diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index d76e4609d..42860da3f 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -5,7 +5,7 @@ body:
   - type: markdown
     attributes:
       value: >
-        **Note:** If this is a support question (e.g. _How do I do XYZ?_), please post on the [Google Developers Community](https://discord.gg/google-dev-community) Discord server's #palm-api channel instead. This is a great place to interact with developers, and to learn, share, and support each other.
+        **Note:** If this is a support question (e.g. _How do I do XYZ?_), please visit the [Discourse forum](https://discuss.ai.google.dev/). This is a great place to interact with developers, and to learn, share, and support each other.
   - type: textarea
     id: description
     attributes:
diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml
index 9d789ff54..af506e50a 100644
--- a/.github/ISSUE_TEMPLATE/feature_request.yml
+++ b/.github/ISSUE_TEMPLATE/feature_request.yml
@@ -5,7 +5,7 @@ body:
   - type: markdown
     attributes:
       value: >
-        **Note:** If this is a support question (e.g. _How do I do XYZ?_), please post on the [Google Developers Community](https://discord.gg/google-dev-community) Discord server's #palm-api channel instead. This is a great place to interact with developers, and to learn, share, and support each other.
+        **Note:** If this is a support question (e.g. _How do I do XYZ?_), please visit the [Discourse forum](https://discuss.ai.google.dev/). This is a great place to interact with developers, and to learn, share, and support each other.
   - type: textarea
     id: description
     attributes:
diff --git a/.github/labeler.yml b/.github/labeler.yml
index 443d0c067..2ff705b08 100644
--- a/.github/labeler.yml
+++ b/.github/labeler.yml
@@ -1,23 +1,32 @@
 'status:awaiting review':
 - '**/*'
 
-'demos:doc-agent':
+'component:demos':
+- demos/**/*
+
+'component:documentation':
+- site/**/*
+
+'demos:docs-agent':
 - demos/palm/python/docs-agent/**/*
 
-'examples:list-it':
+'demos:list-it':
 - demos/palm/web/list-it/**/*
 
-'examples:mood-food':
+'demos:mood-food':
 - demos/palm/web/mood-food/**/*
 
-'examples:quick-prompt':
+'demos:pipet':
+- demos/palm/node/pipet-code-agent/**/*
+
+'demos:quick-prompt':
 - demos/palm/web/quick-prompt/**/*
 
-'examples:talking-character':
+'demos:talking-character':
 - demos/palm/web/talking-character/**/*
 
-'examples:text-fx':
+'demos:text-fx':
 - demos/palm/web/textfx/**/*
 
-'examples:travel-planner':
+'demos:travel-planner':
 - demos/palm/web/travel-planner/**/*
diff --git a/.github/workflows/notebooks.yml b/.github/workflows/notebooks.yml
index ff94f5eff..cce8386b9 100644
--- a/.github/workflows/notebooks.yml
+++ b/.github/workflows/notebooks.yml
@@ -6,11 +6,12 @@ on:
   # Relevant PRs
   pull_request:
     paths:
-    - "site/en/**"
+    - "**.ipynb"
   # Allow manual runs
   workflow_dispatch:
 
 jobs:
+  # Format all notebooks.
   nbfmt:
     name: Notebook format
     runs-on: ubuntu-latest
@@ -28,7 +29,7 @@ jobs:
           readarray -t changed_notebooks < <(git diff --name-only main | grep '\.ipynb$' || true)
         else
           # Manual run, check everything
-          readarray -t changed_notebooks < <(find site/en/ -name '*.ipynb')
+          readarray -t changed_notebooks < <(find -name '*.ipynb')
         fi
         if [[ ${#changed_notebooks[@]} == 0 ]]; then
           echo "No notebooks modified in this pull request."
@@ -44,28 +45,93 @@ jobs:
     steps:
     - uses: actions/checkout@v3
     - uses: actions/setup-python@v4
+    - uses: dorny/paths-filter@v2
+      id: filter
+      with:
+        filters: |
+          website:
+            - 'site/en/**/**.ipynb'
+          github_docs:
+            - 'examples/**/**.ipynb'
+            - 'demos/**/**.ipynb'
+          templates:
+            - 'templates/**/**.ipynb'
     - name: Install tensorflow-docs
       run: python3 -m pip install -U git+https://github.com/tensorflow/docs
     - name: Fetch main branch
       run: git fetch -u origin main:main
-    - name: Lint notebooks
+
+    # Full lint for website notebooks (incl. website button)
+    - name: Lint website notebooks
+      if: steps.filter.outputs.website == 'true'
       run: |
         if [ "${{ github.event_name }}" == "pull_request" ]; then
           # Only check notebooks modified in this pull request
-          readarray -t changed_notebooks < <(git diff --name-only main | grep '\.ipynb$' || true)
+          readarray -t changed_notebooks < <(git diff --name-only main site/en/ |grep '\.ipynb$' || true)
         else
           # Manual run, check everything
           readarray -t changed_notebooks < <(find site/en/ -name '*.ipynb')
         fi
         if [[ ${#changed_notebooks[@]} == 0 ]]; then
-          echo "No notebooks modified in this pull request."
+          echo "No website notebooks modified in this pull request."
           exit 0
         else
           echo "Lint check with nblint:"
           python3 -m tensorflow_docs.tools.nblint \
             --styles=google,tensorflow \
             --arg=repo:google/generative-ai-docs --arg=branch:main \
-            --arg=base_url:https://developers.generativeai.google/ \
+            --arg=base_url:https://ai.google.dev/ \
             --exclude_lint=tensorflow::button_download \
             "${changed_notebooks[@]}"
         fi
+
+    # Reduced lint for notebooks hosted in GitHub
+    - name: Lint documentation notebooks
+      if: steps.filter.outputs.github_docs == 'true'
+      run: |
+        if [ "${{ github.event_name }}" == "pull_request" ]; then
+          # Only check notebooks modified in this pull request
+          readarray -t changed_notebooks < <(git diff --name-only main demos/ examples/ |grep '\.ipynb$' || true)
+        else
+          # Manual run, check everything
+          readarray -t changed_notebooks < <(find demos/ examples/ -name '*.ipynb')
+        fi
+        if [[ ${#changed_notebooks[@]} == 0 ]]; then
+          echo "No GitHub doc notebooks modified in this pull request."
+          exit 0
+        else
+          echo "Lint check with nblint:"
+          python3 -m tensorflow_docs.tools.nblint \
+            --styles=google,tensorflow \
+            --arg=repo:google/generative-ai-docs --arg=branch:main \
+            --exclude_lint=tensorflow::button_download \
+            --exclude_lint=tensorflow::button_website \
+            "${changed_notebooks[@]}"
+        fi
+
+    # Basic lint for template notebooks
+    - name: Lint template notebooks
+      if: steps.filter.outputs.templates == 'true'
+      run: |
+        if [ "${{ github.event_name }}" == "pull_request" ]; then
+          # Only check notebooks modified in this pull request
+          readarray -t changed_notebooks < <(git diff --name-only main templates/ |grep '\.ipynb$' || true)
+        else
+          # Manual run, check everything
+          readarray -t changed_notebooks < <(find templates/ -name '*.ipynb')
+        fi
+        if [[ ${#changed_notebooks[@]} == 0 ]]; then
+          echo "No template notebooks modified in this pull request."
+          exit 0
+        else
+          echo "Lint check with nblint:"
+          python3 -m tensorflow_docs.tools.nblint \
+            --styles=google,tensorflow \
+            --arg=repo:google/generative-ai-docs --arg=branch:main \
+            --exclude_lint=tensorflow::button_download \
+            --exclude_lint=tensorflow::button_website \
+            --exclude_lint=tensorflow::button_colab \
+            --exclude_lint=tensorflow::button_github \
+            "${changed_notebooks[@]}"
+        fi
+
diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml
index bea8d6db7..a4be21a69 100644
--- a/.github/workflows/stale.yml
+++ b/.github/workflows/stale.yml
@@ -24,8 +24,8 @@ jobs:
         repo-token: ${{ secrets.GITHUB_TOKEN }}
         days-before-issue-stale: 14
         days-before-issue-close: 14
-        stale-issue-label: "stale"
-        close-issue-reason: completed
+        stale-issue-label: "status:stale"
+        close-issue-reason: not_planned
         any-of-labels: "status:awaiting user response,status:more data needed"
         stale-issue-message: >
           Marking this issue as stale since it has been open for 14 days with no activity.
@@ -35,7 +35,7 @@ jobs:
           Please post a new issue if you need further assistance. Thanks!
         days-before-pr-stale: 14
         days-before-pr-close: 14
-        stale-pr-label: "stale"
+        stale-pr-label: "status:stale"
         stale-pr-message: >
           Marking this pull request as stale since it has been open for 14 days with no activity.
           This PR will be closed if no further activity occurs.
diff --git a/.gitignore b/.gitignore
index 62d8ea0fe..b25b3fda4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,7 +3,7 @@
 **/.DS_Store
 **/.idea
 **/.ipynb_checkpoints
-**/.vscode
 **/proofreading
 **/venv
 **/.python-version
+**/node_modules
diff --git a/CODEOWNERS b/CODEOWNERS
index 3b81ece5f..eeb169b39 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -1,11 +1,14 @@
 * @google/generative-ai-admin
 
-demos/palm/web/talking-character @lyleaf @jayjicheng @blownhither
-demos/palm/web/mood-food @lyleaf @jayjicheng
-demos/palm/web/travel-planner @lyleaf @jayjicheng
-demos/palm/web/list-it @mrayinteractive @aaron-wade
-demos/palm/web/quick-prompt @mrayinteractive @aaron-wade
-demos/palm/web/textfx @mrayinteractive @aaron-wade
-demos/palm/python/docs-agent @nickvander @rundong08 @Meggin @kyolee415
+# demos/palm/web/talking-character
+# demos/palm/web/mood-food
+# demos/palm/web/travel-planner
+# demos/palm/web/list-it
+# demos/palm/web/quick-prompt
+# demos/palm/web/textfx
+examples/gemini/python/docs-agent @nickvander @rundong08 @Meggin @kyolee415
+examples/gemini/node/flutter_theme_agent/ @khanhnwin @joefernandez
+demos/palm/node/pipet-code-agent @joefernandez @shilpakancharla @markmcd
 
+templates/ @google/ai-studio-team
 site/ @google/generative-ai-site-team
diff --git a/DEMO_MAINTAINERS.md b/DEMO_MAINTAINERS.md
new file mode 100644
index 000000000..fc037ba5c
--- /dev/null
+++ b/DEMO_MAINTAINERS.md
@@ -0,0 +1,33 @@
+# Demo Maintenance
+
+We have several [demo applications](https://github.com/google/generative-ai-docs/tree/main/demos/palm) hosted in this repository that are referenced in the [AI for Developers](https://ai.google.dev/develop/sample-apps) site. We're looking to the community to help maintain them. 
+Thank you in advance for your contributions!
+
+## Responsibilities
+
+While we would love to accept any meaningful contributions to the demos, some tasks we'd particularly like help with include:
+1. Create a process to verify that the app works as desired after any changes are made (preferably automated, but a manual testing process works well to start)
+2. Get dependencies up-to-date
+3. Review and fix any outstanding issues and PRs (you can filter by the `demos:XYZ` [label](https://github.com/google/generative-ai-docs/labels?q=demos%3A))
+4. Migrate from PaLM to Gemini
+
+## Next Steps
+
+If you're interested and commited to maintaining one of the demos, please complete the following:
+- Read through the [Contributing Guide](https://github.com/google/generative-ai-docs/blob/main/CONTRIBUTING.md).
+- Start work on the responsibilities above.
+- Once you have some progress demonstrated, submit a PR to add your GitHub handle next to the demo you're interested in maintaining, in the section below.
+- Non-responsive maintainers will be removed without notice.
+
+## Active Maintainers
+
+| Demo  | Maintainers |
+| ------------- | ------------- |
+| [list-it](https://github.com/google/generative-ai-docs/tree/main/demos/palm/web/list-it)  | @bupd |
+| [mood-food](https://github.com/google/generative-ai-docs/tree/main/demos/palm/web/mood-food)  |   |
+| [quick-prompt](https://github.com/google/generative-ai-docs/tree/main/demos/palm/web/quick-prompt)  |   |
+| [talking-character](https://github.com/google/generative-ai-docs/tree/main/demos/palm/web/talking-character)  |   |
+| [textfx](https://github.com/google/generative-ai-docs/tree/main/demos/palm/web/textfx)  |   |
+| [travel-planner](https://github.com/google/generative-ai-docs/tree/main/demos/palm/web/travel-planner)  |  |
+| [docs-agent](https://github.com/google/generative-ai-docs/tree/main/demos/palm/python/docs-agent)  | @nickvander @rundong08 @Meggin @kyolee415  |
+| [pipet-code-agent](https://github.com/google/generative-ai-docs/tree/main/demos/palm/node/pipet-code-agent)  | @joefernandez @shilpakancharla @markmcd  |
diff --git a/README.md b/README.md
index 5e05ba6e3..1bf3babd9 100644
--- a/README.md
+++ b/README.md
@@ -1,11 +1,23 @@
-# Google Generative AI Documentation
+# Google Gemini API Website & Documentation
 
 These are the source files for the guide and tutorials on
-the [Generative AI developer site](https://developers.generativeai.google/).
+the [Generative AI developer site](https://ai.google.dev/), home to
+the Gemini API and Gemma.
+
+| Path | Description |
+| ---- | ----------- |
+| [`site/`](site/) | Notebooks and other content used directly on ai.google.dev. |
+| [`demos/`](demos/) | Demos apps. Larger than examples, typically consists of working apps. |
+| [`examples/`](examples/) | Examples. Smaller, single-purpose code for demonstrating specific concepts. |
+
+
 
 To contribute to the site documentation, please read
 [CONTRIBUTING.md](CONTRIBUTING.md).
 
+To contribute as a demo app maintainer, please read
+[DEMO_MAINTAINERS.md](DEMO_MAINTAINERS.md).
+
 To file an issue, please use the
 [GitHub issue tracker](https://github.com/google/generative-ai-docs/issues/new).
 
diff --git a/demos/palm/README.md b/demos/palm/README.md
index 45aad33cf..55daa6c6e 100644
--- a/demos/palm/README.md
+++ b/demos/palm/README.md
@@ -6,3 +6,12 @@ Demos are considered to be larger than examples and consist of working apps.
 
 Smaller, specific examples can be found in the
 [`examples/`](../../examples/palm) directory.
+
+## Updates
+
+Some of these code projects have been updated and moved to the /examples/gemini directory, including:
+
+-   **Docs Agent** - /demos/palm/python/docs-agent/ moved to: 
+    [/examples/gemini/python/docs-agent/](https://github.com/google/generative-ai-docs/tree/main/examples/gemini/python/docs-agent)
+-  **Pipet Code Agent** - /demos/palm/node/pipet-code-agent/ moved to: 
+    [/examples/gemini/node/pipet-code-agent/](https://github.com/google/generative-ai-docs/tree/main/examples/gemini/node/pipet-code-agent)
\ No newline at end of file
diff --git a/demos/palm/java/DELETEME b/demos/palm/java/DELETEME
deleted file mode 100644
index 2517fab01..000000000
--- a/demos/palm/java/DELETEME
+++ /dev/null
@@ -1 +0,0 @@
-Placeholder to reserve directory structure until files are added.
diff --git a/demos/palm/python/docs-agent/README.md b/demos/palm/python/docs-agent/README.md
deleted file mode 100644
index 582fc9be1..000000000
--- a/demos/palm/python/docs-agent/README.md
+++ /dev/null
@@ -1,652 +0,0 @@
-# Docs Agent
-
-The Docs Agent demo enables [PaLM API][genai-doc-site] users to launch a chat application
-on a Linux-based host machine using their own set of documents as a source dataset.
-
-**Note**: If you're interested in setting up and launching the Docs Agent sample app on your
-host machine, see the [Set up Docs Agent][set-up-docs-agent] section below.
-
-## Overview
-
-The Docs Agent sample app is being developed to demonstrate an AI-powered chatbot application
-(including a backend server and web UI) that can answer questions specific to any product,
-service, or topic that has a great quantity of information available as documentation (which
-can be from various sources such as Markdown, HTML, Google Docs, chat conversations, etc.).
-
-The main goal of the Docs Agent project is:
-
-- You can supply your own set of documents to enable a PaLM 2 model to synthesize useful,
-  relevant, and accurate responses that are grounded on the documented information.
-
-The Docs Agent sample app is designed to be easily set up and configured in a Linux environment
-and is required that you have access to Google’s [PaLM API][genai-doc-site].
-
-Keep in mind that this approach is not to “fine-tune” an LLM (large language model)
-itself, but the Docs Agent sample app uses a mixture of prompt engineering and
-embeddings techniques on top of a publicly available LLM model such as PaLM 2.
-
-![Docs Agent architecture](docs/images/docs-agent-architecture-01.png)
-
-**Figure 1**. Docs Agent uses a vector database to retrieve context for augmenting prompts.
-
-## Main features
-
-The key features of the Docs Agent sample app are:
-
-- Add context to user questions to augment their prompts to an LLM.
-- Process documents into embeddings and store them in a vector database for context retrieval.
-
-![Docs Agent flow](docs/images/docs-agent-architecture-02.png)
-
-**Figure 2**. A user question is augmented by the Docs Agent server and passed to an LLM.
-
-**Note**: For the moment, the Docs Agent project focuses on providing Python scripts that make it
-easy to process Markdown files into embeddings. However, there is no hard requirement that the
-source documents must exist in Markdown format. What’s important is that the processed content
-is available as embeddings in the vector database.
-
-### Structure of a prompt to a PaLM 2 model
-
-To enable an LLM to answer questions that are not part of the public knowledge (which the LLM
-is likely trained on), the Docs Agent project applies a mixture of prompt engineering and
-embeddings techniques. That is, we process a set of documents (which contain domain specific
-knowledge) into embeddings and store them in a vector database. This vector database allows
-the Docs Agent server to perform semantic search on stored embeddings to find the most relevant
-content from the source documents given user questions.
-
-Once the most relevant content is returned, the Docs Agent server uses the prompt structure
-shown in Figure 3 to augment the user question with a preset **condition** and a list of
-**context**. (When the Docs Agent server starts, the condition value is read from the
-[`condition.txt`][condition-txt] file.) Then the Docs Agent server sends this prompt to a
-PaLM 2 model using the PaLM API and receives a response generated by the model.
-
-![Docs Agent prompt strcture](docs/images/docs-agent-prompt-structure-01.png)
-
-**Figure 3**. Prompt structure for augmenting a user question with related context
-(Context source: [eventhorizontelescope.org][context-source-01])
-
-### Processing of Markdown files into embeddings
-
-To process information into embeddings using the Python scripts in the project, the
-information needs to be stored in Markdown format. Once you have a set of Markdown files
-stored in a directory on your host machine,  you can run the
-[`markdown_to_plain_text.py`][markdown-to-plain-text] script to process those Markdown
-files into small plain text files – the script splits the content by the top three Markdown
-headers (`#`, `##`, and `###`).
-
-Once Markdown files are processed into small plain text files, you can run the
-[`populate_vector_database.py`][populate-vector-database] script to generate embeddings
-for each text file and store those embeddings into a [Chroma][chroma-docs] vector database
-running on the host machine.
-
-The embeddings in this vector database enable the Docs Agent server to perform semantic search
-and retrieve context related to user questions for augmenting prompts.
-
-![Document to embeddings](docs/images/docs-agent-embeddings-01.png)
-
-**Figure 4**. A document is split into small semantic chunks, which are then used to generate
-embeddings.
-
-![Markdown to embeddings](docs/images/docs-agent-embeddings-02.png)
-
-**Figure 5**. A Markdown page is split by headers and processed into embeddings.
-
-## Summary of tasks and features
-
-The following list summarizes the tasks and features of the Docs Agent sample app:
-
-- **Process Markdown**: Split Markdown files into small plain text files. (See the
-  [`markdown_to_plain_text.py`][markdown-to-plain-text] script.)
-- **Generate embeddings**: Use small plain text files to generate embeddings, processed by
-  an embedding model (`embedding-gecko-001`), and store them in a local Chroma vector
-  database. (See the [`populate_vector_database.py`][populate-vector-database] script.)
-- **Semantic search using embeddings**: Compare embeddings in the vector database for most
-  relevant content given user questions (which are also processed into embeddings using
-  the same `embedding-gecko-001` model).
-- **Add context to a user question in a prompt**: Add the list of content returned from
-  the semantic search as context to the user question and send the prompt to a PaLM 2
-  model using the PaLM API.
-- **(Experimental) “Fact-check” responses**: This experimental feature composes a
-  follow-up prompt and asks the PaLM 2 model to “fact-check” its own previous response.
-  (See the [Using a PaLM 2 model to fact-check its own response][fact-check-section] section.)
-- **Generate 5 related questions**: In addition to displaying a response to the user
-  question, the web UI displays five questions generated by the PaLM 2 model based on
-  the context of the user question. (See the
-  [Using a PaLM 2 model to suggest related questions][related-questions-section] section.)
-- **Display URLs of knowledge sources**: The vector database stores URLs as metadata for
-  embeddings. Whenever the vector database is used to retrieve context (for instance, to
-  provide context to user questions), the database can also return the URLs of the sources
-  that were originally used to generate the embeddings.
-- **Submit rewrites and likes**: The web UI includes the buttons at the bottom of the
-  display that allow users to like generated responses or submit rewrites of
-  the responses. (See the
-  [Enabling users to submit a rewrite of a generated response][submit-a-rewrite] and
-  [Enabling users to like generated responses][like-generate-responses] sections.)
-
-## Flow of events
-
-The following events take place in the Docs Agent sample app:
-
-1. The [`markdown_to_plain_text.py`][markdown-to-plain-text] script converts input
-   Markdown documents into small plain text files, split by Markdown headings
-   (`#`, `##`, and `###`).
-2. The [`populate_vector_database.py`][populate-vector-database] script generates
-   embeddings from the small plain text files and populates a vector database.
-3. When the [`chatbot/launch.sh`][launch-script] script is run, it starts the
-   Docs Agent server and vector database, which loads generated embeddings and
-   metadata (URLs and filenames) stored in the `vector_store` directory.
-4. When the user asks a question, the Docs Agent server uses the vector database to
-   perform semantic search on embeddings, which represent content in the source
-   documents.
-5. Using this semantic search capability, the Docs Agent server finds a list of
-   text chunks that are most relevant to the user question.
-6. The Docs Agent server adds this list of text chunks as context (plus a condition
-   for responses) to the user question and constructs them into a prompt.
-7. The system sends the prompt to a PaLM 2 model via the PaLM API.
-8. The PaLM 2 model generates a response and the Docs Agent server renders it on
-   the chat UI.
-
-Additional events for [“fact-checking” a generated response][fact-check-section]:
-
-9. The Docs Agent server prepares another prompt that compares the generated response
-   (in step 8) to the context (in step 6) and asks the PaLM model to look for
-   a discrepancy in the response.
-10. The PaLM model generates a response that points out one major discrepancy
-    (if it exists) between its previous response and the context.
-11. The Docs Agent server renders this response on the chat UI as a call-out note.
-12. The Docs Agent server passes this second response to the vector database to
-    perform semantic search.
-13. The vector database returns a list of relevant content (that is closely related
-    to the second response).
-14. The Docs Agent server renders the top URL of this list on the chat UI and
-    suggests that the user checks out this URL for fact-checking.
-
-Additional events for
-[suggesting 5 questions related to the user question][related-questions-section]:
-
-15. The Docs Agent server prepares another prompt that asks the PaLM model to
-    generate 5 questions based on the context (in step 6).
-16. The PaLM model generates a response that contains a list of questions related
-    to the context.
-17. The Docs Agent server renders the questions on the chat UI.
-
-## Supplementary features
-
-This section describes additional features implemented on the Docs Agent sample app for
-enhancing the usability of the Q&A experience powered by generative AI.
-
-![Docs Agent UI](docs/images/docs-agent-ui-screenshot-01.png)
-
-**Figure 6**. A screenshot of the Docs Agent chat UI showing the sections generated by
-three distinct prompts.
-
-### Using a PaLM 2 model to fact-check its own response
-
-In addition to using the prompt structure above (shown in Figure 3), we‘re currently
-experimenting with the following prompt setup for “fact-checking” responses generated
-by the PaLM model:
-
-- Condition:
-
-  ```
-  You are a helpful chatbot answering questions from users. Read the following context
-  first and answer the question at the end:
-  ```
-
-- Context:
-
-  ```
-  <CONTEXT_USED_IN_THE_PREVIOUS_PROMPT>
-  ```
-
-- Additional condition (for fact-checking):
-
-  ```
-  Compare the following body of text to the context provided in this prompt and write
-  a short message that warns the readers about which part of the text below they
-  should consider fact-checking for themselves? (please keep your response concise and
-  mention only one important point):
-  ```
-
-- Previously generated response
-
-  ```
-  <RESPONSE_RETURNED_FROM_THE_PREVIOUS_PROMPT>
-  ```
-
-This "fact-checking" prompt returns a response similar to the following example:
-
-```
-The text states that Flutter chose to use Dart because it is a fast, productive, object-oriented
-language that is well-suited for building user interfaces. However, the context provided in the
-prompt states that Flutter chose Dart because it is a fast, productive language that is well-suited
-for Flutter's problem domain: creating visual user experiences. Therefore, readers should consider
-fact-checking the claim that Dart is well-suited for building user interfaces.
-```
-
-After the second response, notice that the Docs Agent chat UI also suggests a URL to visit for
-fact-checking  (see Figure 6), which looks similar to the following example:
-
-```
-To verify this information, please check out:
-
-https://docs.flutter.dev/resources/faq
-```
-
-To identify this URL, the Docs Agent server takes the second response (which is the paragraph that
-begins with “The text states that ...” in the example above) and uses it to query the vector
-database. Once the vector database returns a list of the most relevant content to this response,
-the UI only displays the top URL to the user.
-
-Keep in mind that this "fact-checking" prompt setup is currently considered **experimental**
-because we‘ve seen cases where a PaLM model would end up adding incorrect information into its
-second response as well. However, we saw that adding this second response (which brings attention
-to the PaLM model’s possible hallucinations) seems to improve the usability of the system since it
-serves as a reminder to the users that the PaLM model‘s response is far from being perfect, which
-helps encourage the users to take more steps to validate generated responses for themselves.
-
-### Using a PaLM 2 model to suggest related questions
-
-The project‘s latest web UI includes the “Related questions” section, which displays five
-questions that are related to the user question (see Figure 6). These five questions are also
-generated by a PaLM model (via the PaLM API). Using the list of contents returned from the vector
-database as context, the system prepares another prompt asking the PaLM model to generate five
-questions from the included context.
-
-The following is the exact structure of this prompt:
-
-- Condition:
-
-  ```
-  You are a helpful chatbot answering questions from users. Read the following context first
-  and answer the question at the end:
-  ```
-
-- Context:
-
-  ```
-  <CONTEXT_USED_IN_THE_PREVIOUS_PROMPT>
-  ```
-
-- Question:
-
-  ```
-  What are 5 questions developers might ask after reading the context?
-  ```
-
-### Enabling users to submit a rewrite of a generated response
-
-The project‘s latest web UI includes the **Rewrite this response** button at the bottom of
-the panel (see Figure 6). When this button is clicked, a widget opens up, expanding the
-main UI panel, and reveals a textarea containing the generated response to the user's question.
-The user is then allowed to edit this response in the textarea and click the **Submit** button
-to submit the updated response to the system.
-
-The system stores the submitted response as a Markdown file in the project's local `rewrites`
-directory. The user may re-click the **Submit** button to update the submitted rewrite multiple
-times.
-
-### Enabling users to like generated responses
-
-The project's latest web UI includes the **Like this response** button at the bottom of the panel
-(see Figure 6). When this button is clicked, the server logs the event of "like" for the response.
-However, clicking the **Liked** button again will reset the button. Then the server logs this reset
-event of "like" for the response.
-
-The user may click this like button multiple times to toggle the state of the like button. But when
-examining the logs, only the final state of the like button will be considered for the response.
-
-## Issues identified
-
-The following issues have been identified and need to be worked on:
-
-- **Logical content chunking**: When splitting documents, content is divided into chunks only by the
-  current 1500-character limit. This approach splits large docs into small chunks, which results in
-  losing context, especially in large how-to guides with a long sequence of instructions. **[Done]**
-- **Clean plain text for embeddings**: The current Markdown processing method doesn’t fully filter
-  out all Markdown and HTML syntax, which seems to have a negative influence on embeddings.
-- **Database support for embeddings**: The system needs a proper database setup for faster lookup
-  and for enabling us to store metadata (such as URLs) next to embeddings. **[Done]**
-- **Better prompting**: We haven’t widely explored all best practices in prompting. Also consider
-  supporting dynamic prompting given user questions.
-- **Real-world feedback**: We need to set up a feedback loop to collect real-world user
-  interactions, including example prompts and responses, and start using them as part of embeddings.
-
-## Set up Docs Agent
-
-This section provides instructions on how to set up the Docs Agent project on a Linux host machine.
-
-### 1. Prerequisites
-
-1. Update the Linux package repositories on the host machine:
-
-   ```posix-terminal
-   sudo apt update
-   ```
-
-2. Install the following dependencies:
-
-   ```posix-terminal
-   sudo apt install git pip python3-venv
-   ```
-
-3. Install `poetry`:
-
-   ```posix-terminal
-   curl -sSL https://install.python-poetry.org | python3 -
-   ```
-
-   **Important**: Make sure that `$HOME/.local/bin` is in your `PATH` variable.
-
-4. Set the following environment variable:
-
-   ```posix-terminal
-   export PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring
-   ```
-
-   This is a [known issue][poetry-known-issue] in `poetry`.
-
-5. Set the PaLM API key as a environment variable:
-
-   ```
-   export PALM_API_KEY=<YOUR_API_KEY_HERE>
-   ```
-
-   Replace `<YOUR_API_KEY_HERE>` with the API key to
-   [Generative Language API][genai-doc-site].
-
-   **Tip**: To avoid repeating these `export` lines, add them to your
-   `$HOME/.bashrc` file.
-
-### 2. Clone this project repository and install dependencies
-
-**Note**: This guide assumes that you're cloning the `generative-ai-docs` repository
-from your `$HOME` directory.
-
-1. Clone the `generative-ai-docs` repository, for example:
-
-   ```posix-terminal
-   git clone https://github.com/google/generative-ai-docs
-   ```
-
-2. Go to the Docs Agent project directory:
-
-   ```posix-terminal
-   cd ./generative-ai-docs/demos/palm/python/docs-agent
-   ```
-
-4. Install dependencies using `poetry`:
-
-   ```posix-terminal
-   poetry install
-   ```
-
-   This may take some time to complete.
-
-5. Enter the `poetry` shell environment:
-
-   ```posix-terminal
-   poetry shell
-   ```
-
-   **Important**: From this point, all command lines in the sections below need to run
-   in this `poetry shell` environment.
-
-
-Now, the next step is to populate a vector database with your own documents. See the
-[Populate a new vector database from Markdown files][populate-db-steps] section below.
-
-## Populate a new vector database from Markdown files
-
-This section provides instructions on how to bring your own set of documents and create and
-populate a vector database (`vector_stores/chroma`) on your host machine. The Python scripts
-in the project's `scripts` directory can help you populate documents, embeddings and metadata
-from Markdown files (`.md`).
-
-This section uses the [open source Flutter documents][flutter-docs-src] as an example dataset,
-which are the source Markdown files for the [Flutter website][flutter-docs-site]. To download
-the open source Flutter documents on your host machine, run the following command:
-
-```
-git clone --recurse-submodules https://github.com/flutter/website.git
-```
-
-**Note**: The Flutter documents are used in this section as an example dataset only. The
-Python scripts below are designed to work with any documents in the standard Markdown format.
-
-### 1. Convert Markdown files to plain text files
-
-Before generating embeddings, you need to process Markdown files into small chunks of
-plain text files.
-
-To convert Markdown files to plain text files:
-
-1. Go to the Docs Agent project directory, for example:
-
-   ```
-   cd $HOME/generative-ai-docs/demos/palm/python/docs-agent
-   ```
-
-2. Open the `config.yaml` file using a text editor, for example:
-
-   ```
-   nano config.yaml
-   ```
-
-3. (**Optional**) Edit `output_path` to a directory that will store plain text files,
-   for example:
-
-   ```
-   output_path: "data/plain_docs"
-   ```
-
-   The example above creates a new directory named `data/plain_docs` in the current project
-   directory (which results in `generative-ai-docs/demos/palm/python/docs-agent/data/plain_docs`).
-   Then the project uses this `output_path` directory to store the plain text files processed
-   from the input Markdown files.
-
-4. Under the `input` field, define the following entries to specify the directories
-   that contain your source Markdown files.
-
-   - `path`: The directory where the source Markdown files are stored.
-   - `url_prefix`: The prefix used to create URLs for the source Markdown files.
-     If the URLs do not exist for the source files, provide a mock string.
-   - (**Optional**) `exclude_path`: The sub-directory to be excluded from
-     the path directory.
-
-   The example below shows the entries for the Flutter documents downloaded on the
-   host machine (that is, in the `/home/downloads/website` directory):
-
-   ```
-   input:
-     - path: "/home/downloads/website/src"
-       url_prefix: "https://docs.flutter.dev"
-   ```
-
-   You can also provide a number of input directories (`path` and `url_prefix` sets) under
-   the input field, for example:
-
-   ```
-   input:
-     - path: "/home/downloads/website/src/ui"
-       url_prefix: "https://docs.flutter.dev/ui"
-     - path: "/home/downloads/website/src/codelabs"
-       url_prefix: "https://docs.flutter.dev/codelabs"
-   ```
-
-5. Save the file and exit the text editor.
-
-6. Run the Python script:
-
-   ```
-   python3 scripts/markdown_to_plain_text.py
-   ```
-
-   For a large number of Markdown files, it may take a few minutes to process
-   Markdown files.
-
-### 2. Populate a new vector database
-
-**Important**: If the `vector_stores/chroma` directory already exists, delete
-(or move) the `chroma` directory before populating a new vector database. Also,
-if the Docs Agent chat app is already running using this `chroma` directory, shut down
-the app before deleting the directory.
-
-Once you have plain text files processed and stored in the `output_path` directory,
-you can run the `populat_vector_database.py` script to populate a vector database
-with the contents of the plain text files and their embeddings (and metadata).
-
-To populate a new vector database:
-
-1. Go to the Docs Agent project directory, for example:
-
-   ```
-   cd $HOME/generative-ai-docs/demos/palm/python/docs-agent
-   ```
-
-2. Create and populate a new vector database:
-
-   ```
-   python3 ./scripts/populate_vector_database.py
-   ```
-
-   This script uses the `output_path` directory from the `config.yaml` file to locate
-   plain text files and creates a new directory at
-   `generative-ai-docs/demos/palm/python/docs-agent/vector_stores/chroma`, which
-   contains embeddings and metadata.
-
-3. To test the new vector database, run the following script:
-
-   ```
-   python3 ./scripts/test_vector_database.py
-   ```
-
-   **Note**: Adjust `QUESTION` in `scripts/test_vector_database.py` to be suitable for
-   the content in your database.
-
-The next step is to launch the Docs Agent chat app to use the new vector database. See
-the [Start the Docs Agent chat app][start-the-app-steps] section below.
-
-## Start the Docs Agent chat app
-
-**Important**: This section assumes that you've already created a `vector_stores/chroma`
-directory, which contains artifacts for the vector database. If you haven't, see the
-[Populate a new vector database from Markdown files][populate-db-steps] section above.
-
-This Flask app lets users interact with the Docs Agent service through a web browser. The
-`launch.sh` script deploys the Flask app in a Python virtual environment (`poetry`),
-allowing you to easily bring up and destory the Flask app instance.
-
-### 1. Configure the Docs Agent chat app
-
-To customize settings in the Docs Agent chat app, do the following:
-
-1. (**Optional**) Update the `condition.txt` file to provide a more specific prompt condition
-   for your custom dataset, for example:
-
-   ```
-   You are a helpful chatbot answering questions from developers working on Flutter apps.
-   Read the following context first and answer the question at the end:
-   ```
-
-2. Edit the `config.yaml` file to update the following field:
-
-   ```
-   product_name: "My product"
-   ```
-
-   Replace `My product` with your product name (which shows up as the main label on the UI),
-   for example:
-
-   ```
-   product_name: "Flutter"
-   ```
-
-### 2. Launch the Docs Agent chat app
-
-To launch the Docs Agent chat app, do the following:
-
-1. Go to the Docs Agent project directory, for example:
-
-   ```
-   cd $HOME/generative-ai-docs/demos/palm/python/docs-agent
-   ```
-
-2. Launch the Docs Agent chat app:
-
-   ```
-   poetry run ./chatbot/launch.sh
-   ```
-
-   **Note**: The Docs Agent chat app runs on port 5000 by default. If you have an application
-   already running on port 5000 on your host machine, you can use the `-p` flag to specify
-   a different port (for example, `poetry run ./chatbot/launch.sh -p 5050`).
-
-   Once the app starts running, this command prints output similar to the following:
-
-   ```
-   $ poetry run ./chatbot/launch.sh
-   This script starts your flask app in a virtual environment
-   Installing all dependencies through pip...
-   Using the local vector database created at /home/alice/generative-ai-docs/demos/palm/python/docs-agent/vector_database
-   Using embedded DuckDB with persistence: data will be stored in: /home/alice/generative-ai-docs/demos/palm/python/docs-agent/vector_database
-    * Serving Flask app 'chatbot'
-    * Debug mode: on
-   WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
-    * Running on http://example.com:5000
-   Press CTRL+C to quit
-    * Restarting with stat
-   Using the local vector database created at /home/alice/generative-ai-docs/demos/palm/python/docs-agent/vector_database
-   Using embedded DuckDB with persistence: data will be stored in: /home/alice/generative-ai-docs/demos/palm/python/docs-agent/vector_database
-    * Debugger is active!
-    * Debugger PIN: 129-640-957
-   ```
-
-   Notice the line that shows the URL of this server (`http://example.com:5000` in
-   the example above).
-
-3. Open the URL above on a browser.
-
-   Now, users can start asking questions related to the source dataset.
-
-**The Docs Agent chat app is all set!**
-
-## Contribute to Docs Agent
-
-To contribute to the Docs Agent project, do the following:
-
-1. Visit https://cla.developers.google.com/ to see your current agreements
-   or to sign a new one.
-
-2. Fork the [`generative-ai-docs`][gen-ai-docs-repo] repository.
-
-3. Make changes in your forked reposiotry.
-
-4. Create a pull request.
-
-## Contributors
-
-Nick Van der Auwermeulen (`@nickvander`), Rundong Du (`@rundong08`),
-Meggin Kearney (`@Meggin`), and Kyo Lee (`@kyolee415`).
-
-<!-- Reference links -->
-
-[contribute-to-docs-agent]: #contribute-to-docs-agent
-[set-up-docs-agent]: #set-up-docs-agent
-[markdown-to-plain-text]: ./scripts/markdown_to_plain_text.py
-[populate-vector-database]: ./scripts/populate_vector_database.py
-[condition-txt]: ./condition.txt
-[context-source-01]: http://eventhorizontelescope.org
-[fact-check-section]: #using-a-palm-2-model-to-fact-check-its-own-response
-[related-questions-section]: #using-a-palm-2-model-to-suggest-related-questions
-[submit-a-rewrite]: #enabling-users-to-submit-a-rewrite-of-a-generated-response
-[like-generate-responses]: #enabling-users-to-like-generated-responses
-[populate-db-steps]: #populate-a-new-vector-database-from-markdown-files
-[start-the-app-steps]: #start-the-docs-agent-chat-app
-[launch-script]: ./chatbot/launch.sh
-[genai-doc-site]: https://developers.generativeai.google/products/palm
-[chroma-docs]: https://docs.trychroma.com/
-[flutter-docs-src]: https://github.com/flutter/website/tree/main/src
-[flutter-docs-site]: https://docs.flutter.dev/
-[poetry-known-issue]: https://github.com/python-poetry/poetry/issues/1917
-[gen-ai-docs-repo]: https://github.com/google/generative-ai-docs
diff --git a/demos/palm/python/docs-agent/chatbot/chatui.py b/demos/palm/python/docs-agent/chatbot/chatui.py
deleted file mode 100644
index c0f0a3322..000000000
--- a/demos/palm/python/docs-agent/chatbot/chatui.py
+++ /dev/null
@@ -1,286 +0,0 @@
-#
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-"""Chatbot web service for Docs Agent"""
-
-from flask import (
-    Blueprint,
-    render_template,
-    request,
-    redirect,
-    url_for,
-    json,
-)
-import markdown
-from bs4 import BeautifulSoup
-import urllib
-import os
-from datetime import datetime
-from pytz import timezone
-import pytz
-import uuid
-from scripts import read_config
-
-from chroma import Format
-from docs_agent import DocsAgent
-
-
-# Read the configuration file
-config = read_config.ReadConfig()
-# Create the 'rewrites' directory if it does not exist.
-rewrites_dir = "rewrites"
-is_exist = os.path.exists(rewrites_dir)
-if not is_exist:
-    os.makedirs(rewrites_dir)
-
-product = config.returnConfigValue("product_name")
-bp = Blueprint("chatui", __name__)
-docs_agent = DocsAgent()
-
-
-@bp.route("/", methods=["GET", "POST"])
-def index():
-    server_url = request.url_root.replace("http", "https")
-    return render_template("chatui/index.html", product=product, server_url=server_url)
-
-
-@bp.route("/like", methods=["GET", "POST"])
-def like():
-    if request.method == "POST":
-        json_data = json.loads(request.data)
-        is_like = json_data.get("like")
-        uuid_found = json_data.get("uuid")
-        log_like(is_like, str(uuid_found).strip())
-        return "OK"
-    else:
-        return redirect(url_for("chatui.index"))
-
-
-@bp.route("/rewrite", methods=["GET", "POST"])
-def rewrite():
-    if request.method == "POST":
-        json_data = json.loads(request.data)
-        user_id = json_data.get("user_id")
-        question_captured = json_data.get("question")
-        original_response = json_data.get("original_response")
-        rewrite_captured = json_data.get("rewrite")
-        date_format = "%m%d%Y-%H%M%S"
-        date = datetime.now(tz=pytz.utc)
-        date = date.astimezone(timezone("US/Pacific"))
-        print("[" + date.strftime(date_format) + "] A user has submitted a rewrite.")
-        print("Submitted by: " + user_id + "\n")
-        print("# " + question_captured.strip() + "\n")
-        print("## Original response\n")
-        print(original_response.strip() + "\n")
-        print("## Rewrite\n")
-        print(rewrite_captured + "\n")
-        filename = (
-            rewrites_dir
-            + "/"
-            + question_captured.strip()
-            .replace(" ", "-")
-            .replace("?", "")
-            .replace("'", "")
-            .lower()
-            + "-"
-            + date.strftime(date_format)
-            + ".md"
-        )
-        with open(filename, "w", encoding="utf-8") as file:
-            file.write("Submitted by: " + user_id + "\n\n")
-            file.write("# " + question_captured.strip() + "\n\n")
-            file.write("## Original response\n\n")
-            file.write(original_response.strip() + "\n\n")
-            file.write("## Rewrite\n\n")
-            file.write(rewrite_captured + "\n")
-            file.close()
-        return "OK"
-    else:
-        return redirect(url_for("chatui.index"))
-
-
-@bp.route("/result", methods=["GET", "POST"])
-def result():
-    if request.method == "POST":
-        uuid_value = uuid.uuid1()
-        question_captured = request.form["question"]
-        query_result = docs_agent.query_vector_store(question_captured)
-        context = markdown.markdown(query_result.fetch_formatted(Format.CONTEXT))
-        context_with_prefix = docs_agent.add_instruction_to_context(context)
-        response_in_markdown = docs_agent.ask_text_model_with_context(
-            context_with_prefix, question_captured
-        )
-        if response_in_markdown is None:
-            response_in_markdown = (
-                "The PaLM API is not able to answer this question at the moment. "
-                "Try to rephrase the question and ask again."
-            )
-        response_in_html = markdown.markdown(response_in_markdown)
-        metadatas = markdown.markdown(
-            query_result.fetch_formatted(Format.CLICKABLE_URL)
-        )
-        fact_checked_answer_in_markdown = docs_agent.ask_text_model_to_fact_check(
-            context_with_prefix, response_in_markdown
-        )
-        if fact_checked_answer_in_markdown is None:
-            fact_checked_answer_in_markdown = (
-                "The PaLM API is not able to answer this question at the moment. "
-                "Try to rephrase the question and ask again."
-            )
-        fact_checked_answer_in_html = markdown.markdown(fact_checked_answer_in_markdown)
-        new_question = (
-            "What are 5 questions developers might ask after reading the context?"
-        )
-        related_questions = markdown.markdown(
-            docs_agent.ask_text_model_with_context(response_in_markdown, new_question)
-        )
-        soup = BeautifulSoup(related_questions, "html.parser")
-        for item in soup.find_all("li"):
-            if item.string is not None:
-                link = soup.new_tag(
-                    "a",
-                    href=url_for(
-                        "chatui.question", ask=urllib.parse.quote_plus(item.string)
-                    ),
-                )
-                link.string = item.string
-                item.string = ""
-                item.append(link)
-        related_questions = soup
-        fact_link = markdown.markdown(
-            query_result.fetch_nearest_formatted(Format.CLICKABLE_URL)
-        )
-        server_url = request.url_root.replace("http", "https")
-        # Log the question and response to the log file.
-        log_question(uuid_value, question_captured, response_in_markdown)
-        return render_template(
-            "chatui/index.html",
-            question=question_captured,
-            context=context,
-            context_with_prefix=context_with_prefix,
-            response_in_markdown=response_in_markdown,
-            response_in_html=response_in_html,
-            product=product,
-            metadatas=metadatas,
-            fact_checked_answer=fact_checked_answer_in_html,
-            fact_link=fact_link,
-            related_questions=related_questions,
-            server_url=server_url,
-            uuid=uuid_value,
-        )
-    else:
-        return redirect(url_for("chatui.index"))
-
-
-@bp.route("/question/<ask>", methods=["GET", "POST"])
-def question(ask):
-    if request.method == "GET":
-        uuid_value = uuid.uuid1()
-        question_captured = urllib.parse.unquote_plus(ask)
-        query_result = docs_agent.query_vector_store(question_captured)
-        context = markdown.markdown(query_result.fetch_formatted(Format.CONTEXT))
-        context_with_prefix = docs_agent.add_instruction_to_context(context)
-        response_in_markdown = docs_agent.ask_text_model_with_context(
-            context_with_prefix, question_captured
-        )
-        if response_in_markdown is None:
-            response_in_markdown = (
-                "The PaLM API is not able to answer this question at the moment. "
-                "Try to rephrase the question and ask again."
-            )
-        response_in_html = markdown.markdown(response_in_markdown)
-        metadatas = markdown.markdown(
-            query_result.fetch_formatted(Format.CLICKABLE_URL)
-        )
-        fact_checked_answer_in_markdown = docs_agent.ask_text_model_to_fact_check(
-            context_with_prefix, response_in_markdown
-        )
-        if fact_checked_answer_in_markdown is None:
-            fact_checked_answer_in_markdown = (
-                "The PaLM API is not able to answer this question at the moment. "
-                "Try to rephrase the question and ask again."
-            )
-        fact_checked_answer_in_html = markdown.markdown(fact_checked_answer_in_markdown)
-        new_question = (
-            "What are 5 questions developers might ask after reading the context?"
-        )
-        related_questions = markdown.markdown(
-            docs_agent.ask_text_model_with_context(response_in_markdown, new_question)
-        )
-        soup = BeautifulSoup(related_questions, "html.parser")
-        for item in soup.find_all("li"):
-            if item.string is not None:
-                link = soup.new_tag(
-                    "a",
-                    href=url_for(
-                        "chatui.question", ask=urllib.parse.quote_plus(item.string)
-                    ),
-                )
-                link.string = item.string
-                item.string = ""
-                item.append(link)
-        related_questions = soup
-        fact_link = markdown.markdown(
-            query_result.fetch_nearest_formatted(Format.CLICKABLE_URL)
-        )
-        server_url = request.url_root.replace("http", "https")
-        # Log the question and response to the log file.
-        log_question(uuid_value, question_captured, response_in_markdown)
-        return render_template(
-            "chatui/index.html",
-            question=question_captured,
-            context=context,
-            context_with_prefix=context_with_prefix,
-            response_in_markdown=response_in_markdown,
-            response_in_html=response_in_html,
-            product=product,
-            metadatas=metadatas,
-            fact_checked_answer=fact_checked_answer_in_html,
-            fact_link=fact_link,
-            related_questions=related_questions,
-            server_url=server_url,
-            uuid=uuid_value,
-        )
-    else:
-        return redirect(url_for("chatui.index"))
-
-
-# Log the question and response to the server's log file.
-def log_question(uid, user_question, response):
-    date_format = "%m/%d/%Y %H:%M:%S %Z"
-    date = datetime.now(tz=pytz.utc)
-    date = date.astimezone(timezone("US/Pacific"))
-    print("UID: " + str(uid))
-    print("Question: " + user_question.strip() + "\n")
-    print("Response:")
-    print(response.strip() + "\n")
-    with open("chatui_logs.txt", "a", encoding="utf-8") as log_file:
-        log_file.write("[" + date.strftime(date_format) + "][UID " + str(uid) + "]\n")
-        log_file.write("# " + user_question.strip() + "\n\n")
-        log_file.write(response.strip() + "\n\n")
-        log_file.close()
-
-
-def log_like(is_like, uid):
-    date_format = "%m/%d/%Y %H:%M:%S %Z"
-    date = datetime.now(tz=pytz.utc)
-    date = date.astimezone(timezone("US/Pacific"))
-    print("UID: " + str(uid))
-    print("Like: " + str(is_like))
-    with open("chatui_logs.txt", "a", encoding="utf-8") as log_file:
-        log_file.write("[" + date.strftime(date_format) + "][UID " + str(uid) + "]\n")
-        log_file.write("Like: " + str(is_like) + "\n\n")
-        log_file.close()
diff --git a/demos/palm/python/docs-agent/chatbot/launch.sh b/demos/palm/python/docs-agent/chatbot/launch.sh
deleted file mode 100755
index ae94cd222..000000000
--- a/demos/palm/python/docs-agent/chatbot/launch.sh
+++ /dev/null
@@ -1,38 +0,0 @@
-#!/bin/bash
-
-#
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-# Default values
-port=5000
-name='chatbot'
-# Specify port number with -p argument `launch.sh -p 5555`
-while getopts "n:p:h" opt; do
-  case $opt in
-    p) port="${OPTARG}";;
-    h) echo "Usage: $0 [-p port]"; exit 1;;
-    \?) echo "Invalid option: -$OPTARG"; exit 1;;
-  esac
-done
-# Define your hostname
-if [[ -z "$HOSTNAME" ]]; then
-  export HOSTNAME="localhost"
-fi
-export FLASK_PORT=$port
-export FLASK_APP=$name
-export FLASK_DEBUG=true
-
-flask run --host=$HOSTNAME --port=$FLASK_PORT
diff --git a/demos/palm/python/docs-agent/chatbot/static/css/chatbox.css b/demos/palm/python/docs-agent/chatbot/static/css/chatbox.css
deleted file mode 120000
index 3d344232e..000000000
--- a/demos/palm/python/docs-agent/chatbot/static/css/chatbox.css
+++ /dev/null
@@ -1 +0,0 @@
-../../../third_party/css/chatbox.css
\ No newline at end of file
diff --git a/demos/palm/python/docs-agent/chatbot/static/css/style.css b/demos/palm/python/docs-agent/chatbot/static/css/style.css
deleted file mode 100644
index 066e442f0..000000000
--- a/demos/palm/python/docs-agent/chatbot/static/css/style.css
+++ /dev/null
@@ -1,324 +0,0 @@
-/**
- * Copyright 2023 Google LLC
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/* ======= General style for HTML elements ======= */
-
-body {
-  font: 16px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
-  color: #333;
-  font-weight: 300;
-  max-width: 960px;
-  margin: auto;
-  background-color: #d9d9d9;
-  padding-top: 15px;
-  padding-bottom: 15px;
-}
-
-a {
-  color: #22578c;
-}
-
-p {
-  margin: 0 0 1em;
-  line-height: 130%;
-}
-
-h1 {
-  margin: 0 0 0.5em;
-  font-weight: 500;
-  font-size: 2.0em;
-  margin-left: 0.8em;
-  margin-top: 0.3em;
-}
-
-h2 {
-  margin: 0;
-  margin-top: 17px;
-  margin-bottom: 15px;
-}
-
-h3 {
-  margin: 0;
-  margin-top: 10px;
-  margin-bottom: 10px;
-}
-
-h4 {
-  color: #505050;
-  margin: 0;
-  margin-top: 3px;
-  margin-bottom: 10px;
-}
-
-li {
-  margin: 0 0 0.3em;
-}
-
-/* ======= Style layout by ID ======= */
-
-#callout-box {
-  margin: auto;
-  max-width: 800px;
-  font: 13px arial, sans-serif;
-  background-color: white;
-  border-style: solid;
-  border-width: 1px;
-  padding: 10px 25px;
-  box-shadow: 5px 5px 5px grey;
-  border-radius: 15px;
-}
-
-/* ======= Style by class ======= */
-
-.hidden {
-  display: none;
-}
-
-.disable {
-  display: none;
-}
-
-.header-wrapper {
-  display: flex;
-}
-
-.loading {
-  font: 15px arial, sans-serif;
-  width: 100%;
-  margin-left: 12px;
-  color: #505050;
-  padding: 2px;
-}
-
-.notselected {
-  background-color: #303936e6;
-  padding-top: 3px;
-  padding-bottom: 5px;
-}
-
-.notselected:hover {
-  background-color: #121a17e6;
-  cursor:pointer;
-}
-
-.selected {
-  background-color: #1e6a9c;
-  padding-top: 7px;
-  padding-bottom: 7px;
-}
-
-.selected:hover {
-  background-color: #0a619a;
-  cursor:pointer;
-}
-
-.rewrite {
-  padding: 15px;
-  border: 2px solid #000;
-  margin-top: 6px;
-  border-radius: 15px;
-}
-
-.question, .response, .response-text, .fact-checked-text, .related-questions {
-  max-width: 700px;
-  margin-left: 3px;
-}
-
-.full-response {
-  max-width: 700px;
-  margin-left: 10px;
-}
-
-/* ======= Style buttons by ID ======= */
-
-#rewrite-button {
-  border: 0;
-  background-color: #cf633ff2;
-  color: #fff;
-  padding: 7px;
-  border-radius: 5px;
-  cursor:pointer;
-}
-
-#rewrite-button:hover {
-  background: #ce3705f2;
-  cursor:pointer;
-}
-
-#like-button {
-  border: 0;
-  color: #fff;
-  padding-left: 7px;
-  padding-right: 7px;
-  border-radius: 5px;
-  cursor:pointer;
-}
-
-#submit-button {
-  border: 0;
-  background: none;
-  background-color: #CF5C3F;
-  color: #fff;
-  padding: 7px;
-  border-radius: 5px;
-  cursor:pointer;
-}
-
-#submit-button:hover {
-  background: #ce3705f2;
-  cursor:pointer;
-}
-
-#submit-result {
-  color: #027f02d6;
-}
-
-#edit-text-area {
-  font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
-  max-height: 500px;
-  max-width: 650px;
-  height: 300px;
-  width: 650px;
-  padding: 8px;
-}
-
-#rewrite-question-header {
-  margin: 0;
-  margin-bottom: 5px;
-}
-
-#rewrite-response-header {
-  margin: 0;
-  margin-top: 10px;
-  margin-bottom: 5px;
-}
-
-#user-id {
-  margin: 0;
-  margin-top: 10px;
-  margin-bottom: 15px;
-}
-
-#fact-check-url {
-  margin: 0 0 0.7em;
-}
-
-/* ======= Search Box ======= */
-
-.search {
-  border: 2px solid #CF5C3F;
-  overflow: auto;
-  max-width: 700px;
-  margin-top: 15px;
-  margin-left: 10px;
-  margin-bottom: 10px;
-  border-radius: 5px;
-}
-
-.search input[type="text"] {
-  border: 0;
-  width: 91%;
-  padding: 10px;
-}
-
-.search input[type="text"]:focus {
-  outline: 0;
-}
-
-.search input[type="submit"] {
-  border: 0;
-  background: none;
-  background-color: #CF5C3F;
-  color: #fff;
-  float: right;
-  padding: 10px;
-  -moz-border-radius-top-right: 5px;
-  -webkit-border-radius-top-right: 5px;
-  -moz-border-radius-bottom-right: 5px;
-  -webkit-border-radius-bottom-right: 5px;
-  cursor:pointer;
-}
-
-/* ======= Accordion ======= */
-
-.accordion {
-  max-width: 65em;
-  margin-bottom: 1em;
-}
-
-.accordion > input[type="checkbox"] {
-  position: absolute;
-  left: -100vw;
-}
-
-.accordion .content {
-  overflow-y: hidden;
-  height: 0;
-  transition: height 0.3s ease;
-}
-
-.accordion .reference-content {
-  font-size: 13px;
-}
-
-.accordion > input[type="checkbox"]:checked ~ .content {
-  height: auto;
-  overflow: visible;
-  padding: 15px;
-  border: 2px solid #000;
-  margin-top: 6px;
-  border-radius: 15px;
-}
-
-.accordion .handle {
-  margin: 0;
-  font-size: 1.125em;
-  line-height: 1.2em;
-}
-
-.accordion label {
-  display: block;
-  font-weight: normal;
-  border: 2px solid #000;
-  padding: 12px;
-  background: #4490b8ab;
-  border-radius: 15px;
-}
-
-.accordion label:hover,
-.accordion label:focus {
-  background: #d9d9d9;
-  cursor:pointer;
-}
-
-.accordion .handle label::before {
-  font-family: fontawesome, sans-serif;
-  display: inline-block;
-  content: "\2964";
-  margin-right: 10px;
-  font-size: .58em;
-  line-height: 1.556em;
-  vertical-align: middle;
-}
-
-.accordion > input[type="checkbox"]:checked ~ .handle label::before {
-  content: "\2965";
-}
-
-.accordion p:last-child {
-  margin-bottom: 0;
-}
-
diff --git a/demos/palm/python/docs-agent/chatbot/static/javascript/app.js b/demos/palm/python/docs-agent/chatbot/static/javascript/app.js
deleted file mode 100644
index 0903d0961..000000000
--- a/demos/palm/python/docs-agent/chatbot/static/javascript/app.js
+++ /dev/null
@@ -1,146 +0,0 @@
-/**
- * Copyright 2023 Google LLC
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Display the "loading" message when a question is entered and submitted.
-let askButton = document.getElementById('ask-button');
-let loadingDiv = document.getElementById('loading-div');
-
-if (askButton != null){
-  askButton.addEventListener('click',function (){
-    console.log("here");
-    if (loadingDiv.classList.contains("hidden")){
-      loadingDiv.classList.remove("hidden");
-      console.log("there");
-    }
-  });
-}
-
-// Toggle the hidden class on the `rewrite-box` div.
-let rewriteButton = document.getElementById('rewrite-button');
-
-if (rewriteButton != null){
-  rewriteButton.addEventListener('click',function (){
-    let rewriteBox = document.getElementById('rewrite-box');
-    if (rewriteBox.classList.contains("hidden")){
-      rewriteBox.classList.remove("hidden");
-      // Trigger a focus event on the textarea
-      let element = document.getElementById('edit-text-area');
-      element.dispatchEvent(new Event("focus"));
-    }else{
-      rewriteBox.classList.add("hidden");
-    }
-  });
-}
-
-// Toggle the selected class on the `like this response` button.
-let likeButton = document.getElementById('like-button');
-
-if (likeButton != null){
-  likeButton.addEventListener('click',function (){
-    if (likeButton.classList.contains("notselected")) {
-      this.classList.remove("notselected");
-      this.classList.add("selected");
-      this.value = "Liked"
-      let uuidBox = document.getElementById('uuid-box');
-      let uuid = "Unknown";
-      if (uuidBox != null){
-        uuid =  uuidBox.textContent;
-      }
-      let xhr = new XMLHttpRequest();
-      // The value of `urlLike` is specified in the html template,
-      // which is set by the Flask server.
-      // See chatbot/templates/chatui/base.html
-      xhr.open("POST", urlLike, true);
-      xhr.setRequestHeader("Accept", "application/json");
-      xhr.setRequestHeader("Content-Type", "application/json");
-      let data = JSON.stringify({"like": true, "uuid": uuid});
-      xhr.send(data);
-    }else{
-      this.classList.remove("selected");
-      this.classList.add("notselected");
-      this.value = 'Like this response \uD83D\uDC4D';
-      let uuidBox = document.getElementById('uuid-box');
-      let uuid = "Unknown";
-      if (uuidBox != null){
-        uuid =  uuidBox.textContent;
-      }
-      let xhr = new XMLHttpRequest();
-      // The value of `urlLike` is specified in the html template,
-      // which is set by the Flask server.
-      // See chatbot/templates/chatui/base.html
-      xhr.open("POST", urlLike, true);
-      xhr.setRequestHeader("Accept", "application/json");
-      xhr.setRequestHeader("Content-Type", "application/json");
-      let data = JSON.stringify({"like": false, "uuid": uuid});
-      xhr.send(data);
-    }
-  });
-}
-
-// Adjust the size of the `edit-text-area` textarea.
-let rewriteTextArea = document.getElementById('edit-text-area');
-
-if (rewriteTextArea != null){
-  rewriteTextArea.addEventListener('focus', resize_textarea);
-  rewriteTextArea.addEventListener('input', resize_textarea);
-}
-
-function resize_textarea(){
-  this.style.height = "5px";
-  this.style.width = "650px";
-  this.style.height = (this.scrollHeight)+"px";
-  let rewriteSubmitButton = document.getElementById('submit-button');
-  if (rewriteSubmitButton != null){
-    if (rewriteSubmitButton.classList.contains("disable")){
-      rewriteSubmitButton.classList.remove("disable");
-      let submitResult = document.getElementById('submit-result');
-      submitResult.textContent = "Click to re-submit updated rewrite.";
-    }
-  }
-}
-
-// Make a rewrite POST call.
-let rewriteSubmitButton = document.getElementById('submit-button');
-
-if (rewriteSubmitButton != null){
-  rewriteSubmitButton.addEventListener('click',function (){
-    let xhr = new XMLHttpRequest();
-    // The value of `urlRewrite` below is specified in the html template,
-    // which is set by the Flask server.
-    // See chatbot/templates/chatui/base.html
-    xhr.open("POST", urlRewrite, true);
-    xhr.setRequestHeader("Accept", "application/json");
-    xhr.setRequestHeader("Content-Type", "application/json");
-    let rewriteQuestion = document.getElementById('rewrite-question-span');
-    let rewriteOriginalResponse = document.getElementById('rewrite-original-response-span');
-    let rewriteTextArea = document.getElementById('edit-text-area');
-    let userIDInput = document.getElementById('user-id');
-    let userID = userIDInput.value;
-    if (userID == "") {
-      userID = "anonymous";
-    }
-    let data = JSON.stringify({
-      "user_id": userID,
-      "question": rewriteQuestion.textContent,
-      "original_response": rewriteOriginalResponse.textContent,
-      "rewrite": rewriteTextArea.value});
-    xhr.send(data);
-
-    let submitResult = document.getElementById('submit-result');
-    submitResult.textContent = "Rewrite has been submitted. Thank you!";
-    rewriteSubmitButton.classList.add("disable");
-  }, false);
-}
diff --git a/demos/palm/python/docs-agent/chatbot/templates/chatui/result.html b/demos/palm/python/docs-agent/chatbot/templates/chatui/result.html
deleted file mode 100644
index a61396cb9..000000000
--- a/demos/palm/python/docs-agent/chatbot/templates/chatui/result.html
+++ /dev/null
@@ -1,58 +0,0 @@
-<div class="question">
-  <h2>Question</h2>
-  <p>{{ question | replace("+", " ") | replace("%3F", "?")}}</p>
-</div>
-<div class="response-text" id="response-box">
-  <h2>PaLM's answer</h2>
-  <span id="palm-response">
-    {{ response_in_html | safe }}
-  </span>
-  <h4>Important:</h4>
-  {{ fact_checked_answer | safe }}
-  <p id="fact-check-url">To verify this information, please check out:</p>
-  {{ fact_link | safe }}
-</div>
-<div class="related-questions">
-  <h3>Related questions</h3>
-  {{ related_questions | safe }}
-</div>
-<section class="accordion">
-  <input type="checkbox" name="collapse" id="handle1">
-  <h2 class="handle">
-    <label for="handle1">Context</label>
-  </h2>
-  <div class="content">
-    {{ context | safe }}
-    <span class="reference-content">
-      <h4>Reference:</h4>
-      {{ metadatas | safe }}
-    </span>
-  </div>
-</section>
-<div>
-  <input type="button" value="Rewrite this response" id="rewrite-button">
-  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
-</div>
-<div class="rewrite hidden" id="rewrite-box">
-  <h4 id="rewrite-question-header">Question:</h4>
-  <span id="rewrite-question-span">
-    <p>{{ question | replace("+", " ") | replace("%3F", "?")}}</p>
-  </span>
-  <h4 id="rewrite-response-header">Bard's response:</h4>
-  <span id="rewrite-original-response-span">
-    {{ response_in_html | safe }}
-  </span>
-  <h4>Rewrite:</h4>
-  <textarea id="edit-text-area">{{ response_in_markdown | safe }}</textarea>
-  <label for="user-id">User ID:</label>
-  <input type="text" id="user-id" name="user-id" placeholder="Optional">
-  <br>
-  <span id="submit-box">
-    <input type="button" value="Submit" id="submit-button">
-  </span>
-  <span id="submit-result">
-  </span>
-</div>
-<div class="hidden" id="uuid-box">
-  {{ uuid | safe }}
-</div>
diff --git a/demos/palm/python/docs-agent/chroma.py b/demos/palm/python/docs-agent/chroma.py
deleted file mode 100644
index aecc6f940..000000000
--- a/demos/palm/python/docs-agent/chroma.py
+++ /dev/null
@@ -1,200 +0,0 @@
-#
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-"""Chroma wrapper"""
-
-from enum import auto, Enum
-import os
-import string
-
-from absl import logging
-import chromadb
-from chromadb.config import Settings
-from chromadb.utils import embedding_functions
-from chromadb.api.models import Collection
-from chromadb.api.types import QueryResult
-
-from palm import PaLM
-
-
-class Error(Exception):
-    """Base error class for chroma"""
-
-
-class ChromaEmbeddingModelNotSupportedError(Error, RuntimeError):
-    """Raised if the embedding model specified by a collection is not supported."""
-
-
-class Chroma:
-    """Chroma wrapper"""
-
-    def __init__(self, chroma_dir) -> None:
-        self.client = chromadb.Client(
-            Settings(
-                chroma_db_impl="duckdb+parquet",
-                persist_directory=chroma_dir,
-            )
-        )
-
-    def list_collections(self):
-        return self.client.list_collections()
-
-    def get_collection(self, name, embedding_function=None):
-        if embedding_function is not None:
-            return ChromaCollection(
-                self.client.get_collection(name, embedding_function=embedding_function),
-                embedding_function,
-            )
-        # Read embedding meta information from the collection
-        collection = self.client.get_collection(name, lambda x: None)
-        embedding_model = None
-        if collection.metadata:
-            embedding_model = collection.metadata.get("embedding_model", None)
-
-        if embedding_model == "local/all-mpnet-base-v2":
-            base_dir = os.path.dirname(os.path.abspath(__file__))
-            local_model_dir = os.path.join(base_dir, "models/all-mpnet-base-v2")
-            embedding_function = (
-                embedding_functions.SentenceTransformerEmbeddingFunction(
-                    model_name=local_model_dir
-                )
-            )
-        elif embedding_model is None or embedding_model == "palm/embedding-gecko-001":
-            if embedding_model is None:
-                logging.warning(
-                    "Embedding model is not stored in the metadata of "
-                    "the collection %s. Using PaLM as default.",
-                    name,
-                )
-            palm = PaLM(embed_model="models/embedding-gecko-001", find_models=False)
-            # We can not redefine embedding_function with def and
-            # have to assign a lambda to it
-            # pylint: disable-next=unnecessary-lambda-assignment
-            embedding_function = lambda texts: [palm.embed(text) for text in texts]
-
-        else:
-            raise ChromaEmbeddingModelNotSupportedError(
-                f"Embedding model {embedding_model} specified by collection {name} "
-                "is not supported."
-            )
-
-        return ChromaCollection(
-            self.client.get_collection(name, embedding_function=embedding_function),
-            embedding_function,
-        )
-
-
-class Format(Enum):
-    CONTEXT = auto()
-    URL = auto()
-    CLICKABLE_URL = auto()
-
-
-class ChromaQueryResultItem:
-    """Chroma query result item wrapper
-
-    Chroma query result has the following type:
-    ```
-    class QueryResult(TypedDict):
-        ids: List[IDs]
-        embeddings: Optional[List[List[Embedding]]]
-        documents: Optional[List[List[Document]]]
-        metadatas: Optional[List[List[Metadata]]]
-        distances: Optional[List[List[float]]]
-    ```
-    Since the Chroma's query support multiple texts as input, the outer list
-    corresponds to each of the input text. The inner list corresponds to
-    the nearest k documents for a specific input text. Since we always only
-    provide one input text to the query call, our access pattern to the
-    query result will look like `query_result["documents"][0][i]`, where index
-    0 stands for the result for the first (and the only) input text, and index
-    i stands for the i-th nearest document.
-    """
-
-    templates_with_ref_index = {
-        Format.CONTEXT: "$document **[${ref_index}]**",
-        Format.URL: "**[${ref_index}]** $url ($distance)",
-        Format.CLICKABLE_URL: '**[${ref_index}]** <a href="$url">$url</a> ($distance)',
-    }
-
-    templates_without_ref_index = {
-        Format.CONTEXT: "$document",
-        Format.URL: "$url",
-        Format.CLICKABLE_URL: '<a href="$url">$url</a>',
-    }
-
-    def __init__(self, result: QueryResult, index: int) -> None:
-        self.document = result["documents"][0][index]
-        self.metadata = result["metadatas"][0][index]
-        self.distance = result["distances"][0][index]
-
-    def format(self, format_type: Format, ref_index: int = None):
-        d = {
-            "document": self.document,
-            "ref_index": ref_index,
-            "url": self.metadata.get("url", None),
-            "distance": self.distance,
-        }
-        if ref_index is None:
-            template = self.templates_without_ref_index[format_type]
-        else:
-            template = self.templates_with_ref_index[format_type]
-
-        return string.Template(template).substitute(d)
-
-
-class ChromaQueryResult:
-    """Chroma query result wrapper"""
-
-    def __init__(self, result: QueryResult) -> None:
-        self.result = result
-
-    def __len__(self):
-        return len(self.result["documents"][0])
-
-    def fetch(self, distance_threshold=float("inf")):
-        for i in range(len(self)):
-            item = ChromaQueryResultItem(self.result, i)
-            if item.distance < distance_threshold:
-                yield item
-
-    def fetch_formatted(self, format_type: Format, distance_threshold=float("inf")):
-        return "\n\n".join(
-            item.format(format_type, i + 1)
-            for i, item in enumerate(self.fetch(distance_threshold=distance_threshold))
-        )
-
-    def fetch_nearest(self):
-        return ChromaQueryResultItem(self.result, 0)
-
-    def fetch_nearest_formatted(self, format_type: Format):
-        return self.fetch_nearest().format(format_type)
-
-
-class ChromaCollection:
-    """Chroma collection wrapper"""
-
-    def __init__(self, collection: Collection, embedding_function) -> None:
-        self.collection = collection
-        self.embedding_function = embedding_function
-
-    def query(self, text: str, top_k: int = 1):
-        return ChromaQueryResult(
-            self.collection.query(query_texts=[text], n_results=top_k)
-        )
-
-    def embed(self, text: str):
-        return self.embedding_function(text)
diff --git a/demos/palm/python/docs-agent/condition.txt b/demos/palm/python/docs-agent/condition.txt
deleted file mode 100644
index f28fbd493..000000000
--- a/demos/palm/python/docs-agent/condition.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-You are a helpful chatbot answering questions from users. Read the following context first
-and answer the question at the end:
diff --git a/demos/palm/python/docs-agent/config.yaml b/demos/palm/python/docs-agent/config.yaml
deleted file mode 100644
index 13ee5b35b..000000000
--- a/demos/palm/python/docs-agent/config.yaml
+++ /dev/null
@@ -1,49 +0,0 @@
-#
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-# Configuration file for Docs Agent
-
-# `product_name` is the name of your product that appears on the chatbot UI.
-product_name: "My product"
-
-# `output_path` is the directory where the plain text files will be saved
-# after Markdown files are processed by `markdown-to-plain-text.py`. It is
-# relative to the Doc Agent repo folder.
-output_path: "data/plain_docs"
-
-# `vector_db_dir` is the directory that stores artifacts for the Chroma vector
-# database.
-vector_db_dir: "vector_stores/chroma"
-
-# `collection_name` is the name used by the Chroma vector database to identify
-# your dataset collection.
-collection_name: "docs_collection"
-
-# You can list a number of input sources under the `input` field:
-# `path` (Required):         The directory where the source Markdown files are
-#                            stored.
-# `url_prefix` (Required):   The prefix of the URL used to create URLs for the
-#                            source files. If URLs don't exist for the source
-#                            files, you still need to provide a mock string.
-# `exclude_path` (Optional): The sub-directory to be excluded from the `path`
-#                            directory when processing source files.
-input:
-  - path: "data/example/markdown-src-01"
-    url_prefix: "https://example.com/markdown-src-01"
-  - path: "data/example/markdown-src-02"
-    url_prefix: "https://example.com/makrdown-src-02"
-    exclude_path: "/reference/changelogs/"
-
diff --git a/demos/palm/python/docs-agent/docs/images/docs-agent-embeddings-01.png b/demos/palm/python/docs-agent/docs/images/docs-agent-embeddings-01.png
deleted file mode 100644
index 79df22fb6..000000000
Binary files a/demos/palm/python/docs-agent/docs/images/docs-agent-embeddings-01.png and /dev/null differ
diff --git a/demos/palm/python/docs-agent/docs/images/docs-agent-embeddings-02.png b/demos/palm/python/docs-agent/docs/images/docs-agent-embeddings-02.png
deleted file mode 100644
index 6be46d0af..000000000
Binary files a/demos/palm/python/docs-agent/docs/images/docs-agent-embeddings-02.png and /dev/null differ
diff --git a/demos/palm/python/docs-agent/docs_agent.py b/demos/palm/python/docs-agent/docs_agent.py
deleted file mode 100644
index dde2bb19f..000000000
--- a/demos/palm/python/docs-agent/docs_agent.py
+++ /dev/null
@@ -1,151 +0,0 @@
-#
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-"""Docs Agent"""
-
-import os
-import sys
-
-from absl import logging
-import google.api_core
-
-from chroma import Chroma
-from palm import PaLM
-
-from scripts import read_config
-
-### Set up the PaLM API key from the environment ###
-API_KEY = os.getenv("PALM_API_KEY")
-if API_KEY is None:
-    sys.exit("Please set the environment variable PALM_API_KEY to be your API key.")
-
-### Select your PaLM API endpoint ###
-PALM_API_ENDPOINT = "generativelanguage.googleapis.com"
-
-palm = PaLM(api_key=API_KEY, api_endpoint=PALM_API_ENDPOINT)
-
-BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-
-### Set up the path to the chroma vector database ###
-LOCAL_VECTOR_DB_DIR = os.path.join(BASE_DIR, "vector_stores/chroma")
-COLLECTION_NAME = "docs_collection"
-
-IS_CONFIG_FILE = True
-if IS_CONFIG_FILE:
-    config_values = read_config.ReadConfig()
-    LOCAL_VECTOR_DB_DIR = config_values.returnConfigValue("vector_db_dir")
-    COLLECTION_NAME = config_values.returnConfigValue("collection_name")
-
-### Set up the path to the `condition.txt` file that holds custom condition text.  ###
-CONDITION_FILE = os.path.join(BASE_DIR, "condition.txt")
-
-### Select the number of contents to be used for providing context
-NUM_RETURNS = 5
-
-
-class DocsAgent:
-    """DocsAgent class"""
-
-    prompt_condition = (
-        "Answer the question below as truthfully as possible, "
-        "and if you're unsure of the answer, say \"Sorry, I don't know.\""
-    )
-    text_model_error_response = (
-        "I'm a large language model. "
-        "I'm currently not able to help you with that question. "
-        "You may rephrase your question with more specifics and try again."
-    )
-    chat_model_error_response = (
-        "I'm a large language model. "
-        "I'm currently not able to help you with that question."
-    )
-    palm_none_response = (
-        "PaLM is not able to answer this question at the moment. "
-        "You may rephrase the question and ask again."
-    )
-
-    def __init__(self):
-        # Initialize the Chroma vector database
-        logging.info(
-            "Using the local vector database created at %s", LOCAL_VECTOR_DB_DIR
-        )
-        self.chroma = Chroma(LOCAL_VECTOR_DB_DIR)
-        self.collection = self.chroma.get_collection(COLLECTION_NAME)
-        # Update PaLM's condition string
-        self.update_condition_from_file()
-
-    # Use this method for talking to PaLM (Text)
-    def ask_text_model_with_context(self, context, question):
-        new_prompt = f"{context}\nQuestion: {question}"
-        try:
-            response = palm.generate_text(
-                prompt=new_prompt,
-                max_output_tokens=800,
-                candidate_count=1,
-                temperature=0.0,
-            )
-        except google.api_core.exceptions.InvalidArgument:
-            return self.text_model_error_response
-        if response.result is None:
-            print("Block reason: " + str(response.filters))
-            print("Safety feedback: " + str(response.safety_feedback))
-            return self.palm_none_response
-        return response.result
-
-    # Use this method for talking to PaLM (Chat)
-    def ask_chat_model_with_context(self, context, question):
-        try:
-            response = palm.chat(
-                context=context,
-                messages=question,
-                temperature=0.05,
-            )
-        except google.api_core.exceptions.InvalidArgument:
-            return self.chat_model_error_response
-
-        if response.last is None:
-            return self.palm_none_response
-        return response.last
-
-    # Use this method for asking PaLM (Text) for fact-checking
-    def ask_text_model_to_fact_check(self, context, prev_response):
-        question = (
-            "Can you compare the following body of text "
-            "to the context provided in this prompt and "
-            "write a short message that warns the readers about "
-            "which part of the text below they should consider "
-            "fact-checking for themselves? "
-            "(please keep your response concise and mention "
-            "only one important point):\n\n"
-        )
-        question += prev_response
-        return self.ask_text_model_with_context(context, question)
-
-    # Query the local Chroma vector database using the user question
-    def query_vector_store(self, question):
-        return self.collection.query(question, NUM_RETURNS)
-
-    # Add specific instruction as a prefix to the context
-    def add_instruction_to_context(self, context):
-        new_context = ""
-        new_context += self.prompt_condition + "\n" + context
-        return new_context
-
-    # Update the condition string for PaLM from the `condition.txt` file
-    def update_condition_from_file(self):
-        with open(CONDITION_FILE, "r", encoding="utf-8") as text_file:
-            self.prompt_condition = text_file.read()
-            text_file.close()
diff --git a/demos/palm/python/docs-agent/hello_world.py b/demos/palm/python/docs-agent/hello_world.py
deleted file mode 100644
index b8a1ae240..000000000
--- a/demos/palm/python/docs-agent/hello_world.py
+++ /dev/null
@@ -1,60 +0,0 @@
-#
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-"""Hello World for Docs Agent"""
-
-from chroma import Format
-from docs_agent import DocsAgent
-
-# This `hello_world` script contains the minimal set of function calls
-# needed to use Docs Agent.
-#
-# Note: This script assumes that the vector database has already been populated.
-#
-# To run this script:
-# $ python3 hello_world.py
-#
-
-# Initialize Docs Agent.
-print("STATE: Initializing Docs Agent.")
-docs_agent = DocsAgent()
-
-# This question is used for testing.
-question = "What are some differences between apples and oranges?"
-
-# Print the question.
-print("\nQuestion: " + question)
-
-# Pass the question to the vector database and get a list of the most relevant content.
-result = docs_agent.query_vector_store(question)
-context = result.fetch_formatted(Format.CONTEXT)
-
-# Add instruction (see `condition.txt`) as a prefix to the context.
-context_with_prefix = docs_agent.add_instruction_to_context(context)
-
-print("\nSending the prompt to PaLM 2...")
-
-# Pass the context and question to PaLM 2's `text-bison-001` model.
-response_text = docs_agent.ask_text_model_with_context(context_with_prefix, question)
-print("\n[Text answer]:")
-print(response_text)
-
-# Pass the context and question to PaLM 2's `chat-bison-001` model.
-response_chat = docs_agent.ask_chat_model_with_context(context_with_prefix, question)
-print("\n[Chat answer]:")
-print(response_chat)
-
-print("")
diff --git a/demos/palm/python/docs-agent/poetry.lock b/demos/palm/python/docs-agent/poetry.lock
deleted file mode 100644
index fba9000e0..000000000
--- a/demos/palm/python/docs-agent/poetry.lock
+++ /dev/null
@@ -1,4605 +0,0 @@
-# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
-
-[[package]]
-name = "absl-py"
-version = "1.4.0"
-description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "absl-py-1.4.0.tar.gz", hash = "sha256:d2c244d01048ba476e7c080bd2c6df5e141d211de80223460d5b3b8a2a58433d"},
-    {file = "absl_py-1.4.0-py3-none-any.whl", hash = "sha256:0d3fe606adfa4f7db64792dd4c7aee4ee0c38ab75dfd353b7a83ed3e957fcb47"},
-]
-
-[[package]]
-name = "anyio"
-version = "3.6.2"
-description = "High level compatibility layer for multiple asynchronous event loop implementations"
-optional = false
-python-versions = ">=3.6.2"
-files = [
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-]
-
-[package.dependencies]
-idna = ">=2.8"
-sniffio = ">=1.1"
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-[package.extras]
-doc = ["packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["contextlib2", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (<0.15)", "uvloop (>=0.15)"]
-trio = ["trio (>=0.16,<0.22)"]
-
-[[package]]
-name = "appnope"
-version = "0.1.3"
-description = "Disable App Nap on macOS >= 10.9"
-optional = false
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-]
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-[[package]]
-name = "array-record"
-version = "0.2.0"
-description = "A file format that achieves a new frontier of IO efficiency"
-optional = false
-python-versions = ">=3.8"
-files = [
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-]
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-[package.dependencies]
-absl-py = "*"
-etils = {version = "*", extras = ["epath"]}
-
-[[package]]
-name = "astroid"
-version = "2.15.4"
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-optional = false
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-files = [
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-    {file = "astroid-2.15.4.tar.gz", hash = "sha256:c81e1c7fbac615037744d067a9bb5f9aeb655edf59b63ee8b59585475d6f80d8"},
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-[package.dependencies]
-lazy-object-proxy = ">=1.4.0"
-typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
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-    {version = ">=1.14,<2", markers = "python_version >= \"3.11\""},
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-[[package]]
-name = "asttokens"
-version = "2.2.1"
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-optional = false
-python-versions = "*"
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-[package.dependencies]
-six = "*"
-
-[package.extras]
-test = ["astroid", "pytest"]
-
-[[package]]
-name = "astunparse"
-version = "1.6.3"
-description = "An AST unparser for Python"
-optional = false
-python-versions = "*"
-files = [
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-wheel = ">=0.23.0,<1.0"
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-[[package]]
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diff --git a/demos/palm/python/docs-agent/run_console.py b/demos/palm/python/docs-agent/run_console.py
deleted file mode 100644
index 3041aad21..000000000
--- a/demos/palm/python/docs-agent/run_console.py
+++ /dev/null
@@ -1,72 +0,0 @@
-#
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-"""Run the Docs Agent console in the terminal"""
-
-from absl import logging
-from rich.console import Console
-from rich.markdown import Markdown
-from rich.panel import Panel
-import sys
-
-from chroma import Format
-from docs_agent import DocsAgent
-
-# Set logging level to WARNING or above to disable progress bar
-logging.set_verbosity(logging.WARNING)
-
-# Initialize Rich console
-ai_console = Console(width=160)
-ai_console.rule("Fold")
-
-# Initialize Docs Agent
-ai_console.print("STATE: Initializing Docs Agent.")
-docs_agent = DocsAgent()
-ai_console.print("\nHello! I'm PaLM 2.\n")
-
-# First question (for quick testing)
-question = "What are some differences between apples and oranges?"
-
-# User input loop
-while True:
-    # Get context from the vector database
-    result = docs_agent.query_vector_store(question)
-    context = result.fetch_formatted(Format.CONTEXT)
-    # Add instruction to the context
-    context_with_prefix = docs_agent.add_instruction_to_context(context)
-    # Print the context
-    ai_console.print(Panel.fit(Markdown("\nContext: " + context_with_prefix)))
-    # Get URLs of the context from the vector database
-    metadatas = result.fetch_formatted(Format.URL)
-    ai_console.print(Panel.fit(Markdown(metadatas)))
-    # Print the question
-    ai_console.print(Panel.fit("Question: " + question))
-    ai_console.print("\nPaLM 2:")
-    # Pass the context and question to PaLM 2 (Text)
-    response_text = docs_agent.ask_text_model_with_context(context_with_prefix, question)
-    ai_console.print("\n[Text answer]:")
-    ai_console.print(Panel.fit(Markdown(response_text)))
-    # Pass the context and question to PaLM 2 (Chat)
-    response_chat = docs_agent.ask_chat_model_with_context(context_with_prefix, question)
-    ai_console.print("\n[Chat answer]:")
-    ai_console.print(Panel.fit(Markdown(response_chat)))
-    # Keep asking questions to PaLM 2
-    ai_console.print("\n######## Ask PaLM 2 ########")
-    question = input("How can I help?\n> ")
-    ai_console.print("")
-    if question.startswith("exit"):
-        ai_console.print("Goodbye!")
-        sys.exit()
diff --git a/demos/palm/python/docs-agent/scripts/markdown_to_plain_text.py b/demos/palm/python/docs-agent/scripts/markdown_to_plain_text.py
deleted file mode 100644
index cd9ac1549..000000000
--- a/demos/palm/python/docs-agent/scripts/markdown_to_plain_text.py
+++ /dev/null
@@ -1,416 +0,0 @@
-#
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-"""Process Markdown files into plain text"""
-
-from markdown import markdown
-from bs4 import BeautifulSoup
-import os
-import re
-import json
-import frontmatter
-import read_config
-import uuid
-
-### Makrdown splitter ###
-# By default, use the custom Markdown splitter
-# (instead of splitting Markdown by CHUNK_SIZE limit.)
-USE_CUSTOM_MARKDOWN_SPLITTER = True
-
-#### Chunk size ####
-# By default, split Markdown files into 3000 chracters per chunk.
-CHUNK_SIZE = 3000
-
-#### Input variables for the script ####
-#
-# Note: The hardcoded values below are overwritten
-#       if the `input-values.yaml` file is found.
-#
-# MY_INPUT_PATH: An array of directories that contain source Markdown files.
-# URL_PREFIX: An array of prefixes to be used to create URLs for source Markdown files.
-# MY_OUTPUT_PATH: The target directory where processed plain text files will be stored.
-
-MY_INPUT_PATH = ["data/raw/markdown-src-01", "data/raw/markdown-src-02"]
-URL_PREFIX = [
-    "https://my-example.com/markdown-src-01",
-    "https://my-example.com/markdown-src-02",
-]
-MY_OUTPUT_PATH = "data/plain_docs"
-
-#### Read the `input-values-yaml` file ####
-# At a minimum, INPUT_YAML must configure the following values:
-# output_path: The target directory where processed plain text files will be stored.
-# input:
-#  - path: A directory that contains source Markdown files.
-#    url_prefix: A prefix to be used to create URLs for the source Markdown files.
-IS_CONFIG_FILE = True
-if IS_CONFIG_FILE:
-    config = read_config.ReadConfig()
-    MY_OUTPUT_PATH = config.returnConfigValue("output_path")
-    input_len = config.returnInputCount()
-
-print("Started the markdown-to-plain-text.py script.")
-
-BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-
-def resolve_path(rel_or_abs_path: str, base_dir=BASE_DIR):
-    path = rel_or_abs_path.strip()
-    if path.startswith("/"):
-        return path
-    else:
-        return os.path.join(base_dir, path)
-
-
-MY_OUTPUT_PATH = resolve_path(MY_OUTPUT_PATH)
-os.makedirs(MY_OUTPUT_PATH, exist_ok=True)
-
-print("Set the output directory: " + MY_OUTPUT_PATH)
-print("Processing files from " + str(input_len) + " sources.")
-
-
-# This function converts a Markdown string to plain text.
-def markdown_to_text(markdown_string):
-    # Remove <!-- --> lines in Markdown
-    markdown_string = re.sub(r"<\!--(.*?)-->", "", markdown_string)
-
-    # md -> html -> text since BeautifulSoup can extract text cleanly
-    html = markdown(markdown_string)
-
-    # Extract text
-    soup = BeautifulSoup(html, "html.parser")
-    text = "".join(soup.findAll(string=True))
-
-    # Remove [][] in Markdown
-    text = re.sub(r"\[(.*?)\]\[(.*?)\]", "\\1", text)
-
-    # Remove {: } in devsite Markdown
-    text = re.sub(r"\{:(.*?)\}", "", text)
-
-    # Remove {. } in g3doc Markdown
-    text = re.sub(r"\{.(.*?)\}", "", text)
-
-    # Remove a single line `sh` in g3doc Markdown
-    text = re.sub(r"(?m)^sh$", "", text)
-
-    # Remove a single line ````sh` in g3doc Markdown
-    # text = re.sub(r'(?m)^```sh$', '', text)
-
-    # Remove code snippets
-    text = re.sub(r"<pre>(.*?)</pre>", "\\1", text)
-    text = re.sub(r"<code>(.*?)</code>", "\\1", text)
-    text = re.sub(r"(?m)<var>(.*?)</var>", "\\1", text)
-    text = re.sub(
-        r"(^|)(Important|Note|Caution|Tip|Warning|Important|Key Point|Key Term):\s?",
-        "",
-        text,
-    )
-    text = re.sub(
-        r"(^|)(Objective|Success|Beta|Preview|Deprecated):\s?",
-        "",
-        text,
-    )
-    text = re.sub(r"(Project|Book):(.*)\n", "", text)
-    text = text.strip() + "\n"
-    return text
-
-
-# Function to verify that include exists and exports its content
-def read_markdown(file):
-    try:
-        with open(file, "r", encoding="utf-8") as mdfile:
-            output = mdfile.read()
-            return output
-    except FileNotFoundError:
-        print("[FileNotFound] Missing the include file: " + file)
-
-
-# This function converts Markdown page (#), section (##), and subsection (###)
-# headings into plain English.
-def process_page_and_section_titles(markdown_text):
-    updated_markdown = ""
-    page_title = ""
-    section_title = ""
-    subsection_title = ""
-    new_line = ""
-    metadata = {}
-    # Processes the frontmatter in a markdown file
-    data = frontmatter.loads(markdown_text)
-    if "title" in data:
-        page_title = data["title"]
-        markdown_text = data.content
-        metadata = data.metadata
-    for line in markdown_text.split("\n"):
-        new_line = ""
-        skip_this_line = False
-        if line.startswith("#"):
-            match = re.search(r"^(\#*)\s+(.*)$", line)
-            heading = ""
-            captured_title = ""
-            if match:
-                heading = match[1]
-                # Remove {: } in devsite Markdown
-                captured_title = re.sub(r"\{:(.*?)\}", "", match[2])
-                # Special case of RFC pages.
-                if re.search(r"^\{\{\s+(.*)\.(.*)\s+\}\}$", captured_title):
-                    heading = ""
-                    page_title = "RFC"
-                    skip_this_line = True
-
-            # Detect Markdown heading levels
-            if heading == "#":
-                page_title = captured_title.strip()
-                metadata = {"title": page_title}
-                subsection_title = ""
-                section_title = ""
-            elif heading == "##":
-                section_title = captured_title.strip()
-                subsection_title = ""
-            elif heading == "###":
-                subsection_title = captured_title.strip()
-
-            # Convert Markdown headings into plain English
-            # (but keep `#` for the `process_document_into_sections()`
-            # function to detect these headings for splitting).
-            if page_title:
-                new_line = (
-                    '# The "'
-                    + page_title
-                    + '" page contains the following content:\n\n'
-                )
-
-            if section_title:
-                new_line = (
-                    '# The "'
-                    + page_title
-                    + '" page has the "'
-                    + section_title
-                    + '" section that contains the following content:\n'
-                )
-
-            if subsection_title:
-                new_line = (
-                    '# On the "'
-                    + page_title
-                    + '" page, the "'
-                    + section_title
-                    + '" section has the "'
-                    + subsection_title
-                    + '" subsection that contains the following content:\n'
-                )
-
-        if skip_this_line is False:
-            if new_line:
-                updated_markdown += new_line + "\n"
-            else:
-                updated_markdown += line + "\n"
-    return updated_markdown, metadata
-
-
-# This function replaces Markdown's includes sections with content.
-def process_includes(markdown_text, root):
-    updated_markdown = ""
-    for line in markdown_text.split("\n"):
-        new_line = ""
-        # Replaces Markdown includes with content
-        if line.startswith("<<"):
-            include_match = re.search("^<<(.*?)>>", line)
-            if include_match:
-                include_file = os.path.abspath(root + "/" + include_match[1])
-                new_line = read_markdown(include_file)
-        if new_line:
-            updated_markdown += new_line + "\n"
-        else:
-            updated_markdown += line + "\n"
-    return updated_markdown
-
-
-# This function divides Markdown content into sections and
-# returns an array containing these sections.
-# But this function requires pre-processed Markdown headings from
-# the `process_page_and_section_titles()` function, which simplifies
-# three levels of Markdown headings (#, ##, and ###) into just a single #.
-def process_document_into_sections(markdown_text):
-    sections = []
-    buffer = ""
-    first_section = True
-    for line in markdown_text.split("\n"):
-        if line.startswith("#"):
-            match = re.search(r"^(\#*)\s+(.*)$", line)
-            heading = ""
-            if match:
-                heading = match[1]
-            if heading == "#":
-                if first_section is True:
-                    # Ignore the first detection of `#`.
-                    first_section = False
-                else:
-                    # When a new `#` is detected, store the text in `buffer` into
-                    # an array entry and clear the buffer for the next section.
-                    sections.append(buffer)
-                    buffer = ""
-        buffer += line + "\n"
-    # Add the last section on the page.
-    sections.append(buffer)
-    return sections
-
-
-# This function processes Markdown files in the `input_path` directory
-# into plain text files.
-def process_markdown_files_from_source(configs, inputpath, counter, excludepath):
-    f_count = 0
-    for root, dirs, files in os.walk(resolve_path(inputpath)):
-        if IS_CONFIG_FILE:
-            if "exclude_path" in configs[counter]:
-                dirs[:] = [d for d in dirs if d not in excludepath]
-            if "url_prefix" in configs[counter]:
-                namespace_uuid = uuid.uuid3(
-                    uuid.NAMESPACE_DNS, configs[counter]["url_prefix"]
-                )
-        for file in files:
-            f_count += 1
-            # Process only Markdown files
-            if file.endswith(".md"):
-                with open(os.path.join(root, file), "r", encoding="utf-8") as auto:
-                    # Construct a new sub-directory for storing output plain text files
-                    new_path = MY_OUTPUT_PATH + re.sub(
-                        resolve_path(inputpath), "", os.path.join(root, "")
-                    )
-                    is_exist = os.path.exists(new_path)
-                    if not is_exist:
-                        os.makedirs(new_path)
-                    # Grab the filename without the .md extension
-                    new_filename = os.path.join(new_path, file)
-                    # Add filename to a list
-                    file_slash = "/" + file
-                    relative_path = os.path.relpath(root + file_slash, inputpath)
-                    file_index.append(relative_path)
-                    match = re.search(r"(.*)\.md$", new_filename)
-                    new_filename_no_ext = match[1]
-                    # Read the input Markdown content
-                    to_file = auto.read()
-                    # Reformat the page and section titles
-                    to_file, metadata = process_page_and_section_titles(to_file)
-                    # Process includes lines in Markdown
-                    to_file = process_includes(to_file, root)
-                    doc = []
-                    if USE_CUSTOM_MARKDOWN_SPLITTER is True:
-                        # Use a custom splitter to split into small chunks
-                        docs = process_document_into_sections(to_file)
-                    else:
-                        # Use the Markdown splitter to split into small chunks
-                        docs = markdown_splitter.create_documents([to_file])
-                    i = 0
-                    for doc in docs:
-                        # Clean up Makrdown and HTML syntax
-                        if USE_CUSTOM_MARKDOWN_SPLITTER is True:
-                            content = markdown_to_text(doc)
-                        else:
-                            content = markdown_to_text(doc.page_content)
-                        # Save clean plain text to a new filename appended with an index
-                        filename_to_save = new_filename_no_ext + "_" + str(i) + ".md"
-                        # Generate UUID for each plain text chunk and collect its metadata,
-                        # which will be written to the top-level `file_index.json` file.
-                        md_hash = uuid.uuid3(namespace_uuid, content)
-                        uuid_file = uuid.uuid3(namespace_uuid, filename_to_save)
-                        if bool(metadata):
-                            full_file_metadata[filename_to_save] = {
-                                "UUID": str(uuid_file),
-                                "source": input_path,
-                                "source_file": relative_path,
-                                "source_id": counter,
-                                "URL": url_pre,
-                                "md_hash": str(md_hash),
-                                "metadata": metadata,
-                            }
-                        else:
-                            full_file_metadata[filename_to_save] = {
-                                "UUID": str(uuid_file),
-                                "source": input_path,
-                                "source_file": relative_path,
-                                "source_id": counter,
-                                "URL": url_pre,
-                                "md_hash": str(md_hash),
-                            }
-                        with open(filename_to_save, "w", encoding="utf-8") as new_file:
-                            new_file.write(content)
-                            new_file.close()
-                        i = i + 1
-                    auto.close()
-    print("Processed " + str(f_count) + " Markdown files from the source: " + inputpath)
-    return f_count
-
-
-# Write the recorded input variables into a file: `file_index.json`
-def save_file_index_json(src_file_index):
-    json_out_file = MY_OUTPUT_PATH + "/file_index.json"
-    with open(json_out_file, "w", encoding="utf-8") as outfile:
-        json.dump(src_file_index, outfile)
-    print(
-        "Created " + json_out_file + " to store the complete list of processed files."
-    )
-
-
-#### Main ####
-source_file_index = {}
-input_counter = 0
-total_file_count = 0
-
-# Main for-loop
-for input_counter in range(input_len):
-    full_file_metadata = {}
-    file_index = []
-    exclude = []
-    # Process `input-values.yaml` into input variables.
-    if IS_CONFIG_FILE:
-        # Reads all the input values defined in the configuration file
-        config_values = config.returnConfigValue("input")
-        if "path" in config_values[input_counter]:
-            input_path = config_values[input_counter]["path"]
-        if "url_prefix" in config_values[input_counter]:
-            url_pre = config_values[input_counter]["url_prefix"]
-        if "exclude_path" in config_values[input_counter]:
-            exclude = config_values[input_counter]["exclude_path"]
-    else:
-        input_path = MY_INPUT_PATH[input_counter]
-        url_pre = URL_PREFIX[input_counter]
-
-    # Process Markdown files in the `input` path
-    file_count = process_markdown_files_from_source(
-        config_values, input_path, input_counter, exclude
-    )
-    if not input_path.endswith("/"):
-        input_path = input_path + "/"
-    input_path = resolve_path(input_path)
-    # Record the input variables used in this path.
-    file_list = {}
-    for file in file_index:
-        file_obj = {file: {"source": input_path, "URL": url_pre}}
-        file_list[file] = file_obj
-    source_file_index[input_counter] = full_file_metadata
-    input_counter += 1
-    total_file_count += file_count
-
-# Write the recorded input variables into `file_index.json`.
-save_file_index_json(source_file_index)
-
-print(
-    "Processed a total of "
-    + str(total_file_count)
-    + " Markdown files from "
-    + str(input_counter)
-    + " sources."
-)
diff --git a/demos/palm/python/docs-agent/scripts/populate_vector_database.py b/demos/palm/python/docs-agent/scripts/populate_vector_database.py
deleted file mode 100644
index ce4f9414e..000000000
--- a/demos/palm/python/docs-agent/scripts/populate_vector_database.py
+++ /dev/null
@@ -1,302 +0,0 @@
-#
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-"""Populate the vector database with embeddings generated from text chunks"""
-
-import os
-import sys
-import re
-import json
-import chromadb
-import flatdict
-import uuid
-from chromadb.config import Settings
-from chromadb.utils import embedding_functions
-from chromadb.api.types import Documents, Embeddings
-import google.generativeai as palm
-from ratelimit import limits, sleep_and_retry
-import read_config
-
-### Notes on how to use this script ###
-#
-# Prerequisites:
-# - Have plain text files stored in the PLAIN_TEXT_DIR directory
-#   (see `markdown_to_plain_text.py`)
-#
-# Do the following:
-# 1. If you are not using a `input-values.yaml` file,
-#    edit PLAIN_TEXT_DIR in this script (see below).
-# 2. Run:
-#    $ python3 ./scripts/populate-vector-database.py
-#
-# To test, run:
-# $ python3 ./script/test-vector-database.py
-#
-
-BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-### Select the input directory of plain text files, this will be overridden by
-### `input-values.yaml`
-### Set up the path to the local LLM ###
-LOCAL_VECTOR_DB_DIR = os.path.join(BASE_DIR, "vector_stores/chroma")
-COLLECTION_NAME = "docs_collection"
-
-IS_CONFIG_FILE = True
-if IS_CONFIG_FILE:
-    config_values = read_config.ReadConfig()
-    PLAIN_TEXT_DIR = config_values.returnConfigValue("output_path")
-    input_len = config_values.returnInputCount()
-    LOCAL_VECTOR_DB_DIR = config_values.returnConfigValue("vector_db_dir")
-    COLLECTION_NAME = config_values.returnConfigValue("collection_name")
-
-### Select the file index that is generated with your plain text files, same directory
-INPUT_FILE_INDEX = "file_index.json"
-
-# Select the type of embeddings to use, PALM or LOCAL
-EMBEDDINGS_TYPE = "PALM"
-
-### Set up the PaLM API key from the environment ###
-API_KEY = os.getenv("PALM_API_KEY")
-if API_KEY is None:
-    sys.exit("Please set the environment variable PALM_API_KEY to be your API key.")
-
-# PaLM API call limit to 300 per minute
-API_CALLS = 280
-API_CALL_PERIOD = 60
-
-# Enable relative directories.
-if not BASE_DIR.endswith("/"):
-    BASE_DIR = BASE_DIR + "/"
-
-if not PLAIN_TEXT_DIR.endswith("/"):
-    PLAIN_TEXT_DIR = PLAIN_TEXT_DIR + "/"
-
-FULL_BASE_DIR = BASE_DIR + PLAIN_TEXT_DIR
-print("Plain text directory: " + FULL_BASE_DIR + "\n")
-
-FULL_INDEX_PATH = PLAIN_TEXT_DIR + INPUT_FILE_INDEX
-try:
-    with open(FULL_INDEX_PATH, "r", encoding="utf-8") as index_file:
-        index = json.load(index_file)
-except FileNotFoundError:
-    msg = "The file " + FULL_INDEX_PATH + "does not exist."
-
-if EMBEDDINGS_TYPE == "PALM":
-    palm.configure(api_key=API_KEY)
-    # This returns models/embedding-gecko-001"
-    models = [
-        m for m in palm.list_models() if "embedText" in m.supported_generation_methods
-    ]
-    # MODEL = "models/embedding-gecko-001"
-    MODEL = models[0]
-elif EMBEDDINGS_TYPE == "LOCAL":
-    MODEL = os.path.join(BASE_DIR, "models/all-mpnet-base-v2")
-    emb_fn = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=MODEL)
-else:
-    MODEL = os.path.join(BASE_DIR, "models/all-mpnet-base-v2")
-    emb_fn = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=MODEL)
-
-chroma_client = chromadb.Client(
-    Settings(chroma_db_impl="duckdb+parquet", persist_directory=LOCAL_VECTOR_DB_DIR)
-)
-
-
-# Create embed function for PaLM
-# API call limit to 5 qps
-@sleep_and_retry
-@limits(calls=API_CALLS, period=API_CALL_PERIOD)
-def embed_function(texts: Documents) -> Embeddings:
-    # Embed the documents using any supported method
-    return [
-        palm.generate_embeddings(model=MODEL, text=text)["embedding"] for text in texts
-    ]
-
-
-if EMBEDDINGS_TYPE == "PALM":
-    collection = chroma_client.get_or_create_collection(
-        name=COLLECTION_NAME, embedding_function=embed_function
-    )
-elif EMBEDDINGS_TYPE == "LOCAL":
-    collection = chroma_client.get_or_create_collection(
-        name=COLLECTION_NAME, embedding_function=emb_fn
-    )
-else:
-    collection = chroma_client.get_or_create_collection(
-        name=COLLECTION_NAME, embedding_function=emb_fn
-    )
-
-documents = []
-metadatas = []
-ids = []
-i = 0
-updated_count = 0
-new_count = 0
-unchanged_count = 0
-
-# Read plain text files (.md) from the PLAIN_TEXT_DIR dir and
-# add their content to the vector database.
-# Embeddings are generated automatically as they are added to the database.
-for root, dirs, files in os.walk(PLAIN_TEXT_DIR):
-    for file in files:
-        file_update = False
-        # Persists every nth time and if there was an actual update or added file.
-        # However, we don't need to persist, which takes time, if there are no updates.
-        if i % 100 == 0 and file_update == True:
-            chroma_client.persist()
-        if file.endswith(".md"):
-            with open(os.path.join(root, file), "r", encoding="utf-8") as auto:
-                print("Process an entry into the database: " + str(i))
-                print("Opening a file: " + file)
-                # Extract the original filename used (without a file extension)
-                match = re.search(r"(.*)\.md$", file)
-                filename_no_ext = match[1]
-                toFile = auto.read()
-                # Contruct the URL
-                match2 = re.search(r"(.*)_\d*$", filename_no_ext)
-                filename_for_url = match2[1]
-                clean_filename = re.sub(PLAIN_TEXT_DIR, "", os.path.join(root, ""))
-                url = clean_filename + filename_for_url + ".md"
-                url_path = ""
-                md_hash = ""
-                uuid_file = ""
-                # Build the full filename to match entries in file_index.json
-                # Using the full path avoids mismatches
-                full_file_name = FULL_BASE_DIR + clean_filename + file
-                metadata_dict_extra = {}
-                # Reads the metadata associated with files
-                for key in index:
-                    if full_file_name in index[key]:
-                        if (
-                            "URL" in index[key][full_file_name]
-                            and "source_id" in index[key][full_file_name]
-                        ):
-                            # This ensures the URL is retrived from the correct file.
-                            # Avoids issues with common file names such as README.md
-                            if int(key) == index[key][full_file_name]["source_id"]:
-                                if index[key][full_file_name]["URL"]:
-                                    url_path = index[key][full_file_name]["URL"]
-                                else:
-                                    print("No valid URL value for: " + file)
-                        # If metadata exists, add these to a dictionary that is then
-                        # merged with other metadata values
-                        if "metadata" in index[key][full_file_name]:
-                            # Save and flatten dictionary
-                            metadata_dict_extra = flatdict.FlatterDict(
-                                index[key][full_file_name]["metadata"], delimiter="_"
-                            )
-                            metadata_dict_extra = dict(metadata_dict_extra)
-                        else:
-                            metadata_dict_extra = {}
-                        if "UUID" in index[key][full_file_name]:
-                            uuid_file = index[key][full_file_name]["UUID"]
-                        if "md_hash" in index[key][full_file_name]:
-                            md_hash = str(index[key][full_file_name]["md_hash"])
-                # Add a trailing "/" to the url path in case the configuration file
-                # didn't have it.
-                # Do not add slashes to PSAs.
-                if (
-                    not url_path.endswith("/")
-                    and not url_path.startswith("PSA")
-                    and not url.startswith("/")
-                ):
-                    url_path = url_path + "/"
-                url = url_path + url
-                # Remove .md at the end of URLs by default.
-                match3 = re.search(r"(.*)\.md$", url)
-                url = match3[1]
-                # Creates a dictionary with basic metadata values
-                # (i.e. source, URL, and md_hash)
-                metadata_dict_main = {
-                    "source": filename_no_ext,
-                    "url": url,
-                    "md_hash": md_hash,
-                }
-                # Merges dictionaries with main metadata and additional metadata
-                metadata_dict_final = metadata_dict_main | metadata_dict_extra
-                str_uuid_file = str(uuid_file)
-                print("UUID: " + str_uuid_file)
-                print("Markdown hash: " + str(md_hash))
-                print("URL: " + url)
-                if toFile and toFile.strip():
-                    # Skip if the file size is larger than 10000 bytes (API limit)
-                    filesize = len(toFile)
-                    if filesize < 10000:
-                        if md_hash != "" and str_uuid_file != "":
-                            query = {}
-                            # The query looks for the UUID, which is unique and
-                            # compares to see if the hash has changed
-                            query = collection.get(
-                                include=["metadatas"],
-                                ids=str_uuid_file,
-                                where={"md_hash": {"$ne": md_hash}},
-                            )
-                            # Extract any id whose content may have changed
-                            id_to_remove = query["ids"]
-                            if id_to_remove != []:
-                                print("Out of date content.")
-                                # Delete the existing entry
-                                collection.delete(ids=id_to_remove)
-                                # Add a new entry
-                                collection.add(
-                                    documents=toFile,
-                                    metadatas=metadata_dict_final,
-                                    ids=str_uuid_file,
-                                )
-                                print("Updated.")
-                                updated_count += 1
-                                file_update = True
-                            else:
-                                query_2 = collection.get(
-                                    include=["metadatas"],
-                                    ids=str_uuid_file,
-                                    where={"md_hash": {"$eq": md_hash}},
-                                )
-                                id_up_to_date = query_2["ids"]
-                                if id_up_to_date != []:
-                                    print("Up to date content.")
-                                    unchanged_count += 1
-                                else:
-                                    collection.add(
-                                        documents=toFile,
-                                        metadatas=metadata_dict_final,
-                                        ids=str_uuid_file,
-                                    )
-                                    print("Added content.")
-                                    new_count += 1
-                                    file_update = True
-                        i += 1
-                    else:
-                        print(
-                            "[Warning] Skipped "
-                            + file
-                            + " because the file size is too large!"
-                        )
-                else:
-                    print("[Warning] Empty file!")
-                print("")
-                auto.close()
-chroma_client.persist()
-# results = collection.query(
-#     query_texts=["What are some differences between apples and oranges?"],
-#     n_results=3,
-# )
-# print("\nTesting:")
-# print(results)
-
-print("")
-print("Total number of entries: " + str(i))
-print("New entries: " + str(new_count))
-print("Updated entries: " + str(updated_count))
-print("Unchanged entries: " + str(unchanged_count))
diff --git a/demos/palm/python/docs-agent/scripts/read_config.py b/demos/palm/python/docs-agent/scripts/read_config.py
deleted file mode 100644
index 852ab65e2..000000000
--- a/demos/palm/python/docs-agent/scripts/read_config.py
+++ /dev/null
@@ -1,96 +0,0 @@
-#
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-"""Read the configuration file to import user settings"""
-
-import os
-import sys
-import yaml
-
-# The configuration file config.yaml exists in the root of the project
-BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-INPUT_YAML = os.path.join(BASE_DIR, "config.yaml")
-### Set up the path to the local LLM ###
-LOCAL_VECTOR_DB_DIR = os.path.join(BASE_DIR, "vector_stores/chroma")
-
-# Define the required keys to run scripts and chatbot
-required_keys = ["output_path", "input", "product_name", "vector_db_dir"]
-# Define any supported optional keys to run scripts and chatbot
-optional_keys = []
-# Define any required keys that define the properties of input paths
-required_input_keys = ["path", "url_prefix"]
-# Define any optional keys that define the properties of input paths
-optional_input__keys = ["md_extension", "exlude_path"]
-
-
-class ReadConfig:
-    # Tries to ingest the configuration file and validate its keys
-    def __init__(self):
-        try:
-            with open(INPUT_YAML, "r", encoding="utf-8") as inp_yaml:
-                self.config_values = yaml.safe_load(inp_yaml)
-                self.IS_CONFIG_FILE = True
-                print("Configuration defined in: " + INPUT_YAML)
-                # Check that the required keys exist
-                self.validateKeys()
-        except FileNotFoundError:
-            print("The file " + INPUT_YAML + " does not exist.")
-            # Exits the scripts if there is no valid config file
-            return sys.exit(1)
-
-    # Function to return the full configuration file
-    def returnFullConfig(self):
-        return self.config_values
-
-    # Function to return the path of the configuration file
-    def returnConfigFile(self):
-        configFilePath = BASE_DIR + INPUT_YAML
-        return configFilePath
-
-    # Function to count the quantity of input paths
-    def returnInputCount(self):
-        count = len(self.returnConfigValue("input"))
-        return count
-
-    # Validates that a configuratioon file contains the required or optional keys
-    def validateKeys(self):
-        for key in required_keys:
-            if key in self.config_values:
-                # Validates lists such as input with their respective keys
-                if key == "input":
-                    count = 0
-                    for input in self.config_values["input"]:
-                        count += 1
-                        for required_key in required_input_keys:
-                            if required_key not in input:
-                                print(
-                                    "Missing input configuration key: "
-                                    + required_key
-                                    + " from input source "
-                                    + str(count)
-                                )
-            else:
-                print("Missing required configuration key: " + key)
-        for key in optional_keys:
-            if key not in self.config_values:
-                print("Missing optional configuration key: " + key)
-
-    # Checks if a key exists and returns its value
-    def returnConfigValue(self, key):
-        if key in self.config_values:
-            return self.config_values[key]
-        else:
-            print("Error: " + key + " does not exist in the " + INPUT_YAML + " file.")
diff --git a/demos/palm/python/docs-agent/scripts/test_vector_database.py b/demos/palm/python/docs-agent/scripts/test_vector_database.py
deleted file mode 100644
index e87d4735f..000000000
--- a/demos/palm/python/docs-agent/scripts/test_vector_database.py
+++ /dev/null
@@ -1,127 +0,0 @@
-#
-# Copyright 2023 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-"""Test the vector database"""
-
-import os
-import sys
-import google.generativeai as palm
-import chromadb
-from chromadb.config import Settings
-from chromadb.utils import embedding_functions
-from chromadb.api.types import Document, Embedding, Documents, Embeddings
-from rich.console import Console
-from rich.markdown import Markdown
-from rich.panel import Panel
-from ratelimit import limits, sleep_and_retry
-import read_config
-
-BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-# Set the directory path to locate the Chroma vector database
-LOCAL_VECTOR_DB_DIR = os.path.join(BASE_DIR, "vector_stores/chroma")
-COLLECTION_NAME = "docs_collection"
-
-IS_CONFIG_FILE = True
-if IS_CONFIG_FILE:
-    config_values = read_config.ReadConfig()
-    LOCAL_VECTOR_DB_DIR = config_values.returnConfigValue("vector_db_dir")
-    COLLECTION_NAME = config_values.returnConfigValue("collection_name")
-
-# Set a test question
-QUESTION = "What are some differences between apples and oranges?"
-NUM_RETURNS = 5
-
-# Set up the PaLM API key from the environment
-API_KEY = os.getenv("PALM_API_KEY")
-if API_KEY is None:
-    sys.exit("Please set the environment variable PALM_API_KEY to be your API key.")
-
-# Select your PaLM API endpoint
-PALM_API_ENDPOINT = "generativelanguage.googleapis.com"
-palm.configure(api_key=API_KEY, client_options={"api_endpoint": PALM_API_ENDPOINT})
-
-# Set up the path to the local LLM
-# This value is used only when `EMBEDDINGS_TYPE` is set to `LOCAL`
-LOCAL_LLM = os.path.join(BASE_DIR, "models/all-mpnet-base-v2")
-
-# Use the PaLM API for generating embeddings by default
-EMBEDDINGS_TYPE = "PALM"
-
-# PaLM API call limit to 300 per minute
-API_CALLS = 280
-API_CALL_PERIOD = 60
-
-
-# Create embed function for PaLM
-# API call limit to 5 qps
-@sleep_and_retry
-@limits(calls=API_CALLS, period=API_CALL_PERIOD)
-def embed_palm_api_call(text: Document) -> Embedding:
-    return palm.generate_embeddings(model=PALM_EMBEDDING_MODEL, text=text)["embedding"]
-
-
-def embed_palm(texts: Documents) -> Embeddings:
-    # Embed the documents using any supported method
-    return [embed_palm_api_call(text) for text in texts]
-
-
-# Initialize Rich console
-ai_console = Console(width=160)
-ai_console.rule("Fold")
-
-chroma_client = chromadb.Client(
-    Settings(chroma_db_impl="duckdb+parquet", persist_directory=LOCAL_VECTOR_DB_DIR)
-)
-
-if EMBEDDINGS_TYPE == "PALM":
-    PALM_EMBEDDING_MODEL = "models/embedding-gecko-001"
-    emb_fn = embed_palm
-elif EMBEDDINGS_TYPE == "LOCAL":
-    emb_fn = embedding_functions.SentenceTransformerEmbeddingFunction(
-        model_name=LOCAL_LLM
-    )
-else:
-    emb_fn = embedding_functions.SentenceTransformerEmbeddingFunction(
-        model_name=LOCAL_LLM
-    )
-
-collection = chroma_client.get_collection(
-    name=COLLECTION_NAME, embedding_function=emb_fn
-)
-
-results = collection.query(query_texts=[QUESTION], n_results=NUM_RETURNS)
-
-print("")
-ai_console.print(Panel.fit(Markdown("Question: " + QUESTION)))
-print("Results:")
-print(results)
-print("")
-
-i = 0
-for document in results["documents"]:
-    for content in document:
-        print("Content " + str(i) + ": ")
-        ai_console.print(Panel.fit(Markdown(content)))
-        source = results["metadatas"][0][i]
-        this_id = results["ids"][0][i]
-        distance = results["distances"][0][i]
-        print("  source: " + source["source"])
-        print("  URL: " + source["url"])
-        print("  ID: " + this_id)
-        print("  Distance: " + str(distance))
-        print("")
-        i += 1
diff --git a/demos/palm/web/list-it/package-lock.json b/demos/palm/web/list-it/package-lock.json
index cfd660803..f69a9e452 100644
--- a/demos/palm/web/list-it/package-lock.json
+++ b/demos/palm/web/list-it/package-lock.json
@@ -29,7 +29,7 @@
         "eslint-plugin-react-hooks": "^4.6.0",
         "prettier": "^2.8.4",
         "sass": "^1.57.1",
-        "vite": "^4.1.5",
+        "vite": "^4.5.2",
         "vite-plugin-svgr": "^2.4.0"
       }
     },
@@ -47,12 +47,13 @@
       }
     },
     "node_modules/@babel/code-frame": {
-      "version": "7.18.6",
-      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.18.6.tgz",
-      "integrity": "sha512-TDCmlK5eOvH+eH7cdAFlNXeVJqWIQ7gW9tY1GJIpUtFb6CmjVyq2VM3u71bOyR8CRihcCgMUYoDNyLXao3+70Q==",
+      "version": "7.22.13",
+      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.22.13.tgz",
+      "integrity": "sha512-XktuhWlJ5g+3TJXc5upd9Ks1HutSArik6jf2eAjYFyIOf4ej3RN+184cZbzDvbPnuTJIUhPKKJE3cIsYTiAT3w==",
       "dev": true,
       "dependencies": {
-        "@babel/highlight": "^7.18.6"
+        "@babel/highlight": "^7.22.13",
+        "chalk": "^2.4.2"
       },
       "engines": {
         "node": ">=6.9.0"
@@ -98,13 +99,14 @@
       }
     },
     "node_modules/@babel/generator": {
-      "version": "7.20.14",
-      "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.20.14.tgz",
-      "integrity": "sha512-AEmuXHdcD3A52HHXxaTmYlb8q/xMEhoRP67B3T4Oq7lbmSoqroMZzjnGj3+i1io3pdnF8iBYVu4Ilj+c4hBxYg==",
+      "version": "7.23.0",
+      "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.23.0.tgz",
+      "integrity": "sha512-lN85QRR+5IbYrMWM6Y4pE/noaQtg4pNiqeNGX60eqOfo6gtEj6uw/JagelB8vVztSd7R6M5n1+PQkDbHbBRU4g==",
       "dev": true,
       "dependencies": {
-        "@babel/types": "^7.20.7",
+        "@babel/types": "^7.23.0",
         "@jridgewell/gen-mapping": "^0.3.2",
+        "@jridgewell/trace-mapping": "^0.3.17",
         "jsesc": "^2.5.1"
       },
       "engines": {
@@ -145,34 +147,34 @@
       }
     },
     "node_modules/@babel/helper-environment-visitor": {
-      "version": "7.18.9",
-      "resolved": "https://registry.npmjs.org/@babel/helper-environment-visitor/-/helper-environment-visitor-7.18.9.tgz",
-      "integrity": "sha512-3r/aACDJ3fhQ/EVgFy0hpj8oHyHpQc+LPtJoY9SzTThAsStm4Ptegq92vqKoE3vD706ZVFWITnMnxucw+S9Ipg==",
+      "version": "7.22.20",
+      "resolved": "https://registry.npmjs.org/@babel/helper-environment-visitor/-/helper-environment-visitor-7.22.20.tgz",
+      "integrity": "sha512-zfedSIzFhat/gFhWfHtgWvlec0nqB9YEIVrpuwjruLlXfUSnA8cJB0miHKwqDnQ7d32aKo2xt88/xZptwxbfhA==",
       "dev": true,
       "engines": {
         "node": ">=6.9.0"
       }
     },
     "node_modules/@babel/helper-function-name": {
-      "version": "7.19.0",
-      "resolved": "https://registry.npmjs.org/@babel/helper-function-name/-/helper-function-name-7.19.0.tgz",
-      "integrity": "sha512-WAwHBINyrpqywkUH0nTnNgI5ina5TFn85HKS0pbPDfxFfhyR/aNQEn4hGi1P1JyT//I0t4OgXUlofzWILRvS5w==",
+      "version": "7.23.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-function-name/-/helper-function-name-7.23.0.tgz",
+      "integrity": "sha512-OErEqsrxjZTJciZ4Oo+eoZqeW9UIiOcuYKRJA4ZAgV9myA+pOXhhmpfNCKjEH/auVfEYVFJ6y1Tc4r0eIApqiw==",
       "dev": true,
       "dependencies": {
-        "@babel/template": "^7.18.10",
-        "@babel/types": "^7.19.0"
+        "@babel/template": "^7.22.15",
+        "@babel/types": "^7.23.0"
       },
       "engines": {
         "node": ">=6.9.0"
       }
     },
     "node_modules/@babel/helper-hoist-variables": {
-      "version": "7.18.6",
-      "resolved": "https://registry.npmjs.org/@babel/helper-hoist-variables/-/helper-hoist-variables-7.18.6.tgz",
-      "integrity": "sha512-UlJQPkFqFULIcyW5sbzgbkxn2FKRgwWiRexcuaR8RNJRy8+LLveqPjwZV/bwrLZCN0eUHD/x8D0heK1ozuoo6Q==",
+      "version": "7.22.5",
+      "resolved": "https://registry.npmjs.org/@babel/helper-hoist-variables/-/helper-hoist-variables-7.22.5.tgz",
+      "integrity": "sha512-wGjk9QZVzvknA6yKIUURb8zY3grXCcOZt+/7Wcy8O2uctxhplmUPkOdlgoNhmdVee2c92JXbf1xpMtVNbfoxRw==",
       "dev": true,
       "dependencies": {
-        "@babel/types": "^7.18.6"
+        "@babel/types": "^7.22.5"
       },
       "engines": {
         "node": ">=6.9.0"
@@ -231,30 +233,30 @@
       }
     },
     "node_modules/@babel/helper-split-export-declaration": {
-      "version": "7.18.6",
-      "resolved": "https://registry.npmjs.org/@babel/helper-split-export-declaration/-/helper-split-export-declaration-7.18.6.tgz",
-      "integrity": "sha512-bde1etTx6ZyTmobl9LLMMQsaizFVZrquTEHOqKeQESMKo4PlObf+8+JA25ZsIpZhT/WEd39+vOdLXAFG/nELpA==",
+      "version": "7.22.6",
+      "resolved": "https://registry.npmjs.org/@babel/helper-split-export-declaration/-/helper-split-export-declaration-7.22.6.tgz",
+      "integrity": "sha512-AsUnxuLhRYsisFiaJwvp1QF+I3KjD5FOxut14q/GzovUe6orHLesW2C7d754kRm53h5gqrz6sFl6sxc4BVtE/g==",
       "dev": true,
       "dependencies": {
-        "@babel/types": "^7.18.6"
+        "@babel/types": "^7.22.5"
       },
       "engines": {
         "node": ">=6.9.0"
       }
     },
     "node_modules/@babel/helper-string-parser": {
-      "version": "7.19.4",
-      "resolved": "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.19.4.tgz",
-      "integrity": "sha512-nHtDoQcuqFmwYNYPz3Rah5ph2p8PFeFCsZk9A/48dPc/rGocJ5J3hAAZ7pb76VWX3fZKu+uEr/FhH5jLx7umrw==",
+      "version": "7.22.5",
+      "resolved": "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.22.5.tgz",
+      "integrity": "sha512-mM4COjgZox8U+JcXQwPijIZLElkgEpO5rsERVDJTc2qfCDfERyob6k5WegS14SX18IIjv+XD+GrqNumY5JRCDw==",
       "dev": true,
       "engines": {
         "node": ">=6.9.0"
       }
     },
     "node_modules/@babel/helper-validator-identifier": {
-      "version": "7.19.1",
-      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.19.1.tgz",
-      "integrity": "sha512-awrNfaMtnHUr653GgGEs++LlAvW6w+DcPrOliSMXWCKo597CwL5Acf/wWdNkf/tfEQE3mjkeD1YOVZOUV/od1w==",
+      "version": "7.22.20",
+      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.22.20.tgz",
+      "integrity": "sha512-Y4OZ+ytlatR8AI+8KZfKuL5urKp7qey08ha31L8b3BwewJAoJamTzyvxPR/5D+KkdJCGPq/+8TukHBlY10FX9A==",
       "dev": true,
       "engines": {
         "node": ">=6.9.0"
@@ -284,13 +286,13 @@
       }
     },
     "node_modules/@babel/highlight": {
-      "version": "7.18.6",
-      "resolved": "https://registry.npmjs.org/@babel/highlight/-/highlight-7.18.6.tgz",
-      "integrity": "sha512-u7stbOuYjaPezCuLj29hNW1v64M2Md2qupEKP1fHc7WdOA3DgLh37suiSrZYY7haUB7iBeQZ9P1uiRF359do3g==",
+      "version": "7.22.20",
+      "resolved": "https://registry.npmjs.org/@babel/highlight/-/highlight-7.22.20.tgz",
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-        "@esbuild/openbsd-x64": "0.16.17",
-        "@esbuild/sunos-x64": "0.16.17",
-        "@esbuild/win32-arm64": "0.16.17",
-        "@esbuild/win32-ia32": "0.16.17",
-        "@esbuild/win32-x64": "0.16.17"
+      "version": "0.18.20",
+      "resolved": "https://registry.npmjs.org/esbuild/-/esbuild-0.18.20.tgz",
+      "integrity": "sha512-ceqxoedUrcayh7Y7ZX6NdbbDzGROiyVBgC4PriJThBKSVPWnnFHZAkfI1lJT8QFkOwH4qOS2SJkS4wvpGl8BpA==",
+      "dev": true,
+      "requires": {
+        "@esbuild/android-arm": "0.18.20",
+        "@esbuild/android-arm64": "0.18.20",
+        "@esbuild/android-x64": "0.18.20",
+        "@esbuild/darwin-arm64": "0.18.20",
+        "@esbuild/darwin-x64": "0.18.20",
+        "@esbuild/freebsd-arm64": "0.18.20",
+        "@esbuild/freebsd-x64": "0.18.20",
+        "@esbuild/linux-arm": "0.18.20",
+        "@esbuild/linux-arm64": "0.18.20",
+        "@esbuild/linux-ia32": "0.18.20",
+        "@esbuild/linux-loong64": "0.18.20",
+        "@esbuild/linux-mips64el": "0.18.20",
+        "@esbuild/linux-ppc64": "0.18.20",
+        "@esbuild/linux-riscv64": "0.18.20",
+        "@esbuild/linux-s390x": "0.18.20",
+        "@esbuild/linux-x64": "0.18.20",
+        "@esbuild/netbsd-x64": "0.18.20",
+        "@esbuild/openbsd-x64": "0.18.20",
+        "@esbuild/sunos-x64": "0.18.20",
+        "@esbuild/win32-arm64": "0.18.20",
+        "@esbuild/win32-ia32": "0.18.20",
+        "@esbuild/win32-x64": "0.18.20"
       }
     },
     "escalade": {
@@ -8066,9 +8069,9 @@
       "dev": true
     },
     "nanoid": {
-      "version": "3.3.4",
-      "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.4.tgz",
-      "integrity": "sha512-MqBkQh/OHTS2egovRtLk45wEyNXwF+cokD+1YPf9u5VfJiRdAiRwB2froX5Co9Rh20xs4siNPm8naNotSD6RBw==",
+      "version": "3.3.6",
+      "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.6.tgz",
+      "integrity": "sha512-BGcqMMJuToF7i1rt+2PWSNVnWIkGCU78jBG3RxO/bZlnZPK2Cmi2QaffxGO/2RvWi9sL+FAiRiXMgsyxQ1DIDA==",
       "dev": true
     },
     "natural-compare": {
@@ -8284,12 +8287,12 @@
       "dev": true
     },
     "postcss": {
-      "version": "8.4.21",
-      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.21.tgz",
-      "integrity": "sha512-tP7u/Sn/dVxK2NnruI4H9BG+x+Wxz6oeZ1cJ8P6G/PZY0IKk4k/63TDsQf2kQq3+qoJeLm2kIBUNlZe3zgb4Zg==",
+      "version": "8.4.31",
+      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.31.tgz",
+      "integrity": "sha512-PS08Iboia9mts/2ygV3eLpY5ghnUcfLV/EXTOW1E2qYxJKGGBUtNjN76FYHnMs36RmARn41bC0AZmn+rR0OVpQ==",
       "dev": true,
       "requires": {
-        "nanoid": "^3.3.4",
+        "nanoid": "^3.3.6",
         "picocolors": "^1.0.0",
         "source-map-js": "^1.0.2"
       }
@@ -8455,9 +8458,9 @@
       }
     },
     "rollup": {
-      "version": "3.15.0",
-      "resolved": "https://registry.npmjs.org/rollup/-/rollup-3.15.0.tgz",
-      "integrity": "sha512-F9hrCAhnp5/zx/7HYmftvsNBkMfLfk/dXUh73hPSM2E3CRgap65orDNJbLetoiUFwSAk6iHPLvBrZ5iHYvzqsg==",
+      "version": "3.29.4",
+      "resolved": "https://registry.npmjs.org/rollup/-/rollup-3.29.4.tgz",
+      "integrity": "sha512-oWzmBZwvYrU0iJHtDmhsm662rC15FRXmcjCk1xD771dFDx5jJ02ufAQQTn0etB2emNk4J9EZg/yWKpsn9BWGRw==",
       "dev": true,
       "requires": {
         "fsevents": "~2.3.2"
@@ -8744,29 +8747,15 @@
       "integrity": "sha512-MXcSTerfPa4uqyzStbRoTgt5XIe3x5+42+q1sDuy3R5MDk66URdLMOZe5aPX/SQd+kuYAh0FdP/pO28IkQyTeg=="
     },
     "vite": {
-      "version": "4.1.5",
-      "resolved": "https://registry.npmjs.org/vite/-/vite-4.1.5.tgz",
-      "integrity": "sha512-zJ0RiVkf61kpd7O+VtU6r766xgnTaIknP/lR6sJTZq3HtVJ3HGnTo5DaJhTUtYoTyS/CQwZ6yEVdc/lrmQT7dQ==",
+      "version": "4.5.2",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-4.5.2.tgz",
+      "integrity": "sha512-tBCZBNSBbHQkaGyhGCDUGqeo2ph8Fstyp6FMSvTtsXeZSPpSMGlviAOav2hxVTqFcx8Hj/twtWKsMJXNY0xI8w==",
       "dev": true,
       "requires": {
-        "esbuild": "^0.16.14",
+        "esbuild": "^0.18.10",
         "fsevents": "~2.3.2",
-        "postcss": "^8.4.21",
-        "resolve": "^1.22.1",
-        "rollup": "^3.10.0"
-      },
-      "dependencies": {
-        "resolve": {
-          "version": "1.22.1",
-          "resolved": "https://registry.npmjs.org/resolve/-/resolve-1.22.1.tgz",
-          "integrity": "sha512-nBpuuYuY5jFsli/JIs1oldw6fOQCBioohqWZg/2hiaOybXOft4lonv85uDOKXdf8rhyK159cxU5cDcK/NKk8zw==",
-          "dev": true,
-          "requires": {
-            "is-core-module": "^2.9.0",
-            "path-parse": "^1.0.7",
-            "supports-preserve-symlinks-flag": "^1.0.0"
-          }
-        }
+        "postcss": "^8.4.27",
+        "rollup": "^3.27.1"
       }
     },
     "vite-plugin-svgr": {
diff --git a/demos/palm/web/list-it/package.json b/demos/palm/web/list-it/package.json
index c85127f13..05ac0e27b 100644
--- a/demos/palm/web/list-it/package.json
+++ b/demos/palm/web/list-it/package.json
@@ -34,7 +34,7 @@
     "eslint-plugin-react-hooks": "^4.6.0",
     "prettier": "^2.8.4",
     "sass": "^1.57.1",
-    "vite": "^4.1.5",
+    "vite": "^4.5.2",
     "vite-plugin-svgr": "^2.4.0"
   }
 }
diff --git a/demos/palm/web/list-it/yarn.lock b/demos/palm/web/list-it/yarn.lock
index 8ad7e74b6..a42fec95d 100644
--- a/demos/palm/web/list-it/yarn.lock
+++ b/demos/palm/web/list-it/yarn.lock
@@ -17,6 +17,14 @@
   dependencies:
     "@babel/highlight" "^7.18.6"
 
+"@babel/code-frame@^7.22.13":
+  version "7.22.13"
+  resolved "https://registry.yarnpkg.com/@babel/code-frame/-/code-frame-7.22.13.tgz#e3c1c099402598483b7a8c46a721d1038803755e"
+  integrity sha512-XktuhWlJ5g+3TJXc5upd9Ks1HutSArik6jf2eAjYFyIOf4ej3RN+184cZbzDvbPnuTJIUhPKKJE3cIsYTiAT3w==
+  dependencies:
+    "@babel/highlight" "^7.22.13"
+    chalk "^2.4.2"
+
 "@babel/compat-data@^7.20.5":
   version "7.20.14"
   resolved "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.20.14.tgz"
@@ -52,6 +60,16 @@
     "@jridgewell/gen-mapping" "^0.3.2"
     jsesc "^2.5.1"
 
+"@babel/generator@^7.23.0":
+  version "7.23.0"
+  resolved "https://registry.yarnpkg.com/@babel/generator/-/generator-7.23.0.tgz#df5c386e2218be505b34837acbcb874d7a983420"
+  integrity sha512-lN85QRR+5IbYrMWM6Y4pE/noaQtg4pNiqeNGX60eqOfo6gtEj6uw/JagelB8vVztSd7R6M5n1+PQkDbHbBRU4g==
+  dependencies:
+    "@babel/types" "^7.23.0"
+    "@jridgewell/gen-mapping" "^0.3.2"
+    "@jridgewell/trace-mapping" "^0.3.17"
+    jsesc "^2.5.1"
+
 "@babel/helper-compilation-targets@^7.20.7":
   version "7.20.7"
   resolved "https://registry.npmjs.org/@babel/helper-compilation-targets/-/helper-compilation-targets-7.20.7.tgz"
@@ -68,20 +86,25 @@
   resolved "https://registry.npmjs.org/@babel/helper-environment-visitor/-/helper-environment-visitor-7.18.9.tgz"
   integrity sha512-3r/aACDJ3fhQ/EVgFy0hpj8oHyHpQc+LPtJoY9SzTThAsStm4Ptegq92vqKoE3vD706ZVFWITnMnxucw+S9Ipg==
 
-"@babel/helper-function-name@^7.19.0":
-  version "7.19.0"
-  resolved "https://registry.npmjs.org/@babel/helper-function-name/-/helper-function-name-7.19.0.tgz"
-  integrity sha512-WAwHBINyrpqywkUH0nTnNgI5ina5TFn85HKS0pbPDfxFfhyR/aNQEn4hGi1P1JyT//I0t4OgXUlofzWILRvS5w==
+"@babel/helper-environment-visitor@^7.22.20":
+  version "7.22.20"
+  resolved "https://registry.yarnpkg.com/@babel/helper-environment-visitor/-/helper-environment-visitor-7.22.20.tgz#96159db61d34a29dba454c959f5ae4a649ba9167"
+  integrity sha512-zfedSIzFhat/gFhWfHtgWvlec0nqB9YEIVrpuwjruLlXfUSnA8cJB0miHKwqDnQ7d32aKo2xt88/xZptwxbfhA==
+
+"@babel/helper-function-name@^7.23.0":
+  version "7.23.0"
+  resolved "https://registry.yarnpkg.com/@babel/helper-function-name/-/helper-function-name-7.23.0.tgz#1f9a3cdbd5b2698a670c30d2735f9af95ed52759"
+  integrity sha512-OErEqsrxjZTJciZ4Oo+eoZqeW9UIiOcuYKRJA4ZAgV9myA+pOXhhmpfNCKjEH/auVfEYVFJ6y1Tc4r0eIApqiw==
   dependencies:
-    "@babel/template" "^7.18.10"
-    "@babel/types" "^7.19.0"
+    "@babel/template" "^7.22.15"
+    "@babel/types" "^7.23.0"
 
-"@babel/helper-hoist-variables@^7.18.6":
-  version "7.18.6"
-  resolved "https://registry.npmjs.org/@babel/helper-hoist-variables/-/helper-hoist-variables-7.18.6.tgz"
-  integrity sha512-UlJQPkFqFULIcyW5sbzgbkxn2FKRgwWiRexcuaR8RNJRy8+LLveqPjwZV/bwrLZCN0eUHD/x8D0heK1ozuoo6Q==
+"@babel/helper-hoist-variables@^7.22.5":
+  version "7.22.5"
+  resolved "https://registry.yarnpkg.com/@babel/helper-hoist-variables/-/helper-hoist-variables-7.22.5.tgz#c01a007dac05c085914e8fb652b339db50d823bb"
+  integrity sha512-wGjk9QZVzvknA6yKIUURb8zY3grXCcOZt+/7Wcy8O2uctxhplmUPkOdlgoNhmdVee2c92JXbf1xpMtVNbfoxRw==
   dependencies:
-    "@babel/types" "^7.18.6"
+    "@babel/types" "^7.22.5"
 
 "@babel/helper-module-imports@^7.18.6":
   version "7.18.6"
@@ -123,16 +146,33 @@
   dependencies:
     "@babel/types" "^7.18.6"
 
+"@babel/helper-split-export-declaration@^7.22.6":
+  version "7.22.6"
+  resolved "https://registry.yarnpkg.com/@babel/helper-split-export-declaration/-/helper-split-export-declaration-7.22.6.tgz#322c61b7310c0997fe4c323955667f18fcefb91c"
+  integrity sha512-AsUnxuLhRYsisFiaJwvp1QF+I3KjD5FOxut14q/GzovUe6orHLesW2C7d754kRm53h5gqrz6sFl6sxc4BVtE/g==
+  dependencies:
+    "@babel/types" "^7.22.5"
+
 "@babel/helper-string-parser@^7.19.4":
   version "7.19.4"
   resolved "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.19.4.tgz"
   integrity sha512-nHtDoQcuqFmwYNYPz3Rah5ph2p8PFeFCsZk9A/48dPc/rGocJ5J3hAAZ7pb76VWX3fZKu+uEr/FhH5jLx7umrw==
 
+"@babel/helper-string-parser@^7.22.5":
+  version "7.22.5"
+  resolved "https://registry.yarnpkg.com/@babel/helper-string-parser/-/helper-string-parser-7.22.5.tgz#533f36457a25814cf1df6488523ad547d784a99f"
+  integrity sha512-mM4COjgZox8U+JcXQwPijIZLElkgEpO5rsERVDJTc2qfCDfERyob6k5WegS14SX18IIjv+XD+GrqNumY5JRCDw==
+
 "@babel/helper-validator-identifier@^7.18.6", "@babel/helper-validator-identifier@^7.19.1":
   version "7.19.1"
   resolved "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.19.1.tgz"
   integrity sha512-awrNfaMtnHUr653GgGEs++LlAvW6w+DcPrOliSMXWCKo597CwL5Acf/wWdNkf/tfEQE3mjkeD1YOVZOUV/od1w==
 
+"@babel/helper-validator-identifier@^7.22.20":
+  version "7.22.20"
+  resolved "https://registry.yarnpkg.com/@babel/helper-validator-identifier/-/helper-validator-identifier-7.22.20.tgz#c4ae002c61d2879e724581d96665583dbc1dc0e0"
+  integrity sha512-Y4OZ+ytlatR8AI+8KZfKuL5urKp7qey08ha31L8b3BwewJAoJamTzyvxPR/5D+KkdJCGPq/+8TukHBlY10FX9A==
+
 "@babel/helper-validator-option@^7.18.6":
   version "7.18.6"
   resolved "https://registry.npmjs.org/@babel/helper-validator-option/-/helper-validator-option-7.18.6.tgz"
@@ -156,11 +196,25 @@
     chalk "^2.0.0"
     js-tokens "^4.0.0"
 
-"@babel/parser@^7.20.13", "@babel/parser@^7.20.7":
+"@babel/highlight@^7.22.13":
+  version "7.22.20"
+  resolved "https://registry.yarnpkg.com/@babel/highlight/-/highlight-7.22.20.tgz#4ca92b71d80554b01427815e06f2df965b9c1f54"
+  integrity sha512-dkdMCN3py0+ksCgYmGG8jKeGA/8Tk+gJwSYYlFGxG5lmhfKNoAy004YpLxpS1W2J8m/EK2Ew+yOs9pVRwO89mg==
+  dependencies:
+    "@babel/helper-validator-identifier" "^7.22.20"
+    chalk "^2.4.2"
+    js-tokens "^4.0.0"
+
+"@babel/parser@^7.20.7":
   version "7.20.15"
   resolved "https://registry.npmjs.org/@babel/parser/-/parser-7.20.15.tgz"
   integrity sha512-DI4a1oZuf8wC+oAJA9RW6ga3Zbe8RZFt7kD9i4qAspz3I/yHet1VvC3DiSy/fsUvv5pvJuNPh0LPOdCcqinDPg==
 
+"@babel/parser@^7.22.15", "@babel/parser@^7.23.0":
+  version "7.23.0"
+  resolved "https://registry.yarnpkg.com/@babel/parser/-/parser-7.23.0.tgz#da950e622420bf96ca0d0f2909cdddac3acd8719"
+  integrity sha512-vvPKKdMemU85V9WE/l5wZEmImpCtLqbnTvqDS2U1fJ96KrxoW7KrXhNsNCblQlg8Ck4b85yxdTyelsMUgFUXiw==
+
 "@babel/plugin-transform-react-jsx-self@^7.18.6":
   version "7.18.6"
   resolved "https://registry.npmjs.org/@babel/plugin-transform-react-jsx-self/-/plugin-transform-react-jsx-self-7.18.6.tgz"
@@ -182,7 +236,7 @@
   dependencies:
     regenerator-runtime "^0.13.11"
 
-"@babel/template@^7.18.10", "@babel/template@^7.20.7":
+"@babel/template@^7.20.7":
   version "7.20.7"
   resolved "https://registry.npmjs.org/@babel/template/-/template-7.20.7.tgz"
   integrity sha512-8SegXApWe6VoNw0r9JHpSteLKTpTiLZ4rMlGIm9JQ18KiCtyQiAMEazujAHrUS5flrcqYZa75ukev3P6QmUwUw==
@@ -191,23 +245,32 @@
     "@babel/parser" "^7.20.7"
     "@babel/types" "^7.20.7"
 
-"@babel/traverse@^7.20.10", "@babel/traverse@^7.20.12", "@babel/traverse@^7.20.13":
-  version "7.20.13"
-  resolved "https://registry.npmjs.org/@babel/traverse/-/traverse-7.20.13.tgz"
-  integrity sha512-kMJXfF0T6DIS9E8cgdLCSAL+cuCK+YEZHWiLK0SXpTo8YRj5lpJu3CDNKiIBCne4m9hhTIqUg6SYTAI39tAiVQ==
+"@babel/template@^7.22.15":
+  version "7.22.15"
+  resolved "https://registry.yarnpkg.com/@babel/template/-/template-7.22.15.tgz#09576efc3830f0430f4548ef971dde1350ef2f38"
+  integrity sha512-QPErUVm4uyJa60rkI73qneDacvdvzxshT3kksGqlGWYdOTIUOwJ7RDUL8sGqslY1uXWSL6xMFKEXDS3ox2uF0w==
   dependencies:
-    "@babel/code-frame" "^7.18.6"
-    "@babel/generator" "^7.20.7"
-    "@babel/helper-environment-visitor" "^7.18.9"
-    "@babel/helper-function-name" "^7.19.0"
-    "@babel/helper-hoist-variables" "^7.18.6"
-    "@babel/helper-split-export-declaration" "^7.18.6"
-    "@babel/parser" "^7.20.13"
-    "@babel/types" "^7.20.7"
+    "@babel/code-frame" "^7.22.13"
+    "@babel/parser" "^7.22.15"
+    "@babel/types" "^7.22.15"
+
+"@babel/traverse@^7.20.10", "@babel/traverse@^7.20.12", "@babel/traverse@^7.20.13":
+  version "7.23.2"
+  resolved "https://registry.yarnpkg.com/@babel/traverse/-/traverse-7.23.2.tgz#329c7a06735e144a506bdb2cad0268b7f46f4ad8"
+  integrity sha512-azpe59SQ48qG6nu2CzcMLbxUudtN+dOM9kDbUqGq3HXUJRlo7i8fvPoxQUzYgLZ4cMVmuZgm8vvBpNeRhd6XSw==
+  dependencies:
+    "@babel/code-frame" "^7.22.13"
+    "@babel/generator" "^7.23.0"
+    "@babel/helper-environment-visitor" "^7.22.20"
+    "@babel/helper-function-name" "^7.23.0"
+    "@babel/helper-hoist-variables" "^7.22.5"
+    "@babel/helper-split-export-declaration" "^7.22.6"
+    "@babel/parser" "^7.23.0"
+    "@babel/types" "^7.23.0"
     debug "^4.1.0"
     globals "^11.1.0"
 
-"@babel/types@^7.18.6", "@babel/types@^7.19.0", "@babel/types@^7.20.0", "@babel/types@^7.20.2", "@babel/types@^7.20.7":
+"@babel/types@^7.18.6", "@babel/types@^7.20.0", "@babel/types@^7.20.2", "@babel/types@^7.20.7":
   version "7.20.7"
   resolved "https://registry.npmjs.org/@babel/types/-/types-7.20.7.tgz"
   integrity sha512-69OnhBxSSgK0OzTJai4kyPDiKTIe3j+ctaHdIGVbRahTLAT7L3R9oeXHC2aVSuGYt3cVnoAMDmOCgJ2yaiLMvg==
@@ -216,115 +279,124 @@
     "@babel/helper-validator-identifier" "^7.19.1"
     to-fast-properties "^2.0.0"
 
-"@esbuild/android-arm64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/android-arm64/-/android-arm64-0.16.17.tgz#cf91e86df127aa3d141744edafcba0abdc577d23"
-  integrity sha512-MIGl6p5sc3RDTLLkYL1MyL8BMRN4tLMRCn+yRJJmEDvYZ2M7tmAf80hx1kbNEUX2KJ50RRtxZ4JHLvCfuB6kBg==
-
-"@esbuild/android-arm@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/android-arm/-/android-arm-0.16.17.tgz#025b6246d3f68b7bbaa97069144fb5fb70f2fff2"
-  integrity sha512-N9x1CMXVhtWEAMS7pNNONyA14f71VPQN9Cnavj1XQh6T7bskqiLLrSca4O0Vr8Wdcga943eThxnVp3JLnBMYtw==
-
-"@esbuild/android-x64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/android-x64/-/android-x64-0.16.17.tgz#c820e0fef982f99a85c4b8bfdd582835f04cd96e"
-  integrity sha512-a3kTv3m0Ghh4z1DaFEuEDfz3OLONKuFvI4Xqczqx4BqLyuFaFkuaG4j2MtA6fuWEFeC5x9IvqnX7drmRq/fyAQ==
-
-"@esbuild/darwin-arm64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/darwin-arm64/-/darwin-arm64-0.16.17.tgz#edef4487af6b21afabba7be5132c26d22379b220"
-  integrity sha512-/2agbUEfmxWHi9ARTX6OQ/KgXnOWfsNlTeLcoV7HSuSTv63E4DqtAc+2XqGw1KHxKMHGZgbVCZge7HXWX9Vn+w==
-
-"@esbuild/darwin-x64@0.16.17":
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-  resolved "https://registry.npmjs.org/@esbuild/darwin-x64/-/darwin-x64-0.16.17.tgz"
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-
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-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/freebsd-arm64/-/freebsd-arm64-0.16.17.tgz#1f4af488bfc7e9ced04207034d398e793b570a27"
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-
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-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/freebsd-x64/-/freebsd-x64-0.16.17.tgz#636306f19e9bc981e06aa1d777302dad8fddaf72"
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-
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-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/linux-arm64/-/linux-arm64-0.16.17.tgz#a003f7ff237c501e095d4f3a09e58fc7b25a4aca"
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-
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-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/linux-arm/-/linux-arm-0.16.17.tgz#b591e6a59d9c4fe0eeadd4874b157ab78cf5f196"
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-
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-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/linux-ia32/-/linux-ia32-0.16.17.tgz#24333a11027ef46a18f57019450a5188918e2a54"
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-
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-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/linux-loong64/-/linux-loong64-0.16.17.tgz#d5ad459d41ed42bbd4d005256b31882ec52227d8"
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-
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-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/linux-mips64el/-/linux-mips64el-0.16.17.tgz#4e5967a665c38360b0a8205594377d4dcf9c3726"
-  integrity sha512-ezbDkp2nDl0PfIUn0CsQ30kxfcLTlcx4Foz2kYv8qdC6ia2oX5Q3E/8m6lq84Dj/6b0FrkgD582fJMIfHhJfSw==
-
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-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/linux-ppc64/-/linux-ppc64-0.16.17.tgz#206443a02eb568f9fdf0b438fbd47d26e735afc8"
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-
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-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/linux-riscv64/-/linux-riscv64-0.16.17.tgz#c351e433d009bf256e798ad048152c8d76da2fc9"
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-
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-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/linux-s390x/-/linux-s390x-0.16.17.tgz#661f271e5d59615b84b6801d1c2123ad13d9bd87"
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-
-"@esbuild/linux-x64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/linux-x64/-/linux-x64-0.16.17.tgz#e4ba18e8b149a89c982351443a377c723762b85f"
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-
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-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/netbsd-x64/-/netbsd-x64-0.16.17.tgz#7d4f4041e30c5c07dd24ffa295c73f06038ec775"
-  integrity sha512-/PzmzD/zyAeTUsduZa32bn0ORug+Jd1EGGAUJvqfeixoEISYpGnAezN6lnJoskauoai0Jrs+XSyvDhppCPoKOA==
-
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-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/openbsd-x64/-/openbsd-x64-0.16.17.tgz#970fa7f8470681f3e6b1db0cc421a4af8060ec35"
-  integrity sha512-2yaWJhvxGEz2RiftSk0UObqJa/b+rIAjnODJgv2GbGGpRwAfpgzyrg1WLK8rqA24mfZa9GvpjLcBBg8JHkoodg==
-
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-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/sunos-x64/-/sunos-x64-0.16.17.tgz#abc60e7c4abf8b89fb7a4fe69a1484132238022c"
-  integrity sha512-xtVUiev38tN0R3g8VhRfN7Zl42YCJvyBhRKw1RJjwE1d2emWTVToPLNEQj/5Qxc6lVFATDiy6LjVHYhIPrLxzw==
-
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-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/win32-arm64/-/win32-arm64-0.16.17.tgz#7b0ff9e8c3265537a7a7b1fd9a24e7bd39fcd87a"
-  integrity sha512-ga8+JqBDHY4b6fQAmOgtJJue36scANy4l/rL97W+0wYmijhxKetzZdKOJI7olaBaMhWt8Pac2McJdZLxXWUEQw==
-
-"@esbuild/win32-ia32@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/win32-ia32/-/win32-ia32-0.16.17.tgz#e90fe5267d71a7b7567afdc403dfd198c292eb09"
-  integrity sha512-WnsKaf46uSSF/sZhwnqE4L/F89AYNMiD4YtEcYekBt9Q7nj0DiId2XH2Ng2PHM54qi5oPrQ8luuzGszqi/veig==
-
-"@esbuild/win32-x64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/win32-x64/-/win32-x64-0.16.17.tgz#c5a1a4bfe1b57f0c3e61b29883525c6da3e5c091"
-  integrity sha512-y+EHuSchhL7FjHgvQL/0fnnFmO4T1bhvWANX6gcnqTjtnKWbTvUMCpGnv2+t+31d7RzyEAYAd4u2fnIhHL6N/Q==
+"@babel/types@^7.22.15", "@babel/types@^7.22.5", "@babel/types@^7.23.0":
+  version "7.23.0"
+  resolved "https://registry.yarnpkg.com/@babel/types/-/types-7.23.0.tgz#8c1f020c9df0e737e4e247c0619f58c68458aaeb"
+  integrity sha512-0oIyUfKoI3mSqMvsxBdclDwxXKXAUA8v/apZbc+iSyARYou1o8ZGDxbUYyLFoW2arqS2jDGqJuZvv1d/io1axg==
+  dependencies:
+    "@babel/helper-string-parser" "^7.22.5"
+    "@babel/helper-validator-identifier" "^7.22.20"
+    to-fast-properties "^2.0.0"
+
+"@esbuild/android-arm64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/android-arm64/-/android-arm64-0.18.20.tgz#984b4f9c8d0377443cc2dfcef266d02244593622"
+  integrity sha512-Nz4rJcchGDtENV0eMKUNa6L12zz2zBDXuhj/Vjh18zGqB44Bi7MBMSXjgunJgjRhCmKOjnPuZp4Mb6OKqtMHLQ==
+
+"@esbuild/android-arm@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/android-arm/-/android-arm-0.18.20.tgz#fedb265bc3a589c84cc11f810804f234947c3682"
+  integrity sha512-fyi7TDI/ijKKNZTUJAQqiG5T7YjJXgnzkURqmGj13C6dCqckZBLdl4h7bkhHt/t0WP+zO9/zwroDvANaOqO5Sw==
+
+"@esbuild/android-x64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/android-x64/-/android-x64-0.18.20.tgz#35cf419c4cfc8babe8893d296cd990e9e9f756f2"
+  integrity sha512-8GDdlePJA8D6zlZYJV/jnrRAi6rOiNaCC/JclcXpB+KIuvfBN4owLtgzY2bsxnx666XjJx2kDPUmnTtR8qKQUg==
+
+"@esbuild/darwin-arm64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/darwin-arm64/-/darwin-arm64-0.18.20.tgz#08172cbeccf95fbc383399a7f39cfbddaeb0d7c1"
+  integrity sha512-bxRHW5kHU38zS2lPTPOyuyTm+S+eobPUnTNkdJEfAddYgEcll4xkT8DB9d2008DtTbl7uJag2HuE5NZAZgnNEA==
+
+"@esbuild/darwin-x64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/darwin-x64/-/darwin-x64-0.18.20.tgz#d70d5790d8bf475556b67d0f8b7c5bdff053d85d"
+  integrity sha512-pc5gxlMDxzm513qPGbCbDukOdsGtKhfxD1zJKXjCCcU7ju50O7MeAZ8c4krSJcOIJGFR+qx21yMMVYwiQvyTyQ==
+
+"@esbuild/freebsd-arm64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/freebsd-arm64/-/freebsd-arm64-0.18.20.tgz#98755cd12707f93f210e2494d6a4b51b96977f54"
+  integrity sha512-yqDQHy4QHevpMAaxhhIwYPMv1NECwOvIpGCZkECn8w2WFHXjEwrBn3CeNIYsibZ/iZEUemj++M26W3cNR5h+Tw==
+
+"@esbuild/freebsd-x64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/freebsd-x64/-/freebsd-x64-0.18.20.tgz#c1eb2bff03915f87c29cece4c1a7fa1f423b066e"
+  integrity sha512-tgWRPPuQsd3RmBZwarGVHZQvtzfEBOreNuxEMKFcd5DaDn2PbBxfwLcj4+aenoh7ctXcbXmOQIn8HI6mCSw5MQ==
+
+"@esbuild/linux-arm64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/linux-arm64/-/linux-arm64-0.18.20.tgz#bad4238bd8f4fc25b5a021280c770ab5fc3a02a0"
+  integrity sha512-2YbscF+UL7SQAVIpnWvYwM+3LskyDmPhe31pE7/aoTMFKKzIc9lLbyGUpmmb8a8AixOL61sQ/mFh3jEjHYFvdA==
+
+"@esbuild/linux-arm@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/linux-arm/-/linux-arm-0.18.20.tgz#3e617c61f33508a27150ee417543c8ab5acc73b0"
+  integrity sha512-/5bHkMWnq1EgKr1V+Ybz3s1hWXok7mDFUMQ4cG10AfW3wL02PSZi5kFpYKrptDsgb2WAJIvRcDm+qIvXf/apvg==
+
+"@esbuild/linux-ia32@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/linux-ia32/-/linux-ia32-0.18.20.tgz#699391cccba9aee6019b7f9892eb99219f1570a7"
+  integrity sha512-P4etWwq6IsReT0E1KHU40bOnzMHoH73aXp96Fs8TIT6z9Hu8G6+0SHSw9i2isWrD2nbx2qo5yUqACgdfVGx7TA==
+
+"@esbuild/linux-loong64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/linux-loong64/-/linux-loong64-0.18.20.tgz#e6fccb7aac178dd2ffb9860465ac89d7f23b977d"
+  integrity sha512-nXW8nqBTrOpDLPgPY9uV+/1DjxoQ7DoB2N8eocyq8I9XuqJ7BiAMDMf9n1xZM9TgW0J8zrquIb/A7s3BJv7rjg==
+
+"@esbuild/linux-mips64el@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/linux-mips64el/-/linux-mips64el-0.18.20.tgz#eeff3a937de9c2310de30622a957ad1bd9183231"
+  integrity sha512-d5NeaXZcHp8PzYy5VnXV3VSd2D328Zb+9dEq5HE6bw6+N86JVPExrA6O68OPwobntbNJ0pzCpUFZTo3w0GyetQ==
+
+"@esbuild/linux-ppc64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/linux-ppc64/-/linux-ppc64-0.18.20.tgz#2f7156bde20b01527993e6881435ad79ba9599fb"
+  integrity sha512-WHPyeScRNcmANnLQkq6AfyXRFr5D6N2sKgkFo2FqguP44Nw2eyDlbTdZwd9GYk98DZG9QItIiTlFLHJHjxP3FA==
+
+"@esbuild/linux-riscv64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/linux-riscv64/-/linux-riscv64-0.18.20.tgz#6628389f210123d8b4743045af8caa7d4ddfc7a6"
+  integrity sha512-WSxo6h5ecI5XH34KC7w5veNnKkju3zBRLEQNY7mv5mtBmrP/MjNBCAlsM2u5hDBlS3NGcTQpoBvRzqBcRtpq1A==
+
+"@esbuild/linux-s390x@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/linux-s390x/-/linux-s390x-0.18.20.tgz#255e81fb289b101026131858ab99fba63dcf0071"
+  integrity sha512-+8231GMs3mAEth6Ja1iK0a1sQ3ohfcpzpRLH8uuc5/KVDFneH6jtAJLFGafpzpMRO6DzJ6AvXKze9LfFMrIHVQ==
+
+"@esbuild/linux-x64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/linux-x64/-/linux-x64-0.18.20.tgz#c7690b3417af318a9b6f96df3031a8865176d338"
+  integrity sha512-UYqiqemphJcNsFEskc73jQ7B9jgwjWrSayxawS6UVFZGWrAAtkzjxSqnoclCXxWtfwLdzU+vTpcNYhpn43uP1w==
+
+"@esbuild/netbsd-x64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/netbsd-x64/-/netbsd-x64-0.18.20.tgz#30e8cd8a3dded63975e2df2438ca109601ebe0d1"
+  integrity sha512-iO1c++VP6xUBUmltHZoMtCUdPlnPGdBom6IrO4gyKPFFVBKioIImVooR5I83nTew5UOYrk3gIJhbZh8X44y06A==
+
+"@esbuild/openbsd-x64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/openbsd-x64/-/openbsd-x64-0.18.20.tgz#7812af31b205055874c8082ea9cf9ab0da6217ae"
+  integrity sha512-e5e4YSsuQfX4cxcygw/UCPIEP6wbIL+se3sxPdCiMbFLBWu0eiZOJ7WoD+ptCLrmjZBK1Wk7I6D/I3NglUGOxg==
+
+"@esbuild/sunos-x64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/sunos-x64/-/sunos-x64-0.18.20.tgz#d5c275c3b4e73c9b0ecd38d1ca62c020f887ab9d"
+  integrity sha512-kDbFRFp0YpTQVVrqUd5FTYmWo45zGaXe0X8E1G/LKFC0v8x0vWrhOWSLITcCn63lmZIxfOMXtCfti/RxN/0wnQ==
+
+"@esbuild/win32-arm64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/win32-arm64/-/win32-arm64-0.18.20.tgz#73bc7f5a9f8a77805f357fab97f290d0e4820ac9"
+  integrity sha512-ddYFR6ItYgoaq4v4JmQQaAI5s7npztfV4Ag6NrhiaW0RrnOXqBkgwZLofVTlq1daVTQNhtI5oieTvkRPfZrePg==
+
+"@esbuild/win32-ia32@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/win32-ia32/-/win32-ia32-0.18.20.tgz#ec93cbf0ef1085cc12e71e0d661d20569ff42102"
+  integrity sha512-Wv7QBi3ID/rROT08SABTS7eV4hX26sVduqDOTe1MvGMjNd3EjOz4b7zeexIR62GTIEKrfJXKL9LFxTYgkyeu7g==
+
+"@esbuild/win32-x64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/win32-x64/-/win32-x64-0.18.20.tgz#786c5f41f043b07afb1af37683d7c33668858f6d"
+  integrity sha512-kTdfRcSiDfQca/y9QIkng02avJ+NCaQvrMejlsB3RRv5sE9rRoeBPISaZpKxHELzRxZyLvNts1P27W3wV+8geQ==
 
 "@eslint/eslintrc@^2.0.0":
   version "2.0.0"
@@ -789,6 +861,11 @@
   resolved "https://registry.npmjs.org/@jridgewell/resolve-uri/-/resolve-uri-3.1.0.tgz"
   integrity sha512-F2msla3tad+Mfht5cJq7LSXcdudKTWCVYUgw6pLFOOHSTtZlj6SWNYAp+AhuqLmWdBO2X5hPrLcu8cVP8fy28w==
 
+"@jridgewell/resolve-uri@^3.1.0":
+  version "3.1.1"
+  resolved "https://registry.yarnpkg.com/@jridgewell/resolve-uri/-/resolve-uri-3.1.1.tgz#c08679063f279615a3326583ba3a90d1d82cc721"
+  integrity sha512-dSYZh7HhCDtCKm4QakX0xFpsRDqjjtZf/kjI/v3T3Nwt5r8/qz/M19F9ySyOqU94SXBmeG9ttTul+YnR4LOxFA==
+
 "@jridgewell/set-array@^1.0.0", "@jridgewell/set-array@^1.0.1":
   version "1.1.2"
   resolved "https://registry.npmjs.org/@jridgewell/set-array/-/set-array-1.1.2.tgz"
@@ -799,6 +876,19 @@
   resolved "https://registry.npmjs.org/@jridgewell/sourcemap-codec/-/sourcemap-codec-1.4.14.tgz"
   integrity sha512-XPSJHWmi394fuUuzDnGz1wiKqWfo1yXecHQMRf2l6hztTO+nPru658AyDngaBe7isIxEkRsPR3FZh+s7iVa4Uw==
 
+"@jridgewell/sourcemap-codec@^1.4.14":
+  version "1.4.15"
+  resolved "https://registry.yarnpkg.com/@jridgewell/sourcemap-codec/-/sourcemap-codec-1.4.15.tgz#d7c6e6755c78567a951e04ab52ef0fd26de59f32"
+  integrity sha512-eF2rxCRulEKXHTRiDrDy6erMYWqNw4LPdQ8UQA4huuxaQsVeRPFl2oM8oDGxMFhJUWZf9McpLtJasDDZb/Bpeg==
+
+"@jridgewell/trace-mapping@^0.3.17":
+  version "0.3.20"
+  resolved "https://registry.yarnpkg.com/@jridgewell/trace-mapping/-/trace-mapping-0.3.20.tgz#72e45707cf240fa6b081d0366f8265b0cd10197f"
+  integrity sha512-R8LcPeWZol2zR8mmH3JeKQ6QRCFb7XgUhV9ZlGhHLGyg4wpPiPZNQOOWhFZhxKw8u//yTbNGI42Bx/3paXEQ+Q==
+  dependencies:
+    "@jridgewell/resolve-uri" "^3.1.0"
+    "@jridgewell/sourcemap-codec" "^1.4.14"
+
 "@jridgewell/trace-mapping@^0.3.9":
   version "0.3.17"
   resolved "https://registry.npmjs.org/@jridgewell/trace-mapping/-/trace-mapping-0.3.17.tgz"
@@ -1201,7 +1291,7 @@ caniuse-lite@^1.0.30001449:
   resolved "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001452.tgz"
   integrity sha512-Lkp0vFjMkBB3GTpLR8zk4NwW5EdRdnitwYJHDOOKIU85x4ckYCPQ+9WlVvSVClHxVReefkUMtWZH2l9KGlD51w==
 
-chalk@^2.0.0:
+chalk@^2.0.0, chalk@^2.4.2:
   version "2.4.2"
   resolved "https://registry.npmjs.org/chalk/-/chalk-2.4.2.tgz"
   integrity sha512-Mti+f9lpJNcwF4tWV8/OrTTtF1gZi+f8FqlyAdouralcFWFQWF2+NgCHShjkCb+IFBLq9buZwE1xckQU4peSuQ==
@@ -1474,33 +1564,33 @@ es-to-primitive@^1.2.1:
     is-date-object "^1.0.1"
     is-symbol "^1.0.2"
 
-esbuild@^0.16.14:
-  version "0.16.17"
-  resolved "https://registry.npmjs.org/esbuild/-/esbuild-0.16.17.tgz"
-  integrity sha512-G8LEkV0XzDMNwXKgM0Jwu3nY3lSTwSGY6XbxM9cr9+s0T/qSV1q1JVPBGzm3dcjhCic9+emZDmMffkwgPeOeLg==
+esbuild@^0.18.10:
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/esbuild/-/esbuild-0.18.20.tgz#4709f5a34801b43b799ab7d6d82f7284a9b7a7a6"
+  integrity sha512-ceqxoedUrcayh7Y7ZX6NdbbDzGROiyVBgC4PriJThBKSVPWnnFHZAkfI1lJT8QFkOwH4qOS2SJkS4wvpGl8BpA==
   optionalDependencies:
-    "@esbuild/android-arm" "0.16.17"
-    "@esbuild/android-arm64" "0.16.17"
-    "@esbuild/android-x64" "0.16.17"
-    "@esbuild/darwin-arm64" "0.16.17"
-    "@esbuild/darwin-x64" "0.16.17"
-    "@esbuild/freebsd-arm64" "0.16.17"
-    "@esbuild/freebsd-x64" "0.16.17"
-    "@esbuild/linux-arm" "0.16.17"
-    "@esbuild/linux-arm64" "0.16.17"
-    "@esbuild/linux-ia32" "0.16.17"
-    "@esbuild/linux-loong64" "0.16.17"
-    "@esbuild/linux-mips64el" "0.16.17"
-    "@esbuild/linux-ppc64" "0.16.17"
-    "@esbuild/linux-riscv64" "0.16.17"
-    "@esbuild/linux-s390x" "0.16.17"
-    "@esbuild/linux-x64" "0.16.17"
-    "@esbuild/netbsd-x64" "0.16.17"
-    "@esbuild/openbsd-x64" "0.16.17"
-    "@esbuild/sunos-x64" "0.16.17"
-    "@esbuild/win32-arm64" "0.16.17"
-    "@esbuild/win32-ia32" "0.16.17"
-    "@esbuild/win32-x64" "0.16.17"
+    "@esbuild/android-arm" "0.18.20"
+    "@esbuild/android-arm64" "0.18.20"
+    "@esbuild/android-x64" "0.18.20"
+    "@esbuild/darwin-arm64" "0.18.20"
+    "@esbuild/darwin-x64" "0.18.20"
+    "@esbuild/freebsd-arm64" "0.18.20"
+    "@esbuild/freebsd-x64" "0.18.20"
+    "@esbuild/linux-arm" "0.18.20"
+    "@esbuild/linux-arm64" "0.18.20"
+    "@esbuild/linux-ia32" "0.18.20"
+    "@esbuild/linux-loong64" "0.18.20"
+    "@esbuild/linux-mips64el" "0.18.20"
+    "@esbuild/linux-ppc64" "0.18.20"
+    "@esbuild/linux-riscv64" "0.18.20"
+    "@esbuild/linux-s390x" "0.18.20"
+    "@esbuild/linux-x64" "0.18.20"
+    "@esbuild/netbsd-x64" "0.18.20"
+    "@esbuild/openbsd-x64" "0.18.20"
+    "@esbuild/sunos-x64" "0.18.20"
+    "@esbuild/win32-arm64" "0.18.20"
+    "@esbuild/win32-ia32" "0.18.20"
+    "@esbuild/win32-x64" "0.18.20"
 
 escalade@^3.1.1:
   version "3.1.1"
@@ -2331,10 +2421,10 @@ ms@2.1.2:
   resolved "https://registry.npmjs.org/ms/-/ms-2.1.2.tgz"
   integrity sha512-sGkPx+VjMtmA6MX27oA4FBFELFCZZ4S4XqeGOXCv68tT+jb3vk/RyaKWP0PTKyWtmLSM0b+adUTEvbs1PEaH2w==
 
-nanoid@^3.3.4:
-  version "3.3.4"
-  resolved "https://registry.npmjs.org/nanoid/-/nanoid-3.3.4.tgz"
-  integrity sha512-MqBkQh/OHTS2egovRtLk45wEyNXwF+cokD+1YPf9u5VfJiRdAiRwB2froX5Co9Rh20xs4siNPm8naNotSD6RBw==
+nanoid@^3.3.7:
+  version "3.3.7"
+  resolved "https://registry.yarnpkg.com/nanoid/-/nanoid-3.3.7.tgz#d0c301a691bc8d54efa0a2226ccf3fe2fd656bd8"
+  integrity sha512-eSRppjcPIatRIMC1U6UngP8XFcz8MQWGQdt1MTBQ7NaAmvXDfvNxbvWV3x2y6CdEUciCSsDHDQZbhYaB8QEo2g==
 
 natural-compare@^1.4.0:
   version "1.4.0"
@@ -2511,12 +2601,12 @@ picomatch@^2.0.4, picomatch@^2.2.1, picomatch@^2.3.1:
   resolved "https://registry.npmjs.org/picomatch/-/picomatch-2.3.1.tgz"
   integrity sha512-JU3teHTNjmE2VCGFzuY8EXzCDVwEqB2a8fsIvwaStHhAWJEeVd1o1QD80CU6+ZdEXXSLbSsuLwJjkCBWqRQUVA==
 
-postcss@^8.4.21:
-  version "8.4.21"
-  resolved "https://registry.npmjs.org/postcss/-/postcss-8.4.21.tgz"
-  integrity sha512-tP7u/Sn/dVxK2NnruI4H9BG+x+Wxz6oeZ1cJ8P6G/PZY0IKk4k/63TDsQf2kQq3+qoJeLm2kIBUNlZe3zgb4Zg==
+postcss@^8.4.27:
+  version "8.4.35"
+  resolved "https://registry.yarnpkg.com/postcss/-/postcss-8.4.35.tgz#60997775689ce09011edf083a549cea44aabe2f7"
+  integrity sha512-u5U8qYpBCpN13BsiEB0CbR1Hhh4Gc0zLFuedrHJKMctHCHAGrMdG0PRM/KErzAL3CU6/eckEtmHNB3x6e3c0vA==
   dependencies:
-    nanoid "^3.3.4"
+    nanoid "^3.3.7"
     picocolors "^1.0.0"
     source-map-js "^1.0.2"
 
@@ -2654,15 +2744,6 @@ resolve-from@^4.0.0:
   resolved "https://registry.npmjs.org/resolve-from/-/resolve-from-4.0.0.tgz"
   integrity sha512-pb/MYmXstAkysRFx8piNI1tGFNQIFA3vkE3Gq4EuA1dF6gHp/+vgZqsCGJapvy8N3Q+4o7FwvquPJcnZ7RYy4g==
 
-resolve@^1.22.1:
-  version "1.22.1"
-  resolved "https://registry.npmjs.org/resolve/-/resolve-1.22.1.tgz"
-  integrity sha512-nBpuuYuY5jFsli/JIs1oldw6fOQCBioohqWZg/2hiaOybXOft4lonv85uDOKXdf8rhyK159cxU5cDcK/NKk8zw==
-  dependencies:
-    is-core-module "^2.9.0"
-    path-parse "^1.0.7"
-    supports-preserve-symlinks-flag "^1.0.0"
-
 resolve@^2.0.0-next.4:
   version "2.0.0-next.4"
   resolved "https://registry.npmjs.org/resolve/-/resolve-2.0.0-next.4.tgz"
@@ -2684,10 +2765,10 @@ rimraf@^3.0.2:
   dependencies:
     glob "^7.1.3"
 
-rollup@^3.10.0:
-  version "3.15.0"
-  resolved "https://registry.npmjs.org/rollup/-/rollup-3.15.0.tgz"
-  integrity sha512-F9hrCAhnp5/zx/7HYmftvsNBkMfLfk/dXUh73hPSM2E3CRgap65orDNJbLetoiUFwSAk6iHPLvBrZ5iHYvzqsg==
+rollup@^3.27.1:
+  version "3.29.4"
+  resolved "https://registry.yarnpkg.com/rollup/-/rollup-3.29.4.tgz#4d70c0f9834146df8705bfb69a9a19c9e1109981"
+  integrity sha512-oWzmBZwvYrU0iJHtDmhsm662rC15FRXmcjCk1xD771dFDx5jJ02ufAQQTn0etB2emNk4J9EZg/yWKpsn9BWGRw==
   optionalDependencies:
     fsevents "~2.3.2"
 
@@ -2934,15 +3015,14 @@ vite-plugin-svgr@^2.4.0:
     "@rollup/pluginutils" "^5.0.2"
     "@svgr/core" "^6.5.1"
 
-vite@^4.1.5:
-  version "4.1.5"
-  resolved "https://registry.yarnpkg.com/vite/-/vite-4.1.5.tgz#9c93d579f62179ab99c4182fa37acf1b380a374b"
-  integrity sha512-zJ0RiVkf61kpd7O+VtU6r766xgnTaIknP/lR6sJTZq3HtVJ3HGnTo5DaJhTUtYoTyS/CQwZ6yEVdc/lrmQT7dQ==
+vite@^4.5.2:
+  version "4.5.2"
+  resolved "https://registry.yarnpkg.com/vite/-/vite-4.5.2.tgz#d6ea8610e099851dad8c7371599969e0f8b97e82"
+  integrity sha512-tBCZBNSBbHQkaGyhGCDUGqeo2ph8Fstyp6FMSvTtsXeZSPpSMGlviAOav2hxVTqFcx8Hj/twtWKsMJXNY0xI8w==
   dependencies:
-    esbuild "^0.16.14"
-    postcss "^8.4.21"
-    resolve "^1.22.1"
-    rollup "^3.10.0"
+    esbuild "^0.18.10"
+    postcss "^8.4.27"
+    rollup "^3.27.1"
   optionalDependencies:
     fsevents "~2.3.2"
 
diff --git a/demos/palm/web/quick-prompt/package-lock.json b/demos/palm/web/quick-prompt/package-lock.json
index 375d48144..c929a59cf 100644
--- a/demos/palm/web/quick-prompt/package-lock.json
+++ b/demos/palm/web/quick-prompt/package-lock.json
@@ -30,7 +30,7 @@
         "eslint-plugin-react-hooks": "^4.6.0",
         "prettier": "^2.8.4",
         "sass": "^1.57.1",
-        "vite": "^4.1.5"
+        "vite": "^4.5.2"
       }
     },
     "node_modules/@ampproject/remapping": {
@@ -47,12 +47,13 @@
       }
     },
     "node_modules/@babel/code-frame": {
-      "version": "7.18.6",
-      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.18.6.tgz",
-      "integrity": "sha512-TDCmlK5eOvH+eH7cdAFlNXeVJqWIQ7gW9tY1GJIpUtFb6CmjVyq2VM3u71bOyR8CRihcCgMUYoDNyLXao3+70Q==",
+      "version": "7.23.5",
+      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.23.5.tgz",
+      "integrity": "sha512-CgH3s1a96LipHCmSUmYFPwY7MNx8C3avkq7i4Wl3cfa662ldtUe4VM1TPXX70pfmrlWTb6jLqTYrZyT2ZTJBgA==",
       "dev": true,
       "dependencies": {
-        "@babel/highlight": "^7.18.6"
+        "@babel/highlight": "^7.23.4",
+        "chalk": "^2.4.2"
       },
       "engines": {
         "node": ">=6.9.0"
@@ -98,13 +99,14 @@
       }
     },
     "node_modules/@babel/generator": {
-      "version": "7.20.14",
-      "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.20.14.tgz",
-      "integrity": "sha512-AEmuXHdcD3A52HHXxaTmYlb8q/xMEhoRP67B3T4Oq7lbmSoqroMZzjnGj3+i1io3pdnF8iBYVu4Ilj+c4hBxYg==",
+      "version": "7.23.5",
+      "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.23.5.tgz",
+      "integrity": "sha512-BPssCHrBD+0YrxviOa3QzpqwhNIXKEtOa2jQrm4FlmkC2apYgRnQcmPWiGZDlGxiNtltnUFolMe8497Esry+jA==",
       "dev": true,
       "dependencies": {
-        "@babel/types": "^7.20.7",
+        "@babel/types": "^7.23.5",
         "@jridgewell/gen-mapping": "^0.3.2",
+        "@jridgewell/trace-mapping": "^0.3.17",
         "jsesc": "^2.5.1"
       },
       "engines": {
@@ -145,34 +147,34 @@
       }
     },
     "node_modules/@babel/helper-environment-visitor": {
-      "version": "7.18.9",
-      "resolved": "https://registry.npmjs.org/@babel/helper-environment-visitor/-/helper-environment-visitor-7.18.9.tgz",
-      "integrity": "sha512-3r/aACDJ3fhQ/EVgFy0hpj8oHyHpQc+LPtJoY9SzTThAsStm4Ptegq92vqKoE3vD706ZVFWITnMnxucw+S9Ipg==",
+      "version": "7.22.20",
+      "resolved": "https://registry.npmjs.org/@babel/helper-environment-visitor/-/helper-environment-visitor-7.22.20.tgz",
+      "integrity": "sha512-zfedSIzFhat/gFhWfHtgWvlec0nqB9YEIVrpuwjruLlXfUSnA8cJB0miHKwqDnQ7d32aKo2xt88/xZptwxbfhA==",
       "dev": true,
       "engines": {
         "node": ">=6.9.0"
       }
     },
     "node_modules/@babel/helper-function-name": {
-      "version": "7.19.0",
-      "resolved": "https://registry.npmjs.org/@babel/helper-function-name/-/helper-function-name-7.19.0.tgz",
-      "integrity": "sha512-WAwHBINyrpqywkUH0nTnNgI5ina5TFn85HKS0pbPDfxFfhyR/aNQEn4hGi1P1JyT//I0t4OgXUlofzWILRvS5w==",
+      "version": "7.23.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-function-name/-/helper-function-name-7.23.0.tgz",
+      "integrity": "sha512-OErEqsrxjZTJciZ4Oo+eoZqeW9UIiOcuYKRJA4ZAgV9myA+pOXhhmpfNCKjEH/auVfEYVFJ6y1Tc4r0eIApqiw==",
       "dev": true,
       "dependencies": {
-        "@babel/template": "^7.18.10",
-        "@babel/types": "^7.19.0"
+        "@babel/template": "^7.22.15",
+        "@babel/types": "^7.23.0"
       },
       "engines": {
         "node": ">=6.9.0"
       }
     },
     "node_modules/@babel/helper-hoist-variables": {
-      "version": "7.18.6",
-      "resolved": "https://registry.npmjs.org/@babel/helper-hoist-variables/-/helper-hoist-variables-7.18.6.tgz",
-      "integrity": "sha512-UlJQPkFqFULIcyW5sbzgbkxn2FKRgwWiRexcuaR8RNJRy8+LLveqPjwZV/bwrLZCN0eUHD/x8D0heK1ozuoo6Q==",
+      "version": "7.22.5",
+      "resolved": "https://registry.npmjs.org/@babel/helper-hoist-variables/-/helper-hoist-variables-7.22.5.tgz",
+      "integrity": "sha512-wGjk9QZVzvknA6yKIUURb8zY3grXCcOZt+/7Wcy8O2uctxhplmUPkOdlgoNhmdVee2c92JXbf1xpMtVNbfoxRw==",
       "dev": true,
       "dependencies": {
-        "@babel/types": "^7.18.6"
+        "@babel/types": "^7.22.5"
       },
       "engines": {
         "node": ">=6.9.0"
@@ -231,30 +233,30 @@
       }
     },
     "node_modules/@babel/helper-split-export-declaration": {
-      "version": "7.18.6",
-      "resolved": "https://registry.npmjs.org/@babel/helper-split-export-declaration/-/helper-split-export-declaration-7.18.6.tgz",
-      "integrity": "sha512-bde1etTx6ZyTmobl9LLMMQsaizFVZrquTEHOqKeQESMKo4PlObf+8+JA25ZsIpZhT/WEd39+vOdLXAFG/nELpA==",
+      "version": "7.22.6",
+      "resolved": "https://registry.npmjs.org/@babel/helper-split-export-declaration/-/helper-split-export-declaration-7.22.6.tgz",
+      "integrity": "sha512-AsUnxuLhRYsisFiaJwvp1QF+I3KjD5FOxut14q/GzovUe6orHLesW2C7d754kRm53h5gqrz6sFl6sxc4BVtE/g==",
       "dev": true,
       "dependencies": {
-        "@babel/types": "^7.18.6"
+        "@babel/types": "^7.22.5"
       },
       "engines": {
         "node": ">=6.9.0"
       }
     },
     "node_modules/@babel/helper-string-parser": {
-      "version": "7.19.4",
-      "resolved": "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.19.4.tgz",
-      "integrity": "sha512-nHtDoQcuqFmwYNYPz3Rah5ph2p8PFeFCsZk9A/48dPc/rGocJ5J3hAAZ7pb76VWX3fZKu+uEr/FhH5jLx7umrw==",
+      "version": "7.23.4",
+      "resolved": "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.23.4.tgz",
+      "integrity": "sha512-803gmbQdqwdf4olxrX4AJyFBV/RTr3rSmOj0rKwesmzlfhYNDEs+/iOcznzpNWlJlIlTJC2QfPFcHB6DlzdVLQ==",
       "dev": true,
       "engines": {
         "node": ">=6.9.0"
       }
     },
     "node_modules/@babel/helper-validator-identifier": {
-      "version": "7.19.1",
-      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.19.1.tgz",
-      "integrity": "sha512-awrNfaMtnHUr653GgGEs++LlAvW6w+DcPrOliSMXWCKo597CwL5Acf/wWdNkf/tfEQE3mjkeD1YOVZOUV/od1w==",
+      "version": "7.22.20",
+      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.22.20.tgz",
+      "integrity": "sha512-Y4OZ+ytlatR8AI+8KZfKuL5urKp7qey08ha31L8b3BwewJAoJamTzyvxPR/5D+KkdJCGPq/+8TukHBlY10FX9A==",
       "dev": true,
       "engines": {
         "node": ">=6.9.0"
@@ -284,13 +286,13 @@
       }
     },
     "node_modules/@babel/highlight": {
-      "version": "7.18.6",
-      "resolved": "https://registry.npmjs.org/@babel/highlight/-/highlight-7.18.6.tgz",
-      "integrity": "sha512-u7stbOuYjaPezCuLj29hNW1v64M2Md2qupEKP1fHc7WdOA3DgLh37suiSrZYY7haUB7iBeQZ9P1uiRF359do3g==",
+      "version": "7.23.4",
+      "resolved": "https://registry.npmjs.org/@babel/highlight/-/highlight-7.23.4.tgz",
+      "integrity": "sha512-acGdbYSfp2WheJoJm/EBBBLh/ID8KDc64ISZ9DYtBmC8/Q204PZJLHyzeB5qMzJ5trcOkybd78M4x2KWsUq++A==",
       "dev": true,
       "dependencies": {
-        "@babel/helper-validator-identifier": "^7.18.6",
-        "chalk": "^2.0.0",
+        "@babel/helper-validator-identifier": "^7.22.20",
+        "chalk": "^2.4.2",
         "js-tokens": "^4.0.0"
       },
       "engines": {
@@ -298,9 +300,9 @@
       }
     },
     "node_modules/@babel/parser": {
-      "version": "7.20.15",
-      "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.20.15.tgz",
-      "integrity": "sha512-DI4a1oZuf8wC+oAJA9RW6ga3Zbe8RZFt7kD9i4qAspz3I/yHet1VvC3DiSy/fsUvv5pvJuNPh0LPOdCcqinDPg==",
+      "version": "7.23.5",
+      "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.23.5.tgz",
+      "integrity": "sha512-hOOqoiNXrmGdFbhgCzu6GiURxUgM27Xwd/aPuu8RfHEZPBzL1Z54okAHAQjXfcQNwvrlkAmAp4SlRTZ45vlthQ==",
       "dev": true,
       "bin": {
         "parser": "bin/babel-parser.js"
@@ -352,33 +354,33 @@
       }
     },
     "node_modules/@babel/template": {
-      "version": "7.20.7",
-      "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.20.7.tgz",
-      "integrity": "sha512-8SegXApWe6VoNw0r9JHpSteLKTpTiLZ4rMlGIm9JQ18KiCtyQiAMEazujAHrUS5flrcqYZa75ukev3P6QmUwUw==",
+      "version": "7.22.15",
+      "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.22.15.tgz",
+      "integrity": "sha512-QPErUVm4uyJa60rkI73qneDacvdvzxshT3kksGqlGWYdOTIUOwJ7RDUL8sGqslY1uXWSL6xMFKEXDS3ox2uF0w==",
       "dev": true,
       "dependencies": {
-        "@babel/code-frame": "^7.18.6",
-        "@babel/parser": "^7.20.7",
-        "@babel/types": "^7.20.7"
+        "@babel/code-frame": "^7.22.13",
+        "@babel/parser": "^7.22.15",
+        "@babel/types": "^7.22.15"
       },
       "engines": {
         "node": ">=6.9.0"
       }
     },
     "node_modules/@babel/traverse": {
-      "version": "7.20.13",
-      "resolved": "https://registry.npmjs.org/@babel/traverse/-/traverse-7.20.13.tgz",
-      "integrity": "sha512-kMJXfF0T6DIS9E8cgdLCSAL+cuCK+YEZHWiLK0SXpTo8YRj5lpJu3CDNKiIBCne4m9hhTIqUg6SYTAI39tAiVQ==",
-      "dev": true,
-      "dependencies": {
-        "@babel/code-frame": "^7.18.6",
-        "@babel/generator": "^7.20.7",
-        "@babel/helper-environment-visitor": "^7.18.9",
-        "@babel/helper-function-name": "^7.19.0",
-        "@babel/helper-hoist-variables": "^7.18.6",
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-      "integrity": "sha512-tP7u/Sn/dVxK2NnruI4H9BG+x+Wxz6oeZ1cJ8P6G/PZY0IKk4k/63TDsQf2kQq3+qoJeLm2kIBUNlZe3zgb4Zg==",
+      "version": "8.4.31",
+      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.31.tgz",
+      "integrity": "sha512-PS08Iboia9mts/2ygV3eLpY5ghnUcfLV/EXTOW1E2qYxJKGGBUtNjN76FYHnMs36RmARn41bC0AZmn+rR0OVpQ==",
       "dev": true,
       "requires": {
-        "nanoid": "^3.3.4",
+        "nanoid": "^3.3.6",
         "picocolors": "^1.0.0",
         "source-map-js": "^1.0.2"
       }
@@ -8026,17 +8029,6 @@
       "resolved": "https://registry.npmjs.org/require-directory/-/require-directory-2.1.1.tgz",
       "integrity": "sha512-fGxEI7+wsG9xrvdjsrlmL22OMTTiHRwAMroiEeMgq8gzoLC/PQr7RsRDSTLUg/bZAZtF+TVIkHc6/4RIKrui+Q=="
     },
-    "resolve": {
-      "version": "1.22.1",
-      "resolved": "https://registry.npmjs.org/resolve/-/resolve-1.22.1.tgz",
-      "integrity": "sha512-nBpuuYuY5jFsli/JIs1oldw6fOQCBioohqWZg/2hiaOybXOft4lonv85uDOKXdf8rhyK159cxU5cDcK/NKk8zw==",
-      "dev": true,
-      "requires": {
-        "is-core-module": "^2.9.0",
-        "path-parse": "^1.0.7",
-        "supports-preserve-symlinks-flag": "^1.0.0"
-      }
-    },
     "resolve-from": {
       "version": "4.0.0",
       "resolved": "https://registry.npmjs.org/resolve-from/-/resolve-from-4.0.0.tgz",
@@ -8059,9 +8051,9 @@
       }
     },
     "rollup": {
-      "version": "3.15.0",
-      "resolved": "https://registry.npmjs.org/rollup/-/rollup-3.15.0.tgz",
-      "integrity": "sha512-F9hrCAhnp5/zx/7HYmftvsNBkMfLfk/dXUh73hPSM2E3CRgap65orDNJbLetoiUFwSAk6iHPLvBrZ5iHYvzqsg==",
+      "version": "3.29.4",
+      "resolved": "https://registry.npmjs.org/rollup/-/rollup-3.29.4.tgz",
+      "integrity": "sha512-oWzmBZwvYrU0iJHtDmhsm662rC15FRXmcjCk1xD771dFDx5jJ02ufAQQTn0etB2emNk4J9EZg/yWKpsn9BWGRw==",
       "dev": true,
       "requires": {
         "fsevents": "~2.3.2"
@@ -8342,16 +8334,15 @@
       "integrity": "sha512-MXcSTerfPa4uqyzStbRoTgt5XIe3x5+42+q1sDuy3R5MDk66URdLMOZe5aPX/SQd+kuYAh0FdP/pO28IkQyTeg=="
     },
     "vite": {
-      "version": "4.1.5",
-      "resolved": "https://registry.npmjs.org/vite/-/vite-4.1.5.tgz",
-      "integrity": "sha512-zJ0RiVkf61kpd7O+VtU6r766xgnTaIknP/lR6sJTZq3HtVJ3HGnTo5DaJhTUtYoTyS/CQwZ6yEVdc/lrmQT7dQ==",
+      "version": "4.5.2",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-4.5.2.tgz",
+      "integrity": "sha512-tBCZBNSBbHQkaGyhGCDUGqeo2ph8Fstyp6FMSvTtsXeZSPpSMGlviAOav2hxVTqFcx8Hj/twtWKsMJXNY0xI8w==",
       "dev": true,
       "requires": {
-        "esbuild": "^0.16.14",
+        "esbuild": "^0.18.10",
         "fsevents": "~2.3.2",
-        "postcss": "^8.4.21",
-        "resolve": "^1.22.1",
-        "rollup": "^3.10.0"
+        "postcss": "^8.4.27",
+        "rollup": "^3.27.1"
       }
     },
     "webidl-conversions": {
diff --git a/demos/palm/web/quick-prompt/package.json b/demos/palm/web/quick-prompt/package.json
index 7e1f87f70..9ac5e1c42 100644
--- a/demos/palm/web/quick-prompt/package.json
+++ b/demos/palm/web/quick-prompt/package.json
@@ -36,6 +36,6 @@
     "eslint-plugin-react-hooks": "^4.6.0",
     "prettier": "^2.8.4",
     "sass": "^1.57.1",
-    "vite": "^4.1.5"
+    "vite": "^4.5.2"
   }
 }
diff --git a/demos/palm/web/quick-prompt/yarn.lock b/demos/palm/web/quick-prompt/yarn.lock
index 634610503..47641e284 100644
--- a/demos/palm/web/quick-prompt/yarn.lock
+++ b/demos/palm/web/quick-prompt/yarn.lock
@@ -17,6 +17,14 @@
   dependencies:
     "@babel/highlight" "^7.18.6"
 
+"@babel/code-frame@^7.22.13", "@babel/code-frame@^7.23.5":
+  version "7.23.5"
+  resolved "https://registry.yarnpkg.com/@babel/code-frame/-/code-frame-7.23.5.tgz#9009b69a8c602293476ad598ff53e4562e15c244"
+  integrity sha512-CgH3s1a96LipHCmSUmYFPwY7MNx8C3avkq7i4Wl3cfa662ldtUe4VM1TPXX70pfmrlWTb6jLqTYrZyT2ZTJBgA==
+  dependencies:
+    "@babel/highlight" "^7.23.4"
+    chalk "^2.4.2"
+
 "@babel/compat-data@^7.20.5":
   version "7.20.14"
   resolved "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.20.14.tgz"
@@ -52,6 +60,16 @@
     "@jridgewell/gen-mapping" "^0.3.2"
     jsesc "^2.5.1"
 
+"@babel/generator@^7.23.5":
+  version "7.23.5"
+  resolved "https://registry.yarnpkg.com/@babel/generator/-/generator-7.23.5.tgz#17d0a1ea6b62f351d281350a5f80b87a810c4755"
+  integrity sha512-BPssCHrBD+0YrxviOa3QzpqwhNIXKEtOa2jQrm4FlmkC2apYgRnQcmPWiGZDlGxiNtltnUFolMe8497Esry+jA==
+  dependencies:
+    "@babel/types" "^7.23.5"
+    "@jridgewell/gen-mapping" "^0.3.2"
+    "@jridgewell/trace-mapping" "^0.3.17"
+    jsesc "^2.5.1"
+
 "@babel/helper-compilation-targets@^7.20.7":
   version "7.20.7"
   resolved "https://registry.npmjs.org/@babel/helper-compilation-targets/-/helper-compilation-targets-7.20.7.tgz"
@@ -68,20 +86,25 @@
   resolved "https://registry.npmjs.org/@babel/helper-environment-visitor/-/helper-environment-visitor-7.18.9.tgz"
   integrity sha512-3r/aACDJ3fhQ/EVgFy0hpj8oHyHpQc+LPtJoY9SzTThAsStm4Ptegq92vqKoE3vD706ZVFWITnMnxucw+S9Ipg==
 
-"@babel/helper-function-name@^7.19.0":
-  version "7.19.0"
-  resolved "https://registry.npmjs.org/@babel/helper-function-name/-/helper-function-name-7.19.0.tgz"
-  integrity sha512-WAwHBINyrpqywkUH0nTnNgI5ina5TFn85HKS0pbPDfxFfhyR/aNQEn4hGi1P1JyT//I0t4OgXUlofzWILRvS5w==
+"@babel/helper-environment-visitor@^7.22.20":
+  version "7.22.20"
+  resolved "https://registry.yarnpkg.com/@babel/helper-environment-visitor/-/helper-environment-visitor-7.22.20.tgz#96159db61d34a29dba454c959f5ae4a649ba9167"
+  integrity sha512-zfedSIzFhat/gFhWfHtgWvlec0nqB9YEIVrpuwjruLlXfUSnA8cJB0miHKwqDnQ7d32aKo2xt88/xZptwxbfhA==
+
+"@babel/helper-function-name@^7.23.0":
+  version "7.23.0"
+  resolved "https://registry.yarnpkg.com/@babel/helper-function-name/-/helper-function-name-7.23.0.tgz#1f9a3cdbd5b2698a670c30d2735f9af95ed52759"
+  integrity sha512-OErEqsrxjZTJciZ4Oo+eoZqeW9UIiOcuYKRJA4ZAgV9myA+pOXhhmpfNCKjEH/auVfEYVFJ6y1Tc4r0eIApqiw==
   dependencies:
-    "@babel/template" "^7.18.10"
-    "@babel/types" "^7.19.0"
+    "@babel/template" "^7.22.15"
+    "@babel/types" "^7.23.0"
 
-"@babel/helper-hoist-variables@^7.18.6":
-  version "7.18.6"
-  resolved "https://registry.npmjs.org/@babel/helper-hoist-variables/-/helper-hoist-variables-7.18.6.tgz"
-  integrity sha512-UlJQPkFqFULIcyW5sbzgbkxn2FKRgwWiRexcuaR8RNJRy8+LLveqPjwZV/bwrLZCN0eUHD/x8D0heK1ozuoo6Q==
+"@babel/helper-hoist-variables@^7.22.5":
+  version "7.22.5"
+  resolved "https://registry.yarnpkg.com/@babel/helper-hoist-variables/-/helper-hoist-variables-7.22.5.tgz#c01a007dac05c085914e8fb652b339db50d823bb"
+  integrity sha512-wGjk9QZVzvknA6yKIUURb8zY3grXCcOZt+/7Wcy8O2uctxhplmUPkOdlgoNhmdVee2c92JXbf1xpMtVNbfoxRw==
   dependencies:
-    "@babel/types" "^7.18.6"
+    "@babel/types" "^7.22.5"
 
 "@babel/helper-module-imports@^7.18.6":
   version "7.18.6"
@@ -123,16 +146,33 @@
   dependencies:
     "@babel/types" "^7.18.6"
 
+"@babel/helper-split-export-declaration@^7.22.6":
+  version "7.22.6"
+  resolved "https://registry.yarnpkg.com/@babel/helper-split-export-declaration/-/helper-split-export-declaration-7.22.6.tgz#322c61b7310c0997fe4c323955667f18fcefb91c"
+  integrity sha512-AsUnxuLhRYsisFiaJwvp1QF+I3KjD5FOxut14q/GzovUe6orHLesW2C7d754kRm53h5gqrz6sFl6sxc4BVtE/g==
+  dependencies:
+    "@babel/types" "^7.22.5"
+
 "@babel/helper-string-parser@^7.19.4":
   version "7.19.4"
   resolved "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.19.4.tgz"
   integrity sha512-nHtDoQcuqFmwYNYPz3Rah5ph2p8PFeFCsZk9A/48dPc/rGocJ5J3hAAZ7pb76VWX3fZKu+uEr/FhH5jLx7umrw==
 
+"@babel/helper-string-parser@^7.23.4":
+  version "7.23.4"
+  resolved "https://registry.yarnpkg.com/@babel/helper-string-parser/-/helper-string-parser-7.23.4.tgz#9478c707febcbbe1ddb38a3d91a2e054ae622d83"
+  integrity sha512-803gmbQdqwdf4olxrX4AJyFBV/RTr3rSmOj0rKwesmzlfhYNDEs+/iOcznzpNWlJlIlTJC2QfPFcHB6DlzdVLQ==
+
 "@babel/helper-validator-identifier@^7.18.6", "@babel/helper-validator-identifier@^7.19.1":
   version "7.19.1"
   resolved "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.19.1.tgz"
   integrity sha512-awrNfaMtnHUr653GgGEs++LlAvW6w+DcPrOliSMXWCKo597CwL5Acf/wWdNkf/tfEQE3mjkeD1YOVZOUV/od1w==
 
+"@babel/helper-validator-identifier@^7.22.20":
+  version "7.22.20"
+  resolved "https://registry.yarnpkg.com/@babel/helper-validator-identifier/-/helper-validator-identifier-7.22.20.tgz#c4ae002c61d2879e724581d96665583dbc1dc0e0"
+  integrity sha512-Y4OZ+ytlatR8AI+8KZfKuL5urKp7qey08ha31L8b3BwewJAoJamTzyvxPR/5D+KkdJCGPq/+8TukHBlY10FX9A==
+
 "@babel/helper-validator-option@^7.18.6":
   version "7.18.6"
   resolved "https://registry.npmjs.org/@babel/helper-validator-option/-/helper-validator-option-7.18.6.tgz"
@@ -156,11 +196,25 @@
     chalk "^2.0.0"
     js-tokens "^4.0.0"
 
-"@babel/parser@^7.20.13", "@babel/parser@^7.20.7":
+"@babel/highlight@^7.23.4":
+  version "7.23.4"
+  resolved "https://registry.yarnpkg.com/@babel/highlight/-/highlight-7.23.4.tgz#edaadf4d8232e1a961432db785091207ead0621b"
+  integrity sha512-acGdbYSfp2WheJoJm/EBBBLh/ID8KDc64ISZ9DYtBmC8/Q204PZJLHyzeB5qMzJ5trcOkybd78M4x2KWsUq++A==
+  dependencies:
+    "@babel/helper-validator-identifier" "^7.22.20"
+    chalk "^2.4.2"
+    js-tokens "^4.0.0"
+
+"@babel/parser@^7.20.7":
   version "7.20.15"
   resolved "https://registry.npmjs.org/@babel/parser/-/parser-7.20.15.tgz"
   integrity sha512-DI4a1oZuf8wC+oAJA9RW6ga3Zbe8RZFt7kD9i4qAspz3I/yHet1VvC3DiSy/fsUvv5pvJuNPh0LPOdCcqinDPg==
 
+"@babel/parser@^7.22.15", "@babel/parser@^7.23.5":
+  version "7.23.5"
+  resolved "https://registry.yarnpkg.com/@babel/parser/-/parser-7.23.5.tgz#37dee97c4752af148e1d38c34b856b2507660563"
+  integrity sha512-hOOqoiNXrmGdFbhgCzu6GiURxUgM27Xwd/aPuu8RfHEZPBzL1Z54okAHAQjXfcQNwvrlkAmAp4SlRTZ45vlthQ==
+
 "@babel/plugin-transform-react-jsx-self@^7.18.6":
   version "7.18.6"
   resolved "https://registry.npmjs.org/@babel/plugin-transform-react-jsx-self/-/plugin-transform-react-jsx-self-7.18.6.tgz"
@@ -182,7 +236,7 @@
   dependencies:
     regenerator-runtime "^0.13.11"
 
-"@babel/template@^7.18.10", "@babel/template@^7.20.7":
+"@babel/template@^7.20.7":
   version "7.20.7"
   resolved "https://registry.npmjs.org/@babel/template/-/template-7.20.7.tgz"
   integrity sha512-8SegXApWe6VoNw0r9JHpSteLKTpTiLZ4rMlGIm9JQ18KiCtyQiAMEazujAHrUS5flrcqYZa75ukev3P6QmUwUw==
@@ -191,23 +245,32 @@
     "@babel/parser" "^7.20.7"
     "@babel/types" "^7.20.7"
 
-"@babel/traverse@^7.20.10", "@babel/traverse@^7.20.12", "@babel/traverse@^7.20.13":
-  version "7.20.13"
-  resolved "https://registry.npmjs.org/@babel/traverse/-/traverse-7.20.13.tgz"
-  integrity sha512-kMJXfF0T6DIS9E8cgdLCSAL+cuCK+YEZHWiLK0SXpTo8YRj5lpJu3CDNKiIBCne4m9hhTIqUg6SYTAI39tAiVQ==
+"@babel/template@^7.22.15":
+  version "7.22.15"
+  resolved "https://registry.yarnpkg.com/@babel/template/-/template-7.22.15.tgz#09576efc3830f0430f4548ef971dde1350ef2f38"
+  integrity sha512-QPErUVm4uyJa60rkI73qneDacvdvzxshT3kksGqlGWYdOTIUOwJ7RDUL8sGqslY1uXWSL6xMFKEXDS3ox2uF0w==
   dependencies:
-    "@babel/code-frame" "^7.18.6"
-    "@babel/generator" "^7.20.7"
-    "@babel/helper-environment-visitor" "^7.18.9"
-    "@babel/helper-function-name" "^7.19.0"
-    "@babel/helper-hoist-variables" "^7.18.6"
-    "@babel/helper-split-export-declaration" "^7.18.6"
-    "@babel/parser" "^7.20.13"
-    "@babel/types" "^7.20.7"
+    "@babel/code-frame" "^7.22.13"
+    "@babel/parser" "^7.22.15"
+    "@babel/types" "^7.22.15"
+
+"@babel/traverse@^7.20.10", "@babel/traverse@^7.20.12", "@babel/traverse@^7.20.13":
+  version "7.23.5"
+  resolved "https://registry.yarnpkg.com/@babel/traverse/-/traverse-7.23.5.tgz#f546bf9aba9ef2b042c0e00d245990c15508e7ec"
+  integrity sha512-czx7Xy5a6sapWWRx61m1Ke1Ra4vczu1mCTtJam5zRTBOonfdJ+S/B6HYmGYu3fJtr8GGET3si6IhgWVBhJ/m8w==
+  dependencies:
+    "@babel/code-frame" "^7.23.5"
+    "@babel/generator" "^7.23.5"
+    "@babel/helper-environment-visitor" "^7.22.20"
+    "@babel/helper-function-name" "^7.23.0"
+    "@babel/helper-hoist-variables" "^7.22.5"
+    "@babel/helper-split-export-declaration" "^7.22.6"
+    "@babel/parser" "^7.23.5"
+    "@babel/types" "^7.23.5"
     debug "^4.1.0"
     globals "^11.1.0"
 
-"@babel/types@^7.18.6", "@babel/types@^7.19.0", "@babel/types@^7.20.2", "@babel/types@^7.20.7":
+"@babel/types@^7.18.6", "@babel/types@^7.20.2", "@babel/types@^7.20.7":
   version "7.20.7"
   resolved "https://registry.npmjs.org/@babel/types/-/types-7.20.7.tgz"
   integrity sha512-69OnhBxSSgK0OzTJai4kyPDiKTIe3j+ctaHdIGVbRahTLAT7L3R9oeXHC2aVSuGYt3cVnoAMDmOCgJ2yaiLMvg==
@@ -216,115 +279,124 @@
     "@babel/helper-validator-identifier" "^7.19.1"
     to-fast-properties "^2.0.0"
 
-"@esbuild/android-arm64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/android-arm64/-/android-arm64-0.16.17.tgz#cf91e86df127aa3d141744edafcba0abdc577d23"
-  integrity sha512-MIGl6p5sc3RDTLLkYL1MyL8BMRN4tLMRCn+yRJJmEDvYZ2M7tmAf80hx1kbNEUX2KJ50RRtxZ4JHLvCfuB6kBg==
-
-"@esbuild/android-arm@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/android-arm/-/android-arm-0.16.17.tgz#025b6246d3f68b7bbaa97069144fb5fb70f2fff2"
-  integrity sha512-N9x1CMXVhtWEAMS7pNNONyA14f71VPQN9Cnavj1XQh6T7bskqiLLrSca4O0Vr8Wdcga943eThxnVp3JLnBMYtw==
-
-"@esbuild/android-x64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/android-x64/-/android-x64-0.16.17.tgz#c820e0fef982f99a85c4b8bfdd582835f04cd96e"
-  integrity sha512-a3kTv3m0Ghh4z1DaFEuEDfz3OLONKuFvI4Xqczqx4BqLyuFaFkuaG4j2MtA6fuWEFeC5x9IvqnX7drmRq/fyAQ==
-
-"@esbuild/darwin-arm64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.npmjs.org/@esbuild/darwin-arm64/-/darwin-arm64-0.16.17.tgz"
-  integrity sha512-/2agbUEfmxWHi9ARTX6OQ/KgXnOWfsNlTeLcoV7HSuSTv63E4DqtAc+2XqGw1KHxKMHGZgbVCZge7HXWX9Vn+w==
-
-"@esbuild/darwin-x64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/darwin-x64/-/darwin-x64-0.16.17.tgz#42829168730071c41ef0d028d8319eea0e2904b4"
-  integrity sha512-2By45OBHulkd9Svy5IOCZt376Aa2oOkiE9QWUK9fe6Tb+WDr8hXL3dpqi+DeLiMed8tVXspzsTAvd0jUl96wmg==
-
-"@esbuild/freebsd-arm64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/freebsd-arm64/-/freebsd-arm64-0.16.17.tgz#1f4af488bfc7e9ced04207034d398e793b570a27"
-  integrity sha512-mt+cxZe1tVx489VTb4mBAOo2aKSnJ33L9fr25JXpqQqzbUIw/yzIzi+NHwAXK2qYV1lEFp4OoVeThGjUbmWmdw==
-
-"@esbuild/freebsd-x64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/freebsd-x64/-/freebsd-x64-0.16.17.tgz#636306f19e9bc981e06aa1d777302dad8fddaf72"
-  integrity sha512-8ScTdNJl5idAKjH8zGAsN7RuWcyHG3BAvMNpKOBaqqR7EbUhhVHOqXRdL7oZvz8WNHL2pr5+eIT5c65kA6NHug==
-
-"@esbuild/linux-arm64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/linux-arm64/-/linux-arm64-0.16.17.tgz#a003f7ff237c501e095d4f3a09e58fc7b25a4aca"
-  integrity sha512-7S8gJnSlqKGVJunnMCrXHU9Q8Q/tQIxk/xL8BqAP64wchPCTzuM6W3Ra8cIa1HIflAvDnNOt2jaL17vaW+1V0g==
-
-"@esbuild/linux-arm@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/linux-arm/-/linux-arm-0.16.17.tgz#b591e6a59d9c4fe0eeadd4874b157ab78cf5f196"
-  integrity sha512-iihzrWbD4gIT7j3caMzKb/RsFFHCwqqbrbH9SqUSRrdXkXaygSZCZg1FybsZz57Ju7N/SHEgPyaR0LZ8Zbe9gQ==
-
-"@esbuild/linux-ia32@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/linux-ia32/-/linux-ia32-0.16.17.tgz#24333a11027ef46a18f57019450a5188918e2a54"
-  integrity sha512-kiX69+wcPAdgl3Lonh1VI7MBr16nktEvOfViszBSxygRQqSpzv7BffMKRPMFwzeJGPxcio0pdD3kYQGpqQ2SSg==
-
-"@esbuild/linux-loong64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/linux-loong64/-/linux-loong64-0.16.17.tgz#d5ad459d41ed42bbd4d005256b31882ec52227d8"
-  integrity sha512-dTzNnQwembNDhd654cA4QhbS9uDdXC3TKqMJjgOWsC0yNCbpzfWoXdZvp0mY7HU6nzk5E0zpRGGx3qoQg8T2DQ==
-
-"@esbuild/linux-mips64el@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/linux-mips64el/-/linux-mips64el-0.16.17.tgz#4e5967a665c38360b0a8205594377d4dcf9c3726"
-  integrity sha512-ezbDkp2nDl0PfIUn0CsQ30kxfcLTlcx4Foz2kYv8qdC6ia2oX5Q3E/8m6lq84Dj/6b0FrkgD582fJMIfHhJfSw==
-
-"@esbuild/linux-ppc64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/linux-ppc64/-/linux-ppc64-0.16.17.tgz#206443a02eb568f9fdf0b438fbd47d26e735afc8"
-  integrity sha512-dzS678gYD1lJsW73zrFhDApLVdM3cUF2MvAa1D8K8KtcSKdLBPP4zZSLy6LFZ0jYqQdQ29bjAHJDgz0rVbLB3g==
-
-"@esbuild/linux-riscv64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/linux-riscv64/-/linux-riscv64-0.16.17.tgz#c351e433d009bf256e798ad048152c8d76da2fc9"
-  integrity sha512-ylNlVsxuFjZK8DQtNUwiMskh6nT0vI7kYl/4fZgV1llP5d6+HIeL/vmmm3jpuoo8+NuXjQVZxmKuhDApK0/cKw==
-
-"@esbuild/linux-s390x@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/linux-s390x/-/linux-s390x-0.16.17.tgz#661f271e5d59615b84b6801d1c2123ad13d9bd87"
-  integrity sha512-gzy7nUTO4UA4oZ2wAMXPNBGTzZFP7mss3aKR2hH+/4UUkCOyqmjXiKpzGrY2TlEUhbbejzXVKKGazYcQTZWA/w==
-
-"@esbuild/linux-x64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/linux-x64/-/linux-x64-0.16.17.tgz#e4ba18e8b149a89c982351443a377c723762b85f"
-  integrity sha512-mdPjPxfnmoqhgpiEArqi4egmBAMYvaObgn4poorpUaqmvzzbvqbowRllQ+ZgzGVMGKaPkqUmPDOOFQRUFDmeUw==
-
-"@esbuild/netbsd-x64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/netbsd-x64/-/netbsd-x64-0.16.17.tgz#7d4f4041e30c5c07dd24ffa295c73f06038ec775"
-  integrity sha512-/PzmzD/zyAeTUsduZa32bn0ORug+Jd1EGGAUJvqfeixoEISYpGnAezN6lnJoskauoai0Jrs+XSyvDhppCPoKOA==
-
-"@esbuild/openbsd-x64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/openbsd-x64/-/openbsd-x64-0.16.17.tgz#970fa7f8470681f3e6b1db0cc421a4af8060ec35"
-  integrity sha512-2yaWJhvxGEz2RiftSk0UObqJa/b+rIAjnODJgv2GbGGpRwAfpgzyrg1WLK8rqA24mfZa9GvpjLcBBg8JHkoodg==
-
-"@esbuild/sunos-x64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/sunos-x64/-/sunos-x64-0.16.17.tgz#abc60e7c4abf8b89fb7a4fe69a1484132238022c"
-  integrity sha512-xtVUiev38tN0R3g8VhRfN7Zl42YCJvyBhRKw1RJjwE1d2emWTVToPLNEQj/5Qxc6lVFATDiy6LjVHYhIPrLxzw==
-
-"@esbuild/win32-arm64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/win32-arm64/-/win32-arm64-0.16.17.tgz#7b0ff9e8c3265537a7a7b1fd9a24e7bd39fcd87a"
-  integrity sha512-ga8+JqBDHY4b6fQAmOgtJJue36scANy4l/rL97W+0wYmijhxKetzZdKOJI7olaBaMhWt8Pac2McJdZLxXWUEQw==
-
-"@esbuild/win32-ia32@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/win32-ia32/-/win32-ia32-0.16.17.tgz#e90fe5267d71a7b7567afdc403dfd198c292eb09"
-  integrity sha512-WnsKaf46uSSF/sZhwnqE4L/F89AYNMiD4YtEcYekBt9Q7nj0DiId2XH2Ng2PHM54qi5oPrQ8luuzGszqi/veig==
-
-"@esbuild/win32-x64@0.16.17":
-  version "0.16.17"
-  resolved "https://registry.yarnpkg.com/@esbuild/win32-x64/-/win32-x64-0.16.17.tgz#c5a1a4bfe1b57f0c3e61b29883525c6da3e5c091"
-  integrity sha512-y+EHuSchhL7FjHgvQL/0fnnFmO4T1bhvWANX6gcnqTjtnKWbTvUMCpGnv2+t+31d7RzyEAYAd4u2fnIhHL6N/Q==
+"@babel/types@^7.22.15", "@babel/types@^7.22.5", "@babel/types@^7.23.0", "@babel/types@^7.23.5":
+  version "7.23.5"
+  resolved "https://registry.yarnpkg.com/@babel/types/-/types-7.23.5.tgz#48d730a00c95109fa4393352705954d74fb5b602"
+  integrity sha512-ON5kSOJwVO6xXVRTvOI0eOnWe7VdUcIpsovGo9U/Br4Ie4UVFQTboO2cYnDhAGU6Fp+UxSiT+pMft0SMHfuq6w==
+  dependencies:
+    "@babel/helper-string-parser" "^7.23.4"
+    "@babel/helper-validator-identifier" "^7.22.20"
+    to-fast-properties "^2.0.0"
+
+"@esbuild/android-arm64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/android-arm64/-/android-arm64-0.18.20.tgz#984b4f9c8d0377443cc2dfcef266d02244593622"
+  integrity sha512-Nz4rJcchGDtENV0eMKUNa6L12zz2zBDXuhj/Vjh18zGqB44Bi7MBMSXjgunJgjRhCmKOjnPuZp4Mb6OKqtMHLQ==
+
+"@esbuild/android-arm@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/android-arm/-/android-arm-0.18.20.tgz#fedb265bc3a589c84cc11f810804f234947c3682"
+  integrity sha512-fyi7TDI/ijKKNZTUJAQqiG5T7YjJXgnzkURqmGj13C6dCqckZBLdl4h7bkhHt/t0WP+zO9/zwroDvANaOqO5Sw==
+
+"@esbuild/android-x64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/android-x64/-/android-x64-0.18.20.tgz#35cf419c4cfc8babe8893d296cd990e9e9f756f2"
+  integrity sha512-8GDdlePJA8D6zlZYJV/jnrRAi6rOiNaCC/JclcXpB+KIuvfBN4owLtgzY2bsxnx666XjJx2kDPUmnTtR8qKQUg==
+
+"@esbuild/darwin-arm64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/darwin-arm64/-/darwin-arm64-0.18.20.tgz#08172cbeccf95fbc383399a7f39cfbddaeb0d7c1"
+  integrity sha512-bxRHW5kHU38zS2lPTPOyuyTm+S+eobPUnTNkdJEfAddYgEcll4xkT8DB9d2008DtTbl7uJag2HuE5NZAZgnNEA==
+
+"@esbuild/darwin-x64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/darwin-x64/-/darwin-x64-0.18.20.tgz#d70d5790d8bf475556b67d0f8b7c5bdff053d85d"
+  integrity sha512-pc5gxlMDxzm513qPGbCbDukOdsGtKhfxD1zJKXjCCcU7ju50O7MeAZ8c4krSJcOIJGFR+qx21yMMVYwiQvyTyQ==
+
+"@esbuild/freebsd-arm64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/freebsd-arm64/-/freebsd-arm64-0.18.20.tgz#98755cd12707f93f210e2494d6a4b51b96977f54"
+  integrity sha512-yqDQHy4QHevpMAaxhhIwYPMv1NECwOvIpGCZkECn8w2WFHXjEwrBn3CeNIYsibZ/iZEUemj++M26W3cNR5h+Tw==
+
+"@esbuild/freebsd-x64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/freebsd-x64/-/freebsd-x64-0.18.20.tgz#c1eb2bff03915f87c29cece4c1a7fa1f423b066e"
+  integrity sha512-tgWRPPuQsd3RmBZwarGVHZQvtzfEBOreNuxEMKFcd5DaDn2PbBxfwLcj4+aenoh7ctXcbXmOQIn8HI6mCSw5MQ==
+
+"@esbuild/linux-arm64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/linux-arm64/-/linux-arm64-0.18.20.tgz#bad4238bd8f4fc25b5a021280c770ab5fc3a02a0"
+  integrity sha512-2YbscF+UL7SQAVIpnWvYwM+3LskyDmPhe31pE7/aoTMFKKzIc9lLbyGUpmmb8a8AixOL61sQ/mFh3jEjHYFvdA==
+
+"@esbuild/linux-arm@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/linux-arm/-/linux-arm-0.18.20.tgz#3e617c61f33508a27150ee417543c8ab5acc73b0"
+  integrity sha512-/5bHkMWnq1EgKr1V+Ybz3s1hWXok7mDFUMQ4cG10AfW3wL02PSZi5kFpYKrptDsgb2WAJIvRcDm+qIvXf/apvg==
+
+"@esbuild/linux-ia32@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/linux-ia32/-/linux-ia32-0.18.20.tgz#699391cccba9aee6019b7f9892eb99219f1570a7"
+  integrity sha512-P4etWwq6IsReT0E1KHU40bOnzMHoH73aXp96Fs8TIT6z9Hu8G6+0SHSw9i2isWrD2nbx2qo5yUqACgdfVGx7TA==
+
+"@esbuild/linux-loong64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/linux-loong64/-/linux-loong64-0.18.20.tgz#e6fccb7aac178dd2ffb9860465ac89d7f23b977d"
+  integrity sha512-nXW8nqBTrOpDLPgPY9uV+/1DjxoQ7DoB2N8eocyq8I9XuqJ7BiAMDMf9n1xZM9TgW0J8zrquIb/A7s3BJv7rjg==
+
+"@esbuild/linux-mips64el@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/linux-mips64el/-/linux-mips64el-0.18.20.tgz#eeff3a937de9c2310de30622a957ad1bd9183231"
+  integrity sha512-d5NeaXZcHp8PzYy5VnXV3VSd2D328Zb+9dEq5HE6bw6+N86JVPExrA6O68OPwobntbNJ0pzCpUFZTo3w0GyetQ==
+
+"@esbuild/linux-ppc64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/linux-ppc64/-/linux-ppc64-0.18.20.tgz#2f7156bde20b01527993e6881435ad79ba9599fb"
+  integrity sha512-WHPyeScRNcmANnLQkq6AfyXRFr5D6N2sKgkFo2FqguP44Nw2eyDlbTdZwd9GYk98DZG9QItIiTlFLHJHjxP3FA==
+
+"@esbuild/linux-riscv64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/linux-riscv64/-/linux-riscv64-0.18.20.tgz#6628389f210123d8b4743045af8caa7d4ddfc7a6"
+  integrity sha512-WSxo6h5ecI5XH34KC7w5veNnKkju3zBRLEQNY7mv5mtBmrP/MjNBCAlsM2u5hDBlS3NGcTQpoBvRzqBcRtpq1A==
+
+"@esbuild/linux-s390x@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/linux-s390x/-/linux-s390x-0.18.20.tgz#255e81fb289b101026131858ab99fba63dcf0071"
+  integrity sha512-+8231GMs3mAEth6Ja1iK0a1sQ3ohfcpzpRLH8uuc5/KVDFneH6jtAJLFGafpzpMRO6DzJ6AvXKze9LfFMrIHVQ==
+
+"@esbuild/linux-x64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/linux-x64/-/linux-x64-0.18.20.tgz#c7690b3417af318a9b6f96df3031a8865176d338"
+  integrity sha512-UYqiqemphJcNsFEskc73jQ7B9jgwjWrSayxawS6UVFZGWrAAtkzjxSqnoclCXxWtfwLdzU+vTpcNYhpn43uP1w==
+
+"@esbuild/netbsd-x64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/netbsd-x64/-/netbsd-x64-0.18.20.tgz#30e8cd8a3dded63975e2df2438ca109601ebe0d1"
+  integrity sha512-iO1c++VP6xUBUmltHZoMtCUdPlnPGdBom6IrO4gyKPFFVBKioIImVooR5I83nTew5UOYrk3gIJhbZh8X44y06A==
+
+"@esbuild/openbsd-x64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/openbsd-x64/-/openbsd-x64-0.18.20.tgz#7812af31b205055874c8082ea9cf9ab0da6217ae"
+  integrity sha512-e5e4YSsuQfX4cxcygw/UCPIEP6wbIL+se3sxPdCiMbFLBWu0eiZOJ7WoD+ptCLrmjZBK1Wk7I6D/I3NglUGOxg==
+
+"@esbuild/sunos-x64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/sunos-x64/-/sunos-x64-0.18.20.tgz#d5c275c3b4e73c9b0ecd38d1ca62c020f887ab9d"
+  integrity sha512-kDbFRFp0YpTQVVrqUd5FTYmWo45zGaXe0X8E1G/LKFC0v8x0vWrhOWSLITcCn63lmZIxfOMXtCfti/RxN/0wnQ==
+
+"@esbuild/win32-arm64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/win32-arm64/-/win32-arm64-0.18.20.tgz#73bc7f5a9f8a77805f357fab97f290d0e4820ac9"
+  integrity sha512-ddYFR6ItYgoaq4v4JmQQaAI5s7npztfV4Ag6NrhiaW0RrnOXqBkgwZLofVTlq1daVTQNhtI5oieTvkRPfZrePg==
+
+"@esbuild/win32-ia32@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/win32-ia32/-/win32-ia32-0.18.20.tgz#ec93cbf0ef1085cc12e71e0d661d20569ff42102"
+  integrity sha512-Wv7QBi3ID/rROT08SABTS7eV4hX26sVduqDOTe1MvGMjNd3EjOz4b7zeexIR62GTIEKrfJXKL9LFxTYgkyeu7g==
+
+"@esbuild/win32-x64@0.18.20":
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/@esbuild/win32-x64/-/win32-x64-0.18.20.tgz#786c5f41f043b07afb1af37683d7c33668858f6d"
+  integrity sha512-kTdfRcSiDfQca/y9QIkng02avJ+NCaQvrMejlsB3RRv5sE9rRoeBPISaZpKxHELzRxZyLvNts1P27W3wV+8geQ==
 
 "@eslint/eslintrc@^2.0.0":
   version "2.0.0"
@@ -789,6 +861,11 @@
   resolved "https://registry.npmjs.org/@jridgewell/resolve-uri/-/resolve-uri-3.1.0.tgz"
   integrity sha512-F2msla3tad+Mfht5cJq7LSXcdudKTWCVYUgw6pLFOOHSTtZlj6SWNYAp+AhuqLmWdBO2X5hPrLcu8cVP8fy28w==
 
+"@jridgewell/resolve-uri@^3.1.0":
+  version "3.1.1"
+  resolved "https://registry.yarnpkg.com/@jridgewell/resolve-uri/-/resolve-uri-3.1.1.tgz#c08679063f279615a3326583ba3a90d1d82cc721"
+  integrity sha512-dSYZh7HhCDtCKm4QakX0xFpsRDqjjtZf/kjI/v3T3Nwt5r8/qz/M19F9ySyOqU94SXBmeG9ttTul+YnR4LOxFA==
+
 "@jridgewell/set-array@^1.0.0", "@jridgewell/set-array@^1.0.1":
   version "1.1.2"
   resolved "https://registry.npmjs.org/@jridgewell/set-array/-/set-array-1.1.2.tgz"
@@ -799,6 +876,19 @@
   resolved "https://registry.npmjs.org/@jridgewell/sourcemap-codec/-/sourcemap-codec-1.4.14.tgz"
   integrity sha512-XPSJHWmi394fuUuzDnGz1wiKqWfo1yXecHQMRf2l6hztTO+nPru658AyDngaBe7isIxEkRsPR3FZh+s7iVa4Uw==
 
+"@jridgewell/sourcemap-codec@^1.4.14":
+  version "1.4.15"
+  resolved "https://registry.yarnpkg.com/@jridgewell/sourcemap-codec/-/sourcemap-codec-1.4.15.tgz#d7c6e6755c78567a951e04ab52ef0fd26de59f32"
+  integrity sha512-eF2rxCRulEKXHTRiDrDy6erMYWqNw4LPdQ8UQA4huuxaQsVeRPFl2oM8oDGxMFhJUWZf9McpLtJasDDZb/Bpeg==
+
+"@jridgewell/trace-mapping@^0.3.17":
+  version "0.3.20"
+  resolved "https://registry.yarnpkg.com/@jridgewell/trace-mapping/-/trace-mapping-0.3.20.tgz#72e45707cf240fa6b081d0366f8265b0cd10197f"
+  integrity sha512-R8LcPeWZol2zR8mmH3JeKQ6QRCFb7XgUhV9ZlGhHLGyg4wpPiPZNQOOWhFZhxKw8u//yTbNGI42Bx/3paXEQ+Q==
+  dependencies:
+    "@jridgewell/resolve-uri" "^3.1.0"
+    "@jridgewell/sourcemap-codec" "^1.4.14"
+
 "@jridgewell/trace-mapping@^0.3.9":
   version "0.3.17"
   resolved "https://registry.npmjs.org/@jridgewell/trace-mapping/-/trace-mapping-0.3.17.tgz"
@@ -1152,7 +1242,7 @@ caniuse-lite@^1.0.30001449:
   resolved "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001451.tgz"
   integrity sha512-XY7UbUpGRatZzoRft//5xOa69/1iGJRBlrieH6QYrkKLIFn3m7OVEJ81dSrKoy2BnKsdbX5cLrOispZNYo9v2w==
 
-chalk@^2.0.0:
+chalk@^2.0.0, chalk@^2.4.2:
   version "2.4.2"
   resolved "https://registry.npmjs.org/chalk/-/chalk-2.4.2.tgz"
   integrity sha512-Mti+f9lpJNcwF4tWV8/OrTTtF1gZi+f8FqlyAdouralcFWFQWF2+NgCHShjkCb+IFBLq9buZwE1xckQU4peSuQ==
@@ -1402,33 +1492,33 @@ es-to-primitive@^1.2.1:
     is-date-object "^1.0.1"
     is-symbol "^1.0.2"
 
-esbuild@^0.16.14:
-  version "0.16.17"
-  resolved "https://registry.npmjs.org/esbuild/-/esbuild-0.16.17.tgz"
-  integrity sha512-G8LEkV0XzDMNwXKgM0Jwu3nY3lSTwSGY6XbxM9cr9+s0T/qSV1q1JVPBGzm3dcjhCic9+emZDmMffkwgPeOeLg==
+esbuild@^0.18.10:
+  version "0.18.20"
+  resolved "https://registry.yarnpkg.com/esbuild/-/esbuild-0.18.20.tgz#4709f5a34801b43b799ab7d6d82f7284a9b7a7a6"
+  integrity sha512-ceqxoedUrcayh7Y7ZX6NdbbDzGROiyVBgC4PriJThBKSVPWnnFHZAkfI1lJT8QFkOwH4qOS2SJkS4wvpGl8BpA==
   optionalDependencies:
-    "@esbuild/android-arm" "0.16.17"
-    "@esbuild/android-arm64" "0.16.17"
-    "@esbuild/android-x64" "0.16.17"
-    "@esbuild/darwin-arm64" "0.16.17"
-    "@esbuild/darwin-x64" "0.16.17"
-    "@esbuild/freebsd-arm64" "0.16.17"
-    "@esbuild/freebsd-x64" "0.16.17"
-    "@esbuild/linux-arm" "0.16.17"
-    "@esbuild/linux-arm64" "0.16.17"
-    "@esbuild/linux-ia32" "0.16.17"
-    "@esbuild/linux-loong64" "0.16.17"
-    "@esbuild/linux-mips64el" "0.16.17"
-    "@esbuild/linux-ppc64" "0.16.17"
-    "@esbuild/linux-riscv64" "0.16.17"
-    "@esbuild/linux-s390x" "0.16.17"
-    "@esbuild/linux-x64" "0.16.17"
-    "@esbuild/netbsd-x64" "0.16.17"
-    "@esbuild/openbsd-x64" "0.16.17"
-    "@esbuild/sunos-x64" "0.16.17"
-    "@esbuild/win32-arm64" "0.16.17"
-    "@esbuild/win32-ia32" "0.16.17"
-    "@esbuild/win32-x64" "0.16.17"
+    "@esbuild/android-arm" "0.18.20"
+    "@esbuild/android-arm64" "0.18.20"
+    "@esbuild/android-x64" "0.18.20"
+    "@esbuild/darwin-arm64" "0.18.20"
+    "@esbuild/darwin-x64" "0.18.20"
+    "@esbuild/freebsd-arm64" "0.18.20"
+    "@esbuild/freebsd-x64" "0.18.20"
+    "@esbuild/linux-arm" "0.18.20"
+    "@esbuild/linux-arm64" "0.18.20"
+    "@esbuild/linux-ia32" "0.18.20"
+    "@esbuild/linux-loong64" "0.18.20"
+    "@esbuild/linux-mips64el" "0.18.20"
+    "@esbuild/linux-ppc64" "0.18.20"
+    "@esbuild/linux-riscv64" "0.18.20"
+    "@esbuild/linux-s390x" "0.18.20"
+    "@esbuild/linux-x64" "0.18.20"
+    "@esbuild/netbsd-x64" "0.18.20"
+    "@esbuild/openbsd-x64" "0.18.20"
+    "@esbuild/sunos-x64" "0.18.20"
+    "@esbuild/win32-arm64" "0.18.20"
+    "@esbuild/win32-ia32" "0.18.20"
+    "@esbuild/win32-x64" "0.18.20"
 
 escalade@^3.1.1:
   version "3.1.1"
@@ -2244,10 +2334,10 @@ ms@2.1.2:
   resolved "https://registry.npmjs.org/ms/-/ms-2.1.2.tgz"
   integrity sha512-sGkPx+VjMtmA6MX27oA4FBFELFCZZ4S4XqeGOXCv68tT+jb3vk/RyaKWP0PTKyWtmLSM0b+adUTEvbs1PEaH2w==
 
-nanoid@^3.3.4:
-  version "3.3.4"
-  resolved "https://registry.npmjs.org/nanoid/-/nanoid-3.3.4.tgz"
-  integrity sha512-MqBkQh/OHTS2egovRtLk45wEyNXwF+cokD+1YPf9u5VfJiRdAiRwB2froX5Co9Rh20xs4siNPm8naNotSD6RBw==
+nanoid@^3.3.7:
+  version "3.3.7"
+  resolved "https://registry.yarnpkg.com/nanoid/-/nanoid-3.3.7.tgz#d0c301a691bc8d54efa0a2226ccf3fe2fd656bd8"
+  integrity sha512-eSRppjcPIatRIMC1U6UngP8XFcz8MQWGQdt1MTBQ7NaAmvXDfvNxbvWV3x2y6CdEUciCSsDHDQZbhYaB8QEo2g==
 
 natural-compare@^1.4.0:
   version "1.4.0"
@@ -2409,12 +2499,12 @@ picomatch@^2.0.4, picomatch@^2.2.1:
   resolved "https://registry.npmjs.org/picomatch/-/picomatch-2.3.1.tgz"
   integrity sha512-JU3teHTNjmE2VCGFzuY8EXzCDVwEqB2a8fsIvwaStHhAWJEeVd1o1QD80CU6+ZdEXXSLbSsuLwJjkCBWqRQUVA==
 
-postcss@^8.4.21:
-  version "8.4.21"
-  resolved "https://registry.npmjs.org/postcss/-/postcss-8.4.21.tgz"
-  integrity sha512-tP7u/Sn/dVxK2NnruI4H9BG+x+Wxz6oeZ1cJ8P6G/PZY0IKk4k/63TDsQf2kQq3+qoJeLm2kIBUNlZe3zgb4Zg==
+postcss@^8.4.27:
+  version "8.4.35"
+  resolved "https://registry.yarnpkg.com/postcss/-/postcss-8.4.35.tgz#60997775689ce09011edf083a549cea44aabe2f7"
+  integrity sha512-u5U8qYpBCpN13BsiEB0CbR1Hhh4Gc0zLFuedrHJKMctHCHAGrMdG0PRM/KErzAL3CU6/eckEtmHNB3x6e3c0vA==
   dependencies:
-    nanoid "^3.3.4"
+    nanoid "^3.3.7"
     picocolors "^1.0.0"
     source-map-js "^1.0.2"
 
@@ -2552,15 +2642,6 @@ resolve-from@^4.0.0:
   resolved "https://registry.npmjs.org/resolve-from/-/resolve-from-4.0.0.tgz"
   integrity sha512-pb/MYmXstAkysRFx8piNI1tGFNQIFA3vkE3Gq4EuA1dF6gHp/+vgZqsCGJapvy8N3Q+4o7FwvquPJcnZ7RYy4g==
 
-resolve@^1.22.1:
-  version "1.22.1"
-  resolved "https://registry.npmjs.org/resolve/-/resolve-1.22.1.tgz"
-  integrity sha512-nBpuuYuY5jFsli/JIs1oldw6fOQCBioohqWZg/2hiaOybXOft4lonv85uDOKXdf8rhyK159cxU5cDcK/NKk8zw==
-  dependencies:
-    is-core-module "^2.9.0"
-    path-parse "^1.0.7"
-    supports-preserve-symlinks-flag "^1.0.0"
-
 resolve@^2.0.0-next.4:
   version "2.0.0-next.4"
   resolved "https://registry.npmjs.org/resolve/-/resolve-2.0.0-next.4.tgz"
@@ -2582,10 +2663,10 @@ rimraf@^3.0.2:
   dependencies:
     glob "^7.1.3"
 
-rollup@^3.10.0:
-  version "3.15.0"
-  resolved "https://registry.npmjs.org/rollup/-/rollup-3.15.0.tgz"
-  integrity sha512-F9hrCAhnp5/zx/7HYmftvsNBkMfLfk/dXUh73hPSM2E3CRgap65orDNJbLetoiUFwSAk6iHPLvBrZ5iHYvzqsg==
+rollup@^3.27.1:
+  version "3.29.4"
+  resolved "https://registry.yarnpkg.com/rollup/-/rollup-3.29.4.tgz#4d70c0f9834146df8705bfb69a9a19c9e1109981"
+  integrity sha512-oWzmBZwvYrU0iJHtDmhsm662rC15FRXmcjCk1xD771dFDx5jJ02ufAQQTn0etB2emNk4J9EZg/yWKpsn9BWGRw==
   optionalDependencies:
     fsevents "~2.3.2"
 
@@ -2819,15 +2900,14 @@ uuid@^9.0.0:
   resolved "https://registry.npmjs.org/uuid/-/uuid-9.0.0.tgz"
   integrity sha512-MXcSTerfPa4uqyzStbRoTgt5XIe3x5+42+q1sDuy3R5MDk66URdLMOZe5aPX/SQd+kuYAh0FdP/pO28IkQyTeg==
 
-vite@^4.1.5:
-  version "4.1.5"
-  resolved "https://registry.yarnpkg.com/vite/-/vite-4.1.5.tgz#9c93d579f62179ab99c4182fa37acf1b380a374b"
-  integrity sha512-zJ0RiVkf61kpd7O+VtU6r766xgnTaIknP/lR6sJTZq3HtVJ3HGnTo5DaJhTUtYoTyS/CQwZ6yEVdc/lrmQT7dQ==
+vite@^4.5.2:
+  version "4.5.2"
+  resolved "https://registry.yarnpkg.com/vite/-/vite-4.5.2.tgz#d6ea8610e099851dad8c7371599969e0f8b97e82"
+  integrity sha512-tBCZBNSBbHQkaGyhGCDUGqeo2ph8Fstyp6FMSvTtsXeZSPpSMGlviAOav2hxVTqFcx8Hj/twtWKsMJXNY0xI8w==
   dependencies:
-    esbuild "^0.16.14"
-    postcss "^8.4.21"
-    resolve "^1.22.1"
-    rollup "^3.10.0"
+    esbuild "^0.18.10"
+    postcss "^8.4.27"
+    rollup "^3.27.1"
   optionalDependencies:
     fsevents "~2.3.2"
 
diff --git a/demos/palm/web/textfx/package-lock.json b/demos/palm/web/textfx/package-lock.json
index bd2646cb5..c1223be4b 100644
--- a/demos/palm/web/textfx/package-lock.json
+++ b/demos/palm/web/textfx/package-lock.json
@@ -42,7 +42,7 @@
         "eslint-plugin-react": "^7.32.2",
         "eslint-plugin-react-hooks": "^4.6.0",
         "sass": "^1.60.0",
-        "vite": "^4.2.0"
+        "vite": "^4.5.2"
       }
     },
     "node_modules/@ampproject/remapping": {
@@ -58,11 +58,12 @@
       }
     },
     "node_modules/@babel/code-frame": {
-      "version": "7.22.5",
-      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.22.5.tgz",
-      "integrity": "sha512-Xmwn266vad+6DAqEB2A6V/CcZVp62BbwVmcOJc2RPuwih1kw02TjQvWVWlcKGbBPd+8/0V5DEkOcizRGYsspYQ==",
+      "version": "7.22.13",
+      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.22.13.tgz",
+      "integrity": "sha512-XktuhWlJ5g+3TJXc5upd9Ks1HutSArik6jf2eAjYFyIOf4ej3RN+184cZbzDvbPnuTJIUhPKKJE3cIsYTiAT3w==",
       "dependencies": {
-        "@babel/highlight": "^7.22.5"
+        "@babel/highlight": "^7.22.13",
+        "chalk": "^2.4.2"
       },
       "engines": {
         "node": ">=6.9.0"
@@ -106,11 +107,11 @@
       }
     },
     "node_modules/@babel/generator": {
-      "version": "7.21.3",
-      "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.21.3.tgz",
-      "integrity": "sha512-QS3iR1GYC/YGUnW7IdggFeN5c1poPUurnGttOV/bZgPGV+izC/D8HnD6DLwod0fsatNyVn1G3EVWMYIF0nHbeA==",
+      "version": "7.23.0",
+      "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.23.0.tgz",
+      "integrity": "sha512-lN85QRR+5IbYrMWM6Y4pE/noaQtg4pNiqeNGX60eqOfo6gtEj6uw/JagelB8vVztSd7R6M5n1+PQkDbHbBRU4g==",
       "dependencies": {
-        "@babel/types": "^7.21.3",
+        "@babel/types": "^7.23.0",
         "@jridgewell/gen-mapping": "^0.3.2",
         "@jridgewell/trace-mapping": "^0.3.17",
         "jsesc": "^2.5.1"
@@ -231,20 +232,20 @@
       }
     },
     "node_modules/@babel/helper-environment-visitor": {
-      "version": "7.22.5",
-      "resolved": "https://registry.npmjs.org/@babel/helper-environment-visitor/-/helper-environment-visitor-7.22.5.tgz",
-      "integrity": "sha512-XGmhECfVA/5sAt+H+xpSg0mfrHq6FzNr9Oxh7PSEBBRUb/mL7Kz3NICXb194rCqAEdxkhPT1a88teizAFyvk8Q==",
+      "version": "7.22.20",
+      "resolved": "https://registry.npmjs.org/@babel/helper-environment-visitor/-/helper-environment-visitor-7.22.20.tgz",
+      "integrity": "sha512-zfedSIzFhat/gFhWfHtgWvlec0nqB9YEIVrpuwjruLlXfUSnA8cJB0miHKwqDnQ7d32aKo2xt88/xZptwxbfhA==",
       "engines": {
         "node": ">=6.9.0"
       }
     },
     "node_modules/@babel/helper-function-name": {
-      "version": "7.22.5",
-      "resolved": "https://registry.npmjs.org/@babel/helper-function-name/-/helper-function-name-7.22.5.tgz",
-      "integrity": "sha512-wtHSq6jMRE3uF2otvfuD3DIvVhOsSNshQl0Qrd7qC9oQJzHvOL4qQXlQn2916+CXGywIjpGuIkoyZRRxHPiNQQ==",
+      "version": "7.23.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-function-name/-/helper-function-name-7.23.0.tgz",
+      "integrity": "sha512-OErEqsrxjZTJciZ4Oo+eoZqeW9UIiOcuYKRJA4ZAgV9myA+pOXhhmpfNCKjEH/auVfEYVFJ6y1Tc4r0eIApqiw==",
       "dependencies": {
-        "@babel/template": "^7.22.5",
-        "@babel/types": "^7.22.5"
+        "@babel/template": "^7.22.15",
+        "@babel/types": "^7.23.0"
       },
       "engines": {
         "node": ">=6.9.0"
@@ -399,9 +400,9 @@
       }
     },
     "node_modules/@babel/helper-validator-identifier": {
-      "version": "7.22.5",
-      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.22.5.tgz",
-      "integrity": "sha512-aJXu+6lErq8ltp+JhkJUfk1MTGyuA4v7f3pA+BJ5HLfNC6nAQ0Cpi9uOquUj8Hehg0aUiHzWQbOVJGao6ztBAQ==",
+      "version": "7.22.20",
+      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.22.20.tgz",
+      "integrity": "sha512-Y4OZ+ytlatR8AI+8KZfKuL5urKp7qey08ha31L8b3BwewJAoJamTzyvxPR/5D+KkdJCGPq/+8TukHBlY10FX9A==",
       "engines": {
         "node": ">=6.9.0"
       }
@@ -442,12 +443,12 @@
       }
     },
     "node_modules/@babel/highlight": {
-      "version": "7.22.5",
-      "resolved": "https://registry.npmjs.org/@babel/highlight/-/highlight-7.22.5.tgz",
-      "integrity": "sha512-BSKlD1hgnedS5XRnGOljZawtag7H1yPfQp0tdNJCHoH6AZ+Pcm9VvkrK59/Yy593Ypg0zMxH2BxD1VPYUQ7UIw==",
+      "version": "7.22.20",
+      "resolved": "https://registry.npmjs.org/@babel/highlight/-/highlight-7.22.20.tgz",
+      "integrity": "sha512-dkdMCN3py0+ksCgYmGG8jKeGA/8Tk+gJwSYYlFGxG5lmhfKNoAy004YpLxpS1W2J8m/EK2Ew+yOs9pVRwO89mg==",
       "dependencies": {
-        "@babel/helper-validator-identifier": "^7.22.5",
-        "chalk": "^2.0.0",
+        "@babel/helper-validator-identifier": "^7.22.20",
+        "chalk": "^2.4.2",
         "js-tokens": "^4.0.0"
       },
       "engines": {
@@ -455,9 +456,9 @@
       }
     },
     "node_modules/@babel/parser": {
-      "version": "7.22.7",
-      "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.22.7.tgz",
-      "integrity": "sha512-7NF8pOkHP5o2vpmGgNGcfAeCvOYhGLyA3Z4eBQkT1RJlWu47n63bCs93QfJ2hIAFCil7L5P2IWhs1oToVgrL0Q==",
+      "version": "7.23.0",
+      "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.23.0.tgz",
+      "integrity": "sha512-vvPKKdMemU85V9WE/l5wZEmImpCtLqbnTvqDS2U1fJ96KrxoW7KrXhNsNCblQlg8Ck4b85yxdTyelsMUgFUXiw==",
       "bin": {
         "parser": "bin/babel-parser.js"
       },
@@ -2025,31 +2026,31 @@
       }
     },
     "node_modules/@babel/template": {
-      "version": "7.22.5",
-      "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.22.5.tgz",
-      "integrity": "sha512-X7yV7eiwAxdj9k94NEylvbVHLiVG1nvzCV2EAowhxLTwODV1jl9UzZ48leOC0sH7OnuHrIkllaBgneUykIcZaw==",
+      "version": "7.22.15",
+      "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.22.15.tgz",
+      "integrity": "sha512-QPErUVm4uyJa60rkI73qneDacvdvzxshT3kksGqlGWYdOTIUOwJ7RDUL8sGqslY1uXWSL6xMFKEXDS3ox2uF0w==",
       "dependencies": {
-        "@babel/code-frame": "^7.22.5",
-        "@babel/parser": "^7.22.5",
-        "@babel/types": "^7.22.5"
+        "@babel/code-frame": "^7.22.13",
+        "@babel/parser": "^7.22.15",
+        "@babel/types": "^7.22.15"
       },
       "engines": {
         "node": ">=6.9.0"
       }
     },
     "node_modules/@babel/traverse": {
-      "version": "7.21.3",
-      "resolved": "https://registry.npmjs.org/@babel/traverse/-/traverse-7.21.3.tgz",
-      "integrity": "sha512-XLyopNeaTancVitYZe2MlUEvgKb6YVVPXzofHgqHijCImG33b/uTurMS488ht/Hbsb2XK3U2BnSTxKVNGV3nGQ==",
-      "dependencies": {
-        "@babel/code-frame": "^7.18.6",
-        "@babel/generator": "^7.21.3",
-        "@babel/helper-environment-visitor": "^7.18.9",
-        "@babel/helper-function-name": "^7.21.0",
-        "@babel/helper-hoist-variables": "^7.18.6",
-        "@babel/helper-split-export-declaration": "^7.18.6",
-        "@babel/parser": "^7.21.3",
-        "@babel/types": "^7.21.3",
+      "version": "7.23.2",
+      "resolved": "https://registry.npmjs.org/@babel/traverse/-/traverse-7.23.2.tgz",
+      "integrity": "sha512-azpe59SQ48qG6nu2CzcMLbxUudtN+dOM9kDbUqGq3HXUJRlo7i8fvPoxQUzYgLZ4cMVmuZgm8vvBpNeRhd6XSw==",
+      "dependencies": {
+        "@babel/code-frame": "^7.22.13",
+        "@babel/generator": "^7.23.0",
+        "@babel/helper-environment-visitor": "^7.22.20",
+        "@babel/helper-function-name": "^7.23.0",
+        "@babel/helper-hoist-variables": "^7.22.5",
+        "@babel/helper-split-export-declaration": "^7.22.6",
+        "@babel/parser": "^7.23.0",
+        "@babel/types": "^7.23.0",
         "debug": "^4.1.0",
         "globals": "^11.1.0"
       },
@@ -2058,12 +2059,12 @@
       }
     },
     "node_modules/@babel/types": {
-      "version": "7.22.5",
-      "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.22.5.tgz",
-      "integrity": "sha512-zo3MIHGOkPOfoRXitsgHLjEXmlDaD/5KU1Uzuc9GNiZPhSqVxVRtxuPaSBZDsYZ9qV88AjtMtWW7ww98loJ9KA==",
+      "version": "7.23.0",
+      "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.23.0.tgz",
+      "integrity": "sha512-0oIyUfKoI3mSqMvsxBdclDwxXKXAUA8v/apZbc+iSyARYou1o8ZGDxbUYyLFoW2arqS2jDGqJuZvv1d/io1axg==",
       "dependencies": {
         "@babel/helper-string-parser": "^7.22.5",
-        "@babel/helper-validator-identifier": "^7.22.5",
+        "@babel/helper-validator-identifier": "^7.22.20",
         "to-fast-properties": "^2.0.0"
       },
       "engines": {
@@ -10571,9 +10572,9 @@
       }
     },
     "node_modules/postcss": {
-      "version": "8.4.27",
-      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.27.tgz",
-      "integrity": "sha512-gY/ACJtJPSmUFPDCHtX78+01fHa64FaU4zaaWfuh1MhGJISufJAH4cun6k/8fwsHYeK4UQmENQK+tRLCFJE8JQ==",
+      "version": "8.4.31",
+      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.31.tgz",
+      "integrity": "sha512-PS08Iboia9mts/2ygV3eLpY5ghnUcfLV/EXTOW1E2qYxJKGGBUtNjN76FYHnMs36RmARn41bC0AZmn+rR0OVpQ==",
       "dev": true,
       "funding": [
         {
@@ -10873,9 +10874,9 @@
       }
     },
     "node_modules/react-devtools-core": {
-      "version": "4.28.0",
-      "resolved": "https://registry.npmjs.org/react-devtools-core/-/react-devtools-core-4.28.0.tgz",
-      "integrity": "sha512-E3C3X1skWBdBzwpOUbmXG8SgH6BtsluSMe+s6rRcujNKG1DGi8uIfhdhszkgDpAsMoE55hwqRUzeXCmETDBpTg==",
+      "version": "4.28.4",
+      "resolved": "https://registry.npmjs.org/react-devtools-core/-/react-devtools-core-4.28.4.tgz",
+      "integrity": "sha512-IUZKLv3CimeM07G3vX4H4loxVpByrzq3HvfTX7v9migalwvLs9ZY5D3S3pKR33U+GguYfBBdMMZyToFhsSE/iQ==",
       "peer": true,
       "dependencies": {
         "shell-quote": "^1.6.1",
@@ -11392,9 +11393,9 @@
       }
     },
     "node_modules/rollup": {
-      "version": "3.26.3",
-      "resolved": "https://registry.npmjs.org/rollup/-/rollup-3.26.3.tgz",
-      "integrity": "sha512-7Tin0C8l86TkpcMtXvQu6saWH93nhG3dGQ1/+l5V2TDMceTxO7kDiK6GzbfLWNNxqJXm591PcEZUozZm51ogwQ==",
+      "version": "3.29.4",
+      "resolved": "https://registry.npmjs.org/rollup/-/rollup-3.29.4.tgz",
+      "integrity": "sha512-oWzmBZwvYrU0iJHtDmhsm662rC15FRXmcjCk1xD771dFDx5jJ02ufAQQTn0etB2emNk4J9EZg/yWKpsn9BWGRw==",
       "dev": true,
       "bin": {
         "rollup": "dist/bin/rollup"
@@ -12430,14 +12431,14 @@
       }
     },
     "node_modules/vite": {
-      "version": "4.4.7",
-      "resolved": "https://registry.npmjs.org/vite/-/vite-4.4.7.tgz",
-      "integrity": "sha512-6pYf9QJ1mHylfVh39HpuSfMPojPSKVxZvnclX1K1FyZ1PXDOcLBibdq5t1qxJSnL63ca8Wf4zts6mD8u8oc9Fw==",
+      "version": "4.5.2",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-4.5.2.tgz",
+      "integrity": "sha512-tBCZBNSBbHQkaGyhGCDUGqeo2ph8Fstyp6FMSvTtsXeZSPpSMGlviAOav2hxVTqFcx8Hj/twtWKsMJXNY0xI8w==",
       "dev": true,
       "dependencies": {
         "esbuild": "^0.18.10",
-        "postcss": "^8.4.26",
-        "rollup": "^3.25.2"
+        "postcss": "^8.4.27",
+        "rollup": "^3.27.1"
       },
       "bin": {
         "vite": "bin/vite.js"
@@ -12825,11 +12826,12 @@
       }
     },
     "@babel/code-frame": {
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+      "version": "7.22.13",
+      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.22.13.tgz",
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       "requires": {
-        "@babel/highlight": "^7.22.5"
+        "@babel/highlight": "^7.22.13",
+        "chalk": "^2.4.2"
       }
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@@ -12860,11 +12862,11 @@
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+      "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.23.0.tgz",
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       "requires": {
-        "@babel/types": "^7.21.3",
+        "@babel/types": "^7.23.0",
         "@jridgewell/gen-mapping": "^0.3.2",
         "@jridgewell/trace-mapping": "^0.3.17",
         "jsesc": "^2.5.1"
@@ -12954,17 +12956,17 @@
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+      "version": "7.22.20",
+      "resolved": "https://registry.npmjs.org/@babel/helper-environment-visitor/-/helper-environment-visitor-7.22.20.tgz",
+      "integrity": "sha512-zfedSIzFhat/gFhWfHtgWvlec0nqB9YEIVrpuwjruLlXfUSnA8cJB0miHKwqDnQ7d32aKo2xt88/xZptwxbfhA=="
     },
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-      "version": "7.22.5",
-      "resolved": "https://registry.npmjs.org/@babel/helper-function-name/-/helper-function-name-7.22.5.tgz",
-      "integrity": "sha512-wtHSq6jMRE3uF2otvfuD3DIvVhOsSNshQl0Qrd7qC9oQJzHvOL4qQXlQn2916+CXGywIjpGuIkoyZRRxHPiNQQ==",
+      "version": "7.23.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-function-name/-/helper-function-name-7.23.0.tgz",
+      "integrity": "sha512-OErEqsrxjZTJciZ4Oo+eoZqeW9UIiOcuYKRJA4ZAgV9myA+pOXhhmpfNCKjEH/auVfEYVFJ6y1Tc4r0eIApqiw==",
       "requires": {
-        "@babel/template": "^7.22.5",
-        "@babel/types": "^7.22.5"
+        "@babel/template": "^7.22.15",
+        "@babel/types": "^7.23.0"
       }
     },
     "@babel/helper-hoist-variables": {
@@ -13071,9 +13073,9 @@
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     },
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-      "version": "7.22.5",
-      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.22.5.tgz",
-      "integrity": "sha512-aJXu+6lErq8ltp+JhkJUfk1MTGyuA4v7f3pA+BJ5HLfNC6nAQ0Cpi9uOquUj8Hehg0aUiHzWQbOVJGao6ztBAQ=="
+      "version": "7.22.20",
+      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.22.20.tgz",
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       "version": "7.22.5",
@@ -13102,19 +13104,19 @@
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+      "version": "7.22.20",
+      "resolved": "https://registry.npmjs.org/@babel/highlight/-/highlight-7.22.20.tgz",
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       "requires": {
-        "@babel/helper-validator-identifier": "^7.22.5",
-        "chalk": "^2.0.0",
+        "@babel/helper-validator-identifier": "^7.22.20",
+        "chalk": "^2.4.2",
         "js-tokens": "^4.0.0"
       }
     },
     "@babel/parser": {
-      "version": "7.22.7",
-      "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.22.7.tgz",
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+      "version": "7.23.0",
+      "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.23.0.tgz",
+      "integrity": "sha512-vvPKKdMemU85V9WE/l5wZEmImpCtLqbnTvqDS2U1fJ96KrxoW7KrXhNsNCblQlg8Ck4b85yxdTyelsMUgFUXiw=="
     },
     "@babel/plugin-bugfix-safari-id-destructuring-collision-in-function-expression": {
       "version": "7.22.5",
@@ -14149,39 +14151,39 @@
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     },
     "@babel/template": {
-      "version": "7.22.5",
-      "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.22.5.tgz",
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+      "version": "7.22.15",
+      "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.22.15.tgz",
+      "integrity": "sha512-QPErUVm4uyJa60rkI73qneDacvdvzxshT3kksGqlGWYdOTIUOwJ7RDUL8sGqslY1uXWSL6xMFKEXDS3ox2uF0w==",
       "requires": {
-        "@babel/code-frame": "^7.22.5",
-        "@babel/parser": "^7.22.5",
-        "@babel/types": "^7.22.5"
+        "@babel/code-frame": "^7.22.13",
+        "@babel/parser": "^7.22.15",
+        "@babel/types": "^7.22.15"
       }
     },
     "@babel/traverse": {
-      "version": "7.21.3",
-      "resolved": "https://registry.npmjs.org/@babel/traverse/-/traverse-7.21.3.tgz",
-      "integrity": "sha512-XLyopNeaTancVitYZe2MlUEvgKb6YVVPXzofHgqHijCImG33b/uTurMS488ht/Hbsb2XK3U2BnSTxKVNGV3nGQ==",
-      "requires": {
-        "@babel/code-frame": "^7.18.6",
-        "@babel/generator": "^7.21.3",
-        "@babel/helper-environment-visitor": "^7.18.9",
-        "@babel/helper-function-name": "^7.21.0",
-        "@babel/helper-hoist-variables": "^7.18.6",
-        "@babel/helper-split-export-declaration": "^7.18.6",
-        "@babel/parser": "^7.21.3",
-        "@babel/types": "^7.21.3",
+      "version": "7.23.2",
+      "resolved": "https://registry.npmjs.org/@babel/traverse/-/traverse-7.23.2.tgz",
+      "integrity": "sha512-azpe59SQ48qG6nu2CzcMLbxUudtN+dOM9kDbUqGq3HXUJRlo7i8fvPoxQUzYgLZ4cMVmuZgm8vvBpNeRhd6XSw==",
+      "requires": {
+        "@babel/code-frame": "^7.22.13",
+        "@babel/generator": "^7.23.0",
+        "@babel/helper-environment-visitor": "^7.22.20",
+        "@babel/helper-function-name": "^7.23.0",
+        "@babel/helper-hoist-variables": "^7.22.5",
+        "@babel/helper-split-export-declaration": "^7.22.6",
+        "@babel/parser": "^7.23.0",
+        "@babel/types": "^7.23.0",
         "debug": "^4.1.0",
         "globals": "^11.1.0"
       }
     },
     "@babel/types": {
-      "version": "7.22.5",
-      "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.22.5.tgz",
-      "integrity": "sha512-zo3MIHGOkPOfoRXitsgHLjEXmlDaD/5KU1Uzuc9GNiZPhSqVxVRtxuPaSBZDsYZ9qV88AjtMtWW7ww98loJ9KA==",
+      "version": "7.23.0",
+      "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.23.0.tgz",
+      "integrity": "sha512-0oIyUfKoI3mSqMvsxBdclDwxXKXAUA8v/apZbc+iSyARYou1o8ZGDxbUYyLFoW2arqS2jDGqJuZvv1d/io1axg==",
       "requires": {
         "@babel/helper-string-parser": "^7.22.5",
-        "@babel/helper-validator-identifier": "^7.22.5",
+        "@babel/helper-validator-identifier": "^7.22.20",
         "to-fast-properties": "^2.0.0"
       }
     },
@@ -20551,9 +20553,9 @@
       }
     },
     "postcss": {
-      "version": "8.4.27",
-      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.27.tgz",
-      "integrity": "sha512-gY/ACJtJPSmUFPDCHtX78+01fHa64FaU4zaaWfuh1MhGJISufJAH4cun6k/8fwsHYeK4UQmENQK+tRLCFJE8JQ==",
+      "version": "8.4.31",
+      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.31.tgz",
+      "integrity": "sha512-PS08Iboia9mts/2ygV3eLpY5ghnUcfLV/EXTOW1E2qYxJKGGBUtNjN76FYHnMs36RmARn41bC0AZmn+rR0OVpQ==",
       "dev": true,
       "requires": {
         "nanoid": "^3.3.6",
@@ -20765,9 +20767,9 @@
       }
     },
     "react-devtools-core": {
-      "version": "4.28.0",
-      "resolved": "https://registry.npmjs.org/react-devtools-core/-/react-devtools-core-4.28.0.tgz",
-      "integrity": "sha512-E3C3X1skWBdBzwpOUbmXG8SgH6BtsluSMe+s6rRcujNKG1DGi8uIfhdhszkgDpAsMoE55hwqRUzeXCmETDBpTg==",
+      "version": "4.28.4",
+      "resolved": "https://registry.npmjs.org/react-devtools-core/-/react-devtools-core-4.28.4.tgz",
+      "integrity": "sha512-IUZKLv3CimeM07G3vX4H4loxVpByrzq3HvfTX7v9migalwvLs9ZY5D3S3pKR33U+GguYfBBdMMZyToFhsSE/iQ==",
       "peer": true,
       "requires": {
         "shell-quote": "^1.6.1",
@@ -21159,9 +21161,9 @@
       }
     },
     "rollup": {
-      "version": "3.26.3",
-      "resolved": "https://registry.npmjs.org/rollup/-/rollup-3.26.3.tgz",
-      "integrity": "sha512-7Tin0C8l86TkpcMtXvQu6saWH93nhG3dGQ1/+l5V2TDMceTxO7kDiK6GzbfLWNNxqJXm591PcEZUozZm51ogwQ==",
+      "version": "3.29.4",
+      "resolved": "https://registry.npmjs.org/rollup/-/rollup-3.29.4.tgz",
+      "integrity": "sha512-oWzmBZwvYrU0iJHtDmhsm662rC15FRXmcjCk1xD771dFDx5jJ02ufAQQTn0etB2emNk4J9EZg/yWKpsn9BWGRw==",
       "dev": true,
       "requires": {
         "fsevents": "~2.3.2"
@@ -21939,15 +21941,15 @@
       "peer": true
     },
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-      "resolved": "https://registry.npmjs.org/vite/-/vite-4.4.7.tgz",
-      "integrity": "sha512-6pYf9QJ1mHylfVh39HpuSfMPojPSKVxZvnclX1K1FyZ1PXDOcLBibdq5t1qxJSnL63ca8Wf4zts6mD8u8oc9Fw==",
+      "version": "4.5.2",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-4.5.2.tgz",
+      "integrity": "sha512-tBCZBNSBbHQkaGyhGCDUGqeo2ph8Fstyp6FMSvTtsXeZSPpSMGlviAOav2hxVTqFcx8Hj/twtWKsMJXNY0xI8w==",
       "dev": true,
       "requires": {
         "esbuild": "^0.18.10",
         "fsevents": "~2.3.2",
-        "postcss": "^8.4.26",
-        "rollup": "^3.25.2"
+        "postcss": "^8.4.27",
+        "rollup": "^3.27.1"
       }
     },
     "vlq": {
diff --git a/demos/palm/web/textfx/package.json b/demos/palm/web/textfx/package.json
index 04cdff777..979c358a3 100644
--- a/demos/palm/web/textfx/package.json
+++ b/demos/palm/web/textfx/package.json
@@ -26,7 +26,7 @@
     "eslint-plugin-react": "^7.32.2",
     "eslint-plugin-react-hooks": "^4.6.0",
     "sass": "^1.60.0",
-    "vite": "^4.2.0"
+    "vite": "^4.5.2"
   },
   "dependencies": {
     "@tippyjs/react": "^4.2.6",
diff --git a/demos/palm/web/textfx/src/lib/postprocess.js b/demos/palm/web/textfx/src/lib/postprocess.js
index f0c68bf11..809d1c0c6 100644
--- a/demos/palm/web/textfx/src/lib/postprocess.js
+++ b/demos/palm/web/textfx/src/lib/postprocess.js
@@ -103,7 +103,7 @@ const deduplicate = outputs => {
 }
 
 // Remove duplicates in an array of strings, but for each element
-// only the segment occuring BEFORE the specified character is evaluated
+// only the segment occurring BEFORE the specified character is evaluated
 const deduplicateBasedOnSegmentBeforeChar = (outputs, char) => {
   const segments = []
   outputs.forEach(item => {
@@ -121,7 +121,7 @@ const deduplicateBasedOnSegmentBeforeChar = (outputs, char) => {
 }
 
 // Remove duplicates in an array of strings, but for each element,
-// only the segment occuring AFTER the specified character is evaluated
+// only the segment occurring AFTER the specified character is evaluated
 const deduplicateBasedOnSegmentAfterChar = (outputs, char) => {
   const segments = []
   outputs.forEach(item => {
diff --git a/examples/gemini/javascript/langchain_quickstart_node/.gitignore b/examples/gemini/javascript/langchain_quickstart_node/.gitignore
new file mode 100644
index 000000000..9c6e766ce
--- /dev/null
+++ b/examples/gemini/javascript/langchain_quickstart_node/.gitignore
@@ -0,0 +1 @@
+image.jpg
diff --git a/examples/gemini/javascript/langchain_quickstart_node/README.md b/examples/gemini/javascript/langchain_quickstart_node/README.md
new file mode 100644
index 000000000..0f1b1c618
--- /dev/null
+++ b/examples/gemini/javascript/langchain_quickstart_node/README.md
@@ -0,0 +1,61 @@
+# Gemini and LangChain.js quickstart (Node.js)
+
+This example shows you how to invoke
+[Gemini](https://ai.google.dev/docs/gemini_api_overview) models using
+[LangChain.js](https://js.langchain.com/docs/get_started/introduction).
+
+To learn more about the Google AI integration with LangChain.js, see the
+following resources:
+
+* [LangChain.js: Google](https://js.langchain.com/docs/integrations/platforms/google)
+* [LangChain.js: ChatGoogleGenerativeAI](https://js.langchain.com/docs/integrations/chat/google_generativeai)
+* [LangChain.js: Text embedding models: Google AI](https://js.langchain.com/docs/integrations/text_embedding/google_generativeai)
+* [LangChain.js: GoogleGenerativeAIEmbeddings](https://api.js.langchain.com/classes/langchain_google_genai.GoogleGenerativeAIEmbeddings.html)
+
+## Setup
+
+1. Set the `GOOGLE_API_KEY` environment variable, replacing `<API_KEY>` with
+your [API key](https://ai.google.dev/tutorials/setup):
+   ```
+   export GOOGLE_API_KEY=<API_KEY>
+   ```
+   If you don't already have an API key, you can create one through Google AI
+   Studio: [Get an API key](https://makersuite.google.com/app/apikey).
+
+   Note: If you don't want to set an environment variable, you can pass your API
+   key directly to the model:
+
+   ```javascript
+   const model = new ChatGoogleGenerativeAI({
+     apiKey: '<API_KEY>',
+     // ... other params
+   });
+   ```
+
+2. Download an image for testing:
+   ```
+   curl -o image.jpg https://t0.gstatic.com/licensed-image?q=tbn:ANd9GcQ_Kevbk21QBRy-PgB4kQpS79brbmmEG7m3VOTShAn4PecDU5H5UxrJxE3Dw1JiaG17V88QIol19-3TM2wCHw
+   ```
+
+3. Install the package dependencies:
+   ```
+   npm install
+   ```
+
+## Run
+
+```
+npm start
+```
+
+## Learn more
+
+You can also use the
+[Google AI JavaScript SDK](https://github.com/google/generative-ai-js) to
+interact with Gemini. To learn more about using Gemini in your Node.js
+applications, see
+[Quickstart: Get started with the Gemini API in Node.js applications](https://ai.google.dev/tutorials/node_quickstart).
+
+To learn more about the Gemini embedding service, see the
+[embeddings guide](https://ai.google.dev/docs/embeddings_guide).
+
diff --git a/examples/gemini/javascript/langchain_quickstart_node/main.js b/examples/gemini/javascript/langchain_quickstart_node/main.js
new file mode 100644
index 000000000..c8f43b84a
--- /dev/null
+++ b/examples/gemini/javascript/langchain_quickstart_node/main.js
@@ -0,0 +1,94 @@
+/**
+ * Copyright 2024 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+import { readFile } from 'node:fs/promises';
+import { ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings } from '@langchain/google-genai';
+import { HumanMessage } from '@langchain/core/messages';
+
+/**
+ * Creates a Gemini Pro text-only chat model, invokes the model with a single
+ * input, and logs the result.
+ */
+async function invokeGeminiPro() {
+  const model = new ChatGoogleGenerativeAI({
+    modelName: 'gemini-pro',
+    maxOutputTokens: 1024,
+  });
+
+  const result = await model.invoke([
+    [
+      'human',
+      'What is the meaning of life?',
+    ],
+  ]);
+
+  console.log(result);
+}
+
+/**
+ * Creates a Gemini Flash multimodal chat model, invokes the model with an
+ * input containing text and image data, and logs the result.
+ */
+async function invokeGeminiFlash() {
+  const model = new ChatGoogleGenerativeAI({
+    modelName: 'gemini-1.5-flash',
+    maxOutputTokens: 1024,
+  });
+
+  const image = await readFile('./image.jpg', { encoding: 'base64' });
+  const input = [
+    new HumanMessage({
+      content: [
+        {
+          type: 'text',
+          text: 'Write a short, engaging blog post based on this picture. ' +
+              'It should include a description of the meal in the photo ' +
+              'and talk about my journey meal prepping.',
+        },
+        {
+          type: 'image_url',
+          image_url: `data:image/png;base64,${image}`,
+        },
+      ],
+    }),
+  ];
+  const result = await model.invoke(input);
+  console.log(result);
+}
+
+/**
+ * Creates an embedding model, embeds text data, and logs the result.
+ */
+async function embedText() {
+  const model = new GoogleGenerativeAIEmbeddings({
+    modelName: 'embedding-001',
+  });
+  const text = 'The quick brown fox jumps over the lazy dog.';
+  const result = await model.embedQuery(text);
+  console.log(result, result.length)
+}
+
+/**
+ * Runs the example functions. The functions are asynchronous, so we don't know
+ * the order in which they'll return.
+ */
+async function run() {
+  invokeGeminiPro();
+  invokeGeminiFlash();
+  embedText();
+}
+
+run();
diff --git a/examples/gemini/javascript/langchain_quickstart_node/package-lock.json b/examples/gemini/javascript/langchain_quickstart_node/package-lock.json
new file mode 100644
index 000000000..ab1231027
--- /dev/null
+++ b/examples/gemini/javascript/langchain_quickstart_node/package-lock.json
@@ -0,0 +1,480 @@
+{
+  "name": "gemini-langchain-quickstart-node",
+  "version": "1.0.0",
+  "lockfileVersion": 2,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "gemini-langchain-quickstart-node",
+      "version": "1.0.0",
+      "license": "Apache-2.0",
+      "dependencies": {
+        "@langchain/google-genai": "^0.0.7"
+      }
+    },
+    "node_modules/@google/generative-ai": {
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+      "requires": {
+        "is-any-array": "^2.0.0"
+      }
+    },
+    "ml-distance": {
+      "version": "4.0.1",
+      "resolved": "https://registry.npmjs.org/ml-distance/-/ml-distance-4.0.1.tgz",
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+      "requires": {
+        "ml-array-mean": "^1.1.6",
+        "ml-distance-euclidean": "^2.0.0",
+        "ml-tree-similarity": "^1.0.0"
+      }
+    },
+    "ml-distance-euclidean": {
+      "version": "2.0.0",
+      "resolved": "https://registry.npmjs.org/ml-distance-euclidean/-/ml-distance-euclidean-2.0.0.tgz",
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+    },
+    "ml-tree-similarity": {
+      "version": "1.0.0",
+      "resolved": "https://registry.npmjs.org/ml-tree-similarity/-/ml-tree-similarity-1.0.0.tgz",
+      "integrity": "sha512-XJUyYqjSuUQkNQHMscr6tcjldsOoAekxADTplt40QKfwW6nd++1wHWV9AArl0Zvw/TIHgNaZZNvr8QGvE8wLRg==",
+      "requires": {
+        "binary-search": "^1.3.5",
+        "num-sort": "^2.0.0"
+      }
+    },
+    "num-sort": {
+      "version": "2.1.0",
+      "resolved": "https://registry.npmjs.org/num-sort/-/num-sort-2.1.0.tgz",
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+    },
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+    },
+    "p-queue": {
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+      "requires": {
+        "eventemitter3": "^4.0.4",
+        "p-timeout": "^3.2.0"
+      }
+    },
+    "p-retry": {
+      "version": "4.6.2",
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+      "requires": {
+        "@types/retry": "0.12.0",
+        "retry": "^0.13.1"
+      }
+    },
+    "p-timeout": {
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+      "requires": {
+        "p-finally": "^1.0.0"
+      }
+    },
+    "retry": {
+      "version": "0.13.1",
+      "resolved": "https://registry.npmjs.org/retry/-/retry-0.13.1.tgz",
+      "integrity": "sha512-XQBQ3I8W1Cge0Seh+6gjj03LbmRFWuoszgK9ooCpwYIrhhoO80pfq4cUkU5DkknwfOfFteRwlZ56PYOGYyFWdg=="
+    },
+    "uuid": {
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+      "resolved": "https://registry.npmjs.org/uuid/-/uuid-9.0.1.tgz",
+      "integrity": "sha512-b+1eJOlsR9K8HJpow9Ok3fiWOWSIcIzXodvv0rQjVoOVNpWMpxf1wZNpt4y9h10odCNrqnYp1OBzRktckBe3sA=="
+    },
+    "zod": {
+      "version": "3.22.4",
+      "resolved": "https://registry.npmjs.org/zod/-/zod-3.22.4.tgz",
+      "integrity": "sha512-iC+8Io04lddc+mVqQ9AZ7OQ2MrUKGN+oIQyq1vemgt46jwCwLfhq7/pwnBnNXXXZb8VTVLKwp9EDkx+ryxIWmg=="
+    }
+  }
+}
diff --git a/examples/gemini/javascript/langchain_quickstart_node/package.json b/examples/gemini/javascript/langchain_quickstart_node/package.json
new file mode 100644
index 000000000..85cc2abf5
--- /dev/null
+++ b/examples/gemini/javascript/langchain_quickstart_node/package.json
@@ -0,0 +1,17 @@
+{
+  "name": "gemini-langchain-quickstart-node",
+  "version": "1.0.0",
+  "description": "",
+  "main": "main.js",
+  "type": "module",
+  "scripts": {
+    "start": "node main.js"
+  },
+  "keywords": [],
+  "author": "",
+  "license": "Apache-2.0",
+  "private": true,
+  "dependencies": {
+    "@langchain/google-genai": "^0.0.7"
+  }
+}
diff --git a/examples/gemini/node/flutter_theme_agent/.eslintrc.json b/examples/gemini/node/flutter_theme_agent/.eslintrc.json
new file mode 100644
index 000000000..f9b22b793
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/.eslintrc.json
@@ -0,0 +1,24 @@
+{
+    "root": true,
+    "parser": "@typescript-eslint/parser",
+    "parserOptions": {
+        "ecmaVersion": 6,
+        "sourceType": "module"
+    },
+    "plugins": [
+        "@typescript-eslint"
+    ],
+    "rules": {
+        "@typescript-eslint/naming-convention": "warn",
+        "@typescript-eslint/semi": "warn",
+        "curly": "warn",
+        "eqeqeq": "warn",
+        "no-throw-literal": "warn",
+        "semi": "off"
+    },
+    "ignorePatterns": [
+        "out",
+        "dist",
+        "**/*.d.ts"
+    ]
+}
diff --git a/examples/gemini/node/flutter_theme_agent/.gitignore b/examples/gemini/node/flutter_theme_agent/.gitignore
new file mode 100644
index 000000000..0b60dfa12
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/.gitignore
@@ -0,0 +1,5 @@
+out
+dist
+node_modules
+.vscode-test/
+*.vsix
diff --git a/examples/gemini/node/flutter_theme_agent/.vscode/extensions.json b/examples/gemini/node/flutter_theme_agent/.vscode/extensions.json
new file mode 100644
index 000000000..3ac9aeb61
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/.vscode/extensions.json
@@ -0,0 +1,7 @@
+{
+	// See http://go.microsoft.com/fwlink/?LinkId=827846
+	// for the documentation about the extensions.json format
+	"recommendations": [
+		"dbaeumer.vscode-eslint"
+	]
+}
diff --git a/examples/gemini/node/flutter_theme_agent/.vscode/launch.json b/examples/gemini/node/flutter_theme_agent/.vscode/launch.json
new file mode 100644
index 000000000..670d6e66c
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/.vscode/launch.json
@@ -0,0 +1,34 @@
+// A launch configuration that compiles the extension and then opens it inside a new window
+// Use IntelliSense to learn about possible attributes.
+// Hover to view descriptions of existing attributes.
+// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+{
+	"version": "0.2.0",
+	"configurations": [
+		{
+			"name": "Run Extension",
+			"type": "extensionHost",
+			"request": "launch",
+			"args": [
+				"--extensionDevelopmentPath=${workspaceFolder}"
+			],
+			"outFiles": [
+				"${workspaceFolder}/out/**/*.js"
+			],
+			"preLaunchTask": "${defaultBuildTask}"
+		},
+		{
+			"name": "Extension Tests",
+			"type": "extensionHost",
+			"request": "launch",
+			"args": [
+				"--extensionDevelopmentPath=${workspaceFolder}",
+				"--extensionTestsPath=${workspaceFolder}/out/test/suite/index"
+			],
+			"outFiles": [
+				"${workspaceFolder}/out/test/**/*.js"
+			],
+			"preLaunchTask": "${defaultBuildTask}"
+		}
+	]
+}
diff --git a/examples/gemini/node/flutter_theme_agent/.vscode/settings.json b/examples/gemini/node/flutter_theme_agent/.vscode/settings.json
new file mode 100644
index 000000000..30bf8c2d3
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/.vscode/settings.json
@@ -0,0 +1,11 @@
+// Place your settings in this file to overwrite default and user settings.
+{
+    "files.exclude": {
+        "out": false // set this to true to hide the "out" folder with the compiled JS files
+    },
+    "search.exclude": {
+        "out": true // set this to false to include "out" folder in search results
+    },
+    // Turn off tsc task auto detection since we have the necessary tasks as npm scripts
+    "typescript.tsc.autoDetect": "off"
+}
\ No newline at end of file
diff --git a/examples/gemini/node/flutter_theme_agent/.vscode/tasks.json b/examples/gemini/node/flutter_theme_agent/.vscode/tasks.json
new file mode 100644
index 000000000..3b17e53b6
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/.vscode/tasks.json
@@ -0,0 +1,20 @@
+// See https://go.microsoft.com/fwlink/?LinkId=733558
+// for the documentation about the tasks.json format
+{
+	"version": "2.0.0",
+	"tasks": [
+		{
+			"type": "npm",
+			"script": "watch",
+			"problemMatcher": "$tsc-watch",
+			"isBackground": true,
+			"presentation": {
+				"reveal": "never"
+			},
+			"group": {
+				"kind": "build",
+				"isDefault": true
+			}
+		}
+	]
+}
diff --git a/examples/gemini/node/flutter_theme_agent/.vscodeignore b/examples/gemini/node/flutter_theme_agent/.vscodeignore
new file mode 100644
index 000000000..389996760
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/.vscodeignore
@@ -0,0 +1,10 @@
+.vscode/**
+.vscode-test/**
+src/**
+.gitignore
+.yarnrc
+vsc-extension-quickstart.md
+**/tsconfig.json
+**/.eslintrc.json
+**/*.map
+**/*.ts
diff --git a/examples/gemini/node/flutter_theme_agent/CHANGELOG.md b/examples/gemini/node/flutter_theme_agent/CHANGELOG.md
new file mode 100644
index 000000000..57be26dc9
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/CHANGELOG.md
@@ -0,0 +1,7 @@
+# Change Log
+
+All notable changes to the "flutter-theme-agent" extension will be documented in this file.
+
+## v0.0.1
+
+- Initial release. Provides commands for generating Flutter code for a ThemeData and the following components: ButtonStyle, ColorScheme, TextTheme.
\ No newline at end of file
diff --git a/examples/gemini/node/flutter_theme_agent/CONTRIBUTING.md b/examples/gemini/node/flutter_theme_agent/CONTRIBUTING.md
new file mode 100644
index 000000000..ea7316938
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/CONTRIBUTING.md
@@ -0,0 +1,32 @@
+# How to Contribute
+
+We would love to accept your patches and contributions to this project.
+
+## Before you begin
+
+### Sign our Contributor License Agreement
+
+Contributions to this project must be accompanied by a
+[Contributor License Agreement](https://cla.developers.google.com/about) (CLA).
+You (or your employer) retain the copyright to your contribution; this simply
+gives us permission to use and redistribute your contributions as part of the
+project.
+
+If you or your current employer have already signed the Google CLA (even if it
+was for a different project), you probably don't need to do it again.
+
+Visit <https://cla.developers.google.com/> to see your current agreements or to
+sign a new one.
+
+### Review our Community Guidelines
+
+This project follows [Google's Open Source Community
+Guidelines](https://opensource.google/conduct/).
+
+## Contribution process
+
+### Code Reviews
+
+All submissions, including submissions by project members, require review. We
+use [GitHub pull requests](https://docs.github.com/articles/about-pull-requests)
+for this purpose.
\ No newline at end of file
diff --git a/examples/gemini/node/flutter_theme_agent/LICENSE b/examples/gemini/node/flutter_theme_agent/LICENSE
new file mode 100644
index 000000000..f9a86f111
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/LICENSE
@@ -0,0 +1,201 @@
+Apache License
+                          Version 2.0, January 2004
+                      http://www.apache.org/licenses/
+
+  TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+  1. Definitions.
+
+    "License" shall mean the terms and conditions for use, reproduction,
+    and distribution as defined by Sections 1 through 9 of this document.
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+    "Licensor" shall mean the copyright owner or entity authorized by
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+
+    "Legal Entity" shall mean the union of the acting entity and all
+    other entities that control, are controlled by, or are under common
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+    direction or management of such entity, whether by contract or
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+    outstanding shares, or (iii) beneficial ownership of such entity.
+
+    "You" (or "Your") shall mean an individual or Legal Entity
+    exercising permissions granted by this License.
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+    "Source" form shall mean the preferred form for making modifications,
+    including but not limited to software source code, documentation
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+    "Work" shall mean the work of authorship, whether in Source or
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+    "Derivative Works" shall mean any work, whether in Source or Object
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+    of this License, Derivative Works shall not include works that remain
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+    "Contribution" shall mean any work of authorship, including
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+    "Contributor" shall mean Licensor and any individual or Legal Entity
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+
+  2. Grant of Copyright License. Subject to the terms and conditions of
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+    worldwide, non-exclusive, no-charge, royalty-free, irrevocable
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+  4. Redistribution. You may reproduce and distribute copies of the
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+    meet the following conditions:
+
+    (a) You must give any other recipients of the Work or
+        Derivative Works a copy of this License; and
+
+    (b) You must cause any modified files to carry prominent notices
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+
+    (c) You must retain, in the Source form of any Derivative Works
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+
+    (d) If the Work includes a "NOTICE" text file as part of its
+        distribution, then any Derivative Works that You distribute must
+        include a readable copy of the attribution notices contained
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+        of the NOTICE file are for informational purposes only and
+        do not modify the License. You may add Your own attribution
+        notices within Derivative Works that You distribute, alongside
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+
+    You may add Your own copyright statement to Your modifications and
+    may provide additional or different license terms and conditions
+    for use, reproduction, or distribution of Your modifications, or
+    for any such Derivative Works as a whole, provided Your use,
+    reproduction, and distribution of the Work otherwise complies with
+    the conditions stated in this License.
+
+  5. Submission of Contributions. Unless You explicitly state otherwise,
+    any Contribution intentionally submitted for inclusion in the Work
+    by You to the Licensor shall be under the terms and conditions of
+    this License, without any additional terms or conditions.
+    Notwithstanding the above, nothing herein shall supersede or modify
+    the terms of any separate license agreement you may have executed
+    with Licensor regarding such Contributions.
+
+  6. Trademarks. This License does not grant permission to use the trade
+    names, trademarks, service marks, or product names of the Licensor,
+    except as required for reasonable and customary use in describing the
+    origin of the Work and reproducing the content of the NOTICE file.
+
+  7. Disclaimer of Warranty. Unless required by applicable law or
+    agreed to in writing, Licensor provides the Work (and each
+    Contributor provides its Contributions) on an "AS IS" BASIS,
+    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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+    of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+    PARTICULAR PURPOSE. You are solely responsible for determining the
+    appropriateness of using or redistributing the Work and assume any
+    risks associated with Your exercise of permissions under this License.
+
+  8. Limitation of Liability. In no event and under no legal theory,
+    whether in tort (including negligence), contract, or otherwise,
+    unless required by applicable law (such as deliberate and grossly
+    negligent acts) or agreed to in writing, shall any Contributor be
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+  9. Accepting Warranty or Additional Liability. While redistributing
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+    on Your own behalf and on Your sole responsibility, not on behalf
+    of any other Contributor, and only if You agree to indemnify,
+    defend, and hold each Contributor harmless for any liability
+    incurred by, or claims asserted against, such Contributor by reason
+    of your accepting any such warranty or additional liability.
+
+  END OF TERMS AND CONDITIONS
+
+  APPENDIX: How to apply the Apache License to your work.
+
+    To apply the Apache License to your work, attach the following
+    boilerplate notice, with the fields enclosed by brackets "[]"
+    replaced with your own identifying information. (Don't include
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+    file or class name and description of purpose be included on the
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+
+  Copyright [yyyy] [name of copyright owner]
+
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
\ No newline at end of file
diff --git a/examples/gemini/node/flutter_theme_agent/README.md b/examples/gemini/node/flutter_theme_agent/README.md
new file mode 100644
index 000000000..34c786c73
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/README.md
@@ -0,0 +1,106 @@
+# Flutter Theme Agent
+
+Flutter Theme Agent is an AI-powered code assistance tool, built as an extension
+for Microsoft [Visual Studio Code](https://code.visualstudio.com/) (VS Code).
+It uses the Google Gemini API to help you generate 
+components of a Flutter theme, or ThemeData object, including color schemes,
+text styles, and button styles.
+
+![flutter-theme-agent](./flutter-theme-agent.png)
+
+Flutter Theme Agent is provided as a development project, so you must configure and build
+it if you want to run it in your VS Code instance. For more information
+about building, configuring, running, and extending this project, see the
+[Build an AI Flutter code generator with Gemini](https://ai.google.dev/examples/flutter-theme-agent) tutorial.
+
+## Project setup
+
+These instructions walk you through getting the Flutter Theme Agent project set up
+for development. The general steps are Installing some prerequisite
+software, setting a few environment variables, cloning the project from the code
+repository, and running the configuration installation.
+
+Note: You need a Google Gemini API Key to be able to run the project, which you
+can obtain from the [Google Gemini API](https://ai.google.dev/tutorials/setup) page.
+
+### Install the prerequisites
+
+The Flutter Theme Agent project runs as an extension of Microsoft [Visual Studio
+Code](https://code.visualstudio.com/), and uses
+[Node.js](https://nodejs.org/) and npm to manage packages and run the
+application. The following installation instructions are for a Linux host
+machine.
+
+To install the required software:
+
+1.  Install [Visual Studio Code](https://code.visualstudio.com/download) for your platform.
+1.  Install `node` and `npm` by following the [installation instructions](https://nodejs.org/) for your platform.
+
+
+### Clone and configure the project
+
+Download the project code and use the `npm` installation command to download
+the required dependencies and configure the project. You need
+[git](https://git-scm.com/) source control software to retrieve the project
+source code.\
+To download and configure the project code:
+
+1.  Clone the git repository using the following command.\
+    `git clone https://github.com/google/generative-ai-docs`
+1.  Optionally, configure your local git repository to use sparse checkout,
+so you have only the files for the Docs Agent project.`
+cd generative-ai-docs/\
+git sparse-checkout init --cone\
+    git sparse-checkout set examples/gemini/node/flutter_theme_agent/`
+1.  Navigate to the Flutter Theme Agent project root directory.\
+    `cd generative-ai-docs/examples/gemini/node/flutter_theme_agent/`
+1.  Run the install command to download dependencies and configure the project:\
+    `npm install`
+
+### Configure and test the extension
+
+You should now be able to test your installation by running Flutter Theme Agent
+as a development extension in VS Code on your device. The test opens a separate
+VS Code **Extension Development Host** window where the new extension is
+available. In this new window, you configure the API Key the extension uses to
+access the Google Gemini API.
+
+Caution: Treat your API Key like a password and protect it appropriately.
+For some general best practices on key security, review this
+[support article](https://support.google.com/googleapi/answer/6310037).
+
+To configure and test your setup:
+
+1.  Start the VS Code application.
+1.  In VS Code, create a new window by selecting **File > New Window**.
+1.  Open the Flutter Theme Agent project by selecting **File > Open Folder**,
+    and selecting the `flutter_theme_agent/` folder.
+1.  In VS Code, open the `flutter_theme_agent/package.json` file.
+1.  Run the extension in debug mode by selecting **Run > Start Debugging**.
+    This step opens a separate VS Code **Extension Development Host** window.
+1.  Open the VS Code settings by selecting **Code > Settings > Settings**.
+1.  Get a
+    [Google Gemini API Key](https://developers.generativeai.google/tutorials/setup)
+    from the Generative AI Developer site, and copy the key string.
+1.  Set the API key as a configuration setting. In **Search Settings**
+    field, type `flutter theme`, select the **User** tab, and in the **Google >
+    Gemini: Api Key** setting, click the **Edit in settings.json** link, and
+    add your Gemini API key:
+      `"google.ai.apiKey": "your-api-key-here"`
+1.  Save the changes to the `settings.json` file and close the settings tabs.
+
+**Caution:** Treat your API Key like a password and protect it appropriately. Don't
+embed your key in publicly published code.
+
+To test the extension commands:
+
+1.  In the VS Code **Extension Development Host** window, open a Flutter project.
+1.  In your code, write a comment that describes the component you want to generate code for. For example, `// a color scheme that is pink with beige background` and highlight that comment. 
+1.  Open the command palette by selecting **View > Command Palette**.
+1.  In the Command Palette, type `Flutter Theme` and the command for the desired component.
+
+
+## Resources
+
+- Project code tutorial:
+[Build an AI Flutter code generator with Gemini](https://ai.google.dev/examples/flutter-theme-agent) tutorial.
\ No newline at end of file
diff --git a/examples/gemini/node/flutter_theme_agent/flutter-theme-agent.png b/examples/gemini/node/flutter_theme_agent/flutter-theme-agent.png
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Binary files /dev/null and b/examples/gemini/node/flutter_theme_agent/flutter-theme-agent.png differ
diff --git a/examples/gemini/node/flutter_theme_agent/package-lock.json b/examples/gemini/node/flutter_theme_agent/package-lock.json
new file mode 100644
index 000000000..fee5d283e
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/package-lock.json
@@ -0,0 +1,2363 @@
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diff --git a/examples/gemini/node/flutter_theme_agent/package.json b/examples/gemini/node/flutter_theme_agent/package.json
new file mode 100644
index 000000000..301f22d66
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/package.json
@@ -0,0 +1,81 @@
+{
+  "name": "flutter-theme-agent",
+  "displayName": "Flutter Theme Agent",
+  "description": "Extension for generating Flutter ThemeData classes",
+  "version": "0.0.1",
+  "engines": {
+    "vscode": "^1.85.0"
+  },
+  "categories": [
+    "Other"
+  ],
+  "activationEvents": [],
+  "main": "./out/extension.js",
+  "contributes": {
+    "commands": [
+      {
+        "command": "flutter-theme-agent.generateTheme",
+        "title": "Flutter Theme Agent: Create a Flutter ThemeData object."
+      },
+      {
+        "command": "flutter-theme-agent.generateTextTheme",
+        "title": "Flutter Theme Agent: Create a Flutter TextTheme."
+      },
+      {
+        "command": "flutter-theme-agent.generateColorScheme",
+        "title": "Flutter Theme Agent: Create a Flutter ColorScheme."
+      },
+      {
+        "command": "flutter-theme-agent.generateButtonStyle",
+        "title": "Flutter Theme Agent: Create a Flutter ButtonStyle."
+      }
+    ],
+    "configuration": [
+      {
+        "title": "Flutter Theme Agent: Google AI",
+        "properties": {
+          "google.ai.apiKey": {
+            "type": [
+              "string",
+              "null"
+            ],
+            "default": null,
+            "markdownDescription": "Enter your [API Key](https://ai.google.dev/tutorials/setup) for Google AI."
+          },
+          "google.ai.model": {
+            "type": [
+              "string"
+            ],
+            "default": "gemini-pro",
+            "markdownDescription": "Provide the name of the Google AI model you want to use. Choose from the [base models](https://ai.google.dev/models)."
+          }
+        }
+      }
+    ]
+  },
+
+  "scripts": {
+    "vscode:prepublish": "npm run compile",
+    "compile": "tsc -p ./",
+    "watch": "tsc -watch -p ./",
+    "pretest": "npm run compile && npm run lint",
+    "lint": "eslint src --ext ts",
+    "test": "node ./out/test/runTest.js"
+  },
+  "devDependencies": {
+    "@types/glob": "^8.1.0",
+    "@types/mocha": "^10.0.1",
+    "@types/node": "20.2.5",
+    "@types/vscode": "^1.85.0",
+    "@typescript-eslint/eslint-plugin": "^5.59.8",
+    "@typescript-eslint/parser": "^5.59.8",
+    "@vscode/test-electron": "^2.3.2",
+    "eslint": "^8.41.0",
+    "glob": "^8.1.0",
+    "mocha": "^10.2.0",
+    "typescript": "^5.1.3"
+  },
+  "dependencies": {
+    "@google/generative-ai": "^0.1.3"
+  }
+}
diff --git a/examples/gemini/node/flutter_theme_agent/src/components/buttonstyle.ts b/examples/gemini/node/flutter_theme_agent/src/components/buttonstyle.ts
new file mode 100644
index 000000000..848eec325
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/src/components/buttonstyle.ts
@@ -0,0 +1,155 @@
+/**
+ * Copyright 2024 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */ 
+
+import * as vscode from 'vscode';
+
+import { GoogleGenerativeAI} from '@google/generative-ai';
+import {PROMPT_PRIMING} from './component';
+
+const BUTTONSTYLE_CONTEXT=`
+ButtonStyle should only defines properties that exist for a ButtonStyle object. 
+ButtonStyle objects have the following properties. The buttons can ONLY be styled by setting these properties. No other properties:
+alignment → AlignmentGeometry? // The alignment of the button's child.
+animationDuration → Duration? // Defines the duration of animated changes for shape and elevation.
+backgroundColor → MaterialStateProperty<Color?>? // The button's background fill color.
+elevation → MaterialStateProperty<double?>? // The elevation of the button's Material.
+enableFeedback → bool? // Whether detected gestures should provide acoustic and/or haptic feedback.
+fixedSize → MaterialStateProperty<Size?>? // The button's size.
+foregroundColor → MaterialStateProperty<Color?>? // The color for the button's Text and Icon widget descendants.
+iconColor → MaterialStateProperty<Color?>? // The icon's color inside of the button.
+iconSize → MaterialStateProperty<double?>? // The icon's size inside of the button.
+maximumSize → MaterialStateProperty<Size?>? // The maximum size of the button itself.
+minimumSize → MaterialStateProperty<Size?>? // The minimum size of the button itself.
+mouseCursor → MaterialStateProperty<MouseCursor?>? // The cursor for a mouse pointer when it enters or is hovering over this button's InkWell.
+overlayColor → MaterialStateProperty<Color?>? // The highlight color that's typically used to indicate that the button is focused, hovered, or pressed.
+padding → MaterialStateProperty<EdgeInsetsGeometry?>? // The padding between the button's boundary and its child.
+shadowColor → MaterialStateProperty<Color?>? // The shadow color of the button's Material.
+shape → MaterialStateProperty<OutlinedBorder?>? // The shape of the button's underlying Material.
+side → MaterialStateProperty<BorderSide?>? // The color and weight of the button's outline.
+splashFactory → InteractiveInkFeatureFactory? // Creates the InkWell splash factory, which defines the appearance of "ink" splashes that occur in response to taps.
+surfaceTintColor → MaterialStateProperty<Color?>? // The surface tint color of the button's Material.
+tapTargetSize → MaterialTapTargetSize? // Configures the minimum size of the area within which the button may be pressed.
+textStyle → MaterialStateProperty<TextStyle?>? // The style for a button's Text widget descendants.
+visualDensity → VisualDensity? // Defines how compact the button's layout will be.
+
+Available MaterialState Enums:
+hovered → const MaterialState //The state when the user drags their mouse cursor over the given widget.
+focused → const MaterialState // The state when the user navigates with the keyboard to a given widget.
+pressed → const MaterialState // The state when the user is actively pressing down on the given widget.
+dragged → const MaterialState // The state when this widget is being dragged from one place to another by the user.
+selected → const MaterialState // The state when this item has been selected.
+scrolledUnder → const MaterialState // The state when this widget overlaps the content of a scrollable below.
+disabled → const MaterialState // The state when this widget is disabled and cannot be interacted with
+error → const MaterialState // The state when the widget has entered some form of invalid state.
+
+MaterialStateProperty can resolve various states and set properties like this example:
+backgroundColor: MaterialStateProperty.resolveWith((states) {
+  if (states.contains(MaterialState.hovered)) {
+    return Colors.blue;
+  } else {
+    return Colors.green;
+  }
+}),
+In this example, it sets the button's background color to blue when it's being hovered, otherwise the button is green.
+
+MaterialStateProperty.all<T>(T value) → MaterialStateProperty<T>
+Convenience method for creating a MaterialStateProperty that resolves to a single value for all states.
+If you need a const value, use MaterialStatePropertyAll directly.
+
+MaterialStateProperty values should be set as MaterialStateProperty values.
+
+Here's an example prompt: 
+Create a ButtonStyle where the button is green by default and blue on hover state. And elevation is 14, no surface tint color, and the splash effect is turned off.
+Here's an example of good Dart code:
+ButtonStyle(
+  backgroundColor: MaterialStateProperty.resolveWith<Color?>(
+    (Set<MaterialState> states) {
+      if (states.contains(MaterialState.hovered)) {
+        return Colors.blue;
+      } else if (states.contains(MaterialState.pressed)) {
+        return Colors.purple;
+      }
+      return Colors.green;
+    },
+  ),
+  elevation: MaterialStateProperty.all<double>(14),
+  overlayColor: MaterialStateProperty.all<Color>(Colors.transparent),
+  splashFactory: NoSplash.splashFactory,
+)
+
+This is a good example because it resolves the MaterialStateProperty to get its color for when the button is default or hovered. It sets the correct elvation for all MaterialStateProperty and turns off tint and splash effect.
+
+Here's an example prompt: 
+Create a ButtonStyle where the button is green by default and blue on hover state. And elevation is 14, no surface tint color, and the splash effect is turned off.
+Here's an example of good Dart code:
+ButtonStyle(
+  backgroundColor: MaterialStateProperty.all<Color>(Colors.deepPurpleAccent),
+  foregroundColor: MaterialStateProperty.all<Color>(Colors.white),
+  overlayColor: MaterialStateProperty.all<Color>(Colors.transparent),
+  shape: MaterialStateProperty.all<OutlinedBorder>(
+    RoundedRectangleBorder(
+      borderRadius: BorderRadius.circular(8.0),
+      side: BorderSide(color: Colors.deepPurple),
+    ),
+  ),
+  elevation: MaterialStateProperty.all<double>(14),
+)
+
+`;
+
+
+export async function generateButtonStyle(){
+  vscode.window.showInformationMessage('Generating Button Style...');
+
+  // Get API Key from local user configuration
+  const apiKey = vscode.workspace.getConfiguration().get<string>('google.ai.apiKey');
+  if (!apiKey) {
+      vscode.window.showErrorMessage('API key not configured. Check your settings.');
+      return;
+  }
+
+  const genAI = new GoogleGenerativeAI(apiKey);
+  const gemini = genAI.getGenerativeModel({model: "gemini-pro"});
+
+  // Text selection
+  const editor = vscode.window.activeTextEditor;
+  if (!editor) {
+      console.debug('Abandon: no open text editor.');
+      return;
+  }
+
+  const selection = editor.selection;
+  const selectedPrompt = editor.document.getText(selection);
+
+  // Build the full prompt using the template.
+  const fullPrompt = `${PROMPT_PRIMING + BUTTONSTYLE_CONTEXT + selectedPrompt}`;
+
+  const result = await gemini.generateContent(fullPrompt);
+  const response = result.response;
+  
+  if (!response) {
+      console.error('No candidates', response);
+      vscode.window.showErrorMessage('No comment candidates returned. Check debug logs.');
+      return;
+  }
+  const comment = response.text();
+
+  // Insert in place of selection.
+  editor.edit((editBuilder) => {
+      // Insert code inline where the highlighted text is.
+      editBuilder.insert(selection.start, comment);
+  });
+}
\ No newline at end of file
diff --git a/examples/gemini/node/flutter_theme_agent/src/components/colorscheme.ts b/examples/gemini/node/flutter_theme_agent/src/components/colorscheme.ts
new file mode 100644
index 000000000..970f21fee
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/src/components/colorscheme.ts
@@ -0,0 +1,178 @@
+/**
+ * Copyright 2024 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */ 
+
+import * as vscode from 'vscode';
+
+import { GoogleGenerativeAI} from '@google/generative-ai';
+import {PROMPT_PRIMING} from './component';
+
+// ColorScheme
+const COLORSCHEME_CONTEXT=`
+
+ColorScheme objects have the following properties:
+background → Color // A color that typically appears behind scrollable content.
+brightness → Brightness // The overall brightness of this color scheme.
+error → Color // The color to use for input validation errors, e.g. for InputDecoration.errorText.
+errorContainer → Color // A color used for error elements needing less emphasis than error.
+inversePrimary → Color // An accent color used for displaying a highlight color on inverseSurface backgrounds, like button text in a SnackBar.
+inverseSurface → Color // A surface color used for displaying the reverse of what's seen in the surrounding UI, for example in a SnackBar to bring attention to an alert.
+onBackground → Color //A color that's clearly legible when drawn on background.
+onError → Color // A color that's clearly legible when drawn on error.
+onErrorContainer → Color // A color that's clearly legible when drawn on errorContainer.
+onInverseSurface → Color // A color that's clearly legible when drawn on inverseSurface.
+onPrimary → Color // A color that's clearly legible when drawn on primary.
+onPrimaryContainer → Color // A color that's clearly legible when drawn on primaryContainer.
+onSecondary → Color // A color that's clearly legible when drawn on secondary.
+onSecondaryContainer → Color // A color that's clearly legible when drawn on secondaryContainer.
+onSurface → Color // A color that's clearly legible when drawn on surface.
+onSurfaceVariant → Color // A color that's clearly legible when drawn on surfaceVariant.
+onTertiary → Color // A color that's clearly legible when drawn on tertiary.
+onTertiaryContainer → Color // A color that's clearly legible when drawn on tertiaryContainer.
+outline → Color // A utility color that creates boundaries and emphasis to improve usability.
+outlineVariant → Color // A utility color that creates boundaries for decorative elements when a 3:1 contrast isn’t required, such as for dividers or decorative elements.
+primary → Color //The color displayed most frequently across your app's screens and components.
+primaryContainer → Color // A color used for elements needing less emphasis than primary.
+scrim → Color // A color use to paint the scrim around of modal components.
+secondary → Color // An accent color used for less prominent components in the UI, such as filter chips, while expanding the opportunity for color expression.
+secondaryContainer → Color // A color used for elements needing less emphasis than secondary.
+shadow → Color // A color use to paint the drop shadows of elevated components.
+surface → Color // The background color for widgets like Card.
+surfaceTint → Color // A color used as an overlay on a surface color to indicate a component's elevation.
+surfaceVariant → Color // A color variant of surface that can be used for differentiation against a component using surface.
+tertiary → Color // A color used as a contrasting accent that can balance primary and secondary colors or bring heightened attention to an element, such as an input field.
+tertiaryContainer → Color // A color used for elements needing less emphasis than tertiary.
+
+A ColorScheme is:
+- Aesthetically pleasing.
+- Using complementary colors as defined by color theory: https://www.thesprucecrafts.com/definition-of-complementary-colors-2577513
+- Explicitly defining each color role with a Color code.
+- The color scheme must be accessible and high-contrast.
+
+Here's an example user prompt:
+Construct a ColorScheme object in Flutter that has a pastel pink color palette and aesthetically pleasing.
+Here's the example of good Dart code:
+ColorScheme(
+  brightness: Brightness.light,
+  primary: Color(0xffFF80AB),
+  onPrimary: Colors.white,
+  primaryContainer: Color(0xffFFABDE),
+  onPrimaryContainer: Color(0xff21005D),
+  secondary: Color(0xffFFD166),
+  onSecondary: Colors.black,
+  secondaryContainer: Color(0xffffFCD2),
+  onSecondaryContainer: Color(0xff422B08),
+  error: Color(0xffFF3B30),
+  onError: Colors.white,
+  errorContainer: Color(0xffFFDAD4),
+  onErrorContainer: Color(0xff410002),
+  background: Color(0xffFCF8FF),
+  onBackground: Color(0xff201A20),
+  surface: Color(0xffFEF2FE),
+  onSurface: Color(0xff201A20),
+  surfaceVariant: Color(0xffDBD5E0),
+  onSurfaceVariant: Color(0xff49454F),
+  outline: Color(0xff857E92),
+  outlineVariant: Color(0xff68606F),
+  shadow: Color(0xff000000),
+  scrim: Color(0xff000000),
+  inverseSurface: Color(0xff362F33),
+  onInverseSurface: Color(0xffFBF0F3),
+  inversePrimary: Color(0xffD15B9D),
+  surfaceTint: Color(0xffFF80AB),
+)
+This example code is a good because it explicitly defines all of the
+available color properties on a ColorScheme instead of using deprecated properties
+like Color Swatch.
+
+Here's an example user prompt:
+Generate a PURPLE and GREEN hulk themed color scheme.
+Here's the example of good Dart code:
+ColorScheme(
+  brightness: Brightness.dark,
+  primary: Color(0xff6E2C9A),
+  onPrimary: Colors.white,
+  primaryContainer: Colors.purple,
+  onPrimaryContainer: Color(0xffE1BEE7),
+  secondary: Color(0xff388E3C),
+  onSecondary: Colors.black,
+  secondaryContainer: Color(0xffA5D8AA),
+  onSecondaryContainer: Color(0xff003909),
+  error: Color(0xffD32F2F),
+  onError: Colors.white,
+  errorContainer: Color(0xffF2B8B5),
+  onErrorContainer: Color(0xff470001),
+  background: Color(0xff1B1B1F),
+  onBackground: Colors.white,
+  surface: Color(0xff2C2C33),
+  onSurface: Colors.white,
+  surfaceVariant: Color(0xff49454F),
+  onSurfaceVariant: Colors.white,
+  outline: Color(0xff8B858C),
+  outlineVariant: Color(0xff68606F),
+  shadow: Color(0xff000000),
+  scrim: Color(0xff000000),
+  inverseSurface: Color(0xffF4EFF4),
+  onInverseSurface: Color(0xff333039),
+  inversePrimary: Color(0xffD15B9D),
+  surfaceTint: Colors.purple,
+)
+This example code is a good because it explicitly defines all of the
+available color properties on a ColorScheme and it utilizes both colors mentioned
+in the original prompt and there is high contrast.
+`;
+
+export async function generateColorScheme(){
+  vscode.window.showInformationMessage('Generating Color Scheme...');
+
+  // Get API Key from local user configuration
+  const apiKey = vscode.workspace.getConfiguration().get<string>('google.ai.apiKey');
+  if (!apiKey) {
+      vscode.window.showErrorMessage('API key not configured. Check your settings.');
+      return;
+  }
+
+  const genAI = new GoogleGenerativeAI(apiKey);
+  const gemini = genAI.getGenerativeModel({model: "gemini-pro"});
+
+  // Text selection
+  const editor = vscode.window.activeTextEditor;
+  if (!editor) {
+      console.debug('Abandon: no open text editor.');
+      return;
+  }
+
+  const selection = editor.selection;
+  const selectedPrompt = editor.document.getText(selection);
+
+  // Build the full prompt using the template.
+  const fullPrompt = `${PROMPT_PRIMING + COLORSCHEME_CONTEXT + selectedPrompt}`;
+
+  const result = await gemini.generateContent(fullPrompt);
+  const response = result.response;
+  
+  if (!response) {
+      console.error('No candidates', response);
+      vscode.window.showErrorMessage('No comment candidates returned. Check debug logs.');
+      return;
+  }
+  const comment = response.text();
+
+  // Insert in place of selection.
+  editor.edit((editBuilder) => {
+      // Insert code inline where the highlighted text is.
+      editBuilder.insert(selection.start, comment);
+  });
+}
\ No newline at end of file
diff --git a/examples/gemini/node/flutter_theme_agent/src/components/component.ts b/examples/gemini/node/flutter_theme_agent/src/components/component.ts
new file mode 100644
index 000000000..8ee625372
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/src/components/component.ts
@@ -0,0 +1,26 @@
+/**
+ * Copyright 2024 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */ 
+
+// Provide instructions for the AI language model
+// This approach uses a few-shot technique, providing a few examples.
+export const PROMPT_PRIMING = `
+You are an expert Flutter developer, your Flutter code is thorough,
+easily human readable and and up to date with the latest stable
+version of Flutter. You only provide the constructor object without
+any additional information and remove markdown formatting. The code can be inserted
+inline into existing code and works.
+`;
+
diff --git a/examples/gemini/node/flutter_theme_agent/src/components/texttheme.ts b/examples/gemini/node/flutter_theme_agent/src/components/texttheme.ts
new file mode 100644
index 000000000..011a83156
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/src/components/texttheme.ts
@@ -0,0 +1,120 @@
+/**
+ * Copyright 2024 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */ 
+
+import * as vscode from 'vscode';
+
+import { GoogleGenerativeAI} from '@google/generative-ai';
+import {PROMPT_PRIMING} from './component';
+
+// TextTheme
+const TEXTHEME_CONTEXT=`
+TextThemes can be imported from the Google Fonts package. 
+
+All TextTheme objects have the following properties that can be modified:
+bodyLarge → TextStyle? // Largest of the body styles.
+bodyMedium → TextStyle? // Middle size of the body styles.
+bodySmall → TextStyle? // Smallest of the body styles.
+displayLarge → TextStyle? // Largest of the display styles.
+displayMedium → TextStyle? // Middle size of the display styles.
+displaySmall → TextStyle? // Smallest of the display styles.
+headlineLarge → TextStyle? // Largest of the headline styles.
+headlineMedium → TextStyle? // Middle size of the headline styles.
+headlineSmall → TextStyle? // Smallest of the headline styles.
+labelLarge → TextStyle? // Largest of the label styles.
+labelMedium → TextStyle? // Middle size of the label styles.
+labelSmall → TextStyle? // Smallest of the label styles.
+titleLarge → TextStyle? // Largest of the title styles.
+titleMedium → TextStyle? // Middle size of the title styles.
+titleSmall → TextStyle? // Smallest of the title styles.
+
+Here's an example user prompt:
+Add a Google fonts sans-serif text theme to that ThemeData object.
+Here's the example of good Dart code:
+GoogleFonts.rubikTextTheme()
+
+Here's an example prompt: 
+Generate a Google Font text theme that has a script/handwritten font.
+Here's an example of good Dart code:
+GoogleFonts.sacramentoTextTheme()
+
+Here's an example prompt: 
+Generate a Google Font text theme that has a script/handwritten font and make display small text bold. 
+Here's an example of good Dart code:
+GoogleFonts.patrickHandTextTheme().copyWith(
+  displaySmall: TextStyle(
+    fontWeight: FontWeight.bold,
+  ),
+)
+
+Here's an example prompt:
+The foreground property on TextStyles allow effects such as gradients to be applied to text.
+Here we provide a Paint with a ui.Gradient shader. 
+TextStyle(
+  fontSize: 40,
+  foreground: Paint()
+    ..shader = ui.Gradient.linear(
+      const Offset(0, 20),
+      const Offset(150, 20),
+      <Color>[
+        Colors.red,
+        Colors.yellow,
+      ],
+    )
+),
+`
+
+export async function generateTextTheme(){
+  vscode.window.showInformationMessage('Generating Text Theme...');
+
+  // Get API Key from local user configuration
+  const apiKey = vscode.workspace.getConfiguration().get<string>('google.ai.apiKey');
+  if (!apiKey) {
+      vscode.window.showErrorMessage('API key not configured. Check your settings.');
+      return;
+  }
+
+  const genAI = new GoogleGenerativeAI(apiKey);
+  const gemini = genAI.getGenerativeModel({model: "gemini-pro"});
+
+  // Text selection
+  const editor = vscode.window.activeTextEditor;
+  if (!editor) {
+      console.debug('Abandon: no open text editor.');
+      return;
+  }
+
+  const selection = editor.selection;
+  const selectedPrompt = editor.document.getText(selection);
+
+  // Build the full prompt using the template.
+  const fullPrompt = `${PROMPT_PRIMING + TEXTHEME_CONTEXT + selectedPrompt}`;
+
+  const result = await gemini.generateContent(fullPrompt);
+  const response = result.response;
+  
+  if (!response) {
+      console.error('No candidates', response);
+      vscode.window.showErrorMessage('No comment candidates returned. Check debug logs.');
+      return;
+  }
+  const comment = response.text();
+
+  // Insert in place of selection.
+  editor.edit((editBuilder) => {
+      // Insert code inline where the highlighted text is.
+      editBuilder.insert(selection.start, comment);
+  });
+}
\ No newline at end of file
diff --git a/examples/gemini/node/flutter_theme_agent/src/extension.ts b/examples/gemini/node/flutter_theme_agent/src/extension.ts
new file mode 100644
index 000000000..8ae73e5b3
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/src/extension.ts
@@ -0,0 +1,30 @@
+/**
+ * Copyright 2024 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+import * as vscode from 'vscode';
+import { generateTheme } from './theme';
+import { generateButtonStyle} from './components/buttonstyle';
+import { generateColorScheme} from './components/colorscheme';
+import { generateTextTheme } from './components/texttheme';
+
+export function activate(context: vscode.ExtensionContext) {
+	vscode.commands.registerCommand('flutter-theme-agent.generateTextTheme', generateTextTheme);
+	vscode.commands.registerCommand('flutter-theme-agent.generateColorScheme', generateColorScheme);
+	vscode.commands.registerCommand('flutter-theme-agent.generateButtonStyle', generateButtonStyle);
+	vscode.commands.registerCommand('flutter-theme-agent.generateTheme', generateTheme);
+}
+
+export function deactivate() { }
diff --git a/examples/gemini/node/flutter_theme_agent/src/theme.ts b/examples/gemini/node/flutter_theme_agent/src/theme.ts
new file mode 100644
index 000000000..2b817edfc
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/src/theme.ts
@@ -0,0 +1,181 @@
+/**
+ * Copyright 2024 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */ 
+
+import * as vscode from 'vscode';
+
+import { GoogleGenerativeAI} from '@google/generative-ai';
+import {PROMPT_PRIMING} from './components/component';
+
+// Provide instructions for the AI language model
+// This approach uses a few-shot technique, providing a few examples.
+const COMMENT_LABEL = 'Here is the comment:';
+const CODE_LABEL = 'Here is good code:';
+const PROMPT = `
+${COMMENT_LABEL}
+Construct a CardTheme object in flutter that removes elevation and adds a 2px black border with 16px rounded corners.
+${CODE_LABEL}
+CardTheme(
+    elevation: 0,
+    shape: RoundedRectangleBorder(
+        borderRadius: BorderRadius.circular(16),
+        side: BorderSide(
+        color: Colors.black,
+        width: 2,
+        ),
+    ),
+)
+
+${COMMENT_LABEL}
+Construct a ColorScheme object in Flutter that has a pastel pink color palette and aesthetically pleasing.
+${CODE_LABEL}
+ColorScheme(
+    brightness: Brightness.light,
+    primary: Color(0xffFF80AB),
+    onPrimary: Colors.white,
+    primaryContainer: Color(0xffFFABDE),
+    onPrimaryContainer: Color(0xff21005D),
+    secondary: Color(0xffFFD166),
+    onSecondary: Colors.black,
+    secondaryContainer: Color(0xffffFCD2),
+    onSecondaryContainer: Color(0xff422B08),
+    error: Color(0xffFF3B30),
+    onError: Colors.white,
+    errorContainer: Color(0xffFFDAD4),
+    onErrorContainer: Color(0xff410002),
+    background: Color(0xffFCF8FF),
+    onBackground: Color(0xff201A20),
+    surface: Color(0xffFEF2FE),
+    onSurface: Color(0xff201A20),
+    surfaceVariant: Color(0xffDBD5E0),
+    onSurfaceVariant: Color(0xff49454F),
+    outline: Color(0xff857E92),
+    outlineVariant: Color(0xff68606F),
+    shadow: Color(0xff000000),
+    scrim: Color(0xff000000),
+    inverseSurface: Color(0xff362F33),
+    onInverseSurface: Color(0xffFBF0F3),
+    inversePrimary: Color(0xffD15B9D),
+    surfaceTint: Color(0xffFF80AB),
+)
+This example code is a good because it explicitly definess and set all of the
+available color properties on a ColorScheme instead of using deprecated properties
+like Color Swatch.
+
+${COMMENT_LABEL}
+Construct a ThemeData object with a pastel pink color palette that is asthetically 
+pleasing and a the CardTheme that removes elevation and adds a 2px black border.
+${CODE_LABEL}
+ThemeData(
+    cardTheme: CardTheme(
+        elevation: 0,
+        shape: RoundedRectangleBorder(
+        side: BorderSide(
+            color: Colors.black,
+            width: 2,
+        ),
+        ),
+    ),
+    colorScheme: ColorScheme(
+        brightness: Brightness.light,
+        primary: Color(0xffFF80AB),
+        onPrimary: Colors.white,
+        primaryContainer: Color(0xffFFABDE),
+        onPrimaryContainer: Color(0xff21005D),
+        secondary: Color(0xffFFD166),
+        onSecondary: Colors.black,
+        secondaryContainer: Color(0xffffFCD2),
+        onSecondaryContainer: Color(0xff422B08),
+        error: Color(0xffFF3B30),
+        onError: Colors.white,
+        errorContainer: Color(0xffFFDAD4),
+        onErrorContainer: Color(0xff410002),
+        background: Color(0xffFCF8FF),
+        onBackground: Color(0xff201A20),
+        surface: Color(0xffFEF2FE),
+        onSurface: Color(0xff201A20),
+        surfaceVariant: Color(0xffDBD5E0),
+        onSurfaceVariant: Color(0xff49454F),
+        outline: Color(0xff857E92),
+        outlineVariant: Color(0xff68606F),
+        shadow: Color(0xff000000),
+        scrim: Color(0xff000000),
+        inverseSurface: Color(0xff362F33),
+        onInverseSurface: Color(0xffFBF0F3),
+        inversePrimary: Color(0xffD15B9D),
+        surfaceTint: Color(0xffFF80AB),
+    ),
+)
+
+${COMMENT_LABEL}
+Add a Google fonts sans-serif text theme to that ThemeData object.
+${CODE_LABEL}
+ThemeData(
+    textTheme: GoogleFonts.rubikTextTheme(),
+)`
+
+
+export async function generateTheme() {
+    vscode.window.showInformationMessage('Generating comment...');
+
+    // Get API Key from local user configuration
+    const apiKey = vscode.workspace.getConfiguration().get<string>('google.ai.apiKey');
+    if (!apiKey) {
+        vscode.window.showErrorMessage('API key not configured. Check your settings.');
+        return;
+    }
+
+    const genAI = new GoogleGenerativeAI(apiKey);
+    const gemini = genAI.getGenerativeModel({model: "gemini-pro"});
+
+    // Text selection
+    const editor = vscode.window.activeTextEditor;
+    if (!editor) {
+        console.debug('Abandon: no open text editor.');
+        return;
+    }
+
+    const selection = editor.selection;
+    const selectedPrompt = editor.document.getText(selection);
+
+    // Build the full prompt using the template.
+    const fullPrompt = `${PROMPT_PRIMING} + ${PROMPT} + ${selectedPrompt}`;
+
+    const result = await gemini.generateContent(fullPrompt);
+    const response = result.response;
+    
+    if (!response) {
+        console.error('No candidates', response);
+        vscode.window.showErrorMessage('No comment candidates returned. Check debug logs.');
+        return;
+    }
+    const comment = response.text();
+
+    // Insert before selection.
+    editor.edit((editBuilder) => {
+
+        // Copy the indent from the first line of the selection.
+        const trimmed = selectedPrompt.trimStart();
+        const padding = selectedPrompt.substring(0, selectedPrompt.length - trimmed.length);
+
+        let pyComment = comment.split('\n').map((l: string) => `${padding}${l}`).join('\n');
+        if (pyComment.search(/\n$/) === -1) {
+            // Add a final newline if necessary.
+            pyComment += "\n";
+        }
+        
+        editBuilder.insert(selection.start, pyComment);
+    });
+}
diff --git a/examples/gemini/node/flutter_theme_agent/tsconfig.json b/examples/gemini/node/flutter_theme_agent/tsconfig.json
new file mode 100644
index 000000000..64006615a
--- /dev/null
+++ b/examples/gemini/node/flutter_theme_agent/tsconfig.json
@@ -0,0 +1,17 @@
+{
+	"compilerOptions": {
+		"module": "commonjs",
+		"target": "ES2020",
+		"outDir": "out",
+		"lib": [
+			"ES2020"
+		],
+		"sourceMap": true,
+		"rootDir": "src",
+		"strict": false   /* enable all strict type-checking options */
+		/* Additional Checks */
+		// "noImplicitReturns": true, /* Report error when not all code paths in function return a value. */
+		// "noFallthroughCasesInSwitch": true, /* Report errors for fallthrough cases in switch statement. */
+		// "noUnusedParameters": true,  /* Report errors on unused parameters. */
+	}
+}
diff --git a/examples/gemini/node/pipet-code-agent/.eslintrc.json b/examples/gemini/node/pipet-code-agent/.eslintrc.json
new file mode 100644
index 000000000..f9b22b793
--- /dev/null
+++ b/examples/gemini/node/pipet-code-agent/.eslintrc.json
@@ -0,0 +1,24 @@
+{
+    "root": true,
+    "parser": "@typescript-eslint/parser",
+    "parserOptions": {
+        "ecmaVersion": 6,
+        "sourceType": "module"
+    },
+    "plugins": [
+        "@typescript-eslint"
+    ],
+    "rules": {
+        "@typescript-eslint/naming-convention": "warn",
+        "@typescript-eslint/semi": "warn",
+        "curly": "warn",
+        "eqeqeq": "warn",
+        "no-throw-literal": "warn",
+        "semi": "off"
+    },
+    "ignorePatterns": [
+        "out",
+        "dist",
+        "**/*.d.ts"
+    ]
+}
diff --git a/examples/gemini/node/pipet-code-agent/.gitignore b/examples/gemini/node/pipet-code-agent/.gitignore
new file mode 100644
index 000000000..8597d7973
--- /dev/null
+++ b/examples/gemini/node/pipet-code-agent/.gitignore
@@ -0,0 +1,26 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+out
+
+# Editor directories and files
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
+
+tsconfig.tsbuildinfo
+*.tsbuildinfo
diff --git a/examples/gemini/node/pipet-code-agent/.vscode/extensions.json b/examples/gemini/node/pipet-code-agent/.vscode/extensions.json
new file mode 100644
index 000000000..978d6307f
--- /dev/null
+++ b/examples/gemini/node/pipet-code-agent/.vscode/extensions.json
@@ -0,0 +1,5 @@
+{
+	"recommendations": [
+		"dbaeumer.vscode-eslint"
+	]
+}
diff --git a/examples/gemini/node/pipet-code-agent/.vscode/launch.json b/examples/gemini/node/pipet-code-agent/.vscode/launch.json
new file mode 100644
index 000000000..670d6e66c
--- /dev/null
+++ b/examples/gemini/node/pipet-code-agent/.vscode/launch.json
@@ -0,0 +1,34 @@
+// A launch configuration that compiles the extension and then opens it inside a new window
+// Use IntelliSense to learn about possible attributes.
+// Hover to view descriptions of existing attributes.
+// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+{
+	"version": "0.2.0",
+	"configurations": [
+		{
+			"name": "Run Extension",
+			"type": "extensionHost",
+			"request": "launch",
+			"args": [
+				"--extensionDevelopmentPath=${workspaceFolder}"
+			],
+			"outFiles": [
+				"${workspaceFolder}/out/**/*.js"
+			],
+			"preLaunchTask": "${defaultBuildTask}"
+		},
+		{
+			"name": "Extension Tests",
+			"type": "extensionHost",
+			"request": "launch",
+			"args": [
+				"--extensionDevelopmentPath=${workspaceFolder}",
+				"--extensionTestsPath=${workspaceFolder}/out/test/suite/index"
+			],
+			"outFiles": [
+				"${workspaceFolder}/out/test/**/*.js"
+			],
+			"preLaunchTask": "${defaultBuildTask}"
+		}
+	]
+}
diff --git a/examples/gemini/node/pipet-code-agent/.vscode/settings.json b/examples/gemini/node/pipet-code-agent/.vscode/settings.json
new file mode 100644
index 000000000..0429f05ca
--- /dev/null
+++ b/examples/gemini/node/pipet-code-agent/.vscode/settings.json
@@ -0,0 +1,11 @@
+// Place your settings in this file to overwrite default and user settings.
+{
+  "files.exclude": {
+      "out": false // set this to true to hide the "out" folder with the compiled JS files
+  },
+  "search.exclude": {
+      "out": true // set this to false to include "out" folder in search results
+  },
+  // Turn off tsc task auto detection since we have the necessary tasks as npm scripts
+  "typescript.tsc.autoDetect": "off"
+}
\ No newline at end of file
diff --git a/examples/gemini/node/pipet-code-agent/.vscode/tasks.json b/examples/gemini/node/pipet-code-agent/.vscode/tasks.json
new file mode 100644
index 000000000..3b17e53b6
--- /dev/null
+++ b/examples/gemini/node/pipet-code-agent/.vscode/tasks.json
@@ -0,0 +1,20 @@
+// See https://go.microsoft.com/fwlink/?LinkId=733558
+// for the documentation about the tasks.json format
+{
+	"version": "2.0.0",
+	"tasks": [
+		{
+			"type": "npm",
+			"script": "watch",
+			"problemMatcher": "$tsc-watch",
+			"isBackground": true,
+			"presentation": {
+				"reveal": "never"
+			},
+			"group": {
+				"kind": "build",
+				"isDefault": true
+			}
+		}
+	]
+}
diff --git a/examples/gemini/node/pipet-code-agent/.vscodeignore b/examples/gemini/node/pipet-code-agent/.vscodeignore
new file mode 100644
index 000000000..389996760
--- /dev/null
+++ b/examples/gemini/node/pipet-code-agent/.vscodeignore
@@ -0,0 +1,10 @@
+.vscode/**
+.vscode-test/**
+src/**
+.gitignore
+.yarnrc
+vsc-extension-quickstart.md
+**/tsconfig.json
+**/.eslintrc.json
+**/*.map
+**/*.ts
diff --git a/examples/gemini/node/pipet-code-agent/CHANGELOG.md b/examples/gemini/node/pipet-code-agent/CHANGELOG.md
new file mode 100644
index 000000000..2e1028d05
--- /dev/null
+++ b/examples/gemini/node/pipet-code-agent/CHANGELOG.md
@@ -0,0 +1,7 @@
+# Change Log
+
+All notable changes to the "pipet-code-agent" extension are documented in this file.
+
+## v0.0.1
+
+- Initial release. Provides commands for generating code comments and code reviews
diff --git a/examples/gemini/node/pipet-code-agent/CONTRIBUTING.md b/examples/gemini/node/pipet-code-agent/CONTRIBUTING.md
new file mode 100644
index 000000000..ea7316938
--- /dev/null
+++ b/examples/gemini/node/pipet-code-agent/CONTRIBUTING.md
@@ -0,0 +1,32 @@
+# How to Contribute
+
+We would love to accept your patches and contributions to this project.
+
+## Before you begin
+
+### Sign our Contributor License Agreement
+
+Contributions to this project must be accompanied by a
+[Contributor License Agreement](https://cla.developers.google.com/about) (CLA).
+You (or your employer) retain the copyright to your contribution; this simply
+gives us permission to use and redistribute your contributions as part of the
+project.
+
+If you or your current employer have already signed the Google CLA (even if it
+was for a different project), you probably don't need to do it again.
+
+Visit <https://cla.developers.google.com/> to see your current agreements or to
+sign a new one.
+
+### Review our Community Guidelines
+
+This project follows [Google's Open Source Community
+Guidelines](https://opensource.google/conduct/).
+
+## Contribution process
+
+### Code Reviews
+
+All submissions, including submissions by project members, require review. We
+use [GitHub pull requests](https://docs.github.com/articles/about-pull-requests)
+for this purpose.
\ No newline at end of file
diff --git a/examples/gemini/node/pipet-code-agent/LICENSE b/examples/gemini/node/pipet-code-agent/LICENSE
new file mode 100644
index 000000000..989e2c59e
--- /dev/null
+++ b/examples/gemini/node/pipet-code-agent/LICENSE
@@ -0,0 +1,201 @@
+Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
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+      "Licensor" shall mean the copyright owner or entity authorized by
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+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
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+      direction or management of such entity, whether by contract or
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+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
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+      including but not limited to software source code, documentation
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+      "Work" shall mean the work of authorship, whether in Source or
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+      copyright license to reproduce, prepare Derivative Works of,
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+   4. Redistribution. You may reproduce and distribute copies of the
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+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
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+      (c) You must retain, in the Source form of any Derivative Works
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+      (d) If the Work includes a "NOTICE" text file as part of its
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+          notices within Derivative Works that You distribute, alongside
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+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
\ No newline at end of file
diff --git a/examples/gemini/node/pipet-code-agent/README.md b/examples/gemini/node/pipet-code-agent/README.md
new file mode 100644
index 000000000..7d4703972
--- /dev/null
+++ b/examples/gemini/node/pipet-code-agent/README.md
@@ -0,0 +1,103 @@
+# Pipet Code Agent
+
+Pipet Code Agent is an AI-powered code assistance tool, built as an extension
+for Microsoft [Visual Studio Code](https://code.visualstudio.com/) (VS Code).
+Pipet uses the Google Gemini API to help you write code comments and review your
+code by adding commands to the command pallete of VS Code.
+
+![pipet-code-agent](./pipet-snippet.png)
+
+Pipet is provided as a development project, so you must configure and build
+it if you want to run it in your VS Code instance. For more information
+about building, configuring, running, and extending this project, see the
+[Build AI Code Assistant with Pipet Code Agent](https://ai.google.dev/examples/pipet-code-agent) tutorial.
+
+## Project setup
+
+These instructions walk you through getting the Pipet Code Agent project set up
+for development and testing. The general steps are Installing some prerequisite
+software, setting a few environment variables, cloning the project from the code
+repository, and running the configuration installation.
+
+Note: You need a Google Gemini API Key to be able to run the project, which you
+can obtain from the [Google Gemini API](https://ai.google.dev/tutorials/setup) page.
+
+### Install the prerequisites
+
+The Pipet Code Agent project runs as an extension of Microsoft [Visual Studio
+Code](https://code.visualstudio.com/), and uses
+[Node.js](https://nodejs.org/) and npm to manage packages and run the
+application. The following installation instructions are for a Linux host
+machine.
+
+To install the required software:
+
+1.  Install [Visual Studio Code](https://code.visualstudio.com/download)
+    for your platform.
+1.  Install `node` and `npm` by following the [installation
+    instructions](https://nodejs.org/) for your platform.
+
+### Clone and configure the project
+
+Download the project code and use the `npm` installation command to download
+the required dependencies and configure the project. You need
+[git](https://git-scm.com/) source control software to retrieve
+the project source code.
+
+To download and configure the project code:
+
+1.  Clone the git repository using the following command.\
+    `git clone https://github.com/google/generative-ai-docs`
+1.  Optionally, configure your local git repository to use sparse checkout,
+so you have only the files for the Docs Agent project.\
+    `cd generative-ai-docs/`\
+    `git sparse-checkout init --cone`\
+    `git sparse-checkout set examples/gemini/node/pipet-code-agent/`
+1.  Navigate to the Pipet Code Agent project root directory.\
+    `cd generative-ai-docs/examples/gemini/node/pipet-code-agent/`
+1.  Run the install command to download dependencies and configure the project:\
+    `npm install`
+
+### Configure and test the extension
+
+You should now be able to test your installation by running Pipet Code Agent as
+a development extension in VS Code on your device. The test opens a separate VS
+Code **Extension Development Host** window where the new extension is available.
+In this new window, you configure the API Key the extension uses to access the
+Google Gemini API.
+
+To configure and test your setup:
+
+1.  Start the VS Code application.
+1.  In VS Code, create a new window by selecting **File > New Window**.
+1.  Open the Pipet Code Agent project by selecting **File > Open Folder**,
+    and selecting the `pipet-code-agent/` folder.
+1.  Open the `pipet-code-agent/package.json` file.
+1.  Run the extension in debug mode by selecting **Run > Start Debugging**.
+    This step opens a separate VS Code **Extension Development Host** window.
+1.  Open the VS Code settings by selecting **Code > Settings > Settings**.
+1.  Get a [Google Gemini API Key](https://ai.google.dev/tutorials/setup)
+    from the Generative AI Developer site, and copy the key string.
+1.  Set the API key as a configuration setting. In **Search Settings**
+    field, type `pipet`, select the **User** tab, and in the **Google > Gemini
+    Api Key** setting, click the **Edit in settings.json** link, and add your
+    Gemini API key:\
+    `"google.gemini.apiKey": "your-api-key-here"`
+1.  Save the changes to the `settings.json` file and close the settings tabs.
+
+**Caution:** Treat your API Key like a password and protect it appropriately. Don't
+embed your key in publicly published code.
+
+To test the extension commands:
+
+1.  In the VS Code **Extension Development Host** window, select some code
+    in the editor window.
+1.  Open the command palette by selecting **View > Command Palette**.
+1.  In the Command Palette, type `Pipet` and select one of the commands with
+    that prefix.
+
+
+## Resources
+
+- Project code tutorial:
+[Build AI Code Assistant with Pipet Code Agent](https://ai.google.dev/examples/pipet-code-agent) tutorial.
diff --git a/examples/gemini/node/pipet-code-agent/package-lock.json b/examples/gemini/node/pipet-code-agent/package-lock.json
new file mode 100644
index 000000000..a65319f8e
--- /dev/null
+++ b/examples/gemini/node/pipet-code-agent/package-lock.json
@@ -0,0 +1,2360 @@
+{
+  "name": "pipet-code-agent",
+  "version": "0.0.1",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "pipet-code-agent",
+      "version": "0.0.1",
+      "dependencies": {
+        "@google/generative-ai": "^0.3.0",
+        "dotenv": "^16.1.4"
+      },
+      "devDependencies": {
+        "@types/glob": "^8.1.0",
+        "@types/mocha": "^10.0.1",
+        "@types/node": "20.2.5",
+        "@types/vscode": "^1.78.0",
+        "@typescript-eslint/eslint-plugin": "^5.59.8",
+        "@typescript-eslint/parser": "^5.59.8",
+        "@vscode/test-electron": "^2.3.2",
+        "eslint": "^8.41.0",
+        "glob": "^8.1.0",
+        "mocha": "^10.2.0",
+        "typescript": "^5.1.3"
+      },
+      "engines": {
+        "vscode": "^1.78.0"
+      }
+    },
+    "node_modules/@eslint-community/eslint-utils": {
+      "version": "4.4.0",
+      "resolved": "https://registry.npmjs.org/@eslint-community/eslint-utils/-/eslint-utils-4.4.0.tgz",
+      "integrity": "sha512-1/sA4dwrzBAyeUoQ6oxahHKmrZvsnLCg4RfxW3ZFGGmQkSNQPFNLV9CUEFQP1x9EYXHTo5p6xdhZM1Ne9p/AfA==",
+      "dev": true,
+      "dependencies": {
+        "eslint-visitor-keys": "^3.3.0"
+      },
+      "engines": {
+        "node": "^12.22.0 || ^14.17.0 || >=16.0.0"
+      },
+      "peerDependencies": {
+        "eslint": "^6.0.0 || ^7.0.0 || >=8.0.0"
+      }
+    },
+    "node_modules/@eslint-community/regexpp": {
+      "version": "4.5.1",
+      "resolved": "https://registry.npmjs.org/@eslint-community/regexpp/-/regexpp-4.5.1.tgz",
+      "integrity": "sha512-Z5ba73P98O1KUYCCJTUeVpja9RcGoMdncZ6T49FCUl2lN38JtCJ+3WgIDBv0AuY4WChU5PmtJmOCTlN6FZTFKQ==",
+      "dev": true,
+      "engines": {
+        "node": "^12.0.0 || ^14.0.0 || >=16.0.0"
+      }
+    },
+    "node_modules/@eslint/eslintrc": {
+      "version": "2.0.3",
+      "resolved": "https://registry.npmjs.org/@eslint/eslintrc/-/eslintrc-2.0.3.tgz",
+      "integrity": "sha512-+5gy6OQfk+xx3q0d6jGZZC3f3KzAkXc/IanVxd1is/VIIziRqqt3ongQz0FiTUXqTk0c7aDB3OaFuKnuSoJicQ==",
+      "dev": true,
+      "dependencies": {
+        "ajv": "^6.12.4",
+        "debug": "^4.3.2",
+        "espree": "^9.5.2",
+        "globals": "^13.19.0",
+        "ignore": "^5.2.0",
+        "import-fresh": "^3.2.1",
+        "js-yaml": "^4.1.0",
+        "minimatch": "^3.1.2",
+        "strip-json-comments": "^3.1.1"
+      },
+      "engines": {
+        "node": "^12.22.0 || ^14.17.0 || >=16.0.0"
+      },
+      "funding": {
+        "url": "https://opencollective.com/eslint"
+      }
+    },
+    "node_modules/@eslint/js": {
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+      "dev": true
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+    },
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+        "escalade": "^3.1.1",
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+      "dev": true,
+      "dependencies": {
+        "camelcase": "^6.0.0",
+        "decamelize": "^4.0.0",
+        "flat": "^5.0.2",
+        "is-plain-obj": "^2.1.0"
+      },
+      "engines": {
+        "node": ">=10"
+      }
+    },
+    "node_modules/yocto-queue": {
+      "version": "0.1.0",
+      "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz",
+      "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==",
+      "dev": true,
+      "engines": {
+        "node": ">=10"
+      },
+      "funding": {
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+      }
+    }
+  }
+}
diff --git a/examples/gemini/node/pipet-code-agent/package.json b/examples/gemini/node/pipet-code-agent/package.json
new file mode 100644
index 000000000..a2d8462f7
--- /dev/null
+++ b/examples/gemini/node/pipet-code-agent/package.json
@@ -0,0 +1,73 @@
+{
+  "name": "pipet-code-agent",
+  "displayName": "Pipet Code Agent",
+  "description": "",
+  "version": "0.0.1",
+  "engines": {
+    "vscode": "^1.78.0"
+  },
+  "categories": [
+    "Other"
+  ],
+  "activationEvents": [],
+  "main": "./out/extension.js",
+  "contributes": {
+    "commands": [
+      {
+        "command": "pipet-code-agent.commentCode",
+        "title": "Pipet: Add a comment to selected code."
+      },
+      {
+        "command": "pipet-code-agent.reviewCode",
+        "title": "Pipet: Review the selected code."
+      }
+    ],
+    "configuration": [
+      {
+        "title": "Pipet Code Agent: Google Gemini",
+        "properties": {
+          "google.gemini.apiKey": {
+            "type": [
+              "string",
+              "null"
+            ],
+            "default": null,
+            "markdownDescription": "Enter your [API Key](https://aistudio.google.com/app/apikey) for Gemini."
+          },
+          "google.gemini.textModel": {
+            "type": [
+              "string"
+            ],
+            "default": "models/gemini-1.0-pro-latest",
+            "markdownDescription": "Provide the name of the model you want to use. Choose from the [base models](https://ai.google.dev/models/gemini) or your own [tuned model](https://ai.google.dev/docs/model_tuning_guidance)."
+          }
+        }
+      }
+    ]
+  },
+  "scripts": {
+    "vscode:prepublish": "npm run compile",
+    "compile": "tsc -p ./",
+    "watch": "tsc -watch -p ./",
+    "pretest": "npm run compile && npm run lint",
+    "lint": "eslint src --ext ts",
+    "test": "node ./out/test/runTest.js"
+  },
+  "devDependencies": {
+    "@types/glob": "^8.1.0",
+    "@types/mocha": "^10.0.1",
+    "@types/node": "20.2.5",
+    "@types/vscode": "^1.78.0",
+    "@typescript-eslint/eslint-plugin": "^5.59.8",
+    "@typescript-eslint/parser": "^5.59.8",
+    "@vscode/test-electron": "^2.3.2",
+    "eslint": "^8.41.0",
+    "glob": "^8.1.0",
+    "mocha": "^10.2.0",
+    "typescript": "^5.1.3"
+  },
+  "dependencies": {
+    "@google/generative-ai": "^0.3.0",
+    "dotenv": "^16.1.4"
+  }
+}
diff --git a/examples/gemini/node/pipet-code-agent/pipet-snippet.png b/examples/gemini/node/pipet-code-agent/pipet-snippet.png
new file mode 100644
index 000000000..db9500d10
Binary files /dev/null and b/examples/gemini/node/pipet-code-agent/pipet-snippet.png differ
diff --git a/examples/gemini/node/pipet-code-agent/src/comments.ts b/examples/gemini/node/pipet-code-agent/src/comments.ts
new file mode 100644
index 000000000..5dc517478
--- /dev/null
+++ b/examples/gemini/node/pipet-code-agent/src/comments.ts
@@ -0,0 +1,110 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */ 
+
+import * as vscode from 'vscode';
+
+import { GoogleGenerativeAI } from '@google/generative-ai';
+
+// Provide instructions for the AI language model
+// This approach uses a few-shot technique, providing a few examples.
+const CODE_LABEL = 'Here is the code:';
+const COMMENT_LABEL = 'Here is a good comment:';
+const PROMPT = `
+A good code review comment describes the intent behind the code without
+repeating information that's obvious from the code itself. Good comments
+describe "why", explain any "magic" values and non-obvious behaviour.
+Below are some examples of good code comments.
+
+${CODE_LABEL}
+print(f" \\033[33m {msg}\\033[00m", file=sys.stderr)
+${COMMENT_LABEL}
+Use terminal codes to print color output to console.
+
+${CODE_LABEL}
+to_delete = set(data.keys()) - frozenset(keep)
+for key in to_delete:
+  del data[key]
+${COMMENT_LABEL}
+Modifies \`data\` to remove any entry not specified in the \`keep\` list.
+
+${CODE_LABEL}
+lines[text_range.start_line - 1:text_range.end_line - 1] = [repl.new_content]
+${COMMENT_LABEL}
+Replace text from \`lines\` with \`new_content\`, noting that array indices 
+are offset 1 from line numbers.
+
+${CODE_LABEL}
+api_key = os.getenv("GOOGLE_API_KEY")
+${COMMENT_LABEL}
+Attempt to load the API key from the environment.`;
+
+
+export async function generateComment() {
+    vscode.window.showInformationMessage('Generating comment...');
+
+    const modelName = vscode.workspace.getConfiguration().get<string>('google.gemini.textModel', 'models/gemini-1.0-pro-latest');
+
+    // Get API Key from local user configuration
+    const apiKey = vscode.workspace.getConfiguration().get<string>('google.gemini.apiKey');
+    if (!apiKey) {
+        vscode.window.showErrorMessage('API key not configured. Check your settings.');
+        return;
+    }
+
+    const genai = new GoogleGenerativeAI(apiKey);
+    const model = genai.getGenerativeModel({model: modelName});
+
+    // Text selection
+    const editor = vscode.window.activeTextEditor;
+    if (!editor) {
+        console.debug('Abandon: no open text editor.');
+        return;
+    }
+
+    const selection = editor.selection;
+    const selectedCode = editor.document.getText(selection);
+
+    // Build the full prompt using the template.
+    const fullPrompt = `${PROMPT}
+
+${CODE_LABEL}
+${selectedCode}
+${COMMENT_LABEL}
+`;
+
+    const result = await model.generateContent(fullPrompt);
+    const response = await result.response;
+    const comment = response.text();  
+
+    // Insert before selection.
+    editor.edit((editBuilder) => {
+        // TODO(you!): Support other comment styles.
+        const commentPrefix = '# ';
+
+        // Copy the indent from the first line of the selection.
+        const trimmed = selectedCode.trimStart();
+        const padding = selectedCode.substring(0, selectedCode.length - trimmed.length);
+
+        let pyComment = comment.split('\n').map((l: string) => `${padding}${commentPrefix}${l}`).join('\n');
+        if (pyComment.search(/\n$/) === -1) {
+            // Add a final newline if necessary.
+            pyComment += "\n";
+        }
+        let commentIntro = padding + commentPrefix + "Code comment: (generated)\n";
+        editBuilder.insert(selection.start, commentIntro);
+        editBuilder.insert(selection.start, pyComment);
+    });
+}
diff --git a/examples/gemini/node/pipet-code-agent/src/extension.ts b/examples/gemini/node/pipet-code-agent/src/extension.ts
new file mode 100644
index 000000000..67b967078
--- /dev/null
+++ b/examples/gemini/node/pipet-code-agent/src/extension.ts
@@ -0,0 +1,28 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */ 
+
+import * as vscode from 'vscode';
+import { generateComment } from './comments';
+import { generateReview } from './review';
+
+
+export function activate(context: vscode.ExtensionContext) {
+    vscode.commands.registerCommand('pipet-code-agent.commentCode', generateComment);
+    vscode.commands.registerCommand('pipet-code-agent.reviewCode', generateReview);
+}
+
+
+export function deactivate() { }
diff --git a/examples/gemini/node/pipet-code-agent/src/review.ts b/examples/gemini/node/pipet-code-agent/src/review.ts
new file mode 100644
index 000000000..d2fcc7bea
--- /dev/null
+++ b/examples/gemini/node/pipet-code-agent/src/review.ts
@@ -0,0 +1,97 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */ 
+
+import * as vscode from 'vscode';
+import { GoogleGenerativeAI } from '@google/generative-ai';
+const CODE_LABEL = 'Here is the code:';
+const REVIEW_LABEL = 'Here is the review:';
+const PROMPT = `
+Reviewing code involves finding bugs and increasing code quality. Examples of bugs are syntax 
+errors or typos, out of memory errors, and boundary value errors. Increasing code quality 
+entails reducing complexity of code, eliminating duplicate code, and ensuring other developers 
+are able to understand the code. 
+${CODE_LABEL}
+for i in x:
+    pint(f"Iteration {i} provides this {x**2}.")
+${REVIEW_LABEL}
+The command \`print\` is spelled incorrectly.
+${CODE_LABEL}
+height = [1, 2, 3, 4, 5]
+w = [6, 7, 8, 9, 10]
+${REVIEW_LABEL}
+The variable name \`w\` seems vague. Did you mean \`width\` or \`weight\`?
+${CODE_LABEL}
+while i < 0:
+  thrice = i * 3
+  thrice = i * 3
+  twice = i * 2
+${REVIEW_LABEL}
+There are duplicate lines of code in this control structure.
+`;
+
+export async function generateReview() {
+  vscode.window.showInformationMessage('Generating code review...');
+  const modelName = vscode.workspace.getConfiguration().get<string>('google.gemini.textModel', 'models/gemini-1.0-pro-latest');
+
+  // Get API Key from local user configuration
+  const apiKey = vscode.workspace.getConfiguration().get<string>('google.gemini.apiKey');
+  if (!apiKey) {
+    vscode.window.showErrorMessage('API key not configured. Check your settings.');
+    return;
+  }
+
+  const genai = new GoogleGenerativeAI(apiKey);
+  const model = genai.getGenerativeModel({model: modelName});
+
+  // Text selection
+  const editor = vscode.window.activeTextEditor;
+  if (!editor) {
+    console.debug('Abandon: no open text editor.');
+    return;
+  }
+
+  const selection = editor.selection;
+  const selectedCode = editor.document.getText(selection);
+
+  // Build the full prompt using the template.
+  const fullPrompt = `${PROMPT}
+    ${CODE_LABEL}
+    ${selectedCode}
+    ${REVIEW_LABEL}
+    `;
+
+  const result = await model.generateContent(fullPrompt);
+  const response = await result.response;
+  const comment = response.text();  
+
+  // Insert before selection
+  editor.edit((editBuilder) => {
+    // Copy the indent from the first line of the selection.
+    const trimmed = selectedCode.trimStart();
+    const padding = selectedCode.substring(0, selectedCode.length - trimmed.length);
+
+    // TODO(you!): Support other comment styles.
+    const commentPrefix = '# ';
+    let pyComment = comment.split('\n').map((l: string) => `${padding}${commentPrefix}${l}`).join('\n');
+    if (pyComment.search(/\n$/) === -1) {
+      // Add a final newline if necessary.
+      pyComment += "\n";
+    }
+    let reviewIntro = padding + commentPrefix + "Code review: (generated)\n";
+    editBuilder.insert(selection.start, reviewIntro);
+    editBuilder.insert(selection.start, pyComment);
+  });
+}
diff --git a/examples/gemini/node/pipet-code-agent/tsconfig.json b/examples/gemini/node/pipet-code-agent/tsconfig.json
new file mode 100644
index 000000000..315af7ec7
--- /dev/null
+++ b/examples/gemini/node/pipet-code-agent/tsconfig.json
@@ -0,0 +1,17 @@
+{
+	"compilerOptions": {
+		"module": "commonjs",
+		"target": "ES2020",
+		"outDir": "out",
+		"lib": [
+			"ES2020"
+		],
+		"sourceMap": true,
+		"rootDir": "src",
+		"strict": true   /* enable all strict type-checking options */
+		/* Additional Checks */
+		// "noImplicitReturns": true, /* Report error when not all code paths in function return a value. */
+		// "noFallthroughCasesInSwitch": true, /* Report errors for fallthrough cases in switch statement. */
+		// "noUnusedParameters": true,  /* Report errors on unused parameters. */
+	}
+}
diff --git a/demos/palm/python/docs-agent/CONTRIBUTING.md b/examples/gemini/python/docs-agent/CONTRIBUTING.md
similarity index 100%
rename from demos/palm/python/docs-agent/CONTRIBUTING.md
rename to examples/gemini/python/docs-agent/CONTRIBUTING.md
diff --git a/demos/palm/python/docs-agent/LICENSE b/examples/gemini/python/docs-agent/LICENSE
similarity index 100%
rename from demos/palm/python/docs-agent/LICENSE
rename to examples/gemini/python/docs-agent/LICENSE
diff --git a/examples/gemini/python/docs-agent/README.md b/examples/gemini/python/docs-agent/README.md
new file mode 100644
index 000000000..3a916e9d6
--- /dev/null
+++ b/examples/gemini/python/docs-agent/README.md
@@ -0,0 +1,460 @@
+# Docs Agent
+
+The Docs Agent project explores applications and use cases that involve using a large
+corpus of documentation as a knowledge source for AI language models.
+
+Docs Agent provides a set of easy-to-use self-service tools designed to give you and
+your team access to Google's [Gemini API][genai-doc-site] for learning, experimentation,
+and project deployment.
+
+## Docs Agent web app
+
+Docs Agent uses a technique known as **Retrieval Augmented Generation (RAG)**, which
+allows you to bring your own documents as knowledge sources to AI language models.
+This approach helps the AI language models to generate relevant and accurate responses
+that are grounded in the information that you provide and control.
+
+![Docs Agent architecture](docs/images/docs-agent-architecture-01.png)
+
+**Figure 1**. Docs Agent uses a vector database to retrieve context for augmenting prompts.
+
+The Docs Agent chatbot web app is designed to be easily set up and configured in a Linux
+environment. If you want to set up and launch the Docs Agent chat app on your host machine,
+check out the [Set up Docs Agent][set-up-docs-agent] section below.
+
+## Docs Agent tasks
+
+Docs Agent's `agent runtask` command allows you to run pre-defined chains of prompts,
+which are referred to as **tasks**. These tasks simplify complex interactions by defining
+a series of steps that the Docs Agent will execute. The tasks are defined in `.yaml` files
+stored in the [`tasks`][tasks-dir] directory of your Docs Agent project. The tasks are
+designed to be reusable and can be used to automate common workflows, such as generating
+release notes, updating documentation, or analyzing complex information.
+
+A task file example:
+
+```yaml
+tasks:
+  - name: "ExtractWorkflows"
+    model: "models/gemini-1.5-flash-latest"
+    description: "An agent that extracts workflows from a source doc."
+    steps:
+      - prompt: "Summarize the contents of this document in a concise and informative manner. Focus on the key procedures, steps, or workflows described."
+        flags:
+          file: "<INPUT>"
+          default_input: "./README.md"
+      - prompt: "Identify and list all key workflows described in the document. Provide a brief description for each workflow, highlighting its purpose and key steps."
+      - prompt: "Identify all command lines used in the workflows described in the document. Focus on command lines that are essential for executing the workflow steps."
+      - prompt: "For each identified command line, provide a detailed description of its function and purpose. Include specific examples of its usage, showcasing how it is integrated within the workflows."
+```
+
+To set up and run the `agent runtask` command, see [Set up Docs Agent CLI][cli-readme].
+
+## Summary of features
+
+The list below summarizes the tasks and features supported by Docs Agent:
+
+- **Process Markdown**: Split Markdown files into small plain text chunks. (See
+  [Docs Agent chunking process][chunking-process].)
+- **Generate embeddings**: Use an embedding model to process text chunks into embeddings
+  and store them in a vector database.
+- **Perform semantic search**: Compare embeddings in a vector database to retrieve
+  chunks that are most relevant to user questions.
+- **Add context to a user question**: Add chunks returned from a semantic search as
+  [context][prompt-structure] to a prompt.
+- **Fact-check responses**: This [experimental feature][fact-check-section] composes
+  a follow-up prompt and asks the language model to “fact-check” its own previous response.
+- **Generate related questions**: In addition to answering a question, Docs Agent can
+  [suggest related questions][related-questions-section] based on the context of the
+  question.
+- **Return URLs of source documents**: URLs are stored as chunks' metadata. This enables
+  Docs Agent to return the URLs of the source documents.
+- **Collect feedback from users**: Docs Agent's web app has buttons that allow users
+  to [like responses][like-generated-responses] or [submit rewrites][submit-a-rewrite].
+- **Convert Google Docs, PDF, and Gmail into Markdown files**: This feature uses
+  [Apps Script][apps-script-readme] to convert Google Docs, PDF, and Gmail into
+  Markdown files, which then can be used as input datasets for Docs Agent.
+- **Run benchmark test**: Docs Agent can [run benchmark test][benchmark-test] to measure
+  and compare the quality of text chunks, embeddings, and AI-generated responses.
+- **Use the Semantic Retrieval API and AQA model**: Docs Agent can use Gemini's
+  [Semantic Retrieval API][semantic-api] to upload source documents to online corpora
+  and use the [AQA model][aqa-model] for answering questions.
+- **Manage online corpora using the Docs Agent CLI**: The [Docs Agent CLI][cli-reference]
+  lets you create, update and delete online corpora using the Semantic Retrieval AI.
+- **Prevent duplicate chunks and delete obsolete chunks in databases**: Docs Agent
+  uses [metadata in chunks][chunking-process] to prevent uploading duplicate chunks
+  and delete obsolete chunks that are no longer present in the source.
+- **Run the Docs Agent CLI from anywhere in a terminal**:
+  [Set up the Docs Agent CLI][cli-readme] to make requests to the Gemini models
+  from anywhere in a terminal.
+- **Support the Gemini 1.5 models**: Docs Agent works with the Gemini 1.5 models,
+  `gemini-1.5-pro`, `gemini-1.5-flash`, and `text-embedding-004`. The new
+  [`full`][new-15-mode] web app mode uses all three Gemini models to their strength:
+  AQA (`aqa`), Gemini 1.0 Pro (`gemini-pro`), and Gemini 1.5 Pro (`gemini-1.5-pro`).
+- **Complete a task using the Docs Agent CLI**: The `agent runtask` command allows you
+  to run pre-defined chains of prompts, which are referred to as tasks. These tasks
+  simplify complex interactions by defining a series of steps that the Docs Agent will
+  execute. The tasks are defined in .yaml files stored in the [`tasks`][tasks-dir]
+  directory of your Docs Agent project. To run a task in this directory, for example:
+
+  ```sh
+  agent runtask --task DraftReleaseNotes
+  ```
+
+For more information on Docs Agent's architecture and features,
+see the [Docs Agent concepts][docs-agent-concepts] page.
+
+![Docs Agent chat app](docs/images/docs-agent-chat-app-screenshot-01.png)
+
+**Figure 2**. A screenshot of the Docs Agent chat app launched using Flutter docs.
+
+## Set up Docs Agent
+
+**Note**: For instructions on the Docs Agent CLI setup, see the
+[`README.md`][cli-readme] file in the `docs_agent/interfaces` directory.
+
+This section provides instructions on how to set up and launch the Docs Agent
+chatbot web app on a Linux host machine.
+
+### 1. Prerequisites
+
+Setting up Docs Agent requires the following prerequisite items:
+
+- A Linux host machine
+
+- A [Google Cloud][google-cloud] project with the setup below:
+
+  - An API key enabled with the Generative Language API (that is,
+    the [Gemini API][genai-doc-site])
+
+  - (**Optional**) [Authenticated OAuth client credentials][oauth-client]
+    stored on the host machine
+
+### 2 Update your host machine's environment
+
+Update your host machine's environment to prepare for the Docs Agent setup:
+
+1. Update the Linux package repositories on the host machine:
+
+   ```
+   sudo apt update
+   ```
+
+2. Install the following dependencies:
+
+   ```
+   sudo apt install git pipx python3-venv
+   ```
+
+3. Install `poetry`:
+
+   ```
+   pipx install poetry
+   ```
+
+4. To add `$HOME/.local/bin` to your `PATH` variable, run the following
+   command:
+
+   ```
+   pipx ensurepath
+   ```
+
+5. To set the Google API key as a environment variable, add the following
+   line to your `$HOME/.bashrc` file:
+
+   ```
+   export GOOGLE_API_KEY=<YOUR_API_KEY_HERE>
+   ```
+
+   Replace `<YOUR_API_KEY_HERE>` with the API key to the
+   [Gemini API][genai-doc-site].
+
+6. Update your environment:
+
+   ```
+   source ~/.bashrc
+   ```
+
+### 3. (Optional) Authorize credentials for Docs Agent
+
+**This step is needed only if you plan to use [Gemini's AQA model][aqa-model-concept].**
+
+Authorize Google Cloud credentials on your host machine:
+
+1. Download the `client_secret.json` file from your
+   [Google Cloud project][authorize-credentials].
+
+2. Copy the `client_secret.json` file to your host machine.
+
+3. Install the Google Cloud SDK on your host machine:
+
+   ```
+   sudo apt install google-cloud-sdk
+   ```
+
+4. To authenticate credentials, run the following command in the directory of
+   the host machine where the `client_secret.json` file is located:
+
+   ```
+   gcloud auth application-default login --client-id-file=client_secret.json --scopes='https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/generative-language.retriever'
+   ```
+
+   This command opens a browser and asks to log in using your Google account.
+
+5. Follow the instructions on the browser and click **Allow** to authenticate.
+
+   This saves the authenticated credentials for Docs Agent
+   (`application_default_credentials.json`) in the `$HOME/.config/gcloud/`
+   directory of your host machine.
+
+### 4. Clone the Docs Agent project
+
+**Note**: This guide assumes that you're creating a new project directory
+from your `$HOME` directory.
+
+Clone the Docs Agent project and install dependencies:
+
+1. Clone the following repo:
+
+   ```
+   git clone https://github.com/google/generative-ai-docs.git
+   ```
+
+2. Go to the Docs Agent project directory:
+
+   ```
+   cd generative-ai-docs/examples/gemini/python/docs-agent
+   ```
+
+3. Install dependencies using `poetry`:
+
+   ```
+   poetry install
+   ```
+
+4. Enter the `poetry` shell environment:
+
+   ```
+   poetry shell
+   ```
+
+   **Important**: From this point, all `agent` command lines below need to
+   run in this `poetry shell` environment.
+
+### 5. Edit the Docs Agent configuration file
+
+This guide uses the [open source Flutter documents][flutter-docs-src] as an example dataset,
+which are the source Markdown files for the [Flutter website][flutter-docs-site].
+
+To complete this setup walkthrough, run the command below to download the open source
+Flutter documents somewhere on your host machine (for instance, in your `$HOME` directory):
+
+```
+git clone --recurse-submodules https://github.com/flutter/website.git
+```
+
+Update settings in the Docs Agent project to use your custom dataset:
+
+1. Go to the Docs Agent project home directory, for example:
+
+   ```
+   cd $HOME/generative-ai-docs/examples/gemini/python/docs-agent
+   ```
+
+2. Open the [`config.yaml`][config-yaml] file using a text editor, for example:
+
+   ```
+   nano config.yaml
+   ```
+
+3. Edit the file to update the `product_name` field, for example:
+
+   ```
+   product_name: "Flutter"
+   ```
+
+   This product name is displayed on the Docs Agent chat app UI.
+
+4. Under the `inputs` field, define the following entries to specify the directories
+   that contain your source Markdown files.
+
+   - `path`: The directory where the source Markdown files are stored.
+   - `url_prefix`: The prefix used to create URLs for the source Markdown files.
+
+     **Important**: If URLs do not exist for your Markdown files, you still need to
+     provide a placeholder string in the `url_prefix` field.
+
+   The example below shows the entries for the Flutter documents downloaded in the
+  `$HOME/website` directory):
+
+   ```
+   inputs:
+     - path: "/usr/local/home/user01/website/src/content"
+       url_prefix: "https://docs.flutter.dev"
+   ```
+
+   You can also provide multiple input directories (`path` and `url_prefix` sets) under
+   the `inputs` field, for example:
+
+   ```
+   inputs:
+     - path: "/usr/local/home/user01/website/src/content/ui"
+       url_prefix: "https://docs.flutter.dev/ui"
+     - path: "/usr/local/home/user01/website/src/content/tools"
+       url_prefix: "https://docs.flutter.dev/tools"
+   ```
+
+6. If you want to use the `gemini-pro` model with a local vector database setup
+   (`chroma`), use the following settings:
+
+   ```
+   models:
+     - language_model: "models/gemini-pro"
+   ...
+   db_type: "chroma"
+   ```
+
+   (**Optional**) Or if you want to use the Gemini AQA model and populate
+   a corpus online via the [Semantic Retrieval API][semantic-api], use the
+   following settings (and update the `corpus_name` field):
+
+   ```
+   models:
+     - language_model: "models/aqa"
+   ...
+   db_type: "google_semantic_retriever"
+   db_configs:
+     ...
+     - db_type: "google_semantic_retriever"
+       corpus_name: "corpora/flutter-dev"
+   ```
+
+7. Save the `config.yaml` file and exit the text editor.
+
+
+### 6. Populate a new vector database
+
+The Docs Agent CLI can help you chunk documents, generate embeddings extract metadata,
+and populate a vector database from Markdown files and more.
+
+**Note**: The `agent` commands below need to run within the `poetry shell` environment.
+
+To populate a new vector database:
+
+1. Go to the Docs Agent project home directory, for example:
+
+   ```
+   cd $HOME/generative-ai-docs/examples/gemini/python/docs-agent
+   ```
+
+2. Process Markdown files into small text chunks:
+
+   ```
+   agent chunk
+   ```
+
+   The command takes documents under the `inputs` fields (specified in your
+   `config.yaml` file), splits the documents into small text chunk files, and
+   stores them in the `output_path` direcoty.
+
+3. Create and populate a new vector database:
+
+   ```
+   agent populate
+   ```
+
+   This command takes the plain text files in the `output_path` directory
+   and creates a new Chroma collection in the `vector_stores/` directory.
+
+### 7. Launch the Docs Agent chat app
+
+Docs Agent's Flask-based chat app lets users interact with the Docs Agent service through
+a web browser.
+
+**Note**: The `agent chatbot` command needs to run within the `poetry shell` environment.
+
+To start the Docs Agent chat app:
+
+1. Go to the Docs Agent project home directory, for example:
+
+   ```
+   cd $HOME/generative-ai-docs/examples/gemini/python/docs-agent
+   ```
+
+2. Launch the Docs Agent chat app:
+
+   ```
+   agent chatbot
+   ```
+
+   The Docs Agent chat app runs on port 5000 by default. If you have an application
+   already running on port 5000 on your host machine, you can use the `--port` flag to
+   specify a different port (for example, `agent chatbot --port 5050`).
+
+   **Note**: If this `agent chatbot` command fails to run, check the `HOSTNAME` environment
+   variable on your host machine (for example, `echo $HOSTNAME`). If this variable is unset,
+   try setting it to `localhost` by running `export HOSTNAME=localhost`
+
+   Once the app starts running, this command prints output similar to the following:
+
+   ```
+   $ agent chatbot
+   Launching the chatbot UI.
+    * Serving Flask app 'docs_agent.interfaces.chatbot'
+    * Debug mode: on
+   INFO:werkzeug:WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
+    * Running on http://example.com:5000
+   INFO:werkzeug:Press CTRL+C to quit
+   INFO:werkzeug: * Restarting with stat
+   Launching the chatbot UI.
+   WARNING:werkzeug: * Debugger is active!
+   INFO:werkzeug: * Debugger PIN: 391-260-142
+   ```
+
+   Notice the line that shows the URL of this server (`http://example.com:5000`
+   in the example above).
+
+3. Open the URL above on a browser.
+
+   Now, users can start asking questions related to your product.
+
+**The Docs Agent chat app is all set!**
+
+## Contributors
+
+Nick Van der Auwermeulen (`@nickvander`), Rundong Du (`@rundong08`),
+Meggin Kearney (`@Meggin`), and Kyo Lee (`@kyolee415`).
+
+<!-- Reference links -->
+
+[contribute-to-docs-agent]: #contribute-to-docs-agent
+[set-up-docs-agent]: #set-up-docs-agent
+[preprocess-dir]: ./docs_agent/preprocess/
+[populate-vector-database]: ./docs_agent/preprocess/populate_vector_database.py
+[fact-check-section]: ./docs/concepts.md#using-a-language-model-to-fact_check-its-own-response
+[related-questions-section]: ./docs/concepts.md#using-a-language-model-to-suggest-related-questions
+[submit-a-rewrite]: ./docs/concepts.md#enabling-users-to-submit-a-rewrite-of-a-generated-response
+[like-generated-responses]: ./docs/concepts.md#enabling-users-to-like-generated-responses
+[populate-db-steps]: #populate-a-new-vector-database-from-markdown-files
+[start-the-app-steps]: #start-the-docs-agent-chat-app
+[genai-doc-site]: https://ai.google.dev/docs/gemini_api_overview
+[chroma-docs]: https://docs.trychroma.com/
+[flutter-docs-src]: https://github.com/flutter/website/tree/main/src
+[flutter-docs-site]: https://docs.flutter.dev/
+[apps-script-readme]: ./apps_script/README.md
+[scripts-readme]: ./docs_agent/preprocess/README.md
+[config-yaml]: config.yaml
+[benchmark-test]: ./docs_agent/benchmarks/README.md
+[semantic-api]: https://ai.google.dev/docs/semantic_retriever
+[aqa-model]: https://ai.google.dev/models/gemini#model_variations
+[authorize-credentials]: https://ai.google.dev/docs/oauth_quickstart#authorize-credentials
+[aqa-model-concept]: ./docs/concepts.md#using-the-semantic-retrieval-api-and-aqa-model
+[prompt-structure]: ./docs/concepts.md#structure-of-a-prompt-to-a-language-model
+[docs-agent-concepts]: ./docs/concepts.md
+[google-cloud]: https://console.cloud.google.com/
+[oauth-client]: https://ai.google.dev/docs/oauth_quickstart#set-cloud
+[cli-readme]: docs_agent/interfaces/README.md
+[cli-reference]: docs/cli-reference.md
+[chunking-process]: docs/chunking-process.md
+[new-15-mode]: docs/config-reference.md#app_mode
+[tasks-dir]: tasks/
diff --git a/examples/gemini/python/docs-agent/apps_script/README.md b/examples/gemini/python/docs-agent/apps_script/README.md
new file mode 100644
index 000000000..d333f36a8
--- /dev/null
+++ b/examples/gemini/python/docs-agent/apps_script/README.md
@@ -0,0 +1,165 @@
+# Convert Google Docs, PDF, and Gmail to Markdown files
+
+The collection of scripts in this `apps_script` directory allows you to convert
+the contents of Google Drive folders and Gmail to Markdown files that are
+compatible with Docs Agent.
+
+The steps are:
+
+1. [Prepare a Google Drive folder](#1_prepare-a-google-driver-folder).
+2. [Mount Google Drive on your host machine](#2_mount-google-drive-on-your-host-machine).
+3. [Create an Apps Script project](#3_create-an-apps-script-project).
+4. [Edit and run main.gs on Apps Script](#4_edit-and-run-main_gs-on-apps-script).
+5. [Update config.yaml to include the mounted directory](#5_update-config_yaml-to-include-the-mounted-directory).
+
+## 1. Prepare a Google Drive folder
+
+First, create a new folder in Google Drive and add your Google Docs (which will be
+used as source documents to Docs Agent) to the folder.
+
+Do the following:
+
+1. Browser to https://drive.google.com/.
+1. Click **+ New** on the top left corner.
+1. Click **New folder**.
+1. Name your new folder (for example, `my source Google Docs`).
+1. To enter the newly created folder, double click the folder.
+1. Add (or move) your source Google Docs to this new folder.
+
+## 2. Mount Google Drive on your host machine
+
+Mount your Google Drive to your host machine, so that it becomes easy to access the
+folders in Google Drive from your host machine (later in step 5).
+
+There are a variety of methods and tools available online that enable this setup
+(for example, see [`google-drive-ocamlfuse`][google-drive-ocamlfuse] for Linux machines).
+
+## 3. Create an Apps Script project
+
+Create a new Apps Script project and copy all the `.gs` scripts in this
+`apps_script` directory to your new Apps Script project.
+
+Do the following:
+
+1. Browse to https://script.google.com/.
+1. Click **New Project**.
+1. At the top of the page, click **Untitled Project** and enter a meaningful
+   title (for example, `gDocs to Docs Agent`).
+1. Click the **+** icon next to **Files**.
+1. Click **Script**.
+1. Name the new script to be one of the `.gs` files in this `apps_script` directory
+   (for example, `drive_to_markdown`).
+1. Copy the content of the `.gs` file to the new script on your Apps Script project.
+1. To save, click the "Save project" icon in the toolbar.
+1. Repeat the steps until all the `.gs` files are copied to your Apps Script project.
+1. Click the **+** icon next to **Services**.
+1. Scroll down and click **Drive API**.
+1. Select **v2**.
+1. Click **Add**.
+
+You are now ready to edit the parameters on the `main.gs` file to select a folder
+in Google Drive and export emails from Gmail.
+
+![Apps Script project](../docs/images/apps-script-screenshot-01.png)
+
+**Figure 1**. A screenshot of an example Apps Script project.
+
+## 4. Edit and run main.gs on Apps Script
+
+Edit the `main.gs` file on your Apps Script project to select which functions
+(features) you want to run.
+
+Do the following:
+
+1. Browse to your project on https://script.google.com/.
+
+1. Open the `main.gs` file.
+
+1. In the `main` function, comment out any functions that you don't want to run
+   (see Figure 1):
+
+   * `convertDriveFolderToMDForDocsAgent(folderInput)`: This function converts
+     the contents of a Google Drive folder to Markdown files (currently only Google
+     Docs and PDF). Make sure to specify a valid Google Drive folder in the `folderInput`
+     variable. Use the name of the folder created in **step 1** above, for example:
+
+     ```
+     var folderInput = "my source Google Docs"
+     function main() {
+       convertDriveFolderToMDForDocsAgent(folderInput);
+       //exportEmailsToMarkdown(SEARCH_QUERY, folderOutput);
+     }
+     ```
+
+   * `exportEmailsToMarkdown(SEARCH_QUERY, folderOutput)`: This function converts
+     the emails returned from a Gmail search query into Markdown files. Make sure to
+     specify a search query in the `SEARCH_QUERY` variable. You can test this search
+     query directly in the Gmail search bar. Also, specify an output directory for the
+     resulting emails.
+
+1. To save, click the "Save project" icon in the toolbar.
+
+1. Click the "Run" icon in the toolbar.
+
+   When this script runs successfully, the Execution log panel prints output similar
+   to the following:
+
+   ```
+   9:55:59 PM	Notice	Execution completed
+   ```
+
+   Also, the script creates a new folder in your Google Drive and stores the converted
+   Markdown files in this folder. The name of this new folder has `-output` as a postfix.
+   For example, with the folder name `my source Google Docs`, the name of the new folder
+   is `my source Google Docs-output`.
+
+   With Google Drive mounted on your host machine in step 2, you can now directly access
+   this folder from the host machine, for example:
+
+   ```
+   user@hostname:~/DriveFileStream/My Drive/my source Google Docs-output$ ls
+   Copy_of_My_Google_Docs_To_Be_Converted.md
+   ```
+
+## 5. Update config.yaml to include the mounted directory
+
+Once you have your Google Drive mounted on the host machine, you can now
+specify one of its folders as an input source directory for Docs Agent.
+
+Do the following:
+
+1. In the Docs Agent project, open the [`config.yaml`][config-yaml] file
+   with a text editor.
+
+1. Specify your mounted Google Drive folder as an `input` group, for example:
+
+   ```
+   input:
+   - path: "/usr/local/home/user01/DriveFileStream/My Drive/my source Google Docs-output"
+     url_prefix: "docs.google.com"
+   ```
+
+   You **must** specify a value to the `url_prefix` field, such as `docs.google.com`.
+   Currently this value is used to generate hashes for the content.
+
+1. (**Optional**) Add an additional Google Drive folder for your exported emails,
+   for example:
+
+   ```
+   input:
+   - path: "/usr/local/home/user01/DriveFileStream/My Drive/my source Google Docs-output"
+     url_prefix: "docs.google.com"
+   - path: "/usr/local/home/user01/DriveFileStream/My Drive/psa-output"
+     url_prefix: "mail.google.com"
+   ```
+
+1. Save the changes in the `config.yaml` file.
+
+You're all set with a new documentation source for Docs Agent. You can now follow the
+instructions in the project's main [`README`][main-readme] file to launch the Docs Agent app.
+
+<!-- Reference links -->
+
+[config-yaml]: ../config.yaml
+[main-readme]: ../README.md
+[google-drive-ocamlfuse]: https://github.com/astrada/google-drive-ocamlfuse
diff --git a/examples/gemini/python/docs-agent/apps_script/drive_to_markdown.gs b/examples/gemini/python/docs-agent/apps_script/drive_to_markdown.gs
new file mode 100644
index 000000000..b19d33d84
--- /dev/null
+++ b/examples/gemini/python/docs-agent/apps_script/drive_to_markdown.gs
@@ -0,0 +1,240 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+function convertDriveFolderToMDForDocsAgent(folderName, outputFolderName=""){
+  gdoc_count = 0;
+  pdf_count = 0;
+  new_file_count = 0;
+  updated_file_count = 0;
+  unchanged_file_count = 0;
+  gdoc_count, pdf_count, new_file_count, updated_file_count, unchanged_file_count = convertDriveFolder(folderName, outputFolderName=outputFolderName)
+  let conversion_count = pdf_count + gdoc_count
+  let file_count = new_file_count + updated_file_count + unchanged_file_count
+  Logger.log("Converted a total of: " + gdoc_count + " Google Doc files.");
+  Logger.log("Converted a total of: " + pdf_count + " PDF files.");
+  Logger.log("Converted a grand total of: " + conversion_count + " files.");
+  Logger.log("New files: " + new_file_count)
+  Logger.log("Updated a total of: " + updated_file_count + " files.")
+  Logger.log("Files that haven't changed: " + unchanged_file_count);
+  Logger.log("Input directory had a total of: " + file_count + " files.")
+}
+
+function convertDriveFolder(folderName, outputFolderName="", indexFile="") {
+
+  //Checks if input folder exists or exits
+  if(folderExistsInput(folderName)){
+    var file_count = 0;
+    var folders = DriveApp.getFoldersByName(folderName);
+    if (outputFolderName=="") {
+      var folderOutput = folderName + "-output";
+      var output_file_name = folderName + "-index";
+    }
+    else {
+      var folderOutput = outputFolderName + "-output";
+      var output_file_name = outputFolderName + "-index";
+    }
+    Logger.log("Output directory: "+ folderOutput);
+    folderExistsOrCreate(folderOutput);
+    var folderOutputObj = DriveApp.getFoldersByName(folderOutput);
+    if (folderOutputObj.hasNext()){
+      var folderOutputName = folderOutputObj.next();
+    }
+    if (indexFile=="") {
+      var sheet = checkIndexOutputOrCreate(output_file_name, folderOutputName);
+      var timeZone = Session.getScriptTimeZone();
+      var date = Utilities.formatDate(new Date(), timeZone, "MM-dd-yyyy HH:mm:ss z");
+      sheet.appendRow(["Created: ", date])
+      sheet.appendRow(["Name","ID", "URL", "Markdown ID", "Markdown Output", "Date Created", "Last Updated", "Type", "Folder", "MD5 hash", "Status"]);
+    }
+    else {
+      var sheet = indexFile
+    }
+    // var sheet_id = sheet.getId();
+    var foldersnext = folders.next();
+    var myfiles = foldersnext.getFiles();
+    var new_file_count = 0;
+    var unchanged_file_count = 0;
+    var updated_file_count = 0;
+    var gdoc_count = 0;
+    var pdf_count = 0;
+    var start_data_row = 2;
+    var status = "New content";
+
+    while (myfiles.hasNext()) {
+      var myfile = myfiles.next();
+      var ftype = myfile.getMimeType();
+      // If this is a shorcut, retrieve the target file
+      if (ftype == "application/vnd.google-apps.shortcut") {
+        var fid = myfile.getTargetId();
+        var myfile = DriveApp.getFileById(fid);
+        var ftype = myfile.getMimeType();
+      }
+      else{
+        var fid = myfile.getId();
+      }
+      if (ftype == "application/vnd.google-apps.folder") {
+        var folder = DriveApp.getFolderById(fid);
+        Logger.log("Sub-directory: " + folder);
+        sub_gdoc_count = 0;
+        sub_pdf_count = 0;
+        sub_new_file_count = 0;
+        sub_updated_file_count = 0;
+        sub_unchanged_file_count = 0;
+        sub_gdoc_count, sub_pdf_count, sub_new_file_count, sub_updated_file_count, sub_unchanged_file_count = convertDriveFolder(folder, outputFolderName=foldersnext, indexFile=sheet);
+        gdoc_count += sub_gdoc_count;
+        pdf_count += sub_pdf_count;
+        new_file_count += sub_new_file_count;
+        updated_file_count += sub_updated_file_count;
+        unchanged_file_count += sub_unchanged_file_count;
+        continue;
+      }
+      var fname = sanitizeFileName(myfile.getName());
+      var fdate = myfile.getLastUpdated();
+      var furl = myfile.getUrl();
+      var fcreate = myfile.getDateCreated();
+
+      //Function returns an array, assign each array value to seperate variables
+      var backup_results = returnBackupHash(sheet, "Backup", fid, start_data_row, 1, 9, 3);
+      if (backup_results != undefined && backup_results[0] != "no_results") {
+        var backup_fid = backup_results[0];
+        var md5_backup = backup_results[1];
+        var mdoutput_backup_id = backup_results[2];
+      }
+      if (ftype == "application/vnd.google-apps.document") {
+        Logger.log("File: " + fname + " is a Google doc.");
+        let gdoc = DocumentApp.openById(fid);
+        let gdoc_blob = gdoc.getBody().getText();
+        var md5_hash = Utilities.computeDigest(Utilities.DigestAlgorithm.MD5,gdoc_blob,
+                      Utilities.Charset.US_ASCII);
+        var hash_str = byteToStr(md5_hash);
+        if (backup_fid == fid && hash_str == md5_backup) {
+          Logger.log("File is unchanged. Skipping conversion.");
+          if (mdoutput_backup_id){
+            var saved_file = DriveApp.getFileById(mdoutput_backup_id);
+            var saved_file_id = saved_file.getId();
+          }
+          status = "Unchanged content";
+          unchanged_file_count += 1;
+          var convert_file = false;
+        }
+        else if (backup_fid == fid && hash_str != md5_backup){
+          status = "Updated content";
+          updated_file_count += 1;
+          var convert_file = true;
+        }
+        else {
+          status = "New content";
+          new_file_count += 1;
+          var convert_file = true;
+        }
+        if (convert_file){
+          var frontmatter = "---" + "\n";
+          frontmatter += "title: \"" + fname + "\"\n";
+          frontmatter += "type: \"" + ftype + "\"\n";
+          frontmatter += "id: \"" + fid + "\"\n";
+          frontmatter += "created: \"" + fcreate + "\"\n";
+          frontmatter += "updated: \"" + fdate + "\"\n";
+          frontmatter += "URL: \"" + furl + "\"\n";
+          frontmatter += "---" + "\n\n";
+          var saved_file = convertDocumentToMarkdown(gdoc, folderOutputName, frontmatter);
+          var saved_file_id = saved_file.getId();
+          Logger.log("Finished converting file: " + fname + " to markdown.");
+          Logger.log("Markdown file: " + saved_file);
+          status = "New content";
+          gdoc_count += 1;
+        }
+        file_count += 1;
+      }
+      if (ftype == "application/pdf") {
+        // Converts PDFs - First to a temporary Google Doc and then use convertDocumentToMarkdown to convert to markdown with frontmatter
+        Logger.log("File: " + fname + " is a PDF.");
+        let pdfBlob = DriveApp.getFileById(fid).getBlob();
+        let pdfblobText = pdfBlob.getDataAsString();
+        var md5_hash = Utilities.computeDigest(Utilities.DigestAlgorithm.MD5,pdfblobText,
+                       Utilities.Charset.US_ASCII);
+        var hash_str = byteToStr(md5_hash);
+        if (backup_fid == fid && hash_str == md5_backup) {
+          Logger.log("File is unchanged. Skipping conversion.");
+          if (mdoutput_backup_id){
+            var saved_file = DriveApp.getFileById(mdoutput_backup_id);
+            var saved_file_id = saved_file.getId();
+          }
+          status = "Unchanged content";
+          unchanged_file_count += 1;
+          var convert_file = false;
+        }
+        else if (backup_fid == fid && hash_str != md5_backup){
+          status = "Updated content";
+          updated_file_count += 1;
+          var convert_file = true;
+        }
+        else {
+          status = "New content";
+          new_file_count += 1;
+          var convert_file = true;
+        }
+        if (convert_file){
+          let temp_doc_name = pdfBlob.getName() + "-temp";
+          let temp_doc = {title: temp_doc_name, mimeType: pdfBlob.getContentType(), parents: [{id: folderOutputName.getId()}]}
+          let options = {ocr: true};
+          let output = Drive.Files.insert(temp_doc, pdfBlob, options);
+          let output_id = output.getId();
+          let gdoc = DocumentApp.openById(output_id);
+          var frontmatter = "---" + "\n";
+          frontmatter += "title: \"" + fname + "\"\n";
+          frontmatter += "type: \"" + ftype + "\"\n";
+          frontmatter += "id: \"" + fid + "\"\n";
+          frontmatter += "created: \"" + fcreate + "\"\n";
+          frontmatter += "updated: \"" + fdate + "\"\n";
+          frontmatter += "URL: \"" + furl + "\"\n";
+          frontmatter += "---" + "\n\n";
+          var saved_file = convertDocumentToMarkdown(gdoc, folderOutputName, frontmatter);
+          var saved_file_id = saved_file.getId();
+          Logger.log("Finished converting file: "+ fname + " to markdown.");
+          Logger.log("Markdown file: " + saved_file);
+          Logger.log("Clearing temporary gdoc" );
+          let output_file = DriveApp.getFileById(output_id);
+          output_file.setTrashed(true);
+          status = "New content";
+          pdf_count += 1;
+        }
+        file_count += 1;
+      }
+      let md_chip = createRichText(saved_file);
+      let original_chip = createRichText(myfile);
+      let folder_chip = createRichText(foldersnext);
+      metadata = [
+        fname,
+        fid,
+        "original_chip",
+        saved_file_id,
+        "md_chip",
+        fcreate,
+        fdate,
+        ftype,
+        "folder_chip",
+        hash_str,
+        status,
+      ];
+      sheet.appendRow(metadata);
+      // Return final row to inserRichText into correct rows
+      row_number = sheet.getLastRow();
+      insertRichText(sheet, original_chip, "C", row_number);
+      insertRichText(sheet, md_chip, "E", row_number);
+      insertRichText(sheet, folder_chip, "I", row_number);
+    }
+  }
+  return gdoc_count, pdf_count, new_file_count, updated_file_count, unchanged_file_count
+}
diff --git a/examples/gemini/python/docs-agent/apps_script/exportmd.gs b/examples/gemini/python/docs-agent/apps_script/exportmd.gs
new file mode 100644
index 000000000..40a4461ff
--- /dev/null
+++ b/examples/gemini/python/docs-agent/apps_script/exportmd.gs
@@ -0,0 +1,1310 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* Original script is from:
+https://github.com/lmmx/gdocs2md-html/blob/master/exportmd.gs
+and commit: 0d86cfa
+Parsing from mangini/gdocs2md.
+Modified by clearf to add files to the google directory structure.
+Modified by lmmx to write Markdown, going back to HTML-incorporation.
+
+Usage:
+  NB: don't use on top-level doc (in root Drive folder) See comment in setupScript function.
+  Adding this script to your doc:
+    - Tools > Script Manager > New
+    - Select "Blank Project", then paste this code in and save.
+  Running the script:
+    - Tools > Script Manager
+    - Select "convertDocumentToMarkdown" function.
+    - Click Run button.
+    - Converted doc will be added to a "Markdown" folder in the source document's directories.
+    - Images will be added to a subfolder of the "Markdown" folder.
+*/
+
+function onInstall(e) {
+  onOpen(e);
+}
+
+function onOpen() {
+  // Add a menu with some items, some separators, and a sub-menu.
+  setupScript();
+//  In future:
+//  DocumentApp.getUi().createAddonMenu();
+  DocumentApp.getUi().createMenu('Markdown')
+      .addItem('View as markdown', 'markdownPopup')
+      .addSubMenu(DocumentApp.getUi().createMenu('Export \u2192 markdown')
+                  .addItem('Export to local file', 'convertSingleDoc')
+                  .addItem('Export entire folder to local file', 'convertFolder')
+                  .addItem('Customise markdown conversion', 'changeDefaults'))
+      .addSeparator()
+      .addSubMenu(DocumentApp.getUi().createMenu('Toggle comment visibility')
+                 .addItem('Image source URLs', 'toggleImageSourceStatus')
+                 .addItem('All comments', 'toggleCommentStatus'))
+      .addItem("Add comment", 'addCommentDummy')
+      .addToUi();
+}
+
+function changeDefaults() {
+  var ui = DocumentApp.getUi();
+  var default_settings = '{ use your imagination... }';
+  var greeting = ui.alert('This should be set up to display defaults from variables passed to getDocComments etc., e.g. something like:\n\nDefault settings are:'
+                    + '\ncomments - not checking deleted comments.\nDocument - this document (alternatively specify a document ID).'
+                    + '\n\nClick OK to edit these, or cancel.',
+                    ui.ButtonSet.OK_CANCEL);
+  ui.alert("There's not really need for this yet, so this won't proceed, regardless of what you just pressed.");
+  return;
+
+  // Future:
+  if (greeting == ui.Button.CANCEL) {
+    ui.alert("Alright, never mind!");
+    return;
+  }
+  // otherwise user clicked OK
+  // user clicked OK, to proceed with editing these defaults. Ask case by case whether to edit
+
+  var response = ui.prompt('What is x (default y)?', ui.ButtonSet.YES_NO_CANCEL);
+
+  // Example code from docs at https://developers.google.com/apps-script/reference/base/button-set
+  // Process the user's response.
+  if (response.getSelectedButton() == ui.Button.YES) {
+    Logger.log('The user\'s name is %s.', response.getResponseText());
+  } else if (response.getSelectedButton() == ui.Button.NO) {
+    Logger.log('The user didn\'t want to provide a name.');
+  } else {
+    Logger.log('The user clicked the close button in the dialog\'s title bar.');
+  }
+}
+
+function setupScript() {
+  var script_properties = PropertiesService.getScriptProperties();
+  script_properties.setProperty("user_email", Drive.About.get().user.emailAddress);
+
+  // manual way to do the following:
+  // script_properties.setProperty("folder_id", "INSERT_FOLDER_ID_HERE");
+  // script_properties.setProperty("document_id", "INSERT_FILE_ID_HERE");
+
+  var doc_id = DocumentApp.getActiveDocument().getId();
+  script_properties.setProperty("document_id", doc_id);
+  var doc_parents = DriveApp.getFileById(doc_id).getParents();
+  var folders = doc_parents;
+  while (folders.hasNext()) {
+    var folder = folders.next();
+    var folder_id = folder.getId();
+  }
+  script_properties.setProperty("folder_id", folder_id);
+  script_properties.setProperty("image_folder_prefix", ""); // add if modifying image location
+}
+
+function addCommentDummy() {
+  // Dummy function to be switched during development for addComment
+  DocumentApp.getUi()
+    .alert('Cancelling comment entry',
+           "There's not currently a readable anchor for Google Docs - you need to write your own!"
+
+           + "\n\nThe infrastructure for using such an anchoring schema is sketched out in"
+           + " the exportmd.gs script's addComment function, for an anchor defined in anchor_props"
+
+           + "\n\nSee github.com/lmmx/devnotes/wiki/Custom-Google-Docs-comment-anchoring-schema",
+           DocumentApp.getUi().ButtonSet.OK
+          );
+  return;
+}
+
+function addComment() {
+
+  var doc_id = PropertiesService.getScriptProperties().getProperty('document_id');
+  var user_email = PropertiesService.getScriptProperties().getProperty('email');
+/*  Drive.Comments.insert({content: "hello world",
+                         context: {
+                           type: 'text/html',
+                           value: 'hinges'
+                         }
+                        }, document_id); */
+  var revision_list = Drive.Revisions.list(doc_id).items;
+  var recent_revision_id = revision_list[revision_list.length - 1].id;
+  var anchor_props = {
+    revision_id: recent_revision_id,
+    starting_offset: '',
+    offset_length: '',
+    total_chars: ''
+  }
+  insertComment(doc_id, 'hinges', 'Hello world!', my_email, anchor_props);
+}
+
+function insertComment(fileId, selected_text, content, user_email, anchor_props) {
+
+  // NB Deal with handling missing args
+
+  /*
+  anchor_props is an object with 4 properties:
+    - revision_id,
+    - starting_offset,
+    - offset_length,
+    - total_chars
+  */
+
+  var context = Drive.newCommentContext();
+    context.value = selected_text;
+    context.type = 'text/html';
+  var comment = Drive.newComment();
+    comment.kind = 'drive#comment';
+    var author = Drive.newUser();
+      author.kind = 'drive#user';
+      author.displayName = user_email;
+      author.isAuthenticatedUser = true;
+    comment.author = author;
+    comment.content = type;
+    comment.context = context;
+    comment.status = 'open';
+    comment.anchor = "{'r':"
+                     + anchor_props.revision_id
+                     + ",'a':[{'txt':{'o':"
+                     + anchor_props.starting_offset
+                     + ",'l':"
+                     + anchor_props.offset_length
+                     + ",'ml':"
+                     + anchor_props.total_chars
+                     + "}}]}";
+    comment.fileId = fileId;
+  Drive.Comments.insert(comment, fileId);
+}
+
+function decodeScriptSwitches(optional_storage_name) {
+  var property_name = (typeof(optional_storage_name) == 'string') ? optional_storage_name : 'switch_settings';
+  var script_properties = PropertiesService.getScriptProperties();
+  return script_properties
+              .getProperty(property_name)
+              .replace(/{|}/g,'')          // Get the statements out of brackets...
+              .replace(',', ';');          // ...swap the separator for a semi-colon...
+  // ...evaluate the stored object string as statements upon string return and voila, switches interpreted
+}
+
+
+function getDocComments(comment_list_settings) {
+  var possible_settings = ['images', 'include_deleted'];
+
+  // switches are processed and set on a script-wide property called "comment_switches"
+  var property_name = 'comment_switches';
+  switchHandler(comment_list_settings, possible_settings, property_name);
+
+  var script_properties = PropertiesService.getScriptProperties();
+  var comment_switches = decodeScriptSwitches(property_name);
+  eval(comment_switches);
+
+  var document_id = script_properties.getProperty("document_id");
+  var comments_list = Drive.Comments.list(document_id,
+                                          {includeDeleted: include_deleted,
+                                           maxResults: 100 }); // 0 to 100, default 20
+  // See https://developers.google.com/drive/v2/reference/comments/list for all options
+  var comment_array = [];
+  var image_sources = [];
+  // To collect all comments' image URLs to match against inlineImage class elements LINK_URL attribute
+
+  for (var i = 0; i < comments_list.items.length; i++) {
+    var comment = comments_list.items[i];
+    var comment_text = comment.content;
+    var comment_status = comment.status;
+ /*
+    images is a generic parameter passed in as a switch to
+    return image URL-containing comments only.
+
+    If the parameter is provided, it's no longer undefined.
+ */
+    var img_url_regex = /(https?:\/\/.+?\.(png|gif|jpe?g))/;
+    var has_img_url = img_url_regex.test(comment_text);
+
+    if (images && !has_img_url) continue; // no image URL, don't store comment
+    if (has_img_url) image_sources.push(RegExp.$1);
+    comment_array.push(comment);
+  }
+  script_properties.setProperty('image_source_URLs', image_sources)
+  return comment_array;
+}
+
+function isValidAttrib(attribute) { // Sanity check function, called per element in array
+
+  // Possible list of attributes to check against (leaving out unchanging ones like kind)
+  possible_attrs = [
+    'selfLink',
+    'commentId',
+    'createdDate',
+    'modifiedDate',
+    'author',
+    'htmlContent',
+    'content',
+    'deleted',
+    'status',
+    'context',
+    'anchor',
+    'fileId',
+    'fileTitle',
+    'replies',
+    'author'
+  ];
+
+  // Check if attribute(s) provided can be used to match/filter comments:
+
+  if (typeof(attribute) == 'string' || typeof(attribute) == 'object') {
+    // Either a string/object (1-tuple)
+
+    // Generated with Javascript, gist: https://gist.github.com/lmmx/451b301e1d78ed2c10b4
+
+    // Return false from the function if any of the attributes specified are not in the above list
+
+    // If an object, the name is the key, otherwise it's just the string
+    if (attribute.constructor === Object) {
+      var att_keys = [];
+      for (var att_key in attribute) {
+        if (attribute.hasOwnProperty(att_key)) {
+          att_keys.push(att_key);
+        }
+      }
+      for (var n=0; n < att_keys.length; n++) {
+        var attribute_name = att_keys[n];
+        var is_valid_attrib = (possible_attrs.indexOf(attribute_name) > -1);
+
+        // The attribute needs to be one of the possible attributes listed above, match its given value(s),
+        // else returning false will throw an error from onAttribError when within getCommentAttributes
+        return is_valid_attrib;
+      }
+    } else if (typeof(attribute) == 'string') {
+      var attribute_name = attribute;
+      var is_valid_attrib = (possible_attrs.indexOf(attribute_name) > -1);
+      return is_valid_attrib;
+      // Otherwise is a valid (string) attribute
+    } else if (attribute.constructor === Array) {
+      return false; // Again, if within getCommentAttributes this will cause an error - shouldn't pass an array
+    } else {
+      // Wouldn't expect this to happen, so give a custom error message
+      Logger.log('Unknown type (assumed impossible) passed to isValidAttrib: ', attribute, attribute.constructor);
+      throw new TypeError('Unknown passed to isValidAttrib - this should be receiving 1-tuples only, see logs for details.');
+    }
+  } else return false; // Neither string/object / array of strings &/or objects - not a valid attribute
+}
+
+function getCommentAttributes(attributes, comment_list_settings) {
+
+  // A filter function built on Comments.list, for a given list of attributes
+  // Objects' values are ignored here, only their property titles are used to filter comments.
+
+
+  /*
+    - attributes: array of attributes to filter/match on
+    - comment_list_settings: (optional) object with properties corresponding to switches in getDocComments
+
+    This function outputs an array of the same length as the comment list, containing
+    values for all fields matched/filtered on.
+  */
+
+
+  /*
+   *         All possible comment attributes are listed at:
+   *   https://developers.google.com/drive/v2/reference/comments#properties
+   */
+
+  //  Firstly, describe the type in a message to be thrown in case of TypeError:
+
+  var attrib_def_message = "'attributes' should be a string (the attribute to get for each comment), "
+                           + "an object (a key-value pair for attribute and desired value), "
+                           + "or an array of objects (each with key-value pairs)";
+
+  function onAttribError(message) {
+    Logger.log(message);
+    throw new TypeError(message);
+  }
+
+  // If (optional) comment_list_settings isn't set, make a getDocComments call with switches left blank.
+  if (typeof(comment_list_settings) == 'undefined') var comment_list_settings = {};
+  if (typeof(attributes) == 'undefined') onAttribError(attrib_def_message); // no variables specified
+
+  if (isValidAttrib(attributes)) { // This will be true if there's only one attribute, not provided in an array
+
+    /*
+       Make a 1-tuple (array of 1) from either an object or a string,
+       i.e. a single attribute, with or without a defined value respectively.
+    */
+
+    var attributes = Array(attributes);
+
+  } else if (attributes.constructor === Array) {
+
+    // Check each item in the array is a valid attribute specification
+    for (var l = 0; l < attributes.length; l++) {
+      if (! isValidAttrib(attributes[l]) ) {
+        onAttribError('Error in attribute '
+                      + (l+1) + ' of ' + attributes.length
+                      + '\n\n' + + attrib_def_message);
+      }
+    }
+
+  } else { // Neither attribute nor array of attributes
+    throw new TypeError(attrib_def_message);
+  }
+
+  // Attributes now holds an array of string and/or objects specifying a comment match and/or filter query
+
+  var comment_list = getDocComments(comment_list_settings);
+  var comment_attrib_lists = [];
+  for (var i in comment_list) {
+    var comment = comment_list[i];
+    var comment_attrib_list = [];
+    for (var j in attributes) {
+      var comment_attribute = comment_list[i][attributes[j]];
+      comment_attrib_list.push(comment_attribute);
+    }
+    comment_attrib_lists.push(comment_attrib_list);
+  }
+  // The array comment_attrib_lists is now full of the requested attributes,
+  // of length equal to that of attributes
+  return comment_attrib_lists;
+}
+
+// Example function to use getCommentAttributes:
+
+function filterComments(attributes, comment_list_settings) {
+  var comment_attributes = getCommentAttributes(attributes, comment_list_settings);
+  var m = attribs.indexOf('commentId') // no need to keep track of commentID array position
+  comm_attribs.map(function(attrib_pair) {
+     if (attrib_pair[1]);
+  })
+}
+
+function toggleCommentStatus(comment_switches){
+  // Technically just image URL-containing comments, not sources just yet
+  var attribs = ['commentId', 'status'];
+  var comm_attribs = getCommentAttributes(attribs, comment_switches);
+  var rearrangement = [];
+  comm_attribs.map(
+    function(attrib_pair) { // for every comment return with the images_only / images: true comments.list setting,
+      switch (attrib_pair[1]){ // check the status of each
+        case 'open':
+          rearrangement.push([attrib_pair[0],'resolved']);
+          break;
+        case 'resolved':
+          rearrangement.push([attrib_pair[0],'open']);
+          break;
+      }
+    }
+  );
+  var script_properties = PropertiesService.getScriptProperties();
+  var doc_id = script_properties.getProperty("document_id");
+  rearrangement.map(
+    function(new_attrib_pair) { // for every comment ID with flipped status
+      Drive.Comments.patch('{"status": "'
+                           + new_attrib_pair[1]
+                           + '"}', doc_id, new_attrib_pair[0])
+    }
+  );
+  return;
+}
+
+function toggleImageSourceStatus(){
+  toggleCommentStatus({images: true});
+}
+
+function flipResolved() {
+  // Flip the status of resolved comments to open, and open comments to resolved (respectful = true)
+  // I.e. make resolved URL-containing comments visible, without losing track of normal comments' status
+
+  // To force all comments' statuses to switch between resolved and open en masse set respectful to false
+
+  var switch_settings = {};
+    switch_settings.respectful = true;
+    switch_settings.images_only = false; // If true, only switch status of comments with an image URL
+    switch_settings.switch_deleted_comments = false; // If true, also switch status of deleted comments
+
+  var comments_list = getDocComments(
+    { images: switch_settings.images_only,
+      include_deleted: switch_settings.switch_deleted_comments });
+
+  // Note: these parameters are unnecessary if both false (in their absence assumed false)
+  //       but included for ease of later reuse
+
+  if (switch_settings.respectful) {
+    // flip between
+  } else {
+    // flip all based on status of first in list
+  }
+}
+
+function markdownPopup() {
+  var css_style = '<style type="text/css" data-github="https://github.com/lmmx/gdocs2md-html/blob/master/md-preview.css">'
+    + 'h1, h2, h3, h4, h5, h6 {'
+    + '  font-family: "Gibson", sans-serif, "Helvetica Neue", HelveticaNeue, Helvetica, Arial, sans-serif;'
+    + '  font-size: 1.3em;'
+    + '  line-height: 1.4;'
+    + '  margin-top: 9px;'
+    + '}'
+    + 'code,pre,pre *{'
+    + '  font-size: 12px;'
+    + '  line-height: 1.4;'
+    + '  white-space: pre-wrap;'
+    + '  padding-top: 1em;'
+    + '  padding-bottom: 1em;'
+    + '  clear:both;'
+    + '  padding: 0.2em;'
+    + '  margin: 0;'
+    + '  background-color: rgba(0,0,0,0.04);'
+    + '  border-radius: 3px;'
+    + '  font: 11px Consolas, "Liberation Mono", Menlo, Courier, monospace;'
+    + '}'
+    + 'pre textarea {'
+    + '    background: transparent;'
+    + '    border: none;'
+    + '    height: inherit !important;'
+    + '    width: 775px;'
+    + '}'
+    + 'pre {'
+    + '    height: 430px;'
+    + '}'
+    + '</style>';
+
+  // The above was written with js since <link rel="stylesheet" href=[GitHub raw URL]> doesn't work:
+  // https://gist.github.com/lmmx/ec084fc351528395f2bb
+
+  var mdstring = stringMiddleMan();
+
+  var htmlstring =
+      '<!doctype html><html lang="en"><head><meta charset="utf-8">'
+    + css_style
+    + '</head><body><pre><textarea id="md-output" readonly="readonly">'
+    + mdstring
+    + '</textarea></pre><script type="text/javascript">'
+    + 'function mdClick() {'
+    + 'var textbox = document.getElementById("md-output");'
+    + 'textbox.setAttribute("onclick", "this.focus(); this.select();") }'
+    + 'window.onload = mdClick();'
+    + '</script></body></html>';
+
+  var html5 = HtmlService.createHtmlOutput(htmlstring)
+      .setSandboxMode(HtmlService.SandboxMode.IFRAME)
+      .setWidth(800)
+      .setHeight(500);
+
+  DocumentApp.getUi()
+      .showModalDialog(html5, 'Markdown output');
+}
+
+function stringMiddleMan() {
+  var returned_string;
+  convertSingleDoc({"return_string": true}); // for some reason needs the scope to be already set...
+  // could probably rework to use mdstring rather than returned_string, cut out middle man function
+  return this.returned_string;
+}
+
+function convertSingleDoc(optional_switches) {
+  var script_properties = PropertiesService.getScriptProperties();
+  // renew comments list on every export
+  var doc_comments = getDocComments();
+  var image_urls = getDocComments({images: true}); // NB assumed false - any value will do
+  script_properties.setProperty("comments", doc_comments);
+  script_properties.setProperty("image_srcs", image_urls);
+  var folder_id = script_properties.getProperty("folder_id");
+  var document_id = script_properties.getProperty("document_id");
+  var source_folder = DriveApp.getFolderById(folder_id);
+  var markdown_folders = source_folder.getFoldersByName("Markdown");
+
+  var markdown_folder;
+  if (markdown_folders.hasNext()) {
+    markdown_folder = markdown_folders.next();
+  } else {
+    // Create a Markdown folder if it doesn't exist.
+    markdown_folder = source_folder.createFolder("Markdown")
+  }
+
+  convertDocumentToMarkdown(DocumentApp.openById(document_id), markdown_folder, optional_switches);
+}
+
+function convertFolder() {
+  var script_properties = PropertiesService.getScriptProperties();
+  var folder_id = script_properties.getProperty("folder_id");
+  var source_folder = DriveApp.getFolderById(folder_id);
+  var markdown_folders = source_folder.getFoldersByName("Markdown");
+
+
+  var markdown_folder;
+  if (markdown_folders.hasNext()) {
+    markdown_folder = markdown_folders.next();
+  } else {
+    // Create a Markdown folder if it doesn't exist.
+    markdown_folder = source_folder.createFolder("Markdown");
+  }
+
+  // Only try to convert google docs files.
+  var gdoc_files = source_folder.getFilesByType("application/vnd.google-apps.document");
+
+  // For every file in this directory
+  while(gdoc_files.hasNext()) {
+    var gdoc_file = gdoc_files.next()
+
+    var filename = gdoc_file.getName();
+    var md_files = markdown_folder.getFilesByName(filename + ".md");
+    var update_file = false;
+
+    if (md_files.hasNext()) {
+      var md_file = md_files.next();
+
+      if (md_files.hasNext()){ // There are multiple markdown files; delete and rerun
+        update_file = true;
+      } else if (md_file.getLastUpdated() < gdoc_file.getLastUpdated()) {
+        update_file = true;
+      }
+    } else {
+      // There is no folder and the conversion needs to be rerun
+      update_file = true;
+    }
+
+    if (update_file) {
+      convertDocumentToMarkdown(DocumentApp.openById(gdoc_file.getId()), markdown_folder);
+    }
+  }
+}
+
+function switchHandler(input_switches, potential_switches, optional_storage_name) {
+
+  // Firstly, if no input switches were set, make an empty input object
+  if (typeof(input_switches) == 'undefined') input_switches = {};
+
+  // Use optional storage name if it's defined (must be a string), else use default variable name "switch_settings"
+  var property_name = (typeof(optional_storage_name) == 'string') ? optional_storage_name : 'switch_settings';
+
+  // Make a blank object to be populated and stored as the script-wide property named after property_name
+  var switch_settings = {};
+
+  for (var i in potential_switches) {
+    var potential_switch = potential_switches[i];
+
+    // If each switch has been set (in input_switches), evaluate it, else assume it's switched off (false):
+
+    if (input_switches.propertyIsEnumerable(potential_switch)) {
+
+      // Evaluates a string representing a statement which sets switch_settings properties from input_switches
+      // e.g. "switch_settings.images = true" when input_switches = {images: true}
+
+      eval('switch_settings.' + potential_switch + " = " + input_switches[potential_switch]);
+
+    } else {
+
+      // Alternatively, the evaluated statement sets anything absent from the input_switches object as false
+      // e.g. "switch_settings.images = false" when input_switches = {} and potential_switches = ['images']
+
+      eval('switch_settings.' + potential_switch + " = false");
+    }
+  }
+
+  PropertiesService.getScriptProperties().setProperty(property_name, switch_settings);
+
+  /*
+  Looks bad but more sensible than repeatedly checking if arg undefined.
+
+  Sets every variable named in the potential_switches array to false if
+  it wasn't passed into the input_switches object, otherwise evaluates.
+
+  Any arguments not passed in are false, but so are any explicitly passed in as false:
+  all parameters are therefore Boolean until otherwise specified.
+  */
+
+}
+
+function convertDocumentToMarkdown(document, destination_folder, frontmatter_input, optional_switches) {
+  // if returning a string, force_save_images will make the script continue - experimental
+  var possible_switches = ['return_string', 'force_save_images'];
+  var property_name = 'conversion_switches';
+  switchHandler(optional_switches, possible_switches, property_name);
+
+  // TODO switch off image storage if force_save_images is true - not necessary for normal behaviour
+  var script_properties = PropertiesService.getScriptProperties();
+  var comment_switches = decodeScriptSwitches(property_name);
+  eval(comment_switches);
+
+  var image_prefix = script_properties.getProperty("image_folder_prefix");
+  var numChildren = document.getActiveSection().getNumChildren();
+  if (frontmatter_input != "") {
+    var text = frontmatter_input;
+  }
+  else {
+    var text = ""
+  }
+  var md_filename = sanitizeFileName(document.getName()) + ".md";
+  var image_foldername = document.getName()+"_images";
+  var inSrc = false;
+  var inClass = false;
+  var globalImageCounter = 0;
+  var globalListCounters = {};
+  // edbacher: added a variable for indent in src <pre> block. Let style sheet do margin.
+  var srcIndent = "";
+
+  var postHasImages = false;
+
+  var files = [];
+
+  // Walk through all the child elements of the doc.
+  for (var i = 0; i < numChildren; i++) {
+    var child = document.getActiveSection().getChild(i);
+    var result = processParagraph(i, child, inSrc, globalImageCounter, globalListCounters, image_prefix + image_foldername);
+    globalImageCounter += (result && result.images) ? result.images.length : 0;
+    if (result!==null) {
+      if (result.sourceGlossary==="start" && !inSrc) {
+        inSrc=true;
+        text+="<pre class=\"glossary\">\n";
+      } else if (result.sourceGlossary==="end" && inSrc) {
+        inSrc=false;
+        text+="</pre>\n\n";
+      } else if (result.sourceFigCap==="start" && !inSrc) {
+        inSrc=true;
+        text+="<pre class=\"glossary\">\n";
+      } else if (result.sourceFigCap==="end" && inSrc) {
+        inSrc=false;
+        text+="</pre>\n\n";
+      } else if (result.source==="start" && !inSrc) {
+        inSrc=true;
+        text+="<pre>\n";
+      } else if (result.source==="end" && inSrc) {
+        inSrc=false;
+        text+="</pre>\n\n";
+      } else if (result.inClass==="start" && !inClass) {
+        inClass=true;
+        text+="<pre class=\""+result.className+"\">\n";
+      } else if (result.inClass==="end" && inClass) {
+        inClass=false;
+        text+="</pre>\n\n";
+      } else if (inClass) {
+        text+=result.text+"\n\n";
+      } else if (inSrc) {
+        text+=(srcIndent+escapeHTML(result.text)+"\n");
+      } else if (result.text && result.text.length>0) {
+        text+=result.text+"\n\n";
+      }
+
+      if (result.images && result.images.length>0) {
+        for (var j=0; j<result.images.length; j++) {
+          files.push( { "blob": result.images[j].blob } );
+          postHasImages = true;
+        }
+      }
+    } else if (inSrc) { // support empty lines inside source code
+      text+='\n';
+    }
+
+  }
+
+  if (return_string && !force_save_images) {
+    returned_string = text;
+    return returned_string;
+  }
+  // return the string rather than continuing to write files
+
+  // I don't need to handle string return *with* saved images yet so this flag doesn't do anything in absence of return_string
+
+  files.push({"fileName": md_filename, "mimeType": "text/plain", "content": text});
+
+
+  // Cleanup any old folders and files in our destination directory with an identical name
+  var old_folders = destination_folder.getFoldersByName(image_foldername)
+  while (old_folders.hasNext()) {
+    var old_folder = old_folders.next();
+    old_folder.setTrashed(true)
+  }
+
+  // Remove any previously converted markdown files.
+  var old_files = destination_folder.getFilesByName(md_filename)
+  while (old_files.hasNext()) {
+    var old_file = old_files.next();
+    old_file.setTrashed(true)
+  }
+
+  // Create a subfolder for images if they exist
+  var image_folder;
+  if (postHasImages) {
+    image_folder = DriveApp.createFolder(image_foldername);
+    DriveApp.removeFolder(image_folder); // Confusing convention; this just removes the folder from the google drive root.
+    destination_folder.addFolder(image_folder)
+  }
+
+  for (var i = 0; i < files.length; i++) {
+    var saved_file;
+    if (files[i].blob) {
+      saved_file = DriveApp.createFile(files[i].blob)
+      // The images go into a subfolder matching the post title
+      image_folder.addFile(saved_file)
+    } else {
+      // The markdown files all go in the "Markdown" directory
+      saved_file = DriveApp.createFile(files[i]["fileName"], files[i]["content"], files[i]["mimeType"])
+      destination_folder.addFile(saved_file)
+    }
+   DriveApp.removeFile(saved_file) // Removes from google drive root.
+  }
+return saved_file;
+}
+
+function escapeHTML(text) {
+  return text.replace(/</g, '&lt;').replace(/>/g, '&gt;');
+}
+
+function standardQMarks(text) {
+  return text.replace(/\u2018|\u8216|\u2019|\u8217/g,"'").replace(/\u201c|\u8220|\u201d|\u8221/g, '"')
+}
+
+// Process each child element (not just paragraphs).
+function processParagraph(index, element, inSrc, imageCounter, listCounters, image_path) {
+  // First, check for things that require no processing.
+  if (element.getType() === DocumentApp.ElementType.UNSUPPORTED) {
+    return null;
+  }
+  if (element.getNumChildren()==0) {
+    return null;
+  }
+  // Skip on TOC.
+  if (element.getType() === DocumentApp.ElementType.TABLE_OF_CONTENTS) {
+    return {"text": "[[TOC]]"};
+  }
+
+  // Set up for real results.
+  var result = {};
+  var pOut = "";
+  var textElements = [];
+  var imagePrefix = "image_";
+
+  // Handle Table elements. Pretty simple-minded now, but works for simple tables.
+  // Note that Markdown does not process within block-level HTML, so it probably
+  // doesn't make sense to add markup within tables.
+  if (element.getType() === DocumentApp.ElementType.TABLE) {
+    textElements.push("<table>\n");
+    var nCols = element.getChild(0).getNumCells();
+    for (var i = 0; i < element.getNumChildren(); i++) {
+      textElements.push("  <tr>\n");
+      // process this row
+      for (var j = 0; j < nCols; j++) {
+        textElements.push("    <td>" + element.getChild(i).getChild(j).getText() + "</td>\n");
+      }
+      textElements.push("  </tr>\n");
+    }
+    textElements.push("</table>\n");
+  }
+
+  // Need to handle this element type, return null for now
+  if (element.getType() === DocumentApp.ElementType.CODE_SNIPPET) {
+    return null
+  }
+
+  // Process various types (ElementType).
+  for (var i = 0; i < element.getNumChildren(); i++) {
+    var t = element.getChild(i).getType();
+
+    if (t === DocumentApp.ElementType.TABLE_ROW) {
+      // do nothing: already handled TABLE_ROW
+    } else if (t === DocumentApp.ElementType.TEXT) {
+      var txt  = element.getChild(i);
+      pOut += txt.getText();
+      textElements.push(txt);
+    } else if (t === DocumentApp.ElementType.INLINE_IMAGE) {
+      var imglink = element.getChild(i).getLinkUrl();
+      result.images = result.images || [];
+      var blob = element.getChild(i).getBlob()
+      var contentType = blob.getContentType();
+      var extension = "";
+      if (/\/png$/.test(contentType)) {
+        extension = ".png";
+      } else if (/\/gif$/.test(contentType)) {
+        extension = ".gif";
+      } else if (/\/jpe?g$/.test(contentType)) {
+        extension = ".jpg";
+      } else {
+        throw "Unsupported image type: "+contentType;
+      }
+
+      var name = imagePrefix + imageCounter + extension;
+      blob.setName(name);
+
+      imageCounter++;
+      if (!return_string || force_save_images) {
+        textElements.push('![](' + image_path + '/' + name + ')');
+      } else {
+        textElements.push('![](' + imglink + ')');
+      }
+      //result.images.push( {
+      //  "bytes": blob.getBytes(),
+      //  "type": contentType,
+      //  "name": name});
+
+      result.images.push({ "blob" : blob } )
+
+    // Need to fix this case TODO
+    } else if (t === DocumentApp.ElementType.INLINE_DRAWING) {
+
+      imageCounter++;
+      if (!return_string || force_save_images) {
+        textElements.push('![](' + "drawing" + '/' + " name" + ')');
+      } else {
+        textElements.push('![](' + "drawing" + ')');
+      }
+      //result.images.push( {
+      //  "bytes": blob.getBytes(),
+      //  "type": contentType,
+      //  "name": name});
+
+      // result.images.push({ "blob" : blob } )
+
+    }
+     else if (t === DocumentApp.ElementType.PAGE_BREAK) {
+      // ignore
+    } else if (t === DocumentApp.ElementType.HORIZONTAL_RULE) {
+      textElements.push('* * *\n');
+    } else if (t === DocumentApp.ElementType.FOOTNOTE) {
+      textElements.push(' ('+element.getChild(i).getFootnoteContents().getText()+')');
+    // Fixes for new elements
+    } else if (t === DocumentApp.ElementType.EQUATION) {
+      textElements.push(element.getChild(i).getText());
+    } else if (t === DocumentApp.ElementType.DATE) {
+      textElements.push(' ('+element.getChild(i)+')');
+    } else if (t === DocumentApp.ElementType.RICH_LINK) {
+      textElements.push(' ('+element.getChild(i).getUrl()+')');
+    } else if (t === DocumentApp.ElementType.PERSON) {
+      textElements.push(element.getChild(i).getName() + ', ');
+    } else if (t === DocumentApp.ElementType.UNSUPPORTED) {
+      textElements.push(' <UNSUPPORTED> ');
+    } else {
+      Logger.log("Paragraph "+index+" of type "+element.getType()+" has an unsupported child: "
+      +t+" "+(element.getChild(i)["getText"] ? element.getChild(i).getText():'')+" index="+index);
+    }
+  }
+
+  if (textElements.length==0) {
+    // Isn't result empty now?
+    return result;
+  }
+
+// Fix for unrecognized command getIndentFirstLine
+  var ind_f = 0;
+  var ind_s = 0;
+  var ind_e = 0;
+  if (t === DocumentApp.ElementType.PARAGRAPH) {
+
+    var ind_f = element.getIndentFirstLine();
+    var ind_s = element.getIndentStart();
+    var ind_e = element.getIndentEnd();
+  }
+  var i_fse = [ind_f,ind_s,ind_e];
+  var indents = {};
+  for (indt=0;indt<i_fse.length;indt++) {
+    var indname = i_fse[indt];
+    if (eval(indname) > 0) indents[indname] = eval(indname);
+    // lazy test, null (no indent) is not greater than zero, but becomes set if indent 'undone'
+  }
+  var inIndent = (Object.keys(indents).length > 0);
+
+  // evb: Add glossary and figure caption too. (And abbreviations: gloss and fig-cap.)
+  // process source code block:
+  if (/^\s*---\s+gloss\s*$/.test(pOut) || /^\s*---\s+source glossary\s*$/.test(pOut)) {
+    result.sourceGlossary = "start";
+  } else if (/^\s*---\s+fig-cap\s*$/.test(pOut) || /^\s*---\s+source fig-cap\s*$/.test(pOut)) {
+    result.sourceFigCap = "start";
+  } else if (/^\s*---\s+src\s*$/.test(pOut) || /^\s*---\s+source code\s*$/.test(pOut)) {
+    result.source = "start";
+  } else if (/^\s*---\s+class\s+([^ ]+)\s*$/.test(pOut)) {
+    result.inClass = "start";
+    result.className = RegExp.$1.replace(/\./g,' ');
+  } else if (/^\s*---\s*$/.test(pOut)) {
+    result.source = "end";
+    result.sourceGlossary = "end";
+    result.sourceFigCap = "end";
+    result.inClass = "end";
+  } else if (/^\s*---\s+jsperf\s*([^ ]+)\s*$/.test(pOut)) {
+    result.text = '<iframe style="width: 100%; height: 240px; overflow: hidden; border: 0;" '+
+                  'src="http://www.html5rocks.com/static/jsperfview/embed.html?id='+RegExp.$1+
+                  '"></iframe>';
+  } else {
+
+    prefix = findPrefix(inSrc, element, listCounters);
+
+    var pOut = "";
+    for (var i=0; i<textElements.length; i++) {
+      pOut += processTextElement(inSrc, textElements[i]);
+    }
+
+    // replace Unicode quotation marks (double and single)
+    pOut = standardQMarks(pOut);
+
+    result.text = prefix+pOut;
+  }
+
+  var indent_prefix = '> ';
+  var indent_alt_prefix = '> <sub>';
+  if (inIndent && !inSrc) {
+    if (/^#*\s/.test(result.text)) { // don't subscript-prefix header prefix
+      result.text = indent_alt_prefix + result.text;
+    } else {
+      result.text = indent_prefix + result.text;
+    }
+  }
+
+  return result;
+}
+
+// Add correct prefix to list items.
+function findPrefix(inSrc, element, listCounters) {
+  var prefix = "";
+  if (!inSrc) {
+    if (element.getType()===DocumentApp.ElementType.PARAGRAPH) {
+      var paragraphObj = element;
+      switch (paragraphObj.getHeading()) {
+        // Add a # for each heading level. No break, so we accumulate the right number.
+        case DocumentApp.ParagraphHeading.HEADING6: prefix+="#";
+        case DocumentApp.ParagraphHeading.HEADING5: prefix+="#";
+        case DocumentApp.ParagraphHeading.HEADING4: prefix+="#";
+        case DocumentApp.ParagraphHeading.HEADING3: prefix+="#";
+        case DocumentApp.ParagraphHeading.HEADING2: prefix+="#";
+        case DocumentApp.ParagraphHeading.HEADING1: prefix+="# ";
+        default:
+      }
+    } else if (element.getType()===DocumentApp.ElementType.LIST_ITEM) {
+      var listItem = element;
+      var nesting = listItem.getNestingLevel()
+      for (var i=0; i<nesting; i++) {
+        prefix += "    ";
+      }
+      var gt = listItem.getGlyphType();
+      // Bullet list (<ul>):
+      if (gt === DocumentApp.GlyphType.BULLET
+          || gt === DocumentApp.GlyphType.HOLLOW_BULLET
+          || gt === DocumentApp.GlyphType.SQUARE_BULLET) {
+        prefix += "* ";
+      } else {
+        // Ordered list (<ol>):
+        var key = listItem.getListId() + '.' + listItem.getNestingLevel();
+        var counter = listCounters[key] || 0;
+        counter++;
+        listCounters[key] = counter;
+        prefix += counter+". ";
+      }
+    }
+  }
+  return prefix;
+}
+
+function processTextElement(inSrc, txt) {
+  if (typeof(txt) === 'string') {
+    return txt;
+  }
+
+  var pOut = txt.getText();
+  if (! txt.getTextAttributeIndices) {
+    return pOut;
+  }
+
+//  Logger.log("Initial String: " + pOut)
+
+  // CRC introducing reformatted_txt to let us apply rational formatting that we can actually parse
+  var reformatted_txt = txt.copy();
+  reformatted_txt.deleteText(0,pOut.length-1);
+  reformatted_txt = reformatted_txt.setText(pOut);
+
+  var attrs = txt.getTextAttributeIndices();
+  var lastOff = pOut.length;
+  // We will run through this loop multiple times for the things we care about.
+  // Font
+  // URL
+  // Then for alignment
+  // Then for bold
+  // Then for italic.
+
+  // FONTs
+  var lastOff = pOut.length; // loop goes backwards, so this holds
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var font=txt.getFontFamily(off)
+     if (font) {
+       while (i>=1 && txt.getFontFamily(attrs[i-1])==font) {
+          // detect fonts that are in multiple pieces because of errors on formatting:
+          i-=1;
+          off=attrs[i];
+       }
+       reformatted_txt.setFontFamily(off, lastOff-1, font);
+     }
+    lastOff=off;
+  }
+
+  // URL
+  // XXX TODO actually convert to URL text here.
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var url=txt.getLinkUrl(off);
+     if (url) {
+       while (i>=1 && txt.getLinkUrl(attrs[i-1]) == url) {
+          // detect urls that are in multiple pieces because of errors on formatting:
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setLinkUrl(off, lastOff-1, url);
+     }
+    lastOff=off;
+  }
+
+  // alignment
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var alignment=txt.getTextAlignment(off);
+     if (alignment) { //
+       while (i>=1 && txt.getTextAlignment(attrs[i-1]) == alignment) {
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setTextAlignment(off, lastOff-1, alignment);
+     }
+    lastOff=off;
+  }
+
+  // strike
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var strike=txt.isStrikethrough(off);
+     if (strike) {
+       while (i>=1 && txt.isStrikethrough(attrs[i-1])) {
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setStrikethrough(off, lastOff-1, strike);
+     }
+    lastOff=off;
+  }
+
+  // bold
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var bold=txt.isBold(off);
+     if (bold) {
+       while (i>=1 && txt.isBold(attrs[i-1])) {
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setBold(off, lastOff-1, bold);
+     }
+    lastOff=off;
+  }
+
+  // italics
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var italic=txt.isItalic(off);
+     if (italic) {
+       while (i>=1 && txt.isItalic(attrs[i-1])) {
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setItalic(off, lastOff-1, italic);
+     }
+    lastOff=off;
+  }
+
+
+  var mOut=""; // Modified out string
+  var harmonized_attrs = reformatted_txt.getTextAttributeIndices();
+  reformatted_txt.getTextAttributeIndices(); // @lmmx: is this a typo...?
+  pOut = reformatted_txt.getText();
+
+
+  // Markdown is farily picky about how it will let you intersperse spaces around words and strong/italics chars. This regex (hopefully) clears this up
+  // Match any number of \*, followed by spaces/word boundaries against anything that is not the \*, followed by boundaries, spaces and * again.
+  // Test case at http://jsfiddle.net/ovqLv0s9/2/
+
+  var reAlignStars = /(\*+)(\s*\b)([^\*]+)(\b\s*)(\*+)/g;
+
+  var lastOff=pOut.length;
+  for (var i=harmonized_attrs.length-1; i>=0; i--) {
+    var off=harmonized_attrs[i];
+
+    var raw_text = pOut.substring(off, lastOff)
+
+    var d1 = ""; // @lmmx: build up a modifier prefix
+    var d2 = ""; // @lmmx: ...and suffix
+
+    var end_font;
+
+    var mark_bold = false;
+    var mark_italic = false;
+    var mark_code = false;
+    var mark_sup = false;
+    var mark_sub = false;
+    var mark_strike = false;
+
+    // The end of the text block is a special case.
+    if (lastOff == pOut.length) {
+      end_font = reformatted_txt.getFontFamily(lastOff - 1)
+      if (end_font) {
+        if (!inSrc && end_font===end_font.COURIER_NEW) {
+          mark_code = true;
+        }
+      }
+      if (reformatted_txt.isBold(lastOff -1)) {
+        mark_bold = true;
+      }
+      if (reformatted_txt.isItalic(lastOff - 1)) {
+        // edbacher: changed this to handle bold italic properly.
+        mark_italic = true;
+      }
+      if (reformatted_txt.isStrikethrough(lastOff - 1)) {
+        mark_strike = true;
+      }
+      if (reformatted_txt.getTextAlignment(lastOff - 1)===DocumentApp.TextAlignment.SUPERSCRIPT) {
+        mark_sup = true;
+      }
+      if (reformatted_txt.getTextAlignment(lastOff - 1)===DocumentApp.TextAlignment.SUBSCRIPT) {
+        mark_sub = true;
+      }
+    } else {
+      end_font = reformatted_txt.getFontFamily(lastOff -1 )
+      if (end_font) {
+        if (!inSrc && end_font===end_font.COURIER_NEW && reformatted_txt.getFontFamily(lastOff) != end_font) {
+          mark_code=true;
+        }
+      }
+      if (reformatted_txt.isBold(lastOff - 1) && !reformatted_txt.isBold(lastOff) ) {
+        mark_bold=true;
+      }
+      if (reformatted_txt.isStrikethrough(lastOff - 1) && !reformatted_txt.isStrikethrough(lastOff)) {
+        mark_strike=true;
+      }
+      if (reformatted_txt.isItalic(lastOff - 1) && !reformatted_txt.isItalic(lastOff)) {
+        mark_italic=true;
+      }
+      if (reformatted_txt.getTextAlignment(lastOff - 1)===DocumentApp.TextAlignment.SUPERSCRIPT) {
+        if (reformatted_txt.getTextAlignment(lastOff)!==DocumentApp.TextAlignment.SUPERSCRIPT) {
+          mark_sup = true;
+        }
+      }
+      if (reformatted_txt.getTextAlignment(lastOff - 1)===DocumentApp.TextAlignment.SUBSCRIPT) {
+        if (reformatted_txt.getTextAlignment(lastOff)!==DocumentApp.TextAlignment.SUBSCRIPT) {
+          mark_sub = true;
+        }
+      }
+    }
+
+    if (mark_code) {
+      d2 = '`'; // shouldn't these go last? or will it interfere w/ reAlignStars?
+    }
+    if (mark_bold) {
+      d2 = "**" + d2;
+    }
+    if (mark_italic) {
+      d2 = "*" + d2;
+    }
+    if (mark_strike) {
+      d2 = "</strike>" + d2;
+    }
+    if (mark_sup) {
+      d2 = '</sup>' + d2;
+    }
+    if (mark_sub) {
+      d2 = '</sub>' + d2;
+    }
+
+    mark_bold = mark_italic = mark_code = mark_sup = mark_sub = mark_strike = false;
+
+    var font=reformatted_txt.getFontFamily(off);
+    if (off == 0) {
+      if (font) {
+        if (!inSrc && font===font.COURIER_NEW) {
+          mark_code = true;
+        }
+      }
+      if (reformatted_txt.isBold(off)) {
+        mark_bold = true;
+      }
+      if (reformatted_txt.isItalic(off)) {
+        mark_italic = true;
+      }
+      if (reformatted_txt.isStrikethrough(off)) {
+        mark_strike = true;
+      }
+      if (reformatted_txt.getTextAlignment(off)===DocumentApp.TextAlignment.SUPERSCRIPT) {
+        mark_sup = true;
+      }
+      if (reformatted_txt.getTextAlignment(off)===DocumentApp.TextAlignment.SUBSCRIPT) {
+        mark_sub = true;
+      }
+    } else {
+      if (font) {
+        if (!inSrc && font===font.COURIER_NEW && reformatted_txt.getFontFamily(off - 1) != font) {
+          mark_code=true;
+        }
+      }
+      if (reformatted_txt.isBold(off) && !reformatted_txt.isBold(off -1) ) {
+        mark_bold=true;
+      }
+      if (reformatted_txt.isItalic(off) && !reformatted_txt.isItalic(off - 1)) {
+        mark_italic=true;
+      }
+      if (reformatted_txt.isStrikethrough(off) && !reformatted_txt.isStrikethrough(off - 1)) {
+        mark_strike=true;
+      }
+      if (reformatted_txt.getTextAlignment(off)===DocumentApp.TextAlignment.SUPERSCRIPT) {
+        if (reformatted_txt.getTextAlignment(off - 1)!==DocumentApp.TextAlignment.SUPERSCRIPT) {
+          mark_sup = true;
+        }
+      }
+      if (reformatted_txt.getTextAlignment(off)===DocumentApp.TextAlignment.SUBSCRIPT) {
+        if (reformatted_txt.getTextAlignment(off - 1)!==DocumentApp.TextAlignment.SUBSCRIPT) {
+          mark_sub = true;
+        }
+      }
+    }
+
+
+    if (mark_code) {
+      d1 = '`';
+    }
+
+    if (mark_bold) {
+      d1 = d1 + "**";
+    }
+
+    if (mark_italic) {
+      d1 = d1 + "*";
+    }
+
+    if (mark_sup) {
+      d1 = d1 + '<sup>';
+    }
+
+    if (mark_sub) {
+      d1 = d1 + '<sub>';
+    }
+
+    if (mark_strike) {
+      d1 = d1 + '<strike>';
+    }
+
+    var url=reformatted_txt.getLinkUrl(off);
+    if (url) {
+      mOut = d1 + '['+ raw_text +']('+url+')' + d2 + mOut;
+    } else {
+      var new_text = d1 + raw_text + d2;
+      new_text = new_text.replace(reAlignStars, "$2$1$3$5$4");
+      mOut =  new_text + mOut;
+    }
+
+    lastOff=off;
+//    Logger.log("Modified String: " + mOut)
+  }
+
+  mOut = pOut.substring(0, off) + mOut;
+  return mOut;
+}
\ No newline at end of file
diff --git a/examples/gemini/python/docs-agent/apps_script/gmail_to_markdown.gs b/examples/gemini/python/docs-agent/apps_script/gmail_to_markdown.gs
new file mode 100644
index 000000000..3263ef100
--- /dev/null
+++ b/examples/gemini/python/docs-agent/apps_script/gmail_to_markdown.gs
@@ -0,0 +1,137 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+function exportEmailsToMarkdown(search, folderName) {
+  //Checks if input folder exists or exits
+  if(folderExistsOrCreate(folderName)){
+    var output_file_name = folderName + "-index";
+    var folderOutputObj = DriveApp.getFoldersByName(folderName);
+    if (folderOutputObj.hasNext()){
+      var folderOutputName = folderOutputObj.next();
+    }
+    var sheet = checkIndexOutputOrCreate(output_file_name, folderOutputName);
+    console.log(`Searching for: "${search}"`);
+    var start = 0;
+    var max = 500;
+    var threads = GmailApp.search(search, start, max);
+    var threadMax = threads.length;
+    if (threads!=null){
+      console.log(threadMax + " threads found.");
+    } else {
+      console.warn("No threads found with the search criteria");
+      return;
+    }
+    let timeZone = Session.getScriptTimeZone();
+    let created_date = Utilities.formatDate(new Date(), timeZone, "MM-dd-yyyy HH:mm:ss z");
+    sheet.appendRow(["Created: ", created_date])
+    sheet.appendRow(["Date", "From", "Subject", "To", "Markdown ID", "Markdown URL", "Full date", "MD5 hash", "Status"]);
+    var start_data_row = 2;
+    var status = "New content";
+    var newEmails = 0;
+    var unchangedEmails = 0;
+    for (var threadCount in threads) {
+      var msgs = threads[threadCount].getMessages();
+      Logger.log("Processing thread " + threadCount + " of " + threadMax);
+      for (var msgCount in msgs) {
+        var msg = msgs[msgCount];
+        var subject = msg.getSubject().replace(/"/g, "\\\"");;
+        // Removes replies and forwards - Can mostly be noise.
+        if(!subject.toLowerCase().includes("re:") &&
+            !subject.toLowerCase().includes("fwd:") &&
+            !subject.toLowerCase().includes("forwarded message")){
+          // Values to get and store messages
+          var date = msg.getDate();
+          let from_author = msg.getFrom().replace(/"/g, "\\\"");
+          var hash_content = from_author + date + subject;
+          let sanitized_subject = sanitizeString(subject);
+          let date_format = Utilities.formatDate(date, "PST", "MM-dd-yyyy");
+          let to = msg.getTo();
+          let to_array = to.split(", ");
+          for (i in to_array) {
+            to_array[i] = "\"" + to_array[i].replace(/^" "/, "").replace(/"/g, "\\\"") + "\"";
+          }
+          let md5_hash = Utilities.computeDigest(Utilities.DigestAlgorithm.MD5,hash_content,
+                          Utilities.Charset.US_ASCII);
+          let hash_str = byteToStr(md5_hash);
+          //Function returns an array, assign each array value to seperate variables. For emails, only need to retrieve
+          // backup markdown ids
+          var backup_results = returnBackupHash(sheet, "Backup", hash_str, start_data_row, 7, 4, 5);
+          if (backup_results != undefined && backup_results[0] != "no_results") {
+            Logger.log("Email is already in markdown format. Skipping conversion.");
+            var status = "Unchanged content";
+            var markdown_id = backup_results[1];
+            if (markdown_id){
+              var md_file = DriveApp.getFileById(markdown_id);
+            }
+            unchangedEmails += 1;
+          }
+          else {
+            var status = "New content";
+            let message = msg.getPlainBody();
+            let filename = sanitizeFileName(date_format + subject + ".md");
+            // Initialize blank text since file will get updated with URL
+            let email_md = "";
+            // Add count here for emails
+            newEmails += 1;
+            Logger.log("Email number: " + newEmails + "| Saving email to: " + filename);
+            var body = "# " + subject + "\n";
+            // Cleans the reply part of emails
+            body += regexToCleanCharsMD(sanitizeBody(message.replace(/^>/g,""))) + "\n";
+            var destinationFolder = DriveApp.getFoldersByName(folderOutputName).next();
+            // Initialize blank file to retrieve URL which is then added to the frontmatter
+            var destinationFile = destinationFolder.createFile(filename, email_md , MimeType.PLAIN_TEXT);
+            // Create metadata for the object
+            var markdown_id = destinationFile.getId();
+            var md_file = DriveApp.getFileById(markdown_id);
+            let md_url = md_file.getUrl();
+            let frontmatter = "---" + "\n";
+            frontmatter += "title: \"" + sanitized_subject + "\"\n";
+            frontmatter += "type: \"" + "email" + "\"\n";
+            frontmatter += "URL: \"" + md_url + "\"\n";
+            frontmatter += "created: \"" + date + "\"\n";
+            frontmatter += "from: \"" + from_author + "\"\n";
+            frontmatter += "to: \[" + to_array + "\]\n";
+            frontmatter += "---" + "\n\n";
+            email_md = frontmatter + body;
+            var encoded = Utilities.base64Encode(email_md);
+            var byteDataArray = Utilities.base64Decode(encoded);
+            var textAsBlob = Utilities.newBlob(byteDataArray);
+            Drive.Files.update(null,markdown_id, textAsBlob);
+          }
+          let md_chip = createRichText(md_file);
+          metadata = [
+            date_format,
+            from_author,
+            sanitized_subject,
+            to,
+            markdown_id,
+            "md_chip",
+            date,
+            hash_str,
+            status,
+          ];
+          sheet.appendRow(metadata);
+          var emailTotal = newEmails + unchangedEmails;
+          let row_number = emailTotal + start_data_row;
+          insertRichText(sheet, md_chip, "F", row_number);
+        }
+      }
+    }
+    Logger.log("Saved a total of " + newEmails + " new emails.");
+    Logger.log("There is a total of " + unchangedEmails + " unchanged emails.");
+    Logger.log("Grand total of " + emailTotal + " emails.");
+  }
+}
\ No newline at end of file
diff --git a/examples/gemini/python/docs-agent/apps_script/helper_functions.gs b/examples/gemini/python/docs-agent/apps_script/helper_functions.gs
new file mode 100644
index 000000000..3fbf96866
--- /dev/null
+++ b/examples/gemini/python/docs-agent/apps_script/helper_functions.gs
@@ -0,0 +1,214 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// Checks to see if a folder already exists in the drive
+function folderExists(folderName) {
+  const folderIterator = DriveApp.getRootFolder().getFoldersByName(folderName);
+  if(folderIterator.hasNext()) {
+    return true;
+  }
+  else {
+    return false;
+  }
+}
+
+// Checks to see if a folder already exists in the specified root folder
+function folderExistsInRoot(folderName, rootFolder) {
+  const folderIterator = rootFolder.getFoldersByName(folderName);
+  if(folderIterator.hasNext()) {
+    return true;
+  }
+  else {
+    return false;
+  }
+}
+
+// Checks to see if a folder already exists in the drive and exits if it doesn't. Useful for input directories
+function folderExistsInput(folderName){
+  if (folderExists(folderName)) {
+    Logger.log("Folder exists: "+ folderName);
+    return true;
+  }
+  else {
+    Logger.log("Folder does not exist: "+ folderName + ". Please make sure the directory exists.");
+    return false;
+  }
+}
+
+// Checks to see if folder exists or creates it. Useful for output directories
+function folderExistsOrCreate(folderName){
+  if(folderExists(folderName)) {
+    Logger.log("Folder exists: "+ folderName);
+    return true;
+  }
+  else {
+    Logger.log("Folder does not exist: "+ folderName + ". Creating the directory.");
+    DriveApp.createFolder(folderName);
+    return true;
+  }
+}
+
+// Checks to see if folder exists or creates it. Useful for output directories
+function folderExistsOrCreateSubdir(folderName, rootFolder){
+  if(folderExistsInRoot(folderName, rootFolder)) {
+    Logger.log("Folder exists: "+ folderName);
+    return true;
+  }
+  else {
+    Logger.log("Folder does not exist: "+ folderName + ". Creating the directory.");
+    rootFolder.createFolder(folderName);
+    return true;
+  }
+}
+
+// Checks to see if a file exists in a folder
+function checkFileExists(fileName,folderName){
+  let folder = DriveApp.getFoldersByName(folderName);
+  if(!folder.hasNext()){
+  }
+  else{
+    var file = folder.next().getFilesByName(fileName);
+    if(!file.hasNext()){
+       return true;
+    }
+    else{
+       return false;
+    }
+  }
+}
+
+// Function to check if an index output sheet exists or creates it. Returns the file object
+// Specify the file output name and outputdirectory
+function checkIndexOutputOrCreate(fileName, folderOutput, indexFileID="") {
+  var timeZone = Session.getScriptTimeZone();
+  var date = Utilities.formatDate(new Date(), timeZone, "MM-dd-yyyy hh:mm:ss");
+  let file = {title: fileName, mimeType: MimeType.GOOGLE_SHEETS, parents: [{id: folderOutput.getId()}]}
+  let params = "title='" + fileName + "' and parents in '" + folderOutput.getId() + "'";
+  let file_search = DriveApp.searchFiles(params);
+  if (file_search.hasNext()) {
+    if (indexFileID=="") {
+      var fileId = file_search.next().getId();
+    }
+    else {
+      var fileId = indexFileID;
+    }
+    var sheet = SpreadsheetApp.openById(fileId);
+    Logger.log("File index: " + fileName + " exists.");
+    var sheet_index = sheet.getSheetByName("Index");
+    // Checks to see if this is a sub directory
+    if (sheet.getSheetByName("Backup")) {
+      var sheet_backup = sheet.getSheetByName("Backup");
+      sheet.deleteSheet(sheet_backup);
+    }
+    var sheet_backup = sheet.insertSheet("Backup", 1);
+    var sheet_backup_open = sheet.getSheetByName("Backup");
+    sheet_index.getDataRange().copyTo(sheet_backup_open.getRange(1,1));
+    if (sheet_index != null){
+      sheet.deleteSheet(sheet_index);
+    }
+    sheet.insertSheet("Index", 0);
+    sheet_index = sheet.getSheetByName("Index");
+    sheet_index.addDeveloperMetadata("Date", date);
+    }
+  else {
+    Logger.log("File index: " + fileName + " does not exist.");
+    let output = Drive.Files.insert(file).id;
+    var sheet = SpreadsheetApp.openById(output);
+    var sheet_1 = sheet.getSheetByName("Sheet1");
+    sheet.insertSheet("Index", 0);
+    var sheet_index = sheet.getSheetByName("Index")
+    sheet_index.addDeveloperMetadata("Date", date);
+    sheet.deleteSheet(sheet_1);
+  }
+  return sheet;
+}
+
+// Function to convert byte array into a string
+function byteToStr(byteInput){
+  let signatureStr = '';
+  for (i = 0; i < byteInput.length; i++) {
+    let byte = byteInput[i];
+    if (byte < 0)
+      byte += 256;
+    let byteStr = byte.toString(16);
+    if (byteStr.length == 1) byteStr = '0' + byteStr;
+      signatureStr += byteStr;
+    }
+return signatureStr;
+}
+
+// Function to remove special characters for file names
+function sanitizeFileName(fileName){
+  let clean_filename = fileName.replace(/\[/g, "_").replace(/\]/g, "_").replace(/\(/g, "_").replace(/\)/g, "_").replace(/^_/g, "").replace(/,/g, "_").replace(/ /g, "_").replace(/:/g, "").replace(/`/g, "").replace(/\'/g, "").replace(/&/g, "and").replace(/</g, "").replace(/>/g, "").replace(/’/g, "");
+return clean_filename;
+}
+
+// Function to remove special characters for file names
+function sanitizeString(string){
+  let clean_string = string.replace(/\[/g, "").replace(/\]/g, "").replace(/\(/g, "").replace(/\)/g, "").replace(/^_/g, "").replace(/,/g, " ").replace(/:/g, "").replace(/`/g, "").replace(/\'/g, "").replace(/&/g, "and").replace(/</g, "").replace(/>/g, "");
+return clean_string;
+}
+
+function sanitizeBody(string){
+  let clean_body = string.replace(/’/g, "'").replace(/^M/g, "");
+return clean_body;
+}
+
+function regexToCleanCharsMD(string){
+  let clean_string = string.replace(/(\*+)(\s*\b)([^\*]+)(\b\s*)(\*+)/g, "$2$1$3$5$4");
+return clean_string;
+}
+
+// Function to check if a backup sheet exists and return a hash if the file exists
+// Specify the sheet name where the backup is saved, default is "Backup"
+// From your backup sheet specify the column that contains the MD5 hash
+// and the columns for which you return values
+function returnBackupHash(sheet, sheet_name, fid, start_data_row, pos_id, pos_1_col, pos_2_col){
+  if (sheet.getSheetByName(sheet_name)){
+    let backup_sheet = sheet.getSheetByName(sheet_name);
+    if(backup_sheet.getLastRow()> start_data_row){
+      let backup_values = backup_sheet.getDataRange().getValues();
+      for (let row_count = start_data_row; row_count < backup_sheet.getLastRow(); row_count++) {
+        let row_id = backup_values[row_count][pos_id];
+        let pos_1_value = backup_values[row_count][pos_1_col];
+        //Retrieve id of existing markdown conversion
+        let pos_2_value = backup_values[row_count][pos_2_col];
+        if (row_id == fid){
+          var results = [row_id, pos_1_value, pos_2_value];
+          break;
+        }
+        else {
+          var results = ["no_results"];
+        }
+      }
+      return results;
+    }
+  }
+}
+
+// Creates a richText item with item.
+function createRichText (item){
+  let title = item.getName();
+  let url = item.getUrl();
+  let richText = SpreadsheetApp.newRichTextValue().setText(title).setLinkUrl(url).build();
+  return richText;
+}
+
+// Insert a richText item in a specific cell
+function insertRichText (sheetItem, item, column, row){
+  let range = sheetItem.getRange(column + row);
+  range.setRichTextValue(item);
+}
diff --git a/examples/gemini/python/docs-agent/apps_script/main.gs b/examples/gemini/python/docs-agent/apps_script/main.gs
new file mode 100644
index 000000000..2fe88de33
--- /dev/null
+++ b/examples/gemini/python/docs-agent/apps_script/main.gs
@@ -0,0 +1,27 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// Defines the gmail search query for saving emails to markdown
+var SEARCH_QUERY = 'subject: psa to:my-mailing-list@example.com';
+// Defines the directory to output the emails in markdown format
+var folderOutput = "PSA-output"
+// Defines the directory that has your docs content
+var folderInput = "input-folder"
+
+function main() {
+  convertDriveFolderToMDForDocsAgent(folderInput);
+  exportEmailsToMarkdown(SEARCH_QUERY, folderOutput);
+}
\ No newline at end of file
diff --git a/examples/gemini/python/docs-agent/config.yaml b/examples/gemini/python/docs-agent/config.yaml
new file mode 100644
index 000000000..d104ab5f4
--- /dev/null
+++ b/examples/gemini/python/docs-agent/config.yaml
@@ -0,0 +1,50 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+configs:
+  - product_name: "Fuchsia"
+    models:
+      - language_model: "models/gemini-1.5-flash-latest"
+        embedding_model: "models/embedding-001"
+        api_endpoint: "generativelanguage.googleapis.com"
+        embedding_api_call_limit: 1400
+        embedding_api_call_period: 60
+    docs_agent_config: "normal"
+    markdown_splitter: "token_splitter"
+    log_level: "NORMAL"
+    db_type: "chroma"
+    db_configs:
+      - db_type: "chroma"
+        vector_db_dir: "vector_stores/chroma"
+        collection_name: "docs_collection"
+      - db_type: "google_semantic_retriever"
+        corpus_name: "corpora/fuchsia-dev"
+    output_path: "data/plain_docs"
+    inputs:
+      - path: "/usr/local/home/user01/website/src"
+        url_prefix: "https://docs.flutter.dev/"
+    conditions:
+      - condition_text: "You are a helpful chatbot answering questions from users.
+        Read the context below first and answer the user's question at the end.
+        In your answer, provide a summary in three or five sentences. (BUT DO NOT USE
+        ANY INFORMATION YOU KNOW ABOUT THE WORLD.)"
+        fact_check_question: "Can you compare the text below to the information
+        provided in this prompt above and write a short message that warns the readers
+        about which part of the text they should consider fact-checking? (Please keep
+        your response concise, focus on only one important item, but DO NOT USE BOLD
+        TEXT IN YOUR RESPONSE.)"
+        model_error_message: "Gemini is not able to answer this question at the moment.
+        Rephrase the question and try asking again."
diff --git a/examples/gemini/python/docs-agent/docs/chunking-process.md b/examples/gemini/python/docs-agent/docs/chunking-process.md
new file mode 100644
index 000000000..e4c054b92
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs/chunking-process.md
@@ -0,0 +1,68 @@
+# Docs Agent chunking process
+
+This page describes Docs Agent's chunking process and potential optimizations.
+
+Currently, Docs Agent utilizes Markdown headings (`#`, `##`, and `###`) to
+split documents into smaller, manageable chunks. However, the Docs Agent team
+is actively developing more advanced strategies to improve the quality and
+relevance of these chunks for retrieval.
+
+## Chunking technique
+
+In Retrieval Augmented Generation ([RAG][rag]) based systems, ensuring each
+chunk contains the right information and context is crucial for accurate
+retrieval. The goal of an effective chunking process is to ensure that each
+chunk encapsulates a focused topic, which enhances the accuracy of retrieval
+and ultimately leads to better answers. At the same time, the Docs Agent team
+acknowledges the importance of a flexible approach that allows for
+customization based on specific datasets and use cases.
+
+Key characteristics in Docs Agent’s chunking process include:
+
+- **Docs Agent splits documents based on Markdown headings.** However,
+  this approach has limitations, especially when dealing with large sections.
+- **Docs Agent chunks are smaller than 5000 bytes (characters).** This size
+  limit is set by the embedding model used in generating embeddings.
+- **Docs Agent enhances chunks with additional metadata.** The metadata helps
+  Docs Agent to execute operations efficiently, such as preventing duplicate
+  chunks in databases and deleting obsolete chunks that are  no longer
+  present in the source.
+- **Docs Agent retrieves the top 5 chunks and displays the top chunk's URL.**
+  However, this is adjustable in Docs Agent’s configuration (see the `widget`
+  and `experimental` app modes).
+
+The Docs Agent team continues to explore various optimizations to enhance
+the functionality and effectiveness of the chunking process. These efforts
+include refining the chunking algorithm itself and developing advanced
+post-processing techniques, for instance, reconstructing chunks to original
+documents after retrieval.
+
+Additionally, the team has been exploring methods for co-optimizing content
+structure and chunking strategies, which aims to maximize retrieval
+effectiveness by ensuring the structure of the source document itself
+complements the chunking process.
+
+## Chunks retrieval
+
+Docs Agent employs two distinct approaches for storing and retrieving chunks:
+
+- **The local database approach uses a [Chroma][chroma] vector database.**
+  This approach grants greater control over the chunking and retrieval
+  process. This option is recommended for development and experimental
+  setups.
+- **The online corpus approach uses Gemini’s
+  [Semantic Retrieval API][semantic-retrieval].** This approach provides
+  the advantages of centrally hosted online databases, ensuring
+  accessibility for all users throughout the organization. This approach
+  has some drawbacks, as control is reduced because the API may dictate
+  how chunks are selected and where customization can be applied.
+
+Choosing between these approaches depends on the specific needs of the user’s
+deployment situation, which is to balance control and transparency against
+possible improvements in performance, broader reach and ease of use.
+
+<!-- Reference links -->
+
+[rag]: concepts.md
+[chroma]: https://docs.trychroma.com/
+[semantic-retrieval]: https://ai.google.dev/gemini-api/docs/semantic_retrieval
diff --git a/examples/gemini/python/docs-agent/docs/cli-reference.md b/examples/gemini/python/docs-agent/docs/cli-reference.md
new file mode 100644
index 000000000..b6b945740
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs/cli-reference.md
@@ -0,0 +1,358 @@
+# Docs Agent CLI reference
+
+This page provides a list of the Docs Agent command lines and their usages
+and examples.
+
+The Docs Agent CLI helps developers to manage the Docs Agent project and
+interact with language models. It can handle various tasks such as
+processing documents, populating vector databases, launching the chatbot,
+running benchmark test, sending prompts to language models, and more.
+
+**Important**: All `agent` commands need to run in the `poetry shell`
+environment.
+
+## Processing documents
+
+### Chunk Markdown files into small text chunks
+
+The command below splits Markdown files (and other source files) into small
+chunks of plain text files:
+
+```sh
+agent chunk
+```
+
+### Populate a vector database using text chunks
+
+The command below populates a vector database using plain text files (created
+by running the `agent chunk` command):
+
+```sh
+agent populate
+```
+
+### Populate a vector database and delete stale text chunks
+
+The command below deletes stale entries in the existing vector database
+before populating it with the new text chunks:
+
+```sh
+agent populate --enable_delete_chunks
+```
+
+### Show the Docs Agent configuration
+
+The command below prints all the fields and values in the current
+[`config.yaml`][config-yaml] file:
+
+```sh
+agent show-config
+```
+
+### Clean up the Docs Agent development environment
+
+The command below deletes development databases specified in the
+`config.yaml` file:
+
+```sh
+agent cleanup-dev
+```
+
+### Write logs to a CSV file
+
+The command below writes the summaries of all captured debugging information
+(in the `logs/debugs` directory) to  a `.csv` file:
+
+```sh
+agent write-logs-to-csv
+```
+
+## Launching the chatbot web app
+
+### Launch the Docs Agent web app
+
+The command below launches Docs Agent's Flask-based chatbot web application:
+
+```sh
+agent chatbot
+```
+
+### Launch the Docs Agent web app using a different port
+
+The command below launches the Docs Agent web app to run on port 5005:
+
+```sh
+agent chatbot --port 5005
+```
+
+### Launch the Docs Agent web app as a widget
+
+The command below launches the Docs Agent web app to use
+a widget-friendly template:
+
+```sh
+agent chatbot --app_mode widget
+```
+
+### Launch the Docs Agent web app in full mode
+
+The command below launches the Docs Agent web app to use
+a special template that uses three Gemini models (AQA, Gemini 1.5,
+and Gemini 1.0):
+
+```sh
+agent chatbot --app_mode full
+```
+
+### Launch the Docs Agent web app with a log view enabled
+
+The command below launches the Docs Agent web app while enabling
+a log view page (which is accessible at `<APP_URL>/logs`):
+
+```sh
+agent chatbot --enable_show_logs
+```
+
+## Running benchmark test
+
+### Run the Docs Agent benchmark test
+
+The command below runs benchmark test using the questions and answers listed
+in the [`benchmarks.yaml`][benchmarks-yaml] file:
+
+```sh
+agent benchmark
+```
+
+## Interacting with language models
+
+### Ask a question
+
+The command below reads a question from the arguments, asks the Gemini model,
+and prints its response:
+
+```sh
+agent tellme <QUESTION>
+```
+
+Replace `QUESTION` with a question written in plain English, for example:
+
+```sh
+agent tellme does flutter support material design 3?
+```
+
+**Note**: This `agent tellme` command is used to set up the `gemini` command
+in the [Set up Docs Agent CLI][set-up-docs-agent-cli] guide.
+
+### Ask a question to a specific product
+
+The command below enables you to ask a question to a specific product in your
+Docs Agent setup:
+
+```sh
+agent tellme <QUESTION> --product <PRODUCT>
+```
+
+The example below asks the question to the `Flutter` product in your
+Docs Agent setup:
+
+```sh
+agent tellme which modules are available? --product=Flutter
+```
+
+You may also specify multiple products, for example:
+
+```sh
+agent tellme which modules are available? --product=Flutter --product=Angular --product=Android
+```
+
+### Ask for advice
+
+The command below reads a request and a filename from the arguments,
+asks the Gemini model, and prints its response:
+
+```sh
+agent helpme <REQUEST> --file <PATH_TO_FILE>
+```
+
+Replace `REQUEST` with a prompt and `PATH_TO_FILE` with a file's
+absolure or relative path, for example:
+
+```sh
+agent helpme write comments for this C++ file? --file ../my-project/test.cc
+```
+
+### Ask for advice using RAG
+
+The command below uses a local or online vector database (specified in
+the `config.yaml` file) to retrieve relevant context for the request:
+
+```sh
+agent helpme <REQUEST> --file <PATH_TO_FILE> --rag
+```
+
+### Ask for advice in a session
+
+The command below starts a new session (`--new`), which tracks responses,
+before running the `agent helpme` command:
+
+```sh
+agent helpme <REQUEST> --file <PATH_TO_FILE> --new
+```
+
+For example:
+
+```sh
+agent helpme write a draft of all features found in this README file? --file ./README.md --new
+```
+
+After starting a session, use the `--cont` flag to include the previous
+responses as context to the request:
+
+```sh
+agent helpme <REQUEST> --cont
+```
+
+For example:
+
+```sh
+agent helpme write a concept doc that delves into more details of these features? --cont
+```
+
+### Print the context in the current session
+
+The command below prints the questions, files, and responses that
+are being used as context in the current session:
+
+```sh
+agent show-session
+```
+
+### Ask the model to perform the request to each file in a directory
+
+The command below applies the request to each file found in the
+specified directory:
+
+```sh
+agent helpme <REQUEST> --perfile <PATH_TO_DIRECTORY>
+```
+
+For example:
+
+```sh
+agent helpme explain what this file does? --perfile ~/my-project --new
+```
+
+### Ask the model to include all files in a directory as context
+
+The command below includes all files found in the specified directory
+as context to the request:
+
+```sh
+agent helpme <REQUEST> --allfiles <PATH_TO_DIRECTORY>
+```
+
+For example:
+
+```sh
+agent helpme write a concept doc covering all features in this project? --allfiles ~/my-project --new
+```
+
+### Ask the model to run a pre-defined chain of prompts
+
+The command below runs a task (a sequence of prompts) defined in
+a `.yaml` file stored in the [`tasks`][tasks-dir] directory:
+
+```sh
+agent runtask --task <TASK>
+```
+
+For example:
+
+```sh
+agent runtask --task DraftReleaseNotes
+```
+
+### View the list of available Docs Agent tasks
+
+To see the list of all tasks available in your project, run
+`agent runtask` without any arguments:
+
+```sh
+agent runtask
+```
+
+### Ask the model to run a task using custom input
+
+If a task script has a `<INPUT>` placeholder, you can provide
+a custom input string to the task:
+
+```sh
+agent runtask --task <TASK> --custom_input <INPUT_STRING>
+```
+
+For example:
+
+```sh
+agent runtask --task IndexPageGenerator --custom_input ~/my_example/docs/development/
+```
+
+## Managing online corpora
+
+### List all existing online corpora
+
+The command below prints the list of all existing online corpora created
+using the [Semantic Retrieval API][semantic-api]:
+
+```sh
+agent list-corpora
+```
+
+### Share an online corpora with a user
+
+The command below enables `user01@gmail.com` to read text chunks stored in
+`corpora/example01`:
+
+```sh
+agent share-corpus --name corpora/example01 --user user01@gmail.com --role READER
+```
+
+The command below enables `user01@gmail.com` to read and write to
+`corpora/example01`:
+
+```sh
+agent share-corpus --name corpora/example01 --user user01@gmail.com --role WRITER
+```
+
+### Share an online corpora with everyone
+
+The command below enables `EVERYONE` to read text chunks stored in
+`corpora/example01`:
+
+```sh
+agent open-corpus --name corpora/example01
+```
+
+### Remove a user permission from an online corpora
+
+The command below remove an existing user permission set in `corpora/example01`:
+
+```sh
+agent remove-corpus-permission --name corpora/example01/permissions/123456789123456789
+```
+
+### Delete an online corpora
+
+The command below deletes an online corpus:
+
+```sh
+agent delete-corpus --name corpora/example01
+```
+
+<!-- Reference links -->
+
+[config-yaml]: ../config.yaml
+[benchmarks-yaml]: ../docs_agent/benchmarks/benchmarks.yaml
+[set-up-docs-agent-cli]: ../docs_agent/interfaces/README.md
+[semantic-api]: https://ai.google.dev/docs/semantic_retriever
+[tasks-dir]: ../tasks
diff --git a/examples/gemini/python/docs-agent/docs/concepts.md b/examples/gemini/python/docs-agent/docs/concepts.md
new file mode 100644
index 000000000..c8cfb53ac
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs/concepts.md
@@ -0,0 +1,376 @@
+# Docs Agent concepts
+
+**Note**: If you want to set up and launch the Docs Agent chat app on your host machine,
+see the [Set up Docs Agent][set-up-docs-agent] section in README.
+
+This page describes the architecture and features of Docs Agent.
+
+## Overview
+
+The Docs Agent chat app is designed to be easily set up and configured in a Linux environment.
+and require that you have access to Google’s [Gemini API][genai-doc-site].
+
+Docs Agent uses a technique known as Retrieval Augmented Generation (RAG), which allows
+you to bring your own documents as knowledge sources to AI language models. This approach
+helps the AI language models to generate relevant and accurate responses that are grounded
+in the information that you provide and control.
+
+![Docs Agent architecture](./images/docs-agent-architecture-01.png)
+
+**Figure 1**. Docs Agent uses a vector database to retrieve context for augmenting prompts.
+
+## Main features
+
+The key features of the Docs Agent chat app are:
+
+- Add contextual information to user questions to augment prompts for AI language models.
+- Process documents into embeddings and store them in a vector database for semnatic retrieval.
+
+![Docs Agent flow](./images/docs-agent-architecture-02.png)
+
+**Figure 2**. A user question is augmented by the Docs Agent server and passed to an LLM.
+
+For the moment, the Docs Agent project focuses on providing Python scripts that make it
+easy to process Markdown files into embeddings. However, there is no hard requirement that the
+source documents must exist in Markdown format. What’s important is that the processed content
+is available as embeddings in the vector database.
+
+### Structure of a prompt to a language model
+
+To enable an LLM to answer questions that are not part of the public knowledge (which the LLM
+is likely trained on), the Docs Agent project applies a mixture of prompt engineering and
+embeddings techniques. That is, we process a set of documents (which contain domain specific
+knowledge) into embeddings and store them in a vector database. This vector database allows
+the Docs Agent server to perform semantic search on stored embeddings to find the most relevant
+content from the source documents given user questions.
+
+Once the most relevant content is returned, the Docs Agent server uses the prompt structure
+shown in Figure 3 to augment the user question with a preset **condition** and a list of
+**context**. (When the Docs Agent server starts, the condition value is read from the
+[`config.yaml`][config-yaml] file.) Then the Docs Agent server sends this prompt to a
+language model using the Gemini API and receives a response generated by the model.
+
+![Docs Agent prompt strcture](./images/docs-agent-prompt-structure-01.png)
+
+**Figure 3**. Prompt structure for augmenting a user question with related context
+(Context source: [eventhorizontelescope.org][context-source-01])
+
+### Processing of Markdown files into embeddings
+
+To process information into embeddings using the Python scripts in the project, the
+information needs to be stored in Markdown format. Once you have a set of Markdown files
+stored in a directory on your host machine,  you can run the
+[`files_to_plain_text.py`][files-to-plain-text] script to process those Markdown
+files into small plain text files – the script splits the content by the top three Markdown
+headers (`#`, `##`, and `###`).
+
+Once Markdown files are processed into small plain text files, you can run the
+[`populate_vector_database.py`][populate-vector-database] script to generate embeddings
+for each text file and store those embeddings into a [Chroma][chroma-docs] vector database
+running on the host machine.
+
+The embeddings in this vector database enable the Docs Agent server to perform semantic search
+and retrieve context related to user questions for augmenting prompts.
+
+For more information on the processing of Markdown files, see the [`README`][scripts-readme]
+file in the `scripts` directory.
+
+![Document to embeddings](./images/docs-agent-embeddings-01.png)
+
+**Figure 4**. A document is split into small semantic chunks, which are then used to generate
+embeddings.
+
+![Markdown to embeddings](./images/docs-agent-embeddings-02.png)
+
+**Figure 5**. A Markdown page is split by headers and processed into embeddings.
+
+## Summary of tasks and features
+
+The following list summarizes the tasks and features of the Docs Agent chat app:
+
+- **Process Markdown**: Split Markdown files into small plain text files. (See the
+  Python scripts in the [`preprocess`][preprocess-dir] directory.)
+- **Generate embeddings**: Use an embedding model to process small plain text files
+  into embeddings, and store them in a vector database. (See the
+  [`populate_vector_database.py`][populate-vector-database] script.)
+- **Perform semantic search**: Compare embeddings in the vector database to retrieve
+  most relevant content given user questions.
+- **Add context to a user question**: Add a list of text chunks returned from
+  a semantic search as context in a prompt.
+- **(Experimental) “Fact-check” responses**: This experimental feature composes
+  a follow-up prompt and asks the language model to “fact-check” its own previous response.
+  (See the [Using a language model to fact-check its own response][fact-check-section]
+  section.)
+- **Generate related questions**: In addition to displaying a response to the user
+  question, the web UI displays 5 questions generated by the language model based on
+  the context of the user question. (See the
+  [Using a language model to suggest related questions][related-questions-section]
+  section.)
+- **Return URLs of documentation sources**: Docs Agent's vector database stores URLs
+  as metadata next to embeddings. Whenever the vector database is used to retrieve
+  text chunks for context, the database can also return the URLs of the sources used
+  to generate the embeddings.
+- **Collect feedback from users**: Docs Agent's chatbot web UI includes buttons that
+  allow users to [like generated responses][like-generated-responses] or
+  [submit rewrites][submit-a-rewrite].
+- **Convert Google Docs, PDF, and Gmail into Markdown files**: This feature uses
+  Apps Script to convert Google Docs, PDF, and Gmail into Markdown files, which then
+  can be used as input datasets for Docs Agent. (See the
+  [`apps_script`][apps-script-readme] directory.)
+- **Run benchmark test to monitor the quality of AI-generated responses**: Using
+  Docs Agent, you can run [benchmark test][benchmark-test] to measure and compare
+  the quality of text chunks, embeddings, and AI-generated responses.
+- **Use the Semantic Retrieval API and AQA model**: You can use Gemini's
+  [Semantic Retrieval API][semantic-api] to upload source documents to an online
+  corpus and use the [AQA model][aqa-model] that is specifically created for answering
+  questions using an online corpus.
+
+## Flow of events
+
+The following events take place in the Docs Agent chat app:
+
+1. The [`files_to_plain_text.py`][files-to-plain-text] script converts input
+   Markdown documents into small plain text files, split by Markdown headings
+   (`#`, `##`, and `###`).
+2. The [`populate_vector_database.py`][populate-vector-database] script generates
+   embeddings from the small plain text files and populates a vector database.
+3. When the [`agent chatbot`] command is run, it starts the Docs Agent server and
+   vector database, which loads generated embeddings and metadata (URLs and filenames)
+   stored in the `vector_store` directory.
+4. When the user asks a question, the Docs Agent server uses the vector database to
+   perform semantic search on embeddings, which represent content in the source
+   documents.
+5. Using this semantic search capability, the Docs Agent server finds a list of
+   text chunks that are most relevant to the user question.
+6. The Docs Agent server adds this list of text chunks as context (plus a condition
+   for responses) to the user question and constructs them into a prompt.
+7. The system sends the prompt to a language model via the Gemini API.
+8. The language model generates a response and the Docs Agent server renders it on
+   the chat UI.
+
+Additional events for [“fact-checking” a generated response][fact-check-section]:
+
+9. The Docs Agent server prepares another prompt that compares the generated response
+   (in step 8) to the context (in step 6) and asks the language model to look for
+   a discrepancy in the response.
+10. The language model generates a response that points out one major discrepancy
+    (if it exists) between its previous response and the context.
+11. The Docs Agent server renders this response on the chat UI as a call-out note.
+12. The Docs Agent server passes this second response to the vector database to
+    perform semantic search.
+13. The vector database returns a list of relevant content (that is closely related
+    to the second response).
+14. The Docs Agent server renders the top URL of this list on the chat UI and
+    suggests that the user checks out this URL for fact-checking.
+
+Additional events for
+[suggesting 5 questions related to the user question][related-questions-section]:
+
+15. The Docs Agent server prepares another prompt that asks the language model to
+    generate 5 questions based on the context (in step 6).
+16. The language model generates a response that contains a list of questions related
+    to the context.
+17. The Docs Agent server renders the questions on the chat UI.
+
+## Supplementary features
+
+This section describes additional features implemented on the Docs Agent chat app for
+enhancing the usability of the Q&A experience powered by generative AI.
+
+![Docs Agent UI](./images/docs-agent-ui-screenshot-01.png)
+
+**Figure 6**. A screenshot of the Docs Agent chat UI showing the sections generated by
+three distinct prompts.
+
+### Using a language model to fact-check its own response
+
+In addition to using the prompt structure above (shown in Figure 3), we‘re currently
+experimenting with the following prompt setup for “fact-checking” responses generated
+by the language model:
+
+- Condition:
+
+  ```
+  You are a helpful chatbot answering questions from users. Read the following context
+  first and answer the question at the end:
+  ```
+
+- Context:
+
+  ```
+  <CONTEXT_USED_IN_THE_PREVIOUS_PROMPT>
+  ```
+
+- Additional condition (for fact-checking):
+
+  ```
+  Can you compare the text below to the information provided in this prompt above
+  and write a short message that warns the readers about which part of the text they
+  should consider fact-checking? (Please keep your response concise and focus on only
+  one important item.)"
+  ```
+
+- Previously generated response
+
+  ```
+  Text: <RESPONSE_RETURNED_FROM_THE_PREVIOUS_PROMPT>
+  ```
+
+This "fact-checking" prompt returns a response similar to the following example:
+
+```
+The text states that Flutter chose to use Dart because it is a fast, productive, object-oriented
+language that is well-suited for building user interfaces. However, the context provided in the
+prompt states that Flutter chose Dart because it is a fast, productive language that is well-suited
+for Flutter's problem domain: creating visual user experiences. Therefore, readers should consider
+fact-checking the claim that Dart is well-suited for building user interfaces.
+```
+
+After the second response, notice that the Docs Agent chat UI also suggests a URL to visit for
+fact-checking  (see Figure 6), which looks similar to the following example:
+
+```
+To verify this information, please check out:
+
+https://docs.flutter.dev/resources/faq
+```
+
+To identify this URL, the Docs Agent server takes the second response (which is the paragraph that
+begins with “The text states that ...” in the example above) and uses it to query the vector
+database. Once the vector database returns a list of the most relevant content to this response,
+the UI only displays the top URL to the user.
+
+Keep in mind that this "fact-checking" prompt setup is currently considered **experimental**
+because we‘ve seen cases where a language model would end up adding incorrect information into its
+second response as well. However, we saw that adding this second response (which brings attention
+to the language model’s possible hallucinations) seems to improve the usability of the system since it
+serves as a reminder to the users that the language model‘s response is far from being perfect, which
+helps encourage the users to take more steps to validate generated responses for themselves.
+
+### Using a language model to suggest related questions
+
+The project‘s latest web UI includes the “Related questions” section, which displays five
+questions that are related to the user question (see Figure 6). These five questions are also
+generated by a language model (via the Gemini API). Using the list of contents returned from the vector
+database as context, the system prepares another prompt asking the language model to generate five
+questions from the included context.
+
+The following is the exact structure of this prompt:
+
+- Condition:
+
+  ```
+  Read the context below and answer the question at the end:
+  ```
+
+- Context:
+
+  ```
+  <CONTEXT_USED_IN_THE_PREVIOUS_PROMPT>
+  ```
+
+- Question:
+
+  ```
+  What are 5 questions developers might ask after reading the context?
+  ```
+
+### Enabling users to submit a rewrite of a generated response
+
+The project‘s latest web UI includes the **Rewrite this response** button at the bottom of
+the panel (see Figure 6). When this button is clicked, a widget opens up, expanding the
+main UI panel, and reveals a textarea containing the generated response to the user's question.
+The user is then allowed to edit this response in the textarea and click the **Submit** button
+to submit the updated response to the system.
+
+The system stores the submitted response as a Markdown file in the project's local `rewrites`
+directory. The user may re-click the **Submit** button to update the submitted rewrite multiple
+times.
+
+### Enabling users to like generated responses
+
+The project's latest web UI includes the **Like this response** button at the bottom of the panel
+(see Figure 6). When this button is clicked, the server logs the event of "like" for the response.
+However, clicking the **Liked** button again will reset the button. Then the server logs this reset
+event of "like" for the response.
+
+The user may click this like button multiple times to toggle the state of the like button. But when
+examining the logs, only the final state of the like button will be considered for the response.
+
+### Using Google Docs, PDF, or Gmail as input sources
+
+The project includes Apps Script files that allow you to convert various sources of content
+(including Google Docs and PDF) from your Google Drive and Gmail into Markdown files. You can then
+use these Markdown files as additional input sources for Docs Agent. For more information, see the
+[`README`][apps-script-readme] file in the `apps_script` directory.
+
+![Docs Agent pre-processing flow](./images/docs-agent-pre-processing-01.png)
+
+**Figure 7**. Docs Agent's pre-processing flow for various doc types.
+
+### Using the Semantic Retrieval API and AQA model
+
+Docs Agent provides options to use Gemini's [Semantic Retrieval API][semantic-api] for storing text
+chunks in Google Cloud's online storage (and using this online storage for context retrieval),
+in combination with using the [AQA model][aqa-model] for question-answering.
+
+To use the Semantic Retrieval API, update the `config.yaml` file to the following settings:
+
+```
+models:
+  - language_model: "models/aqa"
+
+...
+
+db_type: "google_semantic_retriever"
+```
+
+The setup above uses both the Semantic Retrieval API and the AQA model.
+
+**Note**: At the moment, when `db_type` is set to `google_semantic_retriever`, running the
+`populate_vector_database.py` script will also create and popluate a local vector database using
+Chroma as well as creating and populating an online corpus using the Semantic Retrieval API.
+
+However, if you want to use only the AQA model without using an online corpus, update the
+`config.yaml` file to the following settings instead:
+
+```
+models:
+  - language_model: "models/aqa"
+
+...
+
+db_type: "chroma"
+```
+
+The setup above uses the AQA model with your local Chroma vector database. For more information,
+see the [More Options: AQA Using Inline Passages][inline-passages] section on the
+_Semantic Retriever Quickstart_ page.
+
+**Note**: To use the Semantic Retrieval API, you need to complete the OAuth setup for your Google
+Cloud project from your host machine. For detailed instructions, see the
+[Authentication with OAuth quickstart][oauth-quickstart] page.
+
+<!-- Reference links -->
+
+[set-up-docs-agent]: ../README.md#set-up-docs-agent
+[files-to-plain-text]: ../docs_agent/preprocess/files_to_plain_text.py
+[populate-vector-database]: ../docs_agent/preprocess/populate_vector_database.py
+[context-source-01]: http://eventhorizontelescope.org
+[fact-check-section]: #using-a-language-model-to-fact_check-its-own-response
+[related-questions-section]: #using-a-language-model-to-suggest-related-questions
+[submit-a-rewrite]: #enabling-users-to-submit-a-rewrite-of-a-generated-response
+[like-generated-responses]: #enabling-users-to-like-generated-responses
+[populate-db-steps]: #populate-a-new-vector-database-from-markdown-files
+[genai-doc-site]: https://ai.google.dev/docs/gemini_api_overview
+[chroma-docs]: https://docs.trychroma.com/
+[apps-script-readme]: ../apps_script/README.md
+[scripts-readme]: ../docs_agent/preprocess/README.md
+[config-yaml]: ../config.yaml
+[benchmark-test]: ../docs_agent/benchmarks/README.md
+[semantic-api]: https://ai.google.dev/docs/semantic_retriever
+[aqa-model]: https://ai.google.dev/models/gemini#model_variations
+[oauth-quickstart]: https://ai.google.dev/docs/oauth_quickstart
+[inline-passages]: https://ai.google.dev/docs/semantic_retriever#more_options_aqa_using_inline_passages
+[authorize-credentials]: https://ai.google.dev/docs/oauth_quickstart#authorize-credentials
+[preprocess-dir]: ../docs_agent/preproces/
diff --git a/examples/gemini/python/docs-agent/docs/config-reference.md b/examples/gemini/python/docs-agent/docs/config-reference.md
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@@ -0,0 +1,152 @@
+# Docs Agent configuration reference
+
+This page provides a list of additional options that can be specified
+in the Docs Agent configuration file ([`config.yaml`][config-yaml]).
+
+## Web application options
+
+### app_port
+
+This field sets the port which the Docs Agent web app runs on.
+
+```
+app_port: 5001
+```
+
+By default, the web app is set to use port 5000.
+
+### app_mode
+
+This field controls the user interface mode of the web app.
+
+The options are:
+
+* `widget`: This mode launches a compact widget-style interface, suitable
+  for being embedded within a webpage.
+
+  ```
+  app_mode: "widget"
+  ```
+
+* `full`: This special mode is designed to be used with Gemini 1.5 models.
+
+  ```
+  app_mode: "full"
+  ```
+
+When this field is not specified, the web app is set to use the standard mode.
+
+## User feedback options
+
+### feedback_mode
+
+This field sets the type of feedback mechanism available to users for providing
+the quality or relevance of responses.
+
+The options are:
+
+* `feedback`: This is the default setting.
+
+  ```
+  feedback_mode: "feedback"
+  ```
+
+* `rewrite`: This option provides the "Rewrite this response" button to allows
+  users to suggest alternative responses.
+
+  ```
+  feedback_mode: "rewrite"
+  ```
+
+* `like_and_dislike`: This option provides simple "Like" and "Dislike" buttons.
+
+  ```
+  feedback_mode: "like_and_dislike"
+  ```
+
+## Logging options
+
+### log_level
+
+This field controls the level of detail captured in the logs generated by Docs
+Agent.
+
+Setting it to `VERBOSE` provides more comprehensive logging information:
+
+```
+log_level: "VERBOSE"
+```
+
+This field is set to `NORMAL` by default.
+
+### enable_show_logs
+
+Setting this field to `"True"` allows logs to be displayed on a web browser
+(which is accessible at `<APP_URL>/logs`):
+
+```
+enable_show_logs: "True"
+```
+
+### enable_logs_to_markdown
+
+Setting this field to `"True"` saves the generated answers as Markdown pages
+on the host machine:
+
+```
+enable_logs_to_markdown: "True"
+```
+
+### enable_logs_for_debugging
+
+Setting this field to `"True"` generates detailed logs for debugging purposes:
+
+```
+enable_logs_for_debugging: "True"
+```
+
+## Database management options
+
+### enable_delete_chunks
+
+Setting this field to `"True"` enables the ability to delete outdated, stale
+text chunks from the vector databases:
+
+```
+enable_delete_chunks: "True"
+```
+
+## Secondary database configuration
+
+Docs Agent allows for the use of a secondary database alongside the primary one
+for providing additional context from a different source.
+
+### secondary_db_type
+
+This field specifies the type of secondary database to be used:
+
+```
+secondary_db_type: "google_semantic_retrieval"
+```
+
+or
+
+```
+secondary_db_type: "chroma"
+```
+
+When `chroma` is specified, the collection in the `vector_stores/chroma`
+directory is used as the secondary database.
+
+### secondary_corpus_name
+
+This field defines the name of the corpus for the secondary database,
+for example:
+
+```
+secondary_corpus_name: "corpora/my-example-corpus"
+```
+
+<!-- Reference link -->
+
+[config-yaml]: ../config.yaml
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diff --git a/demos/palm/python/docs-agent/docs/images/docs-agent-ui-screenshot-01.png b/examples/gemini/python/docs-agent/docs/images/docs-agent-ui-screenshot-01.png
similarity index 100%
rename from demos/palm/python/docs-agent/docs/images/docs-agent-ui-screenshot-01.png
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diff --git a/examples/gemini/python/docs-agent/docs/whats-new.md b/examples/gemini/python/docs-agent/docs/whats-new.md
new file mode 100644
index 000000000..7794324ac
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs/whats-new.md
@@ -0,0 +1,100 @@
+# What's new in Docs Agent
+
+## April 2024
+
+* **Focus: Feature enhancements and usability improvements**
+* Expanded CLI functionality with options for managing online corpora and interacting with files.
+* Addressed bug fixes and performed code refactoring for improved stability and maintainability.
+* Added a new chat app template specifically designed for the **Gemini 1.5 model**.
+* Updated GenAI SDK version to `0.5.0`.
+* Introduced a splitter for handling of Fuchsia’s FIDL protocol files in the preprocessing stage.
+
+## March 2024
+
+* **Milestone: Introduction of the Docs Agent CLI**
+* Added the `tellme` command for direct interaction with Gemini from a Linux terminal.
+* Expanded CLI options for corpora management, including creation, deletion, and permission control.
+* Enhanced the chat app UI with a "loading" animation and probability-based response pre-screening.
+* Enabled displaying more URLs retrieved from the AQA model in the widget mode.
+* Added support for including URLs as metadata when uploading chunks to online corpora.
+
+## February 2024
+
+* **Focus: Refining AQA model integration**
+* Improved UI rendering of AQA model responses, especially for code segments.
+* Addressed bug fixes to handle unexpected AQA model responses.
+* Generated related questions by using retrieved context instead of a user question.
+* Started logging `answerable_probability` for AQA model responses.
+
+## January 2024
+
+* **Milestone: Docs Agent uses AQA model and Semantric Retrieval API**
+* Started Logs Agent experiments
+* Benchmark score up ~2.5% with enhancements to embeddings
+
+## December 2023
+
+* **Milestone: Docs Agent uses Gemini model.**
+* Prototyping benchmarking: documentation unit tests.
+* Steady traffic since launch, 861 weekly views, December 14.
+
+## November 2023
+
+* Experimented with context reconstruction.
+* Docs Agent now parsing code blocks.
+* Added new condition using a mixture of best practices to improve answers.
+* Added chunking by tokenization.
+
+## October 2023
+
+* **Milestone: Docs Agent supports Google docs, PDFs, and emails.**
+* Drafted Docs Agent security strategy.
+* Drafted Docs Agent + Talking Character design doc.
+* Top of the charts for generative AI samples: 1216 weekly views.
+* Build for AI series: 16000 watches.
+
+## September 2023
+
+* First open-source feature request: support Google docs.
+* **Milestone: Docs Agent published!**
+* Recorded Build for AI series.
+* Implemented hashing to check existing entries and only generate embeddings for
+  new or updated content.
+
+## August 2023
+
+* Docs Agent demo running with Flutter docs.
+* Docs Agent gets necessary approvals for open-sourcing.
+* Special mention: Docs Agent gets it's name.
+* Added support to read frontmatter, starting with titles.
+
+## July 2023
+
+* Light month, as many of us took vacations :).
+* Created `opensource` branch on internal repo for open-source pushes.
+* Reviewed video script for Build for AI series.
+* Security: meeting on using open-source content and security issues.
+
+## June 2023
+
+* Drafted Docs Agent Readme.
+* Created internal repo to set up infrastructure for open-source pushes.
+* First internal customer tried Docs Agent.
+* Compiled list of Todos to open-source Docs Agent.
+
+## May 2023
+
+* Switched from chunking content based on 3000-char limit to chunking by
+  headings.
+* Cleaned up Markdown processing issues.
+* Privacy: clarified in UI how we are using data.
+* Attempted to create a chat bot for Google chat.
+* Added database admin console.
+* Partially implemented rewrite option.
+* Added related questions.
+
+## April 2023
+
+* Created new UI for chat app: Flask app.
+* Added 'fact-checking' section.
+* **Milestone: started the Docs Agent open-source project.**
diff --git a/demos/palm/python/docs-agent/vector_stores/.gitkeep b/examples/gemini/python/docs-agent/docs_agent/agents/__init__.py
similarity index 100%
rename from demos/palm/python/docs-agent/vector_stores/.gitkeep
rename to examples/gemini/python/docs-agent/docs_agent/agents/__init__.py
diff --git a/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py b/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
new file mode 100644
index 000000000..5eb83c459
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
@@ -0,0 +1,581 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Docs Agent"""
+
+import typing
+
+from absl import logging
+import google.api_core
+import google.ai.generativelanguage as glm
+from chromadb.utils import embedding_functions
+
+from docs_agent.storage.chroma import ChromaEnhanced
+
+from docs_agent.models.google_genai import Gemini
+
+from docs_agent.utilities.config import ProductConfig, Models
+from docs_agent.preprocess.splitters import markdown_splitter
+
+from docs_agent.preprocess.splitters.markdown_splitter import Section as Section
+from docs_agent.postprocess.docs_retriever import SectionDistance as SectionDistance
+from docs_agent.postprocess.docs_retriever import (
+    SectionProbability as SectionProbability,
+)
+
+
+class DocsAgent:
+    """DocsAgent class"""
+
+    # Temporary parameter of init_chroma
+    def __init__(
+        self,
+        config: ProductConfig,
+        init_chroma: bool = True,
+        init_semantic: bool = True,
+    ):
+        # Models settings
+        self.config = config
+        self.language_model = str(self.config.models.language_model)
+        self.embedding_model = str(self.config.models.embedding_model)
+        self.api_endpoint = str(self.config.models.api_endpoint)
+
+        # Initialize the default Gemini model.
+        if self.language_model.startswith("models/gemini"):
+            self.gemini = Gemini(
+                models_config=config.models, conditions=config.conditions
+            )
+            self.context_model = self.language_model
+
+        # Use the new chroma db for all queries
+        # Should make a function for this or clean this behavior
+        if init_chroma:
+            for item in self.config.db_configs:
+                if "chroma" in item.db_type:
+                    self.vector_db_dir = item.vector_db_dir
+                    self.collection_name = item.collection_name
+            self.chroma = ChromaEnhanced(self.vector_db_dir)
+            logging.info(
+                "Using the local vector database created at %s", self.vector_db_dir
+            )
+            self.collection = self.chroma.get_collection(
+                self.collection_name,
+                embedding_model=self.embedding_model,
+                embedding_function=embedding_function_gemini_retrieval(
+                    self.config.models.api_key, self.embedding_model
+                ),
+            )
+
+        # AQA model settings
+        if init_semantic:
+            # Except in "full" and "pro" modes, the semantic retriever option requires
+            # the AQA model. If not, exit the program.
+            if (
+                self.config.app_mode != "full"
+                and self.config.app_mode != "widget-pro"
+                and self.config.db_type == "google_semantic_retriever"
+            ):
+                if self.language_model != "models/aqa":
+                    logging.error(
+                        "The db_type `google_semnatic_retriever` option"
+                        + " requires the AQA model (`models/aqa`)."
+                    )
+                    exit(1)
+            # If the AQA model is selected or the web app is on "full" and "pro" modes.
+            if (
+                self.language_model == "models/aqa"
+                or self.config.app_mode == "full"
+                or self.config.app_mode == "widget-pro"
+            ):
+                # AQA model setup
+                self.generative_service_client = glm.GenerativeServiceClient()
+                self.retriever_service_client = glm.RetrieverServiceClient()
+                self.permission_service_client = glm.PermissionServiceClient()
+                # Start a Gemini model for other tasks
+                self.context_model = "models/gemini-pro"
+                gemini_model_config = Models(
+                    language_model=self.context_model,
+                    embedding_model=self.embedding_model,
+                    api_endpoint=self.api_endpoint,
+                )
+                self.gemini = Gemini(
+                    models_config=gemini_model_config, conditions=config.conditions
+                )
+            # If semantic retriever is selected as the main database.
+            if self.config.db_type == "google_semantic_retriever":
+                for item in self.config.db_configs:
+                    if "google_semantic_retriever" in item.db_type:
+                        self.corpus_name = item.corpus_name
+                        if item.corpus_display:
+                            self.corpus_display = item.corpus_display
+                        else:
+                            self.corpus_display = (
+                                self.config.product_name + " documentation"
+                            )
+                self.aqa_response_buffer = ""
+
+        # Always initialize the Gemini 1.0 pro model for other tasks.
+        gemini_pro_model_config = Models(
+            language_model="models/gemini-pro",
+            embedding_model=self.embedding_model,
+            api_endpoint=self.api_endpoint,
+        )
+        self.gemini_pro = Gemini(
+            models_config=gemini_pro_model_config, conditions=config.conditions
+        )
+
+        if self.config.app_mode == "full" or self.config.app_mode == "widget-pro":
+            # Initialize the Gemini 1.5 model for generating main responses.
+            gemini_15_model_config = Models(
+                language_model=self.language_model,
+                embedding_model=self.embedding_model,
+                api_endpoint=self.api_endpoint,
+            )
+            self.gemini_15 = Gemini(
+                models_config=gemini_15_model_config, conditions=config.conditions
+            )
+        else:
+            self.gemini_15 = self.gemini_pro
+
+    # Use this method for talking to a Gemini content model
+    def ask_content_model_with_context(self, context, question):
+        new_prompt = context + "\n\nQuestion: " + question
+        # Print the prompt for debugging if the log level is VERBOSE.
+        if self.config.log_level == "VERBOSE":
+            self.print_the_prompt(new_prompt)
+        try:
+            response = self.gemini.generate_content(new_prompt)
+        except google.api_core.exceptions.InvalidArgument:
+            return self.config.conditions.model_error_message
+        # for chunk in response:
+        #     if str(chunk.candidates[0].content) == "":
+        #         return self.config.conditions.model_error_message
+        return response
+
+    # Use this method for talking to Gemini's AQA model using inline passages
+    # answer_style can be VERBOSE, ABSTRACTIVE, or EXTRACTIVE
+    def ask_aqa_model_using_local_vector_store(
+        self,
+        question,
+        results_num: int = 5,
+        answer_style: str = "VERBOSE",
+    ):
+        user_query_content = glm.Content(parts=[glm.Part(text=question)])
+        verbose_prompt = "Question: " + question + "\n"
+        # Retrieves from chroma, using up to 30k tokens - max gemini model tokens
+        chroma_search_result, final_context = self.query_vector_store_to_build(
+            question=question,
+            token_limit=30000,
+            results_num=results_num,
+            max_sources=results_num,
+        )
+        # Create the grounding inline passages
+        grounding_passages = glm.GroundingPassages()
+        i = 0
+        aqa_search_result = []
+        for item in chroma_search_result:
+            returned_context = item.section.content
+            new_passage = glm.Content(parts=[glm.Part(text=returned_context)])
+            index_id = str("{:03d}".format(i + 1))
+            i += 1
+            grounding_passages.passages.append(
+                glm.GroundingPassage(content=new_passage, id=index_id)
+            )
+            verbose_prompt += "\nID: " + index_id + "\n" + returned_context + "\n"
+        req = glm.GenerateAnswerRequest(
+            model="models/aqa",
+            contents=[user_query_content],
+            inline_passages=grounding_passages,
+            answer_style=answer_style,
+        )
+        aqa_response = self.generative_service_client.generate_answer(req)
+        self.aqa_response_buffer = aqa_response
+        for item in chroma_search_result:
+            # Builds an object with sections + probability
+            aqa_search_result.append(
+                SectionProbability(
+                    section=item.section,
+                    probability=aqa_response.answerable_probability,
+                )
+            )
+        if self.config.log_level == "VERBOSE":
+            self.print_the_prompt(verbose_prompt)
+        elif self.config.log_level == "DEBUG":
+            self.print_the_prompt(verbose_prompt)
+            print(aqa_response)
+        try:
+            return aqa_response.answer.content.parts[0].text, aqa_search_result
+        except:
+            self.aqa_response_buffer = ""
+            return self.config.conditions.model_error_message, aqa_search_result
+
+    # Get the save response of the AQA model
+    def get_saved_aqa_response_json(self):
+        return self.aqa_response_buffer
+
+    # Retrieve the metadata dictionary from an AQA response grounding attribution entry
+    def get_aqa_response_metadata(self, aqa_response_item):
+        try:
+            chunk_resource_name = (
+                aqa_response_item.source_id.semantic_retriever_chunk.chunk
+            )
+            get_chunk_response = self.retriever_service_client.get_chunk(
+                name=chunk_resource_name
+            )
+            metadata = get_chunk_response.custom_metadata
+            final_metadata = {}
+            for m in metadata:
+                if m.string_value:
+                    value = m.string_value
+                elif m.numeric_value:
+                    value = m.numeric_value
+                else:
+                    value = ""
+                final_metadata[m.key] = value
+        except:
+            final_metadata = {}
+        return final_metadata
+
+    # Use this method for talking to Gemini's AQA model using a corpus
+    # Answer style can be "VERBOSE" or ABSTRACTIVE, EXTRACTIVE
+    def ask_aqa_model_using_corpora(
+        self, question, corpus_name: str = "None", answer_style: str = "VERBOSE"
+    ):
+        search_result = []
+        if corpus_name == "None":
+            corpus_name = self.corpus_name
+        # Prepare parameters for the AQA model
+        user_question_content = glm.Content(
+            parts=[glm.Part(text=question)], role="user"
+        )
+        # Settings to retrieve grounding content from semantic retriever
+        retriever_config = glm.SemanticRetrieverConfig(
+            source=corpus_name, query=user_question_content
+        )
+
+        # Ask the AQA model.
+        req = glm.GenerateAnswerRequest(
+            model="models/aqa",
+            contents=[user_question_content],
+            semantic_retriever=retriever_config,
+            answer_style=answer_style,
+        )
+
+        try:
+            aqa_response = self.generative_service_client.generate_answer(req)
+            self.aqa_response_buffer = aqa_response
+        except:
+            self.aqa_response_buffer = ""
+            return self.config.conditions.model_error_message, search_result
+
+        if self.config.log_level == "VERBOSE":
+            verbose_prompt = "[question]\n" + question + "\n"
+            verbose_prompt += (
+                "\n[answerable_probability]\n"
+                + str(aqa_response.answerable_probability)
+                + "\n"
+            )
+            for attribution in aqa_response.answer.grounding_attributions:
+                verbose_prompt += "\n[grounding_attributions]\n" + str(
+                    attribution.content.parts[0].text
+                )
+            self.print_the_prompt(verbose_prompt)
+        elif self.config.log_level == "DEBUG":
+            print(aqa_response)
+        try:
+            for item in aqa_response.answer.grounding_attributions:
+                metadata = self.get_aqa_response_metadata(item)
+                for part in item.content.parts:
+                    metadata["content"] = part.text
+                section = markdown_splitter.DictionarytoSection(metadata)
+                search_result.append(
+                    SectionProbability(
+                        section=section, probability=aqa_response.answerable_probability
+                    )
+                )
+            # Return the aqa_response object but also the actual text response
+            return aqa_response.answer.content.parts[0].text, search_result
+        except:
+            return self.config.conditions.model_error_message, search_result
+
+    def ask_aqa_model(self, question):
+        response = ""
+        if self.config.db_type == "google_semantic_retriever":
+            response = self.ask_aqa_model_using_corpora(question)
+        else:
+            response = self.ask_aqa_model_using_local_vector_store(question)
+        return response
+
+    # Retrieve and return chunks that are most relevant to the input question.
+    def retrieve_chunks_from_corpus(self, question, corpus_name: str = "None"):
+        if corpus_name == "None":
+            corpus_name = self.corpus_name
+        user_query = question
+        results_count = 5
+        # Quick fix: This was needed to allow the method to be called
+        # even when the model is not set to `models/aqa`.
+        retriever_service_client = glm.RetrieverServiceClient()
+        # Make the request
+        request = glm.QueryCorpusRequest(
+            name=corpus_name, query=user_query, results_count=results_count
+        )
+        query_corpus_response = retriever_service_client.query_corpus(request)
+        return query_corpus_response
+
+    # Use this method for asking a Gemini content model for fact-checking
+    def ask_content_model_to_fact_check(self, context, prev_response):
+        question = self.config.conditions.fact_check_question + "\n\nText: "
+        question += prev_response
+        return self.ask_content_model_with_context(context, question)
+
+    # Query the local Chroma vector database using the user question
+    def query_vector_store(self, question, num_returns: int = 5):
+        return self.collection.query(question, num_returns)
+
+    # Add specific instruction as a prefix to the context
+    def add_instruction_to_context(self, context):
+        new_context = ""
+        new_context += self.config.conditions.condition_text + "\n\n" + context
+        return new_context
+
+    # Add custom instruction as a prefix to the context
+    def add_custom_instruction_to_context(self, condition, context):
+        new_context = ""
+        new_context += condition + "\n\n" + context
+        return new_context
+
+    # Return true if the aqa model used in this Docs Agent setup
+    def check_if_aqa_is_used(self):
+        if (
+            self.config.models.language_model == "models/aqa"
+            or self.config.app_mode == "full"
+            or self.config.app_mode == "widget-pro"
+        ):
+            return True
+        else:
+            return False
+
+    # Return the chroma collection name
+    def return_chroma_collection(self):
+        try:
+            return self.collection_name
+        except:
+            return None
+
+    # Return the vector db name
+    def return_vector_db_dir(self):
+        try:
+            return self.vector_db_dir
+        except:
+            return None
+
+    # Print the prompt on the terminal for debugging
+    def print_the_prompt(self, prompt):
+        print("#########################################")
+        print("#              PROMPT                   #")
+        print("#########################################")
+        print(prompt)
+        print("#########################################")
+        print("#           END OF PROMPT               #")
+        print("#########################################")
+        print("\n")
+
+    # Query the local Chroma vector database. Starts with the number of results
+    # from results
+    # Results_num is the initial result set based on distance to the question
+    # Max_sources is the number of those results_num to use to build a final
+    # context page
+    def query_vector_store_to_build(
+        self,
+        question: str,
+        token_limit: float = 30000,
+        results_num: int = 10,
+        max_sources: int = 4,
+    ):
+        # Looks for contexts related to a question that is limited to an int
+        # Returns a list
+        contexts_query = self.collection.query(question, results_num)
+        # This returns a list of results
+        build_context = contexts_query.returnDBObjList()
+        # Use the token limit and distances to assign a token limit for each
+        # page. For time being split evenly into top max_sources
+        token_limit_temp = token_limit / max_sources
+        token_limit_per_source = []
+        i = 0
+        for i in range(max_sources):
+            token_limit_per_source.append(token_limit_temp)
+        same_document = ""
+        same_metadata = ""
+        # Each item is a chunk result along with all of it's metadata
+        # We can use metadata to identify if one of these chunks comes from the
+        # same page, potentially indicating a better match, so more token allocation
+        # You can see these objects contents with .content, .document, .distance, .metadata
+        plain_content = ""
+        search_result = []
+        same_pages = []
+        # For each result make a SectionDistance object that includes the
+        # Section along with it's distance from the question
+        for item in build_context:
+            # Check if this page was previously added as a source, to avoid
+            # duplicate count. These signals should be used to give a page higher token limits
+            # Make a page based on the section_id (this is where the search
+            # found a match)
+            section = SectionDistance(
+                section=Section(
+                    id=item.metadata.get("section_id", None),
+                    name_id=item.metadata.get("name_id", None),
+                    page_title=item.metadata.get("page_title", None),
+                    section_title=item.metadata.get("section_title", None),
+                    level=item.metadata.get("level", None),
+                    previous_id=item.metadata.get("previous_id", None),
+                    parent_tree=item.metadata.get("parent_tree", None),
+                    token_count=item.metadata.get("token_estimate", None),
+                    content=item.document,
+                    md_hash=item.metadata.get("md_hash", None),
+                    url=item.metadata.get("url", None),
+                    origin_uuid=item.metadata.get("origin_uuid", None),
+                ),
+                distance=item.distance,
+            )
+            search_result.append(section)
+            # From this you can run queries to find all chunks from the same page
+            # since they all share the same origin_uuid which is a hash of the
+            # original source file name
+        # Limits the number of results to go through
+        final_page_content = []
+        final_page_token = []
+        plain_token = 0
+        sources = []
+        final_pages = []
+        # Quick fix: Ensure max_sources is not larger than the array size of search_result.
+        this_range = len(search_result)
+        if this_range > max_sources:
+            this_range = max_sources
+        for i in range(this_range):
+            # The current section that is being built
+            # eval turns str representation of array into an array
+            curr_section_id = search_result[i].section.name_id
+            curr_parent_tree = eval(search_result[i].section.parent_tree)
+            # Assigned token limit for this position in the list
+            page_token_limit = token_limit_per_source[i]
+            # Returns a FullPage which is just a list of Section
+            same_page = self.collection.getPageOriginUUIDList(
+                origin_uuid=search_result[i].section.origin_uuid
+            )
+            same_pages.append(same_page)
+            # Use all sections in experimental, only self when "normal"
+            if self.config.docs_agent_config == "experimental":
+                test_page = same_page.buildSections(
+                    section_id=search_result[i].section.id,
+                    selfSection=True,
+                    children=True,
+                    parent=True,
+                    siblings=True,
+                    token_limit=token_limit_per_source[i],
+                )
+            else:
+                test_page = same_page.buildSections(
+                    section_id=search_result[i].section.id,
+                    selfSection=True,
+                    children=False,
+                    parent=False,
+                    siblings=False,
+                    token_limit=token_limit_per_source[i],
+                )
+            final_pages.append(test_page)
+        # Each item here is a FullPage corresponding to the source
+        final_context = ""
+        for item in final_pages:
+            for source in item.section_list:
+                final_context += source.content + "\n\n"
+        final_context = final_context.strip()
+        # Result contains the search result of Section of the initial hits
+        # final_pages could be returned to get the full Section for displaying
+        # context with metadata
+        return search_result, final_context
+
+    # Use this method for talking to a Gemini content model
+    # Optionally provide a prompt, if not use the one from config.yaml
+    # If prompt is "fact_checker" it will use the fact_check_question from
+    # config.yaml for the prompt
+    def ask_content_model_with_context_prompt(
+        self,
+        context: str,
+        question: str,
+        prompt: typing.Optional[str] = None,
+        model: typing.Optional[str] = None,
+    ):
+        if prompt == None:
+            prompt = self.config.conditions.condition_text
+        elif prompt == "fact_checker":
+            prompt = self.config.conditions.fact_check_question
+        new_prompt = f"{prompt}\n\nContext:\n{context}\nQuestion:\n{question}"
+        # Print the prompt for debugging if the log level is VERBOSE.
+        if self.config.log_level == "VERBOSE":
+            self.print_the_prompt(new_prompt)
+        try:
+            response = ""
+            if model == "gemini-pro":
+                response = self.gemini_pro.generate_content(
+                    contents=new_prompt, log_level=self.config.log_level
+                )
+            elif model == "gemini-1.5":
+                response = self.gemini_15.generate_content(
+                    contents=new_prompt, log_level=self.config.log_level
+                )
+            else:
+                response = self.gemini.generate_content(
+                    contents=new_prompt, log_level=self.config.log_level
+                )
+        except Exception as e:
+            print("Error in generate_content()")
+            print(e)
+            return self.config.conditions.model_error_message, new_prompt
+        return response, new_prompt
+
+    # Use this method for talking to a Gemini content model
+    # Provide a prompt, followed by the content of the file
+    # This isn't in use yet, but can be used to give an LLM a full or partial file
+    def ask_content_model_to_use_file(self, prompt: str, file: str):
+        new_prompt = prompt + file
+        # Print the prompt for debugging if the log level is VERBOSE.
+        if self.config.log_level == "VERBOSE":
+            self.print_the_prompt(new_prompt)
+        try:
+            response = self.gemini.generate_content(contents=new_prompt)
+        except google.api_core.exceptions.InvalidArgument:
+            return self.config.conditions.model_error_message
+        return response
+
+    # Use this method for asking a Gemini content model for fact-checking.
+    # This uses ask_content_model_with_context_prompt w
+    def ask_content_model_to_fact_check_prompt(self, context: str, prev_response: str):
+        question = self.config.conditions.fact_check_question + "\n\nText: "
+        question += prev_response
+        return self.ask_content_model_with_context_prompt(
+            context=context, question=question, prompt=""
+        )
+
+    # Generate an embedding given text input
+    def generate_embedding(self, text, task_type: str = "SEMANTIC_SIMILARITY"):
+        return self.gemini.embed(text, task_type)[0]
+
+
+# Function to give an embedding function for gemini using an API key
+def embedding_function_gemini_retrieval(api_key, embedding_model: str):
+    return embedding_functions.GoogleGenerativeAiEmbeddingFunction(
+        api_key=api_key, model_name=embedding_model, task_type="RETRIEVAL_QUERY"
+    )
diff --git a/examples/gemini/python/docs-agent/docs_agent/benchmarks/README.md b/examples/gemini/python/docs-agent/docs_agent/benchmarks/README.md
new file mode 100644
index 000000000..07cb577f0
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/benchmarks/README.md
@@ -0,0 +1,164 @@
+# Benchmark test for monitoring the quality of embeddings and AI responses
+
+This page explains how to run benchmark test to measure and track
+the quality of embeddings, context chunks, and AI-generated responses.
+
+Docs Agent’s benchmark test currently uses 10 questions and their
+target answers curated by technical writers (see
+[`benchmarks.yaml`][benchmarks-yaml]). The benchmark test asks these
+10 questions to an AI language model to generate responses. The test then
+computes the dot product of the embeddings (vectors) of these AI-generated
+responses and the target answer to measure their similarity values
+(see Figure 1).
+
+![Docs Agent benchmark test](../../docs/images/docs-agent-benchmarks-01.png)
+
+**Figure 1**. The dot product of vectors is computed to measure their similarity.
+
+**Note**: The input questions and answers in the
+[`benchmarks.yaml`][benchmarks-yaml] file checked in the Docs Agent project are
+based on the [FAQ][flutter-faq] page on the Flutter documentation site, whose
+source Markdown files are available in this [Flutter repository][flutter-git]).
+
+## Set up and run the benchmark test
+
+To set up and run benchmark test using Docs Agent, the steps are:
+
+1. [Prepare questions and target answers for your source docs](#1_prepare-questions-and-target-answers-for-your-source-docs).
+2. [Set up Docs Agent](#2_set-up-docs-agent).
+3. [Run the benchmark test](#3_run-the-benchmark-test).
+
+### 1. Prepare questions and target answers for your source docs
+
+List questions and target answers for your source docs in the `benchmarks.yaml`
+file.
+
+An example of a question and target answer pair:
+
+```none
+  - question: "Does Flutter support Material Design?"
+    target_answer: "Yes! The Flutter and Material teams collaborate closely, and Material is fully supported. For more information, check out the Material 2 and Material 3 widgets in the widget catalog."
+```
+
+Based on the information documented in your source docs, come up
+with a list of questions (`question`) and their expected answers
+(`target_answer`). It’s important that these answers are found in
+the source docs and are produced by humans, not AI models.
+
+For instance, the example [`benchmarks.yaml`][benchmarks-yaml] file includes
+10 questions and 10 target answers that are based on the source documents in
+the [Flutter repository][flutter-git]. So if you plan on running benchmark
+test using this `benchmarks.yaml` file, you need to configure your
+Docs Agent setup so that it uses the documents in the Flutter repository
+as a knowledge source, for example:
+
+```yaml
+inputs:
+  - path: "/usr/local/home/user01/website/src"
+    url_prefix: "https://docs.flutter.dev"
+```
+
+(Source: [`config.yaml`][config-yaml])
+
+### 2. Set up Docs Agent
+
+Complete the processing of your source docs into Docs Agent’s vector
+database (by running the `agent chunk` and `agent populate` commands).
+
+**Note**: This benchmark testing uses the same `config.yaml` file as the
+chatbot app (that is, `condition_text`, `vector_db_dir`, and `log_level`
+variables and so on). For instance, set `log_level` to `NORMAL`
+if you do not wish to see the details of prompts to the AI model while
+the benchmark test is running.
+
+### 3. Run the benchmark test
+
+To start benchmark test, run the following command from your Docs Agent
+project home directory:
+
+```sh
+agent benchmark
+```
+
+This command computes the similarity value for each question entry
+in the `benchmarks.yaml` file and writes the test results
+to the [`results.out`][results-out] file. If there already
+exists a `results.out` file, its content will be overwritten.
+
+An example of test results:
+
+```none
+Similarity (-1, 1)    Question
+==================    ========
+0.9693597667161213    What is inside the Flutter SDK?
+0.8810758779307981    Does Flutter work with any editors or IDEs?
+0.8760932771858571    Does Flutter come with a framework?
+0.8924252745816632    Does Flutter come with widgets?
+0.8637181105900334    Does Flutter support Material Design?
+0.9340505894484676    Does Flutter come with a testing framework?
+0.9192416276439515    Does Flutter come with debugging tools?
+0.7491969164696617    Does Flutter come with a dependency injection framework?
+0.7895399136265219    What technology is Flutter built with?
+0.7802681514431923    What language is Flutter written in?
+```
+
+**Note**: The similarity scores shown in the example above are
+computed using only a small set of documents processed from the
+Flutter respository. These scores may vary depending on which
+documents are added into Docs Agent's knowledge source.
+
+## How does this benchmark test work?
+
+When Docs Agent's benchmark test is run, the following events
+take place:
+
+1. Read a `question` and `target_answer` entry from the
+   [`benchmarks.yaml`][benchmarks-yaml] file.
+2. Generate an embedding using `target_answer` (Embedding 1).
+3. Ask `question` to the AI model using the RAG technique.
+4. Generate an embedding using the AI-generated response
+   (Embedding 2).
+5. Compute the similarity between Embedding 1 and Embedding 2.
+6. Repeat the steps until all question entries are read.
+7. Print the test results to the [`results.out`][results-out] file.
+
+## How is the similarity value computed?
+
+To measure the similarity, each benchmark test calculates the
+dot product of the embedding (vector) generated from the target
+answer and the embedding generated from the AI response.
+
+An example of a benchmark test result:
+
+```none
+Question:
+Does Flutter come with debugging tools?
+
+Target answer:
+Yes, Flutter comes with Flutter DevTools (also called Dart DevTools). For more information, see Debugging with Flutter and the Flutter DevTools docs.
+
+AI Response:
+Yes, Flutter has debugging tools. You can debug your app in a few ways:
+
+ • Using DevTools, a suite of debugging and profiling tools that run in a browser and include the Flutter inspector.
+ • Using Android Studio's (or IntelliJ's) built-in debugging features, such as the ability to set breakpoints.
+ • Using the Flutter inspector, directly available in Android Studio and IntelliJ.
+
+Similarity:
+0.9192416276439515
+```
+
+This value estimates the similarity between the human-produced
+and machine-generated answers. The closer the value is to 1,
+the more similar they are. (For more information , see the
+[Embedding guide][embedding-generation] page on the Gemini API
+documentation site.)
+
+<!-- Reference links -->
+
+[benchmarks-yaml]: benchmarks.yaml
+[config-yaml]: ../../config.yaml
+[flutter-faq]: https://docs.flutter.dev/resources/faq
+[flutter-git]: https://github.com/flutter/website/tree/main/src
+[results-out]: results.out
+[embedding-generation]: https://ai.google.dev/docs/embeddings_guide
diff --git a/examples/gemini/python/docs-agent/docs_agent/benchmarks/__init__.py b/examples/gemini/python/docs-agent/docs_agent/benchmarks/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/benchmarks/benchmarks.yaml b/examples/gemini/python/docs-agent/docs_agent/benchmarks/benchmarks.yaml
new file mode 100644
index 000000000..a5a6e4c7e
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/benchmarks/benchmarks.yaml
@@ -0,0 +1,52 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+### Configuration for Docs Agent benchmark tests ###
+
+# Source docs: https://github.com/flutter/website/tree/main/src (flutter.dev)
+#
+# These questions and target answers are captured from https://docs.flutter.dev/resources/faq.
+#
+# For this benchmark testing, use a config.yaml setup similar to the following:
+#
+# inputs:
+#   - path: "/usr/local/home/user01/website/src/ui"
+#     url_prefix: "https://docs.flutter.dev/ui"
+#   - path: "/usr/local/home/user01/website/src/tools"
+#     url_prefix: "https://docs.flutter.dev/tools"
+
+
+benchmarks:
+  - question: "What is inside the Flutter SDK?"
+    target_answer: "Flutter includes: * Heavily optimized, mobile-first 2D rendering engine with excellent support for text * Modern react-style framework * Rich set of widgets implementing Material Design and iOS-style * APIs for unit and integration tests * Interop and plugin APIs to connect to the system and 3rd-party SDKs * Headless test runner for running tests on Windows, Linux, and Mac * Flutter DevTools (also called Dart DevTools) for testing, debugging, and profiling your app * Command-line tools for creating, building, testing, and compiling your apps"
+  - question: "Does Flutter work with any editors or IDEs?"
+    target_answer: "We provide plugins for VS Code, Android Studio, and IntelliJ IDEA. See editor configuration for setup details, and VS Code and Android Studio/IntelliJ for tips on how to use the plugins. Alternatively, you can use the flutter command from a terminal, along with one of the many editors that support editing Dart."
+  - question: "Does Flutter come with a framework?"
+    target_answer: "Yes! Flutter ships with a modern react-style framework. Flutter’s framework is designed to be layered and customizable (and optional). Developers can choose to use only parts of the framework, or even replace upper layers of the framework entirely."
+  - question: "Does Flutter come with widgets?"
+    target_answer: "Yes! Flutter ships with a set of high-quality Material Design and Cupertino (iOS-style) widgets, layouts, and themes. Of course, these widgets are only a starting point. Flutter is designed to make it easy to create your own widgets, or customize the existing widgets."
+  - question: "Does Flutter support Material Design?"
+    target_answer: "Yes! The Flutter and Material teams collaborate closely, and Material is fully supported. For more information, check out the Material 2 and Material 3 widgets in the widget catalog."
+  - question: "Does Flutter come with a testing framework?"
+    target_answer: "Yes, Flutter provides APIs for writing unit and integration tests. Learn more about testing with Flutter. We use our own testing capabilities to test our SDK, and we measure our test coverage on every commit."
+  - question: "Does Flutter come with debugging tools?"
+    target_answer: "Yes, Flutter comes with Flutter DevTools (also called Dart DevTools). For more information, see Debugging with Flutter and the Flutter DevTools docs."
+  - question: "Does Flutter come with a dependency injection framework?"
+    target_answer: "We don’t ship with an opinionated solution, but there are a variety of packages that offer dependency injection and service location, such as injectable, get_it, kiwi, and riverpod."
+  - question: "What technology is Flutter built with?"
+    target_answer: "Flutter is built with C, C++, Dart, Skia (a 2D rendering engine), and Impeller (the default rendering engine on iOS). See this architecture diagram for a better picture of the main components. For a more detailed description of the layered architecture of Flutter, read the architectural overview."
+  - question: "What language is Flutter written in?"
+    target_answer: "Dart, a fast-growing modern language optimized for client apps. The underlying graphics framework and the Dart virtual machine are implemented in C/C++."
diff --git a/examples/gemini/python/docs-agent/docs_agent/benchmarks/results.out b/examples/gemini/python/docs-agent/docs_agent/benchmarks/results.out
new file mode 100644
index 000000000..7418b92cc
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/benchmarks/results.out
@@ -0,0 +1,12 @@
+Similarity (-1, 1)    Question
+==================    ========
+0.9693597667161213    What is inside the Flutter SDK?
+0.8810758779307981    Does Flutter work with any editors or IDEs?
+0.8760932771858571    Does Flutter come with a framework?
+0.8924252745816632    Does Flutter come with widgets?
+0.8637181105900334    Does Flutter support Material Design?
+0.9340505894484676    Does Flutter come with a testing framework?
+0.9192416276439515    Does Flutter come with debugging tools?
+0.7491969164696617    Does Flutter come with a dependency injection framework?
+0.7895399136265219    What technology is Flutter built with?
+0.7802681514431923    What language is Flutter written in?
diff --git a/examples/gemini/python/docs-agent/docs_agent/benchmarks/run_benchmark_tests.py b/examples/gemini/python/docs-agent/docs_agent/benchmarks/run_benchmark_tests.py
new file mode 100644
index 000000000..fe15f9714
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/benchmarks/run_benchmark_tests.py
@@ -0,0 +1,223 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Run benchmark tests to measure the quality of embeddings, context chunks, and AI responses"""
+
+import os
+import sys
+import yaml
+
+import numpy as np
+
+from rich.console import Console
+from rich.markdown import Markdown
+from rich.panel import Panel
+
+from docs_agent.storage.chroma import Format
+from docs_agent.agents.docs_agent import DocsAgent
+from docs_agent.utilities import config
+from docs_agent.utilities.config import ProductConfig
+
+
+# A function that asks the questin to the AI model using the RAG technique.
+def ask_model(question: str, docs_agent: DocsAgent):
+    results_num = 5
+    if "gemini" in docs_agent.config.models.language_model:
+        # print("Asking a Gemini model")
+        (search_result, final_context) = docs_agent.query_vector_store_to_build(
+            question=question,
+            token_limit=30000,
+            results_num=results_num,
+            max_sources=results_num,
+        )
+        response, full_prompt = docs_agent.ask_content_model_with_context_prompt(
+            context=final_context, question=question
+        )
+    elif "aqa" in docs_agent.config.models.language_model:
+        # print("Asking the AQA model")
+        if docs_agent.config.db_type == "google_semantic_retriever":
+            (response, search_result) = docs_agent.ask_aqa_model_using_corpora(
+                question=question
+            )
+        elif docs_agent.config.db_type == "chroma":
+            (
+                response,
+                search_result,
+            ) = docs_agent.ask_aqa_model_using_local_vector_store(
+                question=question, results_num=results_num
+            )
+        else:
+            (response, search_result) = docs_agent.ask_aqa_model_using_corpora(
+                question=question
+            )
+    return response
+
+
+# A customized print function
+def vprint(text: str, VERBOSE: bool = False):
+    if VERBOSE:
+        print(text)
+
+
+# A function that computes cosine similarity between two vectors
+def compute_cosine_similarity(v1, v2):
+    a = np.asarray(v1)
+    b = np.asarray(v2)
+    dot = np.dot(a, b)
+    a_norm = np.linalg.norm(a, 2)
+    b_norm = np.linalg.norm(b, 2)
+    cosine = dot / (a_norm * b_norm)
+    return cosine
+
+
+# Read the `benchmarks.yaml` file in the `benchmarks` directory of the project.
+def read_benchmarks_yaml():
+    BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    BENCHMARKS_YAML = os.path.join(BASE_DIR, "benchmarks/benchmarks.yaml")
+    try:
+        with open(BENCHMARKS_YAML, "r", encoding="utf-8") as b_yaml:
+            read_values = yaml.safe_load(b_yaml)
+    except FileNotFoundError:
+        print("The " + BENCHMARKS_YAML + " file is missing.")
+        sys.exit(1)
+    return read_values
+
+
+def run_benchmarks():
+    # VERBOSE = False
+    BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    # Initialize Rich console
+    my_console = Console(width=160)
+
+    # Read the configuration file (`config.yaml`)
+    config_file = config.ReadConfig().returnProducts()
+    # TODO: This benchmark test only selects the first product
+    #       in the product list in the config file at the moment.
+    product = config_file.products[0]
+    print(f"===========================================")
+    print(f"Benchmark test target product: {product.product_name}")
+    print(f"===========================================")
+
+    # Initialize Docs Agent
+    if product.db_type == "google_semantic_retriever":
+        docs_agent = DocsAgent(config=product, init_chroma=False)
+    else:
+        docs_agent = DocsAgent(config=product)
+
+    # Read the `benchmarks.yaml` file.
+    benchmark_values = read_benchmarks_yaml()
+
+    questions = []
+    results = []
+    index = 0
+    print()
+    for benchmark in benchmark_values["benchmarks"]:
+        embedding_01 = ""
+        embedding_02 = ""
+        response = ""
+        similarity = ""
+
+        # Step 1. Read a `question` and `target_answer` pair.
+        question = benchmark["question"]
+        target_answer = benchmark["target_answer"]
+        questions.append(question)
+        print("================")
+        print("Benchmark " + str(index))
+        print("================")
+        print("Question: " + question)
+        print("Target answer: " + target_answer)
+        print()
+
+        # Step 2. Generate an embedding using `target_answer` - Embedding 1.
+        vprint("################")
+        vprint("# Embedding 1  #")
+        vprint("################")
+        vprint("Input text:")
+        vprint(target_answer)
+        embedding_01 = docs_agent.generate_embedding(target_answer)
+        vprint("")
+        vprint("Embedding:")
+        vprint(str(embedding_01))
+
+        # Step 3. Ask `question` to the AI model.
+        response = ask_model(question, docs_agent)
+        vprint("################")
+        vprint("# Response     #")
+        vprint("################")
+        vprint(response)
+
+        # Step 4. Generate an embedding using the response - Embedding 2.
+        vprint("################")
+        vprint("# Embedding 2  #")
+        vprint("################")
+        vprint("Input text:")
+        vprint(response)
+        embedding_02 = docs_agent.generate_embedding(response)
+        vprint("")
+        vprint("Embedding:")
+        vprint(str(embedding_02))
+
+        # Step 5. Compute the similarity between Embedding 1 and Embedding 2.
+        vprint("################")
+        vprint("# Similarity   #")
+        vprint("################")
+        similarity = compute_cosine_similarity(embedding_01, embedding_02)
+        vprint(similarity)
+        vprint("")
+        results.append(similarity)
+
+        # Step 6. Print the summary of this run.
+        print("################")
+        print("# Result       #")
+        print("################")
+        print("Question:")
+        my_console.print(Panel.fit(Markdown(question)))
+        print()
+        print("Target answer:")
+        my_console.print(Panel.fit(Markdown(target_answer)))
+        print()
+        print("AI Response:")
+        my_console.print(Panel.fit(Markdown(response)))
+        print()
+        print("Similarity:")
+        print(similarity)
+        print()
+
+        index += 1
+
+    # Print the benchmark test results.
+    print("################################")
+    print("# Benchmark tests summary      #")
+    print("################################")
+    print()
+    print("Similarity (-1, 1)" + "    " + "Question")
+    print("==================" + "    " + "========")
+    for i, q in enumerate(questions):
+        print(str("{:.16f}".format(results[i])) + "    " + q)
+    print()
+
+    # Store the benchmark test results into benchmarks/results.out.
+    BENCHMARKS_OUT = os.path.join(BASE_DIR, "benchmarks/results.out")
+    with open(BENCHMARKS_OUT, "w", encoding="utf-8") as outfile:
+        outfile.write("Similarity (-1, 1)" + "    " + "Question\n")
+        outfile.write("==================" + "    " + "========\n")
+        for i, q in enumerate(questions):
+            outfile.write(str("{:.16f}".format(results[i])) + "    " + q + "\n")
+    print("Created " + BENCHMARKS_OUT + " to store the results of the benchmark tests.")
+
+
+if __name__ == "__main__":
+    run_benchmarks()
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md b/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
new file mode 100644
index 000000000..56a3292ea
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
@@ -0,0 +1,368 @@
+# Set up Docs Agent CLI
+
+This guide provides instructions on setting up Docs Agent's command-line
+interface (CLI) on your host machine for running Docs Agent tasks.
+
+Docs Agent's `agent runtask` command allows you to run pre-defined chains of
+prompts, which are referred to as **tasks**. These tasks simplify complex
+interactions by defining a series of steps that the Docs Agent will execute.
+The tasks are defined in `.yaml` files stored in the [`tasks`][docs-agent-tasks]
+directory of your Docs Agent project. The tasks are designed to be reusable and
+can be used to automate common workflows, such as generating release notes,
+updating documentation, or analyzing complex information.
+
+To set up the Docs Agent CLI, the steps are:
+
+1. [Prerequisites](#1-prerequisites)
+2. [Update your host machine's environment](#2-update-your-host-machines-environment)
+3. [Clone the Docs Agent project repository](#3-clone-the-docs-agent-project-repository)
+4. [Try the Docs Agent CLI](#4-try-the-docs-agent-cli)
+
+## 1. Prerequisites
+
+Setting up Docs Agent requires the following prerequisite items:
+
+- A Linux host machine
+
+- A [Google Cloud][google-cloud] project with an API key enabled with the
+  Generative Language API (that is, the [Gemini API][genai-doc-site])
+
+## 2. Update your host machine's environment
+
+1. Update the Linux package repositories on the host machine:
+
+   ```
+   sudo apt update
+   ```
+
+2. Install the following dependencies:
+
+   ```
+   sudo apt install git pipx python3-venv
+   ```
+
+3. Install `poetry`:
+
+   ```
+   pipx install poetry
+   ```
+
+4. To add `$HOME/.local/bin` to your `PATH` variable, run the following
+   command:
+
+   ```
+   pipx ensurepath
+   ```
+
+5. To set the Google API key as a environment variable, add the following
+   line to your `$HOME/.bashrc` file:
+
+   ```
+   export GOOGLE_API_KEY=<YOUR_API_KEY_HERE>
+   ```
+
+   Replace `<YOUR_API_KEY_HERE>` with the API key to the
+   [Gemini API][genai-doc-site].
+
+6. Update your environment:
+
+   ```
+   source ~/.bashrc
+   ```
+
+## 3. Clone the Docs Agent project
+
+**Note**: This guide assumes that you're creating a new project directory
+from your `$HOME` directory.
+
+1. Clone the following repo:
+
+   ```
+   git clone https://github.com/google/generative-ai-docs.git
+   ```
+
+2. Go to the Docs Agent project directory:
+
+   ```
+   cd generative-ai-docs/examples/gemini/python/docs-agent
+   ```
+
+3. Install dependencies using `poetry`:
+
+   ```
+   poetry install
+   ```
+
+## 4. Try the Docs Agent CLI
+
+1. Enter the `poetry shell` environment:
+
+   ```
+   poetry shell
+   ```
+
+   Entering the `poetry shell` environment is **required** for
+   running the `agent` command.
+
+2. Run the `agent helpme` command, for example:
+
+   ```
+   agent helpme how do I cook pasta?
+   ```
+
+   This command returns the Gemini model's response of your input prompt
+   `how do I cook pasta?`.
+
+3. View the list of Docs Agent tasks available in your setup:
+
+   ```
+   agent runtask
+   ```
+
+   This command prints a list of Docs Agent tasks that you can run.
+   (See the `tasks` directory in your local Docs Agent setup.)
+
+4. Run the `agent runtask` command, for example:
+
+   ```
+   agent runtask --task IndexPageGenerator
+   ```
+
+For more details on these commands, see the
+[Interacting with language models][cli-reference-helpme] section in
+the CLI reference page.
+
+## Appendices
+
+### Authorize credentials for Docs Agent
+
+**Note**: This step may not be necessary if you already have OAuth client
+credentials (via `gcloud`) stored on your host machine.
+
+This step is **only necessary** if you plan on using the
+`agent tellme` command to interact with your online corpora on Google Cloud.
+
+Do the following:
+
+1. Download the `client_secret.json` file from your
+   [Google Cloud project][authorize-credentials].
+
+2. Copy the `client_secret.json` file to your host machine.
+
+3. Install the Google Cloud SDK on your host machine:
+
+   ```
+   sudo apt install google-cloud-sdk
+   ```
+
+4. To authenticate credentials, run the following command in the directory of
+   the host machine where the `client_secret.json` file is located:
+
+   ```
+   gcloud auth application-default login --client-id-file=client_secret.json --scopes='https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/generative-language.retriever'
+   ```
+
+   This command opens a browser and asks to log in using your Google account.
+
+5. Follow the instructions on the browser and click **Allow** to authenticate.
+
+   This saves the authenticated credentials for Docs Agent
+   (`application_default_credentials.json`) in the `$HOME/.config/gcloud/`
+   directory of your host machine.
+
+### Set up an alias to the gemini command
+
+This section provides instructions on setting up the Docs Agent CLI to enable
+you to ask questions from anywhere in a terminal.
+
+Using Docs Agent, you can configure your host machine's environment to make
+the `gemini` command run from anywhere in your terminal. The `gemini` command
+(which is an `alias` to Docs Agent's `agent tellme` command) reads a question
+from the arguments, asks the [Gemini AQA][gemini-aqa] model, and prints its
+response in the terminal.
+
+The example below shows that a user can run the `gemini` command directly
+from a terminal:
+
+```
+user@user01:~$ gemini does Flutter support material design 3?
+
+As of the Flutter 3.16 release, Material 3 is enabled by default.
+
+To verify this information, see:
+
+ • https://docs.flutter.dev/ui/design/material/index#more-information
+
+user@user01:~$
+```
+
+In this setup, Docs Agent's AQA model is configured to use an example
+online corpus. However, using the tools available in the Docs Agent project,
+you can [create and populate a new corpus][populate-corpus] with your own
+documents and adjust your Docs Agent configuration to use that corpus
+instead – you can also [share the corpus][share-corpus] with other members
+in your team.
+
+To update your shell environment so that the `gemini` command can be run
+from anywhere in the terminal, do the following:
+
+**Note**: If your Docs Agent project is not cloned in the `$HOME` directory,
+you need to edit the `scripts/tellme.sh` script in your `docs-agent` project
+directory.
+
+1. (**Optional**) Open the `scripts/tellme.sh` file using a text editor,
+   for example:
+
+   ```
+   nano scripts/tellme.sh
+   ```
+
+   If necessary, adjust the path (`$HOME/docs-agent`) to match your
+   `docs-agent` project directory on the host machine:
+
+   ```
+   # IF NECESSARY, ADJUST THIS PATH TO YOUR `docs-agent` DIRECTORY.
+   docs_agent_dir="$HOME/docs-agent"
+   ```
+
+   Save the file and close the text editor.
+
+2. Add the following `alias` line to your `$HOME/.bashrc` file:
+
+   ```
+   alias gemini='$HOME/docs-agent/scripts/tellme.sh'
+   ```
+
+   Similarly, if necessary, you need to adjust the path
+  (`$HOME/docs-agent`) to match your the `docs-agent` project directory
+   on the host machine.
+
+3. Update your environment:
+
+   ```
+   source ~/.bashrc
+   ```
+
+4. Now you can run the `gemini` command from anywhere in your terminal:
+
+   ```
+   gemini <QUESTION>
+   ```
+
+   For example:
+
+   ```
+   user@user01:~/temp$ gemini does flutter support material design 3?
+   ```
+
+### Set up your terminal to run the helpme command
+
+**Note**: This is an experimental setup.
+
+This new feature allows you to freely navigate a codebase setup in your
+terminal and asks Gemini to perform various tasks while automatically
+referencing the output you see in your terminal.
+
+Similar to the `agent tellme` command, the `agent helpme` command allows you to
+ask a question to Gemini directly from your terminal. However, unlike
+the `tellme` command, the `helpme` command uses the Gemini Pro model
+and doesn't depend on an online corpus to retrieve relevant context.
+Instead, this `helpme` setup can read directly from the output of your terminal
+(that is, the last 150 lines at the moment) and automatically adds it as context
+to your prompt.
+
+These tasks include, but not limited to:
+
+- Rewrite `README` file to be instructional and linear.
+- Rewrite `README` file to be more concise and better structured.
+- Format `README` to collect reference links at the bottom.
+- Write a protocol description.
+- Write comments for a C++ source file.
+
+**Note**: Since this setup uses the Gemini Pro model, setting up OAuth on your
+host machine is **not required**.
+
+To set up this `helpme` command in your terminal, do the following:
+
+1. (**Optional**) Open the `scripts/helpme.sh` file using a text editor,
+   for example:
+
+   ```
+   nano scripts/helpme.sh
+   ```
+
+   If necessary, adjust the path (`$HOME/docs-agent`) to match your
+   `docs-agent` project directory on the host machine:
+
+   ```
+   # IF NECESSARY, ADJUST THIS PATH TO YOUR `docs-agent` DIRECTORY.
+   docs_agent_dir="$HOME/docs-agent"
+   ```
+
+   Save the file and close the text editor.
+
+2. Add the following `alias` lines to your `$HOME/.bashrc` file:
+
+   ```
+   alias gemini-pro='$HOME/docs-agent/scripts/helpme.sh'
+   alias start_agent='script -f -o 200MiB -O /tmp/docs_agent_console_input'
+   alias stop_agent='exit'
+   ```
+
+   Similarly, if necessary, you need to adjust the path
+  (`$HOME/docs-agent`) to match your the `docs-agent` project directory
+   on the host machine.
+
+3. Update your environment:
+
+   ```
+   source ~/.bashrc
+   ```
+
+4. When you are ready to let Docs Agent to read output from your terminal,
+   run the following command:
+
+   ```
+   start_agent
+   ```
+
+   **Note**: To stop this process, run `stop_agent`.
+
+5. Navigate to a directory in your terminal and use the `cat` command
+   (or `head` or `tail`) to print the content of a file to your terminal.
+
+   (In fact, you can run any command that prints output to the terminal.)
+
+   For example:
+
+   ```
+   user@user01:~/my-example-project$ cat test.cc
+   <prints the test.cc file here>
+   ```
+
+6. To use the latest output from your terminal, run the `gemini-pro` command
+   immediately after the output:
+
+   ```
+   gemini-pro <REQUEST>
+   ```
+
+   For example:
+
+   ```
+   user@user01:~/my-example-project$ cat test.cc
+   <prints the test.cc file here>
+   user@user01:~/my-example-project$ gemini-pro could you help me write comments for this C++ file above?
+   ```
+
+<!-- Reference links -->
+
+[gemini-aqa]: https://ai.google.dev/docs/semantic_retriever
+[populate-corpus]: ../preprocess/README.md
+[share-corpus]: https://ai.google.dev/docs/semantic_retriever#share_the_corpus
+[google-cloud]: https://console.cloud.google.com/
+[oauth-client]: https://ai.google.dev/docs/oauth_quickstart#set-cloud
+[authorize-credentials]: https://ai.google.dev/docs/oauth_quickstart#authorize-credentials
+[genai-doc-site]: https://ai.google.dev/docs/gemini_api_overview
+[cli-reference-helpme]: ../../docs/cli-reference.md#interacting-with-language-models
+[docs-agent-tasks]: ../../tasks
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/__init__.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/demos/palm/python/docs-agent/chatbot/__init__.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/__init__.py
similarity index 66%
rename from demos/palm/python/docs-agent/chatbot/__init__.py
rename to examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/__init__.py
index e99469e0c..58c4c46d9 100644
--- a/demos/palm/python/docs-agent/chatbot/__init__.py
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/__init__.py
@@ -15,7 +15,11 @@
 #
 
 from flask import Flask
-from chatbot import chatui
+from docs_agent.interfaces.chatbot import chatui
+from docs_agent.utilities import config
 
-app = Flask(__name__)
-app.register_blueprint(chatui.bp)
+
+def create_app(product: config.ProductConfig, app_mode: str = "web"):
+    app = Flask(__name__)
+    app.register_blueprint(chatui.construct_blueprint(product_config=product, app_mode=app_mode))
+    return app
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
new file mode 100644
index 000000000..4b713050d
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
@@ -0,0 +1,605 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Chatbot web service for Docs Agent"""
+
+from flask import Blueprint, render_template, request, redirect, url_for, json, jsonify
+import markdown
+import markdown.extensions.fenced_code
+import urllib
+import os
+import typing
+from datetime import datetime
+from absl import logging
+import pytz
+import uuid
+import re
+
+from docs_agent.utilities.helpers import (
+    parse_related_questions_response_to_html_list,
+    trim_section_for_page_link,
+    named_link_html,
+    md_to_html,
+)
+from docs_agent.utilities import config
+from docs_agent.preprocess.splitters import markdown_splitter
+from docs_agent.postprocess.docs_retriever import SectionProbability
+
+from docs_agent.storage.chroma import Format
+from docs_agent.agents.docs_agent import DocsAgent
+
+from docs_agent.memory.logging import (
+    log_question,
+    log_debug_info_to_file,
+    log_feedback_to_file,
+    log_like,
+    log_dislike,
+)
+
+
+# This is used to define the app blueprint using a productConfig
+def construct_blueprint(
+    product_config: config.ProductConfig, app_mode: typing.Optional[str] = None
+):
+    bp = Blueprint("chatui", __name__)
+    if product_config.db_type == "google_semantic_retriever":
+        if product_config.secondary_db_type == "chroma":
+            docs_agent = DocsAgent(config=product_config, init_chroma=True)
+        else:
+            # A local Chroma DB is not needed for the Semantic Retreiver only mode.
+            docs_agent = DocsAgent(config=product_config, init_chroma=False)
+    elif product_config.db_type == "none":
+        docs_agent = DocsAgent(
+            config=product_config, init_chroma=False, init_semantic=False
+        )
+    else:
+        docs_agent = DocsAgent(config=product_config, init_chroma=True)
+    logging.info(
+        f"Launching the Flask app for product: {product_config.product_name} with app_mode: {app_mode}"
+    )
+    # Assign templates and redirects
+    if app_mode == "web":
+        app_template = "chatui/index.html"
+        redirect_index = "chatui.index"
+    elif app_mode == "experimental":
+        app_template = "chatui-experimental/index.html"
+        redirect_index = "chatui-experimental.index"
+    elif app_mode == "widget":
+        app_template = "chatui-widget/index.html"
+        redirect_index = "chatui-widget.index"
+    elif app_mode == "full":
+        app_template = "chatui-full/index.html"
+        redirect_index = "chatui-full.index"
+    elif app_mode == "widget-pro":
+        app_template = "chatui-widget-pro/index.html"
+        redirect_index = "chatui-widget-pro.index"
+    else:
+        app_template = "chatui/index.html"
+        redirect_index = "chatui.index"
+
+    @bp.route("/", methods=["GET", "POST"])
+    def index():
+        server_url = request.url_root.replace("http", "https")
+        return render_template(
+            app_template,
+            product=product_config.product_name,
+            server_url=server_url,
+        )
+
+    @bp.route("/api/ask-docs-agent", methods=["GET", "POST"])
+    def api():
+        try:
+            input = request.get_json()
+            if input["question"]:
+                (
+                    full_prompt,
+                    response,
+                    context,
+                    search_result,
+                ) = ask_model_with_sources(input["question"], agent=docs_agent)
+                source_array = []
+                # for source in search_result:
+                #     source_array.append(source.returnDictionary())
+                dictionary = {
+                    "response": response,
+                    "full_prompt": full_prompt,
+                    "sources": source_array,
+                }
+                return jsonify(dictionary)
+            else:
+                error = "Must have a valid question key in your JSON"
+                return jsonify({"error": error}), 400
+        except:
+            error = "Must be a valid JSON"
+            return jsonify({"error": error}), 400
+
+    @bp.route("/like", methods=["GET", "POST"])
+    def like():
+        if request.method == "POST":
+            json_data = json.loads(request.data)
+            uuid_found = str(json_data.get("uuid")).strip()
+            is_like = json_data.get("like")
+            if is_like != None:
+                log_like(is_like, uuid_found)
+            is_dislike = json_data.get("dislike")
+            if is_dislike != None:
+                log_dislike(is_dislike, uuid_found)
+            # Check if the server has the `debugs` directory.
+            debug_dir = "logs/debugs"
+            if os.path.exists(debug_dir):
+                log_feedback_to_file(uuid_found, is_like, is_dislike)
+            return "OK"
+        else:
+            return redirect(url_for(redirect_index))
+
+    @bp.route("/rewrite", methods=["GET", "POST"])
+    def rewrite():
+        # Create the 'rewrites' directory if it does not exist.
+        rewrites_dir = "rewrites"
+        is_exist = os.path.exists(rewrites_dir)
+        if not is_exist:
+            os.makedirs(rewrites_dir)
+        if request.method == "POST":
+            json_data = json.loads(request.data)
+            user_id = json_data.get("user_id")
+            question_captured = json_data.get("question")
+            original_response = json_data.get("original_response")
+            rewrite_captured = json_data.get("rewrite")
+            date_format = "%m%d%Y-%H%M%S"
+            date = datetime.now(tz=pytz.utc)
+            date = date.astimezone(pytz.timezone("US/Pacific"))
+            print(
+                "[" + date.strftime(date_format) + "] A user has submitted a rewrite."
+            )
+            print("Submitted by: " + user_id + "\n")
+            print("# " + question_captured.strip() + "\n")
+            print("## Original response\n")
+            print(original_response.strip() + "\n")
+            print("## Rewrite\n")
+            print(rewrite_captured + "\n")
+            filename = (
+                rewrites_dir
+                + "/"
+                + question_captured.strip()
+                .replace(" ", "-")
+                .replace("?", "")
+                .replace("'", "")
+                .lower()
+                + "-"
+                + date.strftime(date_format)
+                + ".md"
+            )
+            with open(filename, "w", encoding="utf-8") as file:
+                file.write("Submitted by: " + user_id + "\n\n")
+                file.write("# " + question_captured.strip() + "\n\n")
+                file.write("## Original response\n\n")
+                file.write(original_response.strip() + "\n\n")
+                file.write("## Rewrite\n\n")
+                file.write(rewrite_captured + "\n")
+                file.close()
+            return "OK"
+        else:
+            return redirect(url_for(redirect_index))
+
+    @bp.route("/feedback", methods=["GET", "POST"])
+    def feedback():
+        # Create the 'feedback' directory if it does not exist.
+        feedback_dir = "feedback"
+        is_exist = os.path.exists(feedback_dir)
+        if not is_exist:
+            os.makedirs(feedback_dir)
+        if request.method == "POST":
+            json_data = json.loads(request.data)
+            user_id = json_data.get("user_id")
+            question = json_data.get("question")
+            response = json_data.get("response")
+            feedback = json_data.get("feedback")
+            date_format = "%m%d%Y-%H%M%S"
+            date = datetime.now(tz=pytz.utc)
+            date = date.astimezone(pytz.timezone("US/Pacific"))
+            print("[" + date.strftime(date_format) + "] A user has submitted feedback.")
+            print("Submitted by: " + user_id + "\n")
+            print("# " + question.strip() + "\n")
+            print("## Response\n")
+            print(response.strip() + "\n")
+            print("## Feedback\n")
+            print(feedback + "\n")
+            filename = (
+                feedback_dir
+                + "/"
+                + question.strip()
+                .replace(" ", "-")
+                .replace("?", "")
+                .replace("'", "")
+                .lower()
+                + "-"
+                + date.strftime(date_format)
+                + ".md"
+            )
+            with open(filename, "w", encoding="utf-8") as file:
+                file.write("Submitted by: " + user_id + "\n\n")
+                file.write("# " + question.strip() + "\n\n")
+                file.write("## Response\n\n")
+                file.write(response.strip() + "\n\n")
+                file.write("## Feedback\n\n")
+                file.write(feedback + "\n")
+                file.close()
+            return "OK"
+        else:
+            return redirect(url_for(redirect_index))
+
+    # Render a response page when the user asks a question
+    # using input text box.
+    @bp.route("/result", methods=["GET", "POST"])
+    def result():
+        if request.method == "POST":
+            question = request.form["question"]
+            return ask_model(question, agent=docs_agent, template=app_template)
+        else:
+            return redirect(url_for(redirect_index))
+
+    # Render a response page when the user clicks a question
+    # from the related questions list.
+    @bp.route("/question/<ask>", methods=["GET", "POST"])
+    def question(ask):
+        if request.method == "GET":
+            question = urllib.parse.unquote_plus(ask)
+            return ask_model(question, agent=docs_agent, template=app_template)
+        else:
+            return redirect(url_for(redirect_index))
+
+    # Render the log view page.
+    @bp.route("/logs", methods=["GET", "POST"])
+    def logs():
+        return show_logs(agent=docs_agent)
+
+    # Render the debug view page.
+    @bp.route("/debugs/<filename>", methods=["GET", "POST"])
+    def debugs(filename):
+        if request.method == "GET":
+            filename = urllib.parse.unquote_plus(filename)
+            return show_debug_info(agent=docs_agent, filename=filename)
+        else:
+            return redirect(url_for(redirect_index))
+
+    return bp
+
+
+# Go through the `seatch_result` object returned from the AQA model
+# and extract context.
+def extract_context_from_search_result(search_result):
+    context = ""
+    context_count = 0
+    for item in search_result:
+        context_count += 1
+        # Add a "Reference[#]" line at the end of each context.
+        context += item.section.content + "\nReference [" + str(context_count) + "]\n\n"
+    context = context.strip()
+    return context
+
+
+# Construct a set of prompts using the user question, send the prompts to
+# the lanaguage model, receive responses, and present them into a page.
+# Use template to specify a custom template for the classic web UI
+def ask_model(question, agent, template: str = "chatui/index.html"):
+    # Returns a built context, a total token count of the context and an array
+    # of sourceOBJ
+    full_prompt = ""
+    final_context = ""
+    docs_agent = agent
+    new_question_count = 5
+    results_num = 5
+    aqa_response_in_html = ""
+
+    # Debugging feature: Do not log this question if it ends with `?do_not_log`.
+    can_be_logged = True
+    question_match = re.search(r"^(.*)\?do_not_log$", question)
+    if question_match:
+        # Update the question to remove `do_not_log`.
+        question = question_match[1] + "?"
+        can_be_logged = False
+
+    # Retrieve context and ask the question.
+    if (
+        docs_agent.config.app_mode == "full"
+        or docs_agent.config.app_mode == "widget-pro"
+        or "aqa" in docs_agent.config.models.language_model
+    ):
+        # For "full" and "pro" modes, use the AQA model for the first request.
+        # For the AQA model, check the DB type.
+        if docs_agent.config.db_type == "chroma":
+            (
+                response,
+                search_result,
+            ) = docs_agent.ask_aqa_model_using_local_vector_store(
+                question=question, results_num=results_num
+            )
+        else:
+            (response, search_result) = docs_agent.ask_aqa_model_using_corpora(
+                question=question
+            )
+        # Extract context from this AQA model's response.
+        final_context = extract_context_from_search_result(search_result)
+        # Save this AQA model's response.
+        aqa_response_json = docs_agent.get_saved_aqa_response_json()
+        # Convert this AQA model's response to HTML for better rendering.
+        if aqa_response_json:
+            aqa_response_in_html = json.dumps(
+                type(aqa_response_json).to_dict(aqa_response_json), indent=2
+            )
+    else:
+        # For the `gemini-*` model, alway use the Chroma database.
+        if docs_agent.config.docs_agent_config == "experimental":
+            results_num = 10
+            new_question_count = 5
+        else:
+            results_num = 5
+            new_question_count = 5
+        # Note: Error if max_sources > results_num, so leave the same for now.
+        if docs_agent.config.db_type == "none":
+            search_result = []
+            final_context = ""
+            # response = ask_content_model_with_context(context="", question=question)
+            # Issue if max_sources > results_num, so leave the same for now
+        else:
+            this_token_limit = 30000
+            if docs_agent.config.models.language_model.startswith("models/gemini-1.5"):
+                this_token_limit = 50000
+            search_result, final_context = docs_agent.query_vector_store_to_build(
+                question=question,
+                token_limit=this_token_limit,
+                results_num=results_num,
+                max_sources=results_num,
+            )
+        try:
+            response, full_prompt = docs_agent.ask_content_model_with_context_prompt(
+                context=final_context, question=question
+            )
+            aqa_response_in_html = ""
+        except:
+            logging.error("Failed to ask content model with context prompt.")
+
+    ### Check the AQA model's answerable_probability field
+    probability = "None"
+    if docs_agent.check_if_aqa_is_used():
+        aqa_response = docs_agent.get_saved_aqa_response_json()
+        try:
+            probability = aqa_response.answerable_probability
+        except:
+            probability = 0.0
+
+    # For "full" and "pro" modes, retrieve additional context from
+    # the secondary knowledge database.
+    additional_context = ""
+    if (
+        docs_agent.config.app_mode == "full"
+        or docs_agent.config.app_mode == "widget-pro"
+    ):
+        if docs_agent.config.secondary_db_type == "chroma":
+            (
+                additional_search_result,
+                additional_context,
+            ) = docs_agent.query_vector_store_to_build(
+                question=question,
+                token_limit=30000,
+                results_num=5,
+                max_sources=5,
+            )
+            # Extract context from this search result.
+            additional_context = extract_context_from_search_result(
+                additional_search_result
+            )
+        elif docs_agent.config.secondary_db_type == "google_semantic_retriever":
+            (
+                additional_response,
+                additional_search_result,
+            ) = docs_agent.ask_aqa_model_using_corpora(
+                question=question,
+                corpus_name=str(docs_agent.config.secondary_corpus_name),
+            )
+            # Extract context from this search result.
+            additional_context = extract_context_from_search_result(
+                additional_search_result
+            )
+
+    ### PROMPT: GET RELATED QUESTIONS.
+    # 1. Use the response from Prompt 1 as context and add a custom condition.
+    # 2. Prepare a new question asking the model to come up with 5 related questions.
+    # 3. Ask the language model with the new question.
+    # 4. Parse the model's response into a list in HTML format.
+    new_condition = f"Read the context below and answer the question at the end:"
+    new_question = f"Can you think of {new_question_count} questions whose answers can be found in the context above?"
+    try:
+        (
+            related_questions_response,
+            new_prompt_questions,
+        ) = docs_agent.ask_content_model_with_context_prompt(
+            context=final_context,
+            question=new_question,
+            prompt=new_condition,
+            model="gemini-pro",
+        )
+        # Clean up the response to a proper html list
+        related_questions = parse_related_questions_response_to_html_list(
+            markdown.markdown(related_questions_response)
+        )
+    except:
+        related_questions = ""
+        logging.error("Failed to ask content model with context prompt.")
+
+    ### PREPARE OTHER ELEMENTS NEEDED BY UI.
+    # - Create a uuid for this request.
+    # - A workaround to get the server's URL to work with the rewrite and like features.
+    new_uuid = uuid.uuid1()
+    server_url = request.url_root.replace("http", "https")
+
+    ### The code below is added for "full" and "pro" modes.
+    # Ask the model to generate the main response.
+    if (
+        docs_agent.config.app_mode == "full"
+        or docs_agent.config.app_mode == "widget-pro"
+    ) and docs_agent.config.db_type != "none":
+        if additional_context != "":
+            extended_context = f"RELEVANT CONTEXT FOUND IN SECONDARY KNOWLEDGE SOURCE:\n\n{additional_context}\n\nRELEVANT CONTEXT FOUND IN PRIMARY KNOWLEDGE SOURCE:\n\n{final_context}\n"
+        else:
+            extended_context = f"{final_context}\n"
+        additional_condition = (
+            "DO NOT INCLUDE THE NAMES OF PEOPLE FOUND IN CONVERSATIONS"
+        )
+        new_condition = f"Read the context below and provide a detailed overview to address the question at the end ({additional_condition}):"
+        (
+            summary_response,
+            summary_prompt,
+        ) = docs_agent.ask_content_model_with_context_prompt(
+            context=extended_context,
+            question=question,
+            prompt=new_condition,
+            model="gemini-1.5",
+        )
+        log_lines = f"{response}\n\n{summary_response}"
+    else:
+        summary_response = ""
+        log_lines = f"{response}"
+
+    ### LOG THIS REQUEST.
+    if can_be_logged:
+        if docs_agent.config.enable_logs_to_markdown == "True":
+            log_question(
+                new_uuid,
+                question,
+                log_lines,
+                probability,
+                save=True,
+                logs_to_markdown="True",
+            )
+        else:
+            log_question(new_uuid, question, log_lines, probability, save=True)
+        # Log debug information.
+
+        if docs_agent.config.enable_logs_for_debugging == "True":
+            top_source_url = ""
+            if len(search_result) > 0:
+                top_source_url = search_result[0].section.url
+            source_urls = ""
+            index = 1
+            for result in search_result:
+                source_urls += "[" + str(index) + "]: " + str(result.section.url) + "\n"
+                index += 1
+            log_debug_info_to_file(
+                uid=new_uuid,
+                user_question=question,
+                response=log_lines,
+                context=final_context,
+                top_source_url=top_source_url,
+                source_urls=source_urls,
+                probability=probability,
+                server_url=server_url,
+            )
+
+    ### Check the feedback mode in the `config.yaml` file.
+    feedback_mode = "feedback"
+    if hasattr(docs_agent.config, "feedback_mode"):
+        feedback_mode = str(docs_agent.config.feedback_mode)
+
+    return render_template(
+        template,
+        question=question,
+        response=response,
+        related_questions=related_questions,
+        product=docs_agent.config.product_name,
+        server_url=server_url,
+        uuid=new_uuid,
+        aqa_response_in_html=aqa_response_in_html,
+        named_link_html=named_link_html,
+        trim_section_for_page_link=trim_section_for_page_link,
+        md_to_html=md_to_html,
+        final_context=final_context,
+        search_result=search_result,
+        summary_response=summary_response,
+        feedback_mode=feedback_mode,
+    )
+
+
+# Not fully implemented
+# This method is used for the API endpoint, so it returns values that can be
+# packaged as JSON
+def ask_model_with_sources(question, agent):
+    docs_agent = agent
+    full_prompt = ""
+    search_result, context = docs_agent.query_vector_store_to_build(
+        question=question, token_limit=30000, results_num=10, max_sources=10
+    )
+    context_with_instruction = docs_agent.add_instruction_to_context(context)
+    if "gemini" in docs_agent.get_language_model_name():
+        response, full_prompt = docs_agent.ask_content_model_with_context_prompt(
+            context=context, question=question
+        )
+    else:
+        response = docs_agent.ask_text_model_with_context(
+            context_with_instruction, question
+        )
+
+    return full_prompt, response, context, search_result
+
+
+# Display a page showing logs
+def show_logs(agent, template: str = "admin/logs.html"):
+    docs_agent = agent
+    product = docs_agent.config.product_name
+    log_filename = "logs/chatui_logs.txt"
+    answerable_log_filename = "logs/answerable_logs.txt"
+    log_contents = ""
+    answerable_contents = ""
+    if docs_agent.config.enable_show_logs == "True":
+        try:
+            with open(log_filename, "r", encoding="utf-8") as file:
+                log_contents = file.read()
+        except:
+            log_contents = "Cannot find or open log files."
+        try:
+            with open(answerable_log_filename, "r", encoding="utf-8") as file:
+                answerable_contents = file.read()
+        except:
+            answerable_contents = (
+                "Cannot find or open a file that contains answerable scores."
+            )
+    return render_template(
+        template,
+        product=product,
+        logs=log_contents,
+        answerable_logs=answerable_contents,
+    )
+
+
+# Display a page showing debug information.
+def show_debug_info(agent, filename: str, template: str = "admin/debugs.html"):
+    docs_agent = agent
+    product = docs_agent.config.product_name
+    debug_dir = "logs/debugs"
+    debug_filename = f"{debug_dir}/{filename}"
+    debug_info = ""
+    if docs_agent.config.enable_logs_for_debugging == "True":
+        try:
+            if debug_filename.endswith("txt"):
+                with open(debug_filename, "r", encoding="utf-8") as file:
+                    debug_info = file.read()
+        except:
+            debug_info = "Cannot find or open this file."
+    return render_template(
+        template,
+        product=product,
+        debug_info=debug_info,
+    )
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/chatbox.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/chatbox.css
new file mode 120000
index 000000000..44ad5badf
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/chatbox.css
@@ -0,0 +1 @@
+../../../../../third_party/css/chatbox.css
\ No newline at end of file
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-full.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-full.css
new file mode 100644
index 000000000..4cdb442e3
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-full.css
@@ -0,0 +1,625 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ======= General style for HTML elements ======= */
+
+body {
+    font: 16px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    color: #333;
+    font-weight: 300;
+    max-width: 960px;
+    margin: auto;
+    background-color: #d9d9d9;
+    padding-top: 15px;
+    padding-bottom: 15px;
+  }
+
+  a {
+    color: #0a619a;
+  }
+
+  p {
+    margin: 0 0 1em;
+    line-height: 130%;
+  }
+
+  h1 {
+    margin: 0 0 0.5em;
+    font-weight: 500;
+    font-size: 1.3em;
+    margin-left: 1.0em;
+    margin-top: 0.3em;
+  }
+
+  h2 {
+    margin: 0;
+    margin-top: 17px;
+    margin-bottom: 15px;
+    font-weight: normal;
+    font-size: 1.5em;
+  }
+
+  h3 {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 10px;
+  }
+
+  h4 {
+    color: #505050;
+    margin-top: 3px;
+    margin-left: 5px;
+    margin-bottom: 8px;
+  }
+
+  li {
+    margin: 0 0 0.3em;
+  }
+
+  code {
+    font-family: math;
+    color: darkgreen;
+    text-wrap: pretty;
+  }
+
+  /* ======= Style layout by ID ======= */
+
+  #callout-box {
+    margin: auto;
+    max-width: 800px;
+    font: 13px arial, sans-serif;
+    background-color: white;
+    border-style: solid;
+    border-width: 1px;
+    padding: 10px 25px;
+    box-shadow: 5px 5px 5px grey;
+    border-radius: 15px;
+  }
+
+  #important-box {
+    font-size: 0.9em;
+    font-family: system-ui;
+    line-height: 150%;
+    word-break: break-word;
+    #padding: 4px;
+    padding-top: 5px;
+    padding-bottom: 5px;
+    padding-left: 10px;
+    padding-right: 10px;
+    background-color: #fcb8a1;
+    border-radius: 5px;
+    border-width: 2px;
+    border-style: solid;
+  }
+
+  #tldr-response-box {
+    font-size: 0.8em;
+    font-family: sans-serif;
+    line-height: 140%;
+    margin-top: 10px;
+    margin-bottom: 25px;
+    border-width: 2px;
+    border-style: solid;
+    padding-top: 5px;
+    padding-bottom: 5px;
+    padding-left: 10px;
+    padding-right: 10px;
+    background-color: #b1d8f1;
+    border-radius: 5px;
+    border-width: 2px;
+    border-style: solid;
+  }
+
+  #response-box {
+    font-size: 1.0em;
+    font-family: sans-serif;
+    line-height: 140%;
+    margin-top: 10px;
+  }
+  #suggested-questions {
+    font-family: sans-serif;
+    word-break: break-word;
+  }
+
+  #context-content{
+    background: #d7dbd7;
+    font-family: sans-serif;
+    word-break: break-all;
+  }
+
+  #context-pre {
+    font-size: small;
+    font-family: monospace;
+    text-wrap: pretty;
+    margin-top: 0.3px;
+  }
+
+  #probability-box {
+    font-size: small;
+    padding: 4px;
+    margin-bottom: 10px;
+  }
+
+  #grounding-box {
+    font-size: small;
+    padding: 4px;
+    word-break: break-all;
+  }
+
+  #grounding-pre {
+    font-size: small;
+    font-family: monospace;
+    text-wrap: pretty;
+  }
+
+  #reference-box {
+    font-size: 0.9em;
+    font-family: system-ui;
+    text-wrap: pretty;
+    word-break: break-all;
+    margin-bottom: 12px;
+    line-height: 1.5em;
+  }
+
+  #reference-box-no-aqa {
+    font-size: 0.9em;
+    font-family: system-ui;
+    text-wrap: pretty;
+    word-break: break-all;
+    line-height: 1.5em;
+  }
+
+  #aqa-content{
+    background: #9fc7db;
+    font-family: math;
+  }
+
+  #aqa-label{
+    background: #49a5d2;
+  }
+
+  #aqa-json {
+    font-family: system-ui;
+    font-size: small;
+    text-wrap: pretty;
+    word-break: break-all;
+    margin: 0;
+  }
+
+  #rewrite-buttons-box {
+    margin-top: 12px;
+  }
+
+  #feedback-buttons-box {
+    margin-top: 12px;
+  }
+
+  #answerable-span {
+    font-size: small;
+    font-family: system-ui;
+    float: right;
+    padding: 10px;
+  }
+
+  /* ======= Style by class ======= */
+
+  .hidden {
+    display: none;
+  }
+
+  .disable {
+    display: none;
+  }
+
+  .header-wrapper {
+    display: flex;
+  }
+
+  .loading {
+    font: 15px arial, sans-serif;
+    width: 100%;
+    margin-left: 12px;
+    color: #505050;
+    padding: 2px;
+  }
+
+  .notselected {
+    background-color: #303936e6;
+    padding-top: 3px;
+    padding-bottom: 5px;
+  }
+
+  .notselected:hover {
+    background-color: #121a17e6;
+    cursor:pointer;
+  }
+
+  #like-button.selected {
+    background-color: #1e6a9c;
+    padding-top: 7px;
+    padding-bottom: 7px;
+  }
+
+  #dislike-button.selected {
+    background-color: #CF5C3F;
+    padding-top: 7px;
+    padding-bottom: 7px;
+  }
+
+  .selected:hover {
+    background-color: #0a619a;
+    cursor:pointer;
+  }
+
+  .rewrite {
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .feedback {
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .question, .response, .response-text, .fact-checked-text {
+    max-width: 700px;
+    margin-left: 3px;
+  }
+
+  .full-response {
+    max-width: 700px;
+    margin-left: 10px;
+  }
+
+  .related-questions {
+    margin-bottom: 20px;
+    font-size: 0.9em;
+    line-height: 140%;
+  }
+
+  /* ======= Style buttons by ID ======= */
+
+  #rewrite-button {
+    border: 0;
+    background-color: #cf633ff2;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+    margin-top: 0.3em;
+  }
+
+  #rewrite-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #feedback-button {
+    border: 0;
+    background-color: #cf633ff2;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+    margin-top: 0.3em;
+  }
+
+  #feedback-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #like-button {
+    border: 0;
+    color: #fff;
+    padding-left: 7px;
+    padding-right: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #dislike-button {
+    border: 0;
+    color: #fff;
+    padding-left: 7px;
+    padding-right: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #submit-button {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #submit-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #submit-result {
+    color: #027f02d6;
+    font-family: fantasy;
+  }
+  #feedback-submit-button {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #feedback-submit-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #feedback-submit-result {
+    color: #027f02d6;
+    font-family: fantasy;
+  }
+
+  #edit-text-area {
+    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    max-height: 500px;
+    max-width: -webkit-fill-available;
+    height: 300px;
+    width: 650px;
+    padding: 8px;
+  }
+
+  #feedback-text-area {
+    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    max-height: 500px;
+    max-width: -webkit-fill-available;
+    height: 300px;
+    width: 580px;
+    padding: 8px;
+  }
+
+  #rewrite-question-header {
+    margin: 0;
+    margin-bottom: 5px;
+  }
+
+  #rewrite-response-header {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 5px;
+  }
+
+  #user-id {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 15px;
+  }
+
+  #fact-check-url {
+    margin: 0 0 0.7em;
+  }
+
+  #source-para {
+    margin: 0 0 0.7em;
+  }
+
+  #distance-para {
+    margin: 0 0 0.7em;
+    font: 11px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+  }
+  /* ======= Search Box ======= */
+
+  .search {
+    border: 2px solid #CF5C3F;
+    overflow: auto;
+    max-width: 700px;
+    margin-top: 15px;
+    margin-left: 10px;
+    margin-bottom: 10px;
+    border-radius: 5px;
+  }
+
+  .search input[type="text"] {
+    border: 0;
+    width: calc(100% - 65px);
+    padding: 10px;
+  }
+
+  .search input[type="text"]:focus {
+    outline: 0;
+  }
+
+  .search input[type="submit"] {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    float: right;
+    padding: 10px;
+    -moz-border-radius-top-right: 5px;
+    -webkit-border-radius-top-right: 5px;
+    -moz-border-radius-bottom-right: 5px;
+    -webkit-border-radius-bottom-right: 5px;
+    cursor:pointer;
+  }
+
+  /* ======= Accordion ======= */
+
+  .accordion {
+    max-width: 65em;
+    #margin-bottom: 1em;
+  }
+
+  .accordion > input[type="checkbox"] {
+    position: absolute;
+    left: -100vw;
+  }
+
+  .accordion .content {
+    overflow-y: hidden;
+    height: 0;
+    transition: height 0.3s ease;
+  }
+
+  .accordion .reference-content {
+    font-size: 15px;
+    font-family: serif;
+  }
+
+  .accordion > input[type="checkbox"]:checked ~ .content {
+    height: auto;
+    overflow: visible;
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .accordion .handle {
+    margin: 0;
+    font-size: 1.0em;
+  }
+
+  .accordion label {
+    display: block;
+    font-weight: normal;
+    border: 2px solid #000;
+    #padding: 12px;
+    background: #4490b8ab;
+    #border-radius: 15px;
+    padding: 5px;
+    #background: #027f023b;
+    border-radius: 10px;
+  }
+
+  .accordion label:hover,
+  .accordion label:focus {
+    background: #d9d9d9;
+    cursor:pointer;
+  }
+
+  .accordion .handle label::before {
+    font-family: fontawesome, sans-serif;
+    display: inline-block;
+    content: "\2964";
+    margin-right: 10px;
+    font-size: .58em;
+    line-height: 1.556em;
+    vertical-align: middle;
+  }
+
+  .accordion > input[type="checkbox"]:checked ~ .handle label::before {
+    content: "\2965";
+  }
+
+  .accordion p:last-child {
+    margin-bottom: 0;
+  }
+
+  /* ======= Accordion Source ======= */
+
+  .accordion-source {
+    max-width: 65em;
+    margin-bottom: 1em;
+  }
+
+  .accordion-source > input[type="checkbox"] {
+    position: absolute;
+    left: -100vw;
+  }
+
+  .accordion-source .content {
+    overflow-y: hidden;
+    height: 0;
+    transition: height 0.3s ease;
+  }
+
+  .accordion-source .content{
+    font-size: 13px;
+  }
+
+  .accordion-source > input[type="checkbox"]:checked ~ .content {
+    height: auto;
+    overflow: visible;
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .accordion-source .handle {
+    margin: 0;
+    font-size: 1em;
+    line-height: 1.2em;
+  }
+
+  .accordion-source label {
+    display: block;
+    font-weight: normal;
+    border: 1px solid #000;
+    padding: 6px;
+    background: #4490b8ab;
+    border-radius: 15px;
+  }
+
+  .accordion-source label:hover,
+  .accordion-source label:focus {
+    background: #d9d9d9;
+    cursor:pointer;
+  }
+
+  .accordion-source .handle label::before {
+    font-family: fontawesome, sans-serif;
+    display: inline-block;
+    content: "\2964";
+    margin-right: 10px;
+    font-size: .58em;
+    line-height: .556em;
+    vertical-align: middle;
+  }
+
+  .accordion-source > input[type="checkbox"]:checked ~ .handle label::before {
+    content: "\2965";
+  }
+
+  .accordion-source p:last-child {
+    margin-bottom: 0;
+  }
+
+/* Loader animation */
+/* Source: https://css-loaders.com/classic/ */
+.loader {
+  width: fit-content;
+  font-family: monospace;
+  font-size: 14px;
+  margin-left: 13px;
+  clip-path: inset(0 3ch 0 0);
+  animation: animation 1s steps(4) infinite;
+}
+.loader:before {
+  content:"Generating a response..."
+}
+@keyframes animation {to{clip-path: inset(0 -1ch 0 0)}}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-widget-pro.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-widget-pro.css
new file mode 100644
index 000000000..b343fb6e1
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-widget-pro.css
@@ -0,0 +1,635 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ======= General style for HTML elements ======= */
+
+body {
+    font: 16px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    color: #333;
+    font-weight: 300;
+    max-width: 960px;
+    margin: auto;
+    background-color: white;
+    padding-top: 15px;
+    padding-bottom: 15px;
+  }
+
+  a {
+    color: #0a619a;
+  }
+
+  p {
+    margin: 0 0 1em;
+    line-height: 130%;
+  }
+
+  h1 {
+    margin: 0 0 0.5em;
+    font-weight: 500;
+    font-size: 1.3em;
+    margin-top: 0.1em;
+    margin-left: 1.0em;
+    margin-bottom: 0.9em;
+  }
+
+  h2 {
+    margin: 0;
+    margin-top: 15px;
+    margin-bottom: 10px;
+    font-weight: normal;
+    font-size: 1.4em;
+  }
+
+  h3 {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 10px;
+  }
+
+  h4 {
+    color: #505050;
+    margin-top: 3px;
+    margin-left: 5px;
+    margin-bottom: 8px;
+  }
+
+  li {
+    margin: 0 0 0.3em;
+  }
+
+  code {
+    font-family: math;
+    color: darkgreen;
+    text-wrap: pretty;
+  }
+
+  /* ======= Style layout by ID ======= */
+
+  #iframe-box {
+    margin: 0px;
+    max-width: 760px;
+    font: 15px arial, sans-serif;
+    background-color: white;
+    padding-bottom: 0px;
+    padding-left: 0px;
+  }
+
+  #callout-box {
+    margin: auto;
+    max-width: 800px;
+    font: 13px arial, sans-serif;
+    background-color: white;
+    border-style: solid;
+    border-width: 1px;
+    padding: 10px 25px;
+    box-shadow: 5px 5px 5px grey;
+    border-radius: 15px;
+  }
+
+  #important-box {
+    font-size: 0.9em;
+    font-family: system-ui;
+    line-height: 150%;
+    word-break: break-word;
+    #padding: 4px;
+    padding-top: 5px;
+    padding-bottom: 5px;
+    padding-left: 10px;
+    padding-right: 10px;
+    background-color: #fcb8a1;
+    border-radius: 5px;
+    border-width: 2px;
+    border-style: solid;
+  }
+
+  #tldr-response-box {
+    font-size: 0.8em;
+    font-family: sans-serif;
+    line-height: 140%;
+    margin-top: 10px;
+    margin-bottom: 25px;
+    border-width: 2px;
+    border-style: solid;
+    padding-top: 5px;
+    padding-bottom: 5px;
+    padding-left: 10px;
+    padding-right: 10px;
+    background-color: #b1d8f1;
+    border-radius: 5px;
+    border-width: 2px;
+    border-style: solid;
+  }
+
+  #response-box {
+    font-size: 1.0em;
+    font-family: sans-serif;
+    line-height: 140%;
+    margin-top: 10px;
+  }
+  #suggested-questions {
+    font-family: sans-serif;
+    word-break: break-word;
+  }
+
+  #context-content{
+    background: #d7dbd7;
+    font-family: sans-serif;
+    word-break: break-all;
+  }
+
+  #context-pre {
+    font-size: small;
+    font-family: monospace;
+    text-wrap: pretty;
+    margin-top: 0.3px;
+  }
+
+  #probability-box {
+    font-size: small;
+    padding: 4px;
+    margin-bottom: 10px;
+  }
+
+  #grounding-box {
+    font-size: small;
+    padding: 4px;
+    word-break: break-all;
+  }
+
+  #grounding-pre {
+    font-size: small;
+    font-family: monospace;
+    text-wrap: pretty;
+  }
+
+  #reference-box {
+    font-size: 0.9em;
+    font-family: system-ui;
+    text-wrap: pretty;
+    word-break: break-all;
+    margin-bottom: 12px;
+    line-height: 1.5em;
+  }
+
+  #reference-box-no-aqa {
+    font-size: 0.9em;
+    font-family: system-ui;
+    text-wrap: pretty;
+    word-break: break-all;
+    line-height: 1.5em;
+  }
+
+  #aqa-content{
+    background: #9fc7db;
+    font-family: math;
+  }
+
+  #aqa-label{
+    background: #49a5d2;
+  }
+
+  #aqa-json {
+    font-family: system-ui;
+    font-size: small;
+    text-wrap: pretty;
+    word-break: break-all;
+    margin: 0;
+  }
+
+  #rewrite-buttons-box {
+    margin-top: 12px;
+  }
+
+  #feedback-buttons-box {
+    margin-top: 12px;
+  }
+
+  #answerable-span {
+    font-size: small;
+    font-family: system-ui;
+    float: right;
+    padding: 10px;
+  }
+
+  /* ======= Style by class ======= */
+
+  .hidden {
+    display: none;
+  }
+
+  .disable {
+    display: none;
+  }
+
+  .header-wrapper {
+    display: flex;
+  }
+
+  .loading {
+    font: 15px arial, sans-serif;
+    width: 100%;
+    margin-left: 12px;
+    color: #505050;
+    padding: 2px;
+  }
+
+  .notselected {
+    background-color: #303936e6;
+    padding-top: 3px;
+    padding-bottom: 5px;
+  }
+
+  .notselected:hover {
+    background-color: #121a17e6;
+    cursor:pointer;
+  }
+
+  #like-button.selected {
+    background-color: #1e6a9c;
+    padding-top: 7px;
+    padding-bottom: 7px;
+  }
+
+  #dislike-button.selected {
+    background-color: #CF5C3F;
+    padding-top: 7px;
+    padding-bottom: 7px;
+  }
+
+  .selected:hover {
+    background-color: #0a619a;
+    cursor:pointer;
+  }
+
+  .rewrite {
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .feedback {
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .question, .response, .response-text, .fact-checked-text {
+    max-width: 700px;
+    margin-left: 3px;
+  }
+
+  .full-response {
+    max-width: 700px;
+    margin-left: 10px;
+  }
+
+  .related-questions {
+    margin-bottom: 20px;
+    font-size: 0.9em;
+    line-height: 140%;
+  }
+
+  /* ======= Style buttons by ID ======= */
+
+  #rewrite-button {
+    border: 0;
+    background-color: #cf633ff2;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+    margin-top: 0.3em;
+  }
+
+  #rewrite-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #feedback-button {
+    border: 0;
+    background-color: #cf633ff2;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+    margin-top: 0.3em;
+  }
+
+  #feedback-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #like-button {
+    border: 0;
+    color: #fff;
+    padding-left: 7px;
+    padding-right: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #dislike-button {
+    border: 0;
+    color: #fff;
+    padding-left: 7px;
+    padding-right: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #submit-button {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #submit-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #submit-result {
+    color: #027f02d6;
+    font-family: fantasy;
+  }
+  #feedback-submit-button {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #feedback-submit-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #feedback-submit-result {
+    color: #027f02d6;
+    font-family: fantasy;
+  }
+
+  #edit-text-area {
+    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    max-height: 500px;
+    max-width: -webkit-fill-available;
+    height: 300px;
+    width: 650px;
+    padding: 8px;
+  }
+
+  #feedback-text-area {
+    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    max-height: 500px;
+    max-width: -webkit-fill-available;
+    height: 300px;
+    width: 580px;
+    padding: 8px;
+  }
+
+  #rewrite-question-header {
+    margin: 0;
+    margin-bottom: 5px;
+  }
+
+  #rewrite-response-header {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 5px;
+  }
+
+  #user-id {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 15px;
+  }
+
+  #fact-check-url {
+    margin: 0 0 0.7em;
+  }
+
+  #source-para {
+    margin: 0 0 0.7em;
+  }
+
+  #distance-para {
+    margin: 0 0 0.7em;
+    font: 11px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+  }
+  /* ======= Search Box ======= */
+
+  .search {
+    border: 2px solid #CF5C3F;
+    overflow: auto;
+    max-width: 700px;
+    margin-top: 15px;
+    margin-left: 10px;
+    margin-bottom: 10px;
+    border-radius: 5px;
+  }
+
+  .search input[type="text"] {
+    border: 0;
+    width: calc(100% - 65px);
+    padding: 10px;
+  }
+
+  .search input[type="text"]:focus {
+    outline: 0;
+  }
+
+  .search input[type="submit"] {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    float: right;
+    padding: 10px;
+    -moz-border-radius-top-right: 5px;
+    -webkit-border-radius-top-right: 5px;
+    -moz-border-radius-bottom-right: 5px;
+    -webkit-border-radius-bottom-right: 5px;
+    cursor:pointer;
+  }
+
+  /* ======= Accordion ======= */
+
+  .accordion {
+    max-width: 65em;
+    #margin-bottom: 1em;
+  }
+
+  .accordion > input[type="checkbox"] {
+    position: absolute;
+    left: -100vw;
+  }
+
+  .accordion .content {
+    overflow-y: hidden;
+    height: 0;
+    transition: height 0.3s ease;
+  }
+
+  .accordion .reference-content {
+    font-size: 15px;
+    font-family: serif;
+  }
+
+  .accordion > input[type="checkbox"]:checked ~ .content {
+    height: auto;
+    overflow: visible;
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .accordion .handle {
+    margin: 0;
+    font-size: 1.0em;
+  }
+
+  .accordion label {
+    display: block;
+    font-weight: normal;
+    border: 2px solid #000;
+    #padding: 12px;
+    background: #4490b8ab;
+    #border-radius: 15px;
+    padding: 5px;
+    #background: #027f023b;
+    border-radius: 10px;
+  }
+
+  .accordion label:hover,
+  .accordion label:focus {
+    background: #d9d9d9;
+    cursor:pointer;
+  }
+
+  .accordion .handle label::before {
+    font-family: fontawesome, sans-serif;
+    display: inline-block;
+    content: "\2964";
+    margin-right: 10px;
+    font-size: .58em;
+    line-height: 1.556em;
+    vertical-align: middle;
+  }
+
+  .accordion > input[type="checkbox"]:checked ~ .handle label::before {
+    content: "\2965";
+  }
+
+  .accordion p:last-child {
+    margin-bottom: 0;
+  }
+
+  /* ======= Accordion Source ======= */
+
+  .accordion-source {
+    max-width: 65em;
+    margin-bottom: 1em;
+  }
+
+  .accordion-source > input[type="checkbox"] {
+    position: absolute;
+    left: -100vw;
+  }
+
+  .accordion-source .content {
+    overflow-y: hidden;
+    height: 0;
+    transition: height 0.3s ease;
+  }
+
+  .accordion-source .content{
+    font-size: 13px;
+  }
+
+  .accordion-source > input[type="checkbox"]:checked ~ .content {
+    height: auto;
+    overflow: visible;
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .accordion-source .handle {
+    margin: 0;
+    font-size: 1em;
+    line-height: 1.2em;
+  }
+
+  .accordion-source label {
+    display: block;
+    font-weight: normal;
+    border: 1px solid #000;
+    padding: 6px;
+    background: #4490b8ab;
+    border-radius: 15px;
+  }
+
+  .accordion-source label:hover,
+  .accordion-source label:focus {
+    background: #d9d9d9;
+    cursor:pointer;
+  }
+
+  .accordion-source .handle label::before {
+    font-family: fontawesome, sans-serif;
+    display: inline-block;
+    content: "\2964";
+    margin-right: 10px;
+    font-size: .58em;
+    line-height: .556em;
+    vertical-align: middle;
+  }
+
+  .accordion-source > input[type="checkbox"]:checked ~ .handle label::before {
+    content: "\2965";
+  }
+
+  .accordion-source p:last-child {
+    margin-bottom: 0;
+  }
+
+/* Loader animation */
+/* Source: https://css-loaders.com/classic/ */
+.loader {
+  width: fit-content;
+  font-family: monospace;
+  font-size: 14px;
+  margin-left: 13px;
+  clip-path: inset(0 3ch 0 0);
+  animation: animation 1s steps(4) infinite;
+}
+.loader:before {
+  content:"Generating a response..."
+}
+@keyframes animation {to{clip-path: inset(0 -1ch 0 0)}}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-widget.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-widget.css
new file mode 100644
index 000000000..9658ef6a7
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-widget.css
@@ -0,0 +1,624 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ======= General style for HTML elements ======= */
+
+body {
+    font: 16px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    color: #333;
+    font-weight: 300;
+    max-width: 960px;
+    margin: auto;
+    background-color: white;
+    padding-top: 15px;
+    padding-bottom: 15px;
+  }
+
+  a {
+    color: #0a619a;
+  }
+
+  p {
+    margin: 0 0 1em;
+    line-height: 130%;
+  }
+
+  h1 {
+    margin: 0 0 0.5em;
+    font-weight: 500;
+    font-size: 1.3em;
+    margin-top: 0.1em;
+    margin-left: 1.0em;
+    margin-bottom: 0.9em;
+  }
+
+  h2 {
+    margin: 0;
+    margin-top: 15px;
+    margin-bottom: 10px;
+    font-weight: normal;
+    font-size: 1.4em;
+  }
+
+  h3 {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 10px;
+  }
+
+  h4 {
+    color: #505050;
+    margin-top: 3px;
+    margin-left: 5px;
+    margin-bottom: 8px;
+  }
+
+  li {
+    margin: 0 0 0.3em;
+  }
+
+  code {
+    font-family: math;
+    color: darkgreen;
+    text-wrap: pretty;
+  }
+
+  /* ======= Style layout by ID ======= */
+
+  #iframe-box {
+    margin: 0px;
+    max-width: 760px;
+    font: 15px arial, sans-serif;
+    background-color: white;
+    padding-bottom: 0px;
+    padding-left: 0px;
+  }
+
+  #callout-box {
+    margin: auto;
+    max-width: 800px;
+    font: 13px arial, sans-serif;
+    background-color: white;
+    border-style: solid;
+    border-width: 1px;
+    padding: 10px 25px;
+    box-shadow: 5px 5px 5px grey;
+    border-radius: 15px;
+  }
+
+  #important-box {
+    font-size: 0.9em;
+    font-family: system-ui;
+    word-break: break-word;
+    line-height: 150%;
+    word-break: break-word;
+    padding: 4px;
+  }
+
+  #response-box {
+    font-size: 1.0em;
+    font-family: sans-serif;
+    line-height: 140%;
+    margin-top: 10px;
+  }
+
+  #suggested-questions {
+    font-family: sans-serif;
+    word-break: break-word;
+  }
+
+  #source-pages {
+    font-family: sans-serif;
+    word-break: break-all;
+  }
+
+  #context-content{
+    background: #d7dbd7;
+    font-family: sans-serif;
+    word-break: break-all;
+  }
+
+  #context-pre {
+    font-size: small;
+    font-family: monospace;
+    text-wrap: pretty;
+    margin-top: 0.3px;
+  }
+
+  #probability-box {
+    font-size: small;
+    padding: 4px;
+    margin-bottom: 10px;
+  }
+
+  #grounding-box {
+    font-size: small;
+    padding: 4px;
+    word-break: break-all;
+  }
+
+  #grounding-pre {
+    font-size: small;
+    font-family: monospace;
+    text-wrap: pretty;
+  }
+
+  #reference-box {
+    font-size: 0.9em;
+    font-family: system-ui;
+    text-wrap: pretty;
+    word-break: break-all;
+    margin-bottom: 12px;
+    line-height: 1.5em;
+  }
+
+  #reference-box-no-aqa {
+    font-size: 0.9em;
+    font-family: system-ui;
+    text-wrap: pretty;
+    word-break: break-all;
+    line-height: 1.5em;
+  }
+
+  #aqa-content{
+    background: #9fc7db;
+    font-family: math;
+  }
+
+  #aqa-label{
+    background: #49a5d2;
+  }
+
+  #aqa-json {
+    font-family: system-ui;
+    font-size: small;
+    text-wrap: pretty;
+    word-break: break-all;
+    margin: 0;
+  }
+
+  #rewrite-buttons-box {
+    margin-top: 12px;
+  }
+
+  #feedback-buttons-box {
+    margin-top: 12px;
+  }
+
+  #answerable-span {
+    font-size: small;
+    font-family: system-ui;
+    float: right;
+    padding: 10px;
+  }
+
+  /* ======= Style by class ======= */
+
+  .hidden {
+    display: none;
+  }
+
+  .disable {
+    display: none;
+  }
+
+  .header-wrapper {
+    display: flex;
+  }
+
+  .loading {
+    font: 15px arial, sans-serif;
+    width: 100%;
+    margin-left: 12px;
+    color: #505050;
+    padding: 2px;
+  }
+
+  .notselected {
+    background-color: #303936e6;
+    padding-top: 3px;
+    padding-bottom: 5px;
+  }
+
+  .notselected:hover {
+    background-color: #121a17e6;
+    cursor:pointer;
+  }
+
+  #like-button.selected {
+    background-color: #1e6a9c;
+    padding-top: 7px;
+    padding-bottom: 7px;
+  }
+
+  #dislike-button.selected {
+    background-color: #CF5C3F;
+    padding-top: 7px;
+    padding-bottom: 7px;
+  }
+
+  .selected:hover {
+    background-color: #0a619a;
+    cursor:pointer;
+  }
+
+  .rewrite {
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .feedback {
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .question, .response, .response-text, .fact-checked-text {
+    max-width: 700px;
+    margin-left: 3px;
+  }
+
+  .full-response {
+    max-width: 700px;
+    margin-left: 10px;
+  }
+
+  .related-questions {
+    margin-bottom: 20px;
+    font-size: 0.9em;
+    line-height: 140%;
+  }
+
+  .relevant-sources {
+    margin-bottom: 20px;
+    font-size: 0.9em;
+    line-height: 140%;
+  }
+
+  /* ======= Style buttons by ID ======= */
+
+  #rewrite-button {
+    border: 0;
+    background-color: #cf633ff2;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+    margin-top: 0.3em;
+  }
+
+  #rewrite-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #feedback-button {
+    border: 0;
+    background-color: #cf633ff2;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+    margin-top: 0.3em;
+  }
+
+  #feedback-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #like-button {
+    border: 0;
+    color: #fff;
+    padding-left: 7px;
+    padding-right: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #dislike-button {
+    border: 0;
+    color: #fff;
+    padding-left: 7px;
+    padding-right: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #submit-button {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #submit-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #submit-result {
+    color: #027f02d6;
+    font-family: fantasy;
+  }
+
+  #feedback-submit-button {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #feedback-submit-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #feedback-submit-result {
+    color: #027f02d6;
+    font-family: fantasy;
+  }
+
+  #edit-text-area {
+    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    max-height: 500px;
+    max-width: -webkit-fill-available;
+    height: 300px;
+    width: 580px;
+    padding: 8px;
+  }
+
+  #feedback-text-area {
+    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    max-height: 500px;
+    max-width: -webkit-fill-available;
+    height: 300px;
+    width: 580px;
+    padding: 8px;
+  }
+
+  #rewrite-question-header {
+    margin: 0;
+    margin-bottom: 5px;
+  }
+
+  #rewrite-response-header {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 5px;
+  }
+
+  #user-id {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 15px;
+  }
+
+  #fact-check-url {
+    margin: 0 0 0.7em;
+  }
+
+  #source-para {
+    margin: 0 0 0.7em;
+  }
+
+  #distance-para {
+    margin: 0 0 0.7em;
+    font: 11px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+  }
+  /* ======= Search Box ======= */
+
+  .search {
+    border: 2px solid #CF5C3F;
+    overflow: auto;
+    max-width: 600px;
+    margin-top: 15px;
+    margin-left: 10px;
+    margin-bottom: 10px;
+    border-radius: 5px;
+  }
+
+  .search input[type="text"] {
+    border: 0;
+    width: calc(100% - 65px);
+    padding: 10px;
+  }
+
+  .search input[type="text"]:focus {
+    outline: 0;
+  }
+
+  .search input[type="submit"] {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    float: right;
+    padding: 10px;
+    -moz-border-radius-top-right: 5px;
+    -webkit-border-radius-top-right: 5px;
+    -moz-border-radius-bottom-right: 5px;
+    -webkit-border-radius-bottom-right: 5px;
+    cursor:pointer;
+  }
+
+  /* ======= Accordion ======= */
+
+  .accordion {
+    max-width: 65em;
+    #margin-bottom: 1em;
+  }
+
+  .accordion > input[type="checkbox"] {
+    position: absolute;
+    left: -100vw;
+  }
+
+  .accordion .content {
+    overflow-y: hidden;
+    height: 0;
+    transition: height 0.3s ease;
+  }
+
+  .accordion .reference-content {
+    font-size: 15px;
+    font-family: serif;
+  }
+
+  .accordion > input[type="checkbox"]:checked ~ .content {
+    height: auto;
+    overflow: visible;
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .accordion .handle {
+    margin: 0;
+    font-size: 1.0em;
+  }
+
+  .accordion label {
+    display: block;
+    font-weight: normal;
+    border: 2px solid #000;
+    #padding: 12px;
+    background: #4490b8ab;
+    #border-radius: 15px;
+    padding: 5px;
+    #background: #027f023b;
+    border-radius: 10px;
+  }
+
+  .accordion label:hover,
+  .accordion label:focus {
+    background: #d9d9d9;
+    cursor:pointer;
+  }
+
+  .accordion .handle label::before {
+    font-family: fontawesome, sans-serif;
+    display: inline-block;
+    content: "\2964";
+    margin-right: 10px;
+    font-size: .58em;
+    line-height: 1.556em;
+    vertical-align: middle;
+  }
+
+  .accordion > input[type="checkbox"]:checked ~ .handle label::before {
+    content: "\2965";
+  }
+
+  .accordion p:last-child {
+    margin-bottom: 0;
+  }
+
+  /* ======= Accordion Source ======= */
+
+  .accordion-source {
+    max-width: 65em;
+    margin-bottom: 1em;
+  }
+
+  .accordion-source > input[type="checkbox"] {
+    position: absolute;
+    left: -100vw;
+  }
+
+  .accordion-source .content {
+    overflow-y: hidden;
+    height: 0;
+    transition: height 0.3s ease;
+  }
+
+  .accordion-source .content{
+    font-size: 13px;
+  }
+
+  .accordion-source > input[type="checkbox"]:checked ~ .content {
+    height: auto;
+    overflow: visible;
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .accordion-source .handle {
+    margin: 0;
+    font-size: 1em;
+    line-height: 1.2em;
+  }
+
+  .accordion-source label {
+    display: block;
+    font-weight: normal;
+    border: 1px solid #000;
+    padding: 6px;
+    background: #4490b8ab;
+    border-radius: 15px;
+  }
+
+  .accordion-source label:hover,
+  .accordion-source label:focus {
+    background: #d9d9d9;
+    cursor:pointer;
+  }
+
+  .accordion-source .handle label::before {
+    font-family: fontawesome, sans-serif;
+    display: inline-block;
+    content: "\2964";
+    margin-right: 10px;
+    font-size: .58em;
+    line-height: .556em;
+    vertical-align: middle;
+  }
+
+  .accordion-source > input[type="checkbox"]:checked ~ .handle label::before {
+    content: "\2965";
+  }
+
+  .accordion-source p:last-child {
+    margin-bottom: 0;
+  }
+
+/* Loader animation */
+/* Source: https://css-loaders.com/classic/ */
+.loader {
+  width: fit-content;
+  font-family: monospace;
+  font-size: 14px;
+  margin-left: 13px;
+  clip-path: inset(0 3ch 0 0);
+  animation: animation 1s steps(4) infinite;
+}
+.loader:before {
+  content:"Generating a response..."
+}
+@keyframes animation {to{clip-path: inset(0 -1ch 0 0)}}
+
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui.css
new file mode 100644
index 000000000..7267c9b22
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui.css
@@ -0,0 +1,601 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ======= General style for HTML elements ======= */
+
+body {
+    font: 16px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    color: #333;
+    font-weight: 300;
+    max-width: 960px;
+    margin: auto;
+    background-color: #d9d9d9;
+    padding-top: 15px;
+    padding-bottom: 15px;
+  }
+
+  a {
+    color: #0a619a;
+  }
+
+  p {
+    margin: 0 0 1em;
+    line-height: 130%;
+  }
+
+  h1 {
+    margin: 0 0 0.5em;
+    font-weight: 500;
+    font-size: 1.3em;
+    margin-left: 1.0em;
+    margin-top: 0.3em;
+  }
+
+  h2 {
+    margin: 0;
+    margin-top: 17px;
+    margin-bottom: 15px;
+    font-weight: normal;
+    font-size: 1.5em;
+  }
+
+  h3 {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 10px;
+  }
+
+  h4 {
+    color: #505050;
+    margin-top: 3px;
+    margin-left: 5px;
+    margin-bottom: 8px;
+  }
+
+  li {
+    margin: 0 0 0.3em;
+  }
+
+  code {
+    font-family: math;
+    color: darkgreen;
+    text-wrap: pretty;
+  }
+
+  /* ======= Style layout by ID ======= */
+
+  #callout-box {
+    margin: auto;
+    max-width: 800px;
+    font: 13px arial, sans-serif;
+    background-color: white;
+    border-style: solid;
+    border-width: 1px;
+    padding: 10px 25px;
+    box-shadow: 5px 5px 5px grey;
+    border-radius: 15px;
+  }
+
+  #important-box {
+    font-size: 0.9em;
+    font-family: system-ui;
+    line-height: 150%;
+    word-break: break-word;
+    padding: 4px;
+  }
+
+  #response-box {
+    font-size: 1.0em;
+    font-family: sans-serif;
+    line-height: 140%;
+    margin-top: 10px;
+  }
+
+  #suggested-questions {
+    font-family: sans-serif;
+    word-break: break-word;
+  }
+
+  #context-content{
+    background: #d7dbd7;
+    font-family: sans-serif;
+    word-break: break-all;
+  }
+
+  #context-pre {
+    font-size: small;
+    font-family: monospace;
+    text-wrap: pretty;
+    margin-top: 0.3px;
+  }
+
+  #probability-box {
+    font-size: small;
+    padding: 4px;
+    margin-bottom: 10px;
+  }
+
+  #grounding-box {
+    font-size: small;
+    padding: 4px;
+    word-break: break-all;
+  }
+
+  #grounding-pre {
+    font-size: small;
+    font-family: monospace;
+    text-wrap: pretty;
+  }
+
+  #reference-box {
+    font-size: 0.9em;
+    font-family: system-ui;
+    text-wrap: pretty;
+    word-break: break-all;
+    margin-bottom: 12px;
+    line-height: 1.5em;
+  }
+
+  #reference-box-no-aqa {
+    font-size: 0.9em;
+    font-family: system-ui;
+    text-wrap: pretty;
+    word-break: break-all;
+    line-height: 1.5em;
+  }
+
+  #aqa-content{
+    background: #9fc7db;
+    font-family: math;
+  }
+
+  #aqa-label{
+    background: #49a5d2;
+  }
+
+  #aqa-json {
+    font-family: system-ui;
+    font-size: small;
+    text-wrap: pretty;
+    word-break: break-all;
+    margin: 0;
+  }
+
+  #rewrite-buttons-box {
+    margin-top: 12px;
+  }
+
+  #feedback-buttons-box {
+    margin-top: 12px;
+  }
+
+  #answerable-span {
+    font-size: small;
+    font-family: system-ui;
+    float: right;
+    padding: 10px;
+  }
+
+  /* ======= Style by class ======= */
+
+  .hidden {
+    display: none;
+  }
+
+  .disable {
+    display: none;
+  }
+
+  .header-wrapper {
+    display: flex;
+  }
+
+  .loading {
+    font: 15px arial, sans-serif;
+    width: 100%;
+    margin-left: 12px;
+    color: #505050;
+    padding: 2px;
+  }
+
+  .notselected {
+    background-color: #303936e6;
+    padding-top: 3px;
+    padding-bottom: 5px;
+  }
+
+  .notselected:hover {
+    background-color: #121a17e6;
+    cursor:pointer;
+  }
+
+  #like-button.selected {
+    background-color: #1e6a9c;
+    padding-top: 7px;
+    padding-bottom: 7px;
+  }
+
+  #dislike-button.selected {
+    background-color: #CF5C3F;
+    padding-top: 7px;
+    padding-bottom: 7px;
+  }
+
+  .selected:hover {
+    background-color: #0a619a;
+    cursor:pointer;
+  }
+
+  .rewrite {
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .feedback {
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .question, .response, .response-text, .fact-checked-text {
+    max-width: 700px;
+    margin-left: 3px;
+  }
+
+  .full-response {
+    max-width: 700px;
+    margin-left: 10px;
+  }
+
+  .related-questions {
+    margin-bottom: 20px;
+    font-size: 0.9em;
+    line-height: 140%;
+  }
+
+  /* ======= Style buttons by ID ======= */
+
+  #rewrite-button {
+    border: 0;
+    background-color: #cf633ff2;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+    margin-top: 0.3em;
+  }
+
+  #rewrite-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #feedback-button {
+    border: 0;
+    background-color: #cf633ff2;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+    margin-top: 0.3em;
+  }
+
+  #feedback-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #like-button {
+    border: 0;
+    color: #fff;
+    padding-left: 7px;
+    padding-right: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #dislike-button {
+    border: 0;
+    color: #fff;
+    padding-left: 7px;
+    padding-right: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #submit-button {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #submit-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #submit-result {
+    color: #027f02d6;
+    font-family: fantasy;
+  }
+
+  #feedback-submit-button {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #feedback-submit-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #feedback-submit-result {
+    color: #027f02d6;
+    font-family: fantasy;
+  }
+
+  #edit-text-area {
+    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    max-height: 500px;
+    max-width: -webkit-fill-available;
+    height: 300px;
+    width: 650px;
+    padding: 8px;
+  }
+
+  #feedback-text-area {
+    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    max-height: 500px;
+    max-width: -webkit-fill-available;
+    height: 300px;
+    width: 580px;
+    padding: 8px;
+  }
+
+  #rewrite-question-header {
+    margin: 0;
+    margin-bottom: 5px;
+  }
+
+  #rewrite-response-header {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 5px;
+  }
+
+  #user-id {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 15px;
+  }
+
+  #fact-check-url {
+    margin: 0 0 0.7em;
+  }
+
+  #source-para {
+    margin: 0 0 0.7em;
+  }
+
+  #distance-para {
+    margin: 0 0 0.7em;
+    font: 11px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+  }
+  /* ======= Search Box ======= */
+
+  .search {
+    border: 2px solid #CF5C3F;
+    overflow: auto;
+    max-width: 700px;
+    margin-top: 15px;
+    margin-left: 10px;
+    margin-bottom: 10px;
+    border-radius: 5px;
+  }
+
+  .search input[type="text"] {
+    border: 0;
+    width: calc(100% - 65px);
+    padding: 10px;
+  }
+
+  .search input[type="text"]:focus {
+    outline: 0;
+  }
+
+  .search input[type="submit"] {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    float: right;
+    padding: 10px;
+    -moz-border-radius-top-right: 5px;
+    -webkit-border-radius-top-right: 5px;
+    -moz-border-radius-bottom-right: 5px;
+    -webkit-border-radius-bottom-right: 5px;
+    cursor:pointer;
+  }
+
+  /* ======= Accordion ======= */
+
+  .accordion {
+    max-width: 65em;
+    #margin-bottom: 1em;
+  }
+
+  .accordion > input[type="checkbox"] {
+    position: absolute;
+    left: -100vw;
+  }
+
+  .accordion .content {
+    overflow-y: hidden;
+    height: 0;
+    transition: height 0.3s ease;
+  }
+
+  .accordion .reference-content {
+    font-size: 15px;
+    font-family: serif;
+  }
+
+  .accordion > input[type="checkbox"]:checked ~ .content {
+    height: auto;
+    overflow: visible;
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .accordion .handle {
+    margin: 0;
+    font-size: 1.0em;
+  }
+
+  .accordion label {
+    display: block;
+    font-weight: normal;
+    border: 2px solid #000;
+    #padding: 12px;
+    background: #4490b8ab;
+    #border-radius: 15px;
+    padding: 5px;
+    #background: #027f023b;
+    border-radius: 10px;
+  }
+
+  .accordion label:hover,
+  .accordion label:focus {
+    background: #d9d9d9;
+    cursor:pointer;
+  }
+
+  .accordion .handle label::before {
+    font-family: fontawesome, sans-serif;
+    display: inline-block;
+    content: "\2964";
+    margin-right: 10px;
+    font-size: .58em;
+    line-height: 1.556em;
+    vertical-align: middle;
+  }
+
+  .accordion > input[type="checkbox"]:checked ~ .handle label::before {
+    content: "\2965";
+  }
+
+  .accordion p:last-child {
+    margin-bottom: 0;
+  }
+
+  /* ======= Accordion Source ======= */
+
+  .accordion-source {
+    max-width: 65em;
+    margin-bottom: 1em;
+  }
+
+  .accordion-source > input[type="checkbox"] {
+    position: absolute;
+    left: -100vw;
+  }
+
+  .accordion-source .content {
+    overflow-y: hidden;
+    height: 0;
+    transition: height 0.3s ease;
+  }
+
+  .accordion-source .content{
+    font-size: 13px;
+  }
+
+  .accordion-source > input[type="checkbox"]:checked ~ .content {
+    height: auto;
+    overflow: visible;
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .accordion-source .handle {
+    margin: 0;
+    font-size: 1em;
+    line-height: 1.2em;
+  }
+
+  .accordion-source label {
+    display: block;
+    font-weight: normal;
+    border: 1px solid #000;
+    padding: 6px;
+    background: #4490b8ab;
+    border-radius: 15px;
+  }
+
+  .accordion-source label:hover,
+  .accordion-source label:focus {
+    background: #d9d9d9;
+    cursor:pointer;
+  }
+
+  .accordion-source .handle label::before {
+    font-family: fontawesome, sans-serif;
+    display: inline-block;
+    content: "\2964";
+    margin-right: 10px;
+    font-size: .58em;
+    line-height: .556em;
+    vertical-align: middle;
+  }
+
+  .accordion-source > input[type="checkbox"]:checked ~ .handle label::before {
+    content: "\2965";
+  }
+
+  .accordion-source p:last-child {
+    margin-bottom: 0;
+  }
+
+/* Loader animation */
+/* Source: https://css-loaders.com/classic/ */
+.loader {
+  width: fit-content;
+  font-family: monospace;
+  font-size: 14px;
+  margin-left: 13px;
+  clip-path: inset(0 3ch 0 0);
+  animation: animation 1s steps(4) infinite;
+}
+.loader:before {
+  content:"Generating a response..."
+}
+@keyframes animation {to{clip-path: inset(0 -1ch 0 0)}}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-logs.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-logs.css
new file mode 100644
index 000000000..e0e884c0a
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-logs.css
@@ -0,0 +1,22 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+.log-pre {
+  font-size: small;
+  font-family: monospace;
+  text-wrap: pretty;
+  word-break: break-all;
+}
diff --git a/demos/palm/python/docs-agent/chatbot/static/images/favicon.png b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/images/favicon.png
similarity index 100%
rename from demos/palm/python/docs-agent/chatbot/static/images/favicon.png
rename to examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/images/favicon.png
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/javascript/app.js b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/javascript/app.js
new file mode 100644
index 000000000..cd3e9aba9
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/javascript/app.js
@@ -0,0 +1,342 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// Display the "loading" message when a question is entered and submitted.
+let askButton = document.getElementById('ask-button');
+let loadingDiv = document.getElementById('loading-div');
+
+if (askButton != null){
+  askButton.addEventListener('click',function (){
+    if (loadingDiv.classList.contains("hidden")){
+      loadingDiv.classList.remove("hidden");
+    }
+  });
+}
+
+// Display the "loading" message when a related question is clicked.
+let relatedQuestions = document.getElementById('suggested-questions');
+
+if (relatedQuestions != null){
+  questions = relatedQuestions.getElementsByTagName('a');
+  for(i=0; i<questions.length; i++){
+    questions[i].addEventListener('click',function (){
+      if (loadingDiv.classList.contains("hidden")){
+        loadingDiv.classList.remove("hidden");
+      }
+    });
+  }
+}
+
+// Display the "aqa-box" div only if the aqa json response is included.
+let aqaContent = document.getElementById('aqa-content');
+let aqaBox = document.getElementById('aqa-box');
+
+if (aqaContent != null){
+  if (aqaContent.innerText.trim() != ""){
+    if (aqaBox.classList.contains("hidden")){
+      aqaBox.classList.remove("hidden");
+    }
+  }
+}
+
+// Toggle the hidden class on the `rewrite-box` div.
+let rewriteButton = document.getElementById('rewrite-button');
+
+if (rewriteButton != null){
+  rewriteButton.addEventListener('click',function (){
+    let rewriteBox = document.getElementById('rewrite-box');
+    if (rewriteBox.classList.contains("hidden")){
+      rewriteBox.classList.remove("hidden");
+      // Trigger a focus event on the textarea
+      let element = document.getElementById('edit-text-area');
+      element.dispatchEvent(new Event("focus"));
+    }else{
+      rewriteBox.classList.add("hidden");
+    }
+  });
+}
+
+// Toggle the hidden class on the `feedback-box` div.
+let feedbackButton = document.getElementById('feedback-button');
+
+if (feedbackButton != null){
+  feedbackButton.addEventListener('click',function (){
+    let feedbackBox = document.getElementById('feedback-box');
+    if (feedbackBox.classList.contains("hidden")){
+      feedbackBox.classList.remove("hidden");
+      // Trigger a focus event on the textarea
+      let element = document.getElementById('feedback-text-area');
+      element.dispatchEvent(new Event("focus"));
+    }else{
+      feedbackBox.classList.add("hidden");
+    }
+  });
+}
+
+// Toggle the selected class on the `like this response` button.
+let likeButton = document.getElementById('like-button');
+let dislikeButton = document.getElementById('dislike-button');
+
+if (likeButton != null){
+  likeButton.addEventListener('click',function (){
+    if (likeButton.classList.contains("notselected")) {
+      this.classList.remove("notselected");
+      this.classList.add("selected");
+      this.value = "Liked"
+      let uuidBox = document.getElementById('uuid-box');
+      let uuid = "Unknown";
+      if (uuidBox != null){
+        uuid =  uuidBox.textContent;
+      }
+      if (dislikeButton != null){
+        dislikeButton.classList.add("hidden");
+        if (dislikeButton.classList.contains("selected")) {
+          dislikeButton.classList.remove("selected");
+          dislikeButton.classList.add("notselected");
+	}
+      }
+      let xhr = new XMLHttpRequest();
+      // The value of `urlLike` is specified in the html template,
+      // which is set by the Flask server.
+      // See chatbot/templates/chatui/base.html
+      xhr.open("POST", urlLike, true);
+      xhr.setRequestHeader("Accept", "application/json");
+      xhr.setRequestHeader("Content-Type", "application/json");
+      let data = JSON.stringify({"like": true, "uuid": uuid});
+      xhr.send(data);
+    }else{
+      this.classList.remove("selected");
+      this.classList.add("notselected");
+      this.value = 'Like this response \uD83D\uDC4D';
+      let uuidBox = document.getElementById('uuid-box');
+      let uuid = "Unknown";
+      if (uuidBox != null){
+        uuid =  uuidBox.textContent;
+      }
+      if (dislikeButton != null){
+        dislikeButton.classList.remove("hidden");
+        if (dislikeButton.classList.contains("selected")) {
+          dislikeButton.classList.remove("selected");
+          dislikeButton.classList.add("notselected");
+	}
+      }
+      let xhr = new XMLHttpRequest();
+      // The value of `urlLike` is specified in the html template,
+      // which is set by the Flask server.
+      // See chatbot/templates/chatui/base.html
+      xhr.open("POST", urlLike, true);
+      xhr.setRequestHeader("Accept", "application/json");
+      xhr.setRequestHeader("Content-Type", "application/json");
+      let data = JSON.stringify({"like": false, "uuid": uuid});
+      xhr.send(data);
+    }
+  });
+}
+
+// Toggle the selected class on the `dislike` button.
+if (dislikeButton != null){
+  dislikeButton.addEventListener('click',function (){
+    if (dislikeButton.classList.contains("notselected")) {
+      this.classList.remove("notselected");
+      this.classList.add("selected");
+      this.value = "Disliked"
+      let uuidBox = document.getElementById('uuid-box');
+      let uuid = "Unknown";
+      if (uuidBox != null){
+        uuid =  uuidBox.textContent;
+      }
+      if (likeButton != null){
+        likeButton.classList.add("hidden");
+        if (likeButton.classList.contains("selected")) {
+          likeButton.classList.remove("selected");
+          likeButton.classList.add("notselected");
+	}
+      }
+      let xhr = new XMLHttpRequest();
+      // The value of `urlLike` is specified in the html template,
+      // which is set by the Flask server.
+      // See chatbot/templates/chatui/base.html
+      xhr.open("POST", urlLike, true);
+      xhr.setRequestHeader("Accept", "application/json");
+      xhr.setRequestHeader("Content-Type", "application/json");
+      let data = JSON.stringify({"dislike": true, "uuid": uuid});
+      xhr.send(data);
+    }else{
+      this.classList.remove("selected");
+      this.classList.add("notselected");
+      this.value = 'Dislike \uD83D\uDC4E';
+      let uuidBox = document.getElementById('uuid-box');
+      let uuid = "Unknown";
+      if (uuidBox != null){
+        uuid =  uuidBox.textContent;
+      }
+      if (likeButton != null){
+        likeButton.classList.remove("hidden");
+        if (likeButton.classList.contains("selected")) {
+          likeButton.classList.remove("selected");
+          likeButton.classList.add("notselected");
+	}
+      }
+      let xhr = new XMLHttpRequest();
+      // The value of `urlLike` is specified in the html template,
+      // which is set by the Flask server.
+      // See chatbot/templates/chatui/base.html
+      xhr.open("POST", urlLike, true);
+      xhr.setRequestHeader("Accept", "application/json");
+      xhr.setRequestHeader("Content-Type", "application/json");
+      let data = JSON.stringify({"dislike": false, "uuid": uuid});
+      xhr.send(data);
+    }
+  });
+}
+
+// Adjust the size of the `edit-text-area` textarea.
+let rewriteTextArea = document.getElementById('edit-text-area');
+
+if (rewriteTextArea != null){
+  rewriteTextArea.addEventListener('focus', resize_textarea);
+  rewriteTextArea.addEventListener('input', resize_textarea);
+}
+
+function resize_textarea(){
+  this.style.height = "5px";
+  this.style.width = "650px";
+  this.style.height = (this.scrollHeight)+"px";
+  let rewriteSubmitButton = document.getElementById('submit-button');
+  if (rewriteSubmitButton != null){
+    if (rewriteSubmitButton.classList.contains("disable")){
+      rewriteSubmitButton.classList.remove("disable");
+      let submitResult = document.getElementById('submit-result');
+      submitResult.textContent = "Click to re-submit updated rewrite.";
+    }
+  }
+}
+
+// Adjust the size of the `feedback-text-area` textarea.
+let feedbackTextArea = document.getElementById('feedback-text-area');
+
+if (feedbackTextArea != null){
+  feedbackTextArea.addEventListener('focus', resize_feedback_textarea);
+  feedbackTextArea.addEventListener('input', resize_feedback_textarea);
+}
+
+function resize_feedback_textarea(){
+  this.style.height = "5px";
+  this.style.width = "650px";
+  this.style.height = (this.scrollHeight)+"px";
+  let feedbackSubmitButton = document.getElementById('feedback-submit-button');
+  if (feedbackSubmitButton != null){
+    if (feedbackSubmitButton.classList.contains("disable")){
+      feedbackSubmitButton.classList.remove("disable");
+      let feedbackResult = document.getElementById('feedback-submit-result');
+      feedbackResult.textContent = "Click to re-submit updated feedback.";
+    }
+  }
+}
+
+// Make a rewrite POST call.
+let rewriteSubmitButton = document.getElementById('submit-button');
+
+if (rewriteSubmitButton != null){
+  rewriteSubmitButton.addEventListener('click',function (){
+    let xhr = new XMLHttpRequest();
+    // The value of `urlRewrite` below is specified in the html template,
+    // which is set by the Flask server.
+    // See chatbot/templates/chatui/base.html
+    xhr.open("POST", urlRewrite, true);
+    xhr.setRequestHeader("Accept", "application/json");
+    xhr.setRequestHeader("Content-Type", "application/json");
+    let rewriteQuestion = document.getElementById('rewrite-question-span');
+    let rewriteOriginalResponse = document.getElementById('rewrite-original-response-span');
+    let rewriteTextArea = document.getElementById('edit-text-area');
+    let userIDInput = document.getElementById('user-id');
+    let userID = userIDInput.value;
+    if (userID == "") {
+      userID = "anonymous";
+    }
+    let data = JSON.stringify({
+      "user_id": userID,
+      "question": rewriteQuestion.textContent,
+      "original_response": rewriteOriginalResponse.textContent,
+      "rewrite": rewriteTextArea.value});
+    xhr.send(data);
+
+    let submitResult = document.getElementById('submit-result');
+    submitResult.textContent = "Rewrite has been submitted. Thank you!";
+    rewriteSubmitButton.classList.add("disable");
+  }, false);
+}
+
+// Make a feedback POST call.
+let feedbackSubmitButton = document.getElementById('feedback-submit-button');
+
+if (feedbackSubmitButton != null){
+  feedbackSubmitButton.addEventListener('click',function (){
+    let xhr = new XMLHttpRequest();
+    // The value of `urlRewrite` below is specified in the html template,
+    // which is set by the Flask server.
+    // See chatbot/templates/chatui/base.html
+    xhr.open("POST", urlFeedback, true);
+    xhr.setRequestHeader("Accept", "application/json");
+    xhr.setRequestHeader("Content-Type", "application/json");
+    let feedbackQuestion = document.getElementById('feedback-question-span');
+    let feedbackResponse = document.getElementById('feedback-response-span');
+    let feedbackTextArea = document.getElementById('feedback-text-area');
+    let userIDInput = document.getElementById('user-id');
+    let userID = userIDInput.value;
+    if (userID == "") {
+      userID = "anonymous";
+    }
+    let data = JSON.stringify({
+      "user_id": userID,
+      "question": feedbackQuestion.textContent,
+      "response": feedbackResponse.textContent,
+      "feedback": feedbackTextArea.value});
+    xhr.send(data);
+
+    let feedbackResult = document.getElementById('feedback-submit-result');
+    feedbackResult.textContent = "Feedback has been submitted. Thank you!";
+    feedbackSubmitButton.classList.add("disable");
+  }, false);
+}
+
+// Monitor the width of the Docs Agent widget.
+let answerableSpan = document.getElementById('answerable-span');
+//let rewriteButton = document.getElementById('rewrite-button');
+const element = document.querySelector('#chat-border');
+
+const resizeObserver = new ResizeObserver(entries => {
+  for (const entry of entries) {
+    const width = entry.contentRect.width;
+    console.log('Element width changed to:', width);
+    if (width < 520) {
+      answerableSpan.classList.add("hidden");
+    }else{
+      if (answerableSpan.classList.contains("hidden")) {
+        answerableSpan.classList.remove("hidden");
+      }
+    }
+    if (width < 300) {
+      rewriteButton.classList.add("hidden");
+    }else{
+      if (rewriteButton.classList.contains("hidden")) {
+        rewriteButton.classList.remove("hidden");
+      }
+    }
+  }
+});
+
+resizeObserver.observe(element);
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/debugs.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/debugs.html
new file mode 100644
index 000000000..76ab6130e
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/debugs.html
@@ -0,0 +1,24 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-chatui-widget.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/chatbox.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-logs.css') }}">
+    <link rel="shortcut icon" href="{{ url_for('static', filename='images/favicon.png') }}">
+    <title>{{ product }} Docs Agent admin page</title>
+</head>
+<body>
+  {% if debug_info %}
+  <div id='widget-box'>
+    <div name="title"><h1>{{ product }} Docs Agent</h1></div>
+    <div id='chat-border'>
+      <div class="body-content">
+	<h2>Debug information<h2>
+	<pre class="log-pre">{{ debug_info }}</pre>
+      </div>
+    </div>
+  </div>
+  {% endif %}
+</body>
+</html>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/logs.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/logs.html
new file mode 100644
index 000000000..77f84f136
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/logs.html
@@ -0,0 +1,26 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-chatui-widget.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/chatbox.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-logs.css') }}">
+    <link rel="shortcut icon" href="{{ url_for('static', filename='images/favicon.png') }}">
+    <title>{{ product }} Docs Agent admin page</title>
+</head>
+<body>
+  {% if logs %}
+  <div id='widget-box'>
+    <div name="title"><h1>{{ product }} Docs Agent</h1></div>
+    <div id='chat-border'>
+      <div class="body-content">
+	<h2>Answerable scores<h2>
+        <pre class="log-pre">{{ answerable_logs }}</pre>
+	<h2>Logs<h2>
+	<pre class="log-pre">{{ logs }}</pre>
+      </div>
+    </div>
+  </div>
+  {% endif %}
+</body>
+</html>
diff --git a/demos/palm/python/docs-agent/chatbot/templates/chatui/base.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-experimental/base.html
similarity index 94%
rename from demos/palm/python/docs-agent/chatbot/templates/chatui/base.html
rename to examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-experimental/base.html
index 7fee30eb6..944984182 100644
--- a/demos/palm/python/docs-agent/chatbot/templates/chatui/base.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-experimental/base.html
@@ -2,7 +2,7 @@
 <html lang="en">
 <head>
     <meta charset="UTF-8">
-    <link rel="stylesheet" href="{{ url_for('static', filename='css/style.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-chatui.css') }}">
     <link rel="stylesheet" href="{{ url_for('static', filename='css/chatbox.css') }}">
     <link rel="shortcut icon" href="{{ url_for('static', filename='images/favicon.png') }}">
     <title>{{ product }} ChatBot</title>
@@ -16,6 +16,8 @@
       </div>
     </div>
   </div>
+  <br>
+  <br>
   <script>
     let urlRewrite = "{{ server_url }}rewrite";
     let urlLike = "{{ server_url }}like";
diff --git a/demos/palm/python/docs-agent/chatbot/templates/chatui/index.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-experimental/index.html
similarity index 84%
rename from demos/palm/python/docs-agent/chatbot/templates/chatui/index.html
rename to examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-experimental/index.html
index 44e3e83e0..97376ac8a 100644
--- a/demos/palm/python/docs-agent/chatbot/templates/chatui/index.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-experimental/index.html
@@ -10,9 +10,9 @@
   <div class="loading hidden" id="loading-div">
     GENERATING A RESPONSE...
   </div>
-  {% if response_in_html %}
+  {% if response %}
   <div class="full-response">
-    {% include 'chatui/result.html' %}
+    {% include 'chatui-experimental/result.html' %}
   </div>
   {% endif %}
-{% endblock %}
+{% endblock %}
\ No newline at end of file
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-experimental/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-experimental/result.html
new file mode 100644
index 000000000..778bfc935
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-experimental/result.html
@@ -0,0 +1,117 @@
+<div class="question">
+  <h2>Question</h2>
+  <p>{{ question | replace("+", " ") | replace("%3F", "?")}}</p>
+</div>
+<div class="response-text" id="response-box">
+  {% if search_result[0].probability %}
+  <h2>AQA's answer</h2>
+  {% else %}
+  <h2>Gemini's answer</h2>
+  {% endif %}
+  <span id="gemini-response">
+    {{ md_to_html(response) | safe }}
+  </span>
+  <!-- {% if fact_checked_response %}
+  <h4>Important - Fact checker results:</h4>
+  <span id="gemini-response">
+  {{md_to_html(fact_checked_response) | safe}}
+  </span>
+  <br>
+    {% for item in fact_checked_response.checks %}
+        <p>Claim: {{item.claim}}</p>
+        <p>Reason: {{item.reason}}</p>
+        Handle various cases when model returns urls, section names, and page names
+        {% if (item.section_name != "" and item.section_name != None) and (item.page_name != "" and item.page_name != None) and (item.URL != "" and item.URL != None and "URL" in item)%}
+          {% set section_url = named_link_html(item.URL, item.section_name) %}
+          {% set page_url = named_link_html(trim_section_for_page_link(item.URL), item.page_name) %}
+          <p>{{section_url | safe}} from the {{page_url | safe}} page.</p>
+        {% elif (item.URL == "" or item.URL == None) and (item.section_name != "" or item.page_name != "") %}
+          {% if item.section_name == "" %}
+            <p>From the {{item.section_name}} section.</p>
+          {% elif item.page_name == "" %}
+            <p>From the {{item.page_name}} page.</p>
+          {% else %}
+            <p>{{item.section_name}} from the {{item.page_name}} page.</p>
+          {% endif %}
+        {% else %}
+      {% endif %}
+    {% endfor %}
+  {% elif fact_checked_response == "" %}
+    <p>The answer that I gave you was based on accurate information from the
+       context. Keep in mind that you still should review my sources if something
+       doesn't seem accurate.</p>
+  {% endif %} -->
+  <p id="source-para">To verify this information, please check out my sources:</p>
+  <div class="sources">
+    <ol>
+    {% for source in search_result %}
+      {% set section_url = named_link_html(source.section.url, source.section.section_title) %}
+      {% set page_url = named_link_html(trim_section_for_page_link(source.section.url), source.section.page_title) %}
+      {% if search_result|length > 1 %}
+      <li>
+      {% endif %}
+        <p>{{section_url | safe}} from the {{page_url | safe}} page.</p>
+        <section class="accordion-source">
+          <input type="checkbox" name="collapse" id="source-{{source.section.page_title}}-{{source.section.section_title}}-{{loop.index}}">
+          <h2 class="handle">
+            <label for="source-{{source.section.page_title}}-{{source.section.section_title}}-{{loop.index}}">Content</label>
+          </h2>
+          <div class="content">
+            {{md_to_html(source.section.content) | safe}}
+          </div>
+          {% if source.distance %}
+          <p id="distance-para">Distance: {{source.distance}}</p>
+          {% elif source.probability %}
+          <p id="probability-para">Probability: {{source.probability}}</p>
+          {% endif %}
+        </section>
+      {% if search_result|length > 1 %}
+      </li>
+      {% endif %}
+    {% endfor %}
+    </ol>
+  </div>
+</div>
+<div class="related-questions">
+  <h3>Related questions</h3>
+  {{ related_questions | safe }}
+</div>
+{% if search_result[0].distance %}
+<section class="accordion">
+  <input type="checkbox" name="collapse" id="handle1">
+  <h2 class="handle">
+    <label for="handle1">Context</label>
+  </h2>
+  <div class="content">
+    {{ md_to_html(final_context) | safe }}
+  </div>
+</section>
+{% else %}
+{% endif %}
+<div>
+  <input type="button" value="Rewrite this response" id="rewrite-button">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+</div>
+<div class="rewrite hidden" id="rewrite-box">
+  <h4 id="rewrite-question-header">Question:</h4>
+  <span id="rewrite-question-span">
+    <p>{{ question | replace("+", " ") | replace("%3F", "?")}}</p>
+  </span>
+  <h4 id="rewrite-response-header">Gemini's response:</h4>
+  <span id="rewrite-original-response-span">
+    {{ md_to_html(response) | safe }}
+  </span>
+  <h4>Rewrite:</h4>
+  <textarea id="edit-text-area">{{ response | safe }}</textarea>
+  <label for="user-id">User ID:</label>
+  <input type="text" id="user-id" name="user-id" placeholder="Optional">
+  <br>
+  <span id="submit-box">
+    <input type="button" value="Submit" id="submit-button">
+  </span>
+  <span id="submit-result">
+  </span>
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
\ No newline at end of file
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/base.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/base.html
new file mode 100644
index 000000000..643e779c7
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/base.html
@@ -0,0 +1,28 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-chatui-full.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/chatbox.css') }}">
+    <link rel="shortcut icon" href="{{ url_for('static', filename='images/favicon.png') }}">
+    <title>{{ product }} ChatBot</title>
+</head>
+<body>
+  <div id='body-box'>
+    <div name="title"><h1>{{ product }} Docs Agent 1.5</h1></div>
+    <div id='chat-border'>
+      <div class="body-content">
+        {% block content %} {% endblock %}
+      </div>
+    </div>
+  </div>
+  <br>
+  <br>
+  <script>
+    let urlRewrite = "{{ server_url }}rewrite";
+    let urlFeedback = "{{ server_url }}feedback";
+    let urlLike = "{{ server_url }}like";
+  </script>
+  <script src="{{ url_for('static', filename='javascript/app.js') }}"></script>
+</body>
+</html>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/index.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/index.html
new file mode 100644
index 000000000..5b57140e4
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/index.html
@@ -0,0 +1,16 @@
+{% extends 'chatui-full/base.html' %}
+
+{% block content %}
+  <div class="search">
+    <form class="search-form" method="POST" action="/result">
+      <input type="text" name="question" placeholder="Ask me about topics related to {{product}}.">
+      <input type="submit" value="Ask" id="ask-button">
+    </form>
+  </div>
+  <div class="loader hidden" id="loading-div"></div>
+  {% if response %}
+  <div class="full-response">
+    {% include 'chatui-full/result.html' %}
+  </div>
+  {% endif %}
+{% endblock %}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/result.html
new file mode 100644
index 000000000..7c72dd00b
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/result.html
@@ -0,0 +1,214 @@
+<!--Sets defaults-->
+{% if search_result[0] %}
+  {% set check_url = search_result[0].section.url %}
+  {% set probability = search_result[0].probability %}
+{% else %}
+  {% set check_url = "None" %}
+  {% set probability = "0.0" %}
+{% endif %}
+
+{% if aqa_response_in_html != "" %}
+  <!--Gemini AQA template-->
+  <div class="question">
+    <h2>{{ question }}</h2>
+  </div>
+  {% if probability|float > 0.7 %}
+  <div class="important-box" id="important-box">
+    <b>Important:</b> The answer below is generated by Gemini 1.5.
+    To verify this answer, please visit: {{named_link_html(check_url, check_url) | safe}}
+  </div>
+  <div class="response-text" id="tldr-response-box">
+    <h3>TL;DR</h3>
+    <span id="gemini-response">
+      {{ md_to_html(response) | safe }}
+    </span>
+  </div>
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      {{ md_to_html(summary_response) | safe }}
+    </span>
+  </div>
+  <div class="related-questions">
+    <h3>Suggested follow-up questions</h3>
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    </span>
+  </div>
+  <section class="accordion">
+    <input type="checkbox" name="collapse" id="handle1">
+    <h2 class="handle">
+      <label for="handle1">Context</label>
+    </h2>
+    <div class="content" id="context-content">
+      <div id="probability-box">
+        <b>Answerable probability</b>: {{"%.6f"|format(probability|float)}}
+      </div>
+      <div id="grounding-box">
+        <b>Grounding attributions</b>:
+	<pre id="grounding-pre">{{ final_context | safe }}</pre>
+      </div>
+      <div id="reference-box">
+      {% for source in search_result %}
+        {% set section_url = named_link_html(source.section.url, source.section.url) %}
+        <b>[{{loop.index}}]</b>{{ section_url | safe}}
+        <br>
+      {% endfor %}
+      </div>
+      <div class="hidden" id="aqa-box">
+        <section class="accordion">
+          <input type="checkbox" name="collapse" id="handle2">
+          <h2 class="handle">
+            <label for="handle2" id="aqa-label">AQA model's JSON response</label>
+          </h2>
+          <div class="content" id="aqa-content">
+	    <pre id="aqa-json">{{ aqa_response_in_html | safe }}</pre>
+          </div>
+        </section>
+      </div>
+    </div>
+  </section>
+  {% else %}
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      I'm sorry, but I cannot answer this question at the moment.
+      <b>The answer to your question does not seem to be available in documentation.</b><br>
+      <br>
+      However, based on the documentation available in the corpus, you could ask questions like:
+    </span>
+  </div>
+  <div class="related-questions">
+    <!--<h4>Suggested questions</h4>-->
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    </span>
+  </div>
+  {% endif %}
+{% else %}
+  <!--Gemini Pro template-->
+  <div class="question">
+    <h2>{{ question }}</h2>
+  </div>
+  <div class="important-box" id="important-box">
+    <!--Uses the URL from the first result-->
+    <b>Important:</b> The answer below is generated by the Gemini 1.5 model.
+    To verify this answer, please visit: {{named_link_html(check_url, check_url) | safe}}
+  </div>
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      {{ md_to_html(response) | safe }}
+    </span>
+  </div>
+  <div class="related-questions">
+    <h4>Suggested questions</h4>
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    <span>
+  </div>
+  <section class="accordion">
+    <input type="checkbox" name="collapse" id="handle1">
+    <h2 class="handle">
+      <label for="handle1">Context</label>
+    </h2>
+    <div class="content" id="context-content">
+      <!-- {{ md_to_html(final_context) | safe }} -->
+      {% for source in search_result %}
+      <pre id="context-pre">{{md_to_html(source.section.content) | safe}}Reference <b>[{{loop.index}}]</b></pre>
+      {% endfor %}
+      <div id="reference-box-no-aqa">
+      {% for source in search_result %}
+        {% set section_url = named_link_html(source.section.url, source.section.url) %}
+        <b>[{{loop.index}}]</b>{{ section_url | safe}} ({{ source.distance }})
+        <br>
+      {% endfor %}
+      </div>
+    </div>
+  </section>
+{% endif %}
+
+<!--Feedback buttons at the bottom of the UI display.-->
+{% if feedback_mode == "rewrite" %}
+<div id="rewrite-buttons-box">
+  <input type="button" value="Rewrite this response" id="rewrite-button">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="rewrite hidden" id="rewrite-box">
+  <h4 id="rewrite-question-header">Question:</h4>
+  <span id="rewrite-question-span">
+    <p>{{ question }}</p>
+  </span>
+  <h4 id="rewrite-response-header">Gemini's response:</h4>
+  <span id="rewrite-original-response-span">
+    {{ md_to_html(response) | safe }}
+  </span>
+  <h4>Rewrite:</h4>
+  <textarea id="edit-text-area">{{ response | safe }}</textarea>
+  <br>
+  <label for="user-id">User ID:</label>
+  <input type="text" id="user-id" name="user-id" placeholder="Optional">
+  <br>
+  <span id="submit-box">
+    <input type="button" value="Submit" id="submit-button">
+  </span>
+  <span id="submit-result">
+  </span>
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+{% elif feedback_mode == "like_and_dislike" %}
+<div id="feedback-buttons-box">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  <input type="button" class="notselected" value="Disike &#128078;" id="dislike-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+<div class="hidden" id="feedback-question-span">
+  {{ question }}
+</div>
+<div class="hidden" id="feedback-response-span">
+  {{ md_to_html(response) | safe }}
+</div>
+{% else %}
+<div id="feedback-buttons-box">
+  <input type="button" value="Provide feedback" id="feedback-button">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="feedback hidden" id="feedback-box">
+  <h4>Feedback:</h4>
+  <textarea id="feedback-text-area"></textarea>
+  <br>
+  <label for="user-id">LDAP:</label>
+  <input type="text" id="user-id" name="user-id" placeholder="Optional">
+  <br>
+  <span id="feedback-submit-box">
+    <input type="button" value="Submit" id="feedback-submit-button">
+  </span>
+  <span id="feedback-submit-result">
+  </span>
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+<div class="hidden" id="feedback-question-span">
+  {{ question }}
+</div>
+<div class="hidden" id="feedback-response-span">
+  {{ md_to_html(response) | safe }}
+</div>
+{% endif %}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/base.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/base.html
new file mode 100644
index 000000000..ec22387df
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/base.html
@@ -0,0 +1,28 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-chatui-widget-pro.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/chatbox.css') }}">
+    <link rel="shortcut icon" href="{{ url_for('static', filename='images/favicon.png') }}">
+    <title>{{ product }} ChatBot</title>
+</head>
+<body>
+  <div id='iframe-box'>
+    <div name="title"><h1>{{ product }} Docs Agent 1.5</h1></div>
+    <div id='chat-border'>
+      <div class="body-content">
+        {% block content %} {% endblock %}
+      </div>
+    </div>
+  </div>
+  <br>
+  <br>
+  <script>
+    let urlRewrite = "{{ server_url }}rewrite";
+    let urlFeedback = "{{ server_url }}feedback";
+    let urlLike = "{{ server_url }}like";
+  </script>
+  <script src="{{ url_for('static', filename='javascript/app.js') }}"></script>
+</body>
+</html>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/index.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/index.html
new file mode 100644
index 000000000..6c084fb06
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/index.html
@@ -0,0 +1,16 @@
+{% extends 'chatui-widget-pro/base.html' %}
+
+{% block content %}
+  <div class="search">
+    <form class="search-form" method="POST" action="/result">
+      <input type="text" name="question" placeholder="Ask me about topics related to {{product}}.">
+      <input type="submit" value="Ask" id="ask-button">
+    </form>
+  </div>
+  <div class="loader hidden" id="loading-div"></div>
+  {% if response %}
+  <div class="full-response">
+    {% include 'chatui-widget-pro/result.html' %}
+  </div>
+  {% endif %}
+{% endblock %}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/result.html
new file mode 100644
index 000000000..77ef339de
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/result.html
@@ -0,0 +1,218 @@
+<!--Sets defaults-->
+{% if search_result[0] %}
+  {% set check_url = search_result[0].section.url %}
+  {% set probability = search_result[0].probability %}
+{% else %}
+  {% set check_url = "None" %}
+  {% set probability = "0.0" %}
+{% endif %}
+
+{% if aqa_response_in_html != "" %}
+  <!--Gemini AQA template-->
+  <div class="question">
+    <h2>{{ question }}</h2>
+  </div>
+  {% if probability|float > 0.7 %}
+  <div class="important-box" id="important-box">
+    <b>Important:</b> The answer below is generated by Gemini 1.5.
+    To verify this answer, please visit: {{named_link_html(check_url, check_url) | safe}}
+  </div>
+  <div class="response-text" id="tldr-response-box">
+    <h3>TL;DR</h3>
+    <span id="gemini-response">
+      {{ md_to_html(response) | safe }}
+    </span>
+  </div>
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      {{ md_to_html(summary_response) | safe }}
+    </span>
+  </div>
+  <div class="related-questions">
+    <h3>Suggested follow-up questions</h3>
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    </span>
+  </div>
+  <div class="hidden">
+  <section class="accordion">
+    <input type="checkbox" name="collapse" id="handle1">
+    <h2 class="handle">
+      <label for="handle1">Context</label>
+    </h2>
+    <div class="content" id="context-content">
+      <div id="probability-box">
+        <b>Answerable probability</b>: {{"%.6f"|format(probability|float)}}
+      </div>
+      <div id="grounding-box">
+        <b>Grounding attributions</b>:
+	<pre id="grounding-pre">{{ final_context | safe }}</pre>
+      </div>
+      <div id="reference-box">
+      {% for source in search_result %}
+        {% set section_url = named_link_html(source.section.url, source.section.url) %}
+        <b>[{{loop.index}}]</b>{{ section_url | safe}}
+        <br>
+      {% endfor %}
+      </div>
+      <div class="hidden" id="aqa-box">
+        <section class="accordion">
+          <input type="checkbox" name="collapse" id="handle2">
+          <h2 class="handle">
+            <label for="handle2" id="aqa-label">AQA model's JSON response</label>
+          </h2>
+          <div class="content" id="aqa-content">
+	    <pre id="aqa-json">{{ aqa_response_in_html | safe }}</pre>
+          </div>
+        </section>
+      </div>
+    </div>
+  </section>
+  </div>
+  {% else %}
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      I'm sorry, but I cannot answer this question at the moment.
+      <b>The answer to your question does not seem to be available in documentation.</b><br>
+      <br>
+      However, based on the documentation available in the corpus, you could ask questions like:
+    </span>
+  </div>
+  <div class="related-questions">
+    <!--<h4>Suggested questions</h4>-->
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    </span>
+  </div>
+  {% endif %}
+{% else %}
+  <!--Gemini Pro template-->
+  <div class="question">
+    <h2>{{ question }}</h2>
+  </div>
+  <div class="important-box" id="important-box">
+    <!--Uses the URL from the first result-->
+    <b>Important:</b> The answer below is generated by the Gemini 1.5 model.
+    To verify this answer, please visit: {{named_link_html(check_url, check_url) | safe}}
+  </div>
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      {{ md_to_html(response) | safe }}
+    </span>
+  </div>
+  <div class="related-questions">
+    <h4>Suggested questions</h4>
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    <span>
+  </div>
+  <div class="hidden">
+  <section class="accordion">
+    <input type="checkbox" name="collapse" id="handle1">
+    <h2 class="handle">
+      <label for="handle1">Context</label>
+    </h2>
+    <div class="content" id="context-content">
+      <!-- {{ md_to_html(final_context) | safe }} -->
+      {% for source in search_result %}
+      <pre id="context-pre">{{md_to_html(source.section.content) | safe}}Reference <b>[{{loop.index}}]</b></pre>
+      {% endfor %}
+      <div id="reference-box-no-aqa">
+      {% for source in search_result %}
+        {% set section_url = named_link_html(source.section.url, source.section.url) %}
+        <b>[{{loop.index}}]</b>{{ section_url | safe}} ({{ source.distance }})
+        <br>
+      {% endfor %}
+      </div>
+    </div>
+  </section>
+  </div>
+{% endif %}
+
+<!--Feedback buttons at the bottom of the UI display.-->
+{% if feedback_mode == "rewrite" %}
+<div id="rewrite-buttons-box">
+  <input type="button" value="Rewrite this response" id="rewrite-button">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="rewrite hidden" id="rewrite-box">
+  <h4 id="rewrite-question-header">Question:</h4>
+  <span id="rewrite-question-span">
+    <p>{{ question }}</p>
+  </span>
+  <h4 id="rewrite-response-header">Gemini's response:</h4>
+  <span id="rewrite-original-response-span">
+    {{ md_to_html(response) | safe }}
+  </span>
+  <h4>Rewrite:</h4>
+  <textarea id="edit-text-area">{{ response | safe }}</textarea>
+  <br>
+  <label for="user-id">User ID:</label>
+  <input type="text" id="user-id" name="user-id" placeholder="Optional">
+  <br>
+  <span id="submit-box">
+    <input type="button" value="Submit" id="submit-button">
+  </span>
+  <span id="submit-result">
+  </span>
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+{% elif feedback_mode == "like_and_dislike" %}
+<div id="feedback-buttons-box">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  <input type="button" class="notselected" value="Disike &#128078;" id="dislike-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+<div class="hidden" id="feedback-question-span">
+  {{ question }}
+</div>
+<div class="hidden" id="feedback-response-span">
+  {{ md_to_html(response) | safe }}
+</div>
+{% else %}
+<div id="feedback-buttons-box">
+  <input type="button" value="Provide feedback" id="feedback-button">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="feedback hidden" id="feedback-box">
+  <h4>Feedback:</h4>
+  <textarea id="feedback-text-area"></textarea>
+  <br>
+  <label for="user-id">LDAP:</label>
+  <input type="text" id="user-id" name="user-id" placeholder="Optional">
+  <br>
+  <span id="feedback-submit-box">
+    <input type="button" value="Submit" id="feedback-submit-button">
+  </span>
+  <span id="feedback-submit-result">
+  </span>
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+<div class="hidden" id="feedback-question-span">
+  {{ question }}
+</div>
+<div class="hidden" id="feedback-response-span">
+  {{ md_to_html(response) | safe }}
+</div>
+{% endif %}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/base.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/base.html
new file mode 100644
index 000000000..720260ca5
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/base.html
@@ -0,0 +1,26 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-chatui-widget.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/chatbox.css') }}">
+    <link rel="shortcut icon" href="{{ url_for('static', filename='images/favicon.png') }}">
+    <title>{{ product }} ChatBot</title>
+</head>
+<body>
+  <div id='iframe-box'>
+    <div name="title"><h1>{{ product }} Docs Agent</h1></div>
+    <div id='chat-border'>
+      <div class="body-content">
+        {% block content %} {% endblock %}
+      </div>
+    </div>
+  </div>
+  <script>
+    let urlRewrite = "{{ server_url }}rewrite";
+    let urlFeedback = "{{ server_url }}feedback";
+    let urlLike = "{{ server_url }}like";
+  </script>
+  <script src="{{ url_for('static', filename='javascript/app.js') }}"></script>
+</body>
+</html>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/index.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/index.html
new file mode 100644
index 000000000..5a6119bd7
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/index.html
@@ -0,0 +1,16 @@
+{% extends 'chatui-widget/base.html' %}
+
+{% block content %}
+  <div class="search">
+    <form class="search-form" method="POST" action="/result">
+      <input type="text" name="question" placeholder="Ask me about topics related to {{ product }}.">
+      <input type="submit" value="Ask" id="ask-button">
+    </form>
+  </div>
+  <div class="loader hidden" id="loading-div"></div>
+  {% if response %}
+  <div class="full-response">
+    {% include 'chatui-widget/result.html' %}
+  </div>
+  {% endif %}
+{% endblock %}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/result.html
new file mode 100644
index 000000000..ae02baac5
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/result.html
@@ -0,0 +1,221 @@
+<!--Sets defaults-->
+{% if search_result[0] %}
+  {% set check_url = search_result[0].section.url %}
+  {% set probability = search_result[0].probability %}
+{% else %}
+  {% set check_url = "None" %}
+  {% set probability = "0.0" %}
+{% endif %}
+
+{% if aqa_response_in_html != "" %}
+  <!--Gemini AQA template-->
+  <div class="question">
+    <h2>{{ question }}</h2>
+  </div>
+  {% if probability|float > 0.7 %}
+  <div class="important-box" id="important-box">
+    <b>Important:</b> The answer below is generated by Gemini. Please
+    verify this answer by visiting the source pages.
+  </div>
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      {{ md_to_html(response) | safe }}
+    </span>
+  </div>
+  <div class="relevant-sources">
+    <h4>Source pages</h4>
+    <span id="source-pages">
+      <ol>
+      {% for source in search_result %}
+        {% set section_url = named_link_html(source.section.url, source.section.url) %}
+        <li>{{ section_url | safe}}</li>
+      {% endfor %}
+      <ol>
+    </span>
+  </div>
+  <div class="related-questions">
+    <h4>Suggested questions</h4>
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    </span>
+  </div>
+  {% else %}
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      I'm sorry, but I cannot answer this question at the moment.
+      <b>The answer to your question does not seem to be available in documentation.</b><br>
+      <br>
+      However, based on the documentation available in the corpus, you could ask questions like:
+    </span>
+  </div>
+  <div class="related-questions">
+    <!--<h4>Suggested questions</h4>-->
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    </span>
+  </div>
+  {% endif %}
+  <div class="hidden">
+  <section class="accordion">
+    <input type="checkbox" name="collapse" id="handle1">
+    <h2 class="handle">
+      <label for="handle1">Context</label>
+    </h2>
+    <div class="content" id="context-content">
+      <div id="probability-box">
+        <b>Answerable probability</b>: {{"%.6f"|format(probability|float)}}
+      </div>
+      <div id="grounding-box">
+        <b>Grounding attributions</b>:
+	<pre id="grounding-pre">{{ final_context | safe }}</pre>
+      </div>
+      <div id="reference-box">
+      {% for source in search_result %}
+        {% set section_url = named_link_html(source.section.url, source.section.url) %}
+        <b>[{{loop.index}}]</b>{{ section_url | safe}}
+        <br>
+      {% endfor %}
+      </div>
+      <div class="hidden" id="aqa-box">
+        <section class="accordion">
+          <input type="checkbox" name="collapse" id="handle2">
+          <h2 class="handle">
+            <label for="handle2" id="aqa-label">AQA model's JSON response</label>
+          </h2>
+          <div class="content" id="aqa-content">
+	    <pre id="aqa-json">{{ aqa_response_in_html | safe }}</pre>
+          </div>
+        </section>
+      </div>
+    </div>
+  </section>
+  </div>
+{% else %}
+  <!--Gemini Pro template-->
+  <div class="question">
+    <h2>{{ question }}</h2>
+  </div>
+  <div class="important-box" id="important-box">
+    <!--Uses the URL from the first result-->
+    <b>Important:</b> The answer below is generated by the Gemini Pro model.
+    To verify this answer, please visit: {{named_link_html(check_url, check_url) | safe}}
+  </div>
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      {{ md_to_html(response) | safe }}
+    </span>
+  </div>
+  <div class="related-questions">
+    <h4>Suggested questions</h4>
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    <span>
+  </div>
+  <section class="accordion">
+    <input type="checkbox" name="collapse" id="handle1">
+    <h2 class="handle">
+      <label for="handle1">Context</label>
+    </h2>
+    <div class="content" id="context-content">
+      <!-- {{ md_to_html(final_context) | safe }} -->
+      {% for source in search_result %}
+      <pre id="context-pre">{{md_to_html(source.section.content) | safe}}Reference <b>[{{loop.index}}]</b></pre>
+      {% endfor %}
+      <div id="reference-box-no-aqa">
+      {% for source in search_result %}
+        {% set section_url = named_link_html(source.section.url, source.section.url) %}
+        <b>[{{loop.index}}]</b>{{ section_url | safe}} ({{ source.distance }})
+        <br>
+      {% endfor %}
+      </div>
+    </div>
+  </section>
+{% endif %}
+
+<!--Feedback buttons at the bottom of the UI display.-->
+{% if feedback_mode == "rewrite" %}
+<div id="rewrite-buttons-box">
+  <input type="button" value="Rewrite this response" id="rewrite-button">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="rewrite hidden" id="rewrite-box">
+  <h4 id="rewrite-question-header">Question:</h4>
+  <span id="rewrite-question-span">
+    <p>{{ question }}</p>
+  </span>
+  <h4 id="rewrite-response-header">Gemini's response:</h4>
+  <span id="rewrite-original-response-span">
+    {{ md_to_html(response) | safe }}
+  </span>
+  <h4>Rewrite:</h4>
+  <textarea id="edit-text-area">{{ response | safe }}</textarea>
+  <br>
+  <label for="user-id">User ID:</label>
+  <input type="text" id="user-id" name="user-id" placeholder="Optional">
+  <br>
+  <span id="submit-box">
+    <input type="button" value="Submit" id="submit-button">
+  </span>
+  <span id="submit-result">
+  </span>
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+{% elif feedback_mode == "like_and_dislike" %}
+<div id="feedback-buttons-box">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  <input type="button" class="notselected" value="Disike &#128078;" id="dislike-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+<div class="hidden" id="feedback-question-span">
+  {{ question }}
+</div>
+<div class="hidden" id="feedback-response-span">
+  {{ md_to_html(response) | safe }}
+</div>
+{% else %}
+<div id="feedback-buttons-box">
+  <input type="button" value="Provide feedback" id="feedback-button">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="feedback hidden" id="feedback-box">
+  <h4>Feedback:</h4>
+  <textarea id="feedback-text-area"></textarea>
+  <br>
+  <label for="user-id">LDAP:</label>
+  <input type="text" id="user-id" name="user-id" placeholder="Optional">
+  <br>
+  <span id="feedback-submit-box">
+    <input type="button" value="Submit" id="feedback-submit-button">
+  </span>
+  <span id="feedback-submit-result">
+  </span>
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+<div class="hidden" id="feedback-question-span">
+  {{ question }}
+</div>
+<div class="hidden" id="feedback-response-span">
+  {{ md_to_html(response) | safe }}
+</div>
+{% endif %}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/base.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/base.html
new file mode 100644
index 000000000..87cece3e4
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/base.html
@@ -0,0 +1,28 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-chatui.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/chatbox.css') }}">
+    <link rel="shortcut icon" href="{{ url_for('static', filename='images/favicon.png') }}">
+    <title>{{ product }} ChatBot</title>
+</head>
+<body>
+  <div id='body-box'>
+    <div name="title"><h1>{{ product }} Docs Agent</h1></div>
+    <div id='chat-border'>
+      <div class="body-content">
+        {% block content %} {% endblock %}
+      </div>
+    </div>
+  </div>
+  <br>
+  <br>
+  <script>
+    let urlRewrite = "{{ server_url }}rewrite";
+    let urlFeedback = "{{ server_url }}feedback";
+    let urlLike = "{{ server_url }}like";
+  </script>
+  <script src="{{ url_for('static', filename='javascript/app.js') }}"></script>
+</body>
+</html>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/index.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/index.html
new file mode 100644
index 000000000..042f18d02
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/index.html
@@ -0,0 +1,16 @@
+{% extends 'chatui/base.html' %}
+
+{% block content %}
+  <div class="search">
+    <form class="search-form" method="POST" action="/result">
+      <input type="text" name="question" placeholder="Ask me about topics related to {{product}}.">
+      <input type="submit" value="Ask" id="ask-button">
+    </form>
+  </div>
+  <div class="loader hidden" id="loading-div"></div>
+  {% if response %}
+  <div class="full-response">
+    {% include 'chatui/result.html' %}
+  </div>
+  {% endif %}
+{% endblock %}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html
new file mode 100644
index 000000000..5c5bcdcaa
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html
@@ -0,0 +1,191 @@
+<!--Sets defaults-->
+{% if search_result[0] %}
+  {% set check_url = search_result[0].section.url %}
+  {% set probability = search_result[0].probability %}
+{% else %}
+  {% set check_url = "None" %}
+  {% set probability = "0.0" %}
+{% endif %}
+
+{% if aqa_response_in_html != "" %}
+  <!--Gemini AQA template-->
+  <div class="question">
+    <h2>{{ question }}</h2>
+  </div>
+  <div class="important-box" id="important-box">
+    <b>Important:</b> The answer below is generated by the Gemini AQA model.
+    To verify this answer, please visit: {{named_link_html(check_url, check_url) | safe}}
+  </div>
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      {{ md_to_html(response) | safe }}
+    </span>
+  </div>
+  <div class="related-questions">
+    <h4>Suggested questions</h4>
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    </span>
+  </div>
+  <section class="accordion">
+    <input type="checkbox" name="collapse" id="handle1">
+    <h2 class="handle">
+      <label for="handle1">Context</label>
+    </h2>
+    <div class="content" id="context-content">
+      <div id="probability-box">
+        <b>Answerable probability</b>: {{"%.6f"|format(probability|float)}}
+      </div>
+      <div id="grounding-box">
+        <b>Grounding attributions</b>:
+	<pre id="grounding-pre">{{ final_context | safe }}</pre>
+      </div>
+      <div id="reference-box">
+      {% for source in search_result %}
+        {% set section_url = named_link_html(source.section.url, source.section.url) %}
+        <b>[{{loop.index}}]</b>{{ section_url | safe}}
+        <br>
+      {% endfor %}
+      </div>
+      <div class="hidden" id="aqa-box">
+        <section class="accordion">
+          <input type="checkbox" name="collapse" id="handle2">
+          <h2 class="handle">
+            <label for="handle2" id="aqa-label">AQA model's JSON response</label>
+          </h2>
+          <div class="content" id="aqa-content">
+	    <pre id="aqa-json">{{ aqa_response_in_html | safe }}</pre>
+          </div>
+        </section>
+      </div>
+    </div>
+  </section>
+{% else %}
+  <!--Gemini Pro template-->
+  <div class="question">
+    <h2>{{ question }}</h2>
+  </div>
+  <div class="important-box" id="important-box">
+    <!--Uses the URL from the first result-->
+    <b>Important:</b> The answer below is generated by the Gemini Pro model.
+    To verify this answer, please visit: {{named_link_html(check_url, check_url) | safe}}
+  </div>
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      {{ md_to_html(response) | safe }}
+    </span>
+  </div>
+  <div class="related-questions">
+    <h4>Suggested questions</h4>
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    <span>
+  </div>
+  <section class="accordion">
+    <input type="checkbox" name="collapse" id="handle1">
+    <h2 class="handle">
+      <label for="handle1">Context</label>
+    </h2>
+    <div class="content" id="context-content">
+      <!-- {{ md_to_html(final_context) | safe }} -->
+      {% for source in search_result %}
+      <pre id="context-pre">{{md_to_html(source.section.content) | safe}}Reference <b>[{{loop.index}}]</b></pre>
+      {% endfor %}
+      <div id="reference-box-no-aqa">
+      {% for source in search_result %}
+        {% set section_url = named_link_html(source.section.url, source.section.url) %}
+        <b>[{{loop.index}}]</b>{{ section_url | safe}} ({{ source.distance }})
+        <br>
+      {% endfor %}
+      </div>
+    </div>
+  </section>
+{% endif %}
+
+<!--Feedback buttons at the bottom of the UI display.-->
+{% if feedback_mode == "rewrite" %}
+<div id="rewrite-buttons-box">
+  <input type="button" value="Rewrite this response" id="rewrite-button">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="rewrite hidden" id="rewrite-box">
+  <h4 id="rewrite-question-header">Question:</h4>
+  <span id="rewrite-question-span">
+    <p>{{ question }}</p>
+  </span>
+  <h4 id="rewrite-response-header">Gemini's response:</h4>
+  <span id="rewrite-original-response-span">
+    {{ md_to_html(response) | safe }}
+  </span>
+  <h4>Rewrite:</h4>
+  <textarea id="edit-text-area">{{ response | safe }}</textarea>
+  <br>
+  <label for="user-id">User ID:</label>
+  <input type="text" id="user-id" name="user-id" placeholder="Optional">
+  <br>
+  <span id="submit-box">
+    <input type="button" value="Submit" id="submit-button">
+  </span>
+  <span id="submit-result">
+  </span>
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+{% elif feedback_mode == "like_and_dislike" %}
+<div id="feedback-buttons-box">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  <input type="button" class="notselected" value="Disike &#128078;" id="dislike-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+<div class="hidden" id="feedback-question-span">
+  {{ question }}
+</div>
+<div class="hidden" id="feedback-response-span">
+  {{ md_to_html(response) | safe }}
+</div>
+{% else %}
+<div id="feedback-buttons-box">
+  <input type="button" value="Provide feedback" id="feedback-button">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="feedback hidden" id="feedback-box">
+  <h4>Feedback:</h4>
+  <textarea id="feedback-text-area"></textarea>
+  <br>
+  <label for="user-id">LDAP:</label>
+  <input type="text" id="user-id" name="user-id" placeholder="Optional">
+  <br>
+  <span id="feedback-submit-box">
+    <input type="button" value="Submit" id="feedback-submit-button">
+  </span>
+  <span id="feedback-submit-result">
+  </span>
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+<div class="hidden" id="feedback-question-span">
+  {{ question }}
+</div>
+<div class="hidden" id="feedback-response-span">
+  {{ md_to_html(response) | safe }}
+</div>
+{% endif %}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/__init__.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli.py
new file mode 100644
index 000000000..b6ff095e2
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli.py
@@ -0,0 +1,37 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Docs Agent CLI wrapper"""
+
+import click
+
+from docs_agent.interfaces.cli.cli_admin import cli_admin
+from docs_agent.interfaces.cli.cli_common import cli_common
+from docs_agent.interfaces.cli.cli_runtask import cli_runtask
+from docs_agent.interfaces.cli.cli_helpme import cli_helpme
+from docs_agent.interfaces.cli.cli_tellme import cli_tellme
+from docs_agent.interfaces.cli.cli_posix import cli_posix
+from docs_agent.interfaces.cli.cli_show_session import cli_show_session
+
+
+cli = click.CommandCollection(
+    sources=[cli_common, cli_admin, cli_runtask, cli_helpme, cli_tellme, cli_posix, cli_show_session],
+    help="With Docs Agent, you can populate vector databases, manage online corpora, and interact with Google's Gemini models.",
+)
+
+
+if __name__ == "__main__":
+    cli()
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_admin.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_admin.py
new file mode 100644
index 000000000..73560d07b
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_admin.py
@@ -0,0 +1,435 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Docs Agent CLI client"""
+
+import click
+import sys
+import typing
+from docs_agent.utilities import config
+from docs_agent.utilities.config import ReadDbConfigs
+from docs_agent.utilities.config import return_config_and_product
+from docs_agent.utilities.helpers import (
+    parallel_backup_dir,
+    return_pure_dir,
+    end_path_backslash,
+    start_path_no_backslash,
+    resolve_path,
+)
+from docs_agent.preprocess import files_to_plain_text as chunker
+from docs_agent.preprocess import populate_vector_database as populate_script
+from docs_agent.benchmarks import run_benchmark_tests as benchmarks
+from docs_agent.interfaces import chatbot as chatbot_flask
+from docs_agent.storage.google_semantic_retriever import SemanticRetriever
+from docs_agent.storage.chroma import ChromaEnhanced
+from docs_agent.memory.logging import write_logs_to_csv_file
+from docs_agent.interfaces.cli.cli_common import common_options
+from docs_agent.interfaces.cli.cli_common import show_config
+import socket
+import os
+
+
+@click.group(invoke_without_command=True)
+@common_options
+@click.pass_context
+def cli_admin(ctx, config_file, product):
+    """With Docs Agent, you can populate vector databases,
+    manage online corpora, and interact with Google's Gemini
+    models."""
+    ctx.ensure_object(dict)
+    # Print config.yaml if agent is run without a command.
+    if ctx.invoked_subcommand is None:
+        click.echo("Docs Agent configuration:\n")
+        show_config()
+
+
+@cli_admin.command()
+@common_options
+def chunk(config_file: typing.Optional[str], product: list[str] = [""]):
+    """Convert files to plain text chunks."""
+    loaded_config, product_config = return_config_and_product(
+        config_file=config_file, product=product
+    )
+    chunker.process_all_products(config_file=product_config)
+    click.echo("\nFiles are successfully converted into text chunks.")
+
+
+@cli_admin.command()
+@click.option(
+    "--enable_delete_chunks",
+    is_flag=True,
+    help="Delete stale chunks in the existing databases.",
+)
+@common_options
+def populate(
+    config_file: typing.Optional[str],
+    enable_delete_chunks: bool = False,
+    product: list[str] = [""],
+):
+    """Populate a vector database using text chunks."""
+    # Loads configurations from common options
+    loaded_config, product_config = return_config_and_product(
+        config_file=config_file, product=product
+    )
+    # If `--enable_delete_chunks` flag is set, update the config object.
+    if enable_delete_chunks:
+        for product in product_config.products:
+            product.enable_delete_chunks = "True"
+
+    populate_script.process_all_products(config_file=product_config)
+    for item in product_config.products:
+        click.echo(f"\nText chunks are successfully added to {item.db_type}.")
+
+
+@cli_admin.command()
+@click.option("--hostname", default=socket.gethostname(), show_default=True)
+@click.option("--port", default=5000, show_default=True, type=int)
+@click.option("--debug", default=True, show_default=True, type=bool)
+@click.option(
+    "--app_mode",
+    default=None,
+    show_default=True,
+    type=click.Choice(
+        ["web", "full", "widget", "widget-pro", "experimental"],
+        case_sensitive=False,
+    ),
+    help="Specify a mode for the chatbot.",
+)
+@click.option(
+    "--enable_show_logs",
+    is_flag=True,
+    help="Enable a page view that shows log files.",
+)
+@click.option(
+    "--enable_logs_to_markdown",
+    is_flag=True,
+    help="Save each question-and-response pair to a Markdown file.",
+)
+@common_options
+def chatbot(
+    hostname: str,
+    port: str,
+    debug: str,
+    app_mode: str,
+    enable_show_logs: str,
+    enable_logs_to_markdown: str,
+    config_file: typing.Optional[str],
+    product: list[str] = [""],
+):
+    """Launch the Flask-based chatbot app."""
+    # Loads configurations from common options
+    loaded_config, product_config = return_config_and_product(
+        config_file=config_file, product=product
+    )
+    # Launch the web app using the first product in the `config.yaml` file.
+    product = product_config.products[0]
+    # Check the `--app_mode` flag
+    if app_mode == None and hasattr(product, "app_mode"):
+        app_mode = product.app_mode
+    elif app_mode != None:
+        # Update the app_mode field in the product config object.
+        product.app_mode = app_mode
+    # Check the `--app_port` flag
+    app_port = int(port)
+    if app_port == 5000 and hasattr(product, "app_port"):
+        app_port = int(product.app_port)
+    # If `--enable_show_logs` flag is set, update the product config object.
+    if enable_show_logs:
+        product.enable_show_logs = "True"
+    # If `--enable_logs_to_markdown` flag is set, update the product config object.
+    if enable_logs_to_markdown:
+        product.enable_logs_to_markdown = "True"
+    app = chatbot_flask.create_app(product=product, app_mode=app_mode)
+    click.echo(
+        f"Launching the chatbot UI for product {product.product_name} in {app_mode} mode."
+    )
+    app.run(host=hostname, port=app_port, debug=debug)
+
+
+@cli_admin.command()
+@click.option(
+    "--model",
+    help="Specify a model to use. Overrides the model (language_model) for all loaded product configurations.",
+    default=None,
+    multiple=False,
+)
+@common_options
+def benchmark(
+    config_file: typing.Optional[str],
+    product: list[str] = [""],
+    model: typing.Optional[str] = None,
+):
+    """Run the Docs Agent benchmark test."""
+    # Loads configurations from common options
+    loaded_config, product_config = return_config_and_product(
+        config_file=config_file, product=product, model=model
+    )
+    benchmarks.run_benchmarks()
+
+
+@cli_admin.command()
+@common_options
+def list_corpora(config_file: typing.Optional[str], product: list[str] = [""]):
+    """List all existing online corpora."""
+    # Loads configurations from common options
+    loaded_config, product_config = return_config_and_product(
+        config_file=config_file, product=product
+    )
+    semantic = SemanticRetriever()
+    response = semantic.list_existing_corpora()
+    click.echo(f"Found the following corpora:\n")
+    click.echo(f"{response}")
+    while hasattr(response, "next_page_token") and response.next_page_token != "":
+        response = semantic.list_existing_corpora(response.next_page_token)
+        click.echo(f"{response}")
+
+
+@cli_admin.command()
+@click.option("--name", default=None)
+@common_options
+def delete_corpus(
+    name: str, config_file: typing.Optional[str], product: list[str] = [""]
+):
+    """Delete an online corpus."""
+    # Loads configurations from common options
+    loaded_config, product_config = return_config_and_product(
+        config_file=config_file, product=product
+    )
+    semantic = SemanticRetriever()
+    # product_file = product_config.products[0]
+    if name == None:
+        click.echo("Usage: agent delete-corpus --name <CORPUS_NAME>")
+    else:
+        click.echo("Deleting " + name)
+        if click.confirm("Do you want to continue?", abort=True):
+            semantic.delete_a_corpus(corpus_name=name, force=True)
+            click.echo("Successfuly deleted " + name)
+            corpora_response = semantic.list_existing_corpora()
+            click.echo(f"Corpora list:\n{corpora_response}")
+
+
+@cli_admin.command()
+@click.option("--name", default=None)
+@common_options
+def open_corpus(
+    name: str, config_file: typing.Optional[str], product: list[str] = [""]
+):
+    """Share an online corpus with everyone."""
+    # Loads configurations from common options
+    loaded_config, product_config = return_config_and_product(
+        config_file=config_file, product=product
+    )
+    semantic = SemanticRetriever()
+    # product_file = product_config.products[0]
+    if name == None:
+        click.echo("Usage: agent open-corpus --name <CORPUS_NAME>")
+    else:
+        click.echo("Sharing " + name)
+        if click.confirm("Do you want to continue?", abort=True):
+            semantic.open_a_corpus(corpus_name=name)
+            click.echo("Successfully shared " + name)
+
+
+@cli_admin.command()
+@click.option("--name", default=None)
+@click.option("--email", default=None)
+@click.option("--role", default="READER")
+@common_options
+def share_corpus(
+    name: str,
+    email: str,
+    role: str,
+    config_file: typing.Optional[str],
+    product: list[str] = [""],
+):
+    """Share an online corpus with a user."""
+    # Loads configurations from common options
+    loaded_config, product_config = return_config_and_product(
+        config_file=config_file, product=product
+    )
+    semantic = SemanticRetriever()
+    # product_file = product_config.products[0]
+    if name == None:
+        click.echo(
+            "Usage: agent share-corpus --name <CORPUS_NAME> --email <EMAIL> --role READER (or WRITER)"
+        )
+    else:
+        click.echo("Sharing " + name + " with " + email + " as " + role + " role.")
+        if click.confirm("Do you want to continue?", abort=True):
+            semantic.share_a_corpus(corpus_name=name, email=email, role=role)
+            click.echo("Successfully shared " + name)
+
+
+@cli_admin.command()
+@click.option("--name", default=None)
+@common_options
+def remove_corpus_permission(
+    name: str, config_file: typing.Optional[str], product: list[str] = [""]
+):
+    """Remove a user permission from an online corpus."""
+    # Loads configurations from common options
+    loaded_config, product_config = return_config_and_product(
+        config_file=config_file, product=product
+    )
+    semantic = SemanticRetriever()
+    # product_file = product_config.products[0]
+    if name == None:
+        click.echo("Usage: agent remove-corpus-permission --name <PERMISSION_NAME>")
+    else:
+        click.echo("Removing " + name)
+        if click.confirm("Do you want to continue?", abort=True):
+            semantic.delete_permission(permission_name=name)
+            click.echo("Successfully removed " + name)
+
+
+@cli_admin.command()
+@click.option("--name", default=None)
+@common_options
+def get_all_docs(
+    name: str, config_file: typing.Optional[str], product: list[str] = [""]
+):
+    """Get the list of all docs in an online corpus."""
+    # Loads configurations from common options
+    loaded_config, product_config = return_config_and_product(
+        config_file=config_file, product=product
+    )
+    semantic = SemanticRetriever()
+    if name == None:
+        click.echo("Usage: agent get_all_docs --name <CORPUS_NAME>")
+    else:
+        click.echo("Getting the list of all docs from " + name)
+        semantic.get_all_docs(corpus_name=name, print_output=True)
+
+
+@cli_admin.command()
+@common_options
+def cleanup_dev(
+    config_file: typing.Optional[str],
+    product: list[str] = [""],
+):
+    """Delete all databases in this Docs Agent development environment."""
+    print("Cleaning up the Docs Agent development environment.")
+    print("Found the following database configuration:")
+    # Loads configurations from common options
+    loaded_config, product_config = return_config_and_product(
+        config_file=config_file, product=product
+    )
+    chroma_dir = ""
+    corpus_name = ""
+    product = product_config.products[0]
+    for db in product.db_configs:
+        if hasattr(db, "vector_db_dir") and db.vector_db_dir != None:
+            print(f"Chroma database: {db.vector_db_dir}")
+            chroma_dir = db.vector_db_dir
+        if hasattr(db, "corpus_name") and db.corpus_name != None:
+            print(f"Corpus name: {db.corpus_name}")
+            corpus_name = db.corpus_name
+    if chroma_dir != "":
+        command = "rm -fr " + chroma_dir
+        if click.confirm(
+            f"\nDeleting the Chroma database {chroma_dir} ({command}).\nDo you want to continue?",
+            abort=True,
+        ):
+            os.system(command)
+            print("Done.")
+    if corpus_name != "":
+        if click.confirm(
+            f"\nDeleting the corpus named {corpus_name}.\nDo you want to continue?",
+            abort=True,
+        ):
+            semantic = SemanticRetriever()
+            semantic.delete_a_corpus(corpus_name=corpus_name)
+            print("Done.")
+
+
+@cli_admin.command()
+@click.option("--input_chroma", default=None)
+@click.option("--output_dir", default=None)
+@common_options
+def backup_chroma(
+    input_chroma: str,
+    output_dir: typing.Optional[str],
+    config_file: typing.Optional[str],
+    product: list[str] = [""],
+):
+    """Backup a chroma database to an output directory."""
+    # Loads configurations from common options
+    loaded_config, product_config = return_config_and_product(
+        config_file=config_file, product=product
+    )
+    if input_chroma == None:
+        # Get first product
+        input_product = product_config.return_first()
+        if input_product.db_type == "chroma":
+            input_chroma = ReadDbConfigs(input_product.db_configs).return_chroma_db()
+        else:
+            click.echo(
+                f"Your product {input_product.product_name} is not configured for chroma."
+            )
+            sys.exit(0)
+    if output_dir == None:
+        output_dir = parallel_backup_dir(input_chroma)
+    else:
+        pure_path = return_pure_dir(input_chroma)
+        output_dir = end_path_backslash(start_path_no_backslash(output_dir)) + pure_path
+    # Initialize chroma and then use backup function
+    chroma_db = ChromaEnhanced(chroma_dir=input_chroma)
+    final_output_dir = chroma_db.backup_chroma(
+        chroma_dir=input_chroma, output_dir=output_dir
+    )
+    if final_output_dir:
+        click.echo(f"Successfully backed up {input_chroma} to {final_output_dir}.")
+    else:
+        click.echo(f"Can't backup chroma database specified: {input_chroma}")
+
+
+@cli_admin.command()
+@click.option("--date", default="None")
+@common_options
+def write_logs_to_csv(
+    date: typing.Optional[str],
+    config_file: typing.Optional[str],
+    product: list[str] = [""],
+):
+    """Write captured debug information to a CSV file."""
+    # Loads configurations from common options
+    loaded_config, product_config = return_config_and_product(
+        config_file=config_file, product=product
+    )
+    output_filename = "debug-info-all.csv"
+    if date != "None":
+        output_filename = "debug-info-" + str(date) + ".csv"
+        click.echo(
+            f"Writing all debug logs from {date} to the logs/{output_filename} file:\n"
+        )
+    else:
+        click.echo(f"Writing all debug logs to the logs/{output_filename} file:\n")
+    # Write the target debug logs to a CSV file.
+    write_logs_to_csv_file(log_date=date)
+    # Print the content of the CSV file.
+    with open("./logs/" + output_filename, "r") as f:
+        print(f.read())
+        f.close()
+
+
+cli = click.CommandCollection(
+    sources=[cli_admin],
+    help="With Docs Agent admin, you can populate vector databases, manage online corpora, and maintain Docs Agent.",
+)
+
+
+if __name__ == "__main__":
+    cli()
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_common.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_common.py
new file mode 100644
index 000000000..596dbf41d
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_common.py
@@ -0,0 +1,67 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Docs Agent CLI common options"""
+
+import click
+import typing
+from functools import wraps
+from docs_agent.utilities.config import return_config_and_product
+
+
+def common_options(func):
+    @wraps(func)
+    @click.option(
+        "--config_file",
+        help="Specify a configuration file. Defaults to config.yaml.",
+        default=None,
+    )
+    @click.option(
+        "--product",
+        help="Specify a product. Defaults to use all products. For web app, defaults to use the first product in config.yaml.",
+        default=None,
+        multiple=True,
+    )
+    def wrapper(*args, **kwargs):
+        return func(*args, **kwargs)
+
+    return wrapper
+
+
+@click.group(invoke_without_command=True)
+@common_options
+@click.pass_context
+def cli_common(ctx, config_file, product):
+    """With Docs Agent, you can populate vector databases,
+    manage online corpora, and interact with Google's Gemini
+    models."""
+    ctx.ensure_object(dict)
+    # Print config.yaml if agent is run without a command.
+    if ctx.invoked_subcommand is None:
+        click.echo("Docs Agent configuration:\n")
+        show_config()
+
+
+@cli_common.command()
+def show_config(config_file: typing.Optional[str] = "config.yaml", product: list[str] = [""]):
+    """Print the Docs Agent configuration."""
+    # Loads configurations from common options
+    loaded_config, product_config = return_config_and_product(
+        config_file=config_file, product=product
+    )
+    for item in product_config.products:
+        print("==========================")
+        print(item)
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_helpme.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_helpme.py
new file mode 100644
index 000000000..d395834a7
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_helpme.py
@@ -0,0 +1,765 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Docs Agent CLI client"""
+
+import click
+import typing
+from docs_agent.utilities import config
+from docs_agent.utilities.config import ConfigFile
+from docs_agent.utilities.config import return_config_and_product, get_project_path
+from docs_agent.utilities.helpers import resolve_path
+
+from docs_agent.interfaces import run_console as console
+from docs_agent.interfaces.cli.cli_common import common_options
+from docs_agent.interfaces.cli.cli_common import show_config
+import os
+import string
+import re
+import time
+import subprocess
+from pathlib import Path
+
+from rich.console import Console
+from rich.markdown import Markdown
+from rich.panel import Panel
+from rich.text import Text
+from rich.style import Style
+
+
+@click.group(invoke_without_command=True)
+@common_options
+@click.pass_context
+def cli_helpme(ctx, config_file, product):
+    """With Docs Agent, you can interact with Google's Gemini
+    models and manage online corpora on Google Cloud."""
+    ctx.ensure_object(dict)
+    # Print config.yaml if agent is run without a command.
+    if ctx.invoked_subcommand is None:
+        click.echo("Docs Agent configuration:\n")
+        show_config()
+
+
+@cli_helpme.command()
+@click.argument("words", nargs=-1)
+@click.option(
+    "--model",
+    help="Specify a language model.",
+    default=None,
+    multiple=False,
+)
+@click.option(
+    "--file",
+    type=click.Path(),
+    help="Specify a file to be included as context.",
+)
+@click.option(
+    "--perfile",
+    type=click.Path(),
+    help="Specify a path where the request will be applied to each file in the directory.",
+)
+@click.option(
+    "--allfiles",
+    type=click.Path(),
+    help="Specify a path where all files in the directory are used as context.",
+)
+@click.option(
+    "--file_ext",
+    help="Works with --perfile and --dir. Specify the file type to be selected. The default is set to use all files.",
+)
+@click.option(
+    "--yaml",
+    is_flag=True,
+    help="Store the output to responses.yaml.",
+)
+@click.option(
+    "--rag",
+    is_flag=True,
+    help="Add entries retrieved from the knowledge database as context.",
+)
+@click.option(
+    "--new",
+    is_flag=True,
+    help="Start a new session.",
+)
+@click.option(
+    "--cont",
+    is_flag=True,
+    help="Use the previous responses in the session as context.",
+)
+@click.option(
+    "--terminal",
+    is_flag=True,
+    help="Use the terminal output as context. (This option requires a special setup in the terminal.)",
+)
+@click.option(
+    "--force",
+    is_flag=True,
+    help="Do not ask the user to confirm interactively.",
+)
+@click.option(
+    "--check",
+    is_flag=True,
+    help="Print the current prompt for testing.",
+    hidden=True,
+)
+@click.option(
+    "--sleep",
+    type=int,
+    default=0,
+    help="Sleep for a specified duration (in seconds) after completing the command.",
+    hidden=True,
+)
+@click.option(
+    "--out",
+    type=click.Path(),
+    help="Specify an output file for saving the response. By default, the file is saved in the project's `agent_out/` directory. Use an absolute path if you want to save the file in a directory outside your current project.",
+)
+@click.option(
+    "--panel",
+    is_flag=True,
+    help="Use a panel for rendering the response.",
+)
+@common_options
+def helpme(
+    words,
+    config_file: typing.Optional[str] = None,
+    file: typing.Optional[str] = None,
+    perfile: typing.Optional[str] = None,
+    allfiles: typing.Optional[str] = None,
+    file_ext: typing.Optional[str] = None,
+    yaml: bool = False,
+    out: typing.Optional[str] = None,
+    rag: bool = False,
+    new: bool = False,
+    cont: bool = False,
+    terminal: bool = False,
+    force: bool = False,
+    check: bool = False,
+    sleep: int = 0,
+    panel: bool = False,
+    product: list[str] = [""],
+    model: typing.Optional[str] = None,
+):
+    """Ask an AI language model to perform a task from the terminal."""
+    # Initialize Rich console
+    ai_console = Console(width=120)
+    console_style = Style(color="cyan", bold=False)
+    use_panel = False
+    if panel:
+        use_panel = True
+    # Loads configurations from common options
+    loaded_config, product_configs = return_config_and_product(
+        config_file=config_file, product=product, model=model
+    )
+    if product == ():
+        # Extracts the first product by default
+        product_config = ConfigFile(products=[product_configs.products[0]])
+    else:
+        product_config = product_configs
+
+    # Get the language model.
+    this_model = product_config.products[0].models.language_model
+    # This feature is only available to the Gemini Pro models (not AQA).
+    if not this_model.startswith("models/gemini"):
+        click.echo(f"File mode is not supported with this model: {this_model}")
+        exit(1)
+
+    # Get the question string.
+    question = ""
+    for word in words:
+        question += word + " "
+    question = question.strip()
+    # This is wrapped around the question when --out flag is set, this will
+    # help format the output of the final file.
+    question_out_wrapper = (
+        f"The answer that you provide to the question below will be saved to a file "
+        + f"named {out}. Your response must only include what will go in this file. "
+        + f"Do your best to ensure that you provide a response that matches the format "
+        + f"of this file extension. For example .md indicates a Markdown file, .py "
+        + f"a Python file, and so on. Markdown files must always be in valid Markdown "
+        + f"format and begin with the # header (for instance, # <PAGE TITLE>).\n\n"
+    )
+    # Set output path to the agent_out directory.
+    if out is not None and out != "" and out != "None":
+        if out.startswith("~/"):
+            out = os.path.expanduser(out)
+        if out.startswith("/"):
+            base_out = os.path.dirname(out)
+            out = Path(out).name
+        # This includes paths like out.startswith("~")
+        else:
+            base_out = os.path.join(get_project_path(), "agent_out")
+        # Creates the output directory if it can't write, it will try home directory.
+        try:
+            os.makedirs(base_out, exist_ok=True)
+        except:
+            base_out = os.path.expanduser("~/docs_agent")
+            base_out = os.path.join(base_out, "agent_out")
+            try:
+                os.makedirs(base_out, exist_ok=True)
+            except:
+                base_out = os.path.join("/tmp/docs_agent", "agent_out")
+                try:
+                    os.makedirs(base_out, exist_ok=True)
+                except:
+                    print(f"Failed to create the output directory: {base_out}")
+                    exit(1)
+        if base_out.endswith("/"):
+            base_out = base_out[:-1]
+        out = base_out + "/" + out
+
+    # Print the prompt for testing.
+    if check:
+        print(f"Prompt: {question}")
+        exit(0)
+
+    # Set the filename for recording exchanges with the Gemini models.
+    history_file = "/tmp/docs_agent_responses"
+
+    # Different modes: Terminal output, Single file, Per file, All files,
+    # Previous exchanges, and No flags.
+    helpme_mode = ""
+    if perfile and perfile != "None":
+        helpme_mode = "PER_FILE"
+    elif allfiles and allfiles != "None":
+        helpme_mode = "ALL_FILES"
+    elif file and file != "None":
+        helpme_mode = "SINGLE_FILE"
+    elif cont:
+        helpme_mode = "PREVIOUS_EXCHANGES"
+    elif terminal:
+        helpme_mode = "TERMINAL_OUTPUT"
+    else:
+        helpme_mode = "NO_FLAGS"
+
+    # Select the mode.
+    if helpme_mode == "PREVIOUS_EXCHANGES":
+        if out is not None and out != "" and out != "None":
+            question = question_out_wrapper + question
+        # Continue mode, which uses the previous exchangs as the main context.
+        this_output = console.ask_model_with_file(
+            question.strip(),
+            product_config,
+            context_file=history_file,
+            rag=rag,
+            return_output=True,
+        )
+
+        # Render the response.
+        if use_panel:
+            ai_console.print("[Response]", style=console_style)
+            ai_console.print(Panel(Markdown(this_output, code_theme="manni")))
+        else:
+            print()
+            print(f"{this_output}")
+            print()
+
+        # Save the response as context.
+        with open(history_file, "a", encoding="utf-8") as out_file:
+            out_file.write(f"QUESTION: {question}\n\n")
+            out_file.write(f"RESPONSE:\n\n{this_output}\n\n")
+            out_file.close()
+
+        # Save this exchange in a YAML file.
+        if yaml is True:
+            output_filename = "./responses.yaml"
+            # Prepare output to be saved in the YAML file.
+            yaml_buffer = (
+                f"  - question: {question}\n" + f"    response: {this_output}\n"
+            )
+            with open(output_filename, "w", encoding="utf-8") as yaml_file:
+                yaml_file.write("logs:\n")
+                yaml_file.write(yaml_buffer)
+                yaml_file.close()
+
+        # Save the response to the `out` file.
+        if out is not None and out != "" and out != "None":
+            try:
+                with open(out, "w", encoding="utf-8") as out_file:
+                    out_file.write(f"{this_output}\n")
+                    out_file.close()
+            except:
+                print(f"Failed to write the output to file: {out}")
+
+    elif helpme_mode == "SINGLE_FILE":
+        if out is not None and out != "" and out != "None":
+            question = question_out_wrapper + question
+        # Single file mode.
+        if file.startswith("~/"):
+            file = os.path.expanduser(file)
+        this_file = os.path.realpath(os.path.join(os.getcwd(), file))
+        this_output = ""
+
+        # if the `--cont` flag is set, inlcude the previous exchanges as additional context.
+        context_file = None
+        if cont:
+            context_file = history_file
+
+        this_output = console.ask_model_with_file(
+            question.strip(),
+            product_config,
+            file=this_file,
+            context_file=context_file,
+            rag=rag,
+            return_output=True,
+        )
+
+        # Render the response.
+        if use_panel:
+            ai_console.print("[Response]", style=console_style)
+            ai_console.print(Panel(Markdown(this_output, code_theme="manni")))
+        else:
+            print()
+            print(f"{this_output}")
+            print()
+
+        # Read the file content to be included in the history file.
+        file_content = ""
+        try:
+            with open(this_file, "r", encoding="utf-8") as target_file:
+                file_content = target_file.read()
+                target_file.close()
+        except:
+            print(f"[Error] This file cannot be opened: {this_file}\n")
+            exit(1)
+
+        # If the `--new` flag is set, overwrite the history file.
+        write_mode = "a"
+        if new:
+            write_mode = "w"
+        # Record this exchange in the history file.
+        with open(history_file, write_mode, encoding="utf-8") as out_file:
+            out_file.write(f"QUESTION: {question}\n\n")
+            out_file.write(f"FILE NAME: {file}\n")
+            out_file.write(f"FILE CONTENT:\n\n{file_content}\n")
+            out_file.write(f"RESPONSE:\n\n{this_output}\n\n")
+            out_file.close()
+
+        # Save this exchange in a YAML file.
+        if yaml is True:
+            output_filename = "./responses.yaml"
+            # Prepare output to be saved in the YAML file.
+            yaml_buffer = (
+                f"  - question: {question}\n"
+                + f"    response: {this_output}\n"
+                + f"    file: {this_file}\n"
+            )
+            with open(output_filename, "w", encoding="utf-8") as yaml_file:
+                yaml_file.write("logs:\n")
+                yaml_file.write(yaml_buffer)
+                yaml_file.close()
+
+        # Save the response to the `out` file.
+        if out is not None and out != "" and out != "None":
+            try:
+                with open(out, "w", encoding="utf-8") as out_file:
+                    out_file.write(f"{this_output}\n")
+                    out_file.close()
+            except:
+                print(f"Failed to write the output to file: {out}")
+
+    elif helpme_mode == "PER_FILE":
+        # Per file mode, which makes the request to each file in the path.
+        if perfile.startswith("~/"):
+            perfile = os.path.expanduser(perfile)
+        this_path = os.path.realpath(resolve_path(perfile))
+        if not os.path.exists(this_path):
+            print(f"[Error] Cannot access the input path: {this_path}")
+            exit(1)
+        # Set the `file_type` variable for display only.
+        file_type = "." + str(file_ext)
+        if file_ext is None or file_ext == "":
+            file_type = "All types"
+
+        # if the `--cont` flag is set, inlcude the previous exchanges as additional context.
+        context_file = None
+        if cont:
+            context_file = history_file
+
+        # Ask the user to confirm.
+        confirm_string = (
+            f"Making a request to each file found in the path below:\n"
+            + f"Question: {question}\nPath: {this_path}\nFile type: {file_type}\n"
+        )
+        if force or click.confirm(
+            f"{confirm_string}" + f"Do you want to continue?",
+            abort=True,
+        ):
+            if force:
+                print(confirm_string)
+            else:
+                print()
+            out_buffer = ""
+            out_buffer_2 = ""
+            yaml_buffer = ""
+            for root, dirs, files in os.walk(resolve_path(perfile)):
+                for file in files:
+                    file_path = os.path.realpath(os.path.join(root, file))
+                    if file_ext == None:
+                        # Apply to all files.
+                        print(f"# File: {file_path}")
+                        try:
+                            with open(file_path, "r", encoding="utf-8") as auto:
+                                content = auto.read()
+                                auto.close()
+                        except:
+                            print(
+                                f"[Warning] Skipping this file because it cannot be opened: {file}\n"
+                            )
+                            continue
+                        this_output = console.ask_model_with_file(
+                            question,
+                            product_config,
+                            file=file_path,
+                            context_file=context_file,
+                            rag=rag,
+                            return_output=True,
+                        )
+                        this_output = this_output.strip()
+                        # Render the response.
+                        if use_panel:
+                            print()
+                            ai_console.print("[Response]", style=console_style)
+                            ai_console.print(
+                                Panel(Markdown(this_output, code_theme="manni"))
+                            )
+                            print()
+                        else:
+                            print()
+                            print(f"{this_output}")
+                            print()
+                        # Read the file content to be included in the history file.
+                        file_content = ""
+                        with open(file_path, "r", encoding="utf-8") as target_file:
+                            file_content = target_file.read()
+                            target_file.close()
+                        # Prepare output to be saved in the history file.
+                        out_buffer += (
+                            f"QUESTION: {question}\n\n"
+                            + f"FILE NAME: {file_path}\n"
+                            + f"FILE CONTENT:\n\n{file_content}\n"
+                            + f"RESPONSE:\n\n{this_output}\n\n"
+                        )
+                        # Prepare output to be saved in the `out` file.
+                        out_buffer_2 += (
+                            f"FILE NAME: {file_path}\n\n" + f"{this_output}\n\n"
+                        )
+                        # Prepare output to be saved in the YAML file.
+                        if yaml is True:
+                            yaml_buffer += (
+                                f"  - question: {question}\n"
+                                + f"    response: {this_output}\n"
+                                + f"    file: {file_path}\n"
+                            )
+                        time.sleep(3)
+                    elif file.endswith(file_ext):
+                        print(f"# File: {file_path}")
+                        try:
+                            with open(file_path, "r", encoding="utf-8") as auto:
+                                content = auto.read()
+                                auto.close()
+                        except:
+                            print(
+                                f"[Warning] Skipping this file becasue it cannot be opened: {file}\n"
+                            )
+                            continue
+                        this_output = console.ask_model_with_file(
+                            question,
+                            product_config,
+                            file=file_path,
+                            context_file=context_file,
+                            rag=rag,
+                            return_output=True,
+                        )
+                        this_output = this_output.strip()
+                        # Render the response.
+                        if use_panel:
+                            print()
+                            ai_console.print("[Response]", style=console_style)
+                            ai_console.print(
+                                Panel(Markdown(this_output, code_theme="manni"))
+                            )
+                            print()
+                        else:
+                            print()
+                            print(f"{this_output}")
+                            print()
+
+                        # Read the file content to be included in the history file.
+                        file_content = ""
+                        with open(file_path, "r", encoding="utf-8") as target_file:
+                            file_content = target_file.read()
+                            target_file.close()
+                        # Prepare output to be saved in the history file.
+                        out_buffer += (
+                            f"QUESTION: {question}\n\n"
+                            + f"FILE NAME: {file_path}\n"
+                            + f"FILE CONTENT:\n\n{file_content}\n"
+                            + f"RESPONSE:\n\n{this_output}\n\n"
+                        )
+                        # Prepare output to be saved in the `out` file.
+                        out_buffer_2 += (
+                            f"FILE NAME: {file_path}\n\n" + f"{this_output}\n\n"
+                        )
+                        # Prepare output to be saved in the YAML file.
+                        if yaml is True:
+                            yaml_buffer += (
+                                f"  - question: {question}\n"
+                                + f"    response: {this_output}\n"
+                                + f"    file: {file_path}\n"
+                            )
+                        time.sleep(3)
+
+            # If the `--new` flag is set, overwrite the history file.
+            write_mode = "a"
+            if new:
+                write_mode = "w"
+            # Record this exchange in the history file.
+            with open(history_file, write_mode, encoding="utf-8") as out_file:
+                out_file.write(out_buffer)
+                out_file.close()
+
+            if yaml is True:
+                output_filename = "./responses.yaml"
+                with open(output_filename, "w", encoding="utf-8") as yaml_file:
+                    yaml_file.write("logs:\n")
+                    yaml_file.write(yaml_buffer)
+                    yaml_file.close()
+
+            # Save the responses to the `out` file.
+            if out is not None and out != "" and out != "None":
+                try:
+                    with open(out, "w", encoding="utf-8") as out_file:
+                        out_file.write(f"{out_buffer_2}")
+                        out_file.close()
+                except:
+                    print(f"Failed to write the output to file: {out}")
+
+    elif helpme_mode == "ALL_FILES":
+        if allfiles.startswith("~/"):
+            allfiles = os.path.expanduser(allfiles)
+        # All files mode, which makes all files in the path to be included as context.
+        this_path = os.path.realpath(resolve_path(allfiles))
+        if not os.path.exists(this_path):
+            print(f"[Error] Cannot access the input path: {this_path}")
+            exit(1)
+        # Set the `file_type` variable for display only.
+        file_type = "." + str(file_ext)
+        if file_ext is None or file_ext == "":
+            file_type = "All types"
+
+        # Ask the user to confirm.
+        confirm_string = (
+            f"Adding all files found in the path below to context:\n"
+            + f"Question: {question}\nPath: {this_path}\nFile type: {file_type}\n"
+        )
+        if force or click.confirm(
+            f"{confirm_string}" + f"Do you want to continue?",
+            abort=True,
+        ):
+            if force:
+                print(confirm_string)
+            else:
+                print()
+            context_buffer = ""
+            for root, dirs, files in os.walk(resolve_path(allfiles)):
+                for file in files:
+                    file_path = os.path.realpath(os.path.join(root, file))
+                    file_content = ""
+                    if file_ext == None:
+                        # Apply to all files.
+                        print(f"# File: {file_path}")
+                        try:
+                            with open(file_path, "r", encoding="utf-8") as auto:
+                                file_content = auto.read()
+                                auto.close()
+                            context_buffer += (
+                                f"FILE NAME: {file_path}\n"
+                                + f"FILE CONTENT:\n\n{file_content}\n\n"
+                            )
+                            print(f"Adding this file to context: {file}\n")
+                        except:
+                            print(
+                                f"[Warning] Skipping this file becasue it cannot be opened: {file}\n"
+                            )
+                            continue
+                    elif file.endswith(file_ext):
+                        print(f"# File: {file_path}")
+                        try:
+                            with open(file_path, "r", encoding="utf-8") as auto:
+                                file_content = auto.read()
+                                auto.close()
+                            context_buffer += (
+                                f"FILE NAME: {file_path}\n"
+                                + f"FILE CONTENT:\n\n{file_content}\n\n"
+                            )
+                            print(f"Adding this file to context: {file}\n")
+                        except:
+                            print(
+                                f"[Warning] Skipping this file becasue it cannot be opened: {file}\n"
+                            )
+                            continue
+
+            context = (
+                "THE FOLLOWING ARE THE CONTENTS OF ALL THE FILES FOUND IN THE TARGET DIRECTORY:\n\n"
+                + context_buffer
+            )
+
+            # If the `--new` flag is set, overwrite the history file.
+            write_mode = "a"
+            if new:
+                write_mode = "w"
+            elif cont:
+                write_mode = "a"
+            # Record this context in the history file.
+            with open(history_file, write_mode, encoding="utf-8") as out_file:
+                out_file.write(context)
+                out_file.close()
+
+            this_output = console.ask_model_with_file(
+                question.strip(),
+                product_config,
+                file=None,
+                context_file=history_file,
+                rag=rag,
+                return_output=True,
+            )
+            this_output = this_output.strip()
+            # Render the response.
+            if use_panel:
+                ai_console.print("[Response]", style=console_style)
+                ai_console.print(Panel(Markdown(this_output, code_theme="manni")))
+            else:
+                print()
+                print(f"{this_output}")
+                print()
+
+            # Record this output in the history file.
+            write_mode = "a"
+            with open(history_file, write_mode, encoding="utf-8") as out_file:
+                out_file.write(f"QUESTION: {question}\n\n")
+                out_file.write(f"RESPONSE:\n\n{this_output}\n\n")
+                out_file.close()
+
+            # Save this exchange in a YAML file.
+            if yaml is True:
+                output_filename = "./responses.yaml"
+                # Prepare output to be saved in the YAML file.
+                yaml_buffer = (
+                    f"  - question: {question}\n"
+                    + f"    response: {this_output}\n"
+                    + f"    path: {this_path}\n"
+                )
+                with open(output_filename, "w", encoding="utf-8") as yaml_file:
+                    yaml_file.write("logs:\n")
+                    yaml_file.write(yaml_buffer)
+                    yaml_file.close()
+
+            # Save the response to the `out` file.
+            if out is not None and out != "" and out != "None":
+                try:
+                    with open(out, "w", encoding="utf-8") as out_file:
+                        out_file.write(f"{this_output}\n")
+                        out_file.close()
+                except:
+                    print(f"Failed to write the output to file: {out}")
+
+    elif helpme_mode == "TERMINAL_OUTPUT":
+        # Terminal output mode, which reads the terminal ouput as context.
+        terminal_output = ""
+        # Set the default filename created from the `script` command.
+        file_path = "/tmp/docs_agent_console_input"
+        # Set the maximum number of lines to read from the terminal.
+        lines_limit = -150
+        # For the new 1.5 pro model, increase the limit to 5000 lines.
+        if this_model.startswith("models/gemini-1.5"):
+            lines_limit = -5000
+        try:
+            with open(file_path, "r", encoding="utf-8") as file:
+                for line in file.readlines()[int(lines_limit) : -2]:
+                    # Filter non-ascii and control characters.
+                    printable = set(string.printable)
+                    line = "".join(filter(lambda x: x in printable, line))
+                    terminal_output += line
+                    # if an Enter key is detected, remove all saved lines.
+                    # This allows to read the output of the last commandline only.
+                    if re.search("^\[\?2004", line):
+                        terminal_output = ""
+        except:
+            terminal_output = "No console output is provided."
+        context = "THE FOLLOWING IS MY CONSOLE OUTPUT:\n\n" + terminal_output
+        console.ask_model_for_help(question, context, product_config)
+    else:
+        # Default mode. No additional context is provided.
+        this_output = console.ask_model_without_context(
+            question.strip(),
+            product_config,
+            return_output=True,
+        )
+
+        # Render the response.
+        if use_panel:
+            ai_console.print("[Response]", style=console_style)
+            ai_console.print(Panel(Markdown(this_output, code_theme="manni")))
+        else:
+            print()
+            print(f"{this_output}")
+            print()
+
+        # If the `--new` flag is set, overwrite the history file.
+        if new:
+            write_mode = "w"
+            # Record this exchange in the history file.
+            with open(history_file, write_mode, encoding="utf-8") as out_file:
+                out_file.write(f"QUESTION: {question}\n\n")
+                out_file.write(f"RESPONSE:\n\n{this_output}\n\n")
+                out_file.close()
+
+        # Save this exchange in a YAML file.
+        if yaml is True:
+            output_filename = "./responses.yaml"
+            # Prepare output to be saved in the YAML file.
+            yaml_buffer = (
+                f"  - question: {question}\n" + f"    response: {this_output}\n"
+            )
+            with open(output_filename, "w", encoding="utf-8") as yaml_file:
+                yaml_file.write("logs:\n")
+                yaml_file.write(yaml_buffer)
+                yaml_file.close()
+
+        # Save the response to the `out` file.
+        if out is not None and out != "" and out != "None":
+            try:
+                with open(out, "w", encoding="utf-8") as out_file:
+                    out_file.write(f"{this_output}\n")
+                    out_file.close()
+            except:
+                print(f"Failed to write the output to file: {out}")
+
+    # When the --sleep flag is provided, sleep for the specified duration.
+    if sleep > 0:
+        time.sleep(int(sleep))
+
+
+cli = click.CommandCollection(
+    sources=[cli_helpme],
+    help="With Docs Agent, you can interact with Google's Gemini models.",
+)
+
+
+if __name__ == "__main__":
+    cli()
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_posix.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_posix.py
new file mode 100644
index 000000000..1b10e8097
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_posix.py
@@ -0,0 +1,114 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Docs Agent CLI client"""
+
+import click
+import typing
+
+from docs_agent.interfaces.cli.cli_common import common_options
+from docs_agent.interfaces.cli.cli_common import show_config
+
+import subprocess
+import os
+import time
+
+
+@click.group(invoke_without_command=True)
+@common_options
+@click.pass_context
+def cli_posix(ctx, config_file, product):
+    """With Docs Agent, you can interact with Google's Gemini
+    models and manage online corpora on Google Cloud."""
+    ctx.ensure_object(dict)
+    # Print config.yaml if agent is run without a command.
+    if ctx.invoked_subcommand is None:
+        click.echo("Docs Agent configuration:\n")
+        show_config()
+
+
+@cli_posix.command(name="posix")
+@click.argument("words", nargs=-1)
+@click.option(
+    "--new",
+    is_flag=True,
+    help="Start a new session.",
+)
+@click.option(
+    "--cont",
+    is_flag=True,
+    help="Use the previous responses in the session as context.",
+)
+@click.option(
+    "--sleep",
+    type=int,
+    default=0,
+    help="Sleep for a specified duration (in seconds) after completing the command.",
+    hidden=True,
+)
+@common_options
+def posix(
+    words,
+    config_file: typing.Optional[str] = None,
+    new: bool = False,
+    cont: bool = False,
+    sleep: int = 0,
+    product: list[str] = [""],
+):
+    """Run a POSIX command and add its output into context."""
+    # Set the filename for recording exchanges with the Gemini models.
+    history_file = "/tmp/docs_agent_responses"
+
+    # Get the question string.
+    command = ""
+    for word in words:
+        if word.startswith("~/"):
+            word = os.path.expanduser(word)
+        command += word + " "
+    command = command.strip()
+
+    print(f"Running a POSIX command: {command}")
+    print()
+    subprocess.run(command.split())
+    result = subprocess.run(command.split(), stdout=subprocess.PIPE)
+    this_output = result.stdout.decode("utf-8")
+
+    # If the `--new` flag is set, overwrite the history file.
+    write_mode = "None"
+    if new:
+        write_mode = "w"
+    elif cont:
+        write_mode = "a"
+    if write_mode != "None":
+        # Record this exchange in the history file.
+        with open(history_file, write_mode, encoding="utf-8") as out_file:
+            out_file.write(f"POSIX COMMAND: {command}\n\n")
+            out_file.write(f"RESPONSE:\n\n{this_output}\n\n")
+            out_file.close()
+
+    # When the --sleep flag is provided, sleep for the specified duration.
+    if sleep > 0:
+        time.sleep(int(sleep))
+
+
+cli = click.CommandCollection(
+    sources=[cli_posix],
+    help="With Docs Agent, you can interact with Google's Gemini models.",
+)
+
+
+if __name__ == "__main__":
+    cli()
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_runtask.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_runtask.py
new file mode 100644
index 000000000..3892f8093
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_runtask.py
@@ -0,0 +1,569 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Docs Agent CLI client - run agent"""
+
+import click
+import typing
+from absl import logging
+from docs_agent.utilities import config
+from docs_agent.interfaces import run_console as console
+from docs_agent.utilities.helpers import get_project_path
+from docs_agent.utilities.config import return_config_and_product
+from docs_agent.utilities.tasks import return_tasks_config
+from docs_agent.utilities.tasks import combine_yaml_files, TaskConfigFile
+from docs_agent.interfaces.cli.cli_common import common_options
+from docs_agent.interfaces.cli.cli_helpme import helpme
+from docs_agent.interfaces.cli.cli_tellme import tellme
+from docs_agent.interfaces.cli.cli_posix import posix
+import os
+import re
+import time
+
+from rich.console import Console
+from rich.markdown import Markdown
+from rich.panel import Panel
+from rich.text import Text
+from rich.style import Style
+
+
+@click.group(invoke_without_command=True)
+@common_options
+@click.pass_context
+def cli_runtask(ctx, config_file, product):
+    """With Docs Agent, you can interact with Google's Gemini
+    models and manage online corpora on Google Cloud."""
+    ctx.ensure_object(dict)
+    # Print config.yaml if task is run without a command.
+    # if ctx.invoked_subcommand is None:
+    #     click.echo("Docs Agent configuration:\n")
+    #     show_config()
+
+
+@cli_runtask.command(name="runtask")
+@click.argument("words", nargs=-1)
+@click.option(
+    "--model",
+    help="Specify a model to use. Overrides the model for all configured help me tasks and configuration. model setting is not overwritten for tellme tasks.",
+    default=None,
+    multiple=False,
+)
+@click.option(
+    "--task_config",
+    help="Specify a yaml file or path that contains configuration of tasks.",
+    default=os.path.join(get_project_path(), "tasks"),
+    multiple=False,
+)
+@click.option(
+    "--force",
+    is_flag=True,
+    help="Do not ask the user to confirm interactively.",
+)
+@click.option(
+    "--task",
+    help="Specify a task to run.",
+    default=None,
+    multiple=True,
+)
+@click.option(
+    "--custom_input",
+    help="Specify an input string for the task.",
+    default=None,
+)
+@click.pass_context
+def runtask(
+    # Words can be used to try to find an agent or match to helpme/tellme
+    ctx,
+    words,
+    task_config: typing.Optional[str],
+    task: typing.Optional[str],
+    custom_input: typing.Optional[str] = None,
+    model: typing.Optional[str] = None,
+    force: bool = False,
+    # task: typing.Optional[str] = None,
+):
+    """Perform tasks defined in a yaml file."""
+    # Initialize Rich console
+    ai_console = Console(width=120)
+    console_style = Style(color="default", bold=True)
+    use_panel = True
+    task_config_user = os.path.join(os.path.expanduser("~/docs_agent"), "tasks")
+    # Read the agent config.
+    if task_config is None:
+        task_config = os.path.join(get_project_path(), "tasks")
+    if task_config.endswith(".yaml"):
+        tasks_config = return_tasks_config(tasks_file=task_config, task=task)
+    else:
+        # Checks for --task flag to make sure we run right tasks
+        tasks_config = combine_yaml_files(
+            path=os.path.join(get_project_path(), "tasks")
+        )
+        try:
+            combined_tasks_config = []
+            tasks_config_user = combine_yaml_files(path=task_config_user)
+            combined_tasks_config.extend(tasks_config_user.tasks)
+            combined_tasks_config.extend(tasks_config.tasks)
+            tasks_config = TaskConfigFile(tasks=combined_tasks_config)
+        except:
+            tasks_config = TaskConfigFile(tasks=tasks_config.tasks)
+        if task is not None and task != ():
+            for item in task:
+                loaded_tasks_config = tasks_config.return_task(task=item)
+                if loaded_tasks_config is None:
+                    print()
+                    print(f"Task {item} not found.")
+                    print()
+                    print("These are the available tasks:")
+                    print()
+                    for item in tasks_config.tasks:
+                        print()
+                        print(f"- {item.name}")
+                        if item.description:
+                            print(f"  Description: {item.description}")
+                        print(f"  Usage: agent runtask --task {item.name}")
+                    print()
+                    print("You can also ask me to try to find a task for you.")
+                    print()
+                    print("Usage: agent runtask how can I generate release notes?")
+                    exit(1)
+                else:
+                    tasks_config = loaded_tasks_config
+        # This checks if a task is specified and if not, suggest a task.
+        elif (task is None or task == ()) and words == "":
+            print()
+            print(
+                f"No task specified. Please specify a task. These are the available tasks:"
+            )
+            for item in tasks_config.tasks:
+                print()
+                print(f"- {item.name}")
+                if item.description:
+                    print(f"  Description: {item.description}")
+                print(f"  Usage: agent runtask --task {item.name}")
+            exit(1)
+    # Get the words before any flags.
+    user_query = ""
+    if (
+        words == ()
+        and (task == () or task == None)
+        and (
+            task_config == ""
+            or task_config == None
+            or task_config == os.path.join(get_project_path(), "tasks")
+        )
+    ):
+        print()
+        print("Please specify a task. These are the available tasks:")
+        for item in tasks_config.tasks:
+            print()
+            print(f"- {item.name}")
+            if item.description:
+                print(f"  Description: {item.description}")
+            print(f"  Usage: agent runtask --task {item.name}")
+        exit(1)
+    for word in words:
+        user_query += word + " "
+    if user_query != "":
+        # Ask the model and retrieve the response.
+        # md_cb_pattern = r"^```(?:\w+)?\s*\n(.*?)(?=^```)```"
+        md_cb_regex = r"```\n(.*)\n```\s*((.|\n|\s)*)"
+        task_assist = f"Given this question: {user_query}, which name of this list of values: {tasks_config} may be the best match? Return the task name in a markdown code block such as name=<name>. Then, justify your response below that. If you cannot find a good match for the task in the provided list and the user is asking a conceptual question, return name=tellme. If you think the user is asking for help with a task and not a conceptual question, return name=helpme. Return name=tellme if you think the user query is a conceptual question.Return name=helpme if you are unsure."
+        # This should be fixed to not rely on product_config
+        if model is None:
+            model = "models/gemini-1.5-flash-latest"
+        loaded_config, product_configs = return_config_and_product(model=model)
+        output = console.ask_model_without_context(
+            task_assist, product_configs, return_output=True
+        )
+        try:
+            result = re.match(md_cb_regex, output)
+            if result is None:
+                print(
+                    f"The LLM did not return a valid response for {user_query}. Try again."
+                )
+                exit(1)
+            found = re.search("^name=(.*)$", result.group(1))
+            if found is None:
+                print(
+                    f"The LLM did not return a valid response for {user_query}. Try again."
+                )
+                exit(1)
+            # Prints the remaining response from model
+            print()
+            print(result.group(2))
+            if (
+                found.group(1) != "helpme"
+                and found.group(1) != "tellme"
+                and click.confirm(
+                    f"Should I execute the {found.group(1)} task?",
+                    abort=True,
+                )
+            ):
+                print()
+                ctx.invoke(
+                    runtask,
+                    words="",
+                    force=True,
+                    task=found.group(1).split(),
+                    model="models/gemini-1.5-flash-latest",
+                )
+                exit(1)
+            elif found.group(1) == "helpme":
+                #     and click.confirm(
+                #     f"Should I try to help you with {user_query}?",
+                #     abort=True,
+                # ):
+                print()
+                ctx.invoke(
+                    helpme,
+                    words=user_query.split(),
+                    force=True,
+                    new=True,
+                    model="models/gemini-1.5-flash-latest",
+                )
+                print(
+                    "Follow-up usage: agent helpme <more text> --cont --models/gemini-1.5-flash-latest"
+                )
+                exit(1)
+            elif found.group(1) == "tellme":
+                # and click.confirm(
+                #     f"Should I tell you more about your question - {user_query}?",
+                #     abort=True,
+                # ):
+                print()
+                # Call helpme instead of tellme until rag is properly hooked in
+                ctx.invoke(
+                    tellme,
+                    words=user_query.split(),
+                )
+                # print("Follow-up usage: agent helpme <more text> --cont")
+                exit(1)
+            else:
+                print()
+                ctx.invoke(
+                    helpme,
+                    words=user_query.split(),
+                    force=True,
+                    model="models/gemini-1.5-flash-latest",
+                )
+                print("Follow-up usage: agent helpme <more text> --cont")
+                exit(1)
+        except:
+            exit(1)
+    for curr_task in tasks_config.tasks:
+        # Extract the top level model specified in the task config.
+        top_level_model = ""
+        if model is not None:
+            top_level_model = model
+        else:
+            top_level_model = curr_task.model
+        if not top_level_model.startswith("models/gemini"):
+            click.echo(
+                f"runtask mode is not supported with this model: {top_level_model} for {curr_task.name}"
+            )
+            exit(1)
+
+        # Extract the preamble in the task config.
+        this_preamble = ""
+        if (
+            hasattr(curr_task, "preamble")
+            and curr_task.preamble is not None
+            and curr_task.preamble != ""
+        ):
+            this_preamble = curr_task.preamble
+
+        # Check if a custom input string is provided.
+        if custom_input is not None:
+            print()
+            print(f"Custom input: {custom_input}")
+            print(
+                f"This input string will replace the <INPUT> placeholder in this task run."
+            )
+
+        # Ask the user to confirm.
+        if force or click.confirm(
+            f"\nPreparing to launch task:\n\n"
+            + f"{curr_task}"
+            + f"Do you want to launch the task?",
+            abort=True,
+        ):
+            print()
+            if use_panel:
+                ai_console.print(f"Starting task: {curr_task.name}", style="cyan")
+            else:
+                print(f"Starting task: {curr_task.name}")
+                # print(f"{curr_task}")
+            list_of_output_files = ""
+            this_step = 0
+            for task in curr_task.steps:
+                this_step += 1
+                # Determine the states of the `is_new` and `is_cont` flags.
+                is_new = False
+                is_cont = True
+                # The first step is always new by default.
+                if int(this_step) == 1:
+                    is_new = True
+                if hasattr(task, "flags"):
+                    if (
+                        hasattr(task.flags, "new")
+                        and task.flags.new is not None
+                        and task.flags.new != ""
+                    ):
+                        if bool(task.flags.new):
+                            is_new = True
+                        else:
+                            is_new = False
+                    # `is_cont` is always True, unless specified.
+                    if (
+                        hasattr(task.flags, "cont")
+                        and task.flags.cont is not None
+                        and task.flags.cont != ""
+                    ):
+                        if bool(task.flags.cont):
+                            is_cont = True
+                        else:
+                            is_cont = False
+
+                # If is_new is True, is_cont is always False.
+                if is_new:
+                    is_cont = False
+
+                # Select the model for this task. Use the top level model by default.
+                this_model = top_level_model
+                if hasattr(task, "flags"):
+                    if hasattr(task.flags, "model"):
+                        if task.flags.model is not None and task.flags.model != "":
+                            this_model = task.flags.model
+
+                # Determine the values for these fields used by helpme and tellme.
+                this_file = None
+                this_perfile = None
+                this_allfiles = None
+                this_file_ext = None
+                this_out = None
+                this_yaml = None
+                this_rag = None
+                this_terminal = None
+                this_default_input = None
+                if hasattr(task, "flags"):
+                    if hasattr(task.flags, "file"):
+                        this_file = task.flags.file
+                    if hasattr(task.flags, "perfile"):
+                        this_perfile = task.flags.perfile
+                    if hasattr(task.flags, "allfiles"):
+                        this_allfiles = task.flags.allfiles
+                    if hasattr(task.flags, "file_ext"):
+                        this_file_ext = task.flags.file_ext
+                    if hasattr(task.flags, "out"):
+                        this_out = task.flags.out
+                    if hasattr(task.flags, "yaml"):
+                        this_yaml = task.flags.yaml
+                    if hasattr(task.flags, "rag"):
+                        this_rag = task.flags.rag
+                    if hasattr(task.flags, "terminal"):
+                        this_terminal = task.flags.terminal
+                    if hasattr(task.flags, "default_input"):
+                        this_default_input = task.flags.default_input
+
+                # Set the out filename to the default name.
+                if this_out is None or this_out == "":
+                    this_out = (
+                        "out-"
+                        + curr_task.name
+                        + "-step-"
+                        + "{:02d}".format(this_step)
+                        + ".md"
+                    )
+
+                list_of_output_files += (
+                    "* Step {:d}:".format(this_step) + " agent_out/" + this_out + "\n"
+                )
+
+                # Update the file-related fields if they are set to <INPUT> in the task file.
+                if custom_input is not None:
+                    # First try to replace them with the custom input value provided by
+                    # the --custom_input flag at runtime
+                    if this_file == "<INPUT>":
+                        this_file = custom_input
+                    if this_perfile == "<INPUT>":
+                        this_perfile = custom_input
+                    if this_allfiles == "<INPUT>":
+                        this_allfiles = custom_input
+                elif this_default_input is not None:
+                    # If no custom_input value is provided at runtime,
+                    # try to replace them with the default input value provided in the task file.
+                    if this_file == "<INPUT>":
+                        this_file = this_default_input
+                    if this_perfile == "<INPUT>":
+                        this_perfile = this_default_input
+                    if this_allfiles == "<INPUT>":
+                        this_allfiles = this_default_input
+                else:
+                    # Error and exit if there is still <INPUT> in any fields.
+                    if (
+                        this_file == "<INPUT>"
+                        or this_perfile == "<INPUT>"
+                        or this_allfiles == "<INPUT>"
+                    ):
+                        print()
+                        print(
+                            f"Error: Detected <INPUT> in the task fields. You must use the --custom_input flag to specify an input string for the task."
+                        )
+                        print(
+                            f"Usage: agent runtask --task <TASK_NAME> --custom_input <CUSTOM_INPUT_STRING>"
+                        )
+                        exit(1)
+
+                # If not specified, set the function field to "helpme".
+                if task.function is None:
+                    task.function = "helpme"
+
+                # If not specified, set the name field to an empty string.
+                if task.name is None:
+                    task.name = ""
+
+                # Select the command type: helpme, tellme, posix
+                if task.function == "helpme":
+                    # helpme Task
+                    # Render this step information.
+                    if use_panel:
+                        print()
+                        ai_console.print(
+                            Panel(
+                                f"Prompt (helpme): {task.prompt}",
+                                title=f"Step {this_step}. {task.name}",
+                                title_align="left",
+                                padding=(1, 2),
+                            ),
+                            style=console_style,
+                        )
+                        print()
+                    else:
+                        print()
+                        print(f"===================")
+                        print(f"Starting task: {task.name}")
+                        print(f"Prompt: {task.prompt}")
+                        print(f"===================")
+                        print()
+                    overwrite_words = (
+                        "FOLLOW THESE RULES FOR THE USER PROMPT: "
+                        + this_preamble
+                        + "\n\nUSER PROMPT: "
+                        + task.prompt
+                    )
+                    overwrite_words = overwrite_words.split()
+                    ctx.invoke(
+                        helpme,
+                        words=overwrite_words,
+                        force=True,
+                        file=this_file,
+                        perfile=this_perfile,
+                        allfiles=this_allfiles,
+                        file_ext=this_file_ext,
+                        out=this_out,
+                        yaml=this_yaml,
+                        rag=this_rag,
+                        new=bool(is_new),
+                        cont=bool(is_cont),
+                        panel=bool(use_panel),
+                        terminal=this_terminal,
+                        model=this_model,
+                    )
+                elif task.function == "tellme":
+                    # tellme Task
+                    # Note: Usually don't want to overwrite model from curr_task.model in
+                    # case it is aqa.
+                    # Render this step information.
+                    if use_panel:
+                        print()
+                        ai_console.print(
+                            Panel(
+                                f"Prompt (tellme): {task.prompt}",
+                                title=f"Step {this_step}. {task.name}",
+                                title_align="left",
+                                padding=(1, 2),
+                            ),
+                            style=console_style,
+                        )
+                        print()
+                    else:
+                        print()
+                        print(f"===================")
+                        print(f"Answering question: {task.name}")
+                        print(f"Prompt: {task.prompt}")
+                        print(f"===================")
+                        print()
+                    overwrite_words = (
+                        "FOLLOW THESE RULES FOR THE USER PROMPT: "
+                        + this_preamble
+                        + "\n\nUSER PROMPT: "
+                        + task.prompt
+                    )
+                    overwrite_words = overwrite_words.split()
+                    ctx.invoke(
+                        tellme,
+                        words=overwrite_words,
+                        new=bool(is_new),
+                        cont=bool(is_cont),
+                        model=this_model,
+                    )
+                elif task.function == "posix":
+                    # Render this step information.
+                    if use_panel:
+                        print()
+                        ai_console.print(
+                            Panel(
+                                f"Command (posix): {task.prompt}",
+                                title=f"Step {this_step}. {task.name}",
+                                title_align="left",
+                                padding=(1, 2),
+                            ),
+                            style=console_style,
+                        )
+                        print()
+                    else:
+                        print()
+                        print(f"===================")
+                        print(f"Running POSIX command: {task.name}")
+                        print(f"Command: {task.prompt}")
+                        print(f"===================")
+                        print()
+                    overwrite_words = task.prompt
+                    overwrite_words = overwrite_words.split()
+                    ctx.invoke(
+                        posix,
+                        words=overwrite_words,
+                        new=bool(is_new),
+                        cont=bool(is_cont),
+                    )
+                else:
+                    logging.error("Unsupported task function: %s", task.function)
+                    exit(1)
+                time.sleep(3)
+
+        print()
+        print("[Output files]\n")
+        print(list_of_output_files)
+
+
+cli = click.CommandCollection(
+    sources=[cli_runtask],
+    help="With Docs Agent, you can interact with Google's Gemini models.",
+)
+
+
+if __name__ == "__main__":
+    cli()
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_show_session.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_show_session.py
new file mode 100644
index 000000000..9a49c0e69
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_show_session.py
@@ -0,0 +1,59 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Docs Agent CLI client"""
+
+import click
+import typing
+from docs_agent.utilities import config
+
+from docs_agent.interfaces.cli.cli_common import common_options
+from docs_agent.interfaces.cli.cli_common import show_config
+
+
+@click.group(invoke_without_command=True)
+@common_options
+@click.pass_context
+def cli_show_session(ctx, config_file, product):
+    """With Docs Agent, you can interact with Google's Gemini
+    models and manage online corpora on Google Cloud."""
+    ctx.ensure_object(dict)
+    # Print config.yaml if agent is run without a command.
+    if ctx.invoked_subcommand is None:
+        click.echo("Docs Agent configuration:\n")
+        show_config()
+
+
+@cli_show_session.command(name="show-session")
+@common_options
+def show_session(config_file: typing.Optional[str] = None, product: list[str] = [""]):
+    """Print the questions and responses being tracked in the current session."""
+    # Set the filename for recording exchanges with the Gemini models.
+    history_file = "/tmp/docs_agent_responses"
+    with open(history_file, "r", encoding="utf-8") as target_file:
+        context = target_file.read()
+        target_file.close()
+    print(context.strip() + "\n")
+
+
+cli = click.CommandCollection(
+    sources=[cli_show_session],
+    help="With Docs Agent, you can interact with Google's Gemini models.",
+)
+
+
+if __name__ == "__main__":
+    cli()
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_tellme.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_tellme.py
new file mode 100644
index 000000000..155c00841
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_tellme.py
@@ -0,0 +1,127 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Docs Agent CLI client"""
+
+import click
+import typing
+from docs_agent.utilities import config
+from docs_agent.utilities.config import ConfigFile
+from docs_agent.utilities.config import return_config_and_product
+
+from docs_agent.interfaces import run_console as console
+from docs_agent.interfaces.cli.cli_common import common_options
+from docs_agent.interfaces.cli.cli_common import show_config
+import time
+
+
+@click.group(invoke_without_command=True)
+@common_options
+@click.pass_context
+def cli_tellme(ctx, config_file, product):
+    """With Docs Agent, you can interact with Google's Gemini
+    models and manage online corpora on Google Cloud."""
+    ctx.ensure_object(dict)
+    # Print config.yaml if agent is run without a command.
+    if ctx.invoked_subcommand is None:
+        click.echo("Docs Agent configuration:\n")
+        show_config()
+
+
+@cli_tellme.command(name="tellme")
+@click.argument("words", nargs=-1)
+@click.option(
+    "--model",
+    help="Specify a language model.",
+    default=None,
+    multiple=False,
+)
+@click.option(
+    "--new",
+    is_flag=True,
+    help="Start a new session.",
+)
+@click.option(
+    "--cont",
+    is_flag=True,
+    help="Use the previous responses in the session as context.",
+)
+@click.option(
+    "--sleep",
+    type=int,
+    default=0,
+    help="Sleep for a specified duration (in seconds) after completing the command.",
+    hidden=True,
+)
+@common_options
+def tellme(
+    words,
+    config_file: typing.Optional[str] = None,
+    new: bool = False,
+    cont: bool = False,
+    sleep: int = 0,
+    product: list[str] = [""],
+    model: typing.Optional[str] = None,
+):
+    """Answer a question related to the product."""
+    # Loads configurations from common options
+    loaded_config, product_configs = return_config_and_product(
+        config_file=config_file, product=product, model=model
+    )
+    if product == ():
+        # Extracts the first product by default
+        product_config = ConfigFile(products=[product_configs.products[0]])
+    else:
+        product_config = product_configs
+
+    # Set the filename for recording exchanges with the Gemini models.
+    history_file = "/tmp/docs_agent_responses"
+
+    # Get the question string.
+    question = ""
+    for word in words:
+        question += word + " "
+    question = question.strip()
+
+    # Ask the model and retrieve the response.
+    this_output = console.ask_model(question, product_config, return_output=True)
+
+    # If the `--new` flag is set, overwrite the history file.
+    write_mode = "None"
+    if new:
+        write_mode = "w"
+    elif cont:
+        write_mode = "a"
+    if write_mode != "None":
+        # Record this exchange in the history file.
+        with open(history_file, write_mode, encoding="utf-8") as out_file:
+            out_file.write(f"QUESTION: {question}\n\n")
+            out_file.write(f"RESPONSE:\n\n{this_output}\n\n")
+            out_file.close()
+
+    # When the --sleep flag is provided, sleep for the specified duration.
+    if sleep > 0:
+        time.sleep(int(sleep))
+
+
+cli = click.CommandCollection(
+    sources=[cli_tellme],
+    help="With Docs Agent, you can interact with Google's Gemini models.",
+)
+
+
+if __name__ == "__main__":
+    cli()
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/hello_world.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/hello_world.py
new file mode 100644
index 000000000..d53cfd739
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/hello_world.py
@@ -0,0 +1,84 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Hello World for Docs Agent"""
+
+from docs_agent.storage.chroma import Format
+from docs_agent.agents.docs_agent import DocsAgent
+
+# This `hello_world` script contains the minimal set of function calls
+# needed to use Docs Agent.
+#
+# Note: This script assumes that the vector database has already been populated.
+#
+# To run this script:
+# $ python3 hello_world.py
+#
+
+
+def main():
+    # Initialize Docs Agent.
+    print("STATE: Initializing Docs Agent.")
+    docs_agent = DocsAgent()
+
+    # This question is used for testing.
+    question = "What are some differences between apples and oranges?"
+
+    # Print the question.
+    print("\nQuestion: " + question)
+
+    # Pass the question to the vector database and get a list of the most relevant content.
+    result = docs_agent.query_vector_store(question)
+    context = result.fetch_formatted(Format.CONTEXT)
+
+    # Add instruction (see `condition.txt`) as a prefix to the context.
+    context_with_prefix = docs_agent.add_instruction_to_context(context)
+
+    print("\nSending prompts to Google's language models...")
+
+    # Pass the context and question to the `gemini-pro` model.
+    if "gemini" in docs_agent.get_language_model_name():
+        response_gemini = docs_agent.ask_content_model_with_context(
+            context_with_prefix, question
+        )
+        print("\n[Genmini answer]:")
+        print(response_gemini)
+
+    # Pass the context and question to the `aqa` model
+    if docs_agent.check_if_aqa_is_used():
+        response_aqa = docs_agent.ask_aqa_model(question)
+        print("\n[AQA answer]:")
+        print(response_aqa)
+
+    # Pass the context and question to PaLM 2's `text-bison-001` model.
+    response_text = docs_agent.ask_text_model_with_context(
+        context_with_prefix, question
+    )
+    print("\n[Text answer]:")
+    print(response_text)
+
+    # Pass the context and question to PaLM 2's `chat-bison-001` model.
+    response_chat = docs_agent.ask_chat_model_with_context(
+        context_with_prefix, question
+    )
+    print("\n[Chat answer]:")
+    print(response_chat)
+
+    print("")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/run_console.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/run_console.py
new file mode 100644
index 000000000..99a216497
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/run_console.py
@@ -0,0 +1,411 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Run the Docs Agent console in the terminal"""
+
+import typing
+from absl import logging
+from rich.console import Console
+from rich.markdown import Markdown
+from rich.panel import Panel
+from rich.text import Text
+from rich.progress import Progress
+
+from docs_agent.agents.docs_agent import DocsAgent
+from docs_agent.utilities.config import ConfigFile
+
+
+# This function is used by the `helpme` command to ask the Gemini Pro model
+# to perform a task based on the console ouput.
+def ask_model_for_help(question: str, context: str, product_configs: ConfigFile):
+    # Initialize Rich console
+    ai_console = Console(width=160)
+    # Filter the input context into text.
+    context_text = Text(context)
+    # Print the console output to be provided as context.
+    ai_console.print("[Context from console output]")
+    ai_console.print(Panel(context_text), markup=False)
+    # Initialize Docs Agent
+    docs_agent = DocsAgent(config=product_configs.products[0], init_chroma=False)
+    (
+        good_response,
+        new_prompt,
+    ) = docs_agent.ask_content_model_with_context_prompt(
+        context=context,
+        question=question,
+        prompt="Examine the console output below and respond to the user's request specified at the end of this prompt:",
+    )
+    # Print the Gemini response
+    ai_console.print()
+    ai_console.print("[Gemini response]")
+    ai_console.print()
+    ai_console.print(good_response)
+
+
+# This function is used by the `tellme` command to ask the Gemini AQA model
+# a question from an online corpus.
+def ask_model(question: str, product_configs: ConfigFile, return_output: bool = False):
+    # Initialize Rich console
+    ai_console = Console(width=160)
+    full_prompt = ""
+    final_context = ""
+    results_num = 5
+    # Initialize Docs Agent
+    with Progress(transient=True) as progress:
+        search_results = []
+        responses = []
+        links = []
+        task_docs_agent = progress.add_task(
+            "[turquoise4 bold]Starting Docs Agent ", total=None, refresh=True
+        )
+        for product in product_configs.products:
+            if "gemini" in product.models.language_model:
+                docs_agent = DocsAgent(config=product)
+                progress.update(
+                    task_docs_agent,
+                    description=f"[turquoise4 bold]Asking Gemini (model: {product.models.language_model}, source: {docs_agent.return_chroma_collection()}) ",
+                    total=None,
+                )
+                if docs_agent.config.docs_agent_config == "experimental":
+                    results_num = 10
+                    new_question_count = 5
+                else:
+                    results_num = 5
+                    new_question_count = 5
+                # Issue if max_sources > results_num, so leave the same for now
+                search_result, final_context = docs_agent.query_vector_store_to_build(
+                    question=question,
+                    token_limit=30000,
+                    results_num=results_num,
+                    max_sources=results_num,
+                )
+                (
+                    response,
+                    full_prompt,
+                ) = docs_agent.ask_content_model_with_context_prompt(
+                    context=final_context, question=question
+                )
+                if len(search_result) >= 1:
+                    if search_result[0].section.url == "":
+                        link = str(search_result[0].section)
+                    else:
+                        link = search_result[0].section.url
+                search_results.append(search_result)
+                responses.append(response)
+                links.append(link)
+            elif "aqa" in product.models.language_model:
+                if product.db_type == "google_semantic_retriever":
+                    docs_agent = DocsAgent(config=product, init_chroma=False)
+                    label = f"[turquoise4 bold]Asking Gemini (model: {product.models.language_model}, "
+                    corpus_name = ""
+                    for db_config in product.db_configs:
+                        if db_config.db_type == "google_semantic_retriever":
+                            corpus_name = db_config.corpus_name
+                    if corpus_name != "":
+                        label += "source: " + corpus_name + ") "
+                    progress.update(
+                        task_docs_agent, description=label, total=None, refresh=True
+                    )
+                    (response, search_result) = docs_agent.ask_aqa_model_using_corpora(
+                        question=question
+                    )
+                    if len(search_result) >= 1:
+                        if search_result[0].section.url == "":
+                            link = str(search_result[0].section)
+                        else:
+                            link = search_result[0].section.url
+                    search_results.append(search_result)
+                    responses.append(response)
+                    links.append(link)
+                elif product.db_type == "chroma":
+                    docs_agent = DocsAgent(config=product, init_chroma=True)
+                    progress.update(
+                        task_docs_agent,
+                        description=f"[turquoise4 bold]Asking Gemini (model: {product.models.language_model}, source: {docs_agent.return_chroma_collection()}) ",
+                        total=None,
+                    )
+                    (
+                        response,
+                        search_result,
+                    ) = docs_agent.ask_aqa_model_using_local_vector_store(
+                        question=question, results_num=results_num
+                    )
+                    if len(search_result) >= 1:
+                        if search_result[0].section.url == "":
+                            link = str(search_result[0].section)
+                        else:
+                            link = search_result[0].section.url
+                    search_results.append(search_result)
+                    responses.append(response)
+                    links.append(link)
+                else:
+                    logging.error(f"Unknown db_type: {product.db_type}")
+        final_search = []
+        final_responses = []
+        final_response_md = ""
+        final_links = []
+        count = 0
+        # Prune the generated answers as needed
+        for item in search_results:
+            # Gemini-pro + chroma will give distance instead of probability
+            # if (item[0].probability != 0):
+            final_search.append(search_results[count])
+            final_responses.append(responses[count])
+            final_response_md += responses[count] + "\n"
+            final_links.append(links[count])
+            count += 1
+
+        # Print the response.
+        count = 0
+        synthesize = False
+        synthesize_product = None
+        # Currently only triggers from a gemini entry into the provided products
+        for product in product_configs.products:
+            if "gemini" in product.models.language_model:
+                synthesize_product = product
+
+        if synthesize and not (synthesize_product == None):
+            docs_agent = DocsAgent(config=synthesize_product, init_chroma=False)
+            progress.update(
+                task_docs_agent,
+                description=f"[turquoise4 bold]Asking {docs_agent.context_model} to synthesize a response",
+                total=None,
+            )
+            new_question = f"Help me synthesize the context above into a cohesive response to help me answer my original question. Only use content from the provided context. My original question: {question}"
+            (
+                good_response,
+                new_prompt,
+            ) = docs_agent.ask_content_model_with_context_prompt(
+                context=final_response_md, question=new_question, prompt=""
+            )
+            progress.update(task_docs_agent, visible=False, refresh=True)
+            # Final printing to console
+            ai_console.print()
+            ai_console.print(Markdown(good_response))
+        else:
+            # This returns the responses as is for each agent interaction
+            progress.update(task_docs_agent, visible=False, refresh=True)
+            ai_console.print()
+            ai_console.print(Markdown(final_response_md))
+
+        # Get the link to the source.
+        md_links = ""
+        for item in final_links:
+            if isinstance(item, str):
+                if not item.startswith("UUID"):
+                    md_links += f"\n* [{item}]({item})\n"
+
+        # Print the link to the source.
+        ai_console.print()
+        ai_console.print(Markdown("To verify this information, see:\n"))
+        ai_console.print(Markdown(md_links))
+
+        # Retrun the output if the `return_output` flag is set.
+        if return_output:
+            return_string = (
+                str(final_response_md.strip())
+                + "\n\nTo verify this information, see:\n"
+                + md_links
+            )
+            return return_string
+
+
+# This function is used by the `helpme` command to ask a Gemini model
+# a question without additional context.
+def ask_model_without_context(
+    question: str,
+    product_configs: ConfigFile,
+    return_output: bool = False,
+):
+    # Initialize Rich console
+    ai_console = Console(width=160)
+    full_prompt = ""
+    final_context = ""
+    response = ""
+
+    # Use the first product by default.
+    product = product_configs.products[0]
+    language_model = product.models.language_model
+    with Progress(transient=True) as progress:
+        task_docs_agent = progress.add_task(
+            "[turquoise4 bold]Starting Docs Agent ", total=None, refresh=True
+        )
+        # Initialize Docs Agent.
+        docs_agent = DocsAgent(config=product, init_chroma=False, init_semantic=False)
+        # Set the progress bar.
+        label = f"[turquoise4 bold]Asking Gemini (model: {language_model}) "
+        progress.update(task_docs_agent, description=label, total=None, refresh=True)
+        final_context = "No additional context is necessary for this question."
+        # Ask Gemini with the question without additional context.
+        response = docs_agent.ask_content_model_with_context(
+            context=final_context, question=question
+        )
+    if return_output:
+        return str(response.strip())
+    ai_console.print()
+    ai_console.print(Markdown(response.strip()))
+
+
+# This function is used by the `helpme` command to ask a Gemini model
+# a question with various context sources.
+def ask_model_with_file(
+    question: str,
+    product_configs: ConfigFile,
+    file: typing.Optional[str] = None,
+    context_file: typing.Optional[str] = None,
+    rag: bool = False,
+    return_output: bool = False,
+):
+    # Initialize Rich console
+    ai_console = Console(width=160)
+    full_prompt = ""
+    final_context = ""
+    response = ""
+
+    # Get the content of the target file.
+    file_content = ""
+    if file != None:
+        try:
+            with open(file, "r", encoding="utf-8") as auto:
+                content = auto.read()
+                auto.close()
+            file_content = f"\nTHE CONTENT BELOW IS FROM THE FILE {file}:\n\n" + content
+        except:
+            print(f"Cannot open the file {file}")
+            exit(1)
+
+    # Get the content of the context file.
+    context_file_content = ""
+    if context_file != None:
+        try:
+            with open(context_file, "r", encoding="utf-8") as auto:
+                content = auto.read()
+                auto.close()
+            context_file_content = (
+                f"\nTHE CONTENT BELOW IS FROM THE PREVIOUS EXCHANGES WITH GEMINI:\n\n"
+                + content
+            )
+            file_content = context_file_content + "\n\n" + file_content
+        except:
+            print(f"Cannot open the context file {file}")
+            exit(1)
+
+    # Use the first product by default.
+    product = product_configs.products[0]
+    language_model = product.models.language_model
+    with Progress(transient=True) as progress:
+        task_docs_agent = progress.add_task(
+            "[turquoise4 bold]Starting Docs Agent ", total=None, refresh=True
+        )
+        if rag:
+            # RAG specified. Retrieve additional context from a database.
+            if product.db_type == "chroma":
+                # Use a local Chroma database.
+                # Initialize Docs Agent.
+                docs_agent = DocsAgent(
+                    config=product, init_chroma=True, init_semantic=False
+                )
+                # Get the Chroma collection name.
+                collection = docs_agent.return_chroma_collection()
+                # Set the progress bar.
+                label = f"[turquoise4 bold]Asking Gemini (model: {language_model}, source: {collection}) "
+                progress.update(
+                    task_docs_agent, description=label, total=None, refresh=True
+                )
+                # Retrieve context from the local Chroma database.
+                (
+                    search_result,
+                    returned_context,
+                ) = docs_agent.query_vector_store_to_build(
+                    question=question,
+                    token_limit=500000,
+                    results_num=5,
+                    max_sources=5,
+                )
+                context_from_database = ""
+                chunk_count = 0
+                for item in search_result:
+                    context_from_database += item.section.content
+                    context_from_database += f"\nReference [{chunk_count}]\n\n"
+                    chunk_count += 1
+                # Construct the final context for the question.
+                final_context = (
+                    "\nTHE CONTENT BELOW IS RETRIEVED FROM THE LOCAL DATABASE:\n\n"
+                    + context_from_database.strip()
+                    + "\n\n"
+                    + str(file_content)
+                )
+            elif product.db_type == "google_semantic_retriever":
+                # Use an online corpus from the Semantic Retrieval API.
+                # Initialize Docs Agent.
+                docs_agent = DocsAgent(
+                    config=product, init_chroma=False, init_semantic=True
+                )
+                # Get the corpus name.
+                corpus_name = ""
+                for db_config in product.db_configs:
+                    if db_config.db_type == "google_semantic_retriever":
+                        corpus_name = db_config.corpus_name
+                # Set the progress bar.
+                label = f"[turquoise4 bold]Asking Gemini (model: {language_model}, source: {corpus_name}) "
+                progress.update(
+                    task_docs_agent, description=label, total=None, refresh=True
+                )
+                # Retrieve context from the online corpus.
+                context_chunks = docs_agent.retrieve_chunks_from_corpus(
+                    question, corpus_name=str(corpus_name)
+                )
+                context_from_corpus = ""
+                chunk_count = 0
+                for chunk in context_chunks.relevant_chunks:
+                    context_from_corpus += chunk.chunk.data.string_value
+                    context_from_corpus += f"\nReference [{chunk_count}]\n\n"
+                    chunk_count += 1
+                # Construct the final context for the question.
+                final_context = (
+                    "\nTHE CONTENT BELOW IS RETRIEVED FROM THE ONLINE DATABASE:\n\n"
+                    + context_from_corpus.strip()
+                    + "\n\n"
+                    + str(file_content)
+                )
+            else:
+                logging.error(f"Unknown db_type: {product.db_type}")
+                exit(1)
+        else:
+            # No RAG specified. No additional context to be retrieved from a database.
+            docs_agent = DocsAgent(
+                config=product, init_chroma=False, init_semantic=False
+            )
+            progress.update(
+                task_docs_agent,
+                description=f"[turquoise4 bold]Asking Gemini (model: {language_model}) ",
+                total=None,
+            )
+            final_context = file_content
+
+        #print(f"Context:\n{final_context}")
+        #print(f"Question:\n{question}")
+        # Ask Gemini with the question and final context.
+        (
+            response,
+            full_prompt,
+        ) = docs_agent.ask_content_model_with_context_prompt(
+            context=final_context, question=question
+        )
+        if return_output:
+            return str(response.strip())
+        ai_console.print()
+        ai_console.print(Markdown(response.strip()))
diff --git a/examples/gemini/python/docs-agent/docs_agent/memory/__init__.py b/examples/gemini/python/docs-agent/docs_agent/memory/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/memory/logging.py b/examples/gemini/python/docs-agent/docs_agent/memory/logging.py
new file mode 100644
index 000000000..c2625e8da
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/memory/logging.py
@@ -0,0 +1,319 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from datetime import datetime
+import pytz
+import os
+import re
+from uuid import UUID
+
+"""Module to log interactions with the chatbot"""
+
+
+# Save this question and response pair as a file.
+def log_question_to_file(user_question: str, response: str, probability: str = "None"):
+    filename = (
+        str(user_question)
+        .lower()
+        .replace(" ", "-")
+        .replace("?", "")
+        .replace("'", "")
+        .replace("`", "")
+    )
+    filename = re.sub("[^a-zA-Z0-9\-]", "", filename)
+    if len(filename) > 64:
+        filename = filename[:64]
+    log_dir = "./logs/responses"
+    log_filename = f"{log_dir}/{filename}.md"
+    if not os.path.exists(log_dir):
+        os.makedirs(log_dir)
+    with open(log_filename, "a", encoding="utf-8") as log_file:
+        log_file.write("# " + user_question.strip() + "\n\n")
+        log_file.write(response.strip() + "\n\n")
+        if probability != "None":
+            log_file.write("(Answerable probability: " + str(probability) + ")\n")
+        log_file.close()
+
+
+# Log the answerable_probability score and question.
+def log_answerable_probability(user_question: str, probability: str):
+    log_dir = "./logs"
+    answerable_list_filename = log_dir + "/answerable_logs.txt"
+    if not os.path.exists(log_dir):
+        os.makedirs(log_dir)
+    with open(answerable_list_filename, "a", encoding="utf-8") as log_file:
+        log_file.write(
+            str("{:.16f}".format(probability)) + "    " + user_question.strip() + "\n"
+        )
+        log_file.close()
+
+
+# Save a detailed record of a question and response pair as a file for debugging.
+def log_debug_info_to_file(
+    uid: UUID,
+    user_question: str,
+    response: str,
+    context: str,
+    top_source_url: str,
+    source_urls: str,
+    probability: str = "None",
+    server_url: str = "None",
+):
+    # Compose a filename
+    date = datetime.now(tz=pytz.utc)
+    date = date.astimezone(pytz.timezone("US/Pacific"))
+    date_formatted = str(date.strftime("%Y-%m-%d-%H-%M-%S"))
+    question_formatted = (
+        str(user_question)
+        .lower()
+        .replace(" ", "-")
+        .replace("?", "")
+        .replace("'", "")
+        .replace("`", "")
+    )
+    question_formatted = re.sub("[^a-zA-Z0-9\-]", "", question_formatted)
+    if len(question_formatted) > 32:
+        question_formatted = question_formatted[:32]
+    filename = date_formatted + "-" + question_formatted + "-" + str(uid) + ".txt"
+    # Set the directory location.
+    debug_dir = "./logs/debugs"
+    debug_filename = f"{debug_dir}/{filename}"
+    if not os.path.exists(debug_dir):
+        os.makedirs(debug_dir)
+    with open(debug_filename, "w", encoding="utf-8") as debug_file:
+        debug_file.write("UID: " + str(uid) + "\n")
+        debug_file.write("DATE: " + str(date) + "\n")
+        debug_file.write("SERVER URL: " + server_url.strip() + "\n")
+        debug_file.write("\n")
+        debug_file.write("TOP SOURCE URL: " + top_source_url.strip() + "\n")
+        debug_file.write("ANSWERABLE PROBABILITY: " + str(probability) + "\n")
+        debug_file.write("\n")
+        debug_file.write("QUESTION: " + user_question.strip() + "\n\n")
+        debug_file.write("RESPONSE:\n\n")
+        debug_file.write(response.strip() + "\n\n")
+        debug_file.write("CONTEXT:\n\n")
+        debug_file.write(context.strip() + "\n\n")
+        debug_file.write("SOURCE URLS:\n\n")
+        debug_file.write(source_urls.strip() + "\n\n")
+        debug_file.close()
+
+
+# Save the like and dislike interactions to the file for debugging.
+def log_feedback_to_file(uid: str, is_like, is_dislike):
+    # Search the debugs directory.
+    debug_dir = "./logs/debugs"
+    target_string = str(uid) + ".txt"
+    debug_filename = ""
+    for root, dirs, files in os.walk(debug_dir):
+        for file in files:
+            if file.endswith(target_string):
+                debug_filename = f"{debug_dir}/{file}"
+    if debug_filename != "":
+        with open(debug_filename, "a", encoding="utf-8") as debug_file:
+            if is_like != None:
+                debug_file.write("LIKE: " + str(is_like) + "\n")
+            if is_dislike != None:
+                debug_file.write("DISLIKE: " + str(is_dislike) + "\n")
+            debug_file.close()
+
+
+# Write captured debug logs into a CSV file.
+def write_logs_to_csv_file(log_date: str = "None"):
+    # Compose the output CSV filename.
+    output_filename = "debug-info-all.csv"
+    if log_date != "None":
+        output_filename = "debug-info-" + str(log_date) + ".csv"
+    # Write a header for this CSV file.
+    log_dir = "./logs"
+    out_csv_filename = log_dir + "/" + output_filename
+    line = f"DATE, UID, QUESTION, PROBABILITY, TOP SOURCE URL, DEBUG LINK, FEEDBACK"
+    with open(out_csv_filename, "w", encoding="utf-8") as csv_file:
+        csv_file.write(line + "\n")
+        csv_file.close()
+    # Search the debugs directory.
+    debug_dir = "./logs/debugs"
+    debug_filename = ""
+    for root, dirs, files in os.walk(debug_dir):
+        for file in files:
+            # Read all files if date is "None" else read files from the input date only.
+            ok_to_read = False
+            if file.endswith("txt"):
+                ok_to_read = True
+            if log_date != "None":
+                if file.startswith(log_date):
+                    ok_to_read = True
+                else:
+                    ok_to_read = False
+            if ok_to_read:
+                debug_filename = f"{debug_dir}/{file}"
+                debug_record = ""
+                # Open and read this debug information file.
+                with open(debug_filename, "r", encoding="utf-8") as debug_file:
+                    debug_record = debug_file.readlines()
+                    debug_file.close()
+                uid = ""
+                date = ""
+                server_url = "None"
+                top_source_url = ""
+                probability = ""
+                question = ""
+                like = "None"
+                dislike = "None"
+                filename = str(file)
+                # Scan the lines from thi debug info and extract fields.
+                for line in debug_record:
+                    match_uid = re.search(r"^UID:\s+(.*)$", line)
+                    match_date = re.search(r"^DATE:\s+(.*)$", line)
+                    match_server_url = re.search(r"^SERVER URL:\s+(.*)$", line)
+                    match_top_url = re.search(r"^TOP SOURCE URL:\s+(.*)$", line)
+                    match_prob = re.search(r"^ANSWERABLE PROBABILITY:\s+(.*)$", line)
+                    match_question = re.search(r"^QUESTION:\s+(.*)$", line)
+                    match_like = re.search(r"^LIKE:\s+(.*)$", line)
+                    match_dislike = re.search(r"^DISLIKE:\s+(.*)$", line)
+                    # Extract fields.
+                    if match_uid:
+                        uid = match_uid.group(1)
+                    elif match_date:
+                        date = match_date.group(1)
+                    elif match_server_url:
+                        server_url = match_server_url.group(1)
+                    elif match_top_url:
+                        top_source_url = match_top_url.group(1)
+                    elif match_prob:
+                        probability = match_prob.group(1)
+                    elif match_question:
+                        question = match_question.group(1)
+                    elif match_like:
+                        like = match_like.group(1)
+                    elif match_dislike:
+                        dislike = match_dislike.group(1)
+                # Write a short version of this debug information to the CSV file.
+                debug_file_link = filename
+                if server_url != "None":
+                    debug_file_link = server_url + "debugs/" + filename
+                feedback = "None"
+                if like == "True":
+                    feedback = "Like"
+                elif dislike == "True":
+                    feedback = "Dislike"
+                log_debug_info_to_csv_file(
+                    output_filename=output_filename,
+                    date=date,
+                    uid=uid,
+                    question=question,
+                    probability=probability,
+                    top_source_url=top_source_url,
+                    debug_file_link=debug_file_link,
+                    feedback=feedback,
+                )
+
+
+# Save the short version of debug information into a CSV file.
+def log_debug_info_to_csv_file(
+    output_filename: str,
+    date: str,
+    uid: str,
+    question: str,
+    probability: str = "None",
+    top_source_url: str = "None",
+    debug_file_link: str = "None",
+    feedback: str = "None",
+):
+    log_dir = "./logs"
+    log_filename = log_dir + "/" + str(output_filename)
+    if not os.path.exists(log_dir):
+        os.makedirs(log_dir)
+    question_formatted = str(question).lower().replace(",", " ").replace(";", "")
+    line = f"{date}, {uid}, {question_formatted}, {probability}, {top_source_url}, {debug_file_link}, {feedback}"
+    with open(log_filename, "a", encoding="utf-8") as log_file:
+        log_file.write(line + "\n")
+        log_file.close()
+
+
+# Print and log the question and response.
+def log_question(
+    uid,
+    user_question: str,
+    response: str,
+    probability: str = "None",
+    save: bool = True,
+    logs_to_markdown: str = "False",
+):
+    date_format = "%m/%d/%Y %H:%M:%S %Z"
+    date = datetime.now(tz=pytz.utc)
+    date = date.astimezone(pytz.timezone("US/Pacific"))
+    print("UID: " + str(uid))
+    print("Question: " + user_question.strip() + "\n")
+    print("Response:")
+    print(response.strip() + "\n")
+    # For the AQA model, also print the response's answerable_probability
+    if probability != "None":
+        print("Answerable probability: " + str(probability) + "\n")
+    if save:
+        log_dir = "./logs"
+        log_filename = log_dir + "/chatui_logs.txt"
+        if not os.path.exists(log_dir):
+            os.makedirs(log_dir)
+        with open(log_filename, "a", encoding="utf-8") as log_file:
+            log_file.write(
+                "[" + date.strftime(date_format) + "][UID " + str(uid) + "]\n"
+            )
+            log_file.write("# " + user_question.strip() + "\n\n")
+            log_file.write(response.strip() + "\n\n")
+            if probability != "None":
+                log_file.write("Answerable probability: " + str(probability) + "\n\n")
+            log_file.close()
+        if probability != "None":
+            # Track the answerable_probability scores.
+            log_answerable_probability(user_question, probability)
+        if logs_to_markdown == "True":
+            log_question_to_file(user_question, response, probability)
+
+
+def log_like(is_like, uid, save: bool = True):
+    date_format = "%m/%d/%Y %H:%M:%S %Z"
+    date = datetime.now(tz=pytz.utc)
+    date = date.astimezone(pytz.timezone("US/Pacific"))
+    print()
+    print("UID: " + str(uid))
+    print("Like: " + str(is_like))
+    if save:
+        log_dir = "./logs"
+        log_filename = log_dir + "/chatui_logs.txt"
+        with open(log_filename, "a", encoding="utf-8") as log_file:
+            log_file.write(
+                "[" + date.strftime(date_format) + "][UID " + str(uid) + "]\n"
+            )
+            log_file.write("Like: " + str(is_like) + "\n\n")
+            log_file.close()
+
+
+def log_dislike(is_dislike, uid, save: bool = True):
+    date_format = "%m/%d/%Y %H:%M:%S %Z"
+    date = datetime.now(tz=pytz.utc)
+    date = date.astimezone(pytz.timezone("US/Pacific"))
+    print()
+    print("UID: " + str(uid))
+    print("Dislike: " + str(is_dislike))
+    if save:
+        log_dir = "./logs"
+        log_filename = log_dir + "/chatui_logs.txt"
+        with open(log_filename, "a", encoding="utf-8") as log_file:
+            log_file.write(
+                "[" + date.strftime(date_format) + "][UID " + str(uid) + "]\n"
+            )
+            log_file.write("Disike: " + str(is_dislike) + "\n\n")
+            log_file.close()
diff --git a/examples/gemini/python/docs-agent/docs_agent/models/__init__.py b/examples/gemini/python/docs-agent/docs_agent/models/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/models/google_genai.py b/examples/gemini/python/docs-agent/docs_agent/models/google_genai.py
new file mode 100644
index 000000000..ab37ac297
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/models/google_genai.py
@@ -0,0 +1,192 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Rate limited Gemini wrapper"""
+
+import typing
+from typing import List
+
+import google.generativeai
+from ratelimit import limits
+from ratelimit import sleep_and_retry
+
+from docs_agent.utilities.config import Models
+from docs_agent.utilities.config import Conditions
+
+
+class Error(Exception):
+    """Base error class for Gemini."""
+
+
+class GoogleNoAPIKeyError(Error, RuntimeError):
+    """Raised if no API key is provided nor found in environment variable."""
+
+    def __init__(self) -> None:
+        super().__init__(
+            "Google API key is not provided "
+            "or set in the environment variable GOOGLE_API_KEY"
+        )
+
+
+class GoogleUnsupportedModelError(Error, RuntimeError):
+    """Raised if a specified model is not supported by the endpoint."""
+
+    def __init__(self, model, api_endpoint) -> None:
+        super().__init__(
+            f"The specified model {model} is not supported "
+            f"on the API endpoint {api_endpoint}"
+        )
+
+
+class Gemini:
+    """Rate limited Gemini wrapper.
+
+    This class exposes Gemini's chat, text, and embedding API, but with a rate
+    limit. Besides the rate limit, the `chat` and `generate_text` method has the
+    same name and behavior as `google.generativeai.chat` and
+    `google.generativeai.generate_text`, respectively. The `embed` method is
+    different from `google.generativeai.generate_embeddings` since `embed`
+    returns List[float] while `google.generativeai.generate_embeddings` returns a
+    dict. And that's why it has a different name.
+    """
+
+    minute = 60  # seconds in a minute
+    max_embed_per_minute = 1400
+    max_text_per_minute = 30
+
+    # MAX_MESSAGE_PER_MINUTE = 30
+    def __init__(
+        self,
+        models_config: Models,
+        conditions: typing.Optional[Conditions] = None,
+    ) -> None:
+        if conditions is None:
+            self.model_error_message = "Gemini model failed to generate"
+            self.prompt_condition = ""
+        else:
+            self.model_error_message = conditions.model_error_message
+            self.prompt_condition = conditions.condition_text
+        self.api_endpoint = models_config.api_endpoint
+        self.api_key = models_config.api_key
+        self.embed_model = models_config.embedding_model
+        self.language_model = models_config.language_model
+        self.embedding_api_call_limit = models_config.embedding_api_call_limit
+        self.embedding_api_call_period = models_config.embedding_api_call_period
+
+        # Configure the model
+        google.generativeai.configure(
+            api_key=self.api_key, client_options={"api_endpoint": self.api_endpoint}
+        )
+        # Check whether the specified models are supported
+        # supported_models = set(
+        #    model.name for model in google.generativeai.list_models()
+        # )
+        # for model in (models_config.language_model, models_config.embedding_model):
+        #  if model not in supported_models:
+        #    raise GoogleUnsupportedModelError(model, self.api_endpoint)
+
+    # TODO: bring in limit values from config files
+    @sleep_and_retry
+    @limits(calls=max_embed_per_minute, period=minute)
+    def embed(
+        self,
+        content,
+        task_type: str = "RETRIEVAL_QUERY",
+        title: typing.Optional[str] = None,
+    ) -> List[float]:
+        if (
+            self.embed_model == "models/embedding-001"
+            or self.embed_model == "models/text-embedding-004"
+        ):
+            return [
+                google.generativeai.embed_content(
+                    model=self.embed_model,
+                    content=content,
+                    task_type=task_type,
+                    title=title,
+                )["embedding"]
+            ]
+        else:
+            raise GoogleUnsupportedModelError(self.embed_model, self.api_endpoint)
+
+    # TODO: bring in limit values from config files
+    @sleep_and_retry
+    @limits(calls=max_text_per_minute, period=minute)
+    def generate_content(self, contents, log_level: typing.Optional[str] = "NORMAL"):
+        if self.language_model is None:
+            raise GoogleUnsupportedModelError(self.language_model, self.api_endpoint)
+        model = google.generativeai.GenerativeModel(model_name=self.language_model)
+        try:
+            response = model.generate_content(contents)
+        except google.api_core.exceptions.InvalidArgument:
+            return self.model_error_message
+        if log_level == "VERBOSE" or log_level == "DEBUG":
+            print("[Response JSON]")
+            print(response)
+            print()
+        for chunk in response:
+            if not hasattr(chunk, "candidates"):
+                return self.model_error_message
+            if len(chunk.candidates) == 0:
+                return self.model_error_message
+            if not hasattr(chunk.candidates[0], "content"):
+                return self.model_error_message
+            if str(chunk.candidates[0].content) == "":
+                return self.model_error_message
+        return response.text
+
+    # Use this method for talking to a Gemini content model
+    # Optionally provide a prompt, if not use the one from config.yaml
+    def ask_content_model_with_context_prompt(
+        self,
+        context: str,
+        question: str,
+        prompt: typing.Optional[str] = None,
+        log_level: typing.Optional[str] = "NORMAL",
+    ):
+        if prompt == None:
+            prompt = self.prompt_condition
+        # elif prompt == "fact_checker":
+        #   prompt = self.fact_check_question
+        new_prompt = f"{prompt}\n\nQuestion: {question}\n\nContext:\n{context}"
+        # Print the prompt for debugging if the log level is VERBOSE.
+        # if LOG_LEVEL == "VERBOSE":
+        #   self.print_the_prompt(new_prompt)
+        try:
+            response = self.generate_content(new_prompt)
+        except google.api_core.exceptions.InvalidArgument:
+            return self.model_error_message
+        if log_level == "VERBOSE" or log_level == "DEBUG":
+            print("[Response JSON]")
+            print(response)
+            print()
+        for chunk in response:
+            if not hasattr(chunk, "candidates"):
+                return self.model_error_message
+            if len(chunk.candidates) == 0:
+                return self.model_error_message
+            if not hasattr(chunk.candidates[0], "content"):
+                return self.model_error_message
+            if str(chunk.candidates[0].content) == "":
+                return self.model_error_message
+        return response.text, new_prompt
+
+
+#   # Use this method for asking a Gemini content model for fact-checking
+#   def ask_content_model_to_fact_check(self, context, prev_response):
+#     question = self.fact_check_question + "\n\nText: "
+#     question += prev_response
+#     return self.ask_content_model_with_context(context, question)
diff --git a/demos/palm/python/docs-agent/palm.py b/examples/gemini/python/docs-agent/docs_agent/models/palm.py
similarity index 78%
rename from demos/palm/python/docs-agent/palm.py
rename to examples/gemini/python/docs-agent/docs_agent/models/palm.py
index 448001b38..ad7e1aec5 100644
--- a/demos/palm/python/docs-agent/palm.py
+++ b/examples/gemini/python/docs-agent/docs_agent/models/palm.py
@@ -81,6 +81,7 @@ def __init__(
         api_endpoint=DEFAULT_ENDPOINT,
         chat_model=None,
         text_model=None,
+        content_model=None,
         embed_model=None,
         find_models=True,
     ) -> None:
@@ -94,13 +95,14 @@ def __init__(
         self.api_endpoint = api_endpoint
         self.chat_model = chat_model
         self.text_model = text_model
+        self.content_model = content_model
         self.embed_model = embed_model
 
         # Check whether the specified models are supported
         supported_models = set(
             model.name for model in google.generativeai.list_models()
         )
-        for model in (chat_model, text_model, embed_model):
+        for model in (chat_model, text_model, content_model, embed_model):
             if model and model not in supported_models:
                 raise PaLMUnsupportedModelError(model, api_endpoint)
 
@@ -108,6 +110,7 @@ def __init__(
         if (not find_models) or (
             chat_model is not None
             and text_model is not None
+            and content_model is not None
             and embed_model is not None
         ):
             return
@@ -116,6 +119,7 @@ def __init__(
             if (
                 self.chat_model is None
                 and "generateMessage" in model.supported_generation_methods
+                and "chat" in model.name
             ):
                 self.chat_model = model.name
             if (
@@ -124,8 +128,13 @@ def __init__(
             ):
                 self.text_model = model.name
             if (
-                self.embed_model is None
-                and "embedText" in model.supported_generation_methods
+                self.content_model is None
+                and "generateContent" in model.supported_generation_methods
+            ):
+                self.content_model = model.name
+            if self.embed_model is None and (
+                "embedText" in model.supported_generation_methods
+                or "embedContent" in model.supported_generation_methods
             ):
                 self.embed_model = model.name
 
@@ -143,11 +152,25 @@ def generate_text(self, *args, **kwargs):
             raise PaLMNoModelError(func_name="generate_text", attr="text_model")
         return google.generativeai.generate_text(*args, model=self.text_model, **kwargs)
 
+    @sleep_and_retry
+    @limits(calls=MAX_TEXT_PER_MINUTE, period=MINUTE)
+    def generate_content(self, text):
+        if self.content_model is None:
+            raise PaLMNoModelError(func_name="generate_content", attr="content_model")
+        model = google.generativeai.GenerativeModel(model_name=self.content_model)
+        return model.generate_content(text)
+
     @sleep_and_retry
     @limits(calls=MAX_EMBED_PER_MINUTE, period=MINUTE)
     def embed(self, text: str) -> List[float]:
         if self.embed_model is None:
             raise PaLMNoModelError(func_name="embed", attr="embed_model")
-        return google.generativeai.generate_embeddings(
-            model=self.embed_model, text=text
-        )["embedding"]
+        if self.embed_model == "models/embedding-001":
+            # Use the `embed_content()` method if it's the new Gemini embedding model.
+            return google.generativeai.embed_content(
+                model=self.embed_model, content=text
+            )["embedding"]
+        else:
+            return google.generativeai.generate_embeddings(
+                model=self.embed_model, text=text
+            )["embedding"]
diff --git a/examples/gemini/python/docs-agent/docs_agent/models/tokenCount.py b/examples/gemini/python/docs-agent/docs_agent/models/tokenCount.py
new file mode 100644
index 000000000..8561e5966
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/models/tokenCount.py
@@ -0,0 +1,126 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+from docs_agent.utilities import config
+
+# Per the docs 60-80 words ~= 100 tokens
+lower_word_limit = 60 / 100
+higher_word_limit = 80 / 100
+
+# Per the docs 4 characters ~= 1 token
+lower_char_limit = 4
+higher_char_limit = 4
+
+"""Estimate or return token count for a given string"""
+
+
+# Hard coded, replace with function for model selection
+# This value should be set in config.yaml file to avoid unecessary API calls as
+# it will mostly remain static until a different model is used
+# todo: clean these functions
+# def returnMaxInputTokenModelAPI() -> float:
+#     model = "models/gemini-pro"
+#     maxInputToken = model.input_token_limit
+#     max_context_tokens_api = maxInputToken - max_token_count_api
+#     print("Max input token: " + str(maxInputToken))
+#     max_context_tokens = maxInputToken - max_token_count_prompt
+#     print("Max input token removing prompt: " + str(max_context_tokens))
+#     print("Max input token removing prompt API: " + str(max_context_tokens_api))
+
+#     number_of_context_per_prompt = 3
+
+#     # Remove 5 tokens to give additional token buffer
+#     max_chunk_size = max_context_tokens / number_of_context_per_prompt - 5
+#     print("Max chunk size is: " + str(max_chunk_size))
+#     max_chunk_size_api = max_context_tokens_api / number_of_context_per_prompt - 5
+#     print("Max chunk size is API: " + str(max_chunk_size_api))
+#     max_token_count_prompt = estimateTokensAverage(CONDITION_TEXT) + 5
+#     max_token_count_api = palm.tokenCount(CONDITION_TEXT) + 5
+
+#     return max_token_count_api
+
+
+# def returnMaxInputTokenModelEstimate() -> float:
+# #    model = "models/gemini-pro"
+#     maxInputToken = model.input_token_limit
+#     max_context_tokens_api = maxInputToken - max_token_count_api
+#     print("Max input token: " + str(maxInputToken))
+#     max_context_tokens = maxInputToken - max_token_count_prompt
+#     print("Max input token removing prompt: " + str(max_context_tokens))
+#     print("Max input token removing prompt API: " + str(max_context_tokens_api))
+
+#     number_of_context_per_prompt = 3
+
+#     # Remove 5 tokens to give additional token buffer
+#     max_chunk_size = max_context_tokens / number_of_context_per_prompt - 5
+#     print("Max chunk size is: " + str(max_chunk_size))
+#     max_chunk_size_api = max_context_tokens_api / number_of_context_per_prompt - 5
+#     print("Max chunk size is API: " + str(max_chunk_size_api))
+#     max_token_estimate = estimateTokensAverage(CONDITION_TEXT) + 5
+
+#     return max_token_estimate
+
+
+# Function to return a character count of a string
+def countChars(input):
+    charCount = len(input)
+    return charCount
+
+
+# Function to return a word count of a string
+def countWords(input):
+    wordCount = len(input.split())
+    return wordCount
+
+
+# Function to estimate token count based on characters
+def estimateTokensFromChars(input, lower_char_limit, higher_char_limit):
+    char_count = countChars(input)
+    lower_token_estimate = char_count / lower_char_limit
+    higher_token_estimate = char_count / higher_char_limit
+    # Estimate is multiplied by 1.27 to more closely match samples
+    average_token_estimate = ((lower_token_estimate + higher_token_estimate) / 2) * 1.27
+    return average_token_estimate
+
+
+# Function to estimate token count based on words
+def estimateTokensFromWords(input, lower_word_limit, higher_word_limit):
+    word_count = countWords(input)
+    lower_token_estimate = word_count / lower_word_limit
+    higher_token_estimate = word_count / higher_word_limit
+    # Estimate is multiplied by 1.55 to more closely match samples
+    average_token_estimate = (
+        ((lower_token_estimate * 1.1) + higher_token_estimate) / 2
+    ) * 1.55
+    return average_token_estimate
+
+
+# Function to return the average between the character and word estimate
+def estimateTokensAverage(input):
+    char_estimate = estimateTokensFromChars(input, lower_char_limit, higher_char_limit)
+    word_estimate = estimateTokensFromWords(input, lower_word_limit, higher_word_limit)
+    average_token_estimate = (char_estimate + word_estimate) / 2
+    return average_token_estimate
+
+
+# Function to return the highest token count between estimateTokensFromChars and
+# estimateTokensFromWords. This improved accuracy to what PaLM returns
+def returnHighestTokens(input):
+    char_estimate = estimateTokensFromChars(input, lower_char_limit, higher_char_limit)
+    word_estimate = estimateTokensFromWords(input, lower_word_limit, higher_word_limit)
+    if char_estimate >= word_estimate:
+        return char_estimate
+    else:
+        return word_estimate
\ No newline at end of file
diff --git a/examples/gemini/python/docs-agent/docs_agent/postprocess/__init__.py b/examples/gemini/python/docs-agent/docs_agent/postprocess/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/postprocess/docs_retriever.py b/examples/gemini/python/docs-agent/docs_agent/postprocess/docs_retriever.py
new file mode 100644
index 000000000..885436f77
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/postprocess/docs_retriever.py
@@ -0,0 +1,242 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# from markdown import markdown
+# from bs4 import BeautifulSoup
+# import re, os
+from docs_agent.models import tokenCount
+from docs_agent.preprocess.splitters.markdown_splitter import Section as Section
+
+"""Objects to handle retrieval of Sections and rebuild original pages"""
+
+
+# Let's you retrieve sections based on an original match while
+# also keeping distance
+class SectionDistance:
+    """Return Section including origin UUID, distance from result, and url"""
+
+    def __init__(self, section: Section, distance: str):
+        self.section = section
+        self.distance = distance
+
+
+# Let's you retrieve sections based on an original match while
+# also keeping probability. This is for AQA
+class SectionProbability:
+    """Return Section including origin UUID, probability of result, and url"""
+
+    def __init__(self, section: Section, probability: float):
+        self.section = section
+        self.probability = probability
+
+
+# Let's you build a full page based on list of Sections and in the order provided
+# Returns a string of final page and a token_count_estimate of the final page
+class FullPage:
+    def __init__(self, section_list: list[Section]):
+        self.section_list = section_list
+
+    def __str__(self):
+        return f"This is a page with the following content:\n"
+
+    def buildPage(self):
+        final_page = ""
+        total_token_count = 0
+        for item in self.section_list:
+            # It is possible to check attributes such as token_count of chunks here
+            # (i.e)if item.token_count > 800:
+            final_page += item.content
+            total_token_count += item.token_count
+        return final_page, total_token_count
+
+    # Given a page, returns only the section that matches the provided id
+    # Also adds a preamble (which can be customized)
+    def returnSelfSection(self, section_id):
+        # Adds initial section from match
+        for item in self.section_list:
+            try:
+                if section_id == item.id:
+                    # Updates the content of a section on the fly with a template
+                    item.updateContentTemplate()
+                    return item
+            except:
+                print(f"Could not find a section with the provided ID {section_id}")
+
+    # Returns all of the children for a given section_id. Any section that
+    # are under the given header. For example, if the provided section_id is
+    # a ##, provide all ### directly under it
+    # Specify a token_limit to limit amount of sections returned
+    def returnChildrenSections(self, section_id, token_limit: float = float("inf")):
+        # Finds the Section given a section_id
+        match = False
+        updated_list = []
+        for item in self.section_list:
+            if section_id == item.id:
+                given_section = item
+                match = True
+                break
+        # If Section doesn't match, just return a FullPage with a blank list
+        if not match:
+            print(f"Could not find a section with the provided ID {section_id}")
+            return FullPage(section_list=updated_list)
+        # Start token count at 0
+        curr_token = 0
+        # updated_list = []
+        for item in self.section_list:
+            direct_parent = item.returnDirectParentId()
+            if int(direct_parent) == int(given_section.id):
+                if (curr_token + item.token_count) < token_limit:
+                    curr_token += item.token_count
+                    # Updates the content of a section on the fly with a template
+                    item.updateContentTemplate()
+                    # Estimate token count for preamble and add it to curr_token
+                    item.token_count = tokenCount.returnHighestTokens(item.content)
+                    curr_token += item.token_count
+                    # Append each Section to a new list to return
+                    updated_list.append(item)
+        updated_page = FullPage(section_list=updated_list)
+        # You can view token count by doing sum of all Section.token_count
+        return updated_page
+
+    # Returns all of the siblings for a given section_id. Any section that
+    # has the same parent_tree
+    def returnSiblingSections(self, section_id, token_limit: float = float("inf")):
+        # Finds the Section given a section_id
+        match = False
+        updated_list = []
+        for item in self.section_list:
+            if section_id == item.id:
+                given_section = item
+                match = True
+                # Break out of loop once a match is found
+                break
+        # If Section doesn't match, just return a FullPage with a blank list
+        if not match:
+            print(f"Could not find a section with the provided ID {section_id}")
+            return FullPage(section_list=updated_list)
+        # Start token count at 0
+        curr_token = 0
+        # updated_list = []
+        given_parent_tree = eval(given_section.parent_tree)
+        for item in self.section_list:
+            direct_parent = item.returnDirectParentId()
+            # Matches parent_tree and skips the same section
+            if (
+                given_parent_tree == eval(item.parent_tree)
+                and given_section.id != item.id
+            ):
+                if (curr_token + item.token_count) < token_limit:
+                    curr_token += item.token_count
+                    # Updates the content of a section on the fly with a template
+                    item.updateContentTemplate()
+                    # Estimate token count for preamble and add it to curr_token
+                    item.token_count = tokenCount.returnHighestTokens(item.content)
+                    curr_token += item.token_count
+                    # Append each Section to a new list to return
+                    updated_list.append(item)
+        updated_page = FullPage(section_list=updated_list)
+        # You can view token count by doing sum of all Section.token_count
+        return updated_page
+
+    # Returns the parent of a given section.
+    # If updated_page contains no parents, return []
+    def returnParentSection(self, section_id, token_limit: float = float("inf")):
+        # Finds the Section given a section_id
+        match = False
+        updated_list = []
+        for item in self.section_list:
+            if section_id == item.id:
+                given_section = item
+                match = True
+                # Break out of loop once a match is found
+                given_parent = given_section.returnDirectParentId()
+                break
+        # If Section doesn't match, just return a FullPage with a blank list
+        if not match:
+            print(f"Could not find a section with the provided ID {section_id}")
+            # return FullPage(section_list=updated_list)
+            return None
+        # Start token count at 0
+        curr_token = 0
+        # updated_list = []
+        for item in self.section_list:
+            direct_parent = item.returnDirectParentId()
+            if int(given_parent) == int(item.id) and given_parent != 0:
+                if (curr_token + item.token_count) < token_limit:
+                    curr_token += item.token_count
+                    # Updates the content of a section on the fly with a template
+                    item.updateContentTemplate()
+                    # Estimate token count for preamble and add it to curr_token
+                    item.token_count = tokenCount.returnHighestTokens(item.content)
+                    curr_token += item.token_count
+                    # A section only can only have a single item
+                    return item
+
+    # Sorts Section by a clause, defaults to id (only supported at the moment)
+    # Include a reverse flag to also do a reverse order
+    def sortSections(self, reverse: bool = False):
+        updated_list = []
+        # In some cases an item may be None, so remove these
+        for item in self.section_list:
+            if item is not None:
+                updated_list.append(item)
+        updated_list.sort(key=lambda x: x.id, reverse=reverse)
+        updated_page = FullPage(section_list=updated_list)
+        return updated_page
+
+    # Builds a page using the additional flags
+    def buildSections(
+        self,
+        section_id,
+        selfSection: bool = True,
+        children: bool = False,
+        parent: bool = False,
+        siblings: bool = False,
+        token_limit: float = float("inf"),
+        reverse: bool = False,
+    ):
+        final_sections = []
+        section_token_count = 0
+        if selfSection:
+            self_section = self.returnSelfSection(section_id=section_id)
+            if self_section is not None:
+                final_sections.append(self_section)
+                section_token_count += self_section.token_count
+        if children:
+            children_sections = self.returnChildrenSections(
+                section_id=section_id, token_limit=(token_limit - section_token_count)
+            )
+            final_sections = final_sections + children_sections.section_list
+            # Adds the tokens for the sections
+            for item in children_sections.section_list:
+                if item is not None:
+                    section_token_count += item.token_count
+        if parent:
+            parent_section = self.returnParentSection(
+                section_id=section_id, token_limit=(token_limit - section_token_count)
+            )
+            final_sections.append(parent_section)
+            if parent_section is not None:
+                section_token_count += parent_section.token_count
+        if siblings:
+            sibling_sections = self.returnSiblingSections(
+                section_id=section_id, token_limit=(token_limit - section_token_count)
+            )
+            final_sections = final_sections + sibling_sections.section_list
+            for item in sibling_sections.section_list:
+                if item is not None:
+                    section_token_count += item.token_count
+        final_page = FullPage(final_sections).sortSections(reverse=reverse)
+        return final_page
\ No newline at end of file
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/README.md b/examples/gemini/python/docs-agent/docs_agent/preprocess/README.md
new file mode 100644
index 000000000..0cb807fd8
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/preprocess/README.md
@@ -0,0 +1,166 @@
+# Processing source documents in Docs Agent
+
+This `README` file provides information on the steps involved in the following two Python scripts,
+which are used to process source documents into plain text chunks and generate embeddings for
+vector databases:
+
+- [`files_to_plain_text.py`][files-to-plain-text]
+- [`populate_vector_database.py`][populate-vector-database]
+
+![Docs Agent pre-processing flow](../../docs/images/docs-agent-pre-processing-01.png)
+
+**Figure 1**. Docs Agent's pre-processing flow from source documents to the vector database.
+
+**Note**: The `markdown_to_plain_text.py` script is deprecated in favor of
+the `files_to_plain_text.py` script.
+
+## Docs Agent chunking technique example
+
+The [`files_to_plain_text.py`][files-to-plain-text] script splits documents
+into smaller chunks based on Markdown headings (#, ##, and ###).
+
+For example, consider the following Markdown page:
+
+```
+# Page title
+
+This is the introduction paragraph of this page.
+
+## Section 1
+
+This is the paragraph of section 1.
+
+### Sub-section 1.1
+
+This is the paragraph of sub-section 1.1.
+
+### Sub-section 1.2
+
+This is the paragraph of sub-section 1.2.
+
+## Section 2
+
+This is the paragraph of section 2.
+```
+
+This example Markdown page is split into the following 5 chunks:
+
+```
+# Page title
+
+This is the introduction paragraph of this page.
+```
+
+```
+## Section 1
+
+This is the paragraph of section 1.
+```
+
+```
+### Sub-section 1.1
+
+This is the paragraph of sub-section 1.1.
+```
+
+```
+### Sub-section 1.2
+
+This is the paragraph of sub-section 1.2.
+```
+
+```
+## Section 2
+
+This is the paragraph of section 2.
+```
+
+Additionally, becasue the token size limitation of embedding models, the script
+recursively splits the chunks above until each chunk's size becomes less than
+5000 bytes (characters).
+
+## Steps in the files_to_plain_text.py script
+
+In the default setting, when processing Markdown files to plain text using the
+[`files_to_plain_text.py`][files-to-plain-text] script, the following events take place:
+
+1. Read the configuration file ([`config.yaml`][config-yaml]) to identify input and output
+   directories.
+1. Construct an array of input sources (which are the `path` entries).
+1. **For** each input source, do the following:
+   1. Extract all input fields (`path`, `url_prefix`, and more).
+   1. Call the `process_files_from_input()` method using these input fields.
+   1. **For** each sub-path in the input directory and **for** each file in these directories:
+      1. Check if the file extension is `.md` (that is, a Markdown file).
+      1. Construct an output directory that preserves the path structure.
+      1. Read the content of the Markdown file.
+      1. Call the `process_page_and_section_titles()` method to reformat the page and section
+         titles.
+         1. Process Front Matter in Markdown.
+         1. Detect (or construct) the title of the page.
+         1. Detect Markdown headings (#, ##, and ###).
+         1. Convert Markdown headings into plain English (to preserve context when generating
+            embeddings).
+      1. Call the `process_document_into_sections()` method to split the content into small
+         text chunks.
+         1. Create a new empty array.
+         1. Divide the content using Markdown headings (#, ##, and ###).
+         1. Insert each chunk into the array and simplify the heading to # (title).
+         1. Return the array.
+      1. **For** each text chunk, do the following:
+         1. Call the `markdown_to_text()` method to clean up Markdown and HTML syntax.
+            1. Remove `<!-- -->` lines in Markdown.
+            1. Convert Markdown to HTML (which makes the plan text extraction easy).
+            1. Use `BeautifulSoup` to extract plain text from the HTML.
+            1. Remove `[][]` in Markdown.
+            1. Remove `{: }` in Markdown.
+            1. Remove `{. }` in Markdown.
+            1. Remove a single line `sh` in Markdown.
+            1. Remove code text and blocks.
+            1. Return the plain text.
+         1. Construct the text chunk’s metadata (including URL) for the `file_index.json` file.
+         1. Write the text chunk into a file in the output directory.
+
+## Steps in the populate_vector_database.py script
+
+When processing plain text chunks to embeddings using the
+[`populate_vector_database.py`][populate-vector-database] script, the following events take place:
+
+1. Read the configuration file ([`config.yaml`][config-yaml]) to identify the plain text directory
+   and Chroma settings.
+1. Set up the Gemini API environment.
+1. Select the embeddings model.
+1. Configure the embedding function (including the API call limit).
+1. **For** each sub-path in the plain text directory and **for** each file in these directories:
+   1. Check if the file extension is `.md` (that is, a Markdown file).
+   1. Read the content of the Markdown file.
+   1. Construct the URL of the text chunk’s source.
+   1. Read the metadata associated with the text chunk file.
+   1. Store the text chunk and metabase to the vector database, which also generates an embedding
+      for the text chunk at the time of insertion.
+   1. Skip if the file size is larger than 5000 bytes (due to the API limit).
+   1. Skip if the text chunk is already in the vector database and the checksum hasn’t changed.
+
+### Delete chunks process
+
+The process below describes how the delete chunks feature is implemented in the
+`populate_vector_database.py` script:
+
+1. Read all exiting entries in the target database.
+2. Read all candidate entries in the `file_index.json` file (created after running the
+   `agent chunk` command).
+3. For each entry in the existing entries found in step 1:
+
+   Compare the `text_chunk_filename` fields (included in the entry's `metadata`).
+
+   1. If not found in the candidate entires in step 2, delete this entry in the database.
+
+   1. If found, compare  the `md_hash` fields:
+
+      If they are different, delete this entry in the database.
+
+<!-- Reference links -->
+
+[files-to-plain-text]: files_to_plain_text.py
+[populate-vector-database]: populate_vector_database.py
+[config-yaml]: ../../config.yaml
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/__init__.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/files_to_plain_text.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/files_to_plain_text.py
new file mode 100644
index 000000000..f8926aee8
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/preprocess/files_to_plain_text.py
@@ -0,0 +1,769 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Process Markdown files into plain text"""
+
+import shutil
+import os
+import re
+import json
+import typing
+import uuid
+from absl import logging
+
+import tqdm
+from docs_agent.utilities import config
+from docs_agent.utilities.config import ProductConfig, ConfigFile, Input
+from docs_agent.models.tokenCount import returnHighestTokens
+from docs_agent.utilities.helpers import (
+    resolve_path,
+    add_scheme_url,
+    end_path_backslash,
+    start_path_no_backslash,
+)
+from docs_agent.preprocess.splitters import (
+    markdown_splitter,
+    html_splitter,
+    fidl_splitter,
+)
+
+
+# Construct a URL from a URL prefix and a relative path.
+def construct_a_url(url_prefix: str, relative_path: str):
+    temp_url = end_path_backslash(add_scheme_url(url=url_prefix, scheme="https"))
+    built_url = temp_url + start_path_no_backslash(relative_path)
+    strip_ext_url = re.search(r"(.*)\.md$", built_url)
+    built_url = strip_ext_url[1]
+    return built_url
+
+
+# This function pre-processes files before they are actually chunked.
+# This allows it to resolve includes of includes, Jinja templates, etc...
+# TODO support Jinja, for this need to support data filters as well
+# {% set doc | jsonloads %} and {% set teams | yamlloads %}
+# Returns the temp_output which can then be deleted
+def pre_process_doc_files(
+    product_config: ProductConfig, inputpathitem: Input, temp_path: str
+) -> str:
+    temp_output = os.path.join(temp_path, product_config.output_path)
+    # Delete directory if it exits, then create it.
+    print(f"Temp output: {temp_output}")
+    print("===========================================")
+    if os.path.exists(temp_output):
+        shutil.rmtree(temp_output)
+        os.makedirs(temp_output)
+    else:
+        os.makedirs(temp_output)
+    # Prepare progress bar
+    file_count = sum(
+        len(files) for _, _, files in os.walk(resolve_path(inputpathitem.path))
+    )
+    progress_bar = tqdm.tqdm(
+        total=file_count,
+        position=0,
+        bar_format="{percentage:3.0f}% | {n_fmt}/{total_fmt} | {elapsed}/{remaining}| {desc}",
+    )
+    for root, dirs, files in os.walk(resolve_path(inputpathitem.path)):
+        if inputpathitem.exclude_path is not None:
+            dirs[:] = [d for d in dirs if d not in inputpathitem.exclude_path]
+        for file in files:
+            # Displays status bar
+            progress_bar.set_description_str(
+                f"Pre-processing file {file}", refresh=True
+            )
+            progress_bar.update(1)
+            # Process only Markdown files that do not begin with _(those should
+            # be imported)
+            # Construct a new sub-directory for storing output plain text files
+            dir_path = os.path.join(
+                temp_output, os.path.relpath(root, inputpathitem.path)
+            )
+            if not os.path.exists(dir_path):
+                os.makedirs(dir_path)
+            relative_path = make_relative_path(
+                file=file, root=root, inputpath=inputpathitem.path
+            )
+            final_filename = temp_output + "/" + relative_path
+            if file.startswith("_") and file.endswith(".md"):
+                with open(os.path.join(root, file), "r", encoding="utf-8") as auto:
+                    # Read the input Markdown content
+                    content = auto.read()
+                    auto.close()
+                # Process includes lines in Markdown
+                file_with_include = markdown_splitter.process_markdown_includes(
+                    content, root
+                )
+                # Process include lines in HTML
+                file_with_include = html_splitter.process_html_includes(
+                    file_with_include, inputpathitem.include_path_html
+                )
+                with open(final_filename, "w", encoding="utf-8") as new_file:
+                    new_file.write(content)
+                    new_file.close()
+            elif file.startswith("_") and (
+                file.endswith(".html") or file.endswith(".htm")
+            ):
+                with open(os.path.join(root, file), "r", encoding="utf-8") as auto:
+                    # Read the input HTML content
+                    content = auto.read()
+                    auto.close()
+                with open(final_filename, "w", encoding="utf-8") as new_file:
+                    new_file.write(content)
+                    new_file.close()
+            else:
+                # Just copy files that that we don't need to preprocess
+                # Such as images or files without underscores
+                initial_file = os.path.join(root, file)
+                # Errors with .gsheet, skip gsheet for now
+                if not (file.endswith(".gsheet")):
+                    shutil.copyfile(initial_file, final_filename)
+    # Return the temporary directory, which can then be deleted once files are fully processed
+    return temp_output
+
+
+# This function processes a Markdown file and
+# splits it into smaller text chunks.
+def process_markdown_file(
+    filename: str,
+    root: str,
+    inputpathitem: Input,
+    splitter: str,
+    new_path: str,
+    file: str,
+    namespace_uuid: uuid.UUID,
+    relative_path: str,
+    url_prefix: str,
+):
+    file_metadata = {}
+    # Read the input Markdown content
+    to_file = ""
+    with open(filename, "r", encoding="utf-8") as auto:
+        to_file = auto.read()
+        auto.close()
+    # Process includes lines in Markdown
+    file_with_include = markdown_splitter.process_markdown_includes(to_file, root)
+    # Process include lines in HTML
+    file_with_include = html_splitter.process_html_includes(
+        file_with_include, inputpathitem.include_path_html
+    )
+    # Estimate of the token count
+    page_token_estimate = returnHighestTokens(file_with_include)
+    # Get the original input path
+    original_input = inputpathitem.path
+    if splitter == "token_splitter":
+        # Returns an array of Section objects along with a Page
+        # Object that contains metadata
+        (
+            page_sections,
+            page,
+        ) = markdown_splitter.process_markdown_page(
+            markdown_text=file_with_include, header_id_spaces="-"
+        )
+        # Process this page's sections into plain text chunks.
+        chunk_number = 0
+        for section in page_sections:
+            filename_to_save = make_chunk_name(
+                new_path=new_path,
+                file=file,
+                index=chunk_number,
+                extension="md",
+            )
+            # Get the text chunk filename string (after `docs-agent/data`).
+            text_chunk_filename = get_relative_path_and_filename(filename_to_save)
+            # Generate UUID for each plain text chunk and collect its metadata,
+            # which will be written to the top-level `file_index.json` file.
+            md_hash = uuid.uuid3(namespace_uuid, section.content)
+            uuid_file = uuid.uuid3(namespace_uuid, filename_to_save)
+            origin_uuid = uuid.uuid3(namespace_uuid, relative_path)
+            # If no URL came from frontmatter, assign URL from config
+            if page.URL == "":
+                page.URL = end_path_backslash(
+                    add_scheme_url(url=url_prefix, scheme="https")
+                )
+            # Strip extension of .md from url
+            # Makes sure that relative_path starts without backslash
+            # page.url will have backslash
+            built_url = page.URL + start_path_no_backslash(relative_path)
+            strip_ext_url = re.search(r"(.*)\.md$", built_url)
+            built_url = strip_ext_url[1]
+            # Build the valid URL for a section including header
+            # Do not add a # if section 1
+            if section.name_id != "" and int(section.level) != 1:
+                built_url = built_url + "#" + section.name_id
+            # Adds additional info so that the section can know its origin
+            file_metadata[filename_to_save] = {
+                "UUID": str(uuid_file),
+                "origin_uuid": str(origin_uuid),
+                "source": str(original_input),
+                "source_file": str(relative_path),
+                "page_title": str(section.page_title),
+                "section_title": str(section.section_title),
+                "section_name_id": str(section.name_id),
+                "section_id": int(section.id),
+                "section_level": int(section.level),
+                "previous_id": int(section.previous_id),
+                "URL": str(built_url),
+                "md_hash": str(md_hash),
+                "text_chunk_filename": str(text_chunk_filename),
+                "token_estimate": float(section.token_count),
+                "full_token_estimate": float(page_token_estimate),
+                "parent_tree": list(section.parent_tree),
+                "metadata": dict(page.metadata),
+            }
+            with open(filename_to_save, "w", encoding="utf-8") as new_file:
+                new_file.write(section.content)
+                new_file.close()
+            chunk_number += 1
+    elif splitter == "process_sections":
+        # Use a custom Markdown splitter to split a Markdown file
+        # into small text chunks
+        to_file = markdown_splitter.process_markdown_includes(to_file, root)
+        # Add the page title and section title into each text chunk
+        (
+            to_file,
+            metadata,
+        ) = markdown_splitter.process_page_and_section_titles(to_file)
+        # Process this page's sections into plain text chunks.
+        docs = markdown_splitter.process_document_into_sections(to_file)
+        # Process each text chunk.
+        chunk_number = 0
+        for doc in docs:
+            filename_to_save = make_chunk_name(
+                new_path=new_path,
+                file=file,
+                index=chunk_number,
+                extension="md",
+            )
+            # Get the text chunk filename string (after `docs-agent/data`).
+            text_chunk_filename = get_relative_path_and_filename(filename_to_save)
+            # Generate UUID for each plain text chunk and collect its metadata,
+            # which will be written to the top-level `file_index.json` file.
+            md_hash = uuid.uuid3(namespace_uuid, file_with_include)
+            uuid_file = uuid.uuid3(namespace_uuid, filename_to_save)
+            # Clean up Markdown and HTML syntax
+            content = markdown_splitter.markdown_to_text(doc)
+            # Contruct a URL
+            built_url = construct_a_url(url_prefix, relative_path)
+            # Get the page title
+            page_title = "None"
+            if "title" in metadata:
+                page_title = metadata["title"]
+            # Construct metadata.
+            file_metadata[filename_to_save] = {
+                "UUID": str(uuid_file),
+                "origin_uuid": str(uuid_file),
+                "source": str(original_input),
+                "source_file": str(relative_path),
+                "page_title": str(page_title),
+                "section_title": str("None"),
+                "section_name_id": str("None"),
+                "section_id": int(1),
+                "section_level": int(1),
+                "previous_id": int(1),
+                "URL": str(built_url),
+                "md_hash": str(md_hash),
+                "text_chunk_filename": str(text_chunk_filename),
+                "token_estimate": float(1.0),
+                "full_token_estimate": float(page_token_estimate),
+                "metadata": dict(metadata),
+            }
+            with open(filename_to_save, "w", encoding="utf-8") as new_file:
+                new_file.write(content)
+                new_file.close()
+            chunk_number += 1
+    else:
+        # Exits if no valid markdown splitter
+        logging.error(
+            f"Select a valid markdown_splitter option in your configuration for your product\n"
+        )
+        exit()
+    return file_metadata
+
+
+# This function processes a FIDL file (.fidl) into small text chunks.
+def process_fidl_file(
+    filename: str,
+    root: str,
+    inputpathitem: Input,
+    splitter: str,
+    new_path: str,
+    file: str,
+    namespace_uuid: uuid.UUID,
+    relative_path: str,
+    url_prefix: str,
+):
+    # Local variables
+    file_metadata = {}
+    library_name = ""
+    filename_prefix = "index"
+    chunk_number = 0
+    # Get the original input path
+    original_input = inputpathitem.path
+    # Read the input FIDL content
+    to_file = ""
+    with open(filename, "r", encoding="utf-8") as auto:
+        to_file = auto.read()
+        auto.close()
+    # Split the FIDL file into a list of FIDL protocols.
+    fidl_protocols = fidl_splitter.split_file_to_protocols(to_file)
+    # Iterate the list of FIDL protocols.
+    for fidl_protocol in fidl_protocols:
+        # Identify the new FIDL chunk file path and name.
+        filename_to_save = make_file_chunk_name(
+            new_path=new_path, filename_prefix=filename_prefix, index=chunk_number
+        )
+        # Get the text chunk filename string (after `docs-agent/data`).
+        text_chunk_filename = get_relative_path_and_filename(filename_to_save)
+        # Prepare metadata for this FIDL protocol chunk.
+        md_hash = uuid.uuid3(namespace_uuid, fidl_protocol)
+        uuid_file = uuid.uuid3(namespace_uuid, filename_to_save)
+        origin_uuid = uuid.uuid3(namespace_uuid, relative_path)
+        # Contruct the URL for this FIDL protocol.
+        match_library = re.search(r"^Library\sname:\s+(.*)\n", fidl_protocol)
+        if match_library:
+            library_name = match_library.group(1)
+        # If no library name is found,
+        # the library name from the previous protocol is used.
+        fidl_url = url_prefix + library_name
+        file_metadata[filename_to_save] = {
+            "UUID": str(uuid_file),
+            "origin_uuid": str(origin_uuid),
+            "source": str(original_input),
+            "source_file": str(relative_path),
+            "source_id": int(chunk_number),
+            "page_title": str(library_name),
+            "section_title": str(library_name),
+            "section_name_id": str("None"),
+            "section_id": int(1),
+            "section_level": int(1),
+            "previous_id": int(1),
+            "URL": str(fidl_url),
+            "md_hash": str(md_hash),
+            "text_chunk_filename": str(text_chunk_filename),
+            "token_estimate": float(1.0),
+            "full_token_estimate": float(1.0),
+        }
+        # Save the FIDL protocol content as a text chunk.
+        with open(filename_to_save, "w", encoding="utf-8") as new_file:
+            new_file.write(fidl_protocol)
+            new_file.close()
+        chunk_number += 1
+    return file_metadata
+
+
+# This function processes a HTML file into small text chunks.
+def process_html_file(
+    filename: str,
+    root: str,
+    inputpathitem: Input,
+    splitter: str,
+    new_path: str,
+    file: str,
+    namespace_uuid: uuid.UUID,
+    relative_path: str,
+    url_prefix: str,
+):
+    # Local variables
+    file_metadata = {}
+    # Read the input HTML content
+    to_file = ""
+    with open(filename, "r", encoding="utf-8") as auto:
+        to_file = auto.read()
+        auto.close()
+    # Process includes lines in HTML
+    file_with_include = html_splitter.process_html_includes(
+        to_file, inputpathitem.include_path_html
+    )
+    return file_metadata
+
+
+# This function processes files specified in the `inputs` field
+# in the config.yaml file into small plain text files.
+# Includes are processed again since preprocess resolves the includes in
+# files prefixed with _, which indicates they are not standalone.
+# inputpath is optional to walk a temporary directory that has been pre-processed.
+# If not, it defaults to path of inputpathitem.
+def process_files_from_input(
+    product_config: ProductConfig,
+    inputpathitem: Input,
+    splitter: str,
+    inputpath: typing.Optional[str] = None,
+    input_path_count: int = 0,
+):
+    # If inputpath isn't specified assign path from item
+    if inputpath is None:
+        inputpath = inputpathitem.path
+    file_count = 0
+    md_count = 0
+    html_count = 0
+    fidl_count = 0
+    file_index = []
+    full_file_metadata = {}
+    resolved_output_path = resolve_path(product_config.output_path)
+    chunk_group_name = "text_chunks_" + "{:03d}".format(input_path_count)
+    # Get the total file count.
+    file_count = sum(len(files) for _, _, files in os.walk(resolve_path(inputpath)))
+    # Set up a status bar for the terminal display.
+    progress_bar = tqdm.tqdm(
+        total=file_count,
+        position=0,
+        bar_format="{percentage:3.0f}% | {n_fmt}/{total_fmt} | {elapsed}/{remaining}| {desc}",
+    )
+    # Process each input path provided in config.yaml.
+    for root, dirs, files in os.walk(resolve_path(inputpath)):
+        if inputpathitem.exclude_path is not None:
+            dirs[:] = [d for d in dirs if d not in inputpathitem.exclude_path]
+        if inputpathitem.url_prefix is not None:
+            # Makes sure that URL ends in backslash
+            url_prefix = end_path_backslash(inputpathitem.url_prefix)
+            namespace_uuid = uuid.uuid3(uuid.NAMESPACE_DNS, url_prefix)
+        # Process the files found in this input path provided in config.yaml.
+        for file in files:
+            # Displays status bar
+            progress_bar.set_description_str(f"Processing file {file}", refresh=True)
+            progress_bar.update(1)
+            # Skip this file if it starts with `_`.
+            if file.startswith("_"):
+                continue
+            # Get the full path to this input file.
+            filename_to_open = os.path.join(root, file)
+            # Construct a new sub-directory for storing output plain text files.
+            new_path = (
+                resolved_output_path
+                + "/"
+                + chunk_group_name
+                + re.sub(resolve_path(inputpath), "", os.path.join(root, ""))
+            )
+            is_exist = os.path.exists(new_path)
+            if not is_exist:
+                os.makedirs(new_path)
+            # Get the relative path to this input file.
+            relative_path = make_relative_path(
+                file=file, root=root, inputpath=inputpath
+            )
+            # Select Splitter mode: Markdown, FIDL, or HTML.
+            if splitter == "token_splitter" or splitter == "process_sections":
+                if file.endswith(".md"):
+                    # Add filename to a list
+                    file_index.append(relative_path)
+                    # Increment the Markdown file count.
+                    md_count += 1
+                    # Process a Markdown file.
+                    this_file_metadata = process_markdown_file(
+                        filename_to_open,
+                        root,
+                        inputpathitem,
+                        splitter,
+                        new_path,
+                        file,
+                        namespace_uuid,
+                        relative_path,
+                        url_prefix,
+                    )
+                    # Merge this file's metadata to the global metadata.
+                    full_file_metadata.update(this_file_metadata)
+            elif splitter == "fidl_splitter":
+                if file.endswith(".fidl"):
+                    # Add filename to a list
+                    file_index.append(relative_path)
+                    # Increment the FIDL file count.
+                    fidl_count += 1
+                    # Process a FIDL protocol file.
+                    this_file_metadata = process_fidl_file(
+                        filename_to_open,
+                        root,
+                        inputpathitem,
+                        splitter,
+                        new_path,
+                        file,
+                        namespace_uuid,
+                        relative_path,
+                        url_prefix,
+                    )
+                    # Merge this file's metadata to the global metadata.
+                    full_file_metadata.update(this_file_metadata)
+            else:
+                if file.endswith(".htm") or file.endswith(".html"):
+                    # Add filename to a list
+                    file_index.append(relative_path)
+                    # Increment the HTML file count.
+                    html_count += 1
+                    # Process a HTML file.
+                    this_file_metadata = process_html_file(
+                        filename_to_open,
+                        root,
+                        inputpathitem,
+                        splitter,
+                        new_path,
+                        file,
+                        namespace_uuid,
+                        relative_path,
+                        url_prefix,
+                    )
+                    # Merge this file's metadata to the global metadata.
+                    full_file_metadata.update(this_file_metadata)
+
+    # The processing of input files is finished.
+    progress_bar.set_description_str(f"Finished processing files.", refresh=False)
+    # Count all processed files.
+    file_count = md_count + html_count + fidl_count
+    # Print the summary of the processed files.
+    print()
+    print("Processed " + str(file_count) + " files from the source: " + str(inputpath))
+    print(str(md_count) + " Markdown files.")
+    print(str(html_count) + " HTML files.")
+    if fidl_count > 0:
+        print(str(fidl_count) + " FIDL files.")
+    print()
+    return file_count, md_count, html_count, file_index, full_file_metadata
+
+
+# Write the recorded input variables into a file: `file_index.json`
+def save_file_index_json(output_path, output_content):
+    json_out_file = resolve_path(output_path) + "/file_index.json"
+    with open(json_out_file, "w", encoding="utf-8") as outfile:
+        json.dump(output_content, outfile)
+    # print("Created " + json_out_file + " to store the complete list of processed files.")
+
+
+# Given a file, root, and inputpath, make a relative path
+def make_relative_path(
+    file: str, inputpath: str, root: typing.Optional[str] = None
+) -> str:
+    file_slash = "/" + file
+    if root is None:
+        relative_path = os.path.relpath(file_slash, inputpath)
+    else:
+        relative_path = os.path.relpath(root + file_slash, inputpath)
+    return relative_path
+
+
+# Given a path, filename_prefix, chunk index, and an optional path extension (to save chunk)
+# Create a file chunk name
+def make_file_chunk_name(
+    new_path: str, filename_prefix: str, index: int, extension: str = "md"
+) -> str:
+    filename_to_save = filename_prefix + "_" + str(index) + "." + extension
+    full_filename = os.path.join(new_path, filename_to_save)
+    return full_filename
+
+
+# Given a path, file, chunk index, and an optional path extension (to save chunk)
+# Create a chunk name
+def make_chunk_name(new_path: str, file: str, index: int, extension: str = "md") -> str:
+    new_filename = os.path.join(new_path, file)
+    filename_to_save = new_filename
+    # Grab the filename without the .md extension
+    match = re.search(r"(.*)\.md$", new_filename)
+    if match:
+        new_filename_no_ext = match[1]
+        # Save the filename appended with an index.
+        filename_to_save = new_filename_no_ext + "_" + str(index) + "." + extension
+    return filename_to_save
+
+
+# Return the relative path after the `docs-agent/data` path
+def get_relative_path_and_filename(full_path: str):
+    path_and_filename = full_path
+    match = re.search(r".*\/docs-agent\/data\/(.*)$", full_path)
+    if match:
+        path_and_filename = match[1]
+    return path_and_filename
+
+
+# Given a path, it resolves the path to an absolute path, and if it exists,
+# deletes it, before re-creating it (essentially making a fresh directory)
+# It then returns the absolute path name
+def resolve_and_clear_path(path: str) -> str:
+    resolved_output_path = resolve_path(path)
+    # Remove the existing output, to make sure stale files are removed
+    if os.path.exists(resolved_output_path):
+        shutil.rmtree(resolved_output_path)
+    os.makedirs(resolved_output_path, exist_ok=True)
+    return resolved_output_path
+
+
+# Processes all inputs from a given ProductConfig object
+def process_inputs_from_product(input_product: ProductConfig, temp_process_path: str):
+    source_file_index = {}
+    total_file_count = 0
+    total_md_count = 0
+    total_html_count = 0
+    final_file_metadata = {}
+    input_path_count = 0
+    for input_path_item in input_product.inputs:
+        print(f"\nInput path {input_path_count}: {input_path_item.path}")
+        temp_output = pre_process_doc_files(
+            product_config=input_product,
+            inputpathitem=input_path_item,
+            temp_path=temp_process_path,
+        )
+        # Process Markdown files in the `input` path, when using pre_proces_doc_files
+        # temp_output should be used as inputpath parameter
+        (
+            file_count,
+            md_count,
+            html_count,
+            file_index,
+            full_file_metadata,
+        ) = process_files_from_input(
+            product_config=input_product,
+            inputpathitem=input_path_item,
+            inputpath=temp_output,
+            splitter=input_product.markdown_splitter,
+            input_path_count=input_path_count,
+        )
+        # Clear the temp_output
+        shutil.rmtree(temp_output)
+        input_path = input_path_item.path
+        if not input_path.endswith("/"):
+            input_path = input_path + "/"
+        input_path = resolve_path(input_path)
+        # Record the input variables used in this path.
+        file_list = {}
+        for file in file_index:
+            file_obj = {file: {"source": input_path, "URL": input_path_item.url_prefix}}
+            file_list[file] = file_obj
+        # Make a single dictionary per product, append each input
+        final_file_metadata = final_file_metadata | full_file_metadata
+        # source_file_index[input_product.product_name] = full_file_metadata
+        total_file_count += file_count
+        total_md_count += md_count
+        total_html_count += html_count
+        input_path_count += 1
+    source_file_index[input_product.product_name] = final_file_metadata
+    # Write the recorded input variables into `file_index.json`.
+    save_file_index_json(
+        output_path=input_product.output_path, output_content=source_file_index
+    )
+    print(
+        "\n[Summary]"
+        + f"\nProduct: {input_product.product_name}"
+        + "\nSources: "
+        + str(len(input_product.inputs))
+        + "\nTotal number of processed source files: "
+        + str(total_file_count)
+        + "\nMarkdown files: "
+        + str(total_md_count)
+        + "\nHTML files: "
+        + str(total_html_count)
+    )
+
+
+# Print the size distribution map of created text chunks.
+def get_chunk_size_distribution_from_product(input_product: ProductConfig):
+    chunk_size_map = {
+        "50": 0,
+        "500": 0,
+        "1000": 0,
+        "1500": 0,
+        "2000": 0,
+        "2500": 0,
+        "3000": 0,
+        "4000": 0,
+        "5000": 0,
+        "6000": 0,
+    }
+    total_file_count = 0
+    chunk_dir = input_product.output_path
+    for root, dirs, files in os.walk(resolve_path(chunk_dir)):
+        for file in files:
+            this_filename = os.path.join(root, file)
+            if this_filename.endswith(".md"):
+                file_stats = os.stat(this_filename)
+                chunk_size = int(file_stats.st_size)
+                if chunk_size <= 50:
+                    count = chunk_size_map["50"]
+                    chunk_size_map["50"] = count + 1
+                elif chunk_size > 50 and chunk_size <= 500:
+                    count = chunk_size_map["500"]
+                    chunk_size_map["500"] = count + 1
+                elif chunk_size > 500 and chunk_size <= 1000:
+                    count = chunk_size_map["1000"]
+                    chunk_size_map["1000"] = count + 1
+                elif chunk_size > 1000 and chunk_size <= 1500:
+                    count = chunk_size_map["1500"]
+                    chunk_size_map["1500"] = count + 1
+                elif chunk_size > 1500 and chunk_size <= 2000:
+                    count = chunk_size_map["2000"]
+                    chunk_size_map["2000"] = count + 1
+                elif chunk_size > 2000 and chunk_size <= 2500:
+                    count = chunk_size_map["2500"]
+                    chunk_size_map["2500"] = count + 1
+                elif chunk_size > 2000 and chunk_size <= 3000:
+                    count = chunk_size_map["3000"]
+                    chunk_size_map["3000"] = count + 1
+                elif chunk_size > 3000 and chunk_size <= 4000:
+                    count = chunk_size_map["4000"]
+                    chunk_size_map["4000"] = count + 1
+                elif chunk_size > 4000 and chunk_size <= 5000:
+                    count = chunk_size_map["5000"]
+                    chunk_size_map["5000"] = count + 1
+                else:
+                    count = chunk_size_map["6000"]
+                    chunk_size_map["6000"] = count + 1
+                total_file_count += 1
+
+    # Print the distribution result.
+    print("\nSpread of text chunk sizes and counts:")
+    prev_size = 0
+    for key in list(chunk_size_map):
+        count = chunk_size_map[key]
+        if int(key) == 50:
+            print(f"- Chunks smaller than {key} bytes: {count}")
+        elif int(key) == 6000:
+            print(f"- Chunks larger than {key} bytes: {count}")
+        else:
+            print(f"- Chunks between {prev_size} and {key} bytes: {count}")
+        prev_size = int(key)
+    print(f"\nTotal number of chunks: {total_file_count}")
+
+
+# Given a ReadConfig object, process all products
+# Default Read config defaults to source of project with config.yaml
+# temp_process_path is where temporary files will be processed and then deleted
+# defaults to /tmp
+def process_all_products(
+    config_file: ConfigFile = config.ReadConfig().returnProducts(),
+    temp_process_path: str = "/tmp",
+):
+    print(f"Starting chunker for {str(len(config_file.products))} products.\n")
+    for index, product in enumerate(config_file.products):
+        print(f"===========================================")
+        print(f"Processing product: {product.product_name}")
+        # logging.error(f"Index: {index}")
+        # if index != 0:
+        #     old_entries = read_file_index_json(output_path=input_product.output_path)
+        #     logging.error(old_entries)
+        # else:
+        #     old_entries = None
+        print("Output directory: " + resolve_and_clear_path(product.output_path))
+        print("Processing files from " + str(len(product.inputs)) + " sources.")
+        process_inputs_from_product(
+            input_product=product, temp_process_path=temp_process_path
+        )
+
+        # Print the distribution map of text chunk sizes.
+        get_chunk_size_distribution_from_product(input_product=product)
+
+
+def main():
+    #### Main ####
+    process_all_products()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/populate_vector_database.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/populate_vector_database.py
new file mode 100644
index 000000000..65a80fd97
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/preprocess/populate_vector_database.py
@@ -0,0 +1,610 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Populate vector databases with embeddings generated from text chunks."""
+
+import json
+import os
+import re
+import sys
+
+from absl import logging
+import chromadb
+from chromadb.utils import embedding_functions
+import flatdict
+import tqdm
+
+from docs_agent.models.google_genai import Gemini
+from docs_agent.preprocess.splitters import markdown_splitter
+from docs_agent.storage.google_semantic_retriever import SemanticRetriever
+from docs_agent.utilities import config
+from docs_agent.utilities.config import ConfigFile
+from docs_agent.utilities.config import ProductConfig
+from docs_agent.utilities.helpers import end_path_backslash
+from docs_agent.utilities.helpers import resolve_path
+
+
+class chromaAddSection:
+    def __init__(
+        self, section: markdown_splitter.Section, doc_title: str, metadata: dict = {}
+    ):
+        self.section = section
+        self.doc_title = doc_title
+        self.metadata = metadata
+
+
+# Get the total number of files in a directory and its subdirectories.
+def get_file_count_in_a_dir(path):
+    file_count = sum(len(files) for _, _, files in os.walk(path))
+    return file_count
+
+
+# Return the relative path after the `docs-agent/data` path
+def get_relative_path_and_filename(full_path: str):
+    path_and_filename = full_path
+    match = re.search(r".*\/docs-agent\/data\/(.*)$", full_path)
+    if match:
+        path_and_filename = match[1]
+    return path_and_filename
+
+
+# Prepare progres bars for showing files being processed and uploaded.
+def init_progress_bars(file_count):
+    print()
+    main = tqdm.tqdm(
+        total=file_count,
+        position=0,
+        bar_format="{percentage:3.0f}% | {n_fmt}/{total_fmt} | {elapsed}/{remaining} | {desc}",
+    )
+    new_file = tqdm.tqdm(position=1, desc="Total new files 0", bar_format="{desc}")
+    unchanged_file = tqdm.tqdm(
+        position=2, desc="Total unchanged files 0", bar_format="{desc}"
+    )
+    update_file = tqdm.tqdm(
+        position=3, desc="Total updated files 0", bar_format="{desc}"
+    )
+    return main, new_file, unchanged_file, update_file
+
+
+# Open a file and return its content.
+def get_file_content(full_path):
+    content_file = ""
+    with open(full_path, "r", encoding="utf-8") as auto:
+        content_file = auto.read()
+        content_file.strip()
+        auto.close()
+    return content_file
+
+
+# Initialize Gemini objects for generating embeddings.
+def init_gemini_model(product_config: ProductConfig):
+    gemini_new = Gemini(models_config=product_config.models)
+    # Use a chromadb function to initialize db
+    embedding_function_gemini = embedding_functions.GoogleGenerativeAiEmbeddingFunction(
+        api_key=product_config.models.api_key,
+        model_name=product_config.models.embedding_model,
+        task_type="RETRIEVAL_DOCUMENT",
+    )
+    return gemini_new, embedding_function_gemini
+
+
+# Upload a text chunk to an online stroage using the Semantic Retrieval API.
+def upload_an_entry_to_a_corpus(
+    semantic, corpus_name, document_name_in_corpus, this_item, is_this_first_chunk
+):
+    document_name = document_name_in_corpus
+    # Check if a document for this chunk exists.
+    if is_this_first_chunk == True:
+        origin_uuid = ""
+        if hasattr(this_item.section, "origin_uuid"):
+            origin_uuid = this_item.section.origin_uuid
+        try:
+            # Create a new document
+            document_name = semantic.create_a_doc(
+                corpus_name=corpus_name,
+                page_title=this_item.section.page_title,
+                uuid=origin_uuid,
+            )
+        except:
+            logging.error(
+                f"Cannot create a new document using the Semantic Retrieval API: {str(this_item.section.page_title)}"
+            )
+    uuid_dict = {"UUID": this_item.section.uuid}
+    dict_with_uuid = this_item.metadata | uuid_dict
+    try:
+        # Create a new chunk
+        semantic.create_a_chunk(
+            doc_name=document_name,
+            text=this_item.section.content,
+            metadata=dict_with_uuid,
+        )
+        logging.info("Added the text chunk using the Semantic Retrieval API.")
+    except:
+        logging.error(dict_with_uuid)
+        logging.error(this_item.section)
+        logging.error("Cannot add the text chunk using the Semantic Retrieval API.")
+    return document_name
+
+
+# Delete entries in the Chroma database if we cannot find matches in the current dataset.
+def delete_unmatched_entries_in_chroma(
+    product_config: ProductConfig, chroma_client, collection
+):
+    print()
+    print(f"Scanning the Chroma database to identify entries to be deleted.")
+    # Arrays to store IDs, text chunk filename, and md hashes of
+    # the existing entries in the local Chroma vector database.
+    existing_online_entry_ids = []
+    existing_online_entry_text_chunk_filenames = []
+    existing_online_entry_md_hashes = []
+    # Get all entries in the vector database.
+    all_entries = collection.get()
+    for entry in all_entries["ids"]:
+        # logging.error(f"ID: {entry}")
+        existing_online_entry_ids.append(str(entry))
+    for entry in all_entries["metadatas"]:
+        # logging.error(f"Metadata: {entry}")
+        text_chunk_filename = ""
+        md_hash = ""
+        if "text_chunk_filename" in entry:
+            text_chunk_filename = entry["text_chunk_filename"]
+        if "md_hash" in entry:
+            md_hash = entry["md_hash"]
+        # logging.error(f"Text chunk filename: {text_chunk_filename}")
+        # logging.error(f"MD HASH: {md_hash}")
+        existing_online_entry_text_chunk_filenames.append(str(text_chunk_filename))
+        existing_online_entry_md_hashes.append(str(md_hash))
+
+    # Examine the new candidate entries in the current `data` directory.
+    candidate_entries = {}
+    (index_object, full_index_path) = load_index(input_path=product_config.output_path)
+    for product in index_object:
+        dictionary_input = index_object[product]
+    # Extract the text chunk name and hash from each chunk data.
+    for item in dictionary_input:
+        chunk_data = dictionary_input[item]
+        text_chunk_filename = ""
+        text_chunk_md_hash = ""
+        # print(f"Candidate text chunk data: {chunk_data}")
+        if "text_chunk_filename" in chunk_data:
+            text_chunk_filename = chunk_data["text_chunk_filename"]
+        if "md_hash" in chunk_data:
+            text_chunk_md_hash = chunk_data["md_hash"]
+        # print(f"Candidate text chunk filename: {text_chunk_filename}")
+        if text_chunk_filename != "":
+            candidate_entries[text_chunk_filename] = text_chunk_md_hash
+
+    # Compare the existing online entries to the candidate entries.
+    to_be_deleted_online_entry_ids = []
+    index = 0
+    for index, item in enumerate(existing_online_entry_text_chunk_filenames):
+        existing_text_chunk = item
+        existing_md_hash = existing_online_entry_md_hashes[index]
+        existing_id = existing_online_entry_ids[index]
+        if existing_text_chunk in candidate_entries:
+            candidate_md_hash = candidate_entries[existing_text_chunk]
+            if existing_md_hash != candidate_md_hash:
+                logging.info(
+                    f"The entry {existing_text_chunk} in the Chroma database "
+                    + "will be deleted because its content has changed."
+                )
+                to_be_deleted_online_entry_ids.append(existing_id)
+        else:
+            logging.info(
+                f"The entry {existing_text_chunk} in the Chroma database "
+                + "will be deleted because it is no longer found in the current dataset."
+            )
+            to_be_deleted_online_entry_ids.append(existing_id)
+
+    # Delete identified entries in the Chroma database.
+    if to_be_deleted_online_entry_ids:
+        collection.delete(ids=to_be_deleted_online_entry_ids)
+        deleted_entries_count = len(to_be_deleted_online_entry_ids)
+        print(f"Deleted entries count: {deleted_entries_count}")
+    else:
+        print(f"Keeping all existing entries in the Chroma database.")
+    return to_be_deleted_online_entry_ids
+
+
+# Delete entries in the online corpus if we cannot find matches in the current dataset.
+def delete_unmatched_entries_in_online_corpus(
+    product_config: ProductConfig, semantic_object, corpus_name
+):
+    print()
+    print(f"Scanning the online corpus to identify chunks to be deleted.")
+    print(f"(This may take some time.)")
+    # Get all chunks in the online corpus.
+    all_chunks = []
+    all_docs = semantic_object.get_all_docs(corpus_name=corpus_name, print_output=False)
+    for doc in all_docs:
+        doc_name = str(doc.name)
+        chunks = semantic_object.get_all_chunks(doc_name=doc_name, print_output=False)
+        for chunk in chunks:
+            all_chunks.append(chunk)
+
+    # Examine the new candidate entries in the current `data` directory.
+    candidate_entries = {}
+    (index_object, full_index_path) = load_index(input_path=product_config.output_path)
+    for product in index_object:
+        dictionary_input = index_object[product]
+    # Extract the text chunk name and hash from each chunk data.
+    for item in dictionary_input:
+        chunk_data = dictionary_input[item]
+        text_chunk_filename = ""
+        text_chunk_md_hash = ""
+        # print(f"Candidate text chunk data: {chunk_data}")
+        if "text_chunk_filename" in chunk_data:
+            text_chunk_filename = chunk_data["text_chunk_filename"]
+        if "md_hash" in chunk_data:
+            text_chunk_md_hash = chunk_data["md_hash"]
+        # print(f"Candidate text chunk filename: {text_chunk_filename}")
+        if text_chunk_filename != "":
+            candidate_entries[text_chunk_filename] = text_chunk_md_hash
+
+    # Compare the existing online entries to the candidate entries.
+    to_be_deleted_online_chunk_names = []
+    for chunk in all_chunks:
+        existing_chunk_name = chunk.name
+        existing_md_hash = ""
+        existing_text_chunk_filename = ""
+        metadata = chunk.custom_metadata
+        for item in metadata:
+            if item.key == "md_hash":
+                # print(f"md_hash: {item.string_value}")
+                existing_md_hash = item.string_value
+            elif item.key == "text_chunk_filename":
+                # print(f"text_chunk_filename: {item.string_value}")
+                existing_text_chunk_filename = item.string_value
+        if existing_text_chunk_filename in candidate_entries:
+            candidate_md_hash = candidate_entries[existing_text_chunk_filename]
+            if existing_md_hash != candidate_md_hash:
+                logging.info(
+                    f"{existing_text_chunk_filename} in the online corpus "
+                    + "will be deleted because its content has changed."
+                )
+                to_be_deleted_online_chunk_names.append(existing_chunk_name)
+        else:
+            logging.info(
+                f"{existing_text_chunk_filename} in the online corpus will be "
+                + "deleted because it is no longer found in the current dataset."
+            )
+            to_be_deleted_online_chunk_names.append(existing_chunk_name)
+
+    # Delete identified chunks in the online corpus.
+    if to_be_deleted_online_chunk_names:
+        # Initialize a progress bar object.
+        progress_bar = tqdm.tqdm(
+            position=0, desc="Deleting the chunk", bar_format="{desc}"
+        )
+        # Loop for deleting chunks online.
+        for chunk_name in to_be_deleted_online_chunk_names:
+            # progress_bar.update(1)
+            progress_bar.set_description_str(
+                f"Deleting the chunk {chunk_name}", refresh=True
+            )
+            semantic_object.delete_a_chunk(chunk_name)
+        delete_count = len(to_be_deleted_online_chunk_names)
+        progress_bar.set_description_str(
+            f"Deleted chunks count: {delete_count}", refresh=False
+        )
+    else:
+        print(f"Keeping all existing chunks in the online corpus.")
+    return to_be_deleted_online_chunk_names
+
+
+# Read plain text files (.md) from an input dir and
+# add their content to the vector database.
+# Embeddings are generated automatically as they are added to the database.
+def populateToDbFromProduct(product_config: ProductConfig):
+    """Populates the vector database with product documentation.
+    Args:
+        product_config: A ProductConfig object containing configuration details.
+    """
+    # Initialize Gemini objects.
+    (gemini_new, embedding_function_gemini) = init_gemini_model(product_config)
+
+    # Initialize the Chroma database.
+    for item in product_config.db_configs:
+        if "chroma" in item.db_type:
+            logging.info("Initializing Chroma for a local storage.")
+            chroma_client = chromadb.PersistentClient(
+                path=resolve_path(item.vector_db_dir)
+            )
+            collection = chroma_client.get_or_create_collection(
+                name=item.collection_name,
+                embedding_function=embedding_function_gemini,
+            )
+            if (
+                hasattr(product_config, "enable_delete_chunks")
+                and product_config.enable_delete_chunks == "True"
+            ):
+                # Delete entries in the database if we cannot find matches
+                # in the current dataset.
+                delete_unmatched_entries_in_chroma(
+                    product_config, chroma_client, collection
+                )
+
+    # Initialzie the Semantic Retreival API.
+    corpus_name = ""
+    if product_config.db_type == "google_semantic_retriever":
+        logging.info("Initializing the Semantic Retrieval API for an online storage.")
+        semantic = SemanticRetriever()
+        for item in product_config.db_configs:
+            if "google_semantic_retriever" in item.db_type:
+                corpus_name = item.corpus_name
+                if semantic.does_this_corpus_exist(corpus_name) == False:
+                    # Create a new corpus.
+                    semantic.create_a_new_corpus(item.corpus_display, corpus_name)
+                elif (
+                    hasattr(product_config, "enable_delete_chunks")
+                    and product_config.enable_delete_chunks == "True"
+                ):
+                    # Delete chunks in the corpus if we cannot find matches in the current dataset.
+                    delete_unmatched_entries_in_online_corpus(
+                        product_config, semantic, corpus_name
+                    )
+
+    # Initialize progress bar objects.
+    file_count = get_file_count_in_a_dir(product_config.output_path)
+    (
+        progress_bar,
+        progress_new_file,
+        progress_unchanged_file,
+        progress_update_file,
+    ) = init_progress_bars(file_count)
+
+    # Get the preprocess information from the `file_index.json` file.
+    (index, full_index_path) = load_index(input_path=product_config.output_path)
+
+    # Local variables track the resource names of documents for the Semantic Retrieval API.
+    document_name_in_corpus = ""
+    dict_document_names_in_corpus = {}
+
+    # Local variables for counting files.
+    total_files = 0
+    updated_count = 0
+    new_count = 0
+    unchanged_count = 0
+
+    # Loop through each `path` in the `config.yaml` file.
+    for root, dirs, files in os.walk(product_config.output_path):
+        # Convert `output_path` to be a fully resolved path.
+        fully_resolved_output = end_path_backslash(
+            resolve_path(product_config.output_path)
+        )
+        # Loop through all files found in the `output_path` directory.
+        for file in files:
+            # Displays status bar, sleep helps to stick the progress
+            progress_bar.update(1)
+            progress_bar.set_description_str(f"Processing file {file}", refresh=True)
+            # Get the full path for the file.
+            full_file_name = resolve_path(os.path.join(root, "")) + file
+            # Process only files with `.md` extension.
+            if file.endswith(".md"):
+                # Open the file and get the content.
+                content_file = get_file_content(os.path.join(root, file))
+                # Get a Section object from the file index object.
+                chroma_add_item = findFileinDict(
+                    input_file_name=full_file_name,
+                    index_object=index,
+                    content_file=content_file,
+                )
+                # Quick fix: If the filename ends with `_##.md`, extract the file prefix
+                # Then check if this prefix exists in a local dict, which tracks document
+                # resource names for the Semantic Retrieval API call.
+                file_page_prefix = ""
+                is_this_first_chunk = False
+                match_file_page = re.search(r"(.*)_\d+\.md$", full_file_name)
+                if match_file_page:
+                    file_page_prefix = match_file_page.group(1)
+                    if file_page_prefix in dict_document_names_in_corpus:
+                        # If the prefix exists in the dict, retrieve the document resource name.
+                        document_name_in_corpus = dict_document_names_in_corpus.get(
+                            file_page_prefix
+                        )
+                    else:
+                        # if not, set the flag to indicate that a new `document` needs
+                        # to be created.
+                        is_this_first_chunk = True
+                        document_name_in_corpus = ""
+                else:
+                    # If the file is not in a group, treat it as its own document.
+                    file_page_prefix = full_file_name
+                # Skip if the file size is larger than 10000 bytes (API limit)
+                if (
+                    chroma_add_item.section.content != ""
+                    and len(chroma_add_item.section.content) < 10000
+                    and chroma_add_item.section.md_hash != ""
+                    and chroma_add_item.section.uuid != ""
+                ):
+                    # Compare the text chunk entries in the local Chroma database
+                    # to check if the hash value has changed.
+                    id_to_not_change = collection.get(
+                        include=["metadatas"],
+                        ids=chroma_add_item.section.uuid,
+                        where={"md_hash": {"$eq": chroma_add_item.section.md_hash}},
+                    )["ids"]
+                    if id_to_not_change != []:
+                        # This text chunk is unchanged. Skip this text chunk.
+                        qty_change = len(id_to_not_change)
+                        progress_unchanged_file.update(qty_change)
+                        unchanged_count += qty_change
+                        progress_unchanged_file.set_description_str(
+                            f"Total unchanged file {unchanged_count}",
+                            refresh=True,
+                        )
+                    else:
+                        # Process this text chunk and store it into the databases.
+                        # Generate an embedding
+                        this_embedding = gemini_new.embed(
+                            content=chroma_add_item.section.content,
+                            task_type="RETRIEVAL_DOCUMENT",
+                            title=chroma_add_item.doc_title,
+                        )[0]
+                        # Store this text chunk entry in Chroma.
+                        collection.add(
+                            documents=[chroma_add_item.section.content],
+                            embeddings=[this_embedding],
+                            metadatas=[chroma_add_item.metadata],
+                            ids=[chroma_add_item.section.uuid],
+                        )
+                        # Update the progress bar.
+                        new_count += 1
+                        progress_new_file.update(1)
+                        progress_new_file.set_description_str(
+                            f"Total new files {new_count}", refresh=True
+                        )
+                        # Add this text chunk to the online storage.
+                        if product_config.db_type == "google_semantic_retriever":
+                            document_name = upload_an_entry_to_a_corpus(
+                                semantic,
+                                corpus_name,
+                                document_name_in_corpus,
+                                chroma_add_item,
+                                is_this_first_chunk,
+                            )
+                            # Store the document resource name
+                            dict_document_names_in_corpus[
+                                file_page_prefix
+                            ] = document_name
+                    total_files += 1
+                else:
+                    if chroma_add_item.section.content == "":
+                        logging.error(f"Skipped {file} because the file is empty.")
+                    else:
+                        logging.error(
+                            f"Skipped {file} because the file is is too large {str(len(chroma_add_item.section.content))}"
+                        )
+            # Skips logging a warning if the file being walked is the index file
+            elif full_file_name == full_index_path:
+                next
+            else:
+                # Logs missing extensions from input directory that may be
+                # processed
+                file_name, extension = os.path.splitext(file)
+                logging.warning(
+                    f"Skipped {file} because there is no configured parser for extension {extension}"
+                )
+
+    progress_bar.set_description_str(
+        f"Finished processing text chunk files (and file_index.json).", refresh=True
+    )
+    progress_unchanged_file.set_description_str(
+        f"Total number of entries: {total_files}", refresh=True
+    )
+
+
+def findFileinDict(input_file_name: str, index_object, content_file):
+    metadata_dict_final = {}
+    for product in index_object:
+        dictionary_input = index_object[product]
+    if input_file_name in dictionary_input:
+        chunk_data = dictionary_input[input_file_name]
+        # Extract the text chunk name from the index object.
+        text_chunk_filename = ""
+        if "text_chunk_filename" in chunk_data:
+            text_chunk_filename = chunk_data["text_chunk_filename"]
+            # logging.error(f"Chunk name: {text_chunk_filename}")
+        # If metadata exists, add these to a dictionary that is then
+        # merged with other metadata values
+        if "metadata" in dictionary_input[input_file_name]:
+            # Save and flatten dictionary
+            metadata_dict_extra = extract_extra_metadata(
+                input_dictionary=dictionary_input[input_file_name]["metadata"]
+            )
+        else:
+            metadata_dict_extra = {}
+        section = markdown_splitter.DictionarytoSection(
+            dictionary_input[input_file_name]
+        )
+        if "URL" in metadata_dict_extra:
+            section.url = metadata_dict_extra["URL"]
+        # Merges dictionaries with main metadata and additional metadata
+        section.content = content_file
+        # Combines Section db in dictionary with extra
+        metadata_dict_final = section.encodeToChromaDBNoContent() | metadata_dict_extra
+        # Add the text chunk filename to the metadata.
+        if text_chunk_filename != "":
+            metadata_dict_final["text_chunk_filename"] = text_chunk_filename
+        # Overide title if it exists from frontmatter
+        if "title" in metadata_dict_final:
+            doc_title = str(metadata_dict_final["title"])
+        else:
+            doc_title = section.createChunkTitle()
+        # print("Title: " + doc_title)
+    else:
+        doc_title = ""
+        section = markdown_splitter.DictionarytoSection(metadata_dict_final)
+        logging.info(f"{input_file_name} not found.")
+    chroma_add = chromaAddSection(
+        section=section, metadata=metadata_dict_final, doc_title=doc_title
+    )
+    return chroma_add
+
+
+# Load the file index information from the file_index.json file.
+def load_index(
+    input_path: str, input_index_name: str = "file_index.json"
+) -> tuple[dict, str]:
+    """Loads the file index.
+    Args:
+        input_path: The path to the input directory.
+
+    Returns:
+        A tuple containing the loaded index and the full index path.
+    """
+    full_index_path = resolve_path(end_path_backslash(input_path) + input_index_name)
+    try:
+        with open(full_index_path, "r", encoding="utf-8") as index_file:
+            logging.info("Using file index: " + full_index_path + "\n")
+            index = json.load(index_file)
+            return index, full_index_path
+    except FileNotFoundError:
+        logging.error(
+            f"The file {full_index_path} does not exist. Re-chunk your project with docsAgent chunk"
+        )
+        return sys.exit(1)
+
+
+# Given a ReadConfig object, process all products
+# Default Read config defaults to source of project with config.yaml
+# temp_process_path is where temporary files will be processed and then deleted
+# defaults to /tmp
+def process_all_products(
+    config_file: ConfigFile = config.ReadConfig().returnProducts(),
+):
+    print(
+        f"Starting to verify files to populate database for {str(len(config_file.products))} products.\n"
+    )
+    for product in config_file.products:
+        print(f"===========================================")
+        print(f"Processing product: {product.product_name}")
+        print(f"Input directory: {resolve_path(product.output_path)}")
+        print(f"Database operation db type: {product.db_type}")
+        print()
+        for item in product.db_configs:
+            print(f"{item}")
+        print(f"===========================================")
+        populateToDbFromProduct(product_config=product)
+
+
+def extract_extra_metadata(input_dictionary):
+    metadata_dict_extra = flatdict.FlatterDict(
+        input_dictionary,
+        delimiter="_",
+    )
+    metadata_dict_extra = dict(metadata_dict_extra)
+    return metadata_dict_extra
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/__init__.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/fidl_splitter.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/fidl_splitter.py
new file mode 100644
index 000000000..50e409b3c
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/fidl_splitter.py
@@ -0,0 +1,129 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import re
+import os
+from absl import logging
+
+
+# Get the byte size of lines.
+def get_byte_size(lines):
+    buffer_size = 0
+    for line in lines:
+        buffer_size += len(line.encode("utf-8"))
+    return buffer_size
+
+
+# Prepare a FIDL protocol into a text chunk to be stored.
+def construct_a_chunk(library_name: str, protocol_name: str, lines):
+    content_to_store = ""
+    content_to_store += "Library name: " + library_name + "\n"
+    content_to_store += "Protocol name: " + protocol_name + "\n\n"
+    content_to_store += (
+        "In Fuchsia's "
+        + library_name
+        + " FIDL library, the "
+        + protocol_name
+        + " protocol interface is defined as the following:\n\n"
+    )
+    content_to_store += "<code>\n"
+    for line in lines:
+        content_to_store += line + "\n"
+    content_to_store += "</code>\n"
+    return content_to_store
+
+
+# Divide a large protocol into two text chunks.
+def divide_a_protocol(lines):
+    half_point = len(lines) // 2
+    first_half = lines[:half_point]
+    second_half = lines[half_point:]
+    return first_half, second_half
+
+
+# Recursively process a FIDL protocol into text chunks.
+def construct_chunks(library_name: str, protocol_name: str, lines):
+    contents = []
+    buffer_size = get_byte_size(lines)
+    if int(buffer_size) > 5000:
+        # If the protocol is larget than 5KB, divide it into two.
+        logging.info(
+            "Found a text chunk ("
+            + str(protocol_name)
+            + ") is greater than 6KB (size: "
+            + str(buffer_size)
+            + ")."
+        )
+        (first_half, second_half) = divide_a_protocol(lines)
+        first_content = construct_chunks(library_name, protocol_name, first_half)
+        second_content = construct_chunks(library_name, protocol_name, second_half)
+        contents += first_content
+        contents += second_content
+    else:
+        # Prepare a text chunk that describes a FIDL protocol.
+        content = construct_a_chunk(library_name, protocol_name, lines)
+        logging.info(
+            "Created a text chunk for "
+            + str(protocol_name)
+            + " (size: "
+            + str(buffer_size)
+            + ")."
+        )
+        contents.append(content)
+    return contents
+
+
+# Split a FIDL file into protocols as text chunks.
+def split_file_to_protocols(this_file):
+    protocols = []
+    line_buffer = []
+    protocol_name = ""
+    library_name = ""
+    index = 0
+    for line in this_file.split("\n"):
+        match_protocol = re.search(r"^closed\s+protocol\s+(.*)\s+\{", line)
+        match_library = re.search(r"^library\s+(.*);", line)
+        match_comment = re.search(r"^\s*\/\/\/\s+(.*)", line)
+        match_new_line = re.search(r"^\s*$", line)
+        match_end_bracket = re.search(r"^};", line)
+        if match_protocol:
+            # print("MATCHED [Protocol]: " + match_protocol.group(1))
+            protocol_name = match_protocol.group(1)
+            line_buffer.append(line)
+        elif match_library:
+            # print("MATCHED [Library]: " + match_library.group(1))
+            library_name = match_library.group(1)
+        elif match_comment:
+            # print("MATCHED [Comment]: " + match_comment.group(1))
+            line_buffer.append(line)
+        elif match_new_line:
+            # print("MATCHED [New line]")
+            line_buffer.append(line)
+        elif match_end_bracket:
+            # print("MATCHED [End bracket]")
+            line_buffer.append(line)
+            if library_name != "" and protocol_name != "":
+                # Prepre a captured FIDL protocl into small text chunks.
+                contents = construct_chunks(library_name, protocol_name, line_buffer)
+                for content in contents:
+                    protocols.append(content)
+            # Clear the line butter and protocol name when an end bracket is found.
+            line_buffer.clear()
+            protocol_name = ""
+        else:
+            line_buffer.append(line)
+        index += 0
+    return protocols
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/html_splitter.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/html_splitter.py
new file mode 100644
index 000000000..952564cac
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/html_splitter.py
@@ -0,0 +1,43 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import re, os
+from docs_agent.preprocess.splitters import markdown_splitter
+
+
+# This function replaces HTML's includes sections with content.
+def process_html_includes(html_text, root):
+    updated_html = ""
+    for line in html_text.split("\n"):
+        # Replaces HTML includes (Jinja) with content (html include can happen
+        # with indents)
+        try:
+            include_match = re.search('{% include "(.*?)" %}', line)
+            include_file = os.path.abspath(root + "/" + include_match[1])
+            # Tries to open include and errors if it doesn't exist
+            try:
+                html_include = markdown_splitter.verify_file(include_file)
+                if html_include is str:
+                    updated_html += html_include + "\n"
+                else:
+                    updated_html += "\n"
+            except FileNotFoundError:
+                # If the file doesn't exist, remove the include statement to
+                # avoid polluting content
+                updated_html += "\n"
+        except:
+            updated_html += line + "\n"
+    return updated_html
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/markdown_splitter.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/markdown_splitter.py
new file mode 100644
index 000000000..ddafcf567
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/markdown_splitter.py
@@ -0,0 +1,674 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import typing
+import markdown
+import bs4
+import re, os
+from absl import logging
+from docs_agent.models import tokenCount
+import frontmatter
+from docs_agent.utilities.helpers import add_scheme_url
+
+
+class Section:
+    def __init__(
+        self,
+        id: int,
+        name_id: str,
+        page_title: str,
+        section_title: str,
+        level: int,
+        previous_id: int,
+        parent_tree: list[int],
+        token_count: float,
+        content: str,
+        url: typing.Optional[str] = None,
+        origin_uuid: typing.Optional[str] = None,
+        md_hash: typing.Optional[str] = None,
+        uuid: typing.Optional[str] = None,
+    ):
+        self.id = id
+        self.name_id = name_id
+        self.page_title = page_title
+        self.section_title = section_title
+        self.level = level
+        self.previous_id = previous_id
+        self.parent_tree = parent_tree
+        self.token_count = token_count
+        self.content = content
+        self.url = url
+        self.origin_uuid = origin_uuid
+        self.md_hash = md_hash
+        self.uuid = uuid
+
+    def __str__(self):
+        return f"UUID: {self.uuid}\n\
+ID: {self.id}\n\
+Name ID: {self.name_id}\n\
+Origin UUID: {self.origin_uuid}\n\
+Page title: {self.page_title}\n\
+Section title: {self.section_title}\n\
+URL: {self.url}\n\
+Level: {self.level}\n\
+Parent ID: {self.previous_id}\n\
+Parent tree: {self.parent_tree}\n\
+Tokens: {self.token_count}\n\
+Content hash: {self.md_hash}\n"
+
+    # Updates the content of a section using page and section title
+    def updateContentTemplate(self):
+        new_content = f"The section titled {self.section_title} is from the page titled {self.page_title} and has this content:\n{self.content}"
+        self.content = new_content
+        return self
+
+    # Given a section, return the id of the parent. If no, parent returns 0
+    # 0 is equivalent to the top of the page
+    def returnDirectParentId(self):
+        parent_tree = eval(self.parent_tree)
+        # Prepare direct_parent variable
+        if len(parent_tree) > 1:
+            direct_parent = parent_tree[len(parent_tree) - 1]
+        # If the curr_parent_tree has a len of 0, this means that no parents
+        if len(parent_tree) == 0:
+            no_parent = True
+            direct_parent = 0
+        else:
+            no_parent = False
+            direct_parent = parent_tree[len(parent_tree) - 1]
+        return direct_parent
+
+    def encodeToChromaDBNoContent(self):
+        metadata = {}
+        metadata.update({"section_id": int(self.id)})
+        metadata.update({"section_name_id": str(self.name_id)})
+        metadata.update({"section_title": str(self.section_title)})
+        metadata.update({"page_title": str(self.page_title)})
+        metadata.update({"section_level": int(self.level)})
+        metadata.update({"previous_id": int(self.previous_id)})
+        # Lists like parent_tree need to be converted to str
+        metadata.update({"parent_tree": str(self.parent_tree)})
+        metadata.update({"token_estimate": float(self.token_count)})
+        metadata.update({"origin_uuid": str(self.origin_uuid)})
+        metadata.update({"md_hash": str(self.md_hash)})
+        metadata.update({"url": str(self.url)})
+        return metadata
+
+    def createChunkTitle(self):
+        if self.page_title == self.section_title:
+            doc_title = self.page_title
+        else:
+            doc_title = f"{self.page_title} - {self.section_title}"
+        return doc_title
+
+    def return_id(self):
+        return self.id
+
+
+def DictionarytoSection(metadata: dict) -> Section:
+    if "section_id" in metadata and metadata["section_id"] != '':
+        section_id = int(metadata["section_id"])
+    else:
+        section_id = ""
+    if "section_name_id" in metadata:
+        section_name_id = str(metadata["section_name_id"])
+    else:
+        section_name_id = ""
+    if "section_title" in metadata:
+        section_title = str(metadata["section_title"])
+    else:
+        section_title = ""
+    if "page_title" in metadata:
+        page_title = str(metadata["page_title"])
+    else:
+        page_title = ""
+    if "section_level" in metadata and metadata["section_level"] != '':
+        section_level = int(metadata["section_level"])
+    else:
+        section_level = ""
+    if "previous_id" in metadata and metadata["previous_id"] != '':
+        previous_id = int(metadata["previous_id"])
+    else:
+        previous_id = ""
+    if "parent_tree" in metadata:
+        parent_tree = metadata["parent_tree"]
+    else:
+        parent_tree = []
+    if "token_estimate" in metadata and metadata["token_estimate"] != '':
+        token_estimate = int(metadata["token_estimate"])
+    else:
+        token_estimate = ""
+    if "content" in metadata:
+        content = str(metadata["content"])
+    else:
+        content = ""
+    if "URL" in metadata:
+        url = str(metadata["URL"])
+    elif "url" in metadata:
+        url = str(metadata["url"])
+    else:
+        url = ""
+    if "origin_uuid" in metadata:
+        origin_uuid = str(metadata["origin_uuid"])
+    else:
+        origin_uuid = ""
+    if "md_hash" in metadata:
+        md_hash = str(metadata["md_hash"])
+    else:
+        md_hash = ""
+    if "UUID" in metadata:
+        UUID = str(metadata["UUID"])
+    else:
+        UUID = ""
+    section = Section(
+        id=section_id,
+        name_id=section_name_id,
+        page_title=page_title,
+        section_title=section_title,
+        level=section_level,
+        previous_id=previous_id,
+        parent_tree=parent_tree,
+        token_count=token_estimate,
+        content=content,
+        url=url,
+        origin_uuid=origin_uuid,
+        md_hash=md_hash,
+        uuid=UUID,
+    )
+    return section
+
+
+class Page:
+    def __init__(
+        self,
+        title: str,
+        URL: str,
+        section_count: int,
+        metadata: typing.Optional[dict] = None,
+    ):
+        self.title = title
+        self.URL = URL
+        self.section_count = section_count
+        self.metadata = metadata
+
+    def __str__(self):
+        return f"This is a page with the following properties:\n\
+Title: {self.title}\n\
+URL: {self.URL}\n\
+Section Count: {self.section_count}\n\
+Metadata: {self.metadata}\n"
+
+
+# This function converts a Markdown string to plain text.
+def markdown_to_text(markdown_string):
+    # Remove <!-- --> lines in Markdown
+    markdown_string = re.sub(r"<\!--(.*?)-->", "", markdown_string)
+    # md -> html -> text since BeautifulSoup can extract text cleanly
+    html = markdown.markdown(markdown_string)
+    # Extract text
+    soup = bs4.BeautifulSoup(html, "html.parser")
+    text = "".join(soup.findAll(string=True))
+    # Remove [][] in Markdown
+    text = re.sub(r"\[(.*?)\]\[(.*?)\]", "\\1", text)
+    # Remove {: } in Markdown
+    text = re.sub(r"\{:(.*?)\}", "", text)
+    # Remove {. } in Markdown
+    text = re.sub(r"\{.(.*?)\}", "", text)
+    # Remove a single line `sh` in Markdown
+    text = re.sub(r"(?m)^sh$", "", text)
+    # Remove a single line ````sh` in Markdown
+    # text = re.sub(r'(?m)^```sh$', '', text)
+    # Remove code snippet tags
+    # text = re.sub(r"<pre>(.*?)</pre>", "\\1", text)
+    # text = re.sub(r"<code>(.*?)</code>", "\\1", text)
+    # Remove variable tags
+    text = re.sub(r"(?m)<var>(.*?)</var>", "\\1", text)
+    text = re.sub(
+        r"(^|)(Important|Note|Caution|Tip|Warning|Important|Key Point|Key Term):\s?",
+        "",
+        text,
+    )
+    text = re.sub(
+        r"(^|)(Objective|Success|Beta|Preview|Deprecated):\s?",
+        "",
+        text,
+    )
+    text = re.sub(r"(Project|Book):(.*)\n", "", text)
+    text = text.strip() + "\n"
+    return text
+
+
+# This function makes a plain text chunk. It can transform markdown headers
+# into plain text for files that can fit in a single chunk or multiple chunks
+# Takes an input of a markdown_text and header_id_spaces on how to treat spaces
+# from Header names to header ids. For example if header_id_spaces="-",
+# ## My section header will get a header id of my-section-header
+# This will try to give more granural context on urls by appending #my-section-header
+# to URL links
+def make_markdown_chunk(markdown_text, header_id_spaces):
+    section_markdown = ""
+    remaining_markdown = ""
+    section_title = ""
+    section_id = ""
+    section_level = ""
+    first_header = True
+    section_done = False
+    # Regular expression to read a header level, title, and an optional anchor id
+    regex_headers = r"^(\#*)\s+(.[^\{]*)(.*?)$"
+    # Regular expression to read an anchor id, format can be {#header-id} or
+    # {:#header-id}
+    regex_anchors = r"(?:\{\#|\{:\#)(.*)\}"
+    # Regular expression to read a special RFC title case (with jinja variables)
+    regex_rfc_title = r"^\{\{\s+(.*)\.(.*)\s+\}\}$"
+    # Regular expression to find parantheses as those are not valid in headers
+    regex_section_name = r"(.[^\(]*)"
+    regex_headers_compiled = re.compile(regex_headers)
+    regex_anchors_compiled = re.compile(regex_anchors)
+    regex_section_name_compiled = re.compile(regex_section_name)
+    regex_rfc_compiled = re.compile(regex_rfc_title)
+    for line in markdown_text.split("\n"):
+        if line.startswith("#") and first_header == True:
+            # Looks for a header in the format of ## Header name {#header-id} or
+            # Just a ## Header name
+            # Level 1 doesn't require header ids as these are page anchors
+            first_header = False
+            match = regex_headers_compiled.search(line)
+            if match:
+                if match[1]:
+                    section_level = len(match[1])
+                if match[2]:
+                    section_title = match[2]
+                    section_id = re.sub(
+                        " ", header_id_spaces, section_title.lower().strip()
+                    )
+                    section_id = clean_section_id(section_id)
+                    if regex_section_name_compiled.search(section_id):
+                        section_id = regex_section_name_compiled.search(section_id)[1]
+                if match[3]:
+                    match_id = regex_anchors_compiled.search(match[3])
+                    if match_id:
+                        section_id = clean_section_id(match_id[1])
+                    # Checks for the special RFC case to assign a title of RFC
+                    # These headers don't have ids as there is no full header title
+                    match_rfc = regex_rfc_compiled.search(match[2] + match[3])
+                    if match_rfc:
+                        section_title = "RFC (request for comment)"
+                # Removing this line as this can be added (if needed) once retrieved from the db
+                # section_markdown += section_intro.format(section_title=section_title)
+        elif len(section_markdown) > 100:
+            # Temp solution: Do not create a chunk if the size is less than 100 chars.
+            if line.startswith("#") and first_header == False:
+                section_done = True
+                remaining_markdown += line + "\n"
+            elif not (line.startswith("#")) and section_done:
+                remaining_markdown += line + "\n"
+            else:
+                section_markdown += line + "\n"
+        else:
+            section_markdown += line + "\n"
+    section_level = level_to_int(section_level)
+    return (
+        section_id,
+        section_level,
+        section_title,
+        section_markdown,
+        remaining_markdown,
+    )
+
+
+# Returns an int of a level to avoid blank string
+def level_to_int(level) -> int:
+    if level == "":
+        level = 0
+    else:
+        level = int(level)
+    return level
+
+
+# In a regular child level the length of the parent_tree should be greater
+# than the current level, so for example a header 2 (level 2) may have a
+# parent_tree of [0, 1], in this case we want to append the previous previous_id
+# Conditions to be aware of:
+# If headers are out of order, you may end up with an array level with
+# the same value, such as a header 4 that ends with a parent_tree of
+# [0, 1, 25, 25] indicates that you jumped from a header 2, right into a 4
+def build_parent_tree(
+    parent_tree: list[int], level: int, previous_id: int
+) -> list[int]:
+    if len(parent_tree) != level:
+        first = True
+        while len(parent_tree) != level:
+            if len(parent_tree) >= level:
+                parent_tree.pop()
+            elif len(parent_tree) <= level:
+                parent_tree.append(previous_id)
+    return parent_tree
+
+
+# This function cleans section ids from special characters
+def clean_section_id(section_id: str) -> str:
+    section_id = re.sub("'", "", section_id)
+    section_id = re.sub("`", "", section_id)
+    section_id = re.sub("\.", "", section_id)
+    section_id = re.sub("\,", "", section_id)
+    section_id = re.sub("#", "", section_id)
+    section_id = re.sub("\?", "", section_id)
+    section_id = re.sub("\/", "", section_id)
+    section_id = re.sub("\{", "", section_id)
+    section_id = re.sub("\}", "", section_id)
+    section_id = re.sub(":", "", section_id)
+    return section_id
+
+
+# This function replaces Markdown's includes sections with content.
+def process_markdown_includes(markdown_text, root):
+    updated_markdown = ""
+    for line in markdown_text.split("\n"):
+        # Replaces Markdown includes with content
+        if line.startswith("<<"):
+            try:
+                include_match = re.search("^<<(.*?)>>", line)
+                include_file = os.path.abspath(root + "/" + include_match[1])
+                with open(include_file, "r", encoding="utf-8") as md_include:
+                    for md_line in md_include:
+                        updated_markdown += md_line + "\n"
+            except:
+                updated_markdown += line + "\n"
+        else:
+            updated_markdown += line + "\n"
+    return updated_markdown
+
+
+# Function to verify that include exists and exports its content if it exists
+def verify_file(file):
+    try:
+        with open(file, "r", encoding="utf-8") as mdfile:
+            output = mdfile.read()
+            mdfile.close()
+            return output
+    except FileNotFoundError:
+        logging.error(f"[FileNotFound] Missing the include file: {file}")
+
+
+# Takes in current section, then returns an array of
+# sections that it split by lines
+def split_sections_by_lines(section: Section):
+    buffer = []
+    for line in section.content.split("\n"):
+        # Special case if line is too long - tends to be comma seperated lists
+        if get_byte_size(line) > 5000:
+            for item in line.split(","):
+                item = item + ","
+                buffer.append(item)
+        else:
+            buffer.append(line)
+    chunks = construct_chunks(buffer)
+    page_sections = []
+    chunk_count = 0
+    for chunk in chunks:
+        # Calculate tokens for new chunk
+        token_count = tokenCount.returnHighestTokens(chunk)
+        # Section id needs to get bumped up
+        new_section = Section(
+            (section.id + chunk_count),
+            section.name_id,
+            section.page_title,
+            section.section_title,
+            section.level,
+            section.previous_id,
+            section.parent_tree.copy(),
+            token_count,
+            markdown_to_text(chunk),
+        )
+        chunk_count += 1
+        page_sections.append(new_section)
+    return page_sections
+    # return page_sections, remaining_content
+
+
+# This function converts Markdown page (#), section (##), and subsection (###)
+# headings into plain English.
+def process_markdown_page(markdown_text, header_id_spaces: str = "_"):
+    page_metadata = {}
+    remaining_content = markdown_text
+    page_title = ""
+    page_sections = []
+    page_url = ""
+    # Processes the frontmatter in a markdown file
+    data = frontmatter.loads(markdown_text)
+    if "title" in data:
+        page_title = data["title"]
+        remaining_content = data.content
+        page_metadata = data.metadata
+    if "URL" in data:
+        page_url = add_scheme_url(url=data["URL"], scheme="https")
+    section_id = 0
+    previous_id = 1
+    parent_level = 0
+    # For each header level the header ID is added, position 0 is header, pos 1 ##, pos 3 ###, etc...
+    parent_tree = [0]
+    while remaining_content != "":
+        name_id, level, title, content, remaining_content = make_markdown_chunk(
+            markdown_text=remaining_content, header_id_spaces=header_id_spaces
+        )
+        # Ensure parent_level is an int
+        parent_level = int(parent_level)
+        # Indicates the first encountered header since parent_level and parent_tree have base values
+        if parent_level == 0 and parent_tree == [0] and "title" not in data:
+            page_title = title
+        # Builds the parent tree based on current section level and the previous_id
+        parent_tree = build_parent_tree(parent_tree, level, previous_id)
+        plain_text_content = markdown_to_text(content)
+        token_count = tokenCount.returnHighestTokens(plain_text_content)
+        # This goes up as sections start at 1
+        section_id += 1
+        # Initializes a Section, content may be replaced if too large
+        # Copy ensures parent_tree doesn't get overwritten
+        section = Section(
+            section_id,
+            name_id,
+            page_title,
+            title,
+            level,
+            previous_id,
+            parent_tree.copy(),
+            token_count,
+            plain_text_content,
+        )
+        # If content is larger than 5KB - split by lines
+        if len(plain_text_content.encode("utf-8")) > 5000:
+            # Return an array of sections that were split and the remain content
+            logging.info("Chunk is too big - splitting by lines")
+            new_sections = split_sections_by_lines(section=section)
+            # Merge the list of sections and bump up the section_id.
+            # Length has to be reduced by 1 since original chunk was already
+            # counted
+            section_id = section_id + (len(new_sections)-1)
+            page_sections += new_sections
+        # Create a Section object for sections under 6k of the doc.
+        else:
+            # If less than 5kb - append to a list of section objects
+            page_sections.append(section)
+        # Prepare previous_id for next section
+        previous_id = int(section_id)
+        parent_level = int(level)
+    sect_count = len(page_sections)
+    page = Page(page_title, page_url, sect_count, page_metadata)
+    # Return an array of Section objects with a single Page object
+    return page_sections, page
+
+
+# This function converts Markdown page (#), section (##), and subsection (###)
+# headings into plain English.
+def process_page_and_section_titles(markdown_text):
+    updated_markdown = ""
+    page_title = ""
+    section_title = ""
+    subsection_title = ""
+    new_line = ""
+    metadata = {}
+    # Processes the frontmatter in a markdown file
+    data = frontmatter.loads(markdown_text)
+    if "title" in data:
+        page_title = data["title"]
+        markdown_text = data.content
+        metadata = data.metadata
+    if "URL" in data:
+        metadata["URL"] = add_scheme_url(url=data["URL"], scheme="https")
+    for line in markdown_text.split("\n"):
+        new_line = ""
+        skip_this_line = False
+        if line.startswith("#"):
+            match = re.search(r"^(\#*)\s+(.*)$", line)
+            heading = ""
+            captured_title = ""
+            if match:
+                heading = match[1]
+                # Remove {: } in devsite Markdown
+                captured_title = re.sub(r"\{:(.*?)\}", "", match[2])
+                # Special case of RFC pages.
+                if re.search(r"^\{\{\s+(.*)\.(.*)\s+\}\}$", captured_title):
+                    heading = ""
+                    page_title = "RFC"
+                    skip_this_line = True
+
+            # Detect Markdown heading levels
+            if heading == "#":
+                page_title = captured_title.strip()
+                metadata["title"] = page_title
+                subsection_title = ""
+                section_title = ""
+            elif heading == "##":
+                section_title = captured_title.strip()
+                subsection_title = ""
+            elif heading == "###":
+                subsection_title = captured_title.strip()
+
+            # Convert Markdown headings into plain English
+            # (but keep `#` for the `process_document_into_sections()`
+            # function to detect these headings for splitting).
+            if page_title:
+                new_line = (
+                    '# The "'
+                    + page_title
+                    + '" page includes the following information:\n\n'
+                )
+
+            if section_title:
+                new_line = (
+                    '# The "'
+                    + page_title
+                    + '" page has the "'
+                    + section_title
+                    + '" section that includes the following information:\n'
+                )
+
+            if subsection_title:
+                new_line = (
+                    '# On the "'
+                    + page_title
+                    + '" page, the "'
+                    + section_title
+                    + '" section has the "'
+                    + subsection_title
+                    + '" subsection that includes the following information:\n'
+                )
+
+        if skip_this_line is False:
+            if new_line:
+                updated_markdown += new_line + "\n"
+            else:
+                updated_markdown += line + "\n"
+    return updated_markdown, metadata
+
+
+# This function divides Markdown content into sections and
+# returns an array containing these sections.
+# But this function requires pre-processed Markdown headings from
+# the `process_page_and_section_titles()` function, which simplifies
+# three levels of Markdown headings (#, ##, and ###) into just a single #.
+def process_document_into_sections(markdown_text):
+    sections = []
+    buffer = []
+    first_section = True
+    for line in markdown_text.split("\n"):
+        if line.startswith("#"):
+            match = re.search(r"^(\#*)\s+(.*)$", line)
+            heading = ""
+            if match:
+                heading = match[1]
+            if heading == "#":
+                if first_section is True:
+                    # Ignore the first detection of `#`.
+                    first_section = False
+                else:
+                    # When a new `#` is detected, store the text in `buffer` into
+                    # an array entry and clear the buffer for the next section.
+                    contents = construct_chunks(buffer)
+                    for content in contents:
+                        sections.append(content)
+                    buffer.clear()
+        buffer.append(line)
+    # Add the last section on the page.
+    contents = construct_chunks(buffer)
+    for content in contents:
+        sections.append(content)
+    return sections
+
+
+# Process an array of Markdwon text into an array of string buffers
+# whose size is smaller than 5KB.
+def construct_chunks(lines):
+    contents = []
+    buffer_size = get_byte_size(lines)
+    if int(buffer_size) > 5000:
+        # If the protocol is larger than 5KB, divide it into two.
+        logging.info(
+            "Found a text chunk greater than 5KB (size: " + str(buffer_size) + ")."
+        )
+        (first_half, second_half) = divide_an_array(lines)
+        first_content = construct_chunks(first_half)
+        second_content = construct_chunks(second_half)
+        contents += first_content
+        contents += second_content
+    else:
+        chunk = convert_array_to_buffer(lines)
+        contents.append(chunk)
+    return contents
+
+
+# Convert an array into a string buffer.
+def convert_array_to_buffer(lines):
+    content = ""
+    for line in lines:
+        content += line + "\n"
+    return content
+
+
+# Get the byte size of lines.
+def get_byte_size(lines):
+    buffer_size = 0
+    for line in lines:
+        buffer_size += len(line.encode("utf-8"))
+    return buffer_size
+
+
+# Divide a large array into two arrays.
+def divide_an_array(lines):
+    half_point = len(lines) // 2
+    first_half = lines[:half_point]
+    second_half = lines[half_point:]
+    return first_half, second_half
diff --git a/examples/gemini/python/docs-agent/docs_agent/storage/__init__.py b/examples/gemini/python/docs-agent/docs_agent/storage/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/storage/chroma.py b/examples/gemini/python/docs-agent/docs_agent/storage/chroma.py
new file mode 100644
index 000000000..de15005bf
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/storage/chroma.py
@@ -0,0 +1,573 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Chroma wrapper"""
+
+from enum import auto, Enum
+import os
+import string
+import shutil
+import typing
+
+from absl import logging
+import chromadb
+from chromadb.utils import embedding_functions
+from chromadb.api.models import Collection
+from chromadb.api.types import QueryResult
+
+from docs_agent.models.palm import PaLM
+from docs_agent.preprocess.splitters.markdown_splitter import Section as Section
+from docs_agent.postprocess.docs_retriever import FullPage as FullPage
+from docs_agent.utilities.helpers import resolve_path, parallel_backup_dir
+
+
+class Error(Exception):
+    """Base error class for chroma"""
+
+
+class ChromaEmbeddingModelNotSupportedError(Error, RuntimeError):
+    """Raised if the embedding model specified by a collection is not supported."""
+
+
+class Chroma:
+    """Chroma wrapper"""
+
+    def __init__(self, chroma_dir) -> None:
+        self.client = chromadb.PersistentClient(path=chroma_dir)
+
+    def list_collections(self):
+        return self.client.list_collections()
+
+    def get_collection(self, name, embedding_function=None, embedding_model=None):
+        if embedding_function is not None:
+            return ChromaCollection(
+                self.client.get_collection(
+                    name=name, embedding_function=embedding_function
+                ),
+                embedding_function,
+            )
+        # Read embedding meta information from the collection
+        collection = self.client.get_collection(name=name)
+        if embedding_model is None and collection.metadata:
+            embedding_model = collection.metadata.get("embedding_model", None)
+            if embedding_model is None:
+                # If embedding_model is not found in the metadata,
+                # use `models/embedding-gecko-001` by default.
+                logging.info(
+                    "Embedding model is not specified in the metadata of "
+                    "the collection %s. Using the default PaLM embedding model.",
+                    name,
+                )
+                embedding_model = "models/embedding-gecko-001"
+        if embedding_model == "local/all-mpnet-base-v2":
+            base_dir = os.path.dirname(os.path.abspath(__file__))
+            local_model_dir = os.path.join(base_dir, "models/all-mpnet-base-v2")
+            embedding_function = (
+                embedding_functions.SentenceTransformerEmbeddingFunction(
+                    model_name=local_model_dir
+                )
+            )
+        else:
+            print("Embedding model: " + str(embedding_model))
+            try:
+                palm = PaLM(embed_model=embedding_model, find_models=False)
+                # We cannot redefine embedding_function with def and
+                # have to assign a lambda to it
+                # pylint: disable-next=unnecessary-lambda-assignment
+                embedding_function = lambda texts: [palm.embed(text) for text in texts]
+            except:
+                raise ChromaEmbeddingModelNotSupportedError(
+                    f"Embedding model {embedding_model} specified by collection {name} "
+                    "is not supported."
+                )
+
+        return ChromaCollection(
+            self.client.get_collection(
+                name=name, embedding_function=embedding_function
+            ),
+            embedding_function,
+        )
+
+
+class Format(Enum):
+    CONTEXT = auto()
+    URL = auto()
+    CLICKABLE_URL = auto()
+
+
+class ChromaQueryResultItem:
+    """Chroma query result item wrapper
+
+    Chroma query result has the following type:
+    ```
+    class QueryResult(TypedDict):
+        ids: List[IDs]
+        embeddings: Optional[List[List[Embedding]]]
+        documents: Optional[List[List[Document]]]
+        metadatas: Optional[List[List[Metadata]]]
+        distances: Optional[List[List[float]]]
+    ```
+    Since the Chroma's query support multiple texts as input, the outer list
+    corresponds to each of the input text. The inner list corresponds to
+    the nearest k documents for a specific input text. Since we always only
+    provide one input text to the query call, our access pattern to the
+    query result will look like `query_result["documents"][0][i]`, where index
+    0 stands for the result for the first (and the only) input text, and index
+    i stands for the i-th nearest document.
+    """
+
+    templates_with_ref_index = {
+        Format.CONTEXT: "$document **[${ref_index}]**",
+        Format.URL: "**[${ref_index}]** $url ($distance)",
+        Format.CLICKABLE_URL: '**[${ref_index}]** <a href="$url">$url</a> ($distance)',
+    }
+
+    templates_without_ref_index = {
+        Format.CONTEXT: "$document",
+        Format.URL: "$url",
+        Format.CLICKABLE_URL: '<a href="$url">$url</a>',
+    }
+
+    def __init__(self, result: QueryResult, index: int) -> None:
+        self.document = result["documents"][0][index]
+        self.metadata = result["metadatas"][0][index]
+        self.distance = result["distances"][0][index]
+
+    def format(self, format_type: Format, ref_index: typing.Optional[int] = None):
+        d = {
+            "document": self.document.strip(),
+            "ref_index": ref_index,
+            "url": self.metadata.get("url", None),
+            "distance": self.distance,
+        }
+        if ref_index is None:
+            template = self.templates_without_ref_index[format_type]
+        else:
+            template = self.templates_with_ref_index[format_type]
+
+        return string.Template(template).substitute(d)
+
+
+class ChromaQueryResult:
+    """Chroma query result wrapper"""
+
+    def __init__(self, result: QueryResult) -> None:
+        self.result = result
+
+    def __len__(self):
+        return len(self.result["documents"][0])
+
+    def fetch(self, distance_threshold=float("inf")):
+        for i in range(len(self)):
+            item = ChromaQueryResultItem(self.result, i)
+            if item.distance < distance_threshold:
+                yield item
+
+    def fetch_formatted(self, format_type: Format, distance_threshold=float("inf")):
+        return "\n\n".join(
+            item.format(format_type, i + 1)
+            for i, item in enumerate(self.fetch(distance_threshold=distance_threshold))
+        )
+
+    def fetch_nearest(self):
+        return ChromaQueryResultItem(self.result, 0)
+
+    def fetch_nearest_formatted(self, format_type: Format):
+        return self.fetch_nearest().format(format_type)
+
+    def fetch_at(self, index):
+        return ChromaQueryResultItem(self.result, index)
+
+    def fetch_at_formatted(self, index, format_type: Format):
+        return self.fetch_at(index).format(format_type)
+
+
+class ChromaCollection:
+    """Chroma collection wrapper"""
+
+    def __init__(self, collection, embedding_function) -> None:
+        self.collection = collection
+        self.embedding_function = embedding_function
+
+    def query(self, text: str, top_k: int = 1):
+        return ChromaQueryResult(
+            self.collection.query(query_texts=[text], n_results=top_k)
+        )
+
+    def embed(self, text: str):
+        return self.embedding_function(text)
+
+
+# New classes to work with Chromadb
+class SectionDB(Enum):
+    SECTION_ID = auto()
+    SECTION_NAME_ID = auto()
+    SECTION_LEVEL = auto()
+    SECTION_TITLE = auto()
+    PREVIOUS_ID = auto()
+    CONTENT = auto()
+    DISTANCE = auto()
+    REF_INDEX = auto()
+    URL = auto()
+    TOKEN_ESTIMATE = auto()
+    PARENT_TREE = auto()
+
+    def decodeSection(self):
+        if self.SECTION_ID:
+            section_id = self.SECTION_ID.value
+        else:
+            section_id = ""
+        if self.SECTION_NAME_ID:
+            section_name_id = self.SECTION_NAME_ID.value
+        else:
+            section_name_id = ""
+        if self.SECTION_TITLE:
+            section_title = self.SECTION_TITLE.value
+        else:
+            section_title = ""
+        if self.SECTION_LEVEL:
+            section_level = self.SECTION_LEVEL.value
+        else:
+            section_level = ""
+        if self.PREVIOUS_ID:
+            previous_id = self.PREVIOUS_ID.value
+        else:
+            previous_id = ""
+        if self.PARENT_TREE:
+            parent_tree = self.PARENT_TREE.value
+            parent_tree_str = str(parent_tree).strip("[]").split(",")
+            parent_tree = []
+            for num in parent_tree_str:
+                parent_tree.append(int(num))
+        else:
+            parent_tree = ""
+        if self.TOKEN_ESTIMATE:
+            token_estimate = self.TOKEN_ESTIMATE.value
+        else:
+            token_estimate = ""
+        if self.CONTENT:
+            content = self.CONTENT
+        else:
+            content = ""
+        section = Section(
+            id=int(section_id),
+            name_id=str(section_name_id),
+            section_title=str(section_title),
+            page_title="",
+            level=int(section_level),
+            previous_id=int(previous_id),
+            parent_tree=parent_tree,
+            token_count=float(token_estimate),
+            content=str(content),
+        )
+        return section
+
+
+class ChromaDBGet:
+    """Chroma query result item wrapper
+
+    Chroma query result has the following type:
+    ```
+    """
+
+    def __init__(self, result) -> None:
+        self.document = result["documents"]
+        self.metadata = result["metadatas"]
+        self.id = result["ids"]
+
+    # Measure how many responses were returned
+    def __len__(self):
+        return len(self.id)
+
+    # def get_section_id(self, index: int = 0):
+    #     return self.id[index]
+
+
+class ChromaSectionDBItem:
+    """Chroma query result item wrapper for SectionDB objects
+
+    Chroma query result has the following type:
+    ```
+    """
+
+    templates_base = {
+        SectionDB.SECTION_ID: "$section_id",
+        SectionDB.SECTION_NAME_ID: "$name_id",
+        SectionDB.SECTION_LEVEL: "$section_level",
+        SectionDB.SECTION_TITLE: "$section_title",
+        SectionDB.PREVIOUS_ID: "$previous_id",
+        SectionDB.TOKEN_ESTIMATE: "$token_estimate",
+        SectionDB.CONTENT: "$content",
+        SectionDB.DISTANCE: "$distance",
+        SectionDB.URL: "$url",
+        SectionDB.PARENT_TREE: "$parent_tree",
+        SectionDB.REF_INDEX: "${ref_index}",
+    }
+
+    def __init__(self, result: QueryResult, index: int) -> None:
+        self.document = result["documents"][0][index]
+        self.metadata = result["metadatas"][0][index]
+        self.distance = result["distances"][0][index]
+        self.id = result["ids"][0][index]
+
+    def __str__(self):
+        return f"This is a section with the following properties:\n\
+Section ID: {SectionDB.SECTION_ID}\n\
+Section Title: {SectionDB.SECTION_TITLE}\n\
+Section URL: {SectionDB.URL}\n"
+
+    def format(self, format_type: SectionDB, ref_index: typing.Optional[int] = None):
+        d = {
+            "content": self.document.strip(),
+            "ref_index": ref_index,
+            "section_id": self.metadata.get("section_id", None),
+            "section_title": self.metadata.get("section_title", None),
+            "section_level": self.metadata.get("section_level", None),
+            "section_name_id": self.metadata.get("section_name_id", None),
+            "tree": self.metadata.get("tree", None),
+            "previous_id": self.metadata.get("previous_id", None),
+            "token_estimate": self.metadata.get("token_estimate", None),
+            "parent_tree": self.metadata.get("tree", None),
+            "url": self.metadata.get("url", None),
+            "distance": self.distance,
+        }
+
+        template = self.templates_base[format_type]
+        result = string.Template(template).substitute(d)
+        return result
+
+
+class ChromaEnhanced:
+    """Chroma wrapper"""
+
+    def __init__(self, chroma_dir) -> None:
+        self.client = chromadb.PersistentClient(path=chroma_dir)
+
+    def list_collections(self):
+        return self.client.list_collections()
+
+    # Returns output_dir if backup was successful, None if it failed
+    # Output dir can only be a child to chroma_dir
+    def backup_chroma(self, chroma_dir: str, output_dir: typing.Optional[str] = None):
+        try:
+            chroma_dir = resolve_path(chroma_dir)
+            if output_dir == None:
+                output_dir = parallel_backup_dir(chroma_dir)
+            shutil.copytree(chroma_dir, output_dir, dirs_exist_ok=True)
+            logging.info(f"Backed up from: {chroma_dir} to {output_dir}")
+            return output_dir
+        except:
+            return None
+
+    # def getSameOriginUUID(self):
+    #     return self.client.get()
+
+    def get_collection(self, name, embedding_function=None, embedding_model=None):
+        if embedding_function is not None:
+            return ChromaCollectionEnhanced(
+                self.client.get_collection(
+                    name=name, embedding_function=embedding_function
+                ),
+                embedding_function,
+            )
+        # Read embedding meta information from the collection
+        collection = self.client.get_collection(name=name)
+        if embedding_model is None and collection.metadata:
+            embedding_model = collection.metadata.get("embedding_model", None)
+            if embedding_model is None:
+                # If embedding_model is not found in the metadata,
+                # use `models/embedding-001` by default.
+                logging.info(
+                    "Embedding model is not specified in the metadata of "
+                    "the collection %s. Using the default embedding model: models/embedding-001",
+                    name,
+                )
+                embedding_model = "models/embedding-001"
+        if embedding_model == "local/all-mpnet-base-v2":
+            base_dir = os.path.dirname(os.path.abspath(__file__))
+            local_model_dir = os.path.join(base_dir, "models/all-mpnet-base-v2")
+            embedding_function = (
+                embedding_functions.SentenceTransformerEmbeddingFunction(
+                    model_name=local_model_dir
+                )
+            )
+        else:
+            print("Embedding model: " + str(embedding_model))
+            try:
+                palm = PaLM(embed_model=embedding_model, find_models=False)
+                # We cannot redefine embedding_function with def and
+                # have to assign a lambda to it
+                # pylint: disable-next=unnecessary-lambda-assignment
+                embedding_function = lambda texts: [palm.embed(text) for text in texts]
+            except:
+                raise ChromaEmbeddingModelNotSupportedError(
+                    f"Embedding model {embedding_model} specified by collection {name} "
+                    "is not supported."
+                )
+
+        return ChromaCollectionEnhanced(
+            self.client.get_collection(
+                name=name, embedding_function=embedding_function
+            ),
+            embedding_function,
+        )
+
+
+class ChromaCollectionEnhanced:
+    """Chroma collection wrapper"""
+
+    def __init__(self, collection, embedding_function) -> None:
+        self.collection = collection
+        self.embedding_function = embedding_function
+
+    def query(self, text: str, top_k: int = 1):
+        dict = {}
+        # dict.update({"token_estimate": {"$gt": 100}})
+        return ChromaQueryResultEnhanced(
+            self.collection.query(query_texts=[text], n_results=top_k, where=dict)
+        )
+
+        # same_page = self.collection.get(include=["documents","metadatas"],
+        #                             where={"origin_uuid": {"$eq": origin_uuid[i]}},)
+
+    # # Return all entries that match an origin_uuid
+    # def getPageOriginUUID(self, origin_uuid):
+    #     return ChromaDBGet(
+    #         self.collection.get(
+    #             include=["metadatas", "documents"],
+    #             where={"origin_uuid": {"$eq": origin_uuid}},
+    #         )
+    #     )
+
+    # Return a FullPage (list of Section) that match an origin_uuid
+    def getPageOriginUUIDList(self, origin_uuid):
+        get_obj = ChromaDBGet(
+            self.collection.get(
+                include=["metadatas", "documents"],
+                where={"origin_uuid": {"$eq": origin_uuid}},
+            )
+        )
+        page = []
+        # added_id = []
+        for i in range(len(get_obj.id)):
+            # Makes sure that each ID is only added once
+            # if get_obj.id not in added_id:
+            section = Section(
+                id=get_obj.metadata[i].get("section_id", None),
+                name_id=get_obj.metadata[i].get("name_id", None),
+                page_title=get_obj.metadata[i].get("page_title", None),
+                section_title=get_obj.metadata[i].get("section_title", None),
+                level=get_obj.metadata[i].get("level", None),
+                previous_id=get_obj.metadata[i].get("previous_id", None),
+                parent_tree=get_obj.metadata[i].get("parent_tree", None),
+                token_count=get_obj.metadata[i].get("token_estimate", None),
+                url=get_obj.metadata[i].get("url", None),
+                uuid=get_obj.id[i],
+                content=get_obj.document[i],
+            )
+            page.append(section)
+        full_page = FullPage(page)
+        return full_page
+
+    def getPageSection(self, section_title):
+        return self.collection.get(
+            include=["metadatas"], where={"section_title": {"$eq": section_title}}
+        )
+
+    def embed(self, text: str):
+        return self.embedding_function(text)
+
+
+class ChromaQueryResultEnhanced:
+    """Chroma query result wrapper"""
+
+    def __init__(self, result: QueryResult) -> None:
+        self.result = result
+
+    def __len__(self):
+        return len(self.result["documents"][0])
+
+    # Get without considering distance
+    def clean_get(self):
+        for i in range(len(self)):
+            item = ChromaSectionDBItem(self.result, i)
+            yield item
+
+    def fetch(self, distance_threshold=float("inf")):
+        for i in range(len(self)):
+            item = ChromaSectionDBItem(self.result, i)
+            if item.distance < distance_threshold:
+                yield item
+
+    def fetch_formatted(self, format_type: SectionDB, distance_threshold=float("inf")):
+        return "\n\n".join(
+            item.format(format_type, i + 1)
+            for i, item in enumerate(self.fetch(distance_threshold=distance_threshold))
+        )
+
+    def fetch_section_list_format(
+        self, format_type: SectionDB, distance_threshold=float("inf")
+    ):
+        results = enumerate(self.fetch(distance_threshold=distance_threshold))
+        contents = []
+        for i, item in results:
+            content_item = item.format(format_type, i + 1)
+            contents.append(content_item)
+        return contents
+
+    def returnSectionObj(self, format_type: SectionDB, distance_threshold=float("inf")):
+        results = self.fetch(distance_threshold=distance_threshold)
+        contents = []
+        # Don't need ids here as we will retrieve those with a get
+        i = 0
+        for item in results:
+            # id = item.id
+            content_item = item.format(format_type, i)
+            # section = item.decodeSection()
+            contents.append(content_item)
+            # ids.append(id)
+            i += 1
+        return contents
+
+    # This function returns a list of ChromaSectionDBItem that match results up to
+    # limit specific in query. You can then access from each list item with
+    # .document, .id, etc...
+    def returnDBObjList(self, distance_threshold=float("inf")):
+        results = self.fetch(distance_threshold=distance_threshold)
+        contents = []
+        for item in results:
+            contents.append(item)
+        return contents
+
+    # This function returns a list of ChromaSectionDBItem that match results up to
+    # limit specific in query. You can then access from each list item with
+    # .document, .id, etc...
+    def returnDBObjListGet(self):
+        results = self.clean_get()
+        contents = []
+        # for item in results:
+        #     content_item = item
+        #     contents.append(content_item)
+        return results
+
+    def fetch_nearest(self):
+        return ChromaSectionDBItem(self.result, 0)
+
+    def fetch_nearest_formatted(self, format_type: SectionDB):
+        return self.fetch_nearest().format(format_type)
+
+    # def return_response(self):
+    #     return ChromaSectionDBItem.returnSection(self)
diff --git a/examples/gemini/python/docs-agent/docs_agent/storage/google_semantic_retriever.py b/examples/gemini/python/docs-agent/docs_agent/storage/google_semantic_retriever.py
new file mode 100644
index 000000000..1a84bc2fd
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/storage/google_semantic_retriever.py
@@ -0,0 +1,385 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Semantic Retrievel module for using the Semantic Retrieval API with AQA"""
+
+import google.ai.generativelanguage as glm
+from absl import logging
+import typing
+
+
+class SemanticRetriever:
+    def __init__(self):
+        # Initialize variables for the Semantic Retrieval API
+        self.generative_service_client = glm.GenerativeServiceClient()
+        self.retriever_service_client = glm.RetrieverServiceClient()
+        self.permission_service_client = glm.PermissionServiceClient()
+
+    def get_corpus(self, corpus_name: str):
+        get_corpus_request = glm.GetCorpusRequest(name=corpus_name)
+        response = self.retriever_service_client.get_corpus(get_corpus_request)
+        return response
+
+    # def get_document(self, document_name: str, metadata):
+    #     get_document_request = glm.GetDocumentRequest(name=corpus_name)
+    #     try:
+    #         response = self.retriever_service_client.get_document(get_document_request)
+    #         return response
+    #     except:
+    #         logging.error(
+    #             f"Document {document_name} does not exist or you do not have permissions"
+    #         )
+
+    # def get_chunk(self, chunk_name: str, metadata):
+    #     get_chunk_request = glm.GetChunkRequest(name=corpus_name)
+    #     try:
+    #         response = self.retriever_service_client.get_chunk(get_chunk_request)
+    #         return response
+    #     except:
+    #         logging.error(
+    #             f"Chunk {chunk_name} does not exist or you do not have permissions"
+    #         )
+
+    def list_existing_corpora(self, page_token: str = ""):
+        corpora_list = glm.ListCorporaRequest(page_size=20, page_token=page_token)
+        response = self.retriever_service_client.list_corpora(corpora_list)
+        return response
+
+    def delete_a_corpus(self, corpus_name: str, force: bool = True):
+        try:
+            delete = glm.DeleteCorpusRequest(name=corpus_name, force=force)
+            delete_corpus_response = self.retriever_service_client.delete_corpus(delete)
+            logging.info(f"Successfully deleted corpus: {corpus_name}")
+        except:
+            logging.info(f"Failed to delete the corpus: {corpus_name}")
+
+    def create_a_new_corpus(self, corpus_display: str, corpus_name: str):
+        try:
+            corpus = glm.Corpus(display_name=corpus_display, name=corpus_name)
+            create_corpus_request = glm.CreateCorpusRequest(corpus=corpus)
+            corpus_response = self.retriever_service_client.create_corpus(
+                create_corpus_request
+            )
+            logging.info(f"Created a new corpus {corpus_name}.\n{corpus_response}")
+        except:
+            logging.error(f"Failed to create a new corpus: {corpus_name}")
+
+    def does_this_corpus_exist(self, corpus_name: str):
+        try:
+            corpus_request = glm.GetCorpusRequest(name=corpus_name)
+            corpus_response = self.retriever_service_client.get_corpus(corpus_request)
+            logging.info(f"{corpus_name} exists.\n{corpus_response}")
+            return True
+        except:
+            return False
+
+    def create_a_doc(
+        self,
+        corpus_name: str,
+        page_title: str,
+        page_url: typing.Optional[str] = None,
+        uuid: typing.Optional[str] = None,
+        metadata: typing.Optional[dict] = None,
+    ):
+        document_resource_name = ""
+        try:
+            # Create a new document with a custom display name.
+            example_document = glm.Document(display_name=page_title)
+            # Add metadata.
+            document_metadata = []
+            if metadata is not None:
+                for key_dict, value_dict in metadata.items():
+                    if isinstance(value_dict, int) or isinstance(value_dict, float):
+                        document_metadata.append(
+                            glm.CustomMetadata(key=key_dict, numeric_value=value_dict)
+                        )
+                    elif isinstance(value_dict, str):
+                        document_metadata.append(
+                            glm.CustomMetadata(key=key_dict, string_value=value_dict)
+                        )
+                    else:
+                        document_metadata.append(
+                            glm.CustomMetadata(
+                                key=key_dict, string_value=str(value_dict)
+                            )
+                        )
+                example_document.custom_metadata.extend(document_metadata)
+            else:
+                if uuid is not None and uuid != "":
+                    document_metadata = [
+                        glm.CustomMetadata(key="uuid", string_value=str(uuid))
+                    ]
+                    example_document.custom_metadata.extend(document_metadata)
+            # Make the request
+            create_document_request = glm.CreateDocumentRequest(
+                parent=corpus_name, document=example_document
+            )
+            create_document_response = self.retriever_service_client.create_document(
+                create_document_request
+            )
+            # Set the `document_resource_name` for subsequent sections.
+            document_resource_name = create_document_response.name
+        except:
+            logging.error(f"Cannot create a new doucment: {page_title}")
+            exit(1)
+        return document_resource_name
+
+    def retrieve_a_doc(self, document_resource_name: str):
+        response = []
+        try:
+            get_document_request = glm.GetDocumentRequest(name=document_resource_name)
+            # Make the request
+            response = self.retriever_service_client.get_document(get_document_request)
+        except:
+            logging.error(f"Cannot retrieve a doucment: {document_resource_name}")
+        return response
+
+    def create_a_chunk(
+        self, doc_name, text, metadata, page_url: typing.Optional[str] = None
+    ):
+        response = ""
+        document_metadata = []
+        if metadata is not None:
+            for key_dict, value_dict in metadata.items():
+                if isinstance(value_dict, int) or isinstance(value_dict, float):
+                    document_metadata.append(
+                        glm.CustomMetadata(key=key_dict, numeric_value=value_dict)
+                    )
+                elif isinstance(value_dict, str):
+                    document_metadata.append(
+                        glm.CustomMetadata(key=key_dict, string_value=value_dict)
+                    )
+                else:
+                    document_metadata.append(
+                        glm.CustomMetadata(key=key_dict, string_value=str(value_dict))
+                    )
+        else:
+            if page_url is not None:
+                document_metadata = [
+                    glm.CustomMetadata(key="url", string_value=page_url)
+                ]
+        try:
+            chunk = glm.Chunk(
+                data={"string_value": text}, custom_metadata=document_metadata
+            )
+            create_chunk_requests = []
+            create_chunk_requests.append(
+                glm.CreateChunkRequest(parent=doc_name, chunk=chunk)
+            )
+            # Make the request
+            request = glm.BatchCreateChunksRequest(
+                parent=doc_name, requests=create_chunk_requests
+            )
+            response = self.retriever_service_client.batch_create_chunks(request)
+            # Print the response
+            logging.info("Created a new text chunk in semantic retriever.\n")
+            # print(response)
+        except:
+            logging.error(f"Failed to create a text chunk for:\n\n{text}")
+            # Failed possibly because the size of the text is too large.
+            # Quick fix: Split the text into 2 chunks
+            lines = text.splitlines()
+            text_01 = ""
+            text_02 = ""
+            text_size = len(lines)
+            half_size = int(text_size / 2)
+            i = 0
+            for line in lines:
+                if i < half_size:
+                    text_01 += line + "\n"
+                else:
+                    text_02 += line + "\n"
+                i += 1
+            self.create_a_chunk(
+                doc_name=doc_name, text=text_01, metadata=document_metadata
+            )
+            self.create_a_chunk(
+                doc_name=doc_name, text=text_02, metadata=document_metadata
+            )
+        return response
+
+    def delete_a_chunk(self, chunk_name: str):
+        response = []
+        try:
+            request = glm.DeleteChunkRequest(name=chunk_name)
+            response = self.retriever_service_client.delete_chunk(request)
+        except:
+            logging.error(f"Cannot delete a chunk: {chunk_name}")
+        return response
+
+    def create_a_doc_chunk(
+        self,
+        corpus_name: str,
+        page_title: str,
+        text,
+        page_url: typing.Optional[str] = None,
+        metadata: typing.Optional[dict] = None,
+    ):
+        try:
+            doc_name = self.create_a_doc(
+                corpus_name=corpus_name,
+                page_title=page_title,
+                page_url=page_url,
+                metadata=metadata,
+            )
+            try:
+                chunk = self.create_a_chunk(
+                    doc_name=doc_name, text=text, metadata=metadata, page_url=page_url
+                )
+                return chunk
+            except:
+                logging.error("Error in creaing a doc chunk: " + page_title)
+                return None
+        except:
+            logging.error("Error in creaing a doc chunk: " + page_title)
+            return None
+
+    def get_all_docs(self, corpus_name: str, print_output: bool = False):
+        all_docs = []
+        try:
+            request = glm.ListDocumentsRequest(parent=corpus_name, page_size=20)
+            response = self.retriever_service_client.list_documents(request)
+            index = 0
+            for docs in response.documents:
+                if print_output:
+                    index += 1
+                    print(f"\nDocument # {index}")
+                    print(f"Name: {docs.name}")
+                    print(f"Display name: {docs.display_name}")
+                    metadata = docs.custom_metadata
+                    for item in metadata:
+                        if item.key == "uuid":
+                            print(f"uuid: {item.string_value}")
+                        elif item.key == "md_hash":
+                            print(f"md_hash: {item.string_value}")
+                all_docs.append(docs)
+            while (
+                hasattr(response, "next_page_token") and response.next_page_token != ""
+            ):
+                request = glm.ListDocumentsRequest(
+                    parent=corpus_name,
+                    page_size=20,
+                    page_token=response.next_page_token,
+                )
+                response = self.retriever_service_client.list_documents(request)
+                for docs in response.documents:
+                    if print_output:
+                        index += 1
+                        print(f"\nDocument # {index}")
+                        print(f"Name: {docs.name}")
+                        print(f"Display name: {docs.display_name}")
+                        metadata = docs.custom_metadata
+                        for item in metadata:
+                            if item.key == "uuid":
+                                print(f"uuid: {item.string_value}")
+                            elif item.key == "md_hash":
+                                print(f"md_hash: {item.string_value}")
+                    all_docs.append(docs)
+            return all_docs
+        except:
+            logging.error("Error in listing all docs: " + corpus_name)
+            return all_docs
+
+    def get_all_chunks(self, doc_name: str, print_output: bool = False):
+        all_chunks = []
+        try:
+            request = glm.ListChunksRequest(parent=doc_name, page_size=20)
+            response = self.retriever_service_client.list_chunks(request)
+            index = 0
+            for chunk in response.chunks:
+                if print_output:
+                    index += 1
+                    print(f"\nChunk # {index}")
+                    print(f"Name: {chunk.name}")
+                    metadata = chunk.custom_metadata
+                    for item in metadata:
+                        if item.key == "uuid":
+                            print(f"uuid: {item.string_value}")
+                        elif item.key == "md_hash":
+                            print(f"md_hash: {item.string_value}")
+                        elif item.key == "text_chunk_filename":
+                            print(f"text_chunk_filename: {item.string_value}")
+                all_chunks.append(chunk)
+            while (
+                hasattr(response, "next_page_token") and response.next_page_token != ""
+            ):
+                request = glm.ListChunksRequest(
+                    parent=doc_name,
+                    page_size=20,
+                    page_token=response.next_page_token,
+                )
+                response = self.retriever_service_client.list_chunks(request)
+                for chunk in response.chunks:
+                    if print_output:
+                        index += 1
+                        print(f"\nChunk # {index}")
+                        print(f"Name: {chunk.name}")
+                        metadata = chunk.custom_metadata
+                        for item in metadata:
+                            if item.key == "uuid":
+                                print(f"uuid: {item.string_value}")
+                            elif item.key == "md_hash":
+                                print(f"md_hash: {item.string_value}")
+                            elif item.key == "text_chunk_filename":
+                                print(f"text_chunk_filename: {item.string_value}")
+                    all_chunks.append(chunk)
+            return all_chunks
+        except:
+            logging.error("Error in listing all chunks: " + doc_name)
+            return all_chunks
+
+    def share_a_corpus(self, corpus_name: str, email: str, role: str):
+        shared_user_email = email
+        user_type = "USER"
+        user_role = role
+        if user_role == "READER" or user_role == "WRITER":
+            # Make the request
+            request = glm.CreatePermissionRequest(
+                parent=corpus_name,
+                permission=glm.Permission(
+                    grantee_type=user_type, email_address=shared_user_email, role=role
+                ),
+            )
+            create_permission_response = (
+                self.permission_service_client.create_permission(request)
+            )
+            print(create_permission_response)
+            return create_permission_response
+        else:
+            return "Invalid input arguments."
+
+    def open_a_corpus(self, corpus_name: str):
+        user_type = "EVERYONE"
+        role = "READER"
+
+        # Make the request
+        request = glm.CreatePermissionRequest(
+            parent=corpus_name,
+            permission=glm.Permission(grantee_type=user_type, role=role),
+        )
+        create_permission_response = self.permission_service_client.create_permission(
+            request
+        )
+        print(create_permission_response)
+        return create_permission_response
+
+    def delete_permission(self, permission_name: str):
+        delete_corpus_name = permission_name
+        request = glm.DeletePermissionRequest(name=delete_corpus_name)
+        delete_permission_response = self.permission_service_client.delete_permission(
+            request
+        )
+        return delete_permission_response
diff --git a/examples/gemini/python/docs-agent/docs_agent/tests/__init__.py b/examples/gemini/python/docs-agent/docs_agent/tests/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/tests/cli_unit_tests.py b/examples/gemini/python/docs-agent/docs_agent/tests/cli_unit_tests.py
new file mode 100644
index 000000000..9cfb28c48
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/tests/cli_unit_tests.py
@@ -0,0 +1,11 @@
+"""Unit tests for Docs Agent CLI."""
+
+import unittest
+
+
+class DocsAgentCLIUnitTest(unittest.TestCase):
+  def test_basic(self):
+    self.assertEqual('hello'.upper(), 'HELLO')
+
+if __name__ == "__main__":
+  unittest.main()
diff --git a/examples/gemini/python/docs-agent/docs_agent/tests/test_vector_database.py b/examples/gemini/python/docs-agent/docs_agent/tests/test_vector_database.py
new file mode 100644
index 000000000..5968f8879
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/tests/test_vector_database.py
@@ -0,0 +1,184 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Test the vector database"""
+
+import os
+import sys
+import google.generativeai as palm
+import chromadb
+from chromadb.config import Settings
+from chromadb.utils import embedding_functions
+from chromadb.api.types import Document, Embedding, Documents, Embeddings
+from rich.console import Console
+from rich.markdown import Markdown
+from rich.panel import Panel
+
+# from rich import print
+from ratelimit import limits, sleep_and_retry
+from docs_agent.utilities import read_config
+from docs_agent.storage.chroma import Chroma, ChromaEnhanced
+
+
+def main():
+    BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+    # Set the directory path to locate the Chroma vector database
+    LOCAL_VECTOR_DB_DIR = os.path.join(BASE_DIR, "vector_stores/chroma")
+    COLLECTION_NAME = "docs_collection"
+    EMBEDDING_MODEL = None
+
+    IS_CONFIG_FILE = True
+    if IS_CONFIG_FILE:
+        config_values = read_config.ReadConfig()
+        LOCAL_VECTOR_DB_DIR = config_values.returnConfigValue("vector_db_dir")
+        COLLECTION_NAME = config_values.returnConfigValue("collection_name")
+        EMBEDDING_MODEL = config_values.returnConfigValue("embedding_model")
+        # Set this in config.yaml as "experimental" to test new features
+        DOCS_AGENT_CONFIG = config_values.returnConfigValue("docs_agent_config")
+
+    # Set a test question
+    QUESTION = "What are some differences between apples and oranges?"
+    NUM_RETURNS = 5
+
+    # Set up the PaLM API key from the environment
+    API_KEY = os.getenv("PALM_API_KEY")
+    if API_KEY is None:
+        sys.exit("Please set the environment variable PALM_API_KEY to be your API key.")
+
+    # Select your PaLM API endpoint
+    PALM_API_ENDPOINT = "generativelanguage.googleapis.com"
+    palm.configure(api_key=API_KEY, client_options={"api_endpoint": PALM_API_ENDPOINT})
+
+    # Set up the path to the local LLM
+    # This value is used only when `EMBEDDINGS_TYPE` is set to `LOCAL`
+    LOCAL_LLM = os.path.join(BASE_DIR, "models/all-mpnet-base-v2")
+
+    # Use the PaLM API for generating embeddings by default
+    EMBEDDINGS_TYPE = "PALM"
+
+    # PaLM API call limit to 300 per minute
+    API_CALLS = 280
+    API_CALL_PERIOD = 60
+
+    # Create embed function for PaLM
+    # API call limit to 5 qps
+    @sleep_and_retry
+    @limits(calls=API_CALLS, period=API_CALL_PERIOD)
+    def embed_palm_api_call(text: Document) -> Embedding:
+        if PALM_EMBEDDING_MODEL == "models/embedding-001":
+            # Use the `embed_content()` method if it's the new Gemini embedding model.
+            return palm.embed_content(model=PALM_EMBEDDING_MODEL, content=text)[
+                "embedding"
+            ]
+        else:
+            return palm.generate_embeddings(model=PALM_EMBEDDING_MODEL, text=text)[
+                "embedding"
+            ]
+
+    def embed_palm(texts: Documents) -> Embeddings:
+        # Embed the documents using any supported method
+        return [embed_palm_api_call(text) for text in texts]
+
+    # Initialize Rich console
+    ai_console = Console(width=160)
+    ai_console.rule("Fold")
+
+    if DOCS_AGENT_CONFIG == "experimental":
+        chroma_client = ChromaEnhanced(LOCAL_VECTOR_DB_DIR)
+    else:
+        chroma_client = Chroma(LOCAL_VECTOR_DB_DIR)
+
+    if EMBEDDINGS_TYPE == "PALM":
+        if EMBEDDING_MODEL is None:
+            PALM_EMBEDDING_MODEL = "models/embedding-gecko-001"
+        else:
+            PALM_EMBEDDING_MODEL = EMBEDDING_MODEL
+        emb_fn = embed_palm
+    elif EMBEDDINGS_TYPE == "LOCAL":
+        emb_fn = embedding_functions.SentenceTransformerEmbeddingFunction(
+            model_name=LOCAL_LLM
+        )
+    else:
+        emb_fn = embedding_functions.SentenceTransformerEmbeddingFunction(
+            model_name=LOCAL_LLM
+        )
+
+    if DOCS_AGENT_CONFIG == "experimental":
+        embedding_function = embedding_functions.GoogleGenerativeAiEmbeddingFunction(
+            api_key=API_KEY, model_name=EMBEDDING_MODEL, task_type="RETRIEVAL_QUERY"
+        )
+    else:
+        embedding_function = emb_fn
+
+    collection = chroma_client.get_collection(
+        name=COLLECTION_NAME, embedding_function=embedding_function
+    )
+
+    if DOCS_AGENT_CONFIG == "experimental":
+        results = collection.query(QUESTION, NUM_RETURNS)
+    else:
+        results = collection.query(QUESTION, NUM_RETURNS)
+        # results = collection.query(text=[QUESTION], top_k=NUM_RETURNS)
+
+    print("")
+    ai_console.print(Panel.fit(Markdown("Question: " + QUESTION)))
+    print("Results:")
+    if DOCS_AGENT_CONFIG == "experimental":
+        objlist = results.returnDBObjList()
+        for obj in objlist:
+            ai_console.print(
+                Panel.fit(
+                    Markdown(
+                        "Page title: "
+                        + obj.metadata["page_title"]
+                        + " ==== Section title: "
+                        + obj.metadata["section_title"]
+                        + "\n\nContent:\n"
+                        + obj.document
+                    )
+                )
+            )
+            ai_console.print(
+                Panel.fit(
+                    Markdown(
+                        "Distance: "
+                        + str(obj.distance)
+                        + "\n\nURL:\n"
+                        + obj.metadata["url"]
+                    )
+                )
+            )
+    else:
+        print(results)
+        i = 0
+        for document in results["documents"]:
+            for content in document:
+                print("Content " + str(i) + ": ")
+                ai_console.print(Panel.fit(Markdown(content)))
+                source = results["metadatas"][0][i]
+                this_id = results["ids"][0][i]
+                distance = results["distances"][0][i]
+                print("  source: " + source["source"])
+                print("  URL: " + source["url"])
+                print("  ID: " + this_id)
+                print("  Distance: " + str(distance))
+                print("")
+                i += 1
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/gemini/python/docs-agent/docs_agent/utilities/__init__.py b/examples/gemini/python/docs-agent/docs_agent/utilities/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/utilities/config.py b/examples/gemini/python/docs-agent/docs_agent/utilities/config.py
new file mode 100644
index 000000000..bf815842a
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/utilities/config.py
@@ -0,0 +1,615 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Read the configuration file to import user settings"""
+
+import os
+import sys
+import yaml
+import typing
+
+from absl import logging
+from docs_agent.utilities.helpers import get_project_path
+
+
+class DbConfig:
+    def __init__(
+        self,
+        # Currently 'chroma' or 'google_semantic_retriever'
+        db_type: str,
+        # These for 'chroma'
+        vector_db_dir: typing.Optional[str] = None,
+        collection_name: typing.Optional[str] = None,
+        # These for 'google_semantic_retriever'
+        corpus_name: typing.Optional[str] = None,
+        # Only used when creating a corpus
+        corpus_display: typing.Optional[str] = None,
+        secondary_db_type: typing.Optional[str] = None,
+        secondary_corpus_name: typing.Optional[str] = None,
+    ):
+        self.db_type = db_type
+        self.vector_db_dir = vector_db_dir
+        self.collection_name = collection_name
+        self.corpus_name = corpus_name
+        self.corpus_display = corpus_display
+        self.secondary_db_type = secondary_db_type
+        self.secondary_corpus_name = secondary_corpus_name
+
+    def __str__(self):
+        help_str = ""
+        help_str += f"Database type: {self.db_type}\n"
+        if self.vector_db_dir is not None and self.vector_db_dir != "":
+            help_str += f"Vector database dir: {self.vector_db_dir}\n"
+        if self.collection_name is not None and self.collection_name != "":
+            help_str += f"Collection name: {self.collection_name}\n"
+        if self.corpus_name is not None and self.corpus_name != "":
+            help_str += f"Corpus name: {self.corpus_name}\n"
+        if self.corpus_display is not None and self.corpus_display != "":
+            help_str += f"Corpus display: {self.corpus_display}\n"
+        if self.secondary_db_type is not None and self.secondary_db_type != "":
+            help_str += f"Secondary database type: {self.secondary_db_type}\n"
+        if self.secondary_corpus_name is not None and self.secondary_corpus_name != "":
+            help_str += f"Secondary corpus name: {self.secondary_corpus_name}\n"
+        return help_str
+
+
+    def return_vector_db_dir(self):
+        return self.vector_db_dir
+
+
+class ReadDbConfigs:
+    # Tries to ingest a list of dictionaries with Input values
+    def __init__(self, input_list: list[dict]):
+        self.input_list = input_list
+
+    def returnDbConfigs(self) -> list[DbConfig]:
+        inputs = []
+        for item in self.input_list:
+            try:
+                # Using .get let's you specify optional keys
+                db_type = item["db_type"]
+                if db_type == "chroma":
+                    input_item = DbConfig(
+                        db_type=db_type,
+                        vector_db_dir=item["vector_db_dir"],
+                        collection_name=item["collection_name"],
+                    )
+                elif db_type == "google_semantic_retriever":
+                    input_item = DbConfig(
+                        db_type=db_type,
+                        corpus_name=item["corpus_name"],
+                        corpus_display=item.get("corpus_display", None),
+                    )
+                else:
+                    logging.error(f"You specified an unsupported db_type {db_type}")
+                inputs.append(input_item)
+            except KeyError as error:
+                logging.error(f"The input is missing a key {error}")
+                # Exits the scripts if there is missing input keys
+                return sys.exit(1)
+        return inputs
+
+    def return_chroma_db(self):
+        for item in self.input_list:
+            if item["vector_db_dir"]:
+                return item["vector_db_dir"]
+            else:
+                return None
+
+    def __str__(self):
+        return self.input_list
+
+
+class Input:
+    def __init__(
+        self,
+        path: str,
+        url_prefix: typing.Optional[str] = None,
+        include_path_html: typing.Optional[str] = None,
+        exclude_path: typing.Optional[str] = None,
+    ):
+        self.path = path
+        self.url_prefix = url_prefix
+        self.include_path_html = include_path_html
+        self.exclude_path = exclude_path
+
+    def __str__(self):
+        help_str = ""
+        if self.path is not None and self.path != "":
+            help_str += f"Path: {self.path}\n"
+        if self.url_prefix is not None and self.url_prefix != "":
+            help_str += f"URL prefix: {self.url_prefix}\n"
+        if self.include_path_html is not None and self.include_path_html != "":
+            help_str += f"Include path html: {self.include_path_html}\n"
+        if self.exclude_path is not None and self.exclude_path != "":
+            help_str += f"Exclude path: {self.exclude_path}\n"
+        return help_str
+
+
+class ReadInputs:
+    # Tries to ingest a list of dictionaries with Input values
+    def __init__(self, input_list: list[dict]):
+        self.input_list = input_list
+
+    def returnInputs(self) -> list[Input]:
+        inputs = []
+        for item in self.input_list:
+            try:
+                # Using .get let's you specify optional keys
+                input_item = Input(
+                    path=item["path"],
+                    url_prefix=item["url_prefix"],
+                    include_path_html=item.get("include_path_html", None),
+                    exclude_path=item.get("exclude_path", None),
+                )
+                inputs.append(input_item)
+            except KeyError as error:
+                logging.error(f"The input is missing a key {error}")
+                # Exits the scripts if there is missing input keys
+                return sys.exit(1)
+        return inputs
+
+    def __str__(self):
+        return self.input_list
+
+
+class Models:
+    def __init__(
+        self,
+        language_model: str,
+        embedding_model: str,
+        api_endpoint: typing.Optional[str] = None,
+        api_key: typing.Optional[str] = None,
+        embedding_api_call_limit: typing.Optional[int] = None,
+        embedding_api_call_period: typing.Optional[int] = None,
+    ):
+        self.language_model = language_model
+        self.embedding_model = embedding_model
+        # Set up the Google API key from the environment.
+        if api_key is None:
+            api_key_var = os.getenv("GOOGLE_API_KEY")
+            self.api_key = api_key_var
+            if api_key_var is None:
+                logging.error(
+                    "Please set the environment variable GOOGLE_API_KEY to be your API key."
+                )
+        else:
+            self.api_key = api_key
+        if api_endpoint is None:
+            self.api_endpoint = "generativelanguage.googleapis.com"
+        else:
+            self.api_endpoint = api_endpoint
+        if embedding_api_call_limit is None:
+            self.embedding_api_call_limit = 1400
+        else:
+            self.embedding_api_call_limit = embedding_api_call_limit
+        if embedding_api_call_period is None:
+            self.embedding_api_call_period = 60
+        else:
+            self.embedding_api_call_period = embedding_api_call_period
+
+    def __str__(self):
+        help_str = ""
+        if self.language_model is not None and self.language_model != "":
+            help_str += f"Language model: {self.language_model}\n"
+        if self.embedding_model is not None and self.embedding_model != "":
+            help_str += f"Embedding model: {self.embedding_model}\n"
+        if self.api_key is not None and self.api_key != "":
+            help_str += f"API key: {self.api_key}\n"
+        if self.api_endpoint is not None and self.api_endpoint != "":
+            help_str += f"API endpoint: {self.api_endpoint}\n"
+        if self.embedding_api_call_limit is not None and self.embedding_api_call_limit != "":
+            help_str += f"Embedding API call limit: {self.embedding_api_call_limit}\n"
+        if self.embedding_api_call_period is not None and self.embedding_api_call_period != "":
+            help_str += f"Embedding API call period: {self.embedding_api_call_period}\n"
+        return help_str
+
+
+class ReadModels:
+    # Tries to ingest a list of dictionaries with Models values
+    def __init__(self, input_list: list[dict]):
+        self.input_list = input_list
+
+    def returnModels(self) -> Models:
+        # Check to make sure there is only 1 set of models per product
+        if len(self.input_list) > 1:
+            logging.error(f"Only 1 set of Models is supported")
+            # Exits the scripts if there is missing input keys
+            return sys.exit(1)
+        models = []
+        for item in self.input_list:
+            try:
+                # Using .get let's you specify optional keys
+                model_item = Models(
+                    language_model=item["language_model"],
+                    embedding_model=item["embedding_model"],
+                    api_endpoint=item.get("api_endpoint", None),
+                    embedding_api_call_limit=item.get("embedding_api_call_limit", None),
+                    embedding_api_call_period=item.get(
+                        "embedding_api_call_period", None
+                    ),
+                )
+                models.append(model_item)
+            except KeyError as error:
+                logging.error(f"The conditions is missing a key {error}")
+                # Exits the scripts if there is missing input keys
+                return sys.exit(1)
+        return models[0]
+
+
+class Conditions:
+    def __init__(
+        self,
+        condition_text: str,
+        fact_check_question: typing.Optional[str] = None,
+        model_error_message: typing.Optional[str] = None,
+    ):
+        default_fact_check_question = (
+            "Can you compare the text below to the information provided in this"
+            " prompt above and write a short message that warns the readers"
+            " about which part of the text they should consider"
+            " fact-checking? (Please keep your response concise, focus on only"
+            " one important item, but DO NOT USE BOLD TEXT IN YOUR RESPONSE.)"
+        )
+        default_model_error_message = (
+            "Gemini is not able to answer this question at the moment."
+            " Rephrase the question and try asking again."
+        )
+        self.condition_text = condition_text
+        if fact_check_question is None:
+            self.fact_check_question = default_fact_check_question
+        else:
+            self.fact_check_question = fact_check_question
+        if model_error_message is None:
+            self.model_error_message = default_model_error_message
+        else:
+            self.model_error_message = model_error_message
+
+    def __str__(self):
+        help_str = ""
+        if self.condition_text is not None and self.condition_text != "":
+            help_str += f"Condition text: {self.condition_text}\n"
+        if self.fact_check_question is not None and self.fact_check_question != "":
+            help_str += f"Fact check question: {self.fact_check_question}\n"
+        if self.model_error_message is not None and self.model_error_message != "":
+            help_str += f"Model error message: {self.model_error_message}\n"
+        return help_str
+
+
+class ReadConditions:
+    # Tries to ingest a list of dictionaries with Conditions values
+    def __init__(self, input_list: list[dict]):
+        self.input_list = input_list
+
+    def returnConditions(self) -> Conditions:
+        # Check to make sure there is only 1 set of conditions per product
+        if len(self.input_list) > 1:
+            logging.error(f"Only 1 set of conditions is supported")
+            # Exits the scripts if there is missing input keys
+            return sys.exit(1)
+        conditions = []
+        for item in self.input_list:
+            try:
+                # Using .get let's you specify optional keys
+                condition_item = Conditions(
+                    condition_text=item["condition_text"],
+                    fact_check_question=item.get("fact_check_question", None),
+                    model_error_message=item.get("model_error_message", None),
+                )
+                conditions.append(condition_item)
+            except KeyError as error:
+                logging.error(f"The conditions is missing a key {error}")
+                # Exits the scripts if there is missing input keys
+                return sys.exit(1)
+        return conditions[0]
+
+
+# Class to build a ProductConfig that contains variables to
+# run docs agent
+class ProductConfig:
+    def __init__(
+        self,
+        product_name: str,
+        models: Models,
+        output_path: str,
+        db_configs: list[DbConfig],
+        inputs: list[Input],
+        conditions: Conditions,
+        log_level: typing.Optional[str] = None,
+        docs_agent_config: typing.Optional[str] = None,
+        markdown_splitter: str = "token_splitter",
+        db_type: str = "chroma",
+        app_mode: str = "web",
+        app_port: int = 5000,
+        feedback_mode: str = "rewrite",
+        enable_show_logs: str = "False",
+        enable_logs_to_markdown: str = "False",
+        enable_logs_for_debugging: str = "False",
+        enable_delete_chunks: str = "False",
+        secondary_db_type: typing.Optional[str] = None,
+        secondary_corpus_name: typing.Optional[str] = None,
+    ):
+        self.product_name = product_name
+        self.docs_agent_config = docs_agent_config
+        self.markdown_splitter = markdown_splitter
+        self.db_type = db_type
+        self.output_path = output_path
+        self.db_configs = db_configs
+        self.models = models
+        self.conditions = conditions
+        self.log_level = log_level
+        self.inputs = inputs
+        self.app_mode = app_mode
+        self.app_port = app_port
+        self.feedback_mode = feedback_mode
+        self.enable_show_logs = enable_show_logs
+        self.enable_logs_to_markdown = enable_logs_to_markdown
+        self.enable_logs_for_debugging = enable_logs_for_debugging
+        self.enable_delete_chunks = enable_delete_chunks
+        self.secondary_db_type = secondary_db_type
+        self.secondary_corpus_name = secondary_corpus_name
+
+    def __str__(self):
+        # Extracts the list of Inputs
+        inputs = []
+        for item in self.inputs:
+            inputs.append(str(item))
+        input_str = "\n".join(inputs)
+        # Extracts the list of DbConfigs
+        dbconfigs = []
+        for item in self.db_configs:
+            dbconfigs.append(str(item))
+        db_config_str = "\n".join(dbconfigs)
+        help_str = ""
+        if self.product_name is not None and self.product_name != "":
+            help_str += f"Product: {self.product_name}\n"
+        if self.docs_agent_config is not None and self.docs_agent_config != "":
+            help_str += f"Docs Agent config: {self.docs_agent_config}\n"
+        if self.app_mode is not None and self.app_mode != "":
+            help_str += f"App mode: {self.app_mode}\n"
+        if self.app_port is not None and self.app_port != "":
+            help_str += f"App port: {self.app_port}\n"
+        if self.feedback_mode is not None and self.feedback_mode != "":
+            help_str += f"Feedback mode: {self.feedback_mode}\n"
+        if self.enable_show_logs is not None and self.enable_show_logs != "":
+            help_str += f"Enable show logs: {self.enable_show_logs}\n"
+        if self.enable_logs_to_markdown is not None and self.enable_logs_to_markdown != "":
+            help_str += f"Enable logs to Markdown: {self.enable_logs_to_markdown}\n"
+        if self.enable_logs_for_debugging is not None and self.enable_logs_for_debugging != "":
+            help_str += f"Enable logs for debugging: {self.enable_logs_for_debugging}\n"
+        if self.enable_delete_chunks is not None and self.enable_delete_chunks != "":
+            help_str += f"Enable delete chunks: {self.enable_delete_chunks}\n"
+        if self.markdown_splitter is not None and self.markdown_splitter != "":
+            help_str += f"Markdown splitter: {self.markdown_splitter}\n"
+        if self.db_type is not None and self.db_type != "":
+            help_str += f"Database type: {self.db_type}\n"
+        if self.secondary_db_type is not None and self.secondary_db_type != "":
+            help_str += f"Secondary database type: {self.secondary_db_type}\n"
+        if self.secondary_corpus_name is not None and self.secondary_corpus_name != "":
+            help_str += f"Secondary corpus name: {self.secondary_corpus_name}\n"
+        if self.output_path is not None and self.output_path != "":
+            help_str += f"Output path: {self.output_path}\n"
+        if db_config_str != "":
+            help_str += f"\nDatabase configs:\n{db_config_str}\n"
+        if self.log_level is not None and self.log_level != "":
+            help_str += f"Log level: {self.log_level}\n"
+        if self.models is not None and self.models != "":
+            help_str += f"\nModels:\n{self.models}\n"
+        if input_str != "":
+            help_str += f"\nInputs:\n{input_str}\n"
+        if self.conditions is not None and self.conditions != "":
+            help_str += f"Conditions:\n{self.conditions}\n"
+        return help_str
+
+
+# This class is used to store the content of a list of
+# ProductConfig
+class ConfigFile:
+    def __init__(
+        self,
+        products: list[ProductConfig],
+    ):
+        self.products = products
+
+    def __str__(self):
+        output = ["Products:"]
+        for item in self.products:
+            output.append(item)
+        return "\n".join(output)
+
+    def return_first(self):
+        return self.products[0]
+
+
+# Class used to read a specific configuration file or defaults
+# to a config.yaml file in the root of the project
+# printing the object returns the path as a string
+# returnProducts() with an optional product flag will return
+# all product configurations or the specified one
+class ReadConfig:
+    # Tries to ingest the configuration file and validate its keys
+    # Defaults to the config.yaml file in the source of the project
+    def __init__(
+        self, yaml_path: str = os.path.join(get_project_path(), "config.yaml")
+    ):
+        self.yaml_path = yaml_path
+        try:
+            with open(yaml_path, "r", encoding="utf-8") as inp_yaml:
+                self.config_values = yaml.safe_load(inp_yaml)
+                # self.yaml_path = yaml_path
+        except FileNotFoundError:
+            logging.error(f"The config file {self.yaml_path} does not exist.")
+            # Exits the scripts if there is no valid config file
+            return sys.exit(1)
+
+    def __str__(self):
+        return self.yaml_path
+
+    def returnProducts(self, product: typing.Optional[str] = None) -> ConfigFile:
+        products = []
+        try:
+            for item in self.config_values["configs"]:
+                name = ""
+                try:
+                    name = item["product_name"]
+                except KeyError as error:
+                    logging.error(f"Your configuration is missing a {error}")
+                    return sys.exit()
+                # Set the default value of `app_mode` to "web"
+                supported_app_modes = [
+                    "web",
+                    "full",
+                    "widget",
+                    "widget-pro",
+                    "experimental",
+                ]
+                try:
+                    app_mode = item["app_mode"]
+                except KeyError:
+                    app_mode = "web"
+                if app_mode not in supported_app_modes:
+                    logging.error(
+                        f"Your configuration is using an invalid mode: {app_mode}. Valid modes are {supported_app_modes}"
+                    )
+                    return sys.exit(1)
+                try:
+                    app_port = int(item["app_port"])
+                except KeyError:
+                    app_port = 5000
+                try:
+                    feedback_mode = item["feedback_mode"]
+                except KeyError:
+                    feedback_mode = "feedback"
+                try:
+                    enable_show_logs = item["enable_show_logs"]
+                except KeyError:
+                    enable_show_logs = "False"
+                try:
+                    enable_logs_to_markdown = item["enable_logs_to_markdown"]
+                except KeyError:
+                    enable_logs_to_markdown = "False"
+                try:
+                    enable_logs_for_debugging = item["enable_logs_for_debugging"]
+                except KeyError:
+                    enable_logs_for_debugging = "False"
+                try:
+                    enable_delete_chunks = item["enable_delete_chunks"]
+                except KeyError:
+                    enable_delete_chunks = "False"
+                try:
+                    secondary_db_type = item["secondary_db_type"]
+                except KeyError:
+                    secondary_db_type = None
+                try:
+                    secondary_corpus_name = item["secondary_corpus_name"]
+                except KeyError:
+                    secondary_corpus_name = None
+                try:
+                    product_config = ProductConfig(
+                        product_name=item["product_name"],
+                        docs_agent_config=item["docs_agent_config"],
+                        markdown_splitter=item["markdown_splitter"],
+                        db_type=item["db_type"],
+                        output_path=item["output_path"],
+                        db_configs=item["db_configs"],
+                        log_level=item["log_level"],
+                        models=item["models"],
+                        conditions=item["conditions"],
+                        inputs=item["inputs"],
+                        app_mode=app_mode,
+                        app_port=app_port,
+                        feedback_mode=feedback_mode,
+                        enable_show_logs=enable_show_logs,
+                        enable_logs_to_markdown=enable_logs_to_markdown,
+                        enable_logs_for_debugging=enable_logs_for_debugging,
+                        enable_delete_chunks=enable_delete_chunks,
+                        secondary_db_type=secondary_db_type,
+                        secondary_corpus_name=secondary_corpus_name,
+                    )
+                    # This is done for keys with children
+                    # Inputs
+                    new_inputs = ReadInputs(input_list=item["inputs"]).returnInputs()
+                    product_config.inputs = new_inputs
+                    product_config.inputs = new_inputs
+                    # Conditions
+                    new_conditions = ReadConditions(
+                        input_list=item["conditions"]
+                    ).returnConditions()
+                    product_config.conditions = new_conditions
+                    # Models
+                    new_models = ReadModels(input_list=item["models"]).returnModels()
+                    product_config.models = new_models
+                    # DbConfigs
+                    new_db_configs = ReadDbConfigs(
+                        input_list=item["db_configs"]
+                    ).returnDbConfigs()
+                    product_config.db_configs = new_db_configs
+                    # Append
+                    products.append(product_config)
+                except KeyError as error:
+                    if name != "":
+                        logging.error(
+                            f"A product configuration, {name} is missing a key {error}"
+                        )
+                        return sys.exit(1)
+            if product is not None:
+                match = False
+                for item in products:
+                    # If product is found, update products to a single entry in products
+                    if item.product_name == product:
+                        match = True
+                        products = [item]
+                        break
+                # Exits if product is not found
+                if not match:
+                    logging.error(
+                        f"The product {product} does not exist in {self.yaml_path}"
+                    )
+                    return sys.exit(1)
+            config_file = ConfigFile(products=products)
+            return config_file
+        except KeyError as error:
+            logging.error(
+                f"Your configuration is missing a configs key {error} in {self.yaml_path}"
+            )
+            # Todo print out an example file
+            return sys.exit(1)
+
+
+# Function to make using common_options simpler
+def return_config_and_product(
+    config_file: typing.Optional[str] = None,
+    product: list[str] = [""],
+    model: typing.Optional[str] = None,
+):
+    if config_file is None:
+        loaded_config = ReadConfig()
+    else:
+        loaded_config = ReadConfig(yaml_path=config_file)
+    final_products = []
+    if product == () or product == [""]:
+        product_config = loaded_config.returnProducts()
+        final_products = product_config.products
+    else:
+        for item in product:
+            product_config = loaded_config.returnProducts(product=item)
+            final_products.append(product_config.products[0])
+    # Overwrites the language_model for all products
+    if model is not None:
+        for item in final_products:
+            item.models.language_model = model
+    product_config = ConfigFile(products=final_products)
+    return loaded_config, product_config
diff --git a/examples/gemini/python/docs-agent/docs_agent/utilities/helpers.py b/examples/gemini/python/docs-agent/docs_agent/utilities/helpers.py
new file mode 100644
index 000000000..2e51d777f
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/utilities/helpers.py
@@ -0,0 +1,183 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""General utility functions"""
+
+import urllib, os
+from flask import url_for
+import bs4
+import typing
+from pathlib import Path, PurePath
+import markdown
+
+
+# This retrieves the project root, regardless of module path
+def get_project_path() -> Path:
+    return Path(__file__).parent.parent.parent
+
+
+# Function to resolve path. If no base_dir is specified, use the project root
+def resolve_path(rel_or_abs_path: str, base_dir: Path = get_project_path()):
+    path = rel_or_abs_path.strip()
+    if path.startswith("/"):
+        return path
+    else:
+        return os.path.join(base_dir, path)
+
+
+# Function to add / to a path.
+def end_path_backslash(input_path: str):
+    if not input_path.endswith("/"):
+        input_path = input_path + "/"
+    return input_path
+
+
+# Function to remove / from a path to combine with url
+def start_path_no_backslash(input_path: str):
+    if input_path.startswith("/"):
+        # Drop first character
+        input_path = input_path[1:]
+    return input_path
+
+
+# Function to create a path to a copy directory in the parent directory.
+# Backup dir is relevant to the input path root
+def parallel_backup_dir(rel_or_abs_path: str, backup_dir_name: str = "backup"):
+    path = Path(resolve_path(rel_or_abs_path))
+    pure_path = PurePath(resolve_path(rel_or_abs_path))
+    backup_dir = (
+        end_path_backslash(str(path.parent.absolute()))
+        + end_path_backslash(start_path_no_backslash(backup_dir_name))
+        + str(pure_path.name)
+    )
+    return backup_dir
+
+
+# Function to return the parent directory
+def return_pure_dir(rel_or_abs_path: str):
+    pure_path = PurePath(resolve_path(rel_or_abs_path))
+    return str(pure_path.name)
+
+
+# This function adds a scheme URL
+def add_scheme_url(url: str, scheme: str = "https"):
+    return url if "://" in url else f"{scheme}://{url}"
+
+
+# Parse a response containing a list of related questions from the language model
+# and convert it into an HTML-based list.
+def parse_related_questions_response_to_html_list(response):
+    soup = bs4.BeautifulSoup(response, "html.parser")
+    for item in soup.find_all("li"):
+        if item.find("code"):
+            # If there are <code> tags, strip the tags.
+            text = item.text
+            link = soup.new_tag(
+                "a",
+                href=url_for("chatui.question", ask=urllib.parse.quote_plus(text)),
+            )
+            link.string = text
+            item.string = ""
+            item.code = ""
+            item.append(link)
+        elif item.find("p"):
+            # If there are <p> tags, strip the tags.
+            text = item.find("p").text
+            link = soup.new_tag(
+                "a",
+                href=url_for("chatui.question", ask=urllib.parse.quote_plus(text)),
+            )
+            link.string = text
+            item.string = ""
+            item.append(link)
+        elif item.string is not None:
+            link = soup.new_tag(
+                "a",
+                href=url_for(
+                    "chatui.question", ask=urllib.parse.quote_plus(item.string)
+                ),
+            )
+            link.string = item.string
+            item.string = ""
+            item.append(link)
+    return soup
+
+
+# Allows us to build a list of html, for example for fact checker and limit to
+# a max_count. Optional section_content to display content chunks along with URLs
+# This is not in use, but a good example to better manipulate data
+def build_list_html_links(
+    urls: list,
+    section_titles: list,
+    page_titles: list,
+    distances: list,
+    section_content: typing.Optional[list] = None,
+    max_count: typing.Optional[int] = None,
+):
+    if max_count == None:
+        max_count = len(urls)
+    md_list = ""
+    for count in range(max_count):
+        section_url = named_link_md(urls[count], section_titles[count])
+        page_url = named_link_md(
+            trim_section_for_page_link(urls[count]), page_titles[count]
+        )
+        if max_count == 1:
+            md_list += f"{section_url} from the {page_url} page"
+            if distances != None:
+                md_list += f"\n\nDistance: {distances[count]}\n"
+            if section_content != None:
+                md_list += f"\n\n{section_content[count]}\n"
+        else:
+            md_list += f"- {section_url} from the {page_url} page\n"
+            md_list += f"\n\n  Distance: {distances[count]}\n"
+            if section_content != None:
+                md_list += f"\n\n  {markdown.markdown(section_content[count])}\n"
+    html_list = markdown.markdown(md_list)
+    return html_list
+
+
+# These functions are made to be used in a Jinja template when rendering a page
+
+
+# Build an html URL link
+def named_link_html(url: str, label: str = "", **kwargs):
+    soup = bs4.BeautifulSoup("")
+    final_url = add_scheme_url(url)
+    attrs = dict(href=f"{final_url}", target=f"_blank", **kwargs)
+    tag = soup.new_tag(name="a", attrs=attrs)
+    # leading and trailing blank space doesn't get removed?
+    tag.string = label.strip()
+    return tag.prettify()
+
+
+def named_link_md(url: str, label: str = ""):
+    final_url = add_scheme_url(url)
+    link = f"[{label}]({final_url})"
+    return link
+
+
+# Create a top level link for a page
+def trim_section_for_page_link(url: str):
+    anchor_marker_url = "#"
+    page_url = url.split(anchor_marker_url, 1)[0]
+    return page_url
+
+
+# Function to convert md to html for flask template
+def md_to_html(md: str):
+    html = markdown.markdown(md)
+    return html
diff --git a/examples/gemini/python/docs-agent/docs_agent/utilities/tasks.py b/examples/gemini/python/docs-agent/docs_agent/utilities/tasks.py
new file mode 100644
index 000000000..5b846bd80
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/utilities/tasks.py
@@ -0,0 +1,399 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Read the configuration file to import tasks"""
+
+import os
+import sys
+import yaml
+import typing
+
+from absl import logging
+from docs_agent.utilities.helpers import get_project_path, resolve_path
+
+
+class Flags:
+    def __init__(
+        self,
+        model: typing.Optional[str] = None,
+        file: typing.Optional[str] = None,
+        perfile: typing.Optional[str] = None,
+        allfiles: typing.Optional[str] = None,
+        file_ext: typing.Optional[str] = None,
+        rag: typing.Optional[bool] = False,
+        yaml: typing.Optional[str] = None,
+        out: typing.Optional[str] = None,
+        new: typing.Optional[bool] = None,
+        cont: typing.Optional[str] = None,
+        terminal: typing.Optional[str] = None,
+        default_input: typing.Optional[str] = None,
+    ):
+        self.model = model
+        self.file = file
+        self.perfile = perfile
+        self.allfiles = allfiles
+        self.file_ext = file_ext
+        self.rag = rag
+        self.yaml = yaml
+        self.out = out
+        self.new = new
+        self.cont = cont
+        self.terminal = terminal
+        self.default_input = default_input
+
+    def __str__(self):
+        help_str = ""
+        if self.model is not None or self.model != "":
+            help_str += f"Model: {self.model}\n"
+        if self.file is not None and self.file != "":
+            help_str += f"File: {self.file}\n"
+        if self.perfile is not None and self.perfile != "":
+            help_str += f"Per file: {self.perfile}\n"
+        if self.allfiles is not None and self.allfiles != "":
+            help_str += f"All files: {self.allfiles}\n"
+        if self.default_input is not None and self.default_input != "":
+            help_str += f"Default input: {self.default_input}\n"
+        if self.file_ext is not None and self.file_ext != "":
+            help_str += f"File ext: {self.file_ext}\n"
+        if self.rag is not None and self.rag != False:
+            help_str += f"RAG: {str(self.rag)}\n"
+        if self.yaml is not None and self.yaml != "":
+            help_str += f"YAML: {self.yaml}\n"
+        if self.out is not None and self.out != "":
+            help_str += f"Out: {self.out}\n"
+        if self.new is not None and self.new != False:
+            help_str += f"New: {str(self.new)}\n"
+        if self.cont is not None and self.cont != "":
+            help_str += f"Cont: {self.cont}\n"
+        if self.terminal is not None and self.terminal != "":
+            help_str += f"Terminal: {self.terminal}\n"
+        return help_str
+
+
+def dictionaryToFlags(flags: dict) -> Flags:
+    if "model" in flags:
+        model = str(flags["model"])
+    else:
+        model = ""
+    if "file" in flags:
+        file = str(flags["file"])
+    else:
+        file = ""
+    if "perfile" in flags:
+        perfile = str(flags["perfile"])
+    else:
+        perfile = ""
+    if "allfiles" in flags:
+        allfiles = str(flags["allfiles"])
+    else:
+        allfiles = ""
+    if "file_ext" in flags:
+        file_ext = str(flags["file_ext"])
+    else:
+        file_ext = ""
+    if "rag" in flags:
+        rag = bool(flags["rag"])
+    else:
+        rag = False
+    if "yaml" in flags:
+        yaml = str(flags["yaml"])
+    else:
+        yaml = ""
+    if "out" in flags:
+        out = str(flags["out"])
+    else:
+        out = ""
+    if "new" in flags:
+        new = bool(flags["new"])
+    else:
+        new = None
+    if "cont" in flags:
+        cont = str(flags["cont"])
+    else:
+        cont = ""
+    if "terminal" in flags:
+        terminal = str(flags["terminal"])
+    else:
+        terminal = ""
+    if "default_input" in flags:
+        default_input = str(flags["default_input"])
+    else:
+        default_input = ""
+    flags = Flags(
+        model=model,
+        file=file,
+        perfile=perfile,
+        allfiles=allfiles,
+        file_ext=file_ext,
+        rag=rag,
+        yaml=yaml,
+        out=out,
+        new=new,
+        cont=cont,
+        terminal=terminal,
+        default_input=default_input,
+    )
+    return flags
+
+
+class Step:
+    def __init__(
+        self,
+        prompt: typing.Optional[str],
+        name: typing.Optional[str] = None,
+        function: typing.Optional[str] = None,
+        flags: typing.Optional[Flags] = None,
+        description: typing.Optional[str] = None,
+    ):
+        self.prompt = prompt
+        self.name = name
+        self.function = function
+        self.flags = flags
+        self.description = description
+
+    def __str__(self):
+        help_str = ""
+        if self.name is not None or self.name != "":
+            help_str += f"Task: {self.name}\n"
+        if self.description is not None or self.description != "":
+            help_str += f"Description: {self.description}\n"
+        if self.function is not None or self.function != "":
+            help_str += f"Function: {self.function}\n"
+        if self.prompt is not None or self.prompt != "":
+            help_str += f"Prompt: {self.prompt}\n"
+        if self.flags is not None:
+            help_str += f"Flags:\n{self.flags}\n"
+        return help_str
+
+
+class ReadSteps:
+    # Tries to ingest a list of dictionaries with step_list values
+    def __init__(self, step_list: list[dict]):
+        self.step_list = step_list
+
+    def returnSteps(self) -> list[Step]:
+        inputs = []
+        for item in self.step_list:
+            try:
+                # Creates a Flags object from the dictionary
+                if "flags" in item:
+                    flags = dictionaryToFlags(item["flags"])
+                else:
+                    flags = None
+                # Using .get let's you specify optional keys
+                step_item = Step(
+                    prompt=item["prompt"],
+                    name=item.get("name", None),
+                    function=item.get("function", None),
+                    flags=flags,
+                    description=item.get("description", None),
+                )
+                inputs.append(step_item)
+            except KeyError as error:
+                logging.error(f"The task is missing a key {error}")
+                # Exits the scripts if there is missing input keys
+                return sys.exit(1)
+        return inputs
+
+    def __str__(self):
+        return self.step_list
+
+
+# Class to define an task that performs tasks
+class TaskConfig:
+    def __init__(
+        self,
+        name: str,
+        steps: list[Step],
+        # Required as a main model. Any task can overwrite this value with it's
+        # own model.
+        model: str,
+        preamble: typing.Optional[str] = None,
+        description: typing.Optional[str] = None,
+    ):
+        self.name = name
+        self.model = model
+        self.preamble = preamble
+        self.description = description
+        self.steps = steps
+
+    def __str__(self):
+        help_str = ""
+        if self.name is not None:
+            help_str += f"Name: {self.name}\n"
+        if self.model is not None:
+            help_str += f"Model: {self.model}\n"
+        if self.description is not None:
+            help_str += f"Description: {self.description}\n"
+        if self.preamble is not None:
+            help_str += f"Preamble: {self.preamble}\n"
+        # Extracts the list of Steps
+        steps = []
+        if self.steps is not None:
+            for step in self.steps:
+                steps.append(str(step))
+            task_str = "\n".join(steps)
+            help_str += f"\nSteps:\n\n{task_str}\n"
+        return help_str
+
+
+# This class is used to store the content of a list of
+# TaskConfig
+class TaskConfigFile:
+    def __init__(
+        self,
+        tasks: list[TaskConfig],
+    ):
+        self.tasks = tasks
+
+    def __str__(self):
+        output = []
+        for item in self.tasks:
+            output.append(str(item))
+        return str(output)
+
+    def return_first(self):
+        return self.tasks[0]
+
+    def return_task(self, task: str):
+        for item in self.tasks:
+            if item.name == task:
+                return TaskConfigFile(tasks=[item])
+
+
+# Class used to read a specific task configuration file or defaults
+# to a yaml file in the tasks directory of the project
+# printing the object returns the path as a string
+# returnTasks() with an optional task flag will return
+# all task configurations or the specified one
+class ReadTaskConfig:
+    # Tries to ingest a task configuration file and validate its keys.
+    def __init__(
+        self,
+        yaml_path: str = os.path.join(
+            get_project_path(), "tasks/release-notes-task.yaml"
+        ),
+    ):
+        self.yaml_path = yaml_path
+        try:
+            with open(self.yaml_path, "r", encoding="utf-8") as inp_yaml:
+                self.config_values = yaml.safe_load(inp_yaml)
+        except FileNotFoundError:
+            logging.error(f"The task file {self.yaml_path} does not exist.")
+            return sys.exit(1)
+
+    def __str__(self):
+        return self.yaml_path
+
+    def returnTasks(self, task: typing.Optional[str] = None) -> TaskConfigFile:
+        tasks = []
+        try:
+            for item in self.config_values["tasks"]:
+                name = ""
+                try:
+                    name = item["name"]
+                except KeyError as error:
+                    logging.error(f"Your task configuration is missing a {error}")
+                    return sys.exit()
+                try:
+                    name = item["model"]
+                except KeyError as error:
+                    logging.error(f"Your task configuration is missing a {error}")
+                    return sys.exit()
+                try:
+                    task_config = TaskConfig(
+                        name=item["name"],
+                        steps=item["steps"],
+                        model=item["model"],
+                        description=item.get("description", None),
+                        preamble=item.get("preamble", None),
+                    )
+                    new_steps = ReadSteps(step_list=item["steps"]).returnSteps()
+                    task_config.steps = new_steps
+                    tasks.append(task_config)
+                except KeyError as error:
+                    if name != "":
+                        logging.error(
+                            f"A product configuration, {name} is missing a key {error}"
+                        )
+                        return sys.exit(1)
+            if task is not None:
+                match = False
+                for item in tasks:
+                    # If task is found, update tasks to a single entry in products
+                    if item.name == task:
+                        match = True
+                        tasks = [item]
+                        break
+                # Exits if product is not found
+                if not match:
+                    logging.error(f"The task {task} does not exist in {self.yaml_path}")
+                    return sys.exit(1)
+            tasks_file = TaskConfigFile(tasks=tasks)
+            return tasks_file
+        except KeyError as error:
+            logging.error(
+                f"Your task configuration file is missing a configuration key {error} in {self.yaml_path}"
+            )
+            return sys.exit(1)
+
+
+# Function to make using common_options simpler
+def return_tasks_config(
+    tasks_file: typing.Optional[str],
+    task: typing.Optional[tuple[str]],
+):
+    loaded_tasks = ReadTaskConfig(yaml_path=tasks_file)
+    final_tasks = []
+    if task == () or task == [""]:
+        task_config = loaded_tasks.returnTasks()
+        final_tasks = task_config.tasks
+    else:
+        for item in task:
+            task_config = loaded_tasks.returnTasks(task=item)
+            final_tasks.append(task_config.tasks[0])
+    task_config = TaskConfigFile(tasks=final_tasks)
+    return task_config
+
+
+# # Function to return a list of tasks from a given task configuration file
+# def return_list_of_tasks(task_config: ReadTaskConfig) -> list[TaskConfig]:
+#     tasks = task_config.returnTasks()
+#     final_tasks = []
+#     for item in tasks:
+#         final_tasks.append(item)
+#     return final_tasks
+
+
+# Function to convert all yaml files inside a path into a single yaml
+def combine_yaml_files(
+    path: typing.Optional[str] = None,
+):
+    final_tasks = []
+    for root, dirs, files in os.walk(resolve_path(path)):
+        for file in files:
+            if file.endswith(".yaml"):
+                full_file_path = os.path.join(resolve_path(path), file)
+                task_config = ReadTaskConfig(yaml_path=full_file_path).returnTasks()
+                if len(task_config.tasks) == 1:
+                    final_tasks.append(task_config.tasks[0])
+                else:
+                    for item in task_config.tasks:
+                        final_tasks.append(item)
+    merged_config = TaskConfigFile(tasks=final_tasks)
+    # for item in merged_config.tasks:
+    #     print(item)
+    return merged_config
diff --git a/examples/gemini/python/docs-agent/poetry.lock b/examples/gemini/python/docs-agent/poetry.lock
new file mode 100644
index 000000000..4992b9b55
--- /dev/null
+++ b/examples/gemini/python/docs-agent/poetry.lock
@@ -0,0 +1,4609 @@
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+
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+trio = ["trio (>=0.23)"]
+
+[[package]]
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+    {file = "wrapt-1.16.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:5f15814a33e42b04e3de432e573aa557f9f0f56458745c2074952f564c50e664"},
+    {file = "wrapt-1.16.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:db2e408d983b0e61e238cf579c09ef7020560441906ca990fe8412153e3b291f"},
+    {file = "wrapt-1.16.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:edfad1d29c73f9b863ebe7082ae9321374ccb10879eeabc84ba3b69f2579d537"},
+    {file = "wrapt-1.16.0-cp39-cp39-win32.whl", hash = "sha256:ed867c42c268f876097248e05b6117a65bcd1e63b779e916fe2e33cd6fd0d3c3"},
+    {file = "wrapt-1.16.0-cp39-cp39-win_amd64.whl", hash = "sha256:eb1b046be06b0fce7249f1d025cd359b4b80fc1c3e24ad9eca33e0dcdb2e4a35"},
+    {file = "wrapt-1.16.0-py3-none-any.whl", hash = "sha256:6906c4100a8fcbf2fa735f6059214bb13b97f75b1a61777fcf6432121ef12ef1"},
+    {file = "wrapt-1.16.0.tar.gz", hash = "sha256:5f370f952971e7d17c7d1ead40e49f32345a7f7a5373571ef44d800d06b1899d"},
+]
+
+[[package]]
+name = "zipp"
+version = "3.18.1"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
+    {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
+]
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.10,<3.12"
+content-hash = "630829af61db0eb47edf2ebf68494279ae5ee16da85f212814646abd264f645e"
diff --git a/demos/palm/python/docs-agent/pylintrc b/examples/gemini/python/docs-agent/pylintrc
similarity index 100%
rename from demos/palm/python/docs-agent/pylintrc
rename to examples/gemini/python/docs-agent/pylintrc
diff --git a/demos/palm/python/docs-agent/pyproject.toml b/examples/gemini/python/docs-agent/pyproject.toml
similarity index 64%
rename from demos/palm/python/docs-agent/pyproject.toml
rename to examples/gemini/python/docs-agent/pyproject.toml
index 9cc8489b6..c39b7fbbb 100644
--- a/demos/palm/python/docs-agent/pyproject.toml
+++ b/examples/gemini/python/docs-agent/pyproject.toml
@@ -1,9 +1,10 @@
 [tool.poetry]
 name = "docs-agent"
-version = "0.1.0"
+version = "0.3.6"
 description = ""
 authors = ["Docs Agent contributors"]
 readme = "README.md"
+packages = [{include = "docs_agent"}]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<3.12"
@@ -11,21 +12,29 @@ rich = "^13.3.5"
 Markdown = "^3.4.3"
 beautifulsoup4 = "^4.12.2"
 protobuf = ">=3.20"
-chromadb = "^0.3.21"
-sentence-transformers = "^2.2.2"
 ratelimit = "^2.2.1"
 absl-py = "^1.4.0"
 python-frontmatter = "^1.0.0"
 flatdict = "^4.0.1"
-google-generativeai = "^0.1.0"
-grpcio = "^1.57.0"
-grpcio-tools = "^1.57.0"
+google-generativeai = "^0.5.0"
 uuid = "^1.30"
+pytz = ">=2020.1"
+chromadb = "^0.4.22"
+click = "^8.1.7"
+pyyaml = "^6.0.1"
+numpy = "^1.26.4"
+tqdm = "^4.66.2"
+flask = "^2.3.2"
 
 [tool.poetry.group.dev.dependencies]
 ipython = "^8.13.2"
 pylint = "^2.17.4"
 black = "^23.3.0"
+deptry = "^0.13.0"
+
+[tool.deptry.per_rule_ignores]
+DEP003 = ["docs_agent"]
+DEP002 = ["uuid"]
 
 [tool.poetry.group.internal]
 optional = true
@@ -33,10 +42,10 @@ optional = true
 [tool.poetry.group.internal.dependencies]
 seqio = "^0.0.16"
 
-[tool.poetry.group.chatbot.dependencies]
-flask = "^2.3.2"
-flask-cors = "^3.0.10"
-
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
+
+
+[tool.poetry.scripts]
+agent = "docs_agent.interfaces.cli.cli:cli"
diff --git a/examples/gemini/python/docs-agent/scripts/helpme.sh b/examples/gemini/python/docs-agent/scripts/helpme.sh
new file mode 100755
index 000000000..183306c7f
--- /dev/null
+++ b/examples/gemini/python/docs-agent/scripts/helpme.sh
@@ -0,0 +1,53 @@
+#!/bin/bash
+
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# IF NECESSARY, ADJUST THIS PATH TO YOUR `docs-agent` DIRECTORY.
+docs_agent_dir="$HOME/docs-agent"
+
+# Initialize request and filename variables.
+request=""
+filename=""
+
+# Loop through the arguments and look for the `--file` flag.
+while [[ $# -gt 0 ]]; do
+  case "$1" in
+    --file)
+      shift
+      filename="$1"
+      shift
+      ;;
+    *)
+      request="$request $1"
+      shift
+      ;;
+  esac
+done
+
+# Get the full path for the filename.
+if [[ -n "$filename" ]]; then
+  filename=$(readlink -m ${filename})
+else
+  filename="None"
+fi
+
+# Check if the POETRY_ACTIVE environment variable is set
+if [ -z "$POETRY_ACTIVE" ]; then
+  cd "$docs_agent_dir" && poetry run agent helpme $request --file "$filename"
+else
+  agent helpme $request --file "$filename"
+fi
diff --git a/demos/palm/python/docs-agent/setup.py b/examples/gemini/python/docs-agent/scripts/tellme.sh
old mode 100644
new mode 100755
similarity index 67%
rename from demos/palm/python/docs-agent/setup.py
rename to examples/gemini/python/docs-agent/scripts/tellme.sh
index 1d9261f80..a51d48554
--- a/demos/palm/python/docs-agent/setup.py
+++ b/examples/gemini/python/docs-agent/scripts/tellme.sh
@@ -1,3 +1,5 @@
+#!/bin/bash
+
 #
 # Copyright 2023 Google LLC
 #
@@ -14,11 +16,13 @@
 # limitations under the License.
 #
 
-"""For setting up local packages in the project."""
-from setuptools import setup
+# IF NECESSARY, ADJUST THIS PATH TO YOUR `docs-agent` DIRECTORY.
+docs_agent_dir="$HOME/docs-agent"
+
+# Check if the POETRY_ACTIVE environment variable is set
+if [ -z "$POETRY_ACTIVE" ]; then
+   cd "$docs_agent_dir" && poetry run agent tellme $@
+else
+   agent tellme $@
+fi
 
-setup(
-    name="docs_agent",
-    packages=["chatbot", "docs_agent"],
-    install_requires=["flask"],
-)
diff --git a/examples/gemini/python/docs-agent/tasks/extract-workflows-task.yaml b/examples/gemini/python/docs-agent/tasks/extract-workflows-task.yaml
new file mode 100644
index 000000000..bb22fcb0c
--- /dev/null
+++ b/examples/gemini/python/docs-agent/tasks/extract-workflows-task.yaml
@@ -0,0 +1,12 @@
+tasks:
+  - name: "ExtractWorkflows"
+    model: "models/gemini-1.5-flash-latest"
+    description: "An agent that extracts workflows from a source doc."
+    steps:
+      - prompt: "Summarize the contents of this document in a concise and informative manner. Focus on the key procedures, steps, or workflows described."
+        flags:
+          file: "<INPUT>"
+          default_input: "./README.md"
+      - prompt: "Identify and list all key workflows described in the document. Provide a brief description for each workflow, highlighting its purpose and key steps."
+      - prompt: "Identify all command lines used in the workflows described in the document. Focus on command lines that are essential for executing the workflow steps."
+      - prompt: "For each identified command line, provide a detailed description of its function and purpose. Include specific examples of its usage, showcasing how it is integrated within the workflows."
diff --git a/examples/gemini/python/docs-agent/tasks/help-polish-prompts-task.yaml b/examples/gemini/python/docs-agent/tasks/help-polish-prompts-task.yaml
new file mode 100644
index 000000000..a1283abed
--- /dev/null
+++ b/examples/gemini/python/docs-agent/tasks/help-polish-prompts-task.yaml
@@ -0,0 +1,13 @@
+tasks:
+  - name: "HelpPolishPrompts"
+    model: "models/gemini-1.5-flash-latest"
+    description: "An agent that helps polish prompts in a task file."
+    preamble: "When writing a prompt, always be direct and concise."
+    steps:
+      - prompt: "Revise the prompts in this task YAML file to improve their effectiveness in generating responses."
+        flags:
+          file: "<INPUT>"
+          default_input: "./tasks/index-page-generator-task.yaml"
+          file_ext: "yaml"
+      - prompt: "Replace the original prompts in the task YAML file with the revised prompts."
+      - prompt: "Suggest additional prompts relevant to the main task in this task YAML file."
diff --git a/examples/gemini/python/docs-agent/tasks/index-page-generator-task.yaml b/examples/gemini/python/docs-agent/tasks/index-page-generator-task.yaml
new file mode 100644
index 000000000..dfed4c666
--- /dev/null
+++ b/examples/gemini/python/docs-agent/tasks/index-page-generator-task.yaml
@@ -0,0 +1,15 @@
+tasks:
+  - name: "IndexPageGenerator"
+    model: "models/gemini-1.5-flash-latest"
+    description: "An agent that generates a draft of an index page from source docs."
+    preamble: "When generating a Markdown file, limit the number of characters per line to 80 or less, and always include a brief description to each page in the list."
+    steps:
+      - prompt: "Explain the files in this directory."
+        flags:
+          allfiles: "<INPUT>"
+          default_input: "./docs_agent"
+          file_ext: "md"
+      - prompt: "Generate a draft of an index page that lists all the pages above so that the pages are organized by related topics for new developers."
+      - prompt: "Update the introduction paragraph of the draft above to be more helpful and descriptive (and keep the rest of the draft intact on the page)."
+      - prompt: "Add Markdown reference-style links that maps certain key words to the files found above (and add a list of the reference-style links at the bottom of the page)."
+
diff --git a/examples/gemini/python/docs-agent/tasks/release-notes-task.yaml b/examples/gemini/python/docs-agent/tasks/release-notes-task.yaml
new file mode 100644
index 000000000..24c9fa1a7
--- /dev/null
+++ b/examples/gemini/python/docs-agent/tasks/release-notes-task.yaml
@@ -0,0 +1,13 @@
+tasks:
+  - name: "DraftReleaseNotes"
+    model: "models/gemini-1.5-flash"
+    description: "An agent that generates a draft of Docs Agent release notes from git commit messages."
+    preamble: "When generating release notes, limit the total number of key features and updates to 5 or fewer entries."
+    steps:
+      - prompt: "git --no-pager log --since=2024-06-15"
+        function: "posix"
+        description: "Load the git log commit messages from the local Docs Agent checkout since a target date."
+      - prompt: "Please extract a list of key features and improvements from the provided git log commit messages, focusing on those that are most impactful and relevant for developers."
+      - prompt: "Based on the extracted key features and improvements, write a concise and informative release notes draft. Use clear and concise language, focusing on the benefits and value provided to developers."
+      - prompt: "Revise the release notes draft to enhance readability and structure. Format the release notes using bullet points for clarity and limit each entry to a maximum of two sentences."
+
diff --git a/demos/palm/python/docs-agent/third_party/css/chatbox.css b/examples/gemini/python/docs-agent/third_party/css/chatbox.css
similarity index 100%
rename from demos/palm/python/docs-agent/third_party/css/chatbox.css
rename to examples/gemini/python/docs-agent/third_party/css/chatbox.css
diff --git a/examples/gemini/python/docs-agent/third_party/g2docsmd-html/LICENSE b/examples/gemini/python/docs-agent/third_party/g2docsmd-html/LICENSE
new file mode 100644
index 000000000..7a4a3ea24
--- /dev/null
+++ b/examples/gemini/python/docs-agent/third_party/g2docsmd-html/LICENSE
@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
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+      "License" shall mean the terms and conditions for use, reproduction,
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+      outstanding shares, or (iii) beneficial ownership of such entity.
+
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+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
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+   6. Trademarks. This License does not grant permission to use the trade
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+   7. Disclaimer of Warranty. Unless required by applicable law or
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+      incurred by, or claims asserted against, such Contributor by reason
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+      To apply the Apache License to your work, attach the following
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\ No newline at end of file
diff --git a/examples/gemini/python/docs-agent/third_party/g2docsmd-html/exportmd.gs b/examples/gemini/python/docs-agent/third_party/g2docsmd-html/exportmd.gs
new file mode 100644
index 000000000..aa8afd00f
--- /dev/null
+++ b/examples/gemini/python/docs-agent/third_party/g2docsmd-html/exportmd.gs
@@ -0,0 +1,1244 @@
+/*
+Parsing from mangini/gdocs2md.
+Modified by clearf to add files to the google directory structure.
+Modified by lmmx to write Markdown, going back to HTML-incorporation.
+
+Usage:
+  NB: don't use on top-level doc (in root Drive folder) See comment in setupScript function.
+  Adding this script to your doc:
+    - Tools > Script Manager > New
+    - Select "Blank Project", then paste this code in and save.
+  Running the script:
+    - Tools > Script Manager
+    - Select "convertDocumentToMarkdown" function.
+    - Click Run button.
+    - Converted doc will be added to a "Markdown" folder in the source document's directories.
+    - Images will be added to a subfolder of the "Markdown" folder.
+*/
+
+function onInstall(e) {
+  onOpen(e);
+}
+
+function onOpen() {
+  // Add a menu with some items, some separators, and a sub-menu.
+  setupScript();
+//  In future:
+//  DocumentApp.getUi().createAddonMenu();
+  DocumentApp.getUi().createMenu('Markdown')
+      .addItem('View as markdown', 'markdownPopup')
+      .addSubMenu(DocumentApp.getUi().createMenu('Export \u2192 markdown')
+                  .addItem('Export to local file', 'convertSingleDoc')
+                  .addItem('Export entire folder to local file', 'convertFolder')
+                  .addItem('Customise markdown conversion', 'changeDefaults'))
+      .addSeparator()
+      .addSubMenu(DocumentApp.getUi().createMenu('Toggle comment visibility')
+                 .addItem('Image source URLs', 'toggleImageSourceStatus')
+                 .addItem('All comments', 'toggleCommentStatus'))
+      .addItem("Add comment", 'addCommentDummy')
+      .addToUi();
+}
+
+function changeDefaults() {
+  var ui = DocumentApp.getUi();
+  var default_settings = '{ use your imagination... }';
+  var greeting = ui.alert('This should be set up to display defaults from variables passed to getDocComments etc., e.g. something like:\n\nDefault settings are:'
+                    + '\ncomments - not checking deleted comments.\nDocument - this document (alternatively specify a document ID).'
+                    + '\n\nClick OK to edit these, or cancel.',
+                    ui.ButtonSet.OK_CANCEL);
+  ui.alert("There's not really need for this yet, so this won't proceed, regardless of what you just pressed.");
+  return;
+
+  // Future:
+  if (greeting == ui.Button.CANCEL) {
+    ui.alert("Alright, never mind!");
+    return;
+  }
+  // otherwise user clicked OK
+  // user clicked OK, to proceed with editing these defaults. Ask case by case whether to edit
+
+  var response = ui.prompt('What is x (default y)?', ui.ButtonSet.YES_NO_CANCEL);
+
+  // Example code from docs at https://developers.google.com/apps-script/reference/base/button-set
+  // Process the user's response.
+  if (response.getSelectedButton() == ui.Button.YES) {
+    Logger.log('The user\'s name is %s.', response.getResponseText());
+  } else if (response.getSelectedButton() == ui.Button.NO) {
+    Logger.log('The user didn\'t want to provide a name.');
+  } else {
+    Logger.log('The user clicked the close button in the dialog\'s title bar.');
+  }
+}
+
+function setupScript() {
+  var script_properties = PropertiesService.getScriptProperties();
+  script_properties.setProperty("user_email", Drive.About.get().user.emailAddress);
+
+  // manual way to do the following:
+  // script_properties.setProperty("folder_id", "INSERT_FOLDER_ID_HERE");
+  // script_properties.setProperty("document_id", "INSERT_FILE_ID_HERE");
+
+  var doc_id = DocumentApp.getActiveDocument().getId();
+  script_properties.setProperty("document_id", doc_id);
+  var doc_parents = DriveApp.getFileById(doc_id).getParents();
+  var folders = doc_parents;
+  while (folders.hasNext()) {
+    var folder = folders.next();
+    var folder_id = folder.getId();
+  }
+  script_properties.setProperty("folder_id", folder_id);
+  script_properties.setProperty("image_folder_prefix", ""); // add if modifying image location
+}
+
+function addCommentDummy() {
+  // Dummy function to be switched during development for addComment
+  DocumentApp.getUi()
+    .alert('Cancelling comment entry',
+           "There's not currently a readable anchor for Google Docs - you need to write your own!"
+
+           + "\n\nThe infrastructure for using such an anchoring schema is sketched out in"
+           + " the exportmd.gs script's addComment function, for an anchor defined in anchor_props"
+
+           + "\n\nSee github.com/lmmx/devnotes/wiki/Custom-Google-Docs-comment-anchoring-schema",
+           DocumentApp.getUi().ButtonSet.OK
+          );
+  return;
+}
+
+function addComment() {
+
+  var doc_id = PropertiesService.getScriptProperties().getProperty('document_id');
+  var user_email = PropertiesService.getScriptProperties().getProperty('email');
+/*  Drive.Comments.insert({content: "hello world",
+                         context: {
+                           type: 'text/html',
+                           value: 'hinges'
+                         }
+                        }, document_id); */
+  var revision_list = Drive.Revisions.list(doc_id).items;
+  var recent_revision_id = revision_list[revision_list.length - 1].id;
+  var anchor_props = {
+    revision_id: recent_revision_id,
+    starting_offset: '',
+    offset_length: '',
+    total_chars: ''
+  }
+  insertComment(doc_id, 'hinges', 'Hello world!', my_email, anchor_props);
+}
+
+function insertComment(fileId, selected_text, content, user_email, anchor_props) {
+
+  // NB Deal with handling missing args
+
+  /*
+  anchor_props is an object with 4 properties:
+    - revision_id,
+    - starting_offset,
+    - offset_length,
+    - total_chars
+  */
+
+  var context = Drive.newCommentContext();
+    context.value = selected_text;
+    context.type = 'text/html';
+  var comment = Drive.newComment();
+    comment.kind = 'drive#comment';
+    var author = Drive.newUser();
+      author.kind = 'drive#user';
+      author.displayName = user_email;
+      author.isAuthenticatedUser = true;
+    comment.author = author;
+    comment.content = type;
+    comment.context = context;
+    comment.status = 'open';
+    comment.anchor = "{'r':"
+                     + anchor_props.revision_id
+                     + ",'a':[{'txt':{'o':"
+                     + anchor_props.starting_offset
+                     + ",'l':"
+                     + anchor_props.offset_length
+                     + ",'ml':"
+                     + anchor_props.total_chars
+                     + "}}]}";
+    comment.fileId = fileId;
+  Drive.Comments.insert(comment, fileId);
+}
+
+function decodeScriptSwitches(optional_storage_name) {
+  var property_name = (typeof(optional_storage_name) == 'string') ? optional_storage_name : 'switch_settings';
+  var script_properties = PropertiesService.getScriptProperties();
+  return script_properties
+              .getProperty(property_name)
+              .replace(/{|}/g,'')          // Get the statements out of brackets...
+              .replace(',', ';');          // ...swap the separator for a semi-colon...
+  // ...evaluate the stored object string as statements upon string return and voila, switches interpreted
+}
+
+
+function getDocComments(comment_list_settings) {
+  var possible_settings = ['images', 'include_deleted'];
+
+  // switches are processed and set on a script-wide property called "comment_switches"
+  var property_name = 'comment_switches';
+  switchHandler(comment_list_settings, possible_settings, property_name);
+
+  var script_properties = PropertiesService.getScriptProperties();
+  var comment_switches = decodeScriptSwitches(property_name);
+  eval(comment_switches);
+
+  var document_id = script_properties.getProperty("document_id");
+  var comments_list = Drive.Comments.list(document_id,
+                                          {includeDeleted: include_deleted,
+                                           maxResults: 100 }); // 0 to 100, default 20
+  // See https://developers.google.com/drive/v2/reference/comments/list for all options
+  var comment_array = [];
+  var image_sources = [];
+  // To collect all comments' image URLs to match against inlineImage class elements LINK_URL attribute
+
+  for (var i = 0; i < comments_list.items.length; i++) {
+    var comment = comments_list.items[i];
+    var comment_text = comment.content;
+    var comment_status = comment.status;
+ /*
+    images is a generic parameter passed in as a switch to
+    return image URL-containing comments only.
+
+    If the parameter is provided, it's no longer undefined.
+ */
+    var img_url_regex = /(https?:\/\/.+?\.(png|gif|jpe?g))/;
+    var has_img_url = img_url_regex.test(comment_text);
+
+    if (images && !has_img_url) continue; // no image URL, don't store comment
+    if (has_img_url) image_sources.push(RegExp.$1);
+    comment_array.push(comment);
+  }
+  script_properties.setProperty('image_source_URLs', image_sources)
+  return comment_array;
+}
+
+function isValidAttrib(attribute) { // Sanity check function, called per element in array
+
+  // Possible list of attributes to check against (leaving out unchanging ones like kind)
+  possible_attrs = [
+    'selfLink',
+    'commentId',
+    'createdDate',
+    'modifiedDate',
+    'author',
+    'htmlContent',
+    'content',
+    'deleted',
+    'status',
+    'context',
+    'anchor',
+    'fileId',
+    'fileTitle',
+    'replies',
+    'author'
+  ];
+
+  // Check if attribute(s) provided can be used to match/filter comments:
+
+  if (typeof(attribute) == 'string' || typeof(attribute) == 'object') {
+    // Either a string/object (1-tuple)
+
+    // Generated with Javascript, gist: https://gist.github.com/lmmx/451b301e1d78ed2c10b4
+
+    // Return false from the function if any of the attributes specified are not in the above list
+
+    // If an object, the name is the key, otherwise it's just the string
+    if (attribute.constructor === Object) {
+      var att_keys = [];
+      for (var att_key in attribute) {
+        if (attribute.hasOwnProperty(att_key)) {
+          att_keys.push(att_key);
+        }
+      }
+      for (var n=0; n < att_keys.length; n++) {
+        var attribute_name = att_keys[n];
+        var is_valid_attrib = (possible_attrs.indexOf(attribute_name) > -1);
+
+        // The attribute needs to be one of the possible attributes listed above, match its given value(s),
+        // else returning false will throw an error from onAttribError when within getCommentAttributes
+        return is_valid_attrib;
+      }
+    } else if (typeof(attribute) == 'string') {
+      var attribute_name = attribute;
+      var is_valid_attrib = (possible_attrs.indexOf(attribute_name) > -1);
+      return is_valid_attrib;
+      // Otherwise is a valid (string) attribute
+    } else if (attribute.constructor === Array) {
+      return false; // Again, if within getCommentAttributes this will cause an error - shouldn't pass an array
+    } else {
+      // Wouldn't expect this to happen, so give a custom error message
+      Logger.log('Unknown type (assumed impossible) passed to isValidAttrib: ', attribute, attribute.constructor);
+      throw new TypeError('Unknown passed to isValidAttrib - this should be receiving 1-tuples only, see logs for details.');
+    }
+  } else return false; // Neither string/object / array of strings &/or objects - not a valid attribute
+}
+
+function getCommentAttributes(attributes, comment_list_settings) {
+
+  // A filter function built on Comments.list, for a given list of attributes
+  // Objects' values are ignored here, only their property titles are used to filter comments.
+
+
+  /*
+    - attributes: array of attributes to filter/match on
+    - comment_list_settings: (optional) object with properties corresponding to switches in getDocComments
+
+    This function outputs an array of the same length as the comment list, containing
+    values for all fields matched/filtered on.
+  */
+
+
+  /*
+   *         All possible comment attributes are listed at:
+   *   https://developers.google.com/drive/v2/reference/comments#properties
+   */
+
+  //  Firstly, describe the type in a message to be thrown in case of TypeError:
+
+  var attrib_def_message = "'attributes' should be a string (the attribute to get for each comment), "
+                           + "an object (a key-value pair for attribute and desired value), "
+                           + "or an array of objects (each with key-value pairs)";
+
+  function onAttribError(message) {
+    Logger.log(message);
+    throw new TypeError(message);
+  }
+
+  // If (optional) comment_list_settings isn't set, make a getDocComments call with switches left blank.
+  if (typeof(comment_list_settings) == 'undefined') var comment_list_settings = {};
+  if (typeof(attributes) == 'undefined') onAttribError(attrib_def_message); // no variables specified
+
+  if (isValidAttrib(attributes)) { // This will be true if there's only one attribute, not provided in an array
+
+    /*
+       Make a 1-tuple (array of 1) from either an object or a string,
+       i.e. a single attribute, with or without a defined value respectively.
+    */
+
+    var attributes = Array(attributes);
+
+  } else if (attributes.constructor === Array) {
+
+    // Check each item in the array is a valid attribute specification
+    for (var l = 0; l < attributes.length; l++) {
+      if (! isValidAttrib(attributes[l]) ) {
+        onAttribError('Error in attribute '
+                      + (l+1) + ' of ' + attributes.length
+                      + '\n\n' + + attrib_def_message);
+      }
+    }
+
+  } else { // Neither attribute nor array of attributes
+    throw new TypeError(attrib_def_message);
+  }
+
+  // Attributes now holds an array of string and/or objects specifying a comment match and/or filter query
+
+  var comment_list = getDocComments(comment_list_settings);
+  var comment_attrib_lists = [];
+  for (var i in comment_list) {
+    var comment = comment_list[i];
+    var comment_attrib_list = [];
+    for (var j in attributes) {
+      var comment_attribute = comment_list[i][attributes[j]];
+      comment_attrib_list.push(comment_attribute);
+    }
+    comment_attrib_lists.push(comment_attrib_list);
+  }
+  // The array comment_attrib_lists is now full of the requested attributes,
+  // of length equal to that of attributes
+  return comment_attrib_lists;
+}
+
+// Example function to use getCommentAttributes:
+
+function filterComments(attributes, comment_list_settings) {
+  var comment_attributes = getCommentAttributes(attributes, comment_list_settings);
+  var m = attribs.indexOf('commentId') // no need to keep track of commentID array position
+  comm_attribs.map(function(attrib_pair) {
+     if (attrib_pair[1]);
+  })
+}
+
+function toggleCommentStatus(comment_switches){
+  // Technically just image URL-containing comments, not sources just yet
+  var attribs = ['commentId', 'status'];
+  var comm_attribs = getCommentAttributes(attribs, comment_switches);
+  var rearrangement = [];
+  comm_attribs.map(
+    function(attrib_pair) { // for every comment return with the images_only / images: true comments.list setting,
+      switch (attrib_pair[1]){ // check the status of each
+        case 'open':
+          rearrangement.push([attrib_pair[0],'resolved']);
+          break;
+        case 'resolved':
+          rearrangement.push([attrib_pair[0],'open']);
+          break;
+      }
+    }
+  );
+  var script_properties = PropertiesService.getScriptProperties();
+  var doc_id = script_properties.getProperty("document_id");
+  rearrangement.map(
+    function(new_attrib_pair) { // for every comment ID with flipped status
+      Drive.Comments.patch('{"status": "'
+                           + new_attrib_pair[1]
+                           + '"}', doc_id, new_attrib_pair[0])
+    }
+  );
+  return;
+}
+
+function toggleImageSourceStatus(){
+  toggleCommentStatus({images: true});
+}
+
+function flipResolved() {
+  // Flip the status of resolved comments to open, and open comments to resolved (respectful = true)
+  // I.e. make resolved URL-containing comments visible, without losing track of normal comments' status
+
+  // To force all comments' statuses to switch between resolved and open en masse set respectful to false
+
+  var switch_settings = {};
+    switch_settings.respectful = true;
+    switch_settings.images_only = false; // If true, only switch status of comments with an image URL
+    switch_settings.switch_deleted_comments = false; // If true, also switch status of deleted comments
+
+  var comments_list = getDocComments(
+    { images: switch_settings.images_only,
+      include_deleted: switch_settings.switch_deleted_comments });
+
+  // Note: these parameters are unnecessary if both false (in their absence assumed false)
+  //       but included for ease of later reuse
+
+  if (switch_settings.respectful) {
+    // flip between
+  } else {
+    // flip all based on status of first in list
+  }
+}
+
+function markdownPopup() {
+  var css_style = '<style type="text/css" data-github="https://github.com/lmmx/gdocs2md-html/blob/master/md-preview.css">'
+    + 'h1, h2, h3, h4, h5, h6 {'
+    + '  font-family: "Gibson", sans-serif, "Helvetica Neue", HelveticaNeue, Helvetica, Arial, sans-serif;'
+    + '  font-size: 1.3em;'
+    + '  line-height: 1.4;'
+    + '  margin-top: 9px;'
+    + '}'
+    + 'code,pre,pre *{'
+    + '  font-size: 12px;'
+    + '  line-height: 1.4;'
+    + '  white-space: pre-wrap;'
+    + '  padding-top: 1em;'
+    + '  padding-bottom: 1em;'
+    + '  clear:both;'
+    + '  padding: 0.2em;'
+    + '  margin: 0;'
+    + '  background-color: rgba(0,0,0,0.04);'
+    + '  border-radius: 3px;'
+    + '  font: 11px Consolas, "Liberation Mono", Menlo, Courier, monospace;'
+    + '}'
+    + 'pre textarea {'
+    + '    background: transparent;'
+    + '    border: none;'
+    + '    height: inherit !important;'
+    + '    width: 775px;'
+    + '}'
+    + 'pre {'
+    + '    height: 430px;'
+    + '}'
+    + '</style>';
+
+  // The above was written with js since <link rel="stylesheet" href=[GitHub raw URL]> doesn't work:
+  // https://gist.github.com/lmmx/ec084fc351528395f2bb
+
+  var mdstring = stringMiddleMan();
+
+  var htmlstring =
+      '<!doctype html><html lang="en"><head><meta charset="utf-8">'
+    + css_style
+    + '</head><body><pre><textarea id="md-output" readonly="readonly">'
+    + mdstring
+    + '</textarea></pre><script type="text/javascript">'
+    + 'function mdClick() {'
+    + 'var textbox = document.getElementById("md-output");'
+    + 'textbox.setAttribute("onclick", "this.focus(); this.select();") }'
+    + 'window.onload = mdClick();'
+    + '</script></body></html>';
+
+  var html5 = HtmlService.createHtmlOutput(htmlstring)
+      .setSandboxMode(HtmlService.SandboxMode.IFRAME)
+      .setWidth(800)
+      .setHeight(500);
+
+  DocumentApp.getUi()
+      .showModalDialog(html5, 'Markdown output');
+}
+
+function stringMiddleMan() {
+  var returned_string;
+  convertSingleDoc({"return_string": true}); // for some reason needs the scope to be already set...
+  // could probably rework to use mdstring rather than returned_string, cut out middle man function
+  return this.returned_string;
+}
+
+function convertSingleDoc(optional_switches) {
+  var script_properties = PropertiesService.getScriptProperties();
+  // renew comments list on every export
+  var doc_comments = getDocComments();
+  var image_urls = getDocComments({images: true}); // NB assumed false - any value will do
+  script_properties.setProperty("comments", doc_comments);
+  script_properties.setProperty("image_srcs", image_urls);
+  var folder_id = script_properties.getProperty("folder_id");
+  var document_id = script_properties.getProperty("document_id");
+  var source_folder = DriveApp.getFolderById(folder_id);
+  var markdown_folders = source_folder.getFoldersByName("Markdown");
+
+  var markdown_folder;
+  if (markdown_folders.hasNext()) {
+    markdown_folder = markdown_folders.next();
+  } else {
+    // Create a Markdown folder if it doesn't exist.
+    markdown_folder = source_folder.createFolder("Markdown")
+  }
+
+  convertDocumentToMarkdown(DocumentApp.openById(document_id), markdown_folder, optional_switches);
+}
+
+function convertFolder() {
+  var script_properties = PropertiesService.getScriptProperties();
+  var folder_id = script_properties.getProperty("folder_id");
+  var source_folder = DriveApp.getFolderById(folder_id);
+  var markdown_folders = source_folder.getFoldersByName("Markdown");
+
+
+  var markdown_folder;
+  if (markdown_folders.hasNext()) {
+    markdown_folder = markdown_folders.next();
+  } else {
+    // Create a Markdown folder if it doesn't exist.
+    markdown_folder = source_folder.createFolder("Markdown");
+  }
+
+  // Only try to convert google docs files.
+  var gdoc_files = source_folder.getFilesByType("application/vnd.google-apps.document");
+
+  // For every file in this directory
+  while(gdoc_files.hasNext()) {
+    var gdoc_file = gdoc_files.next()
+
+    var filename = gdoc_file.getName();
+    var md_files = markdown_folder.getFilesByName(filename + ".md");
+    var update_file = false;
+
+    if (md_files.hasNext()) {
+      var md_file = md_files.next();
+
+      if (md_files.hasNext()){ // There are multiple markdown files; delete and rerun
+        update_file = true;
+      } else if (md_file.getLastUpdated() < gdoc_file.getLastUpdated()) {
+        update_file = true;
+      }
+    } else {
+      // There is no folder and the conversion needs to be rerun
+      update_file = true;
+    }
+
+    if (update_file) {
+      convertDocumentToMarkdown(DocumentApp.openById(gdoc_file.getId()), markdown_folder);
+    }
+  }
+}
+
+function switchHandler(input_switches, potential_switches, optional_storage_name) {
+
+  // Firstly, if no input switches were set, make an empty input object
+  if (typeof(input_switches) == 'undefined') input_switches = {};
+
+  // Use optional storage name if it's defined (must be a string), else use default variable name "switch_settings"
+  var property_name = (typeof(optional_storage_name) == 'string') ? optional_storage_name : 'switch_settings';
+
+  // Make a blank object to be populated and stored as the script-wide property named after property_name
+  var switch_settings = {};
+
+  for (var i in potential_switches) {
+    var potential_switch = potential_switches[i];
+
+    // If each switch has been set (in input_switches), evaluate it, else assume it's switched off (false):
+
+    if (input_switches.propertyIsEnumerable(potential_switch)) {
+
+      // Evaluates a string representing a statement which sets switch_settings properties from input_switches
+      // e.g. "switch_settings.images = true" when input_switches = {images: true}
+
+      eval('switch_settings.' + potential_switch + " = " + input_switches[potential_switch]);
+
+    } else {
+
+      // Alternatively, the evaluated statement sets anything absent from the input_switches object as false
+      // e.g. "switch_settings.images = false" when input_switches = {} and potential_switches = ['images']
+
+      eval('switch_settings.' + potential_switch + " = false");
+    }
+  }
+
+  PropertiesService.getScriptProperties().setProperty(property_name, switch_settings);
+
+  /*
+  Looks bad but more sensible than repeatedly checking if arg undefined.
+
+  Sets every variable named in the potential_switches array to false if
+  it wasn't passed into the input_switches object, otherwise evaluates.
+
+  Any arguments not passed in are false, but so are any explicitly passed in as false:
+  all parameters are therefore Boolean until otherwise specified.
+  */
+
+}
+
+function convertDocumentToMarkdown(document, destination_folder, optional_switches) {
+  // if returning a string, force_save_images will make the script continue - experimental
+  var possible_switches = ['return_string', 'force_save_images'];
+  var property_name = 'conversion_switches';
+  switchHandler(optional_switches, possible_switches, property_name);
+
+  // TODO switch off image storage if force_save_images is true - not necessary for normal behaviour
+  var script_properties = PropertiesService.getScriptProperties();
+  var comment_switches = decodeScriptSwitches(property_name);
+  eval(comment_switches);
+
+  var image_prefix = script_properties.getProperty("image_folder_prefix");
+  var numChildren = document.getActiveSection().getNumChildren();
+  var text = "";
+  var md_filename = document.getName()+".md";
+  var image_foldername = document.getName()+"_images";
+  var inSrc = false;
+  var inClass = false;
+  var globalImageCounter = 0;
+  var globalListCounters = {};
+  // edbacher: added a variable for indent in src <pre> block. Let style sheet do margin.
+  var srcIndent = "";
+
+  var postHasImages = false;
+
+  var files = [];
+
+  // Walk through all the child elements of the doc.
+  for (var i = 0; i < numChildren; i++) {
+    var child = document.getActiveSection().getChild(i);
+    var result = processParagraph(i, child, inSrc, globalImageCounter, globalListCounters, image_prefix + image_foldername);
+    globalImageCounter += (result && result.images) ? result.images.length : 0;
+    if (result!==null) {
+      if (result.sourceGlossary==="start" && !inSrc) {
+        inSrc=true;
+        text+="<pre class=\"glossary\">\n";
+      } else if (result.sourceGlossary==="end" && inSrc) {
+        inSrc=false;
+        text+="</pre>\n\n";
+      } else if (result.sourceFigCap==="start" && !inSrc) {
+        inSrc=true;
+        text+="<pre class=\"glossary\">\n";
+      } else if (result.sourceFigCap==="end" && inSrc) {
+        inSrc=false;
+        text+="</pre>\n\n";
+      } else if (result.source==="start" && !inSrc) {
+        inSrc=true;
+        text+="<pre>\n";
+      } else if (result.source==="end" && inSrc) {
+        inSrc=false;
+        text+="</pre>\n\n";
+      } else if (result.inClass==="start" && !inClass) {
+        inClass=true;
+        text+="<pre class=\""+result.className+"\">\n";
+      } else if (result.inClass==="end" && inClass) {
+        inClass=false;
+        text+="</pre>\n\n";
+      } else if (inClass) {
+        text+=result.text+"\n\n";
+      } else if (inSrc) {
+        text+=(srcIndent+escapeHTML(result.text)+"\n");
+      } else if (result.text && result.text.length>0) {
+        text+=result.text+"\n\n";
+      }
+
+      if (result.images && result.images.length>0) {
+        for (var j=0; j<result.images.length; j++) {
+          files.push( { "blob": result.images[j].blob } );
+          postHasImages = true;
+        }
+      }
+    } else if (inSrc) { // support empty lines inside source code
+      text+='\n';
+    }
+
+  }
+
+  if (return_string && !force_save_images) {
+    returned_string = text;
+    return returned_string;
+  }
+  // return the string rather than continuing to write files
+
+  // I don't need to handle string return *with* saved images yet so this flag doesn't do anything in absence of return_string
+
+  files.push({"fileName": md_filename, "mimeType": "text/plain", "content": text});
+
+
+  // Cleanup any old folders and files in our destination directory with an identical name
+  var old_folders = destination_folder.getFoldersByName(image_foldername)
+  while (old_folders.hasNext()) {
+    var old_folder = old_folders.next();
+    old_folder.setTrashed(true)
+  }
+
+  // Remove any previously converted markdown files.
+  var old_files = destination_folder.getFilesByName(md_filename)
+  while (old_files.hasNext()) {
+    var old_file = old_files.next();
+    old_file.setTrashed(true)
+  }
+
+  // Create a subfolder for images if they exist
+  var image_folder;
+  if (postHasImages) {
+    image_folder = DriveApp.createFolder(image_foldername);
+    DriveApp.removeFolder(image_folder); // Confusing convention; this just removes the folder from the google drive root.
+    destination_folder.addFolder(image_folder)
+  }
+
+  for (var i = 0; i < files.length; i++) {
+    var saved_file;
+    if (files[i].blob) {
+      saved_file = DriveApp.createFile(files[i].blob)
+      // The images go into a subfolder matching the post title
+      image_folder.addFile(saved_file)
+    } else {
+      // The markdown files all go in the "Markdown" directory
+      saved_file = DriveApp.createFile(files[i]["fileName"], files[i]["content"], files[i]["mimeType"])
+      destination_folder.addFile(saved_file)
+    }
+   DriveApp.removeFile(saved_file) // Removes from google drive root.
+  }
+
+}
+
+function escapeHTML(text) {
+  return text.replace(/</g, '&lt;').replace(/>/g, '&gt;');
+}
+
+function standardQMarks(text) {
+  return text.replace(/\u2018|\u8216|\u2019|\u8217/g,"'").replace(/\u201c|\u8220|\u201d|\u8221/g, '"')
+}
+
+// Process each child element (not just paragraphs).
+function processParagraph(index, element, inSrc, imageCounter, listCounters, image_path) {
+  // First, check for things that require no processing.
+  if (element.getNumChildren()==0) {
+    return null;
+  }
+  // Skip on TOC.
+  if (element.getType() === DocumentApp.ElementType.TABLE_OF_CONTENTS) {
+    return {"text": "[[TOC]]"};
+  }
+
+  // Set up for real results.
+  var result = {};
+  var pOut = "";
+  var textElements = [];
+  var imagePrefix = "image_";
+
+  // Handle Table elements. Pretty simple-minded now, but works for simple tables.
+  // Note that Markdown does not process within block-level HTML, so it probably
+  // doesn't make sense to add markup within tables.
+  if (element.getType() === DocumentApp.ElementType.TABLE) {
+    textElements.push("<table>\n");
+    var nCols = element.getChild(0).getNumCells();
+    for (var i = 0; i < element.getNumChildren(); i++) {
+      textElements.push("  <tr>\n");
+      // process this row
+      for (var j = 0; j < nCols; j++) {
+        textElements.push("    <td>" + element.getChild(i).getChild(j).getText() + "</td>\n");
+      }
+      textElements.push("  </tr>\n");
+    }
+    textElements.push("</table>\n");
+  }
+
+  // Process various types (ElementType).
+  for (var i = 0; i < element.getNumChildren(); i++) {
+    var t = element.getChild(i).getType();
+
+    if (t === DocumentApp.ElementType.TABLE_ROW) {
+      // do nothing: already handled TABLE_ROW
+    } else if (t === DocumentApp.ElementType.TEXT) {
+      var txt  = element.getChild(i);
+      pOut += txt.getText();
+      textElements.push(txt);
+    } else if (t === DocumentApp.ElementType.INLINE_IMAGE) {
+      var imglink = element.getChild(i).getLinkUrl();
+      result.images = result.images || [];
+      var blob = element.getChild(i).getBlob()
+      var contentType = blob.getContentType();
+      var extension = "";
+      if (/\/png$/.test(contentType)) {
+        extension = ".png";
+      } else if (/\/gif$/.test(contentType)) {
+        extension = ".gif";
+      } else if (/\/jpe?g$/.test(contentType)) {
+        extension = ".jpg";
+      } else {
+        throw "Unsupported image type: "+contentType;
+      }
+
+      var name = imagePrefix + imageCounter + extension;
+      blob.setName(name);
+
+      imageCounter++;
+      if (!return_string || force_save_images) {
+        textElements.push('![](' + image_path + '/' + name + ')');
+      } else {
+        textElements.push('![](' + imglink + ')');
+      }
+      //result.images.push( {
+      //  "bytes": blob.getBytes(),
+      //  "type": contentType,
+      //  "name": name});
+
+      result.images.push({ "blob" : blob } )
+
+    } else if (t === DocumentApp.ElementType.PAGE_BREAK) {
+      // ignore
+    } else if (t === DocumentApp.ElementType.HORIZONTAL_RULE) {
+      textElements.push('* * *\n');
+    } else if (t === DocumentApp.ElementType.FOOTNOTE) {
+      textElements.push(' ('+element.getChild(i).getFootnoteContents().getText()+')');
+    } else {
+      throw "Paragraph "+index+" of type "+element.getType()+" has an unsupported child: "
+      +t+" "+(element.getChild(i)["getText"] ? element.getChild(i).getText():'')+" index="+index;
+    }
+  }
+
+  if (textElements.length==0) {
+    // Isn't result empty now?
+    return result;
+  }
+
+  var ind_f = element.getIndentFirstLine();
+  var ind_s = element.getIndentStart();
+  var ind_e = element.getIndentEnd();
+  var i_fse = ['ind_f','ind_s','ind_e'];
+  var indents = {};
+  for (indt=0;indt<i_fse.length;indt++) {
+    var indname = i_fse[indt];
+    if (eval(indname) > 0) indents[indname] = eval(indname);
+    // lazy test, null (no indent) is not greater than zero, but becomes set if indent 'undone'
+  }
+  var inIndent = (Object.keys(indents).length > 0);
+
+  // evb: Add glossary and figure caption too. (And abbreviations: gloss and fig-cap.)
+  // process source code block:
+  if (/^\s*---\s+gloss\s*$/.test(pOut) || /^\s*---\s+source glossary\s*$/.test(pOut)) {
+    result.sourceGlossary = "start";
+  } else if (/^\s*---\s+fig-cap\s*$/.test(pOut) || /^\s*---\s+source fig-cap\s*$/.test(pOut)) {
+    result.sourceFigCap = "start";
+  } else if (/^\s*---\s+src\s*$/.test(pOut) || /^\s*---\s+source code\s*$/.test(pOut)) {
+    result.source = "start";
+  } else if (/^\s*---\s+class\s+([^ ]+)\s*$/.test(pOut)) {
+    result.inClass = "start";
+    result.className = RegExp.$1.replace(/\./g,' ');
+  } else if (/^\s*---\s*$/.test(pOut)) {
+    result.source = "end";
+    result.sourceGlossary = "end";
+    result.sourceFigCap = "end";
+    result.inClass = "end";
+  } else if (/^\s*---\s+jsperf\s*([^ ]+)\s*$/.test(pOut)) {
+    result.text = '<iframe style="width: 100%; height: 240px; overflow: hidden; border: 0;" '+
+                  'src="http://www.html5rocks.com/static/jsperfview/embed.html?id='+RegExp.$1+
+                  '"></iframe>';
+  } else {
+
+    prefix = findPrefix(inSrc, element, listCounters);
+
+    var pOut = "";
+    for (var i=0; i<textElements.length; i++) {
+      pOut += processTextElement(inSrc, textElements[i]);
+    }
+
+    // replace Unicode quotation marks (double and single)
+    pOut = standardQMarks(pOut);
+
+    result.text = prefix+pOut;
+  }
+
+  var indent_prefix = '> ';
+# var indent_alt_prefix = '> <sub>';
+  if (inIndent && !inSrc) {
+    if (/^#*\s/.test(result.text)) { // don't subscript-prefix header prefix
+      result.text = indent_alt_prefix + result.text;
+    } else {
+      result.text = indent_prefix + result.text;
+    }
+  }
+
+  return result;
+}
+
+// Add correct prefix to list items.
+function findPrefix(inSrc, element, listCounters) {
+  var prefix = "";
+  if (!inSrc) {
+    if (element.getType()===DocumentApp.ElementType.PARAGRAPH) {
+      var paragraphObj = element;
+      switch (paragraphObj.getHeading()) {
+        // Add a # for each heading level. No break, so we accumulate the right number.
+        case DocumentApp.ParagraphHeading.HEADING6: prefix+="#";
+        case DocumentApp.ParagraphHeading.HEADING5: prefix+="#";
+        case DocumentApp.ParagraphHeading.HEADING4: prefix+="#";
+        case DocumentApp.ParagraphHeading.HEADING3: prefix+="#";
+        case DocumentApp.ParagraphHeading.HEADING2: prefix+="#";
+        case DocumentApp.ParagraphHeading.HEADING1: prefix+="# ";
+        default:
+      }
+    } else if (element.getType()===DocumentApp.ElementType.LIST_ITEM) {
+      var listItem = element;
+      var nesting = listItem.getNestingLevel()
+      for (var i=0; i<nesting; i++) {
+        prefix += "    ";
+      }
+      var gt = listItem.getGlyphType();
+      // Bullet list (<ul>):
+      if (gt === DocumentApp.GlyphType.BULLET
+          || gt === DocumentApp.GlyphType.HOLLOW_BULLET
+          || gt === DocumentApp.GlyphType.SQUARE_BULLET) {
+        prefix += "* ";
+      } else {
+        // Ordered list (<ol>):
+        var key = listItem.getListId() + '.' + listItem.getNestingLevel();
+        var counter = listCounters[key] || 0;
+        counter++;
+        listCounters[key] = counter;
+        prefix += counter+". ";
+      }
+    }
+  }
+  return prefix;
+}
+
+function processTextElement(inSrc, txt) {
+  if (typeof(txt) === 'string') {
+    return txt;
+  }
+
+  var pOut = txt.getText();
+  if (! txt.getTextAttributeIndices) {
+    return pOut;
+  }
+
+//  Logger.log("Initial String: " + pOut)
+
+  // CRC introducing reformatted_txt to let us apply rational formatting that we can actually parse
+  var reformatted_txt = txt.copy();
+  reformatted_txt.deleteText(0,pOut.length-1);
+  reformatted_txt = reformatted_txt.setText(pOut);
+
+  var attrs = txt.getTextAttributeIndices();
+  var lastOff = pOut.length;
+  // We will run through this loop multiple times for the things we care about.
+  // Font
+  // URL
+  // Then for alignment
+  // Then for bold
+  // Then for italic.
+
+  // FONTs
+  var lastOff = pOut.length; // loop goes backwards, so this holds
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var font=txt.getFontFamily(off)
+     if (font) {
+       while (i>=1 && txt.getFontFamily(attrs[i-1])==font) {
+          // detect fonts that are in multiple pieces because of errors on formatting:
+          i-=1;
+          off=attrs[i];
+       }
+       reformatted_txt.setFontFamily(off, lastOff-1, font);
+     }
+    lastOff=off;
+  }
+
+  // URL
+  // XXX TODO actually convert to URL text here.
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var url=txt.getLinkUrl(off);
+     if (url) {
+       while (i>=1 && txt.getLinkUrl(attrs[i-1]) == url) {
+          // detect urls that are in multiple pieces because of errors on formatting:
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setLinkUrl(off, lastOff-1, url);
+     }
+    lastOff=off;
+  }
+
+  // alignment
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var alignment=txt.getTextAlignment(off);
+     if (alignment) { //
+       while (i>=1 && txt.getTextAlignment(attrs[i-1]) == alignment) {
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setTextAlignment(off, lastOff-1, alignment);
+     }
+    lastOff=off;
+  }
+
+  // strike
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var strike=txt.isStrikethrough(off);
+     if (strike) {
+       while (i>=1 && txt.isStrikethrough(attrs[i-1])) {
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setStrikethrough(off, lastOff-1, strike);
+     }
+    lastOff=off;
+  }
+
+  // bold
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var bold=txt.isBold(off);
+     if (bold) {
+       while (i>=1 && txt.isBold(attrs[i-1])) {
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setBold(off, lastOff-1, bold);
+     }
+    lastOff=off;
+  }
+
+  // italics
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var italic=txt.isItalic(off);
+     if (italic) {
+       while (i>=1 && txt.isItalic(attrs[i-1])) {
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setItalic(off, lastOff-1, italic);
+     }
+    lastOff=off;
+  }
+
+
+  var mOut=""; // Modified out string
+  var harmonized_attrs = reformatted_txt.getTextAttributeIndices();
+  reformatted_txt.getTextAttributeIndices(); // @lmmx: is this a typo...?
+  pOut = reformatted_txt.getText();
+
+
+  // Markdown is farily picky about how it will let you intersperse spaces around words and strong/italics chars. This regex (hopefully) clears this up
+  // Match any number of \*, followed by spaces/word boundaries against anything that is not the \*, followed by boundaries, spaces and * again.
+  // Test case at http://jsfiddle.net/ovqLv0s9/2/
+
+  var reAlignStars = /(\*+)(\s*\b)([^\*]+)(\b\s*)(\*+)/g;
+
+  var lastOff=pOut.length;
+  for (var i=harmonized_attrs.length-1; i>=0; i--) {
+    var off=harmonized_attrs[i];
+
+    var raw_text = pOut.substring(off, lastOff)
+
+    var d1 = ""; // @lmmx: build up a modifier prefix
+    var d2 = ""; // @lmmx: ...and suffix
+
+    var end_font;
+
+    var mark_bold = false;
+    var mark_italic = false;
+    var mark_code = false;
+    var mark_sup = false;
+    var mark_sub = false;
+    var mark_strike = false;
+
+    // The end of the text block is a special case.
+    if (lastOff == pOut.length) {
+      end_font = reformatted_txt.getFontFamily(lastOff - 1)
+      if (end_font) {
+        if (!inSrc && end_font===end_font.COURIER_NEW) {
+          mark_code = true;
+        }
+      }
+      if (reformatted_txt.isBold(lastOff -1)) {
+        mark_bold = true;
+      }
+      if (reformatted_txt.isItalic(lastOff - 1)) {
+        // edbacher: changed this to handle bold italic properly.
+        mark_italic = true;
+      }
+      if (reformatted_txt.isStrikethrough(lastOff - 1)) {
+        mark_strike = true;
+      }
+      if (reformatted_txt.getTextAlignment(lastOff - 1)===DocumentApp.TextAlignment.SUPERSCRIPT) {
+        mark_sup = true;
+      }
+      if (reformatted_txt.getTextAlignment(lastOff - 1)===DocumentApp.TextAlignment.SUBSCRIPT) {
+        mark_sub = true;
+      }
+    } else {
+      end_font = reformatted_txt.getFontFamily(lastOff -1 )
+      if (end_font) {
+        if (!inSrc && end_font===end_font.COURIER_NEW && reformatted_txt.getFontFamily(lastOff) != end_font) {
+          mark_code=true;
+        }
+      }
+      if (reformatted_txt.isBold(lastOff - 1) && !reformatted_txt.isBold(lastOff) ) {
+        mark_bold=true;
+      }
+      if (reformatted_txt.isStrikethrough(lastOff - 1) && !reformatted_txt.isStrikethrough(lastOff)) {
+        mark_strike=true;
+      }
+      if (reformatted_txt.isItalic(lastOff - 1) && !reformatted_txt.isItalic(lastOff)) {
+        mark_italic=true;
+      }
+      if (reformatted_txt.getTextAlignment(lastOff - 1)===DocumentApp.TextAlignment.SUPERSCRIPT) {
+        if (reformatted_txt.getTextAlignment(lastOff)!==DocumentApp.TextAlignment.SUPERSCRIPT) {
+          mark_sup = true;
+        }
+      }
+      if (reformatted_txt.getTextAlignment(lastOff - 1)===DocumentApp.TextAlignment.SUBSCRIPT) {
+        if (reformatted_txt.getTextAlignment(lastOff)!==DocumentApp.TextAlignment.SUBSCRIPT) {
+          mark_sub = true;
+        }
+      }
+    }
+
+    if (mark_code) {
+      d2 = '`'; // shouldn't these go last? or will it interfere w/ reAlignStars?
+    }
+    if (mark_bold) {
+      d2 = "**" + d2;
+    }
+    if (mark_italic) {
+      d2 = "*" + d2;
+    }
+    if (mark_strike) {
+      d2 = "</strike>" + d2;
+    }
+    if (mark_sup) {
+      d2 = '</sup>' + d2;
+    }
+    if (mark_sub) {
+      d2 = '</sub>' + d2;
+    }
+
+    mark_bold = mark_italic = mark_code = mark_sup = mark_sub = mark_strike = false;
+
+    var font=reformatted_txt.getFontFamily(off);
+    if (off == 0) {
+      if (font) {
+        if (!inSrc && font===font.COURIER_NEW) {
+          mark_code = true;
+        }
+      }
+      if (reformatted_txt.isBold(off)) {
+        mark_bold = true;
+      }
+      if (reformatted_txt.isItalic(off)) {
+        mark_italic = true;
+      }
+      if (reformatted_txt.isStrikethrough(off)) {
+        mark_strike = true;
+      }
+      if (reformatted_txt.getTextAlignment(off)===DocumentApp.TextAlignment.SUPERSCRIPT) {
+        mark_sup = true;
+      }
+      if (reformatted_txt.getTextAlignment(off)===DocumentApp.TextAlignment.SUBSCRIPT) {
+        mark_sub = true;
+      }
+    } else {
+      if (font) {
+        if (!inSrc && font===font.COURIER_NEW && reformatted_txt.getFontFamily(off - 1) != font) {
+          mark_code=true;
+        }
+      }
+      if (reformatted_txt.isBold(off) && !reformatted_txt.isBold(off -1) ) {
+        mark_bold=true;
+      }
+      if (reformatted_txt.isItalic(off) && !reformatted_txt.isItalic(off - 1)) {
+        mark_italic=true;
+      }
+      if (reformatted_txt.isStrikethrough(off) && !reformatted_txt.isStrikethrough(off - 1)) {
+        mark_strike=true;
+      }
+      if (reformatted_txt.getTextAlignment(off)===DocumentApp.TextAlignment.SUPERSCRIPT) {
+        if (reformatted_txt.getTextAlignment(off - 1)!==DocumentApp.TextAlignment.SUPERSCRIPT) {
+          mark_sup = true;
+        }
+      }
+      if (reformatted_txt.getTextAlignment(off)===DocumentApp.TextAlignment.SUBSCRIPT) {
+        if (reformatted_txt.getTextAlignment(off - 1)!==DocumentApp.TextAlignment.SUBSCRIPT) {
+          mark_sub = true;
+        }
+      }
+    }
+
+
+    if (mark_code) {
+      d1 = '`';
+    }
+
+    if (mark_bold) {
+      d1 = d1 + "**";
+    }
+
+    if (mark_italic) {
+      d1 = d1 + "*";
+    }
+
+    if (mark_sup) {
+      d1 = d1 + '<sup>';
+    }
+
+    if (mark_sub) {
+      d1 = d1 + '<sub>';
+    }
+
+    if (mark_strike) {
+      d1 = d1 + '<strike>';
+    }
+
+    var url=reformatted_txt.getLinkUrl(off);
+    if (url) {
+      mOut = d1 + '['+ raw_text +']('+url+')' + d2 + mOut;
+    } else {
+      var new_text = d1 + raw_text + d2;
+      new_text = new_text.replace(reAlignStars, "$2$1$3$5$4");
+      mOut =  new_text + mOut;
+    }
+
+    lastOff=off;
+//    Logger.log("Modified String: " + mOut)
+  }
+
+  mOut = pOut.substring(0, off) + mOut;
+  return mOut;
+}
\ No newline at end of file
diff --git a/examples/gemini/python/docs-agent/third_party/g2docsmd-html/patches/001-initial-changes-for-docs-agent.patch b/examples/gemini/python/docs-agent/third_party/g2docsmd-html/patches/001-initial-changes-for-docs-agent.patch
new file mode 100644
index 000000000..af6d3aa93
--- /dev/null
+++ b/examples/gemini/python/docs-agent/third_party/g2docsmd-html/patches/001-initial-changes-for-docs-agent.patch
@@ -0,0 +1,153 @@
+--- third_party/g2docsmd-html/exportmd.gs	2023-10-20 22:03:56.577441177 +0000
++++ apps_script/exportmd.gs	2023-10-20 22:45:27.268431292 +0000
+@@ -1,4 +1,22 @@
+-/*
++/**
++ * Copyright 2023 Google LLC
++ *
++ * Licensed under the Apache License, Version 2.0 (the "License");
++ * you may not use this file except in compliance with the License.
++ * You may obtain a copy of the License at
++ *
++ *     http://www.apache.org/licenses/LICENSE-2.0
++ *
++ * Unless required by applicable law or agreed to in writing, software
++ * distributed under the License is distributed on an "AS IS" BASIS,
++ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
++ * See the License for the specific language governing permissions and
++ * limitations under the License.
++ */
++
++/* Original script is from:
++https://github.com/lmmx/gdocs2md-html/blob/master/exportmd.gs
++and commit: 0d86cfa
+ Parsing from mangini/gdocs2md.
+ Modified by clearf to add files to the google directory structure.
+ Modified by lmmx to write Markdown, going back to HTML-incorporation.
+@@ -601,7 +619,7 @@
+ 
+ }
+ 
+-function convertDocumentToMarkdown(document, destination_folder, optional_switches) {
++function convertDocumentToMarkdown(document, destination_folder, frontmatter_input, optional_switches) {
+   // if returning a string, force_save_images will make the script continue - experimental
+   var possible_switches = ['return_string', 'force_save_images'];
+   var property_name = 'conversion_switches';
+@@ -614,8 +632,13 @@
+ 
+   var image_prefix = script_properties.getProperty("image_folder_prefix");
+   var numChildren = document.getActiveSection().getNumChildren();
+-  var text = "";
+-  var md_filename = document.getName()+".md";
++  if (frontmatter_input != "") {
++    var text = frontmatter_input;
++  }
++  else {
++    var text = ""
++  }
++  var md_filename = sanitizeFileName(document.getName()) + ".md";
+   var image_foldername = document.getName()+"_images";
+   var inSrc = false;
+   var inClass = false;
+@@ -724,7 +747,7 @@
+     }
+    DriveApp.removeFile(saved_file) // Removes from google drive root.
+   }
+-
++return saved_file;
+ }
+ 
+ function escapeHTML(text) {
+@@ -738,6 +761,9 @@
+ // Process each child element (not just paragraphs).
+ function processParagraph(index, element, inSrc, imageCounter, listCounters, image_path) {
+   // First, check for things that require no processing.
++  if (element.getType() === DocumentApp.ElementType.UNSUPPORTED) {
++    return null;
++  }
+   if (element.getNumChildren()==0) {
+     return null;
+   }
+@@ -769,6 +795,11 @@
+     textElements.push("</table>\n");
+   }
+ 
++  // Need to handle this element type, return null for now
++  if (element.getType() === DocumentApp.ElementType.CODE_SNIPPET) {
++    return null
++  }
++
+   // Process various types (ElementType).
+   for (var i = 0; i < element.getNumChildren(); i++) {
+     var t = element.getChild(i).getType();
+@@ -811,12 +842,38 @@
+ 
+       result.images.push({ "blob" : blob } )
+ 
+-    } else if (t === DocumentApp.ElementType.PAGE_BREAK) {
++    // Need to fix this case TODO
++    } else if (t === DocumentApp.ElementType.INLINE_DRAWING) {
++
++      imageCounter++;
++      if (!return_string || force_save_images) {
++        textElements.push('![](' + "drawing" + '/' + " name" + ')');
++      } else {
++        textElements.push('![](' + "drawing" + ')');
++      }
++      //result.images.push( {
++      //  "bytes": blob.getBytes(),
++      //  "type": contentType,
++      //  "name": name});
++
++      // result.images.push({ "blob" : blob } )
++
++    }
++     else if (t === DocumentApp.ElementType.PAGE_BREAK) {
+       // ignore
+     } else if (t === DocumentApp.ElementType.HORIZONTAL_RULE) {
+       textElements.push('* * *\n');
+     } else if (t === DocumentApp.ElementType.FOOTNOTE) {
+       textElements.push(' ('+element.getChild(i).getFootnoteContents().getText()+')');
++    // Fixes for new elements
++    } else if (t === DocumentApp.ElementType.DATE) {
++      textElements.push(' ('+element.getChild(i)+')');
++    } else if (t === DocumentApp.ElementType.RICH_LINK) {
++      textElements.push(' ('+element.getChild(i).getUrl()+')');
++    } else if (t === DocumentApp.ElementType.PERSON) {
++      textElements.push(element.getChild(i).getName() + ', ');
++    } else if (t === DocumentApp.ElementType.UNSUPPORTED) {
++      textElements.push(' <UNSUPPORTED> ');
+     } else {
+       throw "Paragraph "+index+" of type "+element.getType()+" has an unsupported child: "
+       +t+" "+(element.getChild(i)["getText"] ? element.getChild(i).getText():'')+" index="+index;
+@@ -828,10 +885,17 @@
+     return result;
+   }
+ 
+-  var ind_f = element.getIndentFirstLine();
+-  var ind_s = element.getIndentStart();
+-  var ind_e = element.getIndentEnd();
+-  var i_fse = ['ind_f','ind_s','ind_e'];
++// Fix for unrecognized command getIndentFirstLine
++  var ind_f = 0;
++  var ind_s = 0;
++  var ind_e = 0;
++  if (t === DocumentApp.ElementType.PARAGRAPH) {
++
++    var ind_f = element.getIndentFirstLine();
++    var ind_s = element.getIndentStart();
++    var ind_e = element.getIndentEnd();
++  }
++  var i_fse = [ind_f,ind_s,ind_e];
+   var indents = {};
+   for (indt=0;indt<i_fse.length;indt++) {
+     var indname = i_fse[indt];
+@@ -876,7 +940,7 @@
+   }
+ 
+   var indent_prefix = '> ';
+-# var indent_alt_prefix = '> <sub>';
++  var indent_alt_prefix = '> <sub>';
+   if (inIndent && !inSrc) {
+     if (/^#*\s/.test(result.text)) { // don't subscript-prefix header prefix
+       result.text = indent_alt_prefix + result.text;
diff --git a/examples/gemini/python/docs-agent/vector_stores/.gitkeep b/examples/gemini/python/docs-agent/vector_stores/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/langchain/Gemini_LangChain_QA_Chroma_WebLoad.ipynb b/examples/gemini/python/langchain/Gemini_LangChain_QA_Chroma_WebLoad.ipynb
new file mode 100644
index 000000000..6d2342a40
--- /dev/null
+++ b/examples/gemini/python/langchain/Gemini_LangChain_QA_Chroma_WebLoad.ipynb
@@ -0,0 +1,684 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0c5ea3f4a75c"
+      },
+      "source": [
+        "# Question Answering using Gemini, LangChain, and Chroma"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "awKO767lQIWh"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/examples/gemini/python/langchain/Gemini_LangChain_QA_Chroma_WebLoad.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/examples/gemini/python/langchain/Gemini_LangChain_QA_Chroma_WebLoad.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "479790a71f3c"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "[Gemini](https://ai.google.dev/models/gemini) is a family of generative AI models that lets developers generate content and solve problems. These models are designed and trained to handle both text and images as input.\n",
+        "\n",
+        "[LangChain](https://www.langchain.com/) is a data framework designed to make integration of Large Language Models (LLM) like Gemini easier for applications.\n",
+        "\n",
+        "[Chroma](https://docs.trychroma.com/) is an open-source embedding database focused on simplicity and developer productivity. Chroma allows users to store embeddings and their metadata, embed documents and queries, and search the embeddings quickly.\n",
+        "\n",
+        "In this notebook, you'll learn how to create an application that answers questions using data from a website with the help of Gemini, LangChain, and Chroma."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_qRjVe1tZhsx"
+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "First, you must install the packages and set the necessary environment variables.\n",
+        "\n",
+        "### Installation\n",
+        "\n",
+        "Install LangChain's Python library, `langchain`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "olK4Ejjzuj76"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m798.0/798.0 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m216.5/216.5 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m48.4/48.4 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install --quiet langchain"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3Ap03KFZjI-"
+      },
+      "source": [
+        "Install LangChain's integration package for Gemini, `langchain-google-genai`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "K1CzIZiaurWv"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install --quiet langchain-google-genai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ba1VjUO3ZwfS"
+      },
+      "source": [
+        "Install Chroma's Python client SDK, `chromadb`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LGBoQhoz3kdy"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m509.0/509.0 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.0/92.0 kB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.3/60.3 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.6/40.6 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.4/5.4 MB\u001b[0m \u001b[31m26.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.4/6.4 MB\u001b[0m \u001b[31m37.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.9/57.9 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.6/105.6 kB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.3/67.3 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m698.9/698.9 kB\u001b[0m \u001b[31m40.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m51.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.6/67.6 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m70.0/70.0 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.8/50.8 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m341.4/341.4 kB\u001b[0m \u001b[31m26.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m51.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m43.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.2/130.2 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "lida 0.0.10 requires kaleido, which is not installed.\n",
+            "lida 0.0.10 requires python-multipart, which is not installed.\n",
+            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0m"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install --quiet chromadb"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wiGHSFmZaniK"
+      },
+      "source": [
+        "### Grab an API Key\n",
+        "\n",
+        "To use Gemini you need an *API key*. You can create an API key with one click in [Google AI Studio](https://makersuite.google.com/).\n",
+        "After creating the API key, you can either set an environment variable named `GOOGLE_API_KEY` to your API Key or pass the API key as an argument when using the `ChatGoogleGenerativeAI` class to access Google's `gemini` and `gemini-vision` models or the `GoogleGenerativeAIEmbeddings` class to access Google's Generative AI embedding model using `LangChain`.\n",
+        "\n",
+        "In this tutorial, you will set the environment variable `GOOGLE_API_KEY` to configure Gemini to use your API key."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xId4sR52utS0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Gemini API Key:··········\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Run this cell and paste the API key in the prompt\n",
+        "import os\n",
+        "import getpass\n",
+        "\n",
+        "os.environ['GOOGLE_API_KEY'] = getpass.getpass('Gemini API Key:')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aEKMUyVmckWI"
+      },
+      "source": [
+        "## Basic steps\n",
+        "LLMs are trained offline on a large corpus of public data. Hence they cannot answer questions based on custom or private data accurately without additional context.\n",
+        "\n",
+        "If you want to make use of LLMs to answer questions based on private data, you have to provide the relevant documents as context alongside your prompt. This approach is called Retrieval Augmented Generation (RAG).\n",
+        "\n",
+        "You will use this approach to create a question-answering assistant using the Gemini text model integrated through LangChain. The assistant is expected to answer questions about the Gemini model. To make this possible you will add more context to the assistant using data from a website.\n",
+        "\n",
+        "In this tutorial, you'll implement the two main components in an RAG-based architecture:\n",
+        "\n",
+        "1. Retriever\n",
+        "\n",
+        "    Based on the user's query, the retriever retrieves relevant snippets that add context from the document. In this tutorial, the document is the website data.\n",
+        "    The relevant snippets are passed as context to the next stage - \"Generator\".\n",
+        "\n",
+        "2. Generator\n",
+        "\n",
+        "    The relevant snippets from the website data are passed to the LLM along with the user's query to generate accurate answers.\n",
+        "\n",
+        "You'll learn more about these stages in the upcoming sections while implementing the application."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kPhs4mDkjdgY"
+      },
+      "source": [
+        "## Import the required libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TcvGPVdXu05F"
+      },
+      "outputs": [],
+      "source": [
+        "from langchain import PromptTemplate\n",
+        "from langchain import hub\n",
+        "from langchain.docstore.document import Document\n",
+        "from langchain.document_loaders import WebBaseLoader\n",
+        "from langchain.schema import StrOutputParser\n",
+        "from langchain.schema.prompt_template import format_document\n",
+        "from langchain.schema.runnable import RunnablePassthrough\n",
+        "from langchain.vectorstores import Chroma"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4461Jihk_rWq"
+      },
+      "source": [
+        "## Retriever\n",
+        "\n",
+        "In this stage, you will perform the following steps:\n",
+        "\n",
+        "1. Read and parse the website data using LangChain.\n",
+        "\n",
+        "2. Create embeddings of the website data.\n",
+        "\n",
+        "    Embeddings are numerical representations (vectors) of text. Hence, text with similar meaning will have similar embedding vectors. You'll make use of Gemini's embedding model to create the embedding vectors of the website data.\n",
+        "\n",
+        "3. Store the embeddings in Chroma's vector store.\n",
+        "    \n",
+        "    Chroma is a vector database. The Chroma vector store helps in the efficient retrieval of similar vectors. Thus, for adding context to the prompt for the LLM, relevant embeddings of the text matching the user's question can be retrieved easily using Chroma.\n",
+        "\n",
+        "4. Create a Retriever from the Chroma vector store.\n",
+        "\n",
+        "    The retriever will be used to pass relevant website embeddings to the LLM along with user queries."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WomGvIAVjZeI"
+      },
+      "source": [
+        "### Read and parse the website data\n",
+        "\n",
+        "LangChain provides a wide variety of document loaders. To read the website data as a document, you will use the `WebBaseLoader` from LangChain.\n",
+        "\n",
+        "To know more about how to read and parse input data from different sources using the document loaders of LangChain, read LangChain's [document loaders guide](https://python.langchain.com/docs/integrations/document_loaders)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DeNX9QFM0V-C"
+      },
+      "outputs": [],
+      "source": [
+        "loader = WebBaseLoader(\"https://blog.google/technology/ai/google-gemini-ai/\")\n",
+        "docs = loader.load()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "olIlIOYrJTlF"
+      },
+      "source": [
+        "If you only want to select a specific portion of the website data to add context to the prompt, you can use regex, text slicing, or text splitting.\n",
+        "\n",
+        "In this example, you'll use Python's `split()` function to extract the required portion of the text. The extracted text should be converted back to LangChain's `Document` format."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EDL9YLRb9Bw2"
+      },
+      "outputs": [],
+      "source": [
+        "# Extract the text from the website data document\n",
+        "text_content = docs[0].page_content\n",
+        "\n",
+        "# The text content between the substrings \"code, audio, image and video.\" to\n",
+        "# \"Cloud TPU v5p\" is relevant for this tutorial. You can use Python's `split()`\n",
+        "# to select the required content.\n",
+        "text_content_1 = text_content.split(\"code, audio, image and video.\",1)[1]\n",
+        "final_text = text_content_1.split(\"Cloud TPU v5p\",1)[0]\n",
+        "\n",
+        "# Convert the text to LangChain's `Document` format\n",
+        "docs =  [Document(page_content=final_text, metadata={\"source\": \"local\"})]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yDsdAg4Fjo5o"
+      },
+      "source": [
+        "### Initialize Gemini's embedding model\n",
+        "\n",
+        "To create the embeddings from the website data, you'll use Gemini's embedding model, **embedding-001** which supports creating text embeddings.\n",
+        "\n",
+        "To use this embedding model, you have to import `GoogleGenerativeAIEmbeddings` from LangChain. To know more about the embedding model, read Google AI's [language documentation](https://ai.google.dev/models/gemini)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8NXNTrjp0jdh"
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_google_genai import GoogleGenerativeAIEmbeddings\n",
+        "\n",
+        "# If there is no environment variable set for the API key, you can pass the API\n",
+        "# key to the parameter `google_api_key` of the `GoogleGenerativeAIEmbeddings`\n",
+        "# function: `google_api_key = \"key\"`.\n",
+        "\n",
+        "gemini_embeddings = GoogleGenerativeAIEmbeddings(model=\"models/embedding-001\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m9Vzw30wpebs"
+      },
+      "source": [
+        "### Store the data using Chroma\n",
+        "\n",
+        "To create a Chroma vector database from the website data, you will use the `from_documents` function of `Chroma`. Under the hood, this function creates embeddings from the documents created by the document loader of LangChain using any specified embedding model and stores them in a Chroma vector database.  \n",
+        "\n",
+        "You have to specify the `docs` you created from the website data using LangChain's `WebBasedLoader` and the `gemini_embeddings` as the embedding model when invoking the `from_documents` function to create the vector database from the website data. You can also specify a directory in the `persist_directory` argument to store the vector store on the disk. If you don't specify a directory, the data will be ephemeral in-memory.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "n1VwhUQMvpcN"
+      },
+      "outputs": [],
+      "source": [
+        "# Save to disk\n",
+        "vectorstore = Chroma.from_documents(\n",
+        "                     documents=docs,                 # Data\n",
+        "                     embedding=gemini_embeddings,    # Embedding model\n",
+        "                     persist_directory=\"./chroma_db\" # Directory to save data\n",
+        "                     )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WFKyb3JXOeaQ"
+      },
+      "source": [
+        "### Create a retriever using Chroma\n",
+        "\n",
+        "You'll now create a retriever that can retrieve website data embeddings from the newly created Chroma vector store. This retriever can be later used to pass embeddings that provide more context to the LLM for answering user's queries.\n",
+        "\n",
+        "\n",
+        "To load the vector store that you previously stored in the disk, you can specify the name of the directory that contains the vector store in `persist_directory` and the embedding model in the `embedding_function` arguments of Chroma's initializer.\n",
+        "\n",
+        "You can then invoke the `as_retriever` function of `Chroma` on the vector store to create a retriever."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "s3t4kmzIOZQq"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "1\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Load from disk\n",
+        "vectorstore_disk = Chroma(\n",
+        "                        persist_directory=\"./chroma_db\",       # Directory of db\n",
+        "                        embedding_function=gemini_embeddings   # Embedding model\n",
+        "                   )\n",
+        "# Get the Retriever interface for the store to use later.\n",
+        "# When an unstructured query is given to a retriever it will return documents.\n",
+        "# Read more about retrievers in the following link.\n",
+        "# https://python.langchain.com/docs/modules/data_connection/retrievers/\n",
+        "#\n",
+        "# Since only 1 document is stored in the Chroma vector store, search_kwargs `k`\n",
+        "# is set to 1 to decrease the `k` value of chroma's similarity search from 4 to\n",
+        "# 1. If you don't pass this value, you will get a warning.\n",
+        "retriever = vectorstore_disk.as_retriever(search_kwargs={\"k\": 1})\n",
+        "\n",
+        "# Check if the retriever is working by trying to fetch the relevant docs related\n",
+        "# to the word 'MMLU' (Massive Multitask Language Understanding). If the length is greater than zero, it means that\n",
+        "# the retriever is functioning well.\n",
+        "print(len(retriever.get_relevant_documents(\"MMLU\")))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LZwcZyRxSO0q"
+      },
+      "source": [
+        "## Generator\n",
+        "\n",
+        "The Generator prompts the LLM for an answer when the user asks a question. The retriever you created in the previous stage from the Chroma vector store will be used to pass relevant embeddings from the website data to the LLM to provide more context to the user's query.\n",
+        "\n",
+        "You'll perform the following steps in this stage:\n",
+        "\n",
+        "1. Chain together the following:\n",
+        "    * A prompt for extracting the relevant embeddings using the retriever.\n",
+        "    * A prompt for answering any question using LangChain.\n",
+        "    * An LLM model from Gemini for prompting.\n",
+        "    \n",
+        "2. Run the created chain with a question as input to prompt the model for an answer.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FtUi5FBIJMDy"
+      },
+      "source": [
+        "### Initialize Gemini\n",
+        "\n",
+        "You must import `ChatGoogleGenerativeAI` from LangChain to initialize your model.\n",
+        " In this example, you will use **gemini-pro**, as it supports text summarization. To know more about the text model, read Google AI's [language documentation](https://ai.google.dev/models/gemini).\n",
+        "\n",
+        "You can configure the model parameters such as ***temperature*** or ***top_p***,  by passing the appropriate values when initializing the `ChatGoogleGenerativeAI` LLM.  To learn more about the parameters and their uses, read Google AI's [concepts guide](https://ai.google.dev/docs/concepts#model_parameters)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CaA1vRCh7s36"
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+        "\n",
+        "# If there is no environment variable set for the API key, you can pass the API\n",
+        "# key to the parameter `google_api_key` of the `ChatGoogleGenerativeAI` function:\n",
+        "# `google_api_key=\"key\"`.\n",
+        "llm = ChatGoogleGenerativeAI(model=\"gemini-pro\",\n",
+        "                 temperature=0.7, top_p=0.85)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jC4QDhiPpDJa"
+      },
+      "source": [
+        "### Create prompt templates\n",
+        "\n",
+        "You'll use LangChain's [PromptTemplate](https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/) to generate prompts to the LLM for answering questions.\n",
+        "\n",
+        "In the `llm_prompt`, the variable `question` will be replaced later by the input question, and the variable `context` will be replaced by the relevant text from the website retrieved from the Chroma vector store."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "90Czqh074dEC"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "input_variables=['context', 'question'] template=\"You are an assistant for question-answering tasks.\\nUse the following context to answer the question.\\nIf you don't know the answer, just say that you don't know.\\nUse five sentences maximum and keep the answer concise.\\n\\nQuestion: {question} \\nContext: {context} \\nAnswer:\"\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Prompt template to query Gemini\n",
+        "llm_prompt_template = \"\"\"You are an assistant for question-answering tasks.\n",
+        "Use the following context to answer the question.\n",
+        "If you don't know the answer, just say that you don't know.\n",
+        "Use five sentences maximum and keep the answer concise.\\n\n",
+        "Question: {question} \\nContext: {context} \\nAnswer:\"\"\"\n",
+        "\n",
+        "llm_prompt = PromptTemplate.from_template(llm_prompt_template)\n",
+        "\n",
+        "print(llm_prompt)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KXDh2jsdp4sr"
+      },
+      "source": [
+        "### Create a stuff documents chain\n",
+        "\n",
+        "LangChain provides [Chains](https://python.langchain.com/docs/modules/chains/) for chaining together LLMs with each other or other components for complex applications. You will create a **stuff documents chain** for this application. A stuff documents chain lets you combine all the relevant documents, insert them into the prompt, and pass that prompt to the LLM.\n",
+        "\n",
+        "You can create a stuff documents chain using the [LangChain Expression Language (LCEL)](https://python.langchain.com/docs/expression_language).\n",
+        "\n",
+        "To learn more about different types of document chains, read LangChain's [chains guide](https://python.langchain.com/docs/modules/chains/document/).\n",
+        "\n",
+        "The stuff documents chain for this application retrieves the relevant website data and passes it as the context to an LLM prompt along with the input question."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gj5sWzpwp7vc"
+      },
+      "outputs": [],
+      "source": [
+        "# Combine data from documents to readable string format.\n",
+        "def format_docs(docs):\n",
+        "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+        "\n",
+        "# Create stuff documents chain using LCEL.\n",
+        "#\n",
+        "# This is called a chain because you are chaining together different elements\n",
+        "# with the LLM. In the following example, to create the stuff chain, you will\n",
+        "# combine the relevant context from the website data matching the question, the\n",
+        "# LLM model, and the output parser together like a chain using LCEL.\n",
+        "#\n",
+        "# The chain implements the following pipeline:\n",
+        "# 1. Extract the website data relevant to the question from the Chroma\n",
+        "#    vector store and save it to the variable `context`.\n",
+        "# 2. `RunnablePassthrough` option to provide `question` when invoking\n",
+        "#    the chain.\n",
+        "# 3. The `context` and `question` are then passed to the prompt where they\n",
+        "#    are populated in the respective variables.\n",
+        "# 4. This prompt is then passed to the LLM (`gemini-pro`).\n",
+        "# 5. Output from the LLM is passed through an output parser\n",
+        "#    to structure the model's response.\n",
+        "rag_chain = (\n",
+        "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
+        "    | llm_prompt\n",
+        "    | llm\n",
+        "    | StrOutputParser()\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cPPqsGCLIrs1"
+      },
+      "source": [
+        "### Prompt the model\n",
+        "\n",
+        "You can now query the LLM by passing any question to the `invoke()` function of the stuff documents chain you created previously."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4vIaopCsIq0B"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "\"Gemini is Google's largest and most capable AI model, designed to efficiently run on various platforms, from data centers to mobile devices. It excels in understanding and reasoning about text, images, audio, and code. Gemini's sophisticated multimodal reasoning capabilities enable it to uncover knowledge from vast amounts of data and explain reasoning in complex subjects like math and physics. It can also generate high-quality code in multiple programming languages.\""
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "rag_chain.invoke(\"What is Gemini?\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lV7T9rqDdjZK"
+      },
+      "source": [
+        "# Conclusion\n",
+        "\n",
+        "That's it. You have successfully created an LLM application that answers questions using data from a website with the help of Gemini, LangChain, and Chroma."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "Gemini_LangChain_QA_Chroma_WebLoad.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/examples/gemini/python/langchain/Gemini_LangChain_QA_Pinecone_WebLoad.ipynb b/examples/gemini/python/langchain/Gemini_LangChain_QA_Pinecone_WebLoad.ipynb
new file mode 100644
index 000000000..6090249d6
--- /dev/null
+++ b/examples/gemini/python/langchain/Gemini_LangChain_QA_Pinecone_WebLoad.ipynb
@@ -0,0 +1,752 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YdsMOBaBfyT0"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "rIIf_RgOf3sr"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TySweisNf_Am"
+      },
+      "source": [
+        "# Question Answering using Gemini, LangChain, and Pinecone"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "awKO767lQIWh"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/examples/gemini/python/langchain/Gemini_LangChain_QA_Pinecone_WebLoad.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/examples/gemini/python/langchain/Gemini_LangChain_QA_Pinecone_WebLoad.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bA5Hys5PU_nt"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "[Gemini](https://ai.google.dev/models/gemini) is a family of generative AI models that lets developers generate content and solve problems. These models are designed and trained to handle both text and images as input.\n",
+        "\n",
+        "[LangChain](https://www.langchain.com/) is a data framework designed to make integration of Large Language Models (LLM) like Gemini easier for applications.\n",
+        "\n",
+        "[Pinecone](https://www.pinecone.io/) is a cloud-first vector database that allows users to search across billions of embeddings with ultra-low query latency.\n",
+        "\n",
+        "In this notebook, you'll learn how to create an application that answers questions using data from a website with the help of Gemini, LangChain, and Pinecone."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_qRjVe1tZhsx"
+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "First, you must install the packages and set the necessary environment variables.\n",
+        "\n",
+        "### Installation\n",
+        "\n",
+        "Install `LangChain`'s python library."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "olK4Ejjzuj76"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m817.0/817.0 kB\u001b[0m \u001b[31m14.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m62.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m250.8/250.8 kB\u001b[0m \u001b[31m28.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.1/63.1 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m138.5/138.5 kB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install --quiet langchain"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3Ap03KFZjI-"
+      },
+      "source": [
+        "Install LangChain's integration package for Gemini, `langchain-google-genai`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "K1CzIZiaurWv"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install --quiet langchain-google-genai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "V7Y51x2AexEf"
+      },
+      "source": [
+        "Install LangChain's integration package for the new version of Pinecone, `langchain-pinecone`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kSxJt9NCerJX"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/211.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━\u001b[0m \u001b[32m194.6/211.0 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.0/211.0 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install --quiet langchain-pinecone"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ba1VjUO3ZwfS"
+      },
+      "source": [
+        "Install Pinecone's python client SDK, `pinecone-client`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LGBoQhoz3kdy"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/201.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━\u001b[0m \u001b[32m194.6/201.4 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m201.4/201.4 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install --quiet pinecone-client==3.0.2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "myebeBlLkqVN"
+      },
+      "source": [
+        "### Grab a Gemini API Key\n",
+        "\n",
+        "To use Gemini you need an *API key*. You can create an API key with one click in [Google AI Studio](https://makersuite.google.com/).\n",
+        "After creating the API key, you can either set an environment variable named `GOOGLE_API_KEY` to your API Key or pass the API key as an argument when using the `ChatGoogleGenerativeAI` class to access Google's `gemini-1.5-flash` or `gemini-1.5-pro` models or the `GoogleGenerativeAIEmbeddings` class to access Google's Generative AI embedding model using `LangChain`.\n",
+        "\n",
+        "In this tutorial, you will set the environment variable `GOOGLE_API_KEY` to configure Gemini to use your API key."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xId4sR52utS0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Gemini API Key:··········\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Run this cell and paste the API key in the prompt\n",
+        "import os\n",
+        "import getpass\n",
+        "\n",
+        "os.environ['GOOGLE_API_KEY'] = getpass.getpass('Gemini API Key:')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MPQLjFvRooqn"
+      },
+      "source": [
+        "### Setup Pinecone\n",
+        "\n",
+        "To use Pinecone in your application, you must have an API key. To create an API key you have to set up a Pinecone account. Visit [Pinecone's app page](https://app.pinecone.io/), and Sign up/Log in to your account. Then navigate to the \"API Keys\" section and copy your API key.\n",
+        "\n",
+        "For more detailed instructions on getting the API key, you can read Pinecone's [Quickstart documentation](https://docs.pinecone.io/docs/quickstart#2-get-your-api-key).\n",
+        "\n",
+        "Set the environment variable `PINECONE_API_KEY` to configure Pinecone to use your API key.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "A7jTZLEApgtm"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Pinecone API Key:··········\n"
+          ]
+        }
+      ],
+      "source": [
+        "os.environ['PINECONE_API_KEY'] = getpass.getpass('Pinecone API Key:')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YGOKV3XflBCe"
+      },
+      "source": [
+        "## Basic steps\n",
+        "LLMs are trained offline on a large corpus of public data. Hence they cannot answer questions based on custom or private data accurately without additional context.\n",
+        "\n",
+        "If you want to make use of LLMs to answer questions based on private data, you have to provide the relevant documents as context alongside your prompt. This approach is called Retrieval Augmented Generation (RAG).\n",
+        "\n",
+        "You will use this approach to create a question-answering assistant using the Gemini text model integrated through LangChain. The assistant is expected to answer questions about Gemini model. To make this possible you will add more context to the assistant using data from a website.\n",
+        "\n",
+        "In this tutorial, you'll implement the two main components in an RAG-based architecture:\n",
+        "\n",
+        "1. Retriever\n",
+        "\n",
+        "    Based on the user's query, the retriever retrieves relevant snippets that add context from the document. In this tutorial, the document is the website data.\n",
+        "    The relevant snippets are passed as context to the next stage - \"Generator\".\n",
+        "\n",
+        "2. Generator\n",
+        "\n",
+        "    The relevant snippets from the website data are passed to the LLM along with the user's query to generate accurate answers.\n",
+        "\n",
+        "You'll learn more about these stages in the upcoming sections while implementing the application."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kPhs4mDkjdgY"
+      },
+      "source": [
+        "## Import the required libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TcvGPVdXu05F"
+      },
+      "outputs": [],
+      "source": [
+        "from langchain import hub\n",
+        "from langchain import PromptTemplate\n",
+        "from langchain.docstore.document import Document\n",
+        "from langchain.document_loaders import WebBaseLoader\n",
+        "from langchain.schema import StrOutputParser\n",
+        "from langchain.schema.prompt_template import format_document\n",
+        "from langchain.schema.runnable import RunnablePassthrough\n",
+        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+        "from langchain_pinecone import Pinecone\n",
+        "\n",
+        "from pinecone import Pinecone as pc\n",
+        "from pinecone import PodSpec"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qZ3tM0T2lbVm"
+      },
+      "source": [
+        "## Retriever\n",
+        "\n",
+        "In this stage, you will perform the following steps:\n",
+        "\n",
+        "1. Read and parse the website data using LangChain.\n",
+        "\n",
+        "2. Create embeddings of the website data.\n",
+        "\n",
+        "    Embeddings are numerical representations (vectors) of text. Hence, text with similar meaning will have similar embedding vectors. You'll make use of Gemini's embedding model to create the embedding vectors of the website data.\n",
+        "\n",
+        "3. Store the embeddings in Pinecone's vector store.\n",
+        "    \n",
+        "    Pinecone is a vector database. The Pinecone vector store helps in the efficient retrieval of similar vectors. Thus, for adding context to the prompt for the LLM, relevant embeddings of the text matching the user's question can be retrieved easily using Pinecone.\n",
+        "\n",
+        "4. Create a Retriever from the Pinecone vector store.\n",
+        "\n",
+        "    The retriever will be used to pass relevant website embeddings to the LLM along with user queries."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "W2N-NCPElqN3"
+      },
+      "source": [
+        "### Read and parse the website data\n",
+        "\n",
+        "LangChain provides a wide variety of document loaders. To read the website data as a document, you will use the `WebBaseLoader` from LangChain.\n",
+        "\n",
+        "To know more about how to read and parse input data from different sources using the document loaders of LangChain, read LangChain's [document loaders guide](https://python.langchain.com/docs/integrations/document_loaders)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DeNX9QFM0V-C"
+      },
+      "outputs": [],
+      "source": [
+        "loader = WebBaseLoader(\"https://blog.google/technology/ai/google-gemini-ai/\")\n",
+        "docs = loader.load()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y2N6RoTDlwsM"
+      },
+      "source": [
+        "If you only want to select a specific portion of the website data to add context to the prompt, you can use regex, text slicing, or text splitting.\n",
+        "\n",
+        "In this example, you'll use Python's `split()` function to extract the required portion of the text. The extracted text should be converted back to LangChain's `Document` format."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qOwDregSBVVG"
+      },
+      "outputs": [],
+      "source": [
+        "# Extract the text from the website data document\n",
+        "text_content = docs[0].page_content\n",
+        "# The text content between the substrings \"code, audio, image and video.\" to\n",
+        "# \"Cloud TPU v5p\" is relevant for this tutorial. You can use Python's `split()`\n",
+        "# to select the required content.\n",
+        "text_content_1 = text_content.split(\"code, audio, image and video.\",1)[1]\n",
+        "final_text = text_content_1.split(\"Cloud TPU v5p\",1)[0]\n",
+        "\n",
+        "# Convert the text to LangChain's `Document` format\n",
+        "docs =  [Document(page_content=final_text, metadata={\"source\": \"local\"})]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sgGVAFqWl20v"
+      },
+      "source": [
+        "### Initialize Gemini's embedding model\n",
+        "\n",
+        "To create the embeddings from the website data, you'll use Gemini's embedding model, **embedding-001** which supports creating text embeddings.\n",
+        "\n",
+        "To use this embedding model, you have to import `GoogleGenerativeAIEmbeddings` from LangChain. To know more about the embedding model, read Google AI's [language documentation](https://ai.google.dev/models/gemini)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8NXNTrjp0jdh"
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_google_genai import GoogleGenerativeAIEmbeddings\n",
+        "\n",
+        "# If there is no environment variable set for the API key, you can pass the API\n",
+        "# key to the parameter `google_api_key` of the `GoogleGenerativeAIEmbeddings`\n",
+        "# function: `google_api_key = \"key\"`.\n",
+        "\n",
+        "gemini_embeddings = GoogleGenerativeAIEmbeddings(model=\"models/embedding-001\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Zr5xeWUXmnUe"
+      },
+      "source": [
+        "### Store the data using Pinecone\n",
+        "\n",
+        "\n",
+        "To create a Pinecone vector database, first, you have to initialize your Pinecone client connection using the API key you set previously.\n",
+        "\n",
+        "In Pinecone, vector embeddings have to be stored in indexes. An index represents the vector data's top-level organizational unit. The vectors in any index must have the same dimensionality and distance metric for calculating similarity. You can read more about indexes in [Pinecone's Indexes documentation](https://docs.pinecone.io/docs/indexes).\n",
+        "\n",
+        "First, you'll create an index using Pinecone's `create_index` function. Pinecone allows you to create two types of indexes, Serverless indexes and Pod-based indexes. Pinecone's free starter plan lets you create only one project and one pod-based starter index with sufficient resources to support 100,000 vectors. For this tutorial, you have to create a pod-based starter index. To know more about different indexes and how they can be created, read Pinecone's [create indexes guide](https://docs.pinecone.io/docs/new-api#creating-indexes).\n",
+        "\n",
+        "\n",
+        "Next, you'll insert the documents you extracted earlier from the website data into the newly created index using LangChain's `Pinecone.from_documents`. Under the hood, this function creates embeddings from the documents created by the document loader of LangChain using any specified embedding model and inserts them into the specified index in a Pinecone vector database.  \n",
+        "\n",
+        "You have to specify the `docs` you created from the website data using LangChain's `WebBasedLoader` and the `gemini_embeddings` as the embedding model when invoking the `from_documents` function to create the vector database from the website data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "n1VwhUQMvpcN"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "creating index\n",
+            "{'dimension': 768,\n",
+            " 'host': 'langchain-demo-xcfkw2a.svc.gcp-starter.pinecone.io',\n",
+            " 'metric': 'cosine',\n",
+            " 'name': 'langchain-demo',\n",
+            " 'spec': {'pod': {'environment': 'gcp-starter',\n",
+            "                  'pod_type': 'starter',\n",
+            "                  'pods': 1,\n",
+            "                  'replicas': 1,\n",
+            "                  'shards': 1}},\n",
+            " 'status': {'ready': True, 'state': 'Ready'}}\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Initialize Pinecone client\n",
+        "\n",
+        "pine_client= pc(\n",
+        "    api_key = os.getenv(\"PINECONE_API_KEY\"),  # API key from app.pinecone.io\n",
+        "    )\n",
+        "index_name = \"langchain-demo\"\n",
+        "\n",
+        "# First, check if the index already exists. If it doesn't, create a new one.\n",
+        "if index_name not in pine_client.list_indexes().names():\n",
+        "    # Create a new index.\n",
+        "    # https://docs.pinecone.io/docs/new-api#creating-a-starter-index\n",
+        "    print(\"Creating index\")\n",
+        "    pine_client.create_index(name=index_name,\n",
+        "                      # `cosine` distance metric compares different documents\n",
+        "                      # for similarity.\n",
+        "                      # Read more about different distance metrics from\n",
+        "                      # https://docs.pinecone.io/docs/indexes#distance-metrics.\n",
+        "                      metric=\"cosine\",\n",
+        "                      # The Gemini embedding model `embedding-001` uses\n",
+        "                      # 768 dimensions.\n",
+        "                      dimension=768,\n",
+        "                      # Specify the pod details.\n",
+        "                      spec=PodSpec(\n",
+        "                        # Starter indexes are hosted in the `gcp-starter`\n",
+        "                        # environment.\n",
+        "                        environment=\"gcp-starter\",\n",
+        "                        pod_type=\"starter\",\n",
+        "                        pods=1)\n",
+        "    )\n",
+        "    print(pine_client.describe_index(index_name))\n",
+        "\n",
+        "# If there is no environment variable set for the API key, you can pass the API\n",
+        "# key to the parameter `pinecone_api_key` of the `Pinecone.from_documents`\n",
+        "# function: `pinecone_api_key = \"key\"`.\n",
+        "vectorstore = Pinecone.from_documents(docs,\n",
+        "                      gemini_embeddings, index_name=index_name)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BuSjapvHnc6T"
+      },
+      "source": [
+        "### Create a retriever using Pinecone\n",
+        "\n",
+        "You'll now create a retriever that can retrieve website data embeddings from the newly created Pinecone vector store. This retriever can be later used to pass embeddings that provide more context to the LLM for answering user's queries.\n",
+        "\n",
+        "Invoke the `as_retriever` function of the vector store you initialized in the last step, to create a retriever."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qndTwf0tnQDv"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "0\n"
+          ]
+        }
+      ],
+      "source": [
+        "retriever = vectorstore.as_retriever()\n",
+        "# Check if the retriever is working by trying to fetch the relevant docs related\n",
+        "# to the word 'MMLU'(Massive Multitask Language Understanding). If the length is\n",
+        "# greater than zero, it means that the retriever is functioning well.\n",
+        "print(len(retriever.get_relevant_documents(\"MMLU\")))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7Qw00lvPnjfR"
+      },
+      "source": [
+        "## Generator\n",
+        "\n",
+        "The Generator prompts the LLM for an answer when the user asks a question. The retriever you created in the previous stage from the Pinecone vector store will be used to pass relevant embeddings from the website data to the LLM to provide more context to the user's query.\n",
+        "\n",
+        "You'll perform the following steps in this stage:\n",
+        "\n",
+        "1. Chain together the following:\n",
+        "    * A prompt for extracting the relevant embeddings using the retriever.\n",
+        "    * A prompt for answering any question using LangChain.\n",
+        "    * An LLM model from Gemini for prompting.\n",
+        "    \n",
+        "2. Run the created chain with a question as input to prompt the model for an answer.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c2MK2wLwnkLg"
+      },
+      "source": [
+        "### Initialize Gemini\n",
+        "\n",
+        "You must import `ChatGoogleGenerativeAI` from LangChain to initialize your model.\n",
+        " In this example, you will use **gemini-pro**, as it supports text summarization. To know more about the text model, read Google AI's [language documentation](https://ai.google.dev/models/gemini).\n",
+        "\n",
+        "You can configure the model parameters such as ***temperature*** or ***top_p***,  by passing the appropriate values when initializing the `ChatGoogleGenerativeAI` LLM.  To learn more about the parameters and their uses, read Google AI's [concepts guide](https://ai.google.dev/docs/concepts#model_parameters)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CaA1vRCh7s36"
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+        "\n",
+        "# If there is no environment variable set for the API key, you can pass the API\n",
+        "# key to the parameter `google_api_key` of the `ChatGoogleGenerativeAI`\n",
+        "# function: `google_api_key=\"key\"`.\n",
+        "\n",
+        "llm = ChatGoogleGenerativeAI(model=\"gemini-pro\",\n",
+        "                 temperature=0.7, top_p=0.85)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2BeLN6RXnuS2"
+      },
+      "source": [
+        "### Create prompt templates\n",
+        "\n",
+        "You'll use LangChain's [PromptTemplate](https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/) to generate prompts to the LLM for answering questions.\n",
+        "\n",
+        "In the `llm_prompt`, the variable `question` will be replaced later by the input question, and the variable `context` will be replaced by the relevant text from the website retrieved from the Pinecone vector store."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "90Czqh074dEC"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "input_variables=['context', 'question'] template=\"You are an assistant for question-answering tasks.\\nUse the following context to answer the question.\\nIf you don't know the answer, just say that you don't know.\\nUse five sentences maximum and keep the answer concise.\\n\\nQuestion: {question} \\nContext: {context} \\nAnswer:\"\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Prompt template to query Gemini\n",
+        "llm_prompt_template = \"\"\"You are an assistant for question-answering tasks.\n",
+        "Use the following context to answer the question.\n",
+        "If you don't know the answer, just say that you don't know.\n",
+        "Use five sentences maximum and keep the answer concise.\n",
+        "\n",
+        "Question: {question}\n",
+        "Context: {context}\n",
+        "Answer:\"\"\"\n",
+        "\n",
+        "llm_prompt = PromptTemplate.from_template(llm_prompt_template)\n",
+        "\n",
+        "print(llm_prompt)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TkWpzMmpnx7b"
+      },
+      "source": [
+        "### Create a stuff documents chain\n",
+        "\n",
+        "LangChain provides [Chains](https://python.langchain.com/docs/modules/chains/) for chaining together LLMs with each other or other components for complex applications. You will create a **stuff documents chain** for this application. A stuff documents chain lets you combine all the relevant documents, insert them into the prompt, and pass that prompt to the LLM.\n",
+        "\n",
+        "You can create a stuff documents chain using the [LangChain Expression Language (LCEL)](https://python.langchain.com/docs/expression_language).\n",
+        "\n",
+        "To learn more about different types of document chains, read LangChain's [chains guide](https://python.langchain.com/docs/modules/chains/document/).\n",
+        "\n",
+        "The stuff documents chain for this application retrieves the relevant website data and passes it as the context to an LLM prompt along with the input question."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gj5sWzpwp7vc"
+      },
+      "outputs": [],
+      "source": [
+        "# Combine data from documents to readable string format.\n",
+        "def format_docs(docs):\n",
+        "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+        "\n",
+        "# Create stuff documents chain using LCEL.\n",
+        "# This is called a chain because you are chaining\n",
+        "# together different elements with the LLM.\n",
+        "# In the following example, to create a stuff chain,\n",
+        "# you will combine content, prompt, LLM model, and\n",
+        "# output parser together like a chain using LCEL.\n",
+        "#\n",
+        "# The chain implements the following pipeline:\n",
+        "# 1. Extract data from documents and save to the variable `context`.\n",
+        "# 2. Use the `RunnablePassthrough` option to provide question during invoke.\n",
+        "# 3. The `context` and `question` are then passed to the prompt and\n",
+        "#    input variables in the prompt are populated.\n",
+        "# 4. The prompt is then passed to the LLM (`gemini-pro`).\n",
+        "# 5. Output from the LLM is passed through an output parser\n",
+        "#    to structure the model response.\n",
+        "rag_chain = (\n",
+        "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
+        "    | llm_prompt\n",
+        "    | llm\n",
+        "    | StrOutputParser()\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gmHx_F7DoMgM"
+      },
+      "source": [
+        "### Prompt the model\n",
+        "\n",
+        "You can now query the LLM by passing any question to the `invoke()` function of the stuff documents chain you created previously."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "95W-sbTjoGGj"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "\"Gemini is Google's largest and most capable AI model. It is also their most flexible model, able to efficiently run on everything from data centers to mobile devices. Its state-of-the-art capabilities significantly enhance the way developers and enterprise customers build and scale with AI.\""
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "rag_chain.invoke(\"What is Gemini?\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "Gemini_LangChain_QA_Pinecone_WebLoad.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/examples/gemini/python/langchain/Gemini_LangChain_Summarization_WebLoad.ipynb b/examples/gemini/python/langchain/Gemini_LangChain_Summarization_WebLoad.ipynb
new file mode 100644
index 000000000..3e789669e
--- /dev/null
+++ b/examples/gemini/python/langchain/Gemini_LangChain_Summarization_WebLoad.ipynb
@@ -0,0 +1,435 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f22a409c18ef"
+      },
+      "source": [
+        "# Summarize large documents using LangChain and Gemini"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "awKO767lQIWh"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/examples/gemini/python/langchain/Gemini_LangChain_Summarization_WebLoad.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/examples/gemini/python/langchain/Gemini_LangChain_Summarization_WebLoad.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f892e8b2c8ef"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "[Gemini](https://ai.google.dev/models/gemini) is a family of generative AI models that lets developers generate content and solve problems. These models are designed and trained to handle both text and images as input.\n",
+        "\n",
+        "[LangChain](https://www.langchain.com/) is a framework designed to make integration of Large Language Models (LLM) like Gemini easier for applications.\n",
+        "\n",
+        "In this notebook, you'll learn how to create an application to summarize large documents using Gemini and LangChain.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iHj4T7hsx1EB"
+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "First, you must install the packages and set the necessary environment variables.\n",
+        "\n",
+        "### Installation\n",
+        "\n",
+        "Install LangChain's Python library, `langchain`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yERdO0eFJpb-"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m803.1/803.1 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m205.7/205.7 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.7/46.7 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install --quiet langchain"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "45MVc1stzNUN"
+      },
+      "source": [
+        "Install LangChain's integration package for Gemini, `langchain-google-genai`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FcMXJTN5JsfU"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install --quiet langchain-google-genai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ycFMUTxn0VoI"
+      },
+      "source": [
+        "### Grab an API Key\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e1dZNWvUzksX"
+      },
+      "source": [
+        "To use Gemini you need an *API key*. You can create an API key with one click in [Google AI Studio](https://makersuite.google.com/).\n",
+        "After creating the API key, you can either set an environment variable named `GOOGLE_API_KEY` to your API Key or pass the API key as an argument when creating the `ChatGoogleGenerativeAI` LLM using `LangChain`.\n",
+        "\n",
+        "In this tutorial, you will set the environment variable `GOOGLE_API_KEY` to configure Gemini to use your API key."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1RO5jvTTddtc"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Gemini API Key:··········\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Run this cell and paste the API key in the prompt\n",
+        "import os\n",
+        "import getpass\n",
+        "\n",
+        "os.environ['GOOGLE_API_KEY'] = getpass.getpass('Gemini API Key:')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i7wgsoiz418u"
+      },
+      "source": [
+        "## Summarize text\n",
+        "\n",
+        "In this tutorial, you are going to summarize the text from a website using the Gemini model integrated through LangChain.\n",
+        "\n",
+        "You'll perform the following steps to achieve the same:\n",
+        "1. Read and parse the website data using LangChain.\n",
+        "2. Chain together the following:\n",
+        "    * A prompt for extracting the required input data from the parsed website data.\n",
+        "    * A prompt for summarizing the text using LangChain.\n",
+        "    * An LLM model (Gemini) for prompting.\n",
+        "\n",
+        "3. Run the created chain to prompt the model for the summary of the website data."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RhL92-zmSB6Z"
+      },
+      "source": [
+        "### Import the required libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rAv0UicpKARZ"
+      },
+      "outputs": [],
+      "source": [
+        "from langchain import PromptTemplate\n",
+        "from langchain.document_loaders import WebBaseLoader\n",
+        "from langchain.schema import StrOutputParser\n",
+        "from langchain.schema.prompt_template import format_document"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4tKpRvmMRX23"
+      },
+      "source": [
+        "### Read and parse the website data\n",
+        "\n",
+        "LangChain provides a wide variety of document loaders. To read the website data as a document, you will use the `WebBaseLoader` from LangChain.\n",
+        "\n",
+        "To know more about how to read and parse input data from different sources using the document loaders of LangChain, read LangChain's [document loaders guide](https://python.langchain.com/docs/integrations/document_loaders)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TTgmyxXzKCSq"
+      },
+      "outputs": [],
+      "source": [
+        "loader = WebBaseLoader(\"https://blog.google/technology/ai/google-gemini-ai/#sundar-note\")\n",
+        "docs = loader.load()\n",
+        "\n",
+        "print(docs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4xlf_F_4B6lB"
+      },
+      "source": [
+        "### Initialize Gemini\n",
+        "\n",
+        "You must import the `ChatGoogleGenerativeAI` LLM from LangChain to initialize your model.\n",
+        " In this example you will use **gemini-pro**, as it supports text summarization. To know more about the text model, read Google AI's [language documentation](https://ai.google.dev/models/gemini).\n",
+        "\n",
+        "You can configure the model parameters such as ***temperature*** or ***top_p***,  by passing the appropriate values when creating the `ChatGoogleGenerativeAI` LLM.  To learn more about the parameters and their uses, read Google AI's [concepts guide](https://ai.google.dev/docs/concepts#model_parameters)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WWA9F0ZqB-8k"
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+        "\n",
+        "# If there is no env variable set for API key, you can pass the API key\n",
+        "# to the parameter `google_api_key` of the `ChatGoogleGenerativeAI` function:\n",
+        "# `google_api_key=\"key\"`.\n",
+        "\n",
+        "llm = ChatGoogleGenerativeAI(model=\"gemini-pro\",\n",
+        "                 temperature=0.7, top_p=0.85)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6TECDzaUSTvS"
+      },
+      "source": [
+        "### Create prompt templates\n",
+        "\n",
+        "You'll use LangChain's [PromptTemplate](https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/) to generate prompts for summarizing the text.\n",
+        "\n",
+        "To summarize the text from the website, you will need the following prompts.\n",
+        "1. Prompt to extract the data from the output of `WebBaseLoader`, named `doc_prompt`\n",
+        "2. Prompt for the LLM model (Gemini) to summarize the extracted text, named `llm_prompt`.\n",
+        "\n",
+        "In the `llm_prompt`, the variable `text` will be replaced later by the text from the website."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rixvvvaNKLe_"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "input_variables=['text'] template='Write a concise summary of the following:\\n\"{text}\"\\nCONCISE SUMMARY:'\n"
+          ]
+        }
+      ],
+      "source": [
+        "# To extract data from WebBaseLoader\n",
+        "doc_prompt = PromptTemplate.from_template(\"{page_content}\")\n",
+        "\n",
+        "# To query Gemini\n",
+        "llm_prompt_template = \"\"\"Write a concise summary of the following:\n",
+        "\"{text}\"\n",
+        "CONCISE SUMMARY:\"\"\"\n",
+        "llm_prompt = PromptTemplate.from_template(llm_prompt_template)\n",
+        "\n",
+        "print(llm_prompt)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-wPBMFyISh13"
+      },
+      "source": [
+        "### Create a Stuff documents chain\n",
+        "\n",
+        "LangChain provides [Chains](https://python.langchain.com/docs/modules/chains/) for chaining together LLMs with each other or other components for complex applications. You will create a **Stuff documents chain** for this application. A **Stuff documents chain** lets you combine all the documents, insert them into the prompt and pass that prompt to the LLM.\n",
+        "\n",
+        "You can create a Stuff documents chain using the [LangChain Expression Language (LCEL)](https://python.langchain.com/docs/expression_language).\n",
+        "\n",
+        "To learn more about different types of document chains, read LangChain's [chains guide](https://python.langchain.com/docs/modules/chains/document/)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EMZomQdyKMr5"
+      },
+      "outputs": [],
+      "source": [
+        "# Create Stuff documents chain using LCEL.\n",
+        "# This is called a chain because you are chaining\n",
+        "# together different elements with the LLM.\n",
+        "# In the following example, to create stuff chain,\n",
+        "# you will combine content, prompt, LLM model and\n",
+        "# output parser together like a chain using LCEL.\n",
+        "#\n",
+        "# The chain implements the following pipeline:\n",
+        "# 1. Extract data from documents and save to variable `text`.\n",
+        "# 2. This `text` is then passed to the prompt and input variable\n",
+        "#    in prompt is populated.\n",
+        "# 3. The prompt is then passed to the LLM (Gemini).\n",
+        "# 4. Output from the LLM is passed through an output parser\n",
+        "#    to structure the model response.\n",
+        "\n",
+        "stuff_chain = (\n",
+        "    # Extract data from the documents and add to the key `text`.\n",
+        "    {\n",
+        "        \"text\": lambda docs: \"\\n\\n\".join(\n",
+        "            format_document(doc, doc_prompt) for doc in docs\n",
+        "        )\n",
+        "    }\n",
+        "    | llm_prompt         # Prompt for Gemini\n",
+        "    | llm                # Gemini function\n",
+        "    | StrOutputParser()  # output parser\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5L0Tvk_5eQzC"
+      },
+      "source": [
+        "### Prompt the model\n",
+        "\n",
+        "To generate the summary of the  the website data, pass the documents extracted using the `WebBaseLoader` (`docs`) to `invoke()`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "k9_GxkA5ePRR"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "\"Google introduces Gemini, its most capable AI model yet. Gemini is multimodal, flexible, and optimized for different sizes. It surpasses state-of-the-art performance on various benchmarks, including text, coding, and multimodal tasks. Gemini's capabilities include sophisticated reasoning, understanding text, images, audio, and advanced coding. It is designed with responsibility and safety at its core, undergoing comprehensive safety evaluations and incorporating safety classifiers. Gemini is being rolled out across Google products, including Bard, Pixel, Search, and Ads. Developers and enterprise customers can access Gemini Pro via the Gemini API. Gemini Ultra will be available to select partners and experts for early experimentation before a broader release. Gemini represents a new era of AI innovation, with future versions expected to advance planning, memory, and context processing capabilities.\""
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "stuff_chain.invoke(docs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nfrBsxUFgZzc"
+      },
+      "source": [
+        "# Conclusion\n",
+        "\n",
+        "That's it. You have successfully created an LLM application to summarize text using LangChain and Gemini."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "Gemini_LangChain_Summarization_WebLoad.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/examples/gemini/python/llamaindex/Gemini_LlamaIndex_QA_Chroma_WebPageReader.ipynb b/examples/gemini/python/llamaindex/Gemini_LlamaIndex_QA_Chroma_WebPageReader.ipynb
new file mode 100644
index 000000000..7e1dd0250
--- /dev/null
+++ b/examples/gemini/python/llamaindex/Gemini_LlamaIndex_QA_Chroma_WebPageReader.ipynb
@@ -0,0 +1,628 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MctthPQNUiMt"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "0sK9GK2mUr4Z"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EecCBd3afA7C"
+      },
+      "source": [
+        "# Question Answering using Gemini, LlamaIndex, and Chroma"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XhAqH8SXfLhn"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/examples/gemini/python/llamaindex/Gemini_LlamaIndex_QA_Chroma_WebPageReader.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/examples/gemini/python/llamaindex/Gemini_LlamaIndex_QA_Chroma_WebPageReader.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "itTLwgnvkrfD"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "[Gemini](https://ai.google.dev/models/gemini) is a family of generative AI models that lets developers generate content and solve problems. These models are designed and trained to handle both text and images as input.\n",
+        "\n",
+        "[LlamaIndex](https://www.llamaindex.ai/) is a simple, flexible data framework that can be used by Large Language Model(LLM) applications to connect custom data sources to LLMs.\n",
+        "\n",
+        "[Chroma](https://docs.trychroma.com/) is an open-source embedding database focused on simplicity and developer productivity. Chroma allows users to store embeddings and their metadata, embed documents and queries, and search the embeddings quickly.\n",
+        "\n",
+        "In this notebook, you'll learn how to create an application that answers questions using data from a website with the help of Gemini, LlamaIndex, and Chroma."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fZQLFShAnNDA"
+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "First, you must install the packages and set the necessary environment variables.\n",
+        "\n",
+        "### Installation\n",
+        "\n",
+        "Install LlamaIndex's python library, `llama-index`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iK3BOx4u6ssQ"
+      },
+      "outputs": [],
+      "source": [
+        "# This guide was tested with 0.10.17, but feel free to try newer versions.\n",
+        "!pip install -q llama-index==0.10.17"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mzd3CN4yKGpw"
+      },
+      "source": [
+        "Install LlamaIndex's integration package for Gemini, `llama-index-llms-gemini`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "aUn_Pdp_KGyM"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q llama-index-llms-gemini"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vWqDXJhuKG-B"
+      },
+      "source": [
+        "Install LlamaIndex's integration package for Gemini embedding model, `llama-index-embeddings-gemini`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IdAKIYmdKHH3"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q llama-index-embeddings-gemini"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_9XYs842I-6G"
+      },
+      "source": [
+        "Install LlamaIndex's web page reader, `llama-index-readers-web`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HnFd2OWXI_M-"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q llama-index-readers-web"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "muHuC24HoCBq"
+      },
+      "source": [
+        "Install Chroma's python client SDK, `chromadb`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "a0OB7mnI8KMw"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q chromadb"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ofnYCgA8ojgz"
+      },
+      "source": [
+        "### Grab an API Key\n",
+        "\n",
+        "To use Gemini you need an *API key*. You can create an API key with one click in [Google AI Studio](https://makersuite.google.com/).\n",
+        "After creating the API key, you can either set an environment variable named `GOOGLE_API_KEY` to your API Key or pass the API key as an argument when using the `Gemini` class to access Google's `gemini-1.5-flash` and `gemini-1.5-pro` models or the `GeminiEmbedding` class to access Google's Generative AI embedding model using `LlamaIndex`.\n",
+        "\n",
+        "In this tutorial, you will set the variable `gemini_api_key` to configure Gemini to use your API key."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Wp3pYcnh60vt"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Gemini API Key:··········\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Run this cell and paste the API key in the prompt\n",
+        "import os\n",
+        "import getpass\n",
+        "\n",
+        "gemini_api_key = getpass.getpass('Gemini API Key:')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gSS20CpqhaDY"
+      },
+      "source": [
+        "## Basic steps\n",
+        "LLMs are trained offline on a large corpus of public data. Hence they cannot answer questions based on custom or private data accurately without additional context.\n",
+        "\n",
+        "If you want to make use of LLMs to answer questions based on private data, you have to provide the relevant documents as context alongside your prompt. This approach is called Retrieval Augmented Generation (RAG).\n",
+        "\n",
+        "You will use this approach to create a question-answering assistant using the Gemini text model integrated through LlamaIndex. The assistant is expected to answer questions about Google's Gemini model. To make this possible you will add more context to the assistant using data from a website.\n",
+        "\n",
+        "In this tutorial, you'll implement the two main components in a RAG-based architecture:\n",
+        "\n",
+        "1. Retriever\n",
+        "\n",
+        "    Based on the user's query, the retriever retrieves relevant snippets that add context from the document. In this tutorial, the document is the website data.\n",
+        "    The relevant snippets are passed as context to the next stage - \"Generator\".\n",
+        "\n",
+        "2. Generator\n",
+        "\n",
+        "    The relevant snippets from the website data are passed to the LLM along with the user's query to generate accurate answers.\n",
+        "\n",
+        "You'll learn more about these stages in the upcoming sections while implementing the application."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nPKvt5_5x6rH"
+      },
+      "source": [
+        "## Import the required libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JXkg7O9PJfe3"
+      },
+      "outputs": [],
+      "source": [
+        "from bs4 import BeautifulSoup\n",
+        "from IPython.display import Markdown, display\n",
+        "from llama_index.core import Document\n",
+        "from llama_index.core import Settings\n",
+        "from llama_index.core import SimpleDirectoryReader\n",
+        "from llama_index.core import StorageContext\n",
+        "from llama_index.core import VectorStoreIndex\n",
+        "from llama_index.readers.web import SimpleWebPageReader\n",
+        "\n",
+        "from llama_index.vector_stores.chroma import ChromaVectorStore\n",
+        "\n",
+        "import chromadb\n",
+        "import re"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fCU_lprVhixQ"
+      },
+      "source": [
+        "## 1. Retriever\n",
+        "\n",
+        "In this stage, you will perform the following steps:\n",
+        "\n",
+        "1. Read and parse the website data using LlamaIndex.\n",
+        "\n",
+        "2. Create embeddings of the website data.\n",
+        "\n",
+        "    Embeddings are numerical representations (vectors) of text. Hence, text with similar meaning will have similar embedding vectors. You'll make use of Gemini's embedding model to create the embedding vectors of the website data.\n",
+        "\n",
+        "3. Store the embeddings in Chroma's vector store.\n",
+        "    \n",
+        "    Chroma is a vector database. The Chroma vector store helps in the efficient retrieval of similar vectors. Thus, for adding context to the prompt for the LLM, relevant embeddings of the text matching the user's question can be retrieved easily using Chroma.\n",
+        "\n",
+        "4. Create a Retriever from the Chroma vector store.\n",
+        "\n",
+        "    The retriever will be used to pass relevant website embeddings to the LLM along with user queries."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FergxGcKh_b_"
+      },
+      "source": [
+        "### Read and parse the website data\n",
+        "\n",
+        "LlamaIndex provides a wide variety of data loaders. To read the website data as a document, you will use the `SimpleWebPageReader` from LlamaIndex.\n",
+        "\n",
+        "To know more about how to read and parse input data from different sources using the data loaders of LlamaIndex, read LlamaIndex's [loading data guide](https://docs.llamaindex.ai/en/stable/understanding/loading/loading.html)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xIYUiPPNjMrr"
+      },
+      "outputs": [],
+      "source": [
+        "web_documents = SimpleWebPageReader().load_data(\n",
+        "    [\"https://blog.google/technology/ai/google-gemini-ai/\"]\n",
+        ")\n",
+        "\n",
+        "# Extract the content from the website data document\n",
+        "html_content = web_documents[0].text"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TamoAP7ckyvB"
+      },
+      "source": [
+        "You can use variety of HTML parsers to extract the required text from the html content.\n",
+        "\n",
+        "In this example, you'll use Python's `BeautifulSoup` library to parse the website data. After processing, the extracted text should be converted back to LlamaIndex's `Document` format."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-90BtEGikzt1"
+      },
+      "outputs": [],
+      "source": [
+        "# Parse the data.\n",
+        "soup = BeautifulSoup(html_content, 'html.parser')\n",
+        "p_tags = soup.findAll('p')\n",
+        "text_content = \"\"\n",
+        "for each in p_tags:\n",
+        "    text_content += each.text + \"\\n\"\n",
+        "\n",
+        "# Convert back to Document format\n",
+        "documents = [Document(text=text_content)]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sq-MBiAgw1ba"
+      },
+      "source": [
+        "### Initialize Gemini's embedding model\n",
+        "\n",
+        "To create the embeddings from the website data, you'll use Gemini's embedding model, **embedding-001** which supports creating text embeddings.\n",
+        "\n",
+        "To use this embedding model, you have to import `GeminiEmbedding` from LlamaIndex. To know more about the embedding model, read Google AI's [language documentation](https://ai.google.dev/models/gemini)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ezv0-TIiFkxv"
+      },
+      "outputs": [],
+      "source": [
+        "from llama_index.embeddings.gemini import GeminiEmbedding\n",
+        "\n",
+        "gemini_embedding_model = GeminiEmbedding(api_key=gemini_api_key, model_name=\"models/embedding-001\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vJB_fdQmoq_6"
+      },
+      "source": [
+        "### Initialize Gemini\n",
+        "\n",
+        "You must import `Gemini` from LlamaIndex to initialize your model.\n",
+        " In this example, you will use **gemini-pro**, as it supports text summarization. To know more about the text model, read Google AI's [model documentation](https://ai.google.dev/models/gemini).\n",
+        "\n",
+        "You can configure the model parameters such as ***temperature*** or ***top_p***,  using the  ***generation_config*** parameter when initializing the `Gemini` LLM.  To learn more about the model parameters and their uses, read Google AI's [concepts guide](https://ai.google.dev/docs/concepts#model_parameters)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Gyq6YIh97quL"
+      },
+      "outputs": [],
+      "source": [
+        "from llama_index.llms.gemini import Gemini\n",
+        "\n",
+        "# To configure model parameters use the `generation_config` parameter.\n",
+        "# eg. generation_config = {\"temperature\": 0.7, \"topP\": 0.8, \"topK\": 40}\n",
+        "# If you only want to set a custom temperature for the model use the\n",
+        "# \"temperature\" parameter directly.\n",
+        "\n",
+        "llm = Gemini(api_key=gemini_api_key, model_name=\"models/gemini-pro\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uNLuJ-TY4utI"
+      },
+      "source": [
+        "### Store the data using Chroma\n",
+        "\n",
+        " Next, you'll store the embeddings of the website data in Chroma's vector store using LlamaIndex.\n",
+        "\n",
+        " First, you have to initiate a Python client in `chromadb`. Since the plan is to save the data to the disk, you will use the `PersistentClient`. You can read more about the different clients in Chroma in the [client reference guide](https://docs.trychroma.com/reference/Client).\n",
+        "\n",
+        "After initializing the client, you have to create a Chroma collection. You'll then initialize the `ChromaVectorStore` class in LlamaIndex using the collection created in the previous step.\n",
+        "\n",
+        "Next, you have to set `Settings` and create storage contexts for the vector store.\n",
+        "\n",
+        "`Settings` is a collection of commonly used resources that are utilized during the indexing and querying phase in a LlamaIndex pipeline. You can specify the LLM, Embedding model, etc that will be used to create the application in the `Settings`. To know more about `Settings`, read the [module guide for Settings](https://docs.llamaindex.ai/en/stable/module_guides/supporting_modules/settings.html).\n",
+        "\n",
+        "`StorageContext` is an abstraction offered by LlamaIndex around different types of storage. To know more about storage context, read the [storage context API guide](https://docs.llamaindex.ai/en/stable/api_reference/storage.html).\n",
+        "\n",
+        "The final step is to load the documents and build an index over them. LlamaIndex offers several indices that help in retrieving relevant context for a user query. Here you'll use the `VectorStoreIndex` since the website embeddings have to be stored in a vector store.\n",
+        "\n",
+        "To create the index you have to pass the storage context along with the documents to the `from_documents` function of `VectorStoreIndex`.\n",
+        "The `VectorStoreIndex` uses the embedding model specified in the `Settings` to create embedding vectors from the documents and stores these vectors in the vector store specified in the storage context. To know more about the\n",
+        "`VectorStoreIndex` you can read the [Using VectorStoreIndex guide](https://docs.llamaindex.ai/en/stable/module_guides/indexing/vector_store_index.html)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1Ohzkf-LJyHO"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a client and a new collection\n",
+        "client = chromadb.PersistentClient(path=\"./chroma_db\")\n",
+        "chroma_collection = client.get_or_create_collection(\"quickstart\")\n",
+        "\n",
+        "# Create a vector store\n",
+        "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
+        "\n",
+        "# Create a storage context\n",
+        "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+        "\n",
+        "# Set Global settings\n",
+        "Settings.llm = llm\n",
+        "Settings.embed_model = gemini_embedding_model\n",
+        "\n",
+        "# Create an index from the documents and save it to the disk.\n",
+        "index = VectorStoreIndex.from_documents(\n",
+        "    documents, storage_context=storage_context\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ir5pUZpNu3ly"
+      },
+      "source": [
+        "### Create a retriever using Chroma\n",
+        "\n",
+        "You'll now create a retriever that can retrieve data embeddings from the newly created Chroma vector store.\n",
+        "\n",
+        "First, initialize the `PersistentClient` with the same path you specified while creating the Chroma vector store. You'll then retrieve the collection `\"quickstart\"` you created previously from Chroma. You can use this collection to initialize the `ChromaVectorStore` in which you store the embeddings of the website data. You can then use the `from_vector_store` function of `VectorStoreIndex` to load the index."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FlAPuVLt4mBr"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "MMLU (massive multitask language understanding) is a benchmark that uses a combination of 57 subjects such as math, physics, history, law, medicine and ethics for testing both world knowledge and problem-solving abilities.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Load from disk\n",
+        "load_client = chromadb.PersistentClient(path=\"./chroma_db\")\n",
+        "\n",
+        "# Fetch the collection\n",
+        "chroma_collection = load_client.get_collection(\"quickstart\")\n",
+        "\n",
+        "# Fetch the vector store\n",
+        "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
+        "\n",
+        "# Get the index from the vector store\n",
+        "index = VectorStoreIndex.from_vector_store(\n",
+        "    vector_store\n",
+        ")\n",
+        "\n",
+        "# Check if the retriever is working by trying to fetch the relevant docs related\n",
+        "# to the phrase 'MMLU' (Multimodal Machine Learning Understanding).\n",
+        "# If the length is greater than zero, it means that the retriever is\n",
+        "# functioning well.\n",
+        "# You can ask questions about your data using a generic interface called\n",
+        "# a query engine. You have to use the `as_query_engine` function of the\n",
+        "# index to create a query engine and use the `query` function of query engine\n",
+        "# to inquire the index.\n",
+        "test_query_engine = index.as_query_engine()\n",
+        "response = test_query_engine.query(\"MMLU\")\n",
+        "print(response)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "10heqY7ilEsi"
+      },
+      "source": [
+        "## 2. Generator\n",
+        "\n",
+        "The Generator prompts the LLM for an answer when the user asks a question. The retriever you created in the previous stage from the Chroma vector store will be used to pass relevant embeddings from the website data to the LLM to provide more context to the user's query.\n",
+        "\n",
+        "You'll perform the following steps in this stage:\n",
+        "\n",
+        "1. Create a prompt for answering any question using LlamaIndex.\n",
+        "    \n",
+        "2. Use a query engine to ask a question and prompt the model for an answer."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iCLTx4zSxSll"
+      },
+      "source": [
+        "### Create prompt templates\n",
+        "\n",
+        "You'll use LlamaIndex's [PromptTemplate](https://docs.llamaindex.ai/en/stable/module_guides/models/prompts.html) to generate prompts to the LLM for answering questions.\n",
+        "\n",
+        "In the `llm_prompt`, the variable `query_str` will be replaced later by the input question, and the variable `context_str` will be replaced by the relevant text from the website retrieved from the Chroma vector store."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "V96dQi1uOzfr"
+      },
+      "outputs": [],
+      "source": [
+        "from llama_index.core import PromptTemplate\n",
+        "\n",
+        "template = (\n",
+        "    \"\"\" You are an assistant for question-answering tasks.\n",
+        "Use the following context to answer the question.\n",
+        "If you don't know the answer, just say that you don't know.\n",
+        "Use five sentences maximum and keep the answer concise.\\n\n",
+        "Question: {query_str} \\nContext: {context_str} \\nAnswer:\"\"\"\n",
+        ")\n",
+        "llm_prompt = PromptTemplate(template)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-aE0YWHT7bal"
+      },
+      "source": [
+        "### Prompt the model using Query Engine\n",
+        "\n",
+        "You will use the `as_query_engine` function of the `VectorStoreIndex` to create a query engine from the index using the `llm_prompt` passed as the value for the `text_qa_template` argument. You can then use the `query` function of the query engine to prompt the LLM. To know more about custom prompting in LlamaIndex, read LlamaIndex's [prompts usage pattern documentation](https://docs.llamaindex.ai/en/stable/module_guides/models/prompts/usage_pattern.html#defining-a-custom-prompt)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "klNUEBbP3xbr"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Gemini is the most capable and general model that Google has ever built. It is a multimodal AI model that can understand and generate text, images, and code. Gemini is being used to power new features in a range of Google products, including Bard, Pixel, Search, and Ads.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Query data from the persisted index\n",
+        "query_engine = index.as_query_engine(text_qa_template=llm_prompt)\n",
+        "response = query_engine.query(\"What is Gemini?\")\n",
+        "print(response)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "Gemini_LlamaIndex_QA_Chroma_WebPageReader.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/examples/gemini/python/vectordb_with_chroma/vectordb_with_chroma.ipynb b/examples/gemini/python/vectordb_with_chroma/vectordb_with_chroma.ipynb
new file mode 100644
index 000000000..77f84cc5d
--- /dev/null
+++ b/examples/gemini/python/vectordb_with_chroma/vectordb_with_chroma.ipynb
@@ -0,0 +1,818 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2023 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CsVPnR8VbXE6"
+      },
+      "source": [
+        "# Document Q&A with ChromaDB"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "awKO767lQIWh"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/examples/gemini/python/vectordb_with_chroma/vectordb_with_chroma.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/examples/gemini/python/vectordb_with_chroma/vectordb_with_chroma.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YtwZ8DZGJfUv"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This tutorial demonstrates how to use the Gemini API to create a vector database and retrieve answers to questions from the database. Moreover, you will use [ChromaDB](https://docs.trychroma.com/){:.external}, an open-source Python tool that creates embedding databases. ChromaDB allows you to:\n",
+        "\n",
+        "* Store embeddings as well as their metadata\n",
+        "* Embed documents and queries\n",
+        "* Search through the database of embeddings\n",
+        "\n",
+        "In this tutorial, you'll use embeddings to retrieve an answer from a database of vectors created with ChromaDB.\n",
+        "\n",
+        "## Prerequisites\n",
+        "\n",
+        "You can run this quickstart in Google Colab.\n",
+        "\n",
+        "To complete this quickstart on your own development environment, ensure that your environment meets the following requirements:\n",
+        "\n",
+        "-  Python 3.9+\n",
+        "-  An installation of `jupyter` to run the notebook."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "akuOzK4dJl3j"
+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "First, download and install ChromaDB and the Gemini API Python library."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JbXe7Oodc5dP"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -U -q google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sNCv-cJPLOZ2"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q chromadb"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jwmKt115PxK8"
+      },
+      "source": [
+        "Then import the modules you'll use in this tutorial."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "muuhsDmmKdHi"
+      },
+      "outputs": [],
+      "source": [
+        "import textwrap\n",
+        "import chromadb\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "import google.generativeai as genai\n",
+        "\n",
+        "# Used to securely store your API key\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "from IPython.display import Markdown\n",
+        "from chromadb import Documents, EmbeddingFunction, Embeddings"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U6tZGHUDOCFW"
+      },
+      "source": [
+        "### Grab an API Key\n",
+        "\n",
+        "Before you can use the Gemini API, you must first obtain an API key. If you don't already have one, create a key with one click in Google AI Studio.\n",
+        "\n",
+        "<a class=\"button button-primary\" href=\"https://makersuite.google.com/app/apikey\" target=\"_blank\" rel=\"noopener noreferrer\">Get an API key</a>\n",
+        "\n",
+        "In Colab, add the key to the secrets manager under the \"🔑\" in the left panel. Give it the name `API_KEY`.\n",
+        "\n",
+        "Once you have the API key, pass it to the SDK. You can do this in two ways:\n",
+        "\n",
+        "* Put the key in the `GOOGLE_API_KEY` environment variable (the SDK will automatically pick it up from there).\n",
+        "* Pass the key to `genai.configure(api_key=...)`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JoCFT6SaiCBX"
+      },
+      "outputs": [],
+      "source": [
+        "# Or use `os.getenv('API_KEY')` to fetch an environment variable.\n",
+        "API_KEY=userdata.get('API_KEY')\n",
+        "\n",
+        "genai.configure(api_key=API_KEY)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fegnGFpMS4AI"
+      },
+      "source": [
+        "Key Point: Next, you will choose a model. Any embedding model will work for this tutorial, but for real applications it's important to choose a specific model and stick with it. The outputs of different models are not compatible with each other.\n",
+        "\n",
+        "**Note**: At this time, the Gemini API is [only available in certain regions](https://ai.google.dev/available_regions)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Km5d13_FS2Q_"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "models/embedding-001\n",
+            "models/embedding-001\n"
+          ]
+        }
+      ],
+      "source": [
+        "for m in genai.list_models():\n",
+        "  if 'embedContent' in m.supported_generation_methods:\n",
+        "    print(m.name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3XWKXoXwOGxS"
+      },
+      "source": [
+        "### Data\n",
+        "\n",
+        "Here is a small set of documents you will use to create an embedding database:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "k8nsbhFJKmG-"
+      },
+      "outputs": [],
+      "source": [
+        "DOCUMENT1 = \"Operating the Climate Control System  Your Googlecar has a climate control system that allows you to adjust the temperature and airflow in the car. To operate the climate control system, use the buttons and knobs located on the center console.  Temperature: The temperature knob controls the temperature inside the car. Turn the knob clockwise to increase the temperature or counterclockwise to decrease the temperature. Airflow: The airflow knob controls the amount of airflow inside the car. Turn the knob clockwise to increase the airflow or counterclockwise to decrease the airflow. Fan speed: The fan speed knob controls the speed of the fan. Turn the knob clockwise to increase the fan speed or counterclockwise to decrease the fan speed. Mode: The mode button allows you to select the desired mode. The available modes are: Auto: The car will automatically adjust the temperature and airflow to maintain a comfortable level. Cool: The car will blow cool air into the car. Heat: The car will blow warm air into the car. Defrost: The car will blow warm air onto the windshield to defrost it.\"\n",
+        "DOCUMENT2 = \"Your Googlecar has a large touchscreen display that provides access to a variety of features, including navigation, entertainment, and climate control. To use the touchscreen display, simply touch the desired icon.  For example, you can touch the \\\"Navigation\\\" icon to get directions to your destination or touch the \\\"Music\\\" icon to play your favorite songs.\"\n",
+        "DOCUMENT3 = \"Shifting Gears Your Googlecar has an automatic transmission. To shift gears, simply move the shift lever to the desired position.  Park: This position is used when you are parked. The wheels are locked and the car cannot move. Reverse: This position is used to back up. Neutral: This position is used when you are stopped at a light or in traffic. The car is not in gear and will not move unless you press the gas pedal. Drive: This position is used to drive forward. Low: This position is used for driving in snow or other slippery conditions.\"\n",
+        "\n",
+        "documents = [DOCUMENT1, DOCUMENT2, DOCUMENT3]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yDzxArLeOexD"
+      },
+      "source": [
+        "## Creating the embedding database with ChromaDB\n",
+        "\n",
+        "You will create a [custom function](https://docs.trychroma.com/embeddings#custom-embedding-functions){:.external} for performing embedding using the Gemini API. By inputting a set of documents into this custom function, you will receive vectors, or embeddings of the documents.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UoHhS32txd_r"
+      },
+      "source": [
+        "### API changes to Embeddings with model embedding-001\n",
+        "\n",
+        "For the new embeddings model, embedding-001, there is a new task type parameter and the optional title (only valid with task_type=`RETRIEVAL_DOCUMENT`).\n",
+        "\n",
+        "These new parameters apply only to the newest embeddings models.The task types are:\n",
+        "\n",
+        "Task Type | Description\n",
+        "---       | ---\n",
+        "RETRIEVAL_QUERY\t| Specifies the given text is a query in a search/retrieval setting.\n",
+        "RETRIEVAL_DOCUMENT | Specifies the given text is a document in a search/retrieval setting.\n",
+        "SEMANTIC_SIMILARITY\t| Specifies the given text will be used for Semantic Textual Similarity (STS).\n",
+        "CLASSIFICATION\t| Specifies that the embeddings will be used for classification.\n",
+        "CLUSTERING\t| Specifies that the embeddings will be used for clustering."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mF7Uu1kCQsT0"
+      },
+      "outputs": [],
+      "source": [
+        "class GeminiEmbeddingFunction(EmbeddingFunction):\n",
+        "  def __call__(self, input: Documents) -> Embeddings:\n",
+        "    model = 'models/embedding-001'\n",
+        "    title = \"Custom query\"\n",
+        "    return genai.embed_content(model=model,\n",
+        "                                content=input,\n",
+        "                                task_type=\"retrieval_document\",\n",
+        "                                title=title)[\"embedding\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HrDWLyopPNBf"
+      },
+      "source": [
+        "Now you will create the vector database. In the `create_chroma_db` function, you will instantiate a [Chroma client](https://docs.trychroma.com/getting-started){:.external}. From there, you will create a collection, which is where you store your embeddings, documents, and any metadata. Note that the embedding function from above is passed as an argument to the `create_collection`.\n",
+        "\n",
+        "Next, you use the `add` method to add the documents to the collection."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OITXgxZlLoXU"
+      },
+      "outputs": [],
+      "source": [
+        "def create_chroma_db(documents, name):\n",
+        "  chroma_client = chromadb.Client()\n",
+        "  db = chroma_client.create_collection(name=name, embedding_function=GeminiEmbeddingFunction())\n",
+        "\n",
+        "  for i, d in enumerate(documents):\n",
+        "    db.add(\n",
+        "      documents=d,\n",
+        "      ids=str(i)\n",
+        "    )\n",
+        "  return db"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RJ3Fq0yzL10B"
+      },
+      "outputs": [],
+      "source": [
+        "# Set up the DB\n",
+        "db = create_chroma_db(documents, \"googlecarsdatabase\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2QbwFgfXp-fL"
+      },
+      "source": [
+        "Confirm that the data was inserted by looking at the database:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kQ9PHUL_l-hf"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a4e28672-c11f-4af0-980e-489e4695cb65\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ids</th>\n",
+              "      <th>embeddings</th>\n",
+              "      <th>metadatas</th>\n",
+              "      <th>documents</th>\n",
+              "      <th>uris</th>\n",
+              "      <th>data</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>[-0.020994942635297775, -0.03876612335443497, ...</td>\n",
+              "      <td>None</td>\n",
+              "      <td>Operating the Climate Control System  Your Goo...</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>[0.017410801723599434, -0.04757162556052208, -...</td>\n",
+              "      <td>None</td>\n",
+              "      <td>Your Googlecar has a large touchscreen display...</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>[-0.03194405511021614, -0.023281503468751907, ...</td>\n",
+              "      <td>None</td>\n",
+              "      <td>Shifting Gears Your Googlecar has an automatic...</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4e28672-c11f-4af0-980e-489e4695cb65')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a4e28672-c11f-4af0-980e-489e4695cb65 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-a4e28672-c11f-4af0-980e-489e4695cb65');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-c5a98be7-0775-4a27-bfe7-4fb2196a7235\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c5a98be7-0775-4a27-bfe7-4fb2196a7235')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-c5a98be7-0775-4a27-bfe7-4fb2196a7235 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "  ids                                         embeddings metadatas  \\\n",
+              "0   0  [-0.020994942635297775, -0.03876612335443497, ...      None   \n",
+              "1   1  [0.017410801723599434, -0.04757162556052208, -...      None   \n",
+              "2   2  [-0.03194405511021614, -0.023281503468751907, ...      None   \n",
+              "\n",
+              "                                           documents  uris  data  \n",
+              "0  Operating the Climate Control System  Your Goo...  None  None  \n",
+              "1  Your Googlecar has a large touchscreen display...  None  None  \n",
+              "2  Shifting Gears Your Googlecar has an automatic...  None  None  "
+            ]
+          },
+          "execution_count": 108,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "pd.DataFrame(db.peek(3))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tu5zRErgsQ8u"
+      },
+      "source": [
+        "## Getting the relevant document\n",
+        "\n",
+        "`db` is a Chroma collection object. You can call `query` on it to perform a nearest neighbors search to find similar embeddings or documents.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gQdJMbTSLtKE"
+      },
+      "outputs": [],
+      "source": [
+        "def get_relevant_passage(query, db):\n",
+        "  passage = db.query(query_texts=[query], n_results=1)['documents'][0][0]\n",
+        "  return passage"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nWYXXKJ6t6Hy"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "Your Googlecar has a large touchscreen display that provides access to a variety of features, including navigation, entertainment, and climate control. To use the touchscreen display, simply touch the desired icon.  For example, you can touch the \"Navigation\" icon to get directions to your destination or touch the \"Music\" icon to play your favorite songs."
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 112,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Perform embedding search\n",
+        "passage = get_relevant_passage(\"touch screen features\", db)\n",
+        "Markdown(passage)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "s8PNRMpOQkm5"
+      },
+      "source": [
+        "Now that you have found the relevant passage in your set of documents, you can use it make a prompt to pass into the Gemini API."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Qkhu4iazLy3G"
+      },
+      "outputs": [],
+      "source": [
+        "def make_prompt(query, relevant_passage):\n",
+        "  escaped = relevant_passage.replace(\"'\", \"\").replace('\"', \"\").replace(\"\\n\", \" \")\n",
+        "  prompt = (\"\"\"You are a helpful and informative bot that answers questions using text from the reference passage included below. \\\n",
+        "  Be sure to respond in a complete sentence, being comprehensive, including all relevant background information. \\\n",
+        "  However, you are talking to a non-technical audience, so be sure to break down complicated concepts and \\\n",
+        "  strike a friendly and converstional tone. \\\n",
+        "  If the passage is irrelevant to the answer, you may ignore it.\n",
+        "  QUESTION: '{query}'\n",
+        "  PASSAGE: '{relevant_passage}'\n",
+        "\n",
+        "    ANSWER:\n",
+        "  \"\"\").format(query=query, relevant_passage=escaped)\n",
+        "\n",
+        "  return prompt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hMEjbz4EswQ6"
+      },
+      "source": [
+        "Pass a query to the prompt:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "b6_Y-GOymaXu"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "You are a helpful and informative bot that answers questions using text from the reference passage included below.   Be sure to respond in a complete sentence, being comprehensive, including all relevant background information.   However, you are talking to a non-technical audience, so be sure to break down complicated concepts and   strike a friendly and converstional tone.   If the passage is irrelevant to the answer, you may ignore it.\n",
+              "  QUESTION: 'How do you shift gears in the Google car?'\n",
+              "  PASSAGE: 'Your Googlecar has a large touchscreen display that provides access to a variety of features, including navigation, entertainment, and climate control. To use the touchscreen display, simply touch the desired icon.  For example, you can touch the Navigation icon to get directions to your destination or touch the Music icon to play your favorite songs.'\n",
+              "\n",
+              "    ANSWER:\n",
+              "  "
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 117,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "query = \"How do you use the touchscreen in the Google car?\"\n",
+        "prompt = make_prompt(query, passage)\n",
+        "Markdown(prompt)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VRy6yXzcPxLB"
+      },
+      "source": [
+        "Now use the `generate_content` method to to generate a response from the model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EwfyxFM6Giy9"
+      },
+      "outputs": [],
+      "source": [
+        "model = genai.GenerativeModel('gemini-pro')\n",
+        "answer = model.generate_content(prompt)\n",
+        "Markdown(answer.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ThTbjAJ7eGP5"
+      },
+      "source": [
+        "## Next steps\n",
+        "\n",
+        "To learn more about how you can use the embeddings, check out the [examples](https://ai.google.dev/examples?keywords=embed) available. To learn how to use other services in the Gemini API, visit the [Python quickstart](https://ai.google.dev/gemini-api/docs/get-started/python)."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "vectordb_with_chroma.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/examples/gemini/python/vectordb_with_qdrant/Qdrant_similarity_search.ipynb b/examples/gemini/python/vectordb_with_qdrant/Qdrant_similarity_search.ipynb
new file mode 100644
index 000000000..5d9ca0ee2
--- /dev/null
+++ b/examples/gemini/python/vectordb_with_qdrant/Qdrant_similarity_search.ipynb
@@ -0,0 +1,535 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lTx8eQlc3cP-"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "cellView": "form",
+        "id": "4HZoi8yf4GEU"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "M9I7LG483nXB"
+      },
+      "source": [
+        "# Similarity Search using Gemini API and Qdrant"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "awKO767lQIWh"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/examples/gemini/python/vectordb_with_qdrant/Qdrant_similarity_search.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/examples/gemini/python/vectordb_with_qdrant/Qdrant_similarity_search.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b1xoF_bU4NCP"
+      },
+      "source": [
+        "## Overview"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CWedABji6bXJ"
+      },
+      "source": [
+        "The [Gemini API](https://ai.google.dev/models/gemini) provides access to a family of generative AI models for generating content and solving problems. These models are designed and trained to handle both text and images as input.\n",
+        "\n",
+        "[Qdrant](https://qdrant.tech/) is a vector similarity search engine that offers an easy-to-use API for managing, storing, and searching vectors, with an additional payload. It is a production-ready service.\n",
+        "\n",
+        "In this notebook, you'll learn how to perform a similarity search on data from a website with the help of Gemini API and Qdrant."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dIAarGkG8VwC"
+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "First, you must install the packages and set the necessary environment variables.\n",
+        "\n",
+        "### Installation\n",
+        "\n",
+        "Install google's python client SDK for the Gemini API, `google-generativeai`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "LnvqwC7AFROK"
+      },
+      "outputs": [],
+      "source": [
+        "! pip install -q google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "70wYOKUC8q1m"
+      },
+      "source": [
+        "Install Qdrant's python client SDK, `qdrant-client`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "mnQbBnA1GKha"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m229.3/229.3 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.8/2.8 MB\u001b[0m \u001b[31m17.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.6/294.6 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.5/57.5 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 4.25.3 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0m"
+          ]
+        }
+      ],
+      "source": [
+        "! pip install -q qdrant-client"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RzppByiY85Uc"
+      },
+      "source": [
+        "### Grab and set the API key\n",
+        "\n",
+        "To use Gemini API you need an *API key*. You can create an API key with one click in [Google AI Studio](https://makersuite.google.com/).\n",
+        "\n",
+        "Once you have the API key, pass it to the SDK. You can do this in two ways:\n",
+        "\n",
+        "1. Assign the key to the `GOOGLE_API_KEY` environment variable (the SDK will automatically pick it up from there) or pass the key to `genai.configure(api_key=...)`.\n",
+        "2. Or provide it explicitly through the `api_key` parameter.\n",
+        "\n",
+        "To run the following cell, your API key must be stored it in a Colab Secret named `GOOGLE_API_KEY`. If you don't already have an API key, or you're not sure how to create a Colab Secret, see the [Authentication](https://github.com/google-gemini/cookbook/blob/main/quickstarts/Authentication.ipynb) guide for an example."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "MWn09K5G8XYZ"
+      },
+      "outputs": [],
+      "source": [
+        "import google.generativeai as genai"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "MWn09K5G87YX"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import userdata\n",
+        "GOOGLE_API_KEY=userdata.get('GOOGLE_API_KEY')\n",
+        "\n",
+        "genai.configure(api_key=GOOGLE_API_KEY)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-go1BAF-_GtV"
+      },
+      "source": [
+        "## Basic steps\n",
+        "\n",
+        "Semantic search is the process using which search engines interpret and match keywords to a user's intent in organic search results. It goes beyond surface-level keyword matching. It uses the meaning of words, phrases, and context using advanced algorithms resulting in more relevant and user-friendly search experiences.\n",
+        "\n",
+        "Semantic searches rely on vector embeddings which can best match the user query to the most similar result.\n",
+        "\n",
+        "In this tutorial, you'll implement the three main components of semantic search:\n",
+        "\n",
+        "1. Create an index\n",
+        "\n",
+        "    Create and store the index for the data in the Qdrant vector store. You will use a Gemini API embedding model to create embedding vectors that can be stored in the Qdrant vector store.\n",
+        "\n",
+        "2. Query the index\n",
+        "\n",
+        "    Query the index using a query string to return the top `n` neighbors of the query.\n",
+        "\n",
+        "You'll learn more about these stages in the upcoming sections while implementing the application."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0egnCR92JKsj"
+      },
+      "source": [
+        "## Import the required libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "LfJN5QosJQqD"
+      },
+      "outputs": [],
+      "source": [
+        "from bs4 import BeautifulSoup\n",
+        "from qdrant_client import models, QdrantClient\n",
+        "from urllib.request import urlopen"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lL7J7BtyJsNQ"
+      },
+      "source": [
+        "## 1. Create an index\n",
+        "\n",
+        "In this stage, you will perform the following steps:\n",
+        "\n",
+        "1. Read and parse the website data using Python's BeautifulSoup library.\n",
+        "\n",
+        "2. Create embeddings of the website data.\n",
+        "\n",
+        "3. Store the embeddings in Qdrant's vector database.\n",
+        "    \n",
+        "    Qdrant is a vector similarity search engine. Along with a convenient API to store, search, and manage points(i.e. vectors), it also provides an option to add an additional payload. The payloads are essentially extra bits of data that you can utilize to refine your search and obtain relevant information that you can then share with your users."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kFlGmkKbRebP"
+      },
+      "source": [
+        "### Read and parse the website data\n",
+        "\n",
+        "To read the website data as text, you will use the `BeautifulSoup` library from Python."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "oMs-ux1gtxOa"
+      },
+      "outputs": [],
+      "source": [
+        "url = \"https://blog.google/outreach-initiatives/sustainability/\"\\\n",
+        "      \"report-ai-sustainability-google-cop28/\"\n",
+        "html = urlopen(url).read()\n",
+        "soup = BeautifulSoup(html, features=\"html.parser\")\n",
+        "\n",
+        "# Remove all script and style elements\n",
+        "for script in soup([\"script\", \"style\"]):\n",
+        "    script.extract()    # Self-destruct\n",
+        "\n",
+        "# Get the text\n",
+        "text_content = soup.get_text()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "toC03rqUSfom"
+      },
+      "source": [
+        "If you only want to select a specific portion of the website data to add context to the prompt, you can use regex, text slicing, or text splitting.\n",
+        "\n",
+        "In this example, you'll use Python's `split()` function to extract the required portion of the text."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "cHJq059duxj7"
+      },
+      "outputs": [],
+      "source": [
+        "# The text content between the substrings \"Later this month at COP28\" to\n",
+        "# \"POSTED IN:\" is relevant for this tutorial. You can use Python's `split()`\n",
+        "# to select the required content.\n",
+        "text_content_1 = text_content.split(\"Later this month at COP28\",1)[1]\n",
+        "final_text = text_content_1.split(\"POSTED IN:\",1)[0]\n",
+        "\n",
+        "texts = final_text.split(\".\")\n",
+        "\n",
+        "documents = []\n",
+        "\n",
+        "# Convert text into a chunk of 3 sentences.\n",
+        "for i in range(0, len(texts), 3):\n",
+        "  documents.append({\"content\": \" \".join(texts[i:i+3])})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-CVPdm0h6aTd"
+      },
+      "source": [
+        "### Initialize the embedding model\n",
+        "\n",
+        "To create the embeddings from the website data, you'll use the **embedding-001** model, which supports creating embeddings from text.\n",
+        "\n",
+        "To use the embedding model, you have to use the `embed_content` function from the `google-generativeai` package. To learn more about the embedding model, read the [model documentation](https://ai.google.dev/gemini-api/docs/models/gemini#embedding).\n",
+        "\n",
+        "One of the arguments passed to the embedding function is `task_type`. Speciefying the `task_type` parameter ensures the model produces appropriate embeddingsfor the expected task and inputs. It is a string that can take on one of the following values:\n",
+        "\n",
+        "| task_type\t  |  Description |\n",
+        "|---|---|\n",
+        "| `RETRIEVAL_QUERY` | Specifies the given text is a query in a search or retrieval setting. |\n",
+        "| `RETRIEVAL_DOCUMENT` | Specifies the given text is a document in a search or retrieval setting. |  \n",
+        "| `SEMANTIC_SIMILARITY` | Specifies the given text will be used for Semantic Textual Similarity (STS). |  \n",
+        "| `CLASSIFICATION` | Specifies that the embeddings will be used for classification. |\n",
+        "| `CLUSTERING` | Specifies that the embeddings will be used for clustering. |"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "EeSW5NOBTS0a"
+      },
+      "outputs": [],
+      "source": [
+        "# Default embedding model\n",
+        "embedding_model = \"models/embedding-001\"\n",
+        "\n",
+        "# Function to convert text to embeddings\n",
+        "def make_embed_text_fn(text, model=embedding_model,\n",
+        "                       task_type=\"retrieval_document\"):\n",
+        "    embedding = genai.embed_content(model=model,\n",
+        "                                    content=text,\n",
+        "                                    task_type=task_type)\n",
+        "    return embedding['embedding']"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9ajByZTxVXal"
+      },
+      "source": [
+        "### Store the data using Qdrant\n",
+        "\n",
+        " Next, you'll store the embeddings of the website data in Qdrant's vector store.\n",
+        "\n",
+        " First, you have to initiate a Qdrant client by creating an instance of `QdrantClient`. In this tutorial, you will store the embeddings in memory. To create an in-memory Qdrant client specify `:memory:` for the `location` argument of the `QdrantClient` class initializer. You can read more about the different types of storage in Qdrant in the [storage reference guide](https://qdrant.tech/documentation/concepts/storage/).\n",
+        "\n",
+        "After initializing the client, you have to create a Qdrant collection using the `recreate_collection` function of `QdrantClient`. You can specify your vector configuration inside the `recreate_collection` function. Pass an instance of `VectorParams` with the `size` set to `768` to match the embedding model and `distance` set to cosine.\n",
+        "\n",
+        "**Note**: Since you will run the script several times during your experiments, `recreate_collection` is appropriate for this tutorial. `recreate_collection` will first try to remove an existing collection with the same name."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "pnURtmtZTImC"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "<ipython-input-9-0cebad4715b7>:5: DeprecationWarning: `recreate_collection` method is deprecated and will be removed in the future. Use `collection_exists` to check collection existence and `create_collection` instead.\n",
+            "  qdrant.recreate_collection(\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Initialize Qdrant client.\n",
+        "qdrant = QdrantClient(\":memory:\")\n",
+        "\n",
+        "# Create a collection named \"GeminiCollection\".\n",
+        "qdrant.recreate_collection(\n",
+        "    collection_name=\"GeminiCollection\",\n",
+        "    vectors_config=models.VectorParams(\n",
+        "        size=768,  # Vector size of `embedding-001`\n",
+        "        distance=models.Distance.COSINE,\n",
+        "    ),\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YQu7FIhLeC0O"
+      },
+      "source": [
+        "You will now insert the `documents` you parsed from the website data into the Qdrant collection you created earlier and index them using the `upsert` function of `QdrantClient`.\n",
+        "\n",
+        "The `upsert` function takes the data to be stored and indexed as an array of `PointsStruct`s.\n",
+        "\n",
+        "Points are the main entity in Qdrant operations. A point is a record consisting of a vector and an optional payload. You can perform a similarity search among the points in one collection. Read more about points in [Qdrant's points documentation](https://qdrant.tech/documentation/concepts/points/).\n",
+        "\n",
+        "You'll create an array of points by enumerating over the documents you prepared earlier from the website data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "uOqivudxSyR9"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "UpdateResult(operation_id=0, status=<UpdateStatus.COMPLETED: 'completed'>)"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Qdrant uses batch loading of points to optimize performance.\n",
+        "# You can create a batch in two ways - record-oriented and column-oriented.\n",
+        "# Here you are using the record-oriented approach.\n",
+        "\n",
+        "qdrant.upsert(\n",
+        "    collection_name=\"GeminiCollection\",\n",
+        "    points=[\n",
+        "        # Use PointStruct function to intialize the point.\n",
+        "        models.PointStruct(\n",
+        "            # Use `make_embed_text_fn` to convert text to embeddings.\n",
+        "            # Pass the same data as payload for a refined search.\n",
+        "            id=idx, vector=make_embed_text_fn(doc[\"content\"]), payload = doc\n",
+        "        )\n",
+        "        for idx, doc in enumerate(documents)\n",
+        "    ]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JdVrKZZ0cTkV"
+      },
+      "source": [
+        "## 2. Query the index\n",
+        "\n",
+        "You'll now query the Qdrant index you created earlier with a question related to the data contained in the website documents.\n",
+        "To query the index, you have to mention the collection name and the query vector. The query vector should be first converted to an embedding vector using the Gemini API embedding model you leveraged to create embedding vectors for the website data. Use the `make_embed_text_fn` you defined earlier for creating an embedding vector from your query. Since you are embedding a query string that is being used to search `retrieval_document` embeddings, the `task_type` must be set to `retrieval_query`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "6LQVKNfMTyOx"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'content': ' Already, it is starting to address climate challenges in three key areas: providing people and organizations with better information to make more sustainable choices, delivering improved predictions to help adapt to climate change, and finding recommendations to optimize climate action for high-impact applications Here’s a look at how, at Google, we’ve used AI to address climate challenges:Providing helpful information: People are looking for information to reduce their environmental footprint  Fuel-efficient routing in Google Maps uses AI to suggest routes that have fewer hills, less traffic, and constant speeds with the same or similar ETA'} score: 0.7711945535904017\n",
+            "{'content': ' Policymakers, in particular, have a central role to play both in harnessing the potential of AI for climate action and in ensuring its sustainable and equitable use  Policymakers can make a difference in accelerating three outcomes:Enabling AI for climate progress by encouraging data sharing, ensuring affordable technology access, building awareness, and supporting the creation and expansion of AI and climate-related upskilling programs for corporations Accelerating the deployment of AI for climate by defining public and private sector priorities, delivering on public sector use cases, and encouraging private sector action'} score: 0.7458781382056137\n",
+            "{'content': '\\n\\n\\n\\n\\nManaging the environmental impact of AIWhile scaling these applications of AI and finding new ways to use it to accelerate climate action is crucial, we need to build AI responsibly and manage the environmental impact associated with it As AI is at an inflection point, predicting the future growth of energy use and emissions from AI compute in our data centers is challenging  Historically, data center energy consumption has grown much more slowly than demand for computing power'} score: 0.7405380973240167\n"
+          ]
+        }
+      ],
+      "source": [
+        "hits = qdrant.search(\n",
+        "    collection_name=\"GeminiCollection\",\n",
+        "    query_vector=make_embed_text_fn(\"How can AI address climate challenges?\",\n",
+        "                                    task_type=\"retrieval_query\"),\n",
+        "    limit=3,\n",
+        ")\n",
+        "for hit in hits:\n",
+        "    print(hit.payload, \"score:\", hit.score)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tt1wSSMIxsf2"
+      },
+      "source": [
+        "##Conclusion\n",
+        "\n",
+        "That's it. You have successfully performed a similarity search using Qdrant with the help of a Gemini API embedding model."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "Qdrant_similarity_search.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/examples/palm/python/google_cloud_functions/main.py b/examples/palm/python/google_cloud_functions/main.py
new file mode 100644
index 000000000..ae6baae0a
--- /dev/null
+++ b/examples/palm/python/google_cloud_functions/main.py
@@ -0,0 +1,39 @@
+import functions_framework
+import google.ai.generativelanguage as glm
+import google.generativeai as palm
+from google.oauth2 import credentials
+import json
+import requests
+
+SCOPES = [
+  'https://www.googleapis.com/auth/cloud-platform',
+  'https://www.googleapis.com/auth/generative-language.tuning',
+]
+
+def get_credentials():
+  """Generate scoped OAuth2 credentials."""
+  token_full_url = 'http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/token?scopes=' + ','.join(SCOPES)
+  token_response = requests.get(token_full_url, headers={'Metadata-Flavor': 'Google'})
+  if token_response.status_code != 200:
+    raise ValueError(f'Cant auth - {token_response.status_code}: {token_response.text}')
+
+  token = json.loads(token_response.text)
+  return credentials.Credentials(token=token['access_token'])
+
+
+@functions_framework.http
+def load_model(request):
+  """Load a PaLM model using a service account."""
+
+  # Build a google.auth.credentials.Credentials object from the running service account's
+  # authorisation, with the desired scopes defined above.
+  o2_creds = get_credentials()
+  # Each PaLM API uses a different client, e.g. ModelServiceClient, TextServiceClient, etc.
+  # You will need to build the respective client for the API you are using.
+  model_client = glm.ModelServiceClient(credentials=o2_creds)
+
+  # Test tuning by passing name=tunedModels/your-model-id. You must ensure that the model is shared
+  # with the running service account, or you will see a permission denied error (in the logs).
+  model_name = request.args.get('name', 'models/text-bison-001')
+  model = palm.get_model(model_name, client=model_client)
+  return f'<pre>{model}</pre>'
diff --git a/site/en/docs/react_gemini_prompting.ipynb b/site/en/docs/react_gemini_prompting.ipynb
new file mode 100644
index 000000000..a7a28be37
--- /dev/null
+++ b/site/en/docs/react_gemini_prompting.ipynb
@@ -0,0 +1,1025 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "T85zXWw3Zs05"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "cellView": "form",
+        "id": "X4uPZ83DbUTq"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "cellView": "form",
+        "id": "FUqzNst0YN9P"
+      },
+      "outputs": [],
+      "source": [
+        "# The non-source code materials on this page are licensed under Creative Commons - Attribution-ShareAlike CC-BY-SA 4.0,\n",
+        "# https://creativecommons.org/licenses/by-sa/4.0/legalcode."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vX-FA27MbYpQ"
+      },
+      "source": [
+        "# ReAct + Gemini: A prompting method for demonstrating reasoning and acting in LLMs"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Pk4Y-PKWc3MU"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/docs/react_gemini_prompting\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/docs/react_gemini_prompting.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/docs/react_gemini_prompting.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sdkuZY1IdRal"
+      },
+      "source": [
+        "This notebook is a minimal implementation of [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629) with the Google `gemini-pro` model.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PSr-BK-5meRo"
+      },
+      "source": [
+        "This notebook demonstrates the use of `gemini-pro` to generate reasoning traces and task-specific actions by leveraging a **Few-shot ReAct Prompt**. In this walkthrough, you will learn how to:\n",
+        "\n",
+        "\n",
+        "1. Set up your development environment and API access to use Gemini.\n",
+        "2. Prompt Gemini with ReAct.\n",
+        "3. Use the newly prompted model for multi-turn conversations (chat).\n",
+        "4. How ReAct overcomes issues of hallucination and error propagation by seeking external groundtruth via **Wikipedia API**.\n",
+        "5. Have conversations with deployed **ReAct prompted Gemini bot 🤖**\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lSkx3VHr3WYb"
+      },
+      "source": [
+        "### Background\n",
+        "\n",
+        "  \n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PqoT0ojAcV9P"
+      },
+      "source": [
+        "ReAct ((Yao, et al. 2022)[https://arxiv.org/abs/2210.03629)) is a prompting method that allows language models to trace reasoning steps involved in answering a user's query. This improves human interpretability and trustworthiness. ReAct prompted models generate **Thought-Action-Observation** triplets for every iteration.\n",
+        "\n",
+        "\n",
+        "Instead of instructing the model to \"Explain step-by-step\", using ReAct encourages models to seek factual information by inducing Thought and Action steps, and using external tools to provide Observation steps.\n",
+        "\n",
+        "\n",
+        "It works by adding structure to the prompt that corresponds to specific actions.\n",
+        "\n",
+        "      - Search[entity]: Search for a specific `entity` (in this guide, it will query the Wikipedia API).\n",
+        "      - Lookup[phrase]: Scan the `Search` results for a specific, exact-match phrase (on a Wikipedia page).\n",
+        "      - Finish[answer]: Return the `answer` to the user."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cVvxnBG-thZG"
+      },
+      "source": [
+        "## Setup\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Xq6NOA99tiHK"
+      },
+      "source": [
+        "### Install the Python SDK\n",
+        "\n",
+        "The Python SDK for the Gemini API, is contained in the [`google-generativeai`](https://pypi.org/project/google-generativeai/) package. Install the dependency using pip:\n",
+        "\n",
+        "You will also need to install the **Wikipedia** API.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "Twc_XZ7h7Bb4"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "7oZwkgQpfrLl"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q wikipedia"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DVWIqdtbffau"
+      },
+      "source": [
+        "Note: The [`wikipedia` package](https://pypi.org/project/wikipedia/) notes that it was \"designed for ease of use and simplicity, not for advanced use\", and that production or heavy use should instead \"use [Pywikipediabot](http://www.mediawiki.org/wiki/Manual:Pywikipediabot) or one of the other more advanced [Python MediaWiki API wrappers](http://en.wikipedia.org/wiki/Wikipedia:Creating_a_bot#Python)\"."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vqv5MnQUuBZJ"
+      },
+      "source": [
+        "### Import packages"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qS5HJk_VuCup"
+      },
+      "source": [
+        "Import the necessary packages."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Jz5HOLy47VX0"
+      },
+      "outputs": [],
+      "source": [
+        "import re\n",
+        "import os\n",
+        "\n",
+        "import wikipedia\n",
+        "from wikipedia.exceptions import DisambiguationError, PageError\n",
+        "\n",
+        "import google.generativeai as genai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4xsPDHz_uSYH"
+      },
+      "source": [
+        "### Set up your API key\n",
+        "\n",
+        "Before you can use the Gemini API, you must first obtain an API key. If you don't already have one, create a key with one click in Google AI Studio.\n",
+        "\n",
+        "<a class=\"button button-primary\" href=\"https://makersuite.google.com/app/apikey\" target=\"_blank\" rel=\"noopener noreferrer\">Get an API key</a>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3QC1DUOxuWDS"
+      },
+      "source": [
+        "In Colab, add the key to the secrets manager under the \"🔑\" in the left panel. Give it the name `GOOGLE_API_KEY`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SAvjxTybuWw-"
+      },
+      "source": [
+        "Once you have the API key, pass it to the SDK. You can do this in two ways:\n",
+        "\n",
+        "* Put the key in the `GOOGLE_API_KEY` environment variable (the SDK will automatically pick it up from there).\n",
+        "* Pass the key to `genai.configure(api_key=...)`\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "JAzIedGr9PdN"
+      },
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    from google.colab import userdata\n",
+        "    GOOGLE_API_KEY=userdata.get('GOOGLE_API_KEY')\n",
+        "except ImportError as e:\n",
+        "    GOOGLE_API_KEY = os.environ['GOOGLE_API_KEY']\n",
+        "genai.configure(api_key=GOOGLE_API_KEY)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Sqkwp87FumIp"
+      },
+      "source": [
+        "## The ReAct prompt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lLv9Kuuu5Ffs"
+      },
+      "source": [
+        "Here, you will be working with the [original ReAct prompt](https://github.com/ysymyth/ReAct/tree/master/prompts) with a few minor adjustments."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "07ed55c29a1d"
+      },
+      "source": [
+        "> Note: The prompt and in-context examples used here are taken from [https://github.com/ysymyth/ReAct](https://github.com/ysymyth/ReAct) which is published under [MIT license](https://opensource.org/licenses/MIT)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "g8klL8df4iXe"
+      },
+      "outputs": [],
+      "source": [
+        "model_instructions = \"\"\"Solve a question answering task with interleaving Thought, Action, Observation steps. Thought can reason about the current situation, Observation is understanding relevant information from an Action's output and Action can be of three types:\n",
+        "(1) <search>entity</search>, which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search and you can try to search the information from those topics.\n",
+        "(2) <lookup>keyword</lookup>, which returns the next sentence containing keyword in the current context. This only does exact matches, so keep your searches short.\n",
+        "(3) <finish>answer</finish>, which returns the answer and finishes the task.\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Fw52CHAG0aRr"
+      },
+      "source": [
+        "### Few-shot prompting to enable in-context learning with Gemini\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-jhaD4ChNv6M"
+      },
+      "source": [
+        "While large language models show good understanding of the instructions they are prompted with, they still may perform poorly on complex tasks in a zero-shot setting. Hence, you will now provide a few examples along with your prompt to steer the model's output according to your needs. This **in-context learning** improves the model's performance significantly."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "tZ7vezr02qv0"
+      },
+      "outputs": [],
+      "source": [
+        "examples = \"\"\"\n",
+        "Here are some examples.\n",
+        "\n",
+        "Question\n",
+        "What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?\n",
+        "\n",
+        "Thought 1\n",
+        "I need to search Colorado orogeny, find the area that the eastern sector of the Colorado orogeny extends into, then find the elevation range of the area.\n",
+        "\n",
+        "Action 1\n",
+        "<search>Colorado orogeny</search>\n",
+        "\n",
+        "Observation 1\n",
+        "The Colorado orogeny was an episode of mountain building (an orogeny) in Colorado and surrounding areas.\n",
+        "\n",
+        "Thought 2\n",
+        "It does not mention the eastern sector. So I need to look up eastern sector.\n",
+        "\n",
+        "Action 2\n",
+        "<lookup>eastern sector</lookup>\n",
+        "\n",
+        "Observation 2\n",
+        "The eastern sector extends into the High Plains and is called the Central Plains orogeny.\n",
+        "\n",
+        "Thought 3\n",
+        "The eastern sector of Colorado orogeny extends into the High Plains. So I need to search High Plains and find its elevation range.\n",
+        "\n",
+        "Action 3\n",
+        "<search>High Plains</search>\n",
+        "\n",
+        "Observation 3\n",
+        "High Plains refers to one of two distinct land regions\n",
+        "\n",
+        "Thought 4\n",
+        "I need to instead search High Plains (United States).\n",
+        "\n",
+        "Action 4\n",
+        "<search>High Plains (United States)</search>\n",
+        "\n",
+        "Observation 4\n",
+        "The High Plains are a subregion of the Great Plains. From east to west, the High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130m).\n",
+        "\n",
+        "Thought 5\n",
+        "High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer is 1,800 to 7,000 ft.\n",
+        "\n",
+        "Action 5\n",
+        "<finish>1,800 to 7,000 ft</finish>\n",
+        "\n",
+        "Question\n",
+        "Musician and satirist Allie Goertz wrote a song about the \"The Simpsons\" character Milhouse, who Matt Groening named after who?\n",
+        "\n",
+        "Thought 1\n",
+        "The question simplifies to \"The Simpsons\" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.\n",
+        "\n",
+        "Action 1\n",
+        "<search>Milhouse</search>\n",
+        "\n",
+        "Observation 1\n",
+        "Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.\n",
+        "\n",
+        "Thought 2\n",
+        "The paragraph does not tell who Milhouse is named after, maybe I can look up \"named after\".\n",
+        "\n",
+        "Action 2\n",
+        "<lookup>named after</lookup>\n",
+        "\n",
+        "Observation 2\n",
+        "Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.\n",
+        "\n",
+        "Thought 3\n",
+        "Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.\n",
+        "\n",
+        "Action 3\n",
+        "<finish>Richard Nixon</finish>\n",
+        "\n",
+        "Question\n",
+        "Which documentary is about Finnish rock groups, Adam Clayton Powell or The Saimaa Gesture?\n",
+        "\n",
+        "Thought 1\n",
+        "I need to search Adam Clayton Powell and The Saimaa Gesture, and find which documentary is about Finnish rock groups.\n",
+        "\n",
+        "Action 1\n",
+        "<search>Adam Clayton Powell</search>\n",
+        "\n",
+        "Observation 1\n",
+        "Could not find [Adam Clayton Powell]. Similar: [’Adam Clayton Powell III’, ’Seventh Avenue (Manhattan)’, ’Adam Clayton Powell Jr. State Office Building’, ’Isabel Washington Powell’, ’Adam Powell’, ’Adam Clayton Powell (film)’, ’Giancarlo Esposito’].\n",
+        "\n",
+        "Thought 2\n",
+        "To find the documentary, I can search Adam Clayton Powell (film).\n",
+        "\n",
+        "Action 2\n",
+        "<search>Adam Clayton Powell (film)</search>\n",
+        "\n",
+        "Observation 2\n",
+        "Adam Clayton Powell is a 1989 American documentary film directed by Richard Kilberg. The film is about the rise and fall of influential African-American politician Adam Clayton Powell Jr.[3][4] It was later aired as part of the PBS series The American Experience.\n",
+        "\n",
+        "Thought 3\n",
+        "Adam Clayton Powell (film) is a documentary about an African-American politician, not Finnish rock groups. So the documentary about Finnish rock groups must instead be The Saimaa Gesture.\n",
+        "\n",
+        "Action 3\n",
+        "<finish>The Saimaa Gesture</finish>\n",
+        "\n",
+        "Question\n",
+        "What profession does Nicholas Ray and Elia Kazan have in common?\n",
+        "\n",
+        "Thought 1\n",
+        "I need to search Nicholas Ray and Elia Kazan, find their professions, then find the profession they have in common.\n",
+        "\n",
+        "Action 1\n",
+        "<search>Nicholas Ray</search>\n",
+        "\n",
+        "Observation 1\n",
+        "Nicholas Ray (born Raymond Nicholas Kienzle Jr., August 7, 1911 - June 16, 1979) was an American film director, screenwriter, and actor best known for the 1955 film Rebel Without a Cause.\n",
+        "\n",
+        "Thought 2\n",
+        "Professions of Nicholas Ray are director, screenwriter, and actor. I need to search Elia Kazan next and find his professions.\n",
+        "\n",
+        "Action 2\n",
+        "<search>Elia Kazan</search>\n",
+        "\n",
+        "Observation 2\n",
+        "Elia Kazan was an American film and theatre director, producer, screenwriter and actor.\n",
+        "\n",
+        "Thought 3\n",
+        "Professions of Elia Kazan are director, producer, screenwriter, and actor. So profession Nicholas Ray and Elia Kazan have in common is director, screenwriter, and actor.\n",
+        "\n",
+        "Action 3\n",
+        "<finish>director, screenwriter, actor</finish>\n",
+        "\n",
+        "Question\n",
+        "Which magazine was started first Arthur’s Magazine or First for Women?\n",
+        "\n",
+        "Thought 1\n",
+        "I need to search Arthur’s Magazine and First for Women, and find which was started first.\n",
+        "\n",
+        "Action 1\n",
+        "<search>Arthur’s Magazine</search>\n",
+        "\n",
+        "Observation 1\n",
+        "Arthur’s Magazine (1844-1846) was an American literary periodical published in Philadelphia in the 19th century.\n",
+        "\n",
+        "Thought 2\n",
+        "Arthur’s Magazine was started in 1844. I need to search First for Women next.\n",
+        "\n",
+        "Action 2\n",
+        "<search>First for Women</search>\n",
+        "\n",
+        "Observation 2\n",
+        "First for Women is a woman’s magazine published by Bauer Media Group in the USA.[1] The magazine was started in 1989.\n",
+        "\n",
+        "Thought 3\n",
+        "First for Women was started in 1989. 1844 (Arthur’s Magazine) < 1989 (First for Women), so Arthur’s Magazine was started first.\n",
+        "\n",
+        "Action 3\n",
+        "<finish>Arthur’s Magazine</finish>\n",
+        "\n",
+        "Question\n",
+        "Were Pavel Urysohn and Leonid Levin known for the same type of work?\n",
+        "\n",
+        "Thought 1\n",
+        "I need to search Pavel Urysohn and Leonid Levin, find their types of work, then find if they are the same.\n",
+        "\n",
+        "Action 1\n",
+        "<search>Pavel Urysohn</search>\n",
+        "\n",
+        "Observation 1\n",
+        "Pavel Samuilovich Urysohn (February 3, 1898 - August 17, 1924) was a Soviet mathematician who is best known for his contributions in dimension theory.\n",
+        "\n",
+        "Thought 2\n",
+        "Pavel Urysohn is a mathematician. I need to search Leonid Levin next and find its type of work.\n",
+        "\n",
+        "Action 2\n",
+        "<search>Leonid Levin</search>\n",
+        "\n",
+        "Observation 2\n",
+        "Leonid Anatolievich Levin is a Soviet-American mathematician and computer scientist.\n",
+        "\n",
+        "Thought 3\n",
+        "Leonid Levin is a mathematician and computer scientist. So Pavel Urysohn and Leonid Levin have the same type of work.\n",
+        "\n",
+        "Action 3\n",
+        "<finish>yes</finish>\n",
+        "\n",
+        "Question\n",
+        "{question}\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xeCImqiN3WiQ"
+      },
+      "source": [
+        "Copy the instructions along with examples in a file called `model_instructions.txt`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "ZyTfAdpk26oB"
+      },
+      "outputs": [],
+      "source": [
+        "ReAct_prompt = model_instructions + examples\n",
+        "with open('model_instructions.txt', 'w') as f:\n",
+        "  f.write(ReAct_prompt)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Is8BIVQP3u95"
+      },
+      "source": [
+        "## Using ReAct with Gemini"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PqEwKVDgM1MF"
+      },
+      "source": [
+        "### Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "T4M3lxEoM3k0"
+      },
+      "source": [
+        "You will now build a class to facilitate multi-turn chat with the ReAct-prompted Gemini model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "vssDZcroN-Ob"
+      },
+      "outputs": [],
+      "source": [
+        "class ReAct:\n",
+        "  def __init__(self, model: str, ReAct_prompt: str | os.PathLike):\n",
+        "    \"\"\"Prepares Gemini to follow a `Few-shot ReAct prompt` by imitating\n",
+        "    `function calling` technique to generate both reasoning traces and\n",
+        "    task-specific actions in an interleaved manner.\n",
+        "\n",
+        "    Args:\n",
+        "        model: name to the model.\n",
+        "        ReAct_prompt: ReAct prompt OR path to the ReAct prompt.\n",
+        "    \"\"\"\n",
+        "    self.model = genai.GenerativeModel(model)\n",
+        "    self.chat = self.model.start_chat(history=[])\n",
+        "    self.should_continue_prompting = True\n",
+        "    self._search_history: list[str] = []\n",
+        "    self._search_urls: list[str] = []\n",
+        "\n",
+        "    try:\n",
+        "      # try to read the file\n",
+        "      with open(ReAct_prompt, 'r') as f:\n",
+        "        self._prompt = f.read()\n",
+        "    except FileNotFoundError:\n",
+        "      # assume that the parameter represents prompt itself rather than path to the prompt file.\n",
+        "      self._prompt = ReAct_prompt\n",
+        "\n",
+        "  @property\n",
+        "  def prompt(self):\n",
+        "    return self._prompt\n",
+        "\n",
+        "  @classmethod\n",
+        "  def add_method(cls, func):\n",
+        "    setattr(cls, func.__name__, func)\n",
+        "\n",
+        "  @staticmethod\n",
+        "  def clean(text: str):\n",
+        "    \"\"\"Helper function for responses.\"\"\"\n",
+        "    text = text.replace(\"\\n\", \" \")\n",
+        "    return text"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xKfThpmhMZYZ"
+      },
+      "source": [
+        "### Define tools\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dnvZ2jqdRHE1"
+      },
+      "source": [
+        "As instructed by the prompt, the model will be generating **Thought-Action-Observation** traces, where every **Action** trace could be one of the following tokens:\n",
+        "\n",
+        "\n",
+        "1.   <search/> : Perform a Wikipedia search via external API.\n",
+        "2.   <lookup/> : Look up specific information on a page with the Wikipedia API.\n",
+        "3.   <finish/> : Stop the execution of the model and return the answer.\n",
+        "\n",
+        "If any of these `Action` tokens are returned, you want to call the relevant tool and update the prompt with an `Observervation`.\n",
+        "\n",
+        "Note: The Gemini API supports [function calling](https://ai.google.dev/docs/function_calling) and you could use this feature to set up your tools. However, for this tutorial, you will use `stop_sequences` parameter in a similar manner.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ysHN4y4FPlJZ"
+      },
+      "source": [
+        "#### Search\n",
+        "Define a method to perform Wikipedia searches"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "yCRB4g4BNzak"
+      },
+      "outputs": [],
+      "source": [
+        "@ReAct.add_method\n",
+        "def search(self, query: str):\n",
+        "    \"\"\"Perfoms search on `query` via Wikipedia api and returns its summary.\n",
+        "\n",
+        "    Args:\n",
+        "        query: Search parameter to query the Wikipedia API with.\n",
+        "\n",
+        "    Returns:\n",
+        "        observation: Summary of Wikipedia search for `query` if found else\n",
+        "        similar search results.\n",
+        "    \"\"\"\n",
+        "    observation = None\n",
+        "    query = query.strip()\n",
+        "    try:\n",
+        "      # try to get the summary for requested `query` from the Wikipedia\n",
+        "      observation = wikipedia.summary(query, sentences=4, auto_suggest=False)\n",
+        "      wiki_url = wikipedia.page(query, auto_suggest=False).url\n",
+        "      observation = self.clean(observation)\n",
+        "\n",
+        "      # if successful, return the first 2-3 sentences from the summary as model's context\n",
+        "      observation = self.model.generate_content(f'Retun the first 2 or 3 \\\n",
+        "          sentences from the following text: {observation}')\n",
+        "      observation = observation.text\n",
+        "\n",
+        "      # keep track of the model's search history\n",
+        "      self._search_history.append(query)\n",
+        "      self._search_urls.append(wiki_url)\n",
+        "      print(f\"Information Source: {wiki_url}\")\n",
+        "\n",
+        "    # if the page is ambiguous/does not exist, return similar search phrases for model's context\n",
+        "    except (DisambiguationError, PageError) as e:\n",
+        "      observation = f'Could not find [\"{query}\"].'\n",
+        "      # get a list of similar search topics\n",
+        "      search_results = wikipedia.search(query)\n",
+        "      observation += f' Similar: {search_results}. You should search for one of those instead.'\n",
+        "\n",
+        "    return observation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "v3fUbHUsPyoF"
+      },
+      "source": [
+        "#### Look up\n",
+        "Look for a specific phrase on the Wikipedia page."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "_F4kAF77O0E_"
+      },
+      "outputs": [],
+      "source": [
+        "@ReAct.add_method\n",
+        "def lookup(self, phrase: str, context_length=200):\n",
+        "    \"\"\"Searches for the `phrase` in the lastest Wikipedia search page\n",
+        "    and returns number of sentences which is controlled by the\n",
+        "    `context_length` parameter.\n",
+        "\n",
+        "    Args:\n",
+        "        phrase: Lookup phrase to search for within a page. Generally\n",
+        "        attributes to some specification of any topic.\n",
+        "\n",
+        "        context_length: Number of words to consider\n",
+        "        while looking for the answer.\n",
+        "\n",
+        "    Returns:\n",
+        "        result: Context related to the `phrase` within the page.\n",
+        "    \"\"\"\n",
+        "    # get the last searched Wikipedia page and find `phrase` in it.\n",
+        "    page = wikipedia.page(self._search_history[-1], auto_suggest=False)\n",
+        "    page = page.content\n",
+        "    page = self.clean(page)\n",
+        "    start_index = page.find(phrase)\n",
+        "\n",
+        "    # extract sentences considering the context length defined\n",
+        "    result = page[max(0, start_index - context_length):start_index+len(phrase)+context_length]\n",
+        "    print(f\"Information Source: {self._search_urls[-1]}\")\n",
+        "    return result"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tc4mq2qlQCnE"
+      },
+      "source": [
+        "#### Finish\n",
+        "Instruct the pipline to terminate its execution."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "0Wxpx8COPak_"
+      },
+      "outputs": [],
+      "source": [
+        "@ReAct.add_method\n",
+        "def finish(self, _):\n",
+        "  \"\"\"Finishes the conversation on encountering <finish> token by\n",
+        "  setting the `self.should_continue_prompting` flag to `False`.\n",
+        "  \"\"\"\n",
+        "  self.should_continue_prompting = False\n",
+        "  print(f\"Information Sources: {self._search_urls}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u9Tl6W98Zhut"
+      },
+      "source": [
+        "### Integrate tools"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0VnX9zpBcdA0"
+      },
+      "source": [
+        "Now that you are all set with function definitions, the next step is to instruct the model to interrupt its execution when it emits any of the action tokens. You will use the `stop_sequences` parameter from the [`genai.GenerativeModel.GenerationConfig`](https://ai.google.dev/api/python/google/generativeai/GenerationConfig) class to instruct the model when to stop. Upon encountering an action token, your system will extract the last action and argument, then call the appropriate tool.\n",
+        "\n",
+        "The response from the function will be added to prompt to continue the process."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "vnQom1aQOsK8"
+      },
+      "outputs": [],
+      "source": [
+        "@ReAct.add_method\n",
+        "def __call__(self, user_question, max_calls: int=8, **generation_kwargs):\n",
+        "  \"\"\"Starts multi-turn conversation with the chat models with function calling\n",
+        "\n",
+        "  Args:\n",
+        "      max_calls: max calls made to the model to get the final answer.\n",
+        "\n",
+        "      generation_kwargs: Same as genai.GenerativeModel.GenerationConfig\n",
+        "              candidate_count: (int | None) = None,\n",
+        "              stop_sequences: (Iterable[str] | None) = None,\n",
+        "              max_output_tokens: (int | None) = None,\n",
+        "              temperature: (float | None) = None,\n",
+        "              top_p: (float | None) = None,\n",
+        "              top_k: (int | None) = None\n",
+        "\n",
+        "  Raises:\n",
+        "      AssertionError: if max_calls is not between 1 and 8\n",
+        "  \"\"\"\n",
+        "\n",
+        "  # hyperparameter fine-tuned according to the paper\n",
+        "  assert 0 < max_calls <= 8, \"max_calls must be between 1 and 8\"\n",
+        "\n",
+        "  if len(self.chat.history) == 0:\n",
+        "    model_prompt = self.prompt.format(question=user_question)\n",
+        "  else:\n",
+        "    model_prompt = user_question\n",
+        "\n",
+        "  # stop_sequences for the model to immitate function calling\n",
+        "  callable_entities = ['</search>', '</lookup>', '</finish>']\n",
+        "\n",
+        "  generation_kwargs.update({'stop_sequences': callable_entities})\n",
+        "\n",
+        "  self.should_continue_prompting = True\n",
+        "  for idx in range(max_calls):\n",
+        "\n",
+        "    self.response = self.chat.send_message(content=[model_prompt],\n",
+        "              generation_config=generation_kwargs, stream=True)\n",
+        "\n",
+        "    for chunk in self.response:\n",
+        "      print(chunk.text, end=' ')\n",
+        "\n",
+        "    response_cmd = self.chat.history[-1].parts[-1].text\n",
+        "\n",
+        "    try:\n",
+        "      # regex to extract <function name writen in between angular brackets>\n",
+        "      cmd = re.findall(r'<(.*)>', response_cmd)[-1]\n",
+        "      print(f'</{cmd}>')\n",
+        "      # regex to extract param\n",
+        "      query = response_cmd.split(f'<{cmd}>')[-1].strip()\n",
+        "      # call to appropriate function\n",
+        "      observation = self.__getattribute__(cmd)(query)\n",
+        "\n",
+        "      if not self.should_continue_prompting:\n",
+        "        break\n",
+        "\n",
+        "      stream_message = f\"\\nObservation {idx + 1}\\n{observation}\"\n",
+        "      print(stream_message)\n",
+        "      # send function's output as user's response\n",
+        "      model_prompt = f\"<{cmd}>{query}</{cmd}>'s Output: {stream_message}\"\n",
+        "\n",
+        "    except (IndexError, AttributeError) as e:\n",
+        "      model_prompt = \"Please try to generate thought-action-observation traces \\\n",
+        "          as instructed by the prompt.\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xtndhebkhW62"
+      },
+      "source": [
+        "### Test ReAct prompted Gemini model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "h_KWkXWwfZ5h"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Thought 1\n",
+            "I need to search the main trio from the new Percy Jackson  and the Olympians TV series, find their ages in real life, then sum them up.\n",
+            "\n",
+            "Action 1\n",
+            "<search>Percy Jackson and the Olymp ians TV series </search>\n",
+            "\n",
+            "Observation 1\n",
+            "Could not find [\"Percy Jackson and the Olympians TV series\"]. Similar: ['Percy Jackson and the Olympians (TV series)', 'Percy Jackson & the Olympians', 'Percy Jackson (film series)', 'Percy Jackson & the Olympians: The Lightning Thief', 'Percy Jackson (disambiguation)', 'Percy Jackson', 'List of characters in mythology novels by Rick Riordan', 'The Lightning Thief', 'The Heroes of Olympus', 'Walker Scobell']. You should search for one of those instead.\n",
+            "Thought 2\n",
+            "I can search Percy Jackson and the Olympians (TV series ) instead.\n",
+            "\n",
+            "Action 2\n",
+            "<search>Percy Jackson and the Olympians (TV series) </search>\n",
+            "Information Source: https://en.wikipedia.org/wiki/Percy_Jackson_and_the_Olympians_(TV_series)\n",
+            "\n",
+            "Observation 2\n",
+            "Percy Jackson and the Olympians is an American fantasy television series created by Rick Riordan and Jonathan E. Steinberg for Disney+, based on the book series of the same name by Riordan. Walker Scobell stars as Percy Jackson, alongside Leah Sava Jeffries and Aryan Simhadri. Development on the series began in May 2020, following a pitch by Riordan to Disney Branded Television.\n",
+            "Thought 3\n",
+            "The main trio from the new Percy Jackson and the Olympians  TV series are Walker Scobell, Leah Sava Jeffries, and Aryan Simhadri. I need to find their ages in real life.\n",
+            "\n",
+            "Action 3\n",
+            " <search>Walker Scobell </search>\n",
+            "Information Source: https://en.wikipedia.org/wiki/Walker_Scobell\n",
+            "\n",
+            "Observation 3\n",
+            "Walker Scobell, born on January 5, 2009, is an American actor who has starred in the 2022 action comedy films The Adam Project and Secret Headquarters.\n",
+            "\n",
+            "In 2023, Scobell began playing the title character of Percy Jackson in the Disney+ fantasy series Percy Jackson and the Olympians.\n",
+            "Thought 4\n",
+            "Walker Scobell was born on January 5, 2 009, so he is 14 years old. I need to find the ages of Leah Sava Jeffries and Aryan Simhadri.\n",
+            "\n",
+            "Action  4\n",
+            "<search>Leah Sava Jeffries </search>\n",
+            "Information Source: https://en.wikipedia.org/wiki/Leah_Jeffries\n",
+            "\n",
+            "Observation 4\n",
+            "Leah Sava Jeffries, born on September 25, 2009, is an American child actress. She made her acting debut in the American musical drama, Empire, in 2015, and later made her feature film debut in the action-thriller Beast, released in 2022.\n",
+            "Thought 5\n",
+            "Leah Sava Jeffries was born on September 25,  2009, so she is 13 years old. I need to find the age of Aryan Simhadri.\n",
+            "\n",
+            "Action 5\n",
+            "<search>Aryan Simhadri </search>\n",
+            "Information Source: https://en.wikipedia.org/wiki/Aryan_Simhadri\n",
+            "\n",
+            "Observation 5\n",
+            "Aryan Simhadri, born on May 6, 2006, is an American actor of Indian descent. He is best known for portraying Grover Underwood in the Disney+ series \"Percy Jackson and the Olympians.\" \n",
+            "\n",
+            "In 2021, Simhadri made his Broadway debut as Walter in the production of \"Trevor: The Musical.\"\n",
+            "Thought 6\n",
+            "Aryan Simhadri was born on May 6,  2006, so he is 17 years old. The sum of ages of the main trio from the new Percy Jackson and the Olympians  TV series is 14 + 13 + 17 = 44.\n",
+            "\n",
+            "Action 6\n",
+            "<finish>44 </finish>\n",
+            "Information Sources: ['https://en.wikipedia.org/wiki/Percy_Jackson_and_the_Olympians_(TV_series)', 'https://en.wikipedia.org/wiki/Walker_Scobell', 'https://en.wikipedia.org/wiki/Leah_Jeffries', 'https://en.wikipedia.org/wiki/Aryan_Simhadri']\n"
+          ]
+        }
+      ],
+      "source": [
+        "gemini_ReAct_chat = ReAct(model='gemini-pro', ReAct_prompt='model_instructions.txt')\n",
+        "# Note: try different combinations of generational_config parameters for variational results\n",
+        "gemini_ReAct_chat(\"What is the total of ages of the main trio from the new Percy Jackson and the Olympians TV series in real life?\", temperature=0.2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZIfeyyI6hoIE"
+      },
+      "source": [
+        "Now, try asking the same question to `gemini-pro` model without the ReAct prompt."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "_NUXNbTuakSC"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'The TV series has not yet been released, so the real-life ages of the main trio are not yet known.'"
+            ]
+          },
+          "execution_count": 22,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "gemini_ReAct_chat.model.generate_content(\"What is the total of ages of the main trio from the new Percy Jackson and the Olympians TV series in real life?\").text"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "B-jsJSyBtrP8"
+      },
+      "source": [
+        "## Summary\n",
+        "\n",
+        "The ReAct prompted Gemini model is grounded by external information sources and hence is less prone to hallucination. Furthermore, **Thought-Action-Observation**  traces generated by the model enhance human interpretability and trustworthiness by allowing users to witness the model's reasoning process for answering the user's query.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vmdNYTm5Lobz"
+      },
+      "source": [
+        "## Further reading\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iTiDOoTkLvH6"
+      },
+      "source": [
+        "Head over to the [Streamlit app](https://mayochat.streamlit.app/) to interact with a ReAct prompted Gemini bot."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "react_gemini_prompting.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/docs/search_reranking_using_embeddings.ipynb b/site/en/docs/search_reranking_using_embeddings.ipynb
new file mode 100644
index 000000000..9369d058e
--- /dev/null
+++ b/site/en/docs/search_reranking_using_embeddings.ipynb
@@ -0,0 +1,1447 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "czPOReizfDMY"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "4Uf6JMEbfGV-"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "cellView": "form",
+        "id": "FUqzNst0YN9P"
+      },
+      "outputs": [],
+      "source": [
+        "# The non-source code materials on this page are licensed under Creative Commons - Attribution-ShareAlike CC-BY-SA 4.0,\n",
+        "# https://creativecommons.org/licenses/by-sa/4.0/legalcode."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oJt4OFKPgO8C"
+      },
+      "source": [
+        "# Search re-ranking using Gemini embeddings"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "k5n0sCc_fKcB"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/docs/search_reranking_using_embeddings\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Google AI</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/docs/search_reranking_using_embeddings.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/docs/search_reranking_using_embeddings.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0UO0Cgq0mRlG"
+      },
+      "source": [
+        "This notebook demonstrates the use of embeddings to re-rank search results. This walkthrough will focus on the following objectives:\n",
+        "\n",
+        "\n",
+        "\n",
+        "1.   Setting up your development environment and API access to use Gemini.\n",
+        "2.   Using Gemini's function calling support to access the Wikipedia API.\n",
+        "3.   Embedding content via Gemini API.\n",
+        "4.   Re-ranking the search results.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-e5Aje-DTQOq"
+      },
+      "source": [
+        "This is how you will implement search re-ranking:\n",
+        "\n",
+        "\n",
+        "1.   User will query the model.\n",
+        "2.   You will use Wikipedia API to return relevant search results.\n",
+        "3.   The search results will be embedded and their relevance will be evaluated by calculating distance metrics like cosine similarity, dot product, etc.\n",
+        "4.   Most relevant result will be returned as the final answer."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "x32Uf6GYDSDg"
+      },
+      "source": [
+        "## Prerequisites\n",
+        "\n",
+        "You can run this quickstart in [Google Colab](https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/docs/search_reranking_using_embeddings.ipynb), which runs this notebook directly in the browser and does not require additional environment configuration.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pwImlRg9DUp7"
+      },
+      "source": [
+        "## Setup\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0aj1YD9lDW_Z"
+      },
+      "source": [
+        "The Python SDK for the Gemini API, is contained in the [`google-generativeai`](https://pypi.org/project/google-generativeai/) package. You will also need to install the **Wikipedia** API.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Nv_6f_qNHe_2"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5999faf240a4"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q wikipedia"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "739f0bb73f05"
+      },
+      "source": [
+        "Note: The [`wikipedia` package](https://pypi.org/project/wikipedia/) notes that it was \"designed for ease of use and simplicity, not for advanced use\", and that production or heavy use should instead \"use [Pywikipediabot](http://www.mediawiki.org/wiki/Manual:Pywikipediabot) or one of the other more advanced [Python MediaWiki API wrappers](http://en.wikipedia.org/wiki/Wikipedia:Creating_a_bot#Python)\"."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZqPr1ViPDhti"
+      },
+      "source": [
+        "Import the necessary packages."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "id": "D0qOtLI3FVid"
+      },
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "import textwrap\n",
+        "\n",
+        "import google.generativeai as genai\n",
+        "\n",
+        "import wikipedia\n",
+        "from wikipedia.exceptions import DisambiguationError, PageError\n",
+        "\n",
+        "import numpy as np\n",
+        "\n",
+        "from IPython.display import Markdown\n",
+        "\n",
+        "def to_markdown(text):\n",
+        "  text = text.replace('•', '  *')\n",
+        "  return Markdown(textwrap.indent(text, '> ', predicate=lambda _: True))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qeDtGeqxDois"
+      },
+      "source": [
+        "### Grab an API key\n",
+        "\n",
+        "Before you can use the Gemini API, you must first obtain an API key. If you don't already have one, create a key with one click in Google AI Studio.\n",
+        "\n",
+        "<a class=\"button button-primary\" href=\"https://makersuite.google.com/app/apikey\" target=\"_blank\" rel=\"noopener noreferrer\">Get an API key</a>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PeFewbQoDqfb"
+      },
+      "source": [
+        "In Colab, add the key to the secrets manager under the \"🔑\" in the left panel. Give it the name `GOOGLE_API_KEY`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aeo_9J9iDtCx"
+      },
+      "source": [
+        "Once you have the API key, pass it to the SDK. You can do this in two ways:\n",
+        "\n",
+        "* Put the key in the `GOOGLE_API_KEY` environment variable (the SDK will automatically pick it up from there).\n",
+        "* Pass the key to `genai.configure(api_key=...)`\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "yxzEH0GqLshI"
+      },
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    from google.colab import userdata\n",
+        "    GOOGLE_API_KEY=userdata.get('GOOGLE_API_KEY')\n",
+        "except ImportError:\n",
+        "    import os\n",
+        "    GOOGLE_API_KEY = os.environ['GOOGLE_API_KEY']\n",
+        "\n",
+        "genai.configure(api_key=GOOGLE_API_KEY)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wpVM0W-5m9fc"
+      },
+      "source": [
+        "Key Point: Next, you will choose a model. Any embedding model will work for this tutorial, but for real applications it's important to choose a specific model and stick with it. The outputs of different models are not compatible with each other.\n",
+        "\n",
+        "**Note**: At this time, the Gemini API is [only available in certain regions](https://developers.generativeai.google/available_regions)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0hkJSFIWI7G1"
+      },
+      "source": [
+        "## Define tools"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7HjN6hsAJkOq"
+      },
+      "source": [
+        "As stated earlier, this tutorial uses Gemini's function calling support to access the Wikipedia API. Please refer to the [docs](https://ai.google.dev/docs/function_calling) to learn more about function calling."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fpFkazXUenkK"
+      },
+      "source": [
+        "### Define the search function"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2fp-m-5Ke0XY"
+      },
+      "source": [
+        "To cater to the search engine needs, you will design this function in the following way:\n",
+        "\n",
+        "\n",
+        "*   For each search query, the search engine will use the `wikipedia.search` method to get relevant topics.\n",
+        "*   From the relevant topics, the engine will choose `n_topics(int)` top candidates and will use `gemini-pro` to extract relevant information from the page.\n",
+        "*   The engine will avoid duplicate entries by maintaining a search history.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "id": "2U27tdP8OBys"
+      },
+      "outputs": [],
+      "source": [
+        "def wikipedia_search(search_queries: list[str]) -> list[str]:\n",
+        "  \"\"\"Search wikipedia for each query and summarize relevant docs.\"\"\"\n",
+        "  n_topics=3\n",
+        "  search_history = set() # tracking search history\n",
+        "  search_urls = []\n",
+        "  mining_model = genai.GenerativeModel('gemini-pro')\n",
+        "  summary_results = []\n",
+        "\n",
+        "  for query in search_queries:\n",
+        "    print(f'Searching for \"{query}\"')\n",
+        "    search_terms = wikipedia.search(query)\n",
+        "\n",
+        "    print(f\"Related search terms: {search_terms[:n_topics]}\")\n",
+        "    for search_term in search_terms[:n_topics]: # select first `n_topics` candidates\n",
+        "      if search_term in search_history: # check if the topic is already covered\n",
+        "        continue\n",
+        "\n",
+        "      print(f'Fetching page: \"{search_term}\"')\n",
+        "      search_history.add(search_term) # add to search history\n",
+        "\n",
+        "      try:\n",
+        "        # extract the relevant data by using `gemini-pro` model\n",
+        "        page = wikipedia.page(search_term, auto_suggest=False)\n",
+        "        url = page.url\n",
+        "        print(f\"Information Source: {url}\")\n",
+        "        search_urls.append(url)\n",
+        "        page = page.content\n",
+        "        response = mining_model.generate_content(textwrap.dedent(f\"\"\"\\\n",
+        "            Extract relevant information\n",
+        "            about user's query: {query}\n",
+        "            From this source:\n",
+        "\n",
+        "            {page}\n",
+        "\n",
+        "            Note: Do not summarize. Only Extract and return the relevant information\n",
+        "        \"\"\"))\n",
+        "\n",
+        "        urls = [url]\n",
+        "        if response.candidates[0].citation_metadata:\n",
+        "          extra_citations = response.candidates[0].citation_metadata.citation_sources\n",
+        "          extra_urls = [source.url for source in extra_citations]\n",
+        "          urls.extend(extra_urls)\n",
+        "          search_urls.extend(extra_urls)\n",
+        "          print(\"Additional citations:\", response.candidates[0].citation_metadata.citation_sources)\n",
+        "        try:\n",
+        "          text = response.text\n",
+        "        except ValueError:\n",
+        "          pass\n",
+        "        else:\n",
+        "          summary_results.append(text + \"\\n\\nBased on:\\n  \" + ',\\n  '.join(urls))\n",
+        "\n",
+        "      except DisambiguationError:\n",
+        "        print(f\"\"\"Results when searching for \"{search_term}\" (originally for \"{query}\")\n",
+        "        were ambiguous, hence skipping\"\"\")\n",
+        "\n",
+        "      except PageError:\n",
+        "        print(f'{search_term} did not match with any page id, hence skipping.')\n",
+        "\n",
+        "  print(f\"Information Sources:\")\n",
+        "  for url in search_urls:\n",
+        "    print('    ', url)\n",
+        "\n",
+        "  return summary_results\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "id": "EWKkkKDXmzOX"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Searching for \"What are LLMs?\"\n",
+            "Related search terms: ['Large language model', 'Prompt engineering', 'Language model']\n",
+            "Fetching page: \"Large language model\"\n",
+            "Information Source: https://en.wikipedia.org/wiki/Large_language_model\n",
+            "Fetching page: \"Prompt engineering\"\n",
+            "Information Source: https://en.wikipedia.org/wiki/Prompt_engineering\n",
+            "Fetching page: \"Language model\"\n",
+            "Information Source: https://en.wikipedia.org/wiki/Language_model\n",
+            "Information Sources:\n",
+            "     https://en.wikipedia.org/wiki/Large_language_model\n",
+            "     https://en.wikipedia.org/wiki/Prompt_engineering\n",
+            "     https://en.wikipedia.org/wiki/Language_model\n"
+          ]
+        }
+      ],
+      "source": [
+        "example = wikipedia_search([\"What are LLMs?\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8bPT114QEPgW"
+      },
+      "source": [
+        "Here is what the search results look like:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "id": "8wy7YUFqEYTv"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "> **Relevant information about LLMs:**\n",
+              "> \n",
+              "> * LLMs are language models notable for their ability to achieve general-purpose language generation and understanding.\n",
+              "> * LLMs are artificial neural networks, the largest and most capable of which are built with a decoder-only transformer-based architecture.\n",
+              "> * LLMs can be used for text generation, a form of generative AI, by taking an input text and repeatedly predicting the next token or word.\n",
+              "> * Some notable LLMs are OpenAI's GPT series of models, Google's PaLM and Gemini, Meta's LLaMA family of open-source models, and Anthropic's Claude models.\n",
+              "> * LLMs are trained using statistical relationships from text documents during a computationally intensive self-supervised and semi-supervised training process.\n",
+              "> * LLMs are thought to acquire knowledge about syntax, semantics, and \"ontology\" inherent in human language corpora, but also inaccuracies and biases present in the corpora.\n",
+              "> * LLMs can be used for a variety of tasks, including text generation, language translation, question answering, and summarization.\n",
+              "> * LLMs have a number of advantages over traditional language models, including their ability to handle longer sequences of text, their ability to learn from unlabeled data, and their ability to generate more coherent and fluent text.\n",
+              "> * LLMs also have a number of limitations, including their tendency to hallucinate facts, their lack of common sense knowledge, and their potential for bias.\n",
+              "> * LLMs are still under development, but they have the potential to revolutionize a wide range of industries, including natural language processing, customer service, and education.\n",
+              "> \n",
+              "> Based on:\n",
+              ">   https://en.wikipedia.org/wiki/Large_language_model"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/markdown": [
+              "> **LLMs** (Large Language Models) are powerful AI models that can understand and generate text. They are designed to perform various language-related tasks, such as answering questions, summarizing documents, translating languages, writing different forms of text, and generating code. LLMs have been significantly improved through techniques such as in-context learning, which allows them to temporarily learn from specific prompts.\n",
+              "> \n",
+              "> **Prompt engineering** involves structuring text prompts to optimize the performance of an LLM. Effective prompts can guide the model's reasoning, provide context, and specify the desired output. Various prompt engineering techniques have been developed, including chain-of-thought prompting, generated knowledge prompting, and complexity-based prompting, each tailored to specific tasks and models.\n",
+              "> \n",
+              "> LLMs have also been adapted to generate images and videos. **Text-to-image** models like DALL-E 2 create art based on textual descriptions, while **text-to-video** models generate videos from textual prompts. These models require specialized prompting techniques that account for their unique capabilities and limitations.\n",
+              "> \n",
+              "> **Non-text prompts** are also used to guide LLMs. **Image prompting** allows users to provide images or image-based information as input, while **gradient descent**-based techniques enable the optimization of soft prompt tokens to enhance model performance.\n",
+              "> \n",
+              "> **Prompt injection** is a security concern where malicious users craft prompts to trick LLMs into performing unintended actions or revealing sensitive information. Mitigation strategies include input filtering, output filtering, and prompt engineering techniques to separate user input from instructions.\n",
+              "> \n",
+              "> LLMs are continually evolving, and new techniques and applications are being developed. They have the potential to revolutionize various industries by automating language-related tasks and enabling novel forms of creativity and communication.\n",
+              "> \n",
+              "> Based on:\n",
+              ">   https://en.wikipedia.org/wiki/Prompt_engineering"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/markdown": [
+              "> - Language models are probabilistic models of natural language.\n",
+              "> - Large language models are a combination of larger datasets, feedforward neural networks, and transformers.\n",
+              "> - Large language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation, optical character recognition, handwriting recognition, grammar induction, and information retrieval.\n",
+              "> - Evaluation of the quality of language models is mostly done by comparison to human-created sample benchmarks created from typical language-oriented tasks.\n",
+              "> \n",
+              "> Based on:\n",
+              ">   https://en.wikipedia.org/wiki/Language_model"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from IPython.display import display\n",
+        "\n",
+        "for e in example:\n",
+        "  display(to_markdown(e))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SKcH1LICeg5Z"
+      },
+      "source": [
+        "### Pass the tools to the model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qQcxR7cjLjGM"
+      },
+      "source": [
+        "If you pass a list of functions to the `GenerativeModel`'s `tools` argument,\n",
+        "it will extract a schema from the function's signature and type hints, and then pass schema along to the API calls. In response the model may return a `FunctionCall` object asking to call the function.\n",
+        "\n",
+        "Note: This approach only handles annotations of `AllowedTypes = int | float | str | dict | list['AllowedTypes']`\n",
+        "\n",
+        "The `GenerativeModel` will keep a reference to the function inself, so that it _can_ execute the function locally later."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {
+        "id": "39kqTnBRLDeQ"
+      },
+      "outputs": [],
+      "source": [
+        "model = genai.GenerativeModel(\n",
+        "    'gemini-pro',\n",
+        "    tools=[wikipedia_search],\n",
+        "    generation_config={'temperature': 0.6})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VedPbpzlh6jX"
+      },
+      "source": [
+        "## Generate supporting search queries"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5sLKZik7isBW"
+      },
+      "source": [
+        "In order to have multiple supporting search queries to the user's original query, you will ask the model to generate more such queries. This would help the engine to cover the asked question on comprehensive levels."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "id": "Z-Ym3H9KIosY"
+      },
+      "outputs": [],
+      "source": [
+        "instructions = \"\"\"You have access to the Wikipedia API which you will be using\n",
+        "to answer a user's query. Your job is to generate a list of search queries which\n",
+        "might answer a user's question. Be creative by using various key-phrases from\n",
+        "the user's query. To generate variety of queries, ask questions which are\n",
+        "related to  the user's query that might help to find the answer. The more\n",
+        "queries you generate the better are the odds of you finding the correct answer.\n",
+        "Here is an example:\n",
+        "\n",
+        "user: Tell me about Cricket World cup 2023 winners.\n",
+        "\n",
+        "function_call: wikipedia_search(['What is the name of the team that\n",
+        "won the Cricket World Cup 2023?', 'Who was the captain of the Cricket World Cup\n",
+        "2023 winning team?', 'Which country hosted the Cricket World Cup 2023?', 'What\n",
+        "was the venue of the Cricket World Cup 2023 final match?', 'Cricket World cup 2023',\n",
+        "'Who lifted the Cricket World Cup 2023 trophy?'])\n",
+        "\n",
+        "The search function will return a list of article summaries, use these to\n",
+        "answer the  user's question.\n",
+        "\n",
+        "Here is the user's query: {query}\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3Wyn3lV-d5S_"
+      },
+      "source": [
+        "In order to yield creative and a more random variety of questions, you will set the model's temperature parameter to a value higher. Values can range from [0.0,1.0], inclusive. A value closer to 1.0 will produce responses that are more varied and creative, while a value closer to 0.0 will typically result in more straightforward responses from the model.\n",
+        "\n",
+        "> Note: Explore more parameters from [genai.GenerativeModel.GenerationConfig](https://ai.google.dev/api/python/google/generativeai/GenerationConfig) class to control model's response in a better way."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TD1vU6FXhYO5"
+      },
+      "source": [
+        "## Enable automatic function calling and call the API"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qyUSnXJ5hg6x"
+      },
+      "source": [
+        "Now start a new chat with `enable_automatic_function_calling=True`. With it enabled, the `genai.ChatSession` will handle the back and forth required to call the function, and return the final response:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "metadata": {
+        "id": "ZNJqA60yKNpT"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Searching for \"How does deep-sea life survive?\"\n",
+            "Related search terms: ['Deep sea', 'Deep-sea community', 'Hydrothermal vent']\n",
+            "Fetching page: \"Deep sea\"\n",
+            "Information Source: https://en.wikipedia.org/wiki/Deep_sea\n",
+            "Fetching page: \"Deep-sea community\"\n",
+            "Information Source: https://en.wikipedia.org/wiki/Deep-sea_community\n",
+            "Fetching page: \"Hydrothermal vent\"\n",
+            "Information Source: https://en.wikipedia.org/wiki/Hydrothermal_vent\n",
+            "Searching for \"What adaptations have deep-sea life developed to survive?\"\n",
+            "Related search terms: ['Deep sea', 'Deep-sea fish', 'Deep-sea community']\n",
+            "Fetching page: \"Deep-sea fish\"\n",
+            "Information Source: https://en.wikipedia.org/wiki/Deep-sea_fish\n",
+            "Searching for \"What are the unique characteristics of deep-sea life?\"\n",
+            "Related search terms: ['Deep-sea fish', 'Deep-sea community', 'Deep sea']\n",
+            "Searching for \"What are the challenges deep-sea life faces?\"\n",
+            "Related search terms: ['Deep sea', 'Marine habitat', 'Sea']\n",
+            "Fetching page: \"Marine habitat\"\n",
+            "Information Source: https://en.wikipedia.org/wiki/Marine_habitat\n",
+            "Fetching page: \"Sea\"\n",
+            "Information Source: https://en.wikipedia.org/wiki/Sea\n",
+            "Searching for \"How has deep-sea life evolved to cope with the extreme conditions?\"\n",
+            "Related search terms: ['Deep-sea fish', 'Marine life', 'Hydrothermal vent microbial communities']\n",
+            "Fetching page: \"Marine life\"\n",
+            "Information Source: https://en.wikipedia.org/wiki/Marine_life\n",
+            "Fetching page: \"Hydrothermal vent microbial communities\"\n",
+            "Information Source: https://en.wikipedia.org/wiki/Hydrothermal_vent_microbial_communities\n",
+            "Information Sources:\n",
+            "     https://en.wikipedia.org/wiki/Deep_sea\n",
+            "     https://en.wikipedia.org/wiki/Deep-sea_community\n",
+            "     https://en.wikipedia.org/wiki/Hydrothermal_vent\n",
+            "     https://en.wikipedia.org/wiki/Deep-sea_fish\n",
+            "     https://en.wikipedia.org/wiki/Marine_habitat\n",
+            "     https://en.wikipedia.org/wiki/Sea\n",
+            "     https://en.wikipedia.org/wiki/Marine_life\n",
+            "     https://en.wikipedia.org/wiki/Hydrothermal_vent_microbial_communities\n"
+          ]
+        }
+      ],
+      "source": [
+        "model = genai.GenerativeModel(\n",
+        "    'gemini-pro', tools=[wikipedia_search], generation_config={'temperature': 0.6})\n",
+        "\n",
+        "chat = model.start_chat(enable_automatic_function_calling=True)\n",
+        "\n",
+        "query = \"Explain how deep-sea life survives.\"\n",
+        "\n",
+        "res = chat.send_message(instructions.format(query=query))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {
+        "id": "1l8KWb13M_lJ"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "> Deep-sea life has evolved remarkable adaptations to survive the extreme conditions of the deep ocean. They have adapted to withstand high pressure, cold temperatures, and low oxygen levels. They have also developed unique ways to find food and communicate in the darkness of the deep sea.\n",
+              "> \n",
+              "> Some of the adaptations of deep-sea life include:\n",
+              "> \n",
+              "> * **High pressure tolerance:** Deep-sea organisms have evolved strong bodies to withstand the immense pressure of the deep ocean. Their bodies are often filled with a gelatinous substance that helps them to withstand the pressure.\n",
+              "> * **Cold tolerance:** Deep-sea organisms have adapted to the cold temperatures of the deep ocean. They have enzymes that function at low temperatures, and their bodies are often covered in a thick layer of insulation.\n",
+              "> * **Low oxygen tolerance:** Deep-sea organisms have adapted to the low oxygen levels of the deep ocean. They have evolved efficient respiratory systems that allow them to extract oxygen from the water.\n",
+              "> * ** Bioluminescence:** Many deep-sea organisms produce their own light, a process called bioluminescence. They use bioluminescence to attract prey, communicate with each other, and defend themselves from predators.\n",
+              "> * **Chemosynthesis:** Some deep-sea organisms do not rely on sunlight for food. Instead, they use a process called chemosynthesis to create food from chemicals in the water.\n",
+              "> \n",
+              "> These are just a few of the adaptations that deep-sea life has evolved to survive in the extreme conditions of the deep ocean. These adaptations are a testament to the resilience and adaptability of life on Earth."
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 48,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "to_markdown(res.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ua7DPlj7HZyO"
+      },
+      "source": [
+        "Check for additional citations:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "id": "ASotq7EcHAeo"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'No citations found'"
+            ]
+          },
+          "execution_count": 49,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "res.candidates[0].citation_metadata or 'No citations found'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A0CfKMjYFSQm"
+      },
+      "source": [
+        "That looks like it worked. You can go through the chat history to see the details of what was sent and received in the function calls:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "metadata": {
+        "id": "_9h2b8saNnxh"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "user -> {\n",
+            "  \"text\": \"You have access to the Wikipedia API which you will be using\\nto answer a user's query. Your job is to generate a list of search queries which\\nmight answer a user's question. Be creative by using various key-phrases from\\nthe user's query. To generate variety of queries, ask questions which are\\nrelated to  the user's query that might help to find the answer. The more\\nqueries you generate the better are the odds of you finding the correct answer.\\nHere is an example:\\n\\nuser: Tell me about Cricket World cup 2023 winners.\\n\\nfunction_call: wikipedia_search(['What is the name of the team that\\nwon the Cricket World Cup 2023?', 'Who was the captain of the Cricket World Cup\\n2023 winning team?', 'Which country hosted the Cricket World Cup 2023?', 'What\\nwas the venue of the Cricket World Cup 2023 final match?', 'Cricket World cup 2023',\\n'Who lifted the Cricket World Cup 2023 trophy?'])\\n\\nThe search function will return a list of article summaries, use these to\\nanswer the  user's question.\\n\\nHere is the user's query: Explain how deep-sea life survives.\\n\"\n",
+            "}\n",
+            "------------------------------------------------------------\n",
+            "model -> {\n",
+            "  \"function_call\": {\n",
+            "    \"name\": \"wikipedia_search\",\n",
+            "    \"args\": {\n",
+            "      \"search_queries\": [\n",
+            "        \"How does deep-sea life survive?\",\n",
+            "        \"What adaptations have deep-sea life developed to survive?\",\n",
+            "        \"What are the unique characteristics of deep-sea life?\",\n",
+            "        \"What are the challenges deep-sea life faces?\",\n",
+            "        \"How has deep-sea life evolved to cope with the extreme conditions?\"\n",
+            "      ]\n",
+            "    }\n",
+            "  }\n",
+            "}\n",
+            "------------------------------------------------------------\n",
+            "user -> {\n",
+            "  \"function_response\": {\n",
+            "    \"name\": \"wikipedia_search\",\n",
+            "    \"response\": {\n",
+            "      \"result\": [\n",
+            "        \"**Environmental Characteristics**\\n- Pressure: Pressure increases by about 1 atmosphere for every 10 meters of depth. Deep-sea organisms must have adaptations to withstand this pressure.\\n- Salinity: Salinity is remarkably constant throughout the deep sea, with no significant ecological differences.\\n- Temperature: The two areas of greatest temperature gradient are the transition zone between the surface waters and the deep waters (the thermocline) and the transition between the deep-sea floor and the hot water flows at the hydrothermal vents.\\n- Light: Natural light does not penetrate the deep ocean, except for the upper parts of the mesopelagic. Organisms must rely on energy sources from elsewhere, such as organic material drifting down from the photic zone.\\n\\n**Biology**\\n- Regions below the epipelagic are divided into further zones: bathyal zone (200-3000 meters), abyssal zone (3000-6000 meters), and hadal zone (6000-11,000 meters).\\n- Food: Deep-sea organisms rely on falling organic matter known as 'marine snow' and carcasses derived from the productive zone above.\\n- Adaptations: Deep-sea organisms have various adaptations to survive in extreme conditions, including: \\n 1. Jelly-like flesh to provide buoyancy\\n 2. Floaters filled with ammonium chloride that are lighter than the surrounding water\\n 3. Small size, slow metabolism, and elongated bodies\\n 4. Enhanced eyesight, such as larger eyes and rod cells for detecting light in low-light conditions\\n 5. Bioluminescence for camouflage and attracting prey\\n 6. Modifications in proteins, anatomical structures and metabolic systems to cope with high hydrostatic pressure\\n\\n**Chemosynthesis**\\n- Some species in the deep sea do not rely on dissolved organic matter for their food.\\n- These species form communities around hydrothermal vents and rely on chemosynthesis, a process where bacteria use chemical energy to produce organic matter. The tube worm Riftia is an example of an organism that benefits from this process.\\n\\n**Adaptation to Hydrostatic Pressure:**\\n- Deep-sea organisms have developed unique adaptations to survive hydrostatic pressure.\\n- Proteins can be affected by hydrostatic pressure, so deep-sea organisms have specific substitutions in the active sites of proteins like actin.\\n- These substitutions allow for better stabilization in ATP binding and subunit arrangement.\\n- Osmolytes like Trimethylamine N-oxide (TMAO) are adjusted in deep-sea fish to assist in protein stabilization.\\n- Molecular adaptations include modified Osteocalcin genes, which lead to open skulls and cartilage-based bone formation in species like the Mariana hadal snailfish. These adaptations are crucial for withstanding high pressure in the deep sea.\\n\\nBased on:\\n  https://en.wikipedia.org/wiki/Deep_sea\",\n",
+            "        \"Deep-sea life survives due to various adaptations and energy sources:\\n\\n**Adaptations:**\\n- Size: Smaller size to withstand pressure.\\n- Gelatinous flesh and minimal skeletal structure.\\n- Elimination of excess cavities to prevent collapse.\\n- Eyes adapted for low light conditions.\\n- Tolerance to cold temperatures and low oxygen levels.\\n\\n**Energy Sources:**\\n\\n**Marine Snow:**\\n- Repackaged organic matter that sinks quickly, providing food for bottom-dwelling organisms.\\n\\n**Whale Falls:**\\n- Dead whales provide a significant amount of organic matter, supporting a diverse community of scavengers and other organisms.\\n- Stages of whale fall progression: mobile scavenger, opportunistic, and sulfophilic.\\n\\n**Chemosynthesis:**\\n\\n**Hydrothermal Vents:**\\n- Spew forth chemicals that bacteria can transform into energy.\\n- Support giant tube worms and other unique species.\\n- Entire ecosystems independent from sunlight.\\n\\n**Cold Seeps:**\\n- Hydrogen sulfide, methane, and other hydrocarbon-rich fluids provide energy for chemosynthetic organisms.\\n\\nBased on:\\n  https://en.wikipedia.org/wiki/Deep-sea_community\",\n",
+            "        \"**How does deep-sea life survive?**\\n\\n* Life around hydrothermal vents is based on chemosynthesis, where organisms use chemical compounds as energy sources instead of sunlight.\\n* Chemosynthetic bacteria form the base of the food chain and support diverse organisms.\\n* Specialized adaptations allow organisms to withstand extreme conditions, such as high temperatures and pressure, and toxic chemicals.\\n* They have symbiotic relationships with chemoautotrophic microbial symbionts that convert inorganic molecules into organic molecules for nutrition.\\n* Their metabolism allows them to survive in environments where sunlight is absent and oxygen is limited.\\n\\nBased on:\\n  https://en.wikipedia.org/wiki/Hydrothermal_vent\",\n",
+            "        \"**Adaptations of Deep-Sea Fish:**\\n\\n* **Vision**:\\n    * Large, sensitive eyes for low-light environments\\n    * Bioluminescence to attract prey or illuminate the area\\n\\n* **Sensory adaptations**:\\n    * Enhanced sensitivity to pressure and smell\\n    * Loss of eyesight in some species\\n\\n* **Buoyancy control**:\\n    * Reduction in swim bladders (in bathypelagic fish)\\n    * Hydrofoils to provide lift\\n    * High fat content and low bone density to reduce buoyancy\\n\\n* **Metabolic adaptations**:\\n    * Slow metabolism\\n    * Increased proportion of unsaturated fatty acids in cell membranes for fluidity\\n\\n* **Adaptations to high pressure**:\\n    * Gelatinous layer for buoyancy\\n    * Modifications in protein structure and reaction criteria\\n    * Rigid proteins to resist pressure\\n    * High tolerance of Na+/K+-ATPase to hydrostatic pressure\\n\\n* **Feeding adaptations**:\\n    * Long feelers to locate prey\\n    * Large mouths with sharp teeth for consuming large prey\\n    * Expandable bodies to accommodate large prey items\\n\\n* **Mating and reproduction**:\\n    * Bioluminescence to attract mates\\n    * Hermaphroditism in some species\\n    * Extreme sexual dimorphism in anglerfish (male attached to female)\\n\\nBased on:\\n  https://en.wikipedia.org/wiki/Deep-sea_fish\",\n",
+            "        \"**Challenges deep-sea life faces:**\\n\\n- Extreme water pressure\\n- No sunlight\\n- Cold temperatures\\n- Limited food resources\\n- Pollution\\n- Human activities (e.g., fishing, mining)\\n\\nBased on:\\n  https://en.wikipedia.org/wiki/Marine_habitat\",\n",
+            "        \"**Challenges deep-sea life faces:**\\n\\n* **Low light:** sunlight only penetrates the top 200 meters, making it difficult for plants to grow and limiting the food available for other organisms.\\n* **High pressure:** the pressure increases with depth, making it difficult for organisms to maintain their body structure and function.\\n* **Cold temperatures:** the temperature decreases with depth, making it difficult for organisms to regulate their body temperature.\\n* **Low oxygen levels:** the oxygen content of the water decreases with depth, making it difficult for organisms to breathe.\\n* **Nutrient scarcity:** the availability of nutrients decreases with depth, making it difficult for organisms to find food.\\n* **Pollution:** pollutants from human activities can accumulate in the deep sea, harming organisms and disrupting the ecosystem.\\n* **Climate change:** climate change is altering the conditions in the deep sea, such as temperature, acidity, and oxygen levels, which can be harmful to organisms.\\n\\nBased on:\\n  https://en.wikipedia.org/wiki/Sea\",\n",
+            "        \"Deep-sea life has evolved to cope with the extreme conditions of the deep ocean. There is no sunlight, so primary producers must use chemosynthesis to create food. The water is cold and dark, so animals must adapt to the low temperatures and lack of light. The pressure is immense, so animals must develop strong bodies to withstand the crushing force.\\nOne of the most striking adaptations of deep-sea life is the use of bioluminescence. This is the ability to produce light, and it is used by many deep-sea animals to attract prey, communicate with each other, and defend themselves from predators. Bioluminescence is often produced by a chemical reaction that involves a luciferase enzyme and a luciferin substrate.\\nAnother adaptation of deep-sea life is the use of gigantism. This is the tendency for deep-sea animals to be larger than their shallow-water counterparts. Gigantism is thought to be an adaptation to the low food availability in the deep sea. Larger animals have a greater chance of finding food, and they can also store more energy in their bodies.\\nDeep-sea life is a fascinating and diverse group of organisms that have evolved to cope with the extreme conditions of the deep ocean. These animals have developed a range of adaptations that allow them to survive and thrive in this unique environment.\\n\\nBased on:\\n  https://en.wikipedia.org/wiki/Marine_life\",\n",
+            "        \"**Adaptations** \\n\\n- Microbes that inhabit hydrothermal vents have adapted to extreme conditions, such as high temperatures, pressure, and chemical concentrations. \\n\\n- Hyperthermophiles, microorganisms that grow at temperatures above 90 \\u00b0C, are found where fluids from the vents are expelled and mixed with the surrounding water. \\n\\n- Hyperthermophilic microbes are thought to contain proteins that have extended stability at higher temperatures due to intramolecular interactions. \\n\\n- Microbes are also found in symbiotic relationships with other organisms in the hydrothermal vent environment due to their ability to have a detoxification mechanism that allows them to metabolize the sulfide-rich waters which would otherwise be toxic to the organisms and the microbes.\\n\\nBased on:\\n  https://en.wikipedia.org/wiki/Hydrothermal_vent_microbial_communities\"\n",
+            "      ]\n",
+            "    }\n",
+            "  }\n",
+            "}\n",
+            "------------------------------------------------------------\n",
+            "model -> {\n",
+            "  \"text\": \"Deep-sea life has evolved remarkable adaptations to survive the extreme conditions of the deep ocean. They have adapted to withstand high pressure, cold temperatures, and low oxygen levels. They have also developed unique ways to find food and communicate in the darkness of the deep sea.\\n\\nSome of the adaptations of deep-sea life include:\\n\\n* **High pressure tolerance:** Deep-sea organisms have evolved strong bodies to withstand the immense pressure of the deep ocean. Their bodies are often filled with a gelatinous substance that helps them to withstand the pressure.\\n* **Cold tolerance:** Deep-sea organisms have adapted to the cold temperatures of the deep ocean. They have enzymes that function at low temperatures, and their bodies are often covered in a thick layer of insulation.\\n* **Low oxygen tolerance:** Deep-sea organisms have adapted to the low oxygen levels of the deep ocean. They have evolved efficient respiratory systems that allow them to extract oxygen from the water.\\n* ** Bioluminescence:** Many deep-sea organisms produce their own light, a process called bioluminescence. They use bioluminescence to attract prey, communicate with each other, and defend themselves from predators.\\n* **Chemosynthesis:** Some deep-sea organisms do not rely on sunlight for food. Instead, they use a process called chemosynthesis to create food from chemicals in the water.\\n\\nThese are just a few of the adaptations that deep-sea life has evolved to survive in the extreme conditions of the deep ocean. These adaptations are a testament to the resilience and adaptability of life on Earth.\"\n",
+            "}\n",
+            "------------------------------------------------------------\n"
+          ]
+        }
+      ],
+      "source": [
+        "for content in chat.history:\n",
+        "  part = content.parts[0]\n",
+        "\n",
+        "  print(f'{content.role} -> ', end='')\n",
+        "  print(json.dumps(type(part).to_dict(part), indent=2))\n",
+        "  print('---' * 20)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WDWc9Fj9Ig6A"
+      },
+      "source": [
+        "In the chat history you can see all 4 steps:\n",
+        "\n",
+        "1. The user sent the query.\n",
+        "2. The model replied with a `genai.protos.FunctionCall` calling the `wikipedia_search` with a number of relevant searches.\n",
+        "3. Because you set `enable_automatic_function_calling=True` when creating the `genai.ChatSession`, it  executed the search function and returned the list of article summaries to the model.\n",
+        "4. Folliwing the instructions in the prompt, the model generated a final answer based on those summaries.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PJP1EAgfnPUA"
+      },
+      "source": [
+        "## [Optional] Manually execute the function call"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Sa5ke2ssKl7M"
+      },
+      "source": [
+        "If you want to understand what happened behind the scenes, this section executes the `FunctionCall` manually to demonstrate."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 53,
+      "metadata": {
+        "id": "wavbHrL3K5vo"
+      },
+      "outputs": [],
+      "source": [
+        "chat = model.start_chat()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 54,
+      "metadata": {
+        "id": "ON4LTcmiLs2E"
+      },
+      "outputs": [],
+      "source": [
+        "result = chat.send_message(instructions.format(query=query))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "__JN3YuHe7fR"
+      },
+      "source": [
+        "Initially the model returns a FunctionCall:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 65,
+      "metadata": {
+        "id": "Lgngdvcdi06F"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{\n",
+            "  \"name\": \"wikipedia_search\",\n",
+            "  \"args\": {\n",
+            "    \"search_queries\": [\n",
+            "      \"How do deep-sea animals survive?\",\n",
+            "      \"What are the adaptations of deep-sea creatures?\",\n",
+            "      \"How do deep-sea animals cope with extreme pressure?\",\n",
+            "      \"What are the unique characteristics of deep-sea organisms?\",\n",
+            "      \"How do deep-sea animals find food?\",\n",
+            "      \"How do deep-sea animals reproduce?\",\n",
+            "      \"What are the challenges faced by deep-sea animals?\",\n",
+            "      \"What is the role of deep-sea animals in the marine ecosystem?\"\n",
+            "    ]\n",
+            "  }\n",
+            "}\n"
+          ]
+        }
+      ],
+      "source": [
+        "fc = result.candidates[0].content.parts[0].function_call\n",
+        "fc = type(fc).to_dict(fc)\n",
+        "print(json.dumps(fc, indent=2))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 66,
+      "metadata": {
+        "id": "Bfp1Kqv7PE8N"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'wikipedia_search'"
+            ]
+          },
+          "execution_count": 66,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "fc['name']"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xpD3axtDmfZb"
+      },
+      "source": [
+        "Call the function with generated arguments to get the results."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 67,
+      "metadata": {
+        "id": "Ek4g-CTAPSou"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Searching for \"How do deep-sea animals survive?\"\n",
+            "Related search terms: ['Deep sea', 'Marine life', 'Deep-sea fish']\n",
+            "Fetching page: \"Deep sea\"\n",
+            "Information Source: https://en.wikipedia.org/wiki/Deep_sea\n",
+            "Fetching page: \"Marine life\"\n",
+            "Information Source: https://en.wikipedia.org/wiki/Marine_life\n",
+            "Fetching page: \"Deep-sea fish\"\n",
+            "Information Source: https://en.wikipedia.org/wiki/Deep-sea_fish\n",
+            "Searching for \"What are the adaptations of deep-sea creatures?\"\n",
+            "Related search terms: ['Deep sea', 'Deep-sea community', 'Deep-sea fish']\n",
+            "Fetching page: \"Deep-sea community\"\n",
+            "Information Source: https://en.wikipedia.org/wiki/Deep-sea_community\n",
+            "Searching for \"How do deep-sea animals cope with extreme pressure?\"\n",
+            "Related search terms: ['Deep sea', 'Deep-sea fish', 'Deep-sea community']\n",
+            "Searching for \"What are the unique characteristics of deep-sea organisms?\"\n",
+            "Related search terms: ['Deep-sea fish', 'Deep sea', 'Deep-sea community']\n",
+            "Searching for \"How do deep-sea animals find food?\"\n",
+            "Related search terms: ['Deep-sea community', 'Deep-sea fish', 'Marine life']\n",
+            "Searching for \"How do deep-sea animals reproduce?\"\n",
+            "Related search terms: ['Marine life', 'Sea cucumber', 'Deep-water coral']\n",
+            "Fetching page: \"Sea cucumber\"\n",
+            "Information Source: https://en.wikipedia.org/wiki/Sea_cucumber\n",
+            "Fetching page: \"Deep-water coral\"\n",
+            "Information Source: https://en.wikipedia.org/wiki/Deep-water_coral\n",
+            "Searching for \"What are the challenges faced by deep-sea animals?\"\n",
+            "Related search terms: ['Deep sea', 'Marine life', 'Marine habitat']\n",
+            "Fetching page: \"Marine habitat\"\n",
+            "Information Source: https://en.wikipedia.org/wiki/Marine_habitat\n",
+            "Searching for \"What is the role of deep-sea animals in the marine ecosystem?\"\n",
+            "Related search terms: ['Marine ecosystem', 'Deep-sea community', 'Marine life']\n",
+            "Fetching page: \"Marine ecosystem\"\n",
+            "Information Source: https://en.wikipedia.org/wiki/Marine_ecosystem\n",
+            "Information Sources:\n",
+            "     https://en.wikipedia.org/wiki/Deep_sea\n",
+            "     https://en.wikipedia.org/wiki/Marine_life\n",
+            "     https://en.wikipedia.org/wiki/Deep-sea_fish\n",
+            "     https://en.wikipedia.org/wiki/Deep-sea_community\n",
+            "     https://en.wikipedia.org/wiki/Sea_cucumber\n",
+            "     https://en.wikipedia.org/wiki/Deep-water_coral\n",
+            "     https://en.wikipedia.org/wiki/Marine_habitat\n",
+            "     https://en.wikipedia.org/wiki/Marine_ecosystem\n"
+          ]
+        }
+      ],
+      "source": [
+        "summaries = wikipedia_search(**fc['args'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kv4WGnG_gT3F"
+      },
+      "source": [
+        "Now send the `FunctionResult` to the model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 68,
+      "metadata": {
+        "id": "bcLuieHqj9PW"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "> **Deep-sea life survives by adapting to the extreme conditions of the deep ocean, including high pressure, low temperatures, and lack of light.**\n",
+              "> \n",
+              "> **Adaptations for survival include:**\n",
+              "> \n",
+              "> * **High internal pressure:** Deep-sea animals have high internal pressure that matches the external pressure, preventing them from being crushed.\n",
+              "> * **Buoyancy adaptations:** Many deep-sea fish have a gelatinous layer below the skin or around the spine for buoyancy and swimming efficiency. They also have low tissue density, achieved through high fat content, reduced skeletal weight, and water accumulation, allowing them to float without a swim bladder.\n",
+              "> * **Light and vision:** Deep-sea fish lack sunlight, so they rely on other senses, such as sensitivity to pressure changes and smell, for locating prey and mates. Many deep-sea fish are bioluminescent, using light to communicate, attract prey, or camouflage themselves. Some have sensitive eyes with high numbers of Rh1 genes, helping them see in low light conditions.\n",
+              "> * **Feeding mechanisms:** Deep-sea fish often have large mouths and sharp teeth for consuming prey of similar or larger sizes. They use feelers to locate prey in the darkness.\n",
+              "> * **Behavior:** Mesopelagic fish make vertical migrations following zooplankton prey, returning to deeper depths during the day. Bathypelagic fish are sedentary, waiting for prey to come close enough or being lured by bioluminescence. Some deep-sea fish are hermaphrodites, increasing their chances of reproduction in the sparse environment.\n",
+              "> * **Physiological adaptations:** Deep-sea animals have slow metabolisms and unspecialized diets, allowing them to survive with limited food availability. Their proteins are structurally modified to withstand high pressure, ensuring enzymatic reactions and cellular processes function properly. Na+/K+ -ATPase, involved in osmoregulation, is more tolerant of pressure in deep-sea fish compared to shallow-water species."
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 68,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "response = chat.send_message(\n",
+        "    genai.protos.Content(\n",
+        "      parts=[genai.protos.Part(\n",
+        "          function_response = genai.protos.FunctionResponse(\n",
+        "            name='wikipedia_search',\n",
+        "            response={'result': summaries}\n",
+        "          )\n",
+        "      )]\n",
+        "    )\n",
+        ")\n",
+        "\n",
+        "to_markdown(response.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "L2Vfv8xpmuV1"
+      },
+      "source": [
+        "## Re-ranking the search results"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cQ3JUJLeGGzA"
+      },
+      "source": [
+        "Helper function to embed the content:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 69,
+      "metadata": {
+        "id": "mSkE7EynJBwF"
+      },
+      "outputs": [],
+      "source": [
+        "def get_embeddings(content: list[str]) -> np.ndarray:\n",
+        "  embeddings = genai.embed_content('models/embedding-001', content, 'SEMANTIC_SIMILARITY')\n",
+        "  embds = embeddings.get('embedding', None)\n",
+        "  embds = np.array(embds).reshape(len(embds), -1)\n",
+        "  return embds"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tip8ArqJf_ep"
+      },
+      "source": [
+        "Please refer to the [embeddings guide](https://ai.google.dev/docs/embeddings_guide) for more information on embeddings."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nSPyycFuFj-_"
+      },
+      "source": [
+        "Your next step is to define functions that you can use to calculate similarity scores between two embedding vectors. These scores will help you decide which embedding vector is the most relevant vector to the user's query.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ltbB0vDsKQtI"
+      },
+      "source": [
+        "You will now implement **cosine similarity** as your metric. Here returned embedding vectors will be of unit length and hence their L1 norm (`np.linalg.norm()`) will be ~1. Hence, calculating **cosine similarity** is esentially same as calculating their **dot product score**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 70,
+      "metadata": {
+        "id": "9iDFdzq_JWJW"
+      },
+      "outputs": [],
+      "source": [
+        "def dot_product(a: np.ndarray, b: np.ndarray):\n",
+        "  return (a @ b.T)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MrF_1c_M_Hw3"
+      },
+      "source": [
+        "### Similarity with user's query\n",
+        "\n",
+        "Now it's time to find the most relevant search result returned by the Wikipedia API.\n",
+        "\n",
+        "Use Gemini API to get embeddings for user's query and search results."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 71,
+      "metadata": {
+        "id": "gK9ryjftGDNe"
+      },
+      "outputs": [],
+      "source": [
+        "search_res = get_embeddings(summaries)\n",
+        "embedded_query = get_embeddings([query])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2wwWq30uGRG3"
+      },
+      "source": [
+        "Calculate similarity score:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 72,
+      "metadata": {
+        "id": "jWlFNYIsGV0X"
+      },
+      "outputs": [],
+      "source": [
+        "sim_value = dot_product(search_res, embedded_query)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bJW1pQQXG2w2"
+      },
+      "source": [
+        "using `np.argmax` best candidate is selected.\n",
+        "\n",
+        "**Users's Input:** Explain how deep-sea life survives.\n",
+        "\n",
+        "**Answer:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 73,
+      "metadata": {
+        "id": "8vDMDnCsG8Wn"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "In this document, there is no information about how deep-sea animals survive.\n",
+            "\n",
+            "Based on:\n",
+            "  https://en.wikipedia.org/wiki/Marine_life\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(summaries[np.argmax(sim_value)])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ozn6mIFvoyJU"
+      },
+      "source": [
+        "### Similarity with Hypothetical Document Embeddings (HyDE)\n",
+        "\n",
+        "Drawing inspiration from [[Gao et al](https://arxiv.org/abs/2212.10496)] the objective here is to generate a template answer to the user's query using `gemini-pro`'s internal knowledge. This hypothetical answer will serve as a baseline to calculate relevance of all the search results."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 74,
+      "metadata": {
+        "id": "J7m5KAkMREBH"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "> In the enigmatic depths where sunlight surrenders to inky blackness, life perseveres, illuminated by the faintest of luminescent flickers.\n",
+              "> \n",
+              "> Imagine a realm where extreme pressure could crush the mightiest of vessels. Yet, in this unforgiving abyss, creatures have evolved with bodies resilient as the very seafloor. Their flexible exoskeletons or gelatinous tissues withstand the crushing weight gracefully.\n",
+              "> \n",
+              "> Oxygen, a lifeline for most creatures, grows scarce with depth. Enter our deep-sea dwellers, whose bodies have ingeniously adapted. They absorb oxygen directly through their skin or gills, maximizing every molecule they find.\n",
+              "> \n",
+              "> Nutrient scarcity plagues these depths, where sunlight cannot penetrate to foster photosynthesis. Instead, these creatures rely on chemosynthesis, a remarkable process that utilizes chemicals from hydrothermal vents or decaying matter.\n",
+              "> \n",
+              "> In the perpetual darkness, vision becomes obsolete. Instead, sensory organs have evolved to detect minute vibrations, bioluminescence, and heat gradients, guiding them through the shadowy labyrinth.\n",
+              "> \n",
+              "> Temperature fluctuations can be drastic, from freezing cold to scalding heat. But deep-sea creatures have mastered the art of thermoregulation, their internal systems finely tuned to withstand the extremes.\n",
+              "> \n",
+              "> Growth is a slow and arduous process in these unforgiving depths. Many species exhibit extreme longevity, surviving for centuries or even millennia. Their life cycles are meticulously paced, ensuring their survival in this harsh environment.\n",
+              "> \n",
+              "> Reproduction is a perilous task, with offspring often vulnerable and exposed. Some deep-sea creatures protect their young with parental care, nurturing them until they can fend for themselves in this unforgiving realm.\n",
+              "> \n",
+              "> The deep sea, a testament to the resilience and adaptability of life, is a fascinating and mysterious world. Its inhabitants continue to inspire awe and wonder, reminding us of the extraordinary diversity and ingenuity that exists within our planet's watery depths."
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 74,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "hypothetical_ans_model = genai.GenerativeModel('gemini-pro')\n",
+        "res = hypothetical_ans_model.generate_content(f\"\"\"Generate a hypothetical answer\n",
+        "to the user's query by using your own knowledge. Assume that you know everything\n",
+        "about the said topic. Do not use factual information, instead use placeholders\n",
+        "to complete your answer. Your answer should feel like it has been written by a human.\n",
+        "\n",
+        "query: {query}\"\"\")\n",
+        "\n",
+        "to_markdown(res.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "85f4MpLgrYbW"
+      },
+      "source": [
+        "Use Gemini API to get embeddings for the baseline answer and compare them with search results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 75,
+      "metadata": {
+        "id": "fSI6-5eYI3me"
+      },
+      "outputs": [],
+      "source": [
+        "hypothetical_ans = get_embeddings([res.text])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WxYGEWbqrh44"
+      },
+      "source": [
+        "Calculate similarity scores to rank the search results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 76,
+      "metadata": {
+        "id": "99sjvMtlJfL_"
+      },
+      "outputs": [],
+      "source": [
+        "sim_value = dot_product(search_res, hypothetical_ans)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 77,
+      "metadata": {
+        "id": "C32IhIapQFNa"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([[0.72687077],\n",
+              "       [0.73694087],\n",
+              "       [0.77235092],\n",
+              "       [0.75185433],\n",
+              "       [0.63363508],\n",
+              "       [0.62639701],\n",
+              "       [0.71418557],\n",
+              "       [0.70211815]])"
+            ]
+          },
+          "execution_count": 77,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "sim_value"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wJ7pklxursSk"
+      },
+      "source": [
+        "using `np.argmax` best candidate is selected.\n",
+        "\n",
+        "**Users's Input:** Explain how deep-sea life survives.\n",
+        "\n",
+        "**Answer:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 78,
+      "metadata": {
+        "id": "MzTfyU_mJ8-M"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "> **How do deep-sea animals survive?**\n",
+              "> \n",
+              "> **Adaptations to Pressure:**\n",
+              "> \n",
+              "> * Deep-sea animals have high internal pressure that matches the external pressure, preventing them from being crushed.\n",
+              "> * Their cell membranes contain a higher proportion of unsaturated fatty acids, which increases membrane fluidity in high-pressure environments.\n",
+              "> \n",
+              "> **Buoyancy Adaptations:**\n",
+              "> \n",
+              "> * Many deep-sea fish have a gelatinous layer below the skin or around the spine for buoyancy and swimming efficiency.\n",
+              "> * They have low tissue density, achieved through high fat content, reduced skeletal weight, and water accumulation, allowing them to float without a swim bladder.\n",
+              "> \n",
+              "> **Light and Vision:**\n",
+              "> \n",
+              "> * Deep-sea fish lack sunlight, so they rely on other senses, such as sensitivity to pressure changes and smell, for locating prey and mates.\n",
+              "> * Many deep-sea fish are bioluminescent, using light to communicate, attract prey, or camouflage themselves.\n",
+              "> * Some have sensitive eyes with high numbers of Rh1 genes, helping them see in low light conditions.\n",
+              "> \n",
+              "> **Feeding Mechanisms:**\n",
+              "> \n",
+              "> * Deep-sea fish often have large mouths and sharp teeth for consuming prey of similar or larger sizes.\n",
+              "> * They use feelers to locate prey in the darkness.\n",
+              "> \n",
+              "> **Behavior:**\n",
+              "> \n",
+              "> * Mesopelagic fish make vertical migrations following zooplankton prey, returning to deeper depths during the day.\n",
+              "> * Bathypelagic fish are sedentary, waiting for prey to come close enough or being lured by bioluminescence.\n",
+              "> * Some deep-sea fish are hermaphrodites, increasing their chances of reproduction in the sparse environment.\n",
+              "> \n",
+              "> **Physiological Adaptations:**\n",
+              "> \n",
+              "> * Deep-sea animals have slow metabolisms and unspecialized diets, allowing them to survive with limited food availability.\n",
+              "> * Their proteins are structurally modified to withstand high pressure, ensuring enzymatic reactions and cellular processes function properly.\n",
+              "> * Na+/K+ -ATPase, involved in osmoregulation, is more tolerant of pressure in deep-sea fish compared to shallow-water species.\n",
+              "> \n",
+              "> Based on:\n",
+              ">   https://en.wikipedia.org/wiki/Deep-sea_fish"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 78,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "to_markdown(summaries[np.argmax(sim_value)])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UjDN2OUpsL6M"
+      },
+      "source": [
+        "You have now created a search re-ranking engine using embeddings!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AKH2vs2Lc1R1"
+      },
+      "source": [
+        "## Next steps\n",
+        "\n",
+        "To learn how to use other services in the Gemini API, visit the [Python quickstart](https://ai.google.dev/tutorials/python_quickstart). To learn more about how you can use the embeddings, check out the [examples](https://ai.google.dev/examples?keywords=embed) available."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "search_reranking_using_embeddings.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/examples/anomaly_detection.ipynb b/site/en/examples/anomaly_detection.ipynb
deleted file mode 100644
index 86b238d4b..000000000
--- a/site/en/examples/anomaly_detection.ipynb
+++ /dev/null
@@ -1,2203 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "CrFqUhKys-Xn"
-      },
-      "source": [
-        "##### Copyright 2023 Google LLC."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "LUAcL9sxtIzH"
-      },
-      "outputs": [],
-      "source": [
-        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-        "# you may not use this file except in compliance with the License.\n",
-        "# You may obtain a copy of the License at\n",
-        "#\n",
-        "# https://www.apache.org/licenses/LICENSE-2.0\n",
-        "#\n",
-        "# Unless required by applicable law or agreed to in writing, software\n",
-        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-        "# See the License for the specific language governing permissions and\n",
-        "# limitations under the License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "PkzOKBirz271"
-      },
-      "source": [
-        "# Anomaly detection with embeddings"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ZFWzQEqNosrS"
-      },
-      "source": [
-        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
-        "  <td>\n",
-        "    <a target=\"_blank\" href=https://developers.generativeai.google/examples/anomaly_detection\"><img src=\"https://developers.generativeai.google/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Generative AI</a>\n",
-        "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/examples/anomaly_detection.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
-        "  </td>\n",
-        "  <td>\n",
-        "    <a target=\"_blank\" href=\" https://github.com/google/generative-ai-docs/blob/main/site/en/examples/anomaly_detection.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
-        "  </td>\n",
-        "</table>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "BQPvHyHCz7mk"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "This tutorial demonstrates how to use the embeddings from the PaLM API to detect potential outliers in your dataset. You will visualize a subset of the 20 Newsgroup dataset using [t-SNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html){:.external} and detect outliers outside a particular radius of the central point of each categorical cluster.\n",
-        "\n",
-        "For more information on getting started with embeddings generated from the PaLM API, check out the [quickstart](../tutorials/embeddings_quickstart.ipynb).\n",
-        "\n",
-        "## Setup\n",
-        "\n",
-        "First, download and install the PaLM API Python library."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "LyLLYVEhzud8"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -q google-generativeai"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Z5GJi99k0Ctz"
-      },
-      "outputs": [],
-      "source": [
-        "import google.generativeai as palm\n",
-        "\n",
-        "import re\n",
-        "import tqdm\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns\n",
-        "\n",
-        "from sklearn.datasets import fetch_20newsgroups\n",
-        "from sklearn.manifold import TSNE"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Yi0kitgd5aLG"
-      },
-      "source": [
-        "### Grab an API Key\n",
-        "\n",
-        "To get started, you'll need to [create an API key](/tutorials/setup)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "6OeEZ5Bj5Zr8"
-      },
-      "outputs": [],
-      "source": [
-        "palm.configure(api_key='PALM_KEY')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ce6MdcP170Uv"
-      },
-      "source": [
-        "Key Point: Next, you will choose a model. Any embedding model will work for this tutorial, but for real applications it's important to choose a specific model and stick with it. The outputs of different models are not compatible with each other.\n",
-        "\n",
-        "**Note**: At this time, the PaLM API is [only available in certain regions](https://developers.generativeai.google/available_regions)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "h3mqsrUB7zsE"
-      },
-      "outputs": [],
-      "source": [
-        "models = [m for m in palm.list_models() if 'embedText' in m.supported_generation_methods]\n",
-        "\n",
-        "model = models[0]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "qhWtEhZ6BO58"
-      },
-      "source": [
-        "## Prepare dataset\n",
-        "\n",
-        "The [20 Newsgroups Text Dataset](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html){:.external} contains 18,000 newsgroups posts on 20 topics divided into training and test sets. The split between the training and test datasets are based on messages posted before and after a specific date. This tutorial uses the training subset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "YtHABp9BBTIt"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "['alt.atheism',\n",
-              " 'comp.graphics',\n",
-              " 'comp.os.ms-windows.misc',\n",
-              " 'comp.sys.ibm.pc.hardware',\n",
-              " 'comp.sys.mac.hardware',\n",
-              " 'comp.windows.x',\n",
-              " 'misc.forsale',\n",
-              " 'rec.autos',\n",
-              " 'rec.motorcycles',\n",
-              " 'rec.sport.baseball',\n",
-              " 'rec.sport.hockey',\n",
-              " 'sci.crypt',\n",
-              " 'sci.electronics',\n",
-              " 'sci.med',\n",
-              " 'sci.space',\n",
-              " 'soc.religion.christian',\n",
-              " 'talk.politics.guns',\n",
-              " 'talk.politics.mideast',\n",
-              " 'talk.politics.misc',\n",
-              " 'talk.religion.misc']"
-            ]
-          },
-          "execution_count": 8,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "newsgroups_train = fetch_20newsgroups(subset='train')\n",
-        "\n",
-        "# View list of class names for dataset\n",
-        "newsgroups_train.target_names"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "LPKgmQDQC3zd"
-      },
-      "source": [
-        "Here is the first example in the training set."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "CSXYP0JwBXHh"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Lines: 15\n",
-            "\n",
-            " I was wondering if anyone out there could enlighten me on this car I saw\n",
-            "the other day. It was a 2-door sports car, looked to be from the late 60s/\n",
-            "early 70s. It was called a Bricklin. The doors were really small. In addition,\n",
-            "the front bumper was separate from the rest of the body. This is \n",
-            "all I know. If anyone can tellme a model name, engine specs, years\n",
-            "of production, where this car is made, history, or whatever info you\n",
-            "have on this funky looking car, please e-mail.\n",
-            "\n",
-            "Thanks,\n",
-            "- IL\n",
-            "   ---- brought to you by your neighborhood Lerxst ----\n",
-            "\n",
-            "\n",
-            "\n",
-            "\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "idx = newsgroups_train.data[0].index('Lines')\n",
-        "print(newsgroups_train.data[0][idx:])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Raafa2naC6Ec"
-      },
-      "outputs": [],
-      "source": [
-        "# Apply functions to remove names, emails, and extraneous words from data points in newsgroups.data\n",
-        "newsgroups_train.data = [re.sub(r'[\\w\\.-]+@[\\w\\.-]+', '', d) for d in newsgroups_train.data] # Remove email\n",
-        "newsgroups_train.data = [re.sub(r\"\\([^()]*\\)\", \"\", d) for d in newsgroups_train.data] # Remove names\n",
-        "newsgroups_train.data = [d.replace(\"From: \", \"\") for d in newsgroups_train.data] # Remove \"From: \"\n",
-        "newsgroups_train.data = [d.replace(\"\\nSubject: \", \"\") for d in newsgroups_train.data] # Remove \"\\nSubject: \""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ZjE_Lsr6IhEd"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
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-              "      <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Text</th>\n",
-              "      <th>Label</th>\n",
-              "      <th>Class Name</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>WHAT car is this!?\\nNntp-Posting-Host: rac3.w...</td>\n",
-              "      <td>7</td>\n",
-              "      <td>rec.autos</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>SI Clock Poll - Final Call\\nSummary: Final ca...</td>\n",
-              "      <td>4</td>\n",
-              "      <td>comp.sys.mac.hardware</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>PB questions...\\nOrganization: Purdue Univers...</td>\n",
-              "      <td>4</td>\n",
-              "      <td>comp.sys.mac.hardware</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>Re: Weitek P9000 ?\\nOrganization: Harris Comp...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>comp.graphics</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>Re: Shuttle Launch Question\\nOrganization: Sm...</td>\n",
-              "      <td>14</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>11309</th>\n",
-              "      <td>Re: Migraines and scans\\nDistribution: world...</td>\n",
-              "      <td>13</td>\n",
-              "      <td>sci.med</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>11310</th>\n",
-              "      <td>Screen Death: Mac Plus/512\\nLines: 22\\nOrganiz...</td>\n",
-              "      <td>4</td>\n",
-              "      <td>comp.sys.mac.hardware</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>11311</th>\n",
-              "      <td>Mounting CPU Cooler in vertical case\\nOrganiz...</td>\n",
-              "      <td>3</td>\n",
-              "      <td>comp.sys.ibm.pc.hardware</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>11312</th>\n",
-              "      <td>Re: Sphere from 4 points?\\nOrganization: Cent...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>comp.graphics</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>11313</th>\n",
-              "      <td>stolen CBR900RR\\nOrganization: California Ins...</td>\n",
-              "      <td>8</td>\n",
-              "      <td>rec.motorcycles</td>\n",
-              "    </tr>\n",
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-              "<p>11314 rows × 3 columns</p>\n",
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-              "              title=\"Generate charts.\"\n",
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-              "      </button>\n",
-              "    </div>\n",
-              "    \n",
-              "  <style>\n",
-              "    .colab-df-quickchart {\n",
-              "      background-color: #E8F0FE;\n",
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-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-quickchart:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-quickchart {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-quickchart:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#df-e9a99fc1-79d2-4c70-a675-adb26bdf6949 button.colab-df-quickchart');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function quickchart(key) {\n",
-              "        const containerElement = document.querySelector('#df-e9a99fc1-79d2-4c70-a675-adb26bdf6949');\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'generateCharts', [key], {});\n",
-              "      }\n",
-              "    </script>\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      flex-wrap:wrap;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-3e763a03-3bc2-4228-9c9e-a5482527d5da button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-3e763a03-3bc2-4228-9c9e-a5482527d5da');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ],
-            "text/plain": [
-              "                                                    Text  Label  \\\n",
-              "0       WHAT car is this!?\\nNntp-Posting-Host: rac3.w...      7   \n",
-              "1       SI Clock Poll - Final Call\\nSummary: Final ca...      4   \n",
-              "2       PB questions...\\nOrganization: Purdue Univers...      4   \n",
-              "3       Re: Weitek P9000 ?\\nOrganization: Harris Comp...      1   \n",
-              "4       Re: Shuttle Launch Question\\nOrganization: Sm...     14   \n",
-              "...                                                  ...    ...   \n",
-              "11309    Re: Migraines and scans\\nDistribution: world...     13   \n",
-              "11310  Screen Death: Mac Plus/512\\nLines: 22\\nOrganiz...      4   \n",
-              "11311   Mounting CPU Cooler in vertical case\\nOrganiz...      3   \n",
-              "11312   Re: Sphere from 4 points?\\nOrganization: Cent...      1   \n",
-              "11313   stolen CBR900RR\\nOrganization: California Ins...      8   \n",
-              "\n",
-              "                     Class Name  \n",
-              "0                     rec.autos  \n",
-              "1         comp.sys.mac.hardware  \n",
-              "2         comp.sys.mac.hardware  \n",
-              "3                 comp.graphics  \n",
-              "4                     sci.space  \n",
-              "...                         ...  \n",
-              "11309                   sci.med  \n",
-              "11310     comp.sys.mac.hardware  \n",
-              "11311  comp.sys.ibm.pc.hardware  \n",
-              "11312             comp.graphics  \n",
-              "11313           rec.motorcycles  \n",
-              "\n",
-              "[11314 rows x 3 columns]"
-            ]
-          },
-          "execution_count": 11,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Put training points into a dataframe\n",
-        "df_train = pd.DataFrame(newsgroups_train.data, columns=['Text'])\n",
-        "df_train['Label'] = newsgroups_train.target\n",
-        "# Match label to target name index\n",
-        "df_train['Class Name'] = df_train['Label'].map(newsgroups_train.target_names.__getitem__)\n",
-        "\n",
-        "df_train"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "f7OHvTBaImpB"
-      },
-      "source": [
-        "Next, sample some of the data by taking 150 data points in the training dataset and choosing a few categories. This tutorial uses the science categories."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "yPxwl05BIjWX"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "\n",
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-              "    <div class=\"colab-df-container\">\n",
-              "      <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
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-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>index</th>\n",
-              "      <th>Text</th>\n",
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-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>1650</td>\n",
-              "      <td>Re: The Old Key Registration Idea...\\nOrganiz...</td>\n",
-              "      <td>11</td>\n",
-              "      <td>sci.crypt</td>\n",
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-              "    <tr>\n",
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-              "      <td>1651</td>\n",
-              "      <td>Re: What the clipper nay-sayers sound like to...</td>\n",
-              "      <td>11</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>1652</td>\n",
-              "      <td>Re: White House Public Encryption Management ...</td>\n",
-              "      <td>11</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>1653</td>\n",
-              "      <td>Re: Clipper Chip and crypto key-escrow\\nOrgan...</td>\n",
-              "      <td>11</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>1654</td>\n",
-              "      <td>Marc VanHeyningen &lt;&gt;Re: Clipper chip -- techni...</td>\n",
-              "      <td>11</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>595</th>\n",
-              "      <td>2245</td>\n",
-              "      <td>Re: Space Station Redesign, JSC Alternative #...</td>\n",
-              "      <td>14</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>596</th>\n",
-              "      <td>2246</td>\n",
-              "      <td>Re: Level 5?\\nOrganization: U of Toronto Zool...</td>\n",
-              "      <td>14</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>597</th>\n",
-              "      <td>2247</td>\n",
-              "      <td>Space FAQ 15/15 - Orbital and Planetary Launc...</td>\n",
-              "      <td>14</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>598</th>\n",
-              "      <td>2248</td>\n",
-              "      <td>Nasa incentives\\nX-Added: Forwarded by Space ...</td>\n",
-              "      <td>14</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>599</th>\n",
-              "      <td>2249</td>\n",
-              "      <td>Re: Elevator to the top floor\\nOrganization: ...</td>\n",
-              "      <td>14</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>600 rows × 4 columns</p>\n",
-              "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-073bb1a0-0ab0-4ea3-a63d-1f3835745962')\"\n",
-              "              title=\"Convert this dataframe to an interactive table.\"\n",
-              "              style=\"display:none;\">\n",
-              "        \n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
-              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
-              "  </svg>\n",
-              "      </button>\n",
-              "      \n",
-              "  \n",
-              "    <div id=\"df-7f33f343-faf7-4b7e-b422-1fef2aa7b78a\">\n",
-              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7f33f343-faf7-4b7e-b422-1fef2aa7b78a')\"\n",
-              "              title=\"Generate charts.\"\n",
-              "              style=\"display:none;\">\n",
-              "        \n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "      <g>\n",
-              "          <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "      </g>\n",
-              "  </svg>\n",
-              "      </button>\n",
-              "    </div>\n",
-              "    \n",
-              "  <style>\n",
-              "    .colab-df-quickchart {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-quickchart:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-quickchart {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-quickchart:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#df-7f33f343-faf7-4b7e-b422-1fef2aa7b78a button.colab-df-quickchart');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function quickchart(key) {\n",
-              "        const containerElement = document.querySelector('#df-7f33f343-faf7-4b7e-b422-1fef2aa7b78a');\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'generateCharts', [key], {});\n",
-              "      }\n",
-              "    </script>\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      flex-wrap:wrap;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-073bb1a0-0ab0-4ea3-a63d-1f3835745962 button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-073bb1a0-0ab0-4ea3-a63d-1f3835745962');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ],
-            "text/plain": [
-              "     index                                               Text  Label  \\\n",
-              "0     1650   Re: The Old Key Registration Idea...\\nOrganiz...     11   \n",
-              "1     1651   Re: What the clipper nay-sayers sound like to...     11   \n",
-              "2     1652   Re: White House Public Encryption Management ...     11   \n",
-              "3     1653   Re: Clipper Chip and crypto key-escrow\\nOrgan...     11   \n",
-              "4     1654  Marc VanHeyningen <>Re: Clipper chip -- techni...     11   \n",
-              "..     ...                                                ...    ...   \n",
-              "595   2245   Re: Space Station Redesign, JSC Alternative #...     14   \n",
-              "596   2246   Re: Level 5?\\nOrganization: U of Toronto Zool...     14   \n",
-              "597   2247   Space FAQ 15/15 - Orbital and Planetary Launc...     14   \n",
-              "598   2248   Nasa incentives\\nX-Added: Forwarded by Space ...     14   \n",
-              "599   2249   Re: Elevator to the top floor\\nOrganization: ...     14   \n",
-              "\n",
-              "    Class Name  \n",
-              "0    sci.crypt  \n",
-              "1    sci.crypt  \n",
-              "2    sci.crypt  \n",
-              "3    sci.crypt  \n",
-              "4    sci.crypt  \n",
-              "..         ...  \n",
-              "595  sci.space  \n",
-              "596  sci.space  \n",
-              "597  sci.space  \n",
-              "598  sci.space  \n",
-              "599  sci.space  \n",
-              "\n",
-              "[600 rows x 4 columns]"
-            ]
-          },
-          "execution_count": 12,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Take a sample of each label category from df_train\n",
-        "SAMPLE_SIZE = 150\n",
-        "df_train = (df_train.groupby('Label', as_index = False)\n",
-        "                    .apply(lambda x: x.sample(SAMPLE_SIZE))\n",
-        "                    .reset_index(drop=True))\n",
-        "\n",
-        "# Choose categories about science\n",
-        "df_train = df_train[df_train['Class Name'].str.contains('sci')]\n",
-        "\n",
-        "# Reset the index\n",
-        "df_train = df_train.reset_index()\n",
-        "df_train"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "UjTrEnmdIo5P"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "sci.crypt          150\n",
-              "sci.electronics    150\n",
-              "sci.med            150\n",
-              "sci.space          150\n",
-              "Name: Class Name, dtype: int64"
-            ]
-          },
-          "execution_count": 13,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "df_train['Class Name'].value_counts()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DUgv8SOwXfAX"
-      },
-      "source": [
-        "## Create the embeddings\n",
-        "\n",
-        "In this section, you will see how to generate embeddings for the different texts in the dataframe using the embeddings from the PaLM API."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "jkS_EWfAXcxc"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "100%|██████████| 600/600 [04:10<00:00,  2.39it/s]\n"
-          ]
-        }
-      ],
-      "source": [
-        "from google.api_core import retry\n",
-        "\n",
-        "# Define a function with automatic retry logic to compute embeddings and wait\n",
-        "# a bit once the quota limit has been reached.\n",
-        "@retry.Retry(timeout=300.0)\n",
-        "def embed_text(model, text):\n",
-        "  x = palm.generate_embeddings(model=model, text=text)\n",
-        "  return np.array(x['embedding'])\n",
-        "\n",
-        "# Create embeddings for each document and add that as a column to the dataframe\n",
-        "tqdm.tqdm.pandas()\n",
-        "df_train['Embeddings'] = df_train['Text'].progress_apply(lambda x: embed_text(model=model,\n",
-        "                                                                              text=x))\n",
-        "df_train.drop('index', axis=1, inplace=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "hNTjKcD_aluG"
-      },
-      "source": [
-        "## Dimensionality reduction\n",
-        "\n",
-        "The dimension of the document embedding vector is 768. In order to visualize how the embedded documents are grouped together, you will need to apply dimensionality reduction as you can only visualize the embeddings in 2D or 3D space. Contextually similar documents should be closer together in space as opposed to documents that are not as similar."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "BJDHDQmeZqy2"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "768"
-            ]
-          },
-          "execution_count": 15,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "len(df_train['Embeddings'][0])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "S5-XU-twaoK6"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(600, 768)"
-            ]
-          },
-          "execution_count": 16,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Convert df_train['Embeddings'] Pandas series to a np.array of float32\n",
-        "X = np.array(df_train['Embeddings'].to_list(), dtype=np.float32)\n",
-        "X.shape"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "AV-Y7iEtbAkm"
-      },
-      "source": [
-        "You will apply the t-Distributed Stochastic Neighbor Embedding (t-SNE) to perform dimensionality reduction. This technique reduces the number of dimensions, while preserving clusters (points that are close together stay close together). For the original data, the model tries to construct a distribution over which other data points are \"neighbors\" (e.g., they share a similar meaning). It then optimizes an objective function to keep a similar distribution in the visualization."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "FhYKF-lObC04"
-      },
-      "outputs": [],
-      "source": [
-        "tsne = TSNE(random_state=0, n_iter=1000)\n",
-        "tsne_results = tsne.fit_transform(X)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "31wdqnp_bH9B"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "\n",
-              "  <div id=\"df-bae13ed4-dc74-4c9a-9931-763fa431e1f0\">\n",
-              "    <div class=\"colab-df-container\">\n",
-              "      <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>TSNE1</th>\n",
-              "      <th>TSNE2</th>\n",
-              "      <th>Class Name</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>-22.163635</td>\n",
-              "      <td>19.868490</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>-7.135291</td>\n",
-              "      <td>26.222147</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>-21.182516</td>\n",
-              "      <td>30.351313</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>-16.046413</td>\n",
-              "      <td>22.734932</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>-19.539093</td>\n",
-              "      <td>22.545391</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>595</th>\n",
-              "      <td>-10.613983</td>\n",
-              "      <td>-23.675396</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>596</th>\n",
-              "      <td>7.599044</td>\n",
-              "      <td>-12.451307</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>597</th>\n",
-              "      <td>-7.096947</td>\n",
-              "      <td>-15.042100</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>598</th>\n",
-              "      <td>-0.572135</td>\n",
-              "      <td>-19.784580</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>599</th>\n",
-              "      <td>2.162482</td>\n",
-              "      <td>-18.406704</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>600 rows × 3 columns</p>\n",
-              "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bae13ed4-dc74-4c9a-9931-763fa431e1f0')\"\n",
-              "              title=\"Convert this dataframe to an interactive table.\"\n",
-              "              style=\"display:none;\">\n",
-              "        \n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
-              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
-              "  </svg>\n",
-              "      </button>\n",
-              "      \n",
-              "  \n",
-              "    <div id=\"df-a7cfd3b1-6153-451b-b28f-ca022265a4c1\">\n",
-              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a7cfd3b1-6153-451b-b28f-ca022265a4c1')\"\n",
-              "              title=\"Generate charts.\"\n",
-              "              style=\"display:none;\">\n",
-              "        \n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "      <g>\n",
-              "          <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "      </g>\n",
-              "  </svg>\n",
-              "      </button>\n",
-              "    </div>\n",
-              "    \n",
-              "  <style>\n",
-              "    .colab-df-quickchart {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-quickchart:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-quickchart {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-quickchart:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#df-a7cfd3b1-6153-451b-b28f-ca022265a4c1 button.colab-df-quickchart');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function quickchart(key) {\n",
-              "        const containerElement = document.querySelector('#df-a7cfd3b1-6153-451b-b28f-ca022265a4c1');\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'generateCharts', [key], {});\n",
-              "      }\n",
-              "    </script>\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      flex-wrap:wrap;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-bae13ed4-dc74-4c9a-9931-763fa431e1f0 button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-bae13ed4-dc74-4c9a-9931-763fa431e1f0');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ],
-            "text/plain": [
-              "         TSNE1      TSNE2 Class Name\n",
-              "0   -22.163635  19.868490  sci.crypt\n",
-              "1    -7.135291  26.222147  sci.crypt\n",
-              "2   -21.182516  30.351313  sci.crypt\n",
-              "3   -16.046413  22.734932  sci.crypt\n",
-              "4   -19.539093  22.545391  sci.crypt\n",
-              "..         ...        ...        ...\n",
-              "595 -10.613983 -23.675396  sci.space\n",
-              "596   7.599044 -12.451307  sci.space\n",
-              "597  -7.096947 -15.042100  sci.space\n",
-              "598  -0.572135 -19.784580  sci.space\n",
-              "599   2.162482 -18.406704  sci.space\n",
-              "\n",
-              "[600 rows x 3 columns]"
-            ]
-          },
-          "execution_count": 55,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "df_tsne = pd.DataFrame(tsne_results, columns=['TSNE1', 'TSNE2'])\n",
-        "df_tsne['Class Name'] = df_train['Class Name'] # Add labels column from df_train to df_tsne\n",
-        "df_tsne"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "pTj8HfhpbJ9X"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 800x600 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "fig, ax = plt.subplots(figsize=(8,6)) # Set figsize\n",
-        "sns.set_style('darkgrid', {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
-        "sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Class Name', palette='Set2')\n",
-        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
-        "plt.title('Scatter plot of news using t-SNE')\n",
-        "plt.xlabel('TSNE1')\n",
-        "plt.ylabel('TSNE2');"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "8JQbX4pcMdBe"
-      },
-      "source": [
-        "## Outlier detection\n",
-        "\n",
-        "To determine which points are anomalous, you will determine which points are inliers and outliers. Start by finding the centroid, or location that represents the center of the cluster, and use the distance to determine the points that are outliers.\n",
-        "\n",
-        "Start by getting the centroid of each category."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "nUIkLxtMK4qC"
-      },
-      "outputs": [
-        {
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-            "text/plain": [
-              "                     TSNE1      TSNE2\n",
-              "Class Name                           \n",
-              "sci.crypt       -15.899219  21.129622\n",
-              "sci.electronics  -0.609658   5.234158\n",
-              "sci.med          29.365065  -8.778655\n",
-              "sci.space        -5.070405 -21.039291"
-            ]
-          },
-          "execution_count": 20,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "def get_centroids(df_tsne):\n",
-        "  # Get the centroid of each cluster\n",
-        "  centroids = df_tsne.groupby('Class Name').mean()\n",
-        "  return centroids\n",
-        "\n",
-        "centroids = get_centroids(df_tsne)\n",
-        "centroids"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "GJH4Oo6E-r_6"
-      },
-      "outputs": [],
-      "source": [
-        "def get_embedding_centroids(df):\n",
-        "  emb_centroids = dict()\n",
-        "  grouped = df.groupby('Class Name')\n",
-        "  for c in grouped.groups:\n",
-        "    sub_df = grouped.get_group(c)\n",
-        "    # Get the centroid value of dimension 768\n",
-        "    emb_centroids[c] = np.mean(sub_df['Embeddings'], axis=0)\n",
-        "\n",
-        "  return emb_centroids"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "1tas9Yg4_iyq"
-      },
-      "outputs": [],
-      "source": [
-        "emb_c = get_embedding_centroids(df_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "aMvdYLjKl32a"
-      },
-      "source": [
-        "Plot each centroid you have found against the rest of the points."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "jpN02WY3Ogji"
-      },
-      "outputs": [
-        {
-          "data": {
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\n",
-            "text/plain": [
-              "<Figure size 800x600 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# Plot the centroids against the cluster\n",
-        "fig, ax = plt.subplots(figsize=(8,6)) # Set figsize\n",
-        "sns.set_style('darkgrid', {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
-        "sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Class Name', palette='Set2');\n",
-        "sns.scatterplot(data=centroids, x='TSNE1', y='TSNE2', color=\"black\", marker='X', s=100, label='Centroids')\n",
-        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
-        "plt.title('Scatter plot of news using t-SNE with centroids')\n",
-        "plt.xlabel('TSNE1')\n",
-        "plt.ylabel('TSNE2');"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "onFfUf1XoEQW"
-      },
-      "source": [
-        "Choose a radius. Anything beyond this bound from the centroid of that category is considered an outlier."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "87cDfNpvOu7f"
-      },
-      "outputs": [],
-      "source": [
-        "def calculate_euclidean_distance(p1, p2):\n",
-        "  return np.sqrt(np.sum(np.square(p1 - p2)))\n",
-        "\n",
-        "def detect_outlier(df, emb_centroids, radius):\n",
-        "  for idx, row in df.iterrows():\n",
-        "    class_name = row['Class Name'] # Get class name of row\n",
-        "    # Compare centroid distances\n",
-        "    dist = calculate_euclidean_distance(row['Embeddings'],\n",
-        "                                        emb_centroids[class_name])\n",
-        "    df.at[idx, 'Outlier'] = dist > radius\n",
-        "\n",
-        "  return len(df[df['Outlier'] == True])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "CsVsod5MKd3X"
-      },
-      "outputs": [],
-      "source": [
-        "range_ = np.arange(0.3, 0.75, 0.02).round(decimals=2).tolist()\n",
-        "num_outliers = []\n",
-        "for i in range_:\n",
-        "  num_outliers.append(detect_outlier(df_train, emb_c, i))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "vReUSOjbNHQv"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 1400x800 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# Plot range_ and num_outliers\n",
-        "fig = plt.figure(figsize = (14, 8))\n",
-        "plt.rcParams.update({'font.size': 12})\n",
-        "plt.bar(list(map(str, range_)), num_outliers)\n",
-        "plt.title(\"Number of outliers vs. distance of points from centroid\")\n",
-        "plt.xlabel(\"Distance\")\n",
-        "plt.ylabel(\"Number of outliers\")\n",
-        "for i in range(len(range_)):\n",
-        "  plt.text(i, num_outliers[i], num_outliers[i], ha = 'center')\n",
-        "\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gNISxrzwGvBH"
-      },
-      "source": [
-        "Depending on how sensitive you want your anomaly detector to be, you can choose which radius you would like to use. For now, 0.58 is used, but you can change this value."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "PMNFFSDOTELn"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "\n",
-              "  <div id=\"df-be30e135-5f86-4bcc-a1d5-8a0e661347b1\">\n",
-              "    <div class=\"colab-df-container\">\n",
-              "      <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Text</th>\n",
-              "      <th>Label</th>\n",
-              "      <th>Class Name</th>\n",
-              "      <th>Embeddings</th>\n",
-              "      <th>Outlier</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>19</th>\n",
-              "      <td>Re: **Sorry folks** \\nOriginator: \\nNntp-Post...</td>\n",
-              "      <td>11</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "      <td>[-0.0032022626, -0.040697347, 0.025153195, -0....</td>\n",
-              "      <td>True</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>23</th>\n",
-              "      <td>freely distributable public key cryptography ...</td>\n",
-              "      <td>11</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "      <td>[0.016041335, -0.031121235, 0.028244283, 0.030...</td>\n",
-              "      <td>True</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>171</th>\n",
-              "      <td>Re: Does someone know what is the news group ...</td>\n",
-              "      <td>12</td>\n",
-              "      <td>sci.electronics</td>\n",
-              "      <td>[0.047017973, -0.0058603217, -0.026950218, 0.0...</td>\n",
-              "      <td>True</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>176</th>\n",
-              "      <td>Re: What do Nuclear Site's Cooling Towers do?...</td>\n",
-              "      <td>12</td>\n",
-              "      <td>sci.electronics</td>\n",
-              "      <td>[-0.027654478, -0.016448569, -0.015691927, 0.0...</td>\n",
-              "      <td>True</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>194</th>\n",
-              "      <td>Read only if going to ISCAS93 in Chicago\\nKey...</td>\n",
-              "      <td>12</td>\n",
-              "      <td>sci.electronics</td>\n",
-              "      <td>[-0.0005354147, -0.034046683, -0.003146662, 0....</td>\n",
-              "      <td>True</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
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-              "      fill: #174EA6;\n",
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-              "\n",
-              "    [theme=dark] .colab-df-quickchart {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
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-              "    [theme=dark] .colab-df-quickchart:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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-              "        const containerElement = document.querySelector('#df-a7fcb373-5c75-4649-b1c6-c8f7b87883dd');\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'generateCharts', [key], {});\n",
-              "      }\n",
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-              "  <style>\n",
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-              "      display:flex;\n",
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-              "    .colab-df-convert {\n",
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-              "      width: 32px;\n",
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-              "      fill: #174EA6;\n",
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-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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-              "\n",
-              "      <script>\n",
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-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-be30e135-5f86-4bcc-a1d5-8a0e661347b1');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
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-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
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-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ],
-            "text/plain": [
-              "                                                  Text  Label  \\\n",
-              "19    Re: **Sorry folks** \\nOriginator: \\nNntp-Post...     11   \n",
-              "23    freely distributable public key cryptography ...     11   \n",
-              "171   Re: Does someone know what is the news group ...     12   \n",
-              "176   Re: What do Nuclear Site's Cooling Towers do?...     12   \n",
-              "194   Read only if going to ISCAS93 in Chicago\\nKey...     12   \n",
-              "\n",
-              "          Class Name                                         Embeddings  \\\n",
-              "19         sci.crypt  [-0.0032022626, -0.040697347, 0.025153195, -0....   \n",
-              "23         sci.crypt  [0.016041335, -0.031121235, 0.028244283, 0.030...   \n",
-              "171  sci.electronics  [0.047017973, -0.0058603217, -0.026950218, 0.0...   \n",
-              "176  sci.electronics  [-0.027654478, -0.016448569, -0.015691927, 0.0...   \n",
-              "194  sci.electronics  [-0.0005354147, -0.034046683, -0.003146662, 0....   \n",
-              "\n",
-              "    Outlier  \n",
-              "19     True  \n",
-              "23     True  \n",
-              "171    True  \n",
-              "176    True  \n",
-              "194    True  "
-            ]
-          },
-          "execution_count": 59,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# View the points that are outliers\n",
-        "RADIUS = 0.62\n",
-        "detect_outlier(df_train, emb_c, RADIUS)\n",
-        "df_outliers = df_train[df_train['Outlier'] == True]\n",
-        "df_outliers.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "h_wbM5yYE4MS"
-      },
-      "outputs": [],
-      "source": [
-        "# Use the index to map the outlier points back to the projected TSNE points\n",
-        "outliers_projected = df_tsne.loc[df_outliers['Outlier'].index]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "xCt4wfYdoTJz"
-      },
-      "source": [
-        "Plot the outliers and denote them using a transparent red color."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "IrAKwBp0TaNu"
-      },
-      "outputs": [
-        {
-          "data": {
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\n",
-            "text/plain": [
-              "<Figure size 800x600 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "fig, ax = plt.subplots(figsize=(8,6)) # Set figsize\n",
-        "plt.rcParams.update({'font.size': 10})\n",
-        "sns.set_style('darkgrid', {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
-        "sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Class Name', palette='Set2');\n",
-        "sns.scatterplot(data=centroids, x='TSNE1', y='TSNE2', color=\"black\", marker='X', s=100, label='Centroids')\n",
-        "# Draw a red circle around the outliers\n",
-        "sns.scatterplot(data=outliers_projected, x='TSNE1', y='TSNE2', color='red', marker='o', alpha=0.5, s=90, label='Outliers')\n",
-        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
-        "plt.title('Scatter plot of news with outliers projected with t-SNE')\n",
-        "plt.xlabel('TSNE1')\n",
-        "plt.ylabel('TSNE2');"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "RVm_A9HmGwEN"
-      },
-      "source": [
-        "Use the index values of the datafames to print a few examples of what outliers can look like in each category. Here, the first data point from each category is printed out. Explore other points in each category to see data that are deemed as outliers, or anomalies."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "lpZ-hcDvG13M"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            " Re: **Sorry folks** \n",
-            "Originator: \n",
-            "Nntp-Posting-Host: eff.org\n",
-            "Organization: Enormes_Rebajas_Online\n",
-            "Distribution: na\n",
-            "Lines: 15\n",
-            "\n",
-            "In article <>   writes:\n",
-            "\n",
-            ">I just found out from my source that this article was a joke.  Heh heh..  \n",
-            ">It seemed pretty damn convincing to me from the start -- I just didn't\n",
-            ">notice the smiley at the end of the article, and there were a few other\n",
-            ">hints which I should of caught.\n",
-            "\n",
-            "\tPeople took this article seriously?  I mean, I know it's the\n",
-            "Net and all, but the prankster didn't even have Clinton's sound-bites\n",
-            "right.\n",
-            "\n",
-            "\n",
-            "-- \n",
-            "Rita Rouvalis\n",
-            "\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "sci_crypt_outliers = df_outliers[df_outliers['Class Name'] == 'sci.crypt']\n",
-        "print(sci_crypt_outliers['Text'].iloc[0])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "SPsQB3eHJN25"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            " Re: Does someone know what is the news group for IEEE.\n",
-            "Reply-To: \n",
-            "Distribution: usa\n",
-            "Organization: SFBAC\n",
-            "Lines: 11\n",
-            "X-Newsreader: Helldiver 1.07 \n",
-            "\n",
-            "In <>   writes:\n",
-            ">            Thanks a lot.\n",
-            "\n",
-            "ieee.general\n",
-            "\n",
-            "and\n",
-            "\n",
-            "ieee.announce\n",
-            "\n",
-            "\n",
-            "are the most frequently used groups.\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "sci_elec_outliers = df_outliers[df_outliers['Class Name'] == 'sci.electronics']\n",
-        "print(sci_elec_outliers['Text'].iloc[0])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "APPg8TURJ9yt"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            " Re: Can men get yeast infections?\n",
-            "Lines: 13\n",
-            "\n",
-            " To:  \n",
-            "\n",
-            " LB> I know from personal experience that men CAN get yeast infections. I \n",
-            " LB> get rather nasty ones from time to time, mostly in the area of the\n",
-            " LB> scrotum and the base of the penis. \n",
-            "\n",
-            "I used to have problems with recurrent athlete's foot until I \n",
-            "started drying between my toes with my blow drier after each time \n",
-            "I bathe.  I also dry my pubic area while I am at it to prevent \n",
-            "problems.  You might want to try it.\n",
-            "\n",
-            "... My cat types with his tail.\n",
-            " * Origin: ONE WORLD Los Angeles 310/372-0987 32b \n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "sci_med_outliers = df_outliers[df_outliers['Class Name'] == 'sci.med']\n",
-        "print(sci_med_outliers['Text'].iloc[0])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "WeoJF7c8KB49"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            " Stereo Pix of planets?\n",
-            "Organization: California State University, Sacramento\n",
-            "Lines: 5\n",
-            "\n",
-            "Can anyone tell me where I might find stereo images of planetary and\n",
-            "planetary satellite surfaces?  GIFs preferred, but any will do.  I'm\n",
-            "especially interested in stereos of the surfaces of Phobos, Deimos, Mars\n",
-            "and the Moon .\n",
-            "  Thanks. \n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "sci_space_outliers = df_outliers[df_outliers['Class Name'] == 'sci.space']\n",
-        "print(sci_space_outliers['Text'].iloc[0])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "siaPlEJhh0pr"
-      },
-      "source": [
-        "## Next steps\n",
-        "\n",
-        "You've now created an anomaly detector using embeddings! Try using your own textual data to visualize them as embeddings, and choose some bound such that you can detect outliers. You can perform dimensionality reduction in order to complete the visualization step. Note that TSNE is good at clustering inputs, but can take a longer time to converge or might get stuck at local minima. If you run into this issue, another technique you could consider are [principal components analysis (PCA)](https://en.wikipedia.org/wiki/Principal_component_analysis){:.external}.\n",
-        "\n",
-        "To learn more about how you can use the embeddings, check out the examples available. To learn how to create them from scratch, see TensorFlow's [Word Embeddings](https://www.tensorflow.org/text/guide/word_embeddings) tutorial. To learn how to use other services in the PaLM API, visit the various quickstart guides:\n",
-        "\n",
-        "* [Chat quickstart](../tutorials/chat_quickstart.ipynb)\n",
-        "\n",
-        "* [Text generation quickstart](../tutorials/text_quickstart.ipynb)"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "name": "anomaly_detection.ipynb",
-      "toc_visible": true
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
diff --git a/site/en/examples/chat_calculator.ipynb b/site/en/examples/chat_calculator.ipynb
index bb856ff69..7e154edd7 100644
--- a/site/en/examples/chat_calculator.ipynb
+++ b/site/en/examples/chat_calculator.ipynb
@@ -6,7 +6,7 @@
         "id": "Tce3stUlHN0L"
       },
       "source": [
-        "##### Copyright 2023 Google LLC."
+        "##### Copyright 2024 Google LLC."
       ]
     },
     {
@@ -48,7 +48,7 @@
       "source": [
         "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://developers.generativeai.google/examples/chat_calculator\"><img src=\"https://developers.generativeai.google/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Generative AI</a>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/examples/chat_calculator\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/examples/chat_calculator.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
@@ -66,7 +66,7 @@
       },
       "source": [
         "For some use cases, you may want to stop the generation from a model to insert specific results. For example, language models may have trouble with complicated arithmetic problems like word problems.\n",
-        "This tutorial shows an example of using an external tool with the `palm.chat` method to output the correct answer to a word problem.\n",
+        "This tutorial shows an example of using an external tool with the `genai.chat` method to output the correct answer to a word problem.\n",
         "\n",
         "This particular example uses the [`numexpr`](https://github.com/pydata/numexpr) tool to perform the arithmetic but you can use this same procedure to integrate other tools specific to your use case. The following is an outline of the steps:\n",
         "\n",
@@ -74,7 +74,7 @@
         "1. Create a prompt instructing the model how to use the tags in its response.\n",
         "1. From the model response, take the text between the `start` and `end` tags as input to the tool.\n",
         "1. Drop everything after the `end` tag.\n",
-        "1. Run the tool and add it's output as your reply.\n",
+        "1. Run the tool and add its output as your reply.\n",
         "1. The model will take into account the tools's output in its reply."
       ]
     },
@@ -84,19 +84,9 @@
       "metadata": {
         "id": "v8d0FtO2KJ3O"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.2/122.2 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m113.3/113.3 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
-        "pip install -q google.generativeai"
+        "pip install -q google-generativeai"
       ]
     },
     {
@@ -122,7 +112,7 @@
         "\n",
         "@retry.Retry()\n",
         "def retry_chat(**kwargs):\n",
-        "  return palm.chat(**kwargs)\n",
+        "  return genai.chat(**kwargs)\n",
         "\n",
         "@retry.Retry()\n",
         "def retry_reply(self, arg):\n",
@@ -137,8 +127,8 @@
       },
       "outputs": [],
       "source": [
-        "import google.generativeai as palm\n",
-        "palm.configure(api_key=\"YOUR API KEY\")"
+        "import google.generativeai as genai\n",
+        "genai.configure(api_key=\"YOUR API KEY\")"
       ]
     },
     {
@@ -149,7 +139,7 @@
       },
       "outputs": [],
       "source": [
-        "models = [m for m in palm.list_models() if 'generateMessage' in m.supported_generation_methods]\n",
+        "models = [m for m in genai.list_models() if 'generateMessage' in m.supported_generation_methods]\n",
         "model = models[0].name\n",
         "print(model)"
       ]
@@ -371,14 +361,14 @@
         "  # keep everything before opening the calc tag.\n",
         "  text, remainder = result.split('<calc>', 1)\n",
         "  # drop everything after closing the c alc tag.\n",
-        "  experssion, junk = remainder.split('</calc>', 1)\n",
+        "  expression, junk = remainder.split('</calc>', 1)\n",
         "\n",
         "  # Remove the units like \"7 cats / hour\" -> \"7\"\n",
-        "  experssion = re.sub(\"[a-zA-Z][ /a-zA-Z]*[a-zA-Z]\",'', experssion)\n",
+        "  expression = re.sub(\"[a-zA-Z][ /a-zA-Z]*[a-zA-Z]\",'', expression)\n",
         "\n",
         "  # `eval` is unsafe use numexpr\n",
-        "  result = f\"{text}<calc>{experssion}</calc>\"\n",
-        "  return result, str(numexpr.evaluate(experssion))"
+        "  result = f\"{text}<calc>{expression}</calc>\"\n",
+        "  return result, str(numexpr.evaluate(expression))"
       ]
     },
     {
diff --git a/site/en/examples/clustering_with_embeddings.ipynb b/site/en/examples/clustering_with_embeddings.ipynb
deleted file mode 100644
index 31f3e0f63..000000000
--- a/site/en/examples/clustering_with_embeddings.ipynb
+++ /dev/null
@@ -1,1459 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Tce3stUlHN0L"
-      },
-      "source": [
-        "##### Copyright 2023 Google LLC."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "cellView": "form",
-        "id": "tuOe1ymfHZPu"
-      },
-      "outputs": [],
-      "source": [
-        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-        "# you may not use this file except in compliance with the License.\n",
-        "# You may obtain a copy of the License at\n",
-        "#\n",
-        "# https://www.apache.org/licenses/LICENSE-2.0\n",
-        "#\n",
-        "# Unless required by applicable law or agreed to in writing, software\n",
-        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-        "# See the License for the specific language governing permissions and\n",
-        "# limitations under the License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "xPixuBZFck9b"
-      },
-      "source": [
-        "# Visualizing embeddings with t-SNE\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "M43FZggHDEr5"
-      },
-      "source": [
-        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
-        "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://developers.generativeai.google/examples/clustering_with_embeddings\"><img src=\"https://developers.generativeai.google/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Generative AI</a>\n",
-        "  </td>\n",
-        "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/examples/clustering_with_embeddings.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
-        "  </td>\n",
-        "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/examples/clustering_with_embeddings.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
-        "  </td>\n",
-        "</table>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "PMCPLbMYsljk"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "This tutorial demonstrates how to visualize and perform clustering with the embeddings from the PaLM API. You will visualize a subset of the 20 Newsgroup dataset using [t-SNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html){:.external} and cluster that subset using the KMeans algorithm.\n",
-        "\n",
-        "For more information on getting started with embeddings generated from the PaLM API, check out the [quickstart](../tutorials/embeddings_quickstart.ipynb).\n",
-        "\n",
-        "## Setup\n",
-        "\n",
-        "First, download and install the PaLM API Python library."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "id": "VYACbzJqseql"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -q google-generativeai"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "id": "d7bEYfTFmvy9"
-      },
-      "outputs": [],
-      "source": [
-        "import google.generativeai as palm\n",
-        "\n",
-        "import re\n",
-        "import tqdm\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns\n",
-        "\n",
-        "from sklearn.datasets import fetch_20newsgroups\n",
-        "from sklearn.manifold import TSNE\n",
-        "from sklearn.cluster import KMeans\n",
-        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "qEunxaDOHzfi"
-      },
-      "source": [
-        "### Get an API Key\n",
-        "\n",
-        "To get started, you'll need to [create an API key](https://developers.generativeai.google/tutorials/setup)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "id": "7CItpYF3uOEf"
-      },
-      "outputs": [],
-      "source": [
-        "palm.configure(api_key='YOUR_API_KEY')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dJTxEH7RAOfq"
-      },
-      "source": [
-        "Key Point: Next, you will choose a model. Any embedding model will work for this tutorial, but for real applications it's important to choose a specific model and stick with it. The outputs of different models are not compatible with each other.\n",
-        "\n",
-        "**Note**: At this time, the PaLM API is [only available in certain regions](https://developers.generativeai.google/available_regions)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "id": "sLeRMa1bz9Ad"
-      },
-      "outputs": [],
-      "source": [
-        "models = [m for m in palm.list_models() if 'embedText' in m.supported_generation_methods]\n",
-        "\n",
-        "model = models[0]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "pnICLtwna2UU"
-      },
-      "source": [
-        "## Dataset\n",
-        "\n",
-        "The [20 Newsgroups Text Dataset](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html){:.external} contains 18,000 newsgroups posts on 20 topics divided into training and test sets. The split between the training and test datasets are based on messages posted before and after a specific date. For this tutorial, you will be using the training subset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "id": "7j4Y2198bdnm"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "['alt.atheism',\n",
-              " 'comp.graphics',\n",
-              " 'comp.os.ms-windows.misc',\n",
-              " 'comp.sys.ibm.pc.hardware',\n",
-              " 'comp.sys.mac.hardware',\n",
-              " 'comp.windows.x',\n",
-              " 'misc.forsale',\n",
-              " 'rec.autos',\n",
-              " 'rec.motorcycles',\n",
-              " 'rec.sport.baseball',\n",
-              " 'rec.sport.hockey',\n",
-              " 'sci.crypt',\n",
-              " 'sci.electronics',\n",
-              " 'sci.med',\n",
-              " 'sci.space',\n",
-              " 'soc.religion.christian',\n",
-              " 'talk.politics.guns',\n",
-              " 'talk.politics.mideast',\n",
-              " 'talk.politics.misc',\n",
-              " 'talk.religion.misc']"
-            ]
-          },
-          "execution_count": 6,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "newsgroups_train = fetch_20newsgroups(subset='train')\n",
-        "\n",
-        "# View list of class names for dataset\n",
-        "newsgroups_train.target_names"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "k-XyGQsTcdSR"
-      },
-      "source": [
-        "Here is the first example in the training set."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "id": "KDELgM0xbpkt"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Lines: 15\n",
-            "\n",
-            " I was wondering if anyone out there could enlighten me on this car I saw\n",
-            "the other day. It was a 2-door sports car, looked to be from the late 60s/\n",
-            "early 70s. It was called a Bricklin. The doors were really small. In addition,\n",
-            "the front bumper was separate from the rest of the body. This is \n",
-            "all I know. If anyone can tellme a model name, engine specs, years\n",
-            "of production, where this car is made, history, or whatever info you\n",
-            "have on this funky looking car, please e-mail.\n",
-            "\n",
-            "Thanks,\n",
-            "- IL\n",
-            "   ---- brought to you by your neighborhood Lerxst ----\n",
-            "\n",
-            "\n",
-            "\n",
-            "\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "idx = newsgroups_train.data[0].index('Lines')\n",
-        "print(newsgroups_train.data[0][idx:])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "id": "G6ldbA4XfPpP"
-      },
-      "outputs": [],
-      "source": [
-        "# Apply functions to remove names, emails, and extraneous words from data points in newsgroups.data\n",
-        "newsgroups_train.data = [re.sub(r'[\\w\\.-]+@[\\w\\.-]+', '', d) for d in newsgroups_train.data] # Remove email\n",
-        "newsgroups_train.data = [re.sub(r\"\\([^()]*\\)\", \"\", d) for d in newsgroups_train.data] # Remove names\n",
-        "newsgroups_train.data = [d.replace(\"From: \", \"\") for d in newsgroups_train.data] # Remove \"From: \"\n",
-        "newsgroups_train.data = [d.replace(\"\\nSubject: \", \"\") for d in newsgroups_train.data] # Remove \"\\nSubject: \""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "id": "26qIj6fJccVI"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Text</th>\n",
-              "      <th>Label</th>\n",
-              "      <th>Class Name</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>WHAT car is this!?\\nNntp-Posting-Host: rac3.w...</td>\n",
-              "      <td>7</td>\n",
-              "      <td>rec.autos</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>SI Clock Poll - Final Call\\nSummary: Final ca...</td>\n",
-              "      <td>4</td>\n",
-              "      <td>comp.sys.mac.hardware</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>PB questions...\\nOrganization: Purdue Univers...</td>\n",
-              "      <td>4</td>\n",
-              "      <td>comp.sys.mac.hardware</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>Re: Weitek P9000 ?\\nOrganization: Harris Comp...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>comp.graphics</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>Re: Shuttle Launch Question\\nOrganization: Sm...</td>\n",
-              "      <td>14</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>11309</th>\n",
-              "      <td>Re: Migraines and scans\\nDistribution: world...</td>\n",
-              "      <td>13</td>\n",
-              "      <td>sci.med</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>11310</th>\n",
-              "      <td>Screen Death: Mac Plus/512\\nLines: 22\\nOrganiz...</td>\n",
-              "      <td>4</td>\n",
-              "      <td>comp.sys.mac.hardware</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>11311</th>\n",
-              "      <td>Mounting CPU Cooler in vertical case\\nOrganiz...</td>\n",
-              "      <td>3</td>\n",
-              "      <td>comp.sys.ibm.pc.hardware</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>11312</th>\n",
-              "      <td>Re: Sphere from 4 points?\\nOrganization: Cent...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>comp.graphics</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>11313</th>\n",
-              "      <td>stolen CBR900RR\\nOrganization: California Ins...</td>\n",
-              "      <td>8</td>\n",
-              "      <td>rec.motorcycles</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>11314 rows × 3 columns</p>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "                                                    Text  Label   \n",
-              "0       WHAT car is this!?\\nNntp-Posting-Host: rac3.w...      7  \\\n",
-              "1       SI Clock Poll - Final Call\\nSummary: Final ca...      4   \n",
-              "2       PB questions...\\nOrganization: Purdue Univers...      4   \n",
-              "3       Re: Weitek P9000 ?\\nOrganization: Harris Comp...      1   \n",
-              "4       Re: Shuttle Launch Question\\nOrganization: Sm...     14   \n",
-              "...                                                  ...    ...   \n",
-              "11309    Re: Migraines and scans\\nDistribution: world...     13   \n",
-              "11310  Screen Death: Mac Plus/512\\nLines: 22\\nOrganiz...      4   \n",
-              "11311   Mounting CPU Cooler in vertical case\\nOrganiz...      3   \n",
-              "11312   Re: Sphere from 4 points?\\nOrganization: Cent...      1   \n",
-              "11313   stolen CBR900RR\\nOrganization: California Ins...      8   \n",
-              "\n",
-              "                     Class Name  \n",
-              "0                     rec.autos  \n",
-              "1         comp.sys.mac.hardware  \n",
-              "2         comp.sys.mac.hardware  \n",
-              "3                 comp.graphics  \n",
-              "4                     sci.space  \n",
-              "...                         ...  \n",
-              "11309                   sci.med  \n",
-              "11310     comp.sys.mac.hardware  \n",
-              "11311  comp.sys.ibm.pc.hardware  \n",
-              "11312             comp.graphics  \n",
-              "11313           rec.motorcycles  \n",
-              "\n",
-              "[11314 rows x 3 columns]"
-            ]
-          },
-          "execution_count": 9,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Put training points into a dataframe\n",
-        "df_train = pd.DataFrame(newsgroups_train.data, columns=['Text'])\n",
-        "df_train['Label'] = newsgroups_train.target\n",
-        "# Match label to target name index\n",
-        "df_train['Class Name'] = df_train['Label'].map(newsgroups_train.target_names.__getitem__)\n",
-        "\n",
-        "df_train"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sZnW2_Tx2_L1"
-      },
-      "source": [
-        "Next, you will sample some of the data by taking 100 data points in the training dataset, and dropping a few of the categories to run through this tutorial. Choose the science categories to compare."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "id": "L5LWfJMY3Ii7"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>index</th>\n",
-              "      <th>Text</th>\n",
-              "      <th>Label</th>\n",
-              "      <th>Class Name</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>1650</td>\n",
-              "      <td>Competing standard\\nNntp-Posting-Host: mizzou...</td>\n",
-              "      <td>11</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>1651</td>\n",
-              "      <td>Subject: Re: Don't fight Clipper Chip, subvert...</td>\n",
-              "      <td>11</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>1652</td>\n",
-              "      <td>Re: Pgp, PEM, and RFC's \\nNntp-Posting-Host: ...</td>\n",
-              "      <td>11</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>1653</td>\n",
-              "      <td>THE CLIPPER CHIP: A TECHNICAL SUMMARY\\nDistrib...</td>\n",
-              "      <td>11</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>1654</td>\n",
-              "      <td>Re: Pgp, PEM, and RFC's \\nOrganization: Oce G...</td>\n",
-              "      <td>11</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>595</th>\n",
-              "      <td>2245</td>\n",
-              "      <td>Re: Jemison on Star Trek \\nOrganization: Expr...</td>\n",
-              "      <td>14</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>596</th>\n",
-              "      <td>2246</td>\n",
-              "      <td>Re: Alaska Pipeline and Space Station!\\nOrgan...</td>\n",
-              "      <td>14</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>597</th>\n",
-              "      <td>2247</td>\n",
-              "      <td>Space Station Redesign, JSC Alternative #4\\nOr...</td>\n",
-              "      <td>14</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>598</th>\n",
-              "      <td>2248</td>\n",
-              "      <td>Re: Solar Sail Data\\nOrganization: Sun Micros...</td>\n",
-              "      <td>14</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>599</th>\n",
-              "      <td>2249</td>\n",
-              "      <td>Eco-Freaks forcing Space Mining.\\nArticle-I.D....</td>\n",
-              "      <td>14</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>600 rows × 4 columns</p>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "     index                                               Text  Label   \n",
-              "0     1650   Competing standard\\nNntp-Posting-Host: mizzou...     11  \\\n",
-              "1     1651  Subject: Re: Don't fight Clipper Chip, subvert...     11   \n",
-              "2     1652   Re: Pgp, PEM, and RFC's \\nNntp-Posting-Host: ...     11   \n",
-              "3     1653  THE CLIPPER CHIP: A TECHNICAL SUMMARY\\nDistrib...     11   \n",
-              "4     1654   Re: Pgp, PEM, and RFC's \\nOrganization: Oce G...     11   \n",
-              "..     ...                                                ...    ...   \n",
-              "595   2245   Re: Jemison on Star Trek \\nOrganization: Expr...     14   \n",
-              "596   2246   Re: Alaska Pipeline and Space Station!\\nOrgan...     14   \n",
-              "597   2247  Space Station Redesign, JSC Alternative #4\\nOr...     14   \n",
-              "598   2248   Re: Solar Sail Data\\nOrganization: Sun Micros...     14   \n",
-              "599   2249  Eco-Freaks forcing Space Mining.\\nArticle-I.D....     14   \n",
-              "\n",
-              "    Class Name  \n",
-              "0    sci.crypt  \n",
-              "1    sci.crypt  \n",
-              "2    sci.crypt  \n",
-              "3    sci.crypt  \n",
-              "4    sci.crypt  \n",
-              "..         ...  \n",
-              "595  sci.space  \n",
-              "596  sci.space  \n",
-              "597  sci.space  \n",
-              "598  sci.space  \n",
-              "599  sci.space  \n",
-              "\n",
-              "[600 rows x 4 columns]"
-            ]
-          },
-          "execution_count": 10,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Take a sample of each label category from df_train\n",
-        "SAMPLE_SIZE = 150\n",
-        "df_train = (df_train.groupby('Label', as_index = False)\n",
-        "                    .apply(lambda x: x.sample(SAMPLE_SIZE))\n",
-        "                    .reset_index(drop=True))\n",
-        "\n",
-        "# Choose categories about science\n",
-        "df_train = df_train[df_train['Class Name'].str.contains('sci')]\n",
-        "\n",
-        "# Reset the index\n",
-        "df_train = df_train.reset_index()\n",
-        "df_train"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {
-        "id": "FI1FDqirsz3O"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "Class Name\n",
-              "sci.crypt          150\n",
-              "sci.electronics    150\n",
-              "sci.med            150\n",
-              "sci.space          150\n",
-              "Name: count, dtype: int64"
-            ]
-          },
-          "execution_count": 11,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "df_train['Class Name'].value_counts()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5NbA2hpDH3nl"
-      },
-      "source": [
-        "## Create the embeddings\n",
-        "\n",
-        "In this section, you will see how to generate embeddings for the different texts in the dataframe using the embeddings from the PaLM API."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "metadata": {
-        "id": "g1NC0e6McsQx"
-      },
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "71539ef786934a36a55f6d6e4e269b42",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "  0%|          | 0/600 [00:00<?, ?it/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "from tqdm.auto import tqdm\n",
-        "tqdm.pandas()\n",
-        "\n",
-        "from google.api_core import retry\n",
-        "\n",
-        "def make_embed_text_fn(model):\n",
-        "\n",
-        "  @retry.Retry(timeout=300.0)\n",
-        "  def embed_fn(text: str) -> list[float]:\n",
-        "    return palm.generate_embeddings(model=model, text=text)['embedding']\n",
-        "\n",
-        "  return embed_fn\n",
-        "\n",
-        "def create_embeddings(model, df):\n",
-        "  df['Embeddings'] = df['Text'].progress_apply(make_embed_text_fn(model))\n",
-        "  return df\n",
-        "\n",
-        "df_train = create_embeddings(model, df_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "t-1QKCK8DHsI"
-      },
-      "source": [
-        "## Dimensionality reduction\n",
-        "\n",
-        "The length of the document embedding vector is 768. In order to visualize how the embedded documents are grouped together, you will need to apply dimensionality reduction as you can only visualize the embeddings in 2D or 3D space. Contextually similar documents should be closer together in space as opposed to documents that are not as similar."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {
-        "id": "XODHZlFcFnn6"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "768"
-            ]
-          },
-          "execution_count": 13,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "len(df_train['Embeddings'][0])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 14,
-      "metadata": {
-        "id": "73aAdKo1UCrL"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(600, 768)"
-            ]
-          },
-          "execution_count": 14,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Convert df_train['Embeddings'] Pandas series to a np.array of float32\n",
-        "X = np.array(df_train['Embeddings'].to_list(), dtype=np.float32)\n",
-        "X.shape"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "JB3bsmi4Iuak"
-      },
-      "source": [
-        "You will apply the t-Distributed Stochastic Neighbor Embedding (t-SNE) to perform dimensionality reduction. This technique reduces the number of dimensions, while preserving clusters (points that are close together stay close together). For the original data, the model tries to construct a distribution over which other data points are \"neighbors\" (e.g., they share a similar meaning). It then optimizes an objective function to keep a similar distribution in the visualization.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 15,
-      "metadata": {
-        "id": "OpyoE-RVSzfe"
-      },
-      "outputs": [],
-      "source": [
-        "tsne = TSNE(random_state=0, n_iter=1000)\n",
-        "tsne_results = tsne.fit_transform(X)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 16,
-      "metadata": {
-        "id": "BbsWqQlxJHas"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>TSNE1</th>\n",
-              "      <th>TSNE2</th>\n",
-              "      <th>Class Name</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>-1.212522</td>\n",
-              "      <td>-22.677013</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>3.995803</td>\n",
-              "      <td>-11.590203</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>9.159574</td>\n",
-              "      <td>-10.091983</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>-5.450058</td>\n",
-              "      <td>-15.795723</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>7.922492</td>\n",
-              "      <td>-10.505751</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>595</th>\n",
-              "      <td>0.272435</td>\n",
-              "      <td>35.359333</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>596</th>\n",
-              "      <td>4.217656</td>\n",
-              "      <td>24.452595</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>597</th>\n",
-              "      <td>4.366329</td>\n",
-              "      <td>20.885956</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>598</th>\n",
-              "      <td>-1.971566</td>\n",
-              "      <td>24.719038</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>599</th>\n",
-              "      <td>12.387172</td>\n",
-              "      <td>25.825365</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>600 rows × 3 columns</p>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "         TSNE1      TSNE2 Class Name\n",
-              "0    -1.212522 -22.677013  sci.crypt\n",
-              "1     3.995803 -11.590203  sci.crypt\n",
-              "2     9.159574 -10.091983  sci.crypt\n",
-              "3    -5.450058 -15.795723  sci.crypt\n",
-              "4     7.922492 -10.505751  sci.crypt\n",
-              "..         ...        ...        ...\n",
-              "595   0.272435  35.359333  sci.space\n",
-              "596   4.217656  24.452595  sci.space\n",
-              "597   4.366329  20.885956  sci.space\n",
-              "598  -1.971566  24.719038  sci.space\n",
-              "599  12.387172  25.825365  sci.space\n",
-              "\n",
-              "[600 rows x 3 columns]"
-            ]
-          },
-          "execution_count": 16,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "df_tsne = pd.DataFrame(tsne_results, columns=['TSNE1', 'TSNE2'])\n",
-        "df_tsne['Class Name'] = df_train['Class Name'] # Add labels column from df_train to df_tsne\n",
-        "df_tsne"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 17,
-      "metadata": {
-        "id": "z4N7d8MlpVCS"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(-39.6663013458252, 42.38134346008301, -35.120894813537596, 38.98466053009033)"
-            ]
-          },
-          "execution_count": 17,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": 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TjIyMgGs3c+ZMPvzwQ/+YR44cyR9//MGPP/7oD3wGDhzIvn37eO+99xgyZIj/d/jII4/QpUsXZFnGaDRy8cUX+wOuAwcOBB3H4XbU2dnZ/m07d+4M+N1NmzaNRo0a4XQ60Wq1DBw4kPfff7/G8x5Phw4dsFgsLFiwIGD7/Pnzj/vYY3+fx2Zp0tPTUalUJCcn89FHHwX9O8zMzAQIGQwBpKam8uqrr7Jy5UoWLlxYm5clCIIgnCEiQyQI5xmVSsW7777LqFGjuPDCC7nvvvto1aoVNpuNJUuWMH36dF544QUiIyMBmDRpEvfeey9xcXFceumlZGZm8uyzz3LfffcRGRlJ165dmT17Np07dyYlJYVff/2VyZMno1Ao/HM8LBYL4Jvr0qpVK7p16+bPCH3++ed0796ddu3aMXr0aO666y7279/PhRdeSGZmJk8++SSNGzcmIyPjpF7vrbfeyosvvkhcXBxTpkyhqqqKp556qtpxSqWSyZMnc9ttt3HDDTdw0003UVRUxMSJE4mKimLcuHHAkUzW999/T2RkJO3btw/6nG+//TajRo3iueeeo3Hjxnz77bd89NFHPPvss/5z1JVx48Yxc+ZMBg8ezCOPPEJ0dDTz5s1jxowZAa22Bw4ciEql4vvvv/cHfp07dyY8PJw1a9bw4Ycf+o8dNGgQr7/+OrfeeiujR4/G5XLxyiuvEBUVxcCBA4OOY8CAARgMBv773//y/PPPY7VaefbZZ/3Zt8NjeOyxx7j88su57777UKvVvPfee+h0On+53okKDw9n/PjxPPPMM/4s18qVK3n33XeBIyWGwURERLB27VqWL19Ox44d/X/3x3rzzTfp378/nTp14sEHH6Rjx454vV5WrVrF1KlTueeee467ftJdd93Fl19+ydKlS4P+DWzatClohhJ8pYbByjAFQRCEOlCvLR0EQag3GzZskK+77jo5JSVF1ul0stlslvv37y9//fXX1Y795JNP5DZt2sharVZu0qSJ/MILL8hut1uWZVnev3+/fPHFF8sWi0W2WCxyly5d5FmzZsnDhw+Xu3Tp4j/HuHHjZJPJJEdGRsoul0s+dOiQ3KVLF1mj0chjx46VZVmW3W63/Nxzz8lNmjSRNRqNnJKSIo8dO9bflU2Wa98V7HCnrwULFsjp6emy0WiUhwwZIv/111/+Y4J1Rfvqq6/kzp07y1qtVo6JiZFHjx4tZ2Vl+fd7vV75+uuvl/V6vdymTZuQz19YWCjfcccdcmxsrKzT6eT27dvLH374YcAxte0yd+wxwbr27d69W7766qvlyMhI2WAwBH0+WZb9LdFzc3P920aMGCErFAo5Ly8v4Ng5c+bInTp1ksPCwuTw8HD5oosukv/+++8ax/vjjz/K7du3l7VarZyRkSHPnj1bHjZsWEAnwsWLF8u9evWSzWazbDQa5b59+8orV6707w/WZe7ZZ58NeJ5juxRKkiS/8MILcmpqqqzVauU+ffrIU6dOlQF5w4YNIce7fPlyOS0tTdZqtfLs2bNrfG3//POPfPvtt8uNGzeW9Xq9bDKZ5K5du8ozZswI6Lx37NiOduDAATk8PPyEuswB8tSpU2scmyAIgnDyFLJ8ArOeBUEQzhKHF38Vb3HnPo/Hw5w5cxgwYACpqan+7W+//TYPPPAAxcXFdZ6VEwRBEM4domROEARBOKup1Wpefvllpk2bxlNPPUVMTAxbtmzhqaee4uabbxbBkCAIglAjERAJgiAIZ71FixbxxBNPMHbsWMrKykhLS+Phhx/miSeeqO+hCYIgCA2cKJkTBEEQBEEQBOG8JdpuC4IgCIIgCIJw3hIBkSAIgiAIgiAI5y0REAmCIAiCIAiCcN46b5sqSJJETk4O4eHhKBSK+h6OIAiCIAiCcAxZlqmoqCApKanGRZYF4VSctwFRTk5OwHoVgiAIgiAIQsN08OBBUlJS6nsYwjnqvA2IwsPDAd//YGazuZ5HIwiCIAiCIBzLarWSmprqv28ThNPhvA2IDpfJmc1mERAJgiAIgiA0YGJ6g3A6iWJMQRAEQRAEQRDOWyIgEgRBEARBEAThvCUCIkEQBEEQBEEQzlvn7RwiQRAEQRAE4ewnyzIejwev11vfQxEaEJVKhVqtrtX8MxEQCYIgCIIgCGcll8tFTk4OVVW2+h6K0ACFhZlITExEq9XWeJwIiARBEARBEISzjiRJ7N27F1lWEBERjVqtqe8hCQ2Ix+PGai1j7969ZGRk1LiwrwiIBEEQBEEQhLOOy+XC65WIjo5Hp9PX93CEBkar1aFSqSkuzsflcqHXh/4bEU0VBEEQBEEQhLOWWKNICKW2fxsiIBIEQRAEQRAE4bwlAiJBEARBEARBEM5bIiASBEEQBEEQhJNktVp5443Xufzyi+nXryfXXnsFc+fORpIk/zHdu3diw4b1Z2xMixZ9S/funfj993XV9o0dexczZrx3xsZyNhABkSAIgiAIgiCchPLyMm6//SZ27tzOhAnP8PnnX3LnnXfz6acf8frrU+p7eLz66ku4XK76HkaDJ7rMCYIgCIIgCMJJePvtt9BqtUyb9jY6nQ6ApKRk9Ho948eP45prriMtrVG9jM1sNlNSUsLMmZ9wxx1j6mUMZwuRIRIE4bzidki47GI189PB65Gw5rkoy3ZRWeBCluX6HpIgCMJp43K5+PnnxVx11bX+YOiw3r37Mn36eyQkJFZ7XEFBAU888ShDhvSjT59u3HzzDWze/Jd//7x5nzNq1Aj69u3OrbfeyF9/bfLve/fdtxg5cij9+vVg7Ni72Lt3T8jxGY0mxowZy2effUx29sGgx8iyzCeffMjll19M795dufjioXzwwf/8+8eOvYvZsz/j/vvH0q9fD26//SYOHsxi8uTnGTCgF1dddRkbN27wH79nz27+858x9OvXg2uuuZyvvvriuNexIRABkSAI54WqEg8Hfq9kxWu5/DIlj39+KaeywF3fwzpnVBa62fxlCd8/kc23j2Tx4zOH2PWzFXupp76HJgiCcFocOpSNzWajdevW1fYpFAo6d+6CVquttm/ixKeQJIkZMz7hs88+Jy4ujlde+T8AMjN3Mn36NB599Anmzp1Phw4dmTDhMSRJYsWK5XzzzQJefPFlZs/+gujoaF54YWKNY7zqqmto1Kgxr776ctD9P/ywiHnz5vDkk0/zxRffcPvtY/jgg/+xc+cO/zEffjiDUaOu4JNPZlNZWcntt99EdHQMH388i6ZNm/H6668A4HA4ePjh+2nfvgMzZ87j/vsf4qOPZvDjj4tqe0nrjSiZEwThnGcrcfPruwXkbbH7t+VvtxORomXA+ETC4xr+6uZ2qwen1UtBpgO3QyK+pQG9RUVYTP2PvarEzYbZRRxYV+XfZi/z8vuHhbiqJFpfHIFKLdYJEQTh3FJRUQGAyRRW68fIsky/fv0ZMGAQcXHxgC9oGTfuAQByc3NQKBQkJCSSlJTE3XffS69efZAkidzcHDQaNQkJCSQkJPLf/z7GgQP7a3w+lUrF+PFPMGbMbSxf/jMDBw4O2J+QkMBTT02kS5duAFxxxVV8+OH77Nu3h5YtWwHQu3cfBg0aAkDfvv35+efF3HXXPSgUCi677Aoee2wcAEuW/ERkZCR33/0fANLS0sjNzWXu3DlcdNHFtb5G9UEERIIgnPOK9jgDgqHDyrJd7P+tgjaXRKBUNdyEua3MQ/aGKn7/qBD5qGq/Rj1MdL4hhrDY+g2KXJVSQDB0tC3flNCouwlzQvVvSQVBEM5mFosFOBIY1YZCoeCKK65m6dLFbNmymf3795OZucPfka579x40bdqMG2+8hoyMlvTt24/LLrsCtVrN0KHD+eqrL7jiikto2/YC+vXrzyWXjDruc7Zt247LLrucadNeo3v3ngH7OnfuwtatW3jnnbfYv38fu3btpLi4CK/3SIe8pKRk/3/rdDoSEhL9C57qdDrcbl+1xf79+9i9+x8GDOjlP16SJFQqVa2vT31puHcAgiAIdcDjlNi1zBpy/55VFVQVN+w5RY4yD+s+CAyGAA6srSJ7QxWSVL9zday5oTsYeRwyrkop5H5BEISzVXJyCmFhYQHlZUd79NGH+eOP3wO2SZLEAw+MZc6cmcTHJzB69M0888xz/v16vYEPP/yMt9/+H506dWbRom+55ZYbKCgoIDo6hnnzvmbKlKk0bdqMWbM+4847b8HhqP6F37HGjr0ft9tVrd32woULeOCBsbhcTgYMGMhbb73nz1wddmxAo1AEDx+8Xg8XXtiFzz773P9v1qx5fPrpnOOOr76JgEgQhHOaLIPsDR0wyNK/BzVgu1dWQIgh7vipnKqi+p2nozXV/O2fSivK5QRBOPeo1WqGDBnGV1/N82dJDlu9eiWrV68kNjY2YPu+fXvZtGkjb731Hrfeege9evWhqKgI8JXTbdmymU8//YjOnbvw0EP/5YsvFuByOdm8eRO//rqahQu/oVevPjz22JPMmjWXrKwD7N69+7hjNZvN3HffQ3zxxVwOHcr2b1+w4Ctuv/0uHnroES666GIiIiIpKSkm5IdODdLS0snKyiIpKZnU1DRSU9PYunULX34574TPdaaJgEgQhHOaRq+kSe/wkPtTu5hQNuD5LbIkYysOHfDYyzzI9ZwhMsWo0VuCB0WxGXp04eKjRhCEc9Odd95NVVUVDz54Lxs3biA7+yDffvsNzz//LNdeez2NGzcJOD48PBylUsnSpYvJzc1h+fKf+eADX9bG5XKh0+n58MP3WbhwATk5OSxduhi73U6zZs2RJIm33prKihXLycnJYdGib9Hr9bVu6z1y5CW0a9eOgoJ8/zaLxcKff/5OVtYBdu7czlNPPYbH48HlOvGmQ8OHj8DhcPDSSy+yf/8+fvttDVOnTiEyMvKEz3WmiU8pQRDOebEt9ESl66ptN8WoadI7nPKchrtonUKpILGdMeT+mKZ6NIb6fSs3RKno93ACGmPgOEwxanqMicMYWf+NHwRBEE6H6OgY3n//I5KTk3n22QnceOM1zJ07m7vuuocHHhhX7fi4uHjGj3+CmTM/4YYbrubTTz9i3LjxqFRqdu3aSUZGCyZMeJbZsz/juuuu4NNPP2LixBdo3LgJffr04667xjJt2mtcd90V/PzzEl55ZSpms7nW4x0//knU6iMtBB5++FGqqqq46abrePzxR2jWLIN+/Qawa9fOE74WJpOJqVPf4uDBA9x88/VMnvw8V111LbfccvsJn+tMU8jn6UIRVqsVi8VCeXn5Cf0hCYJw9rGVunFVSmRvtLH31wpkj0zKhSYa9whj37pKHOVeet0Tf/wT1RNrnoufnj2EozxwEpFCAcMnJRObYainkR3htnuxlXop2u3AmusmuqmOiBStaKYgCMIpqel+zeFwsGfPXmJiEtBqq3/pJQgul5OiojyaNm2CXq8PeZzoMicIwrlPVrBiah6thlvoMSYOZBlnlZd1HxZStNvJBVc07HS+OUHL0KeT+PPTInL/7ZZnTtLQ9dYYLCkNI+DQGFRYDCosSQ1jPIIgCIJQWyIgEgThnKe3qGjSO5zfPyoKuj+9Z+g5Rg1FRIqOPg/E47RKSJKM1qjEFC1K0QRBEAThVImASBCEc55SpaBZfzO5W2zk73AE7Ot+Zyym6LPjrVAfrkbf8GM3QRAEQTirnB13AYIgCKfIGKWm70MJVOS7ydlsQxumIrm9EUOkut6bEgiCIAiCUH9EQCQIwnnDYFFjsKiJawBNCARBEARBaBjE16KCIAiCIAiCIJy3RIZIEAThFNjLPEheGaVagcEi3lIFQRAE4WwjPr0FQRBOgsPqJW+nnaJddlxVMo4yD80GmIlvZUAXrqrv4QmCIAiCUEuiZE4QBOEEeRwSJfsdSE6ZynwPthIPCe2MSF6ZA79X4nVL9T1EQRAEQRBqSQREgiAIJ8he7uGf5RWseTufrD+ryNlsY/1nRWz+sgRjtBp7mbe+hygIgiCcQxYt+pZRo0bW9zBYv/4P9u3bW9/DqHMiIBIEQThBlQUeDqyrrLa9PMdN7hY7HqfIEAmCIAh1Z/DgoXz88az6Hgb33XcPJSUl9T2MOicCIkEQhBMgSzK7V1hD7t+/tgIUijM4IkEQBOFUyJKEe/dOnBt/x717J7LU8L7U0uv1REZG1vcwzlkiIBIEQTgBMuB1yyH3Sx4ZlWhXIwiCcFZw/r2BsufGY317CpUz38f69hTKnhuP8+8Np/V55837nFGjRtC3b3duvfVG/vprEwDbt29jzJjb6devJ1dfPYqlSxcDxy+ZW7v2V26++Qb69evJ6NHX8uefvwMwY8Z7jB8/jnvuuYOhQ/vzwQf/Y+jQAXg8Hv9jly9fxmWXjUCWZUaNGsncubO58cZr6N+/J+PGPUBxcRGA//nvvXcMM2a8d1quS30RAZEgCMIJUCoVNOsfHnJ/WtcwDJFnf0TkcUlY89z8s7ycLQtKKMi0Yy/zHP+BgiAIZwnn3xuo/PgdpPLSgO1SeSmVH79z2oKizMydTJ8+jUcffYK5c+fToUNHJkx4jJKSEh54YCwZGRl89tkcbrnldp577hn++WdXjefbu3cPjzzyMP37D2TmzLkMHTqc8ePH+QOZVatWMHToRUyf/j+uv340TqeTDRv+9D9+2bIlDB48BMW/1Q0zZvyP0aNv4YMPPsXhcPD4448C+Ev2Jk+ewo033nw6Lk29Ofs/tQVBEM6wqHQ9Mc11FP3jDNiuC1fS9tII1Nqz+7smj1Pi0F82Vr2Rh3xU5UhMcx39Hk7AFKWpv8EJgiDUAVmSsM3/vMZjbAvmom3bEYWybt/Tc3NzUCgUJCQkkpSUxN1330uvXn1YunQxZrOFcePGo1QqadQoHavVitPpqPF83377DRdc0J7bb78TgJtvvg273U5FRQUAUVHRXHHFVf7je/fuw7JlP9OtWw8cDju//baGd9+d4d9/ySWXctFFvmzQU089yxVXXMKePbtp2rQZAGazBaPRWKfXpL6d3Z/agiAI9cAYpab/w4l0uTUGc6IGY7SaViMsjHghlbC4sz9YsJV6WDUtMBgCKPrHyY4fys7btuIet4Q110XxXgdlh1y4bKKboCCcrTx7d1XLDB1LKivBs7fm7MzJ6N69B02bNuPGG6/h5ptvYPbsz0hPb0JW1n4yMlqgPCoAu+GG0bRte0GN58vKOkDLlq0Ctt19939IT28MQGJiYsC+oUOHs2rVL3g8Hn79dQ0xMbG0bNnav/+CCzr4/zspKRmz2cL+/ftO9uWeFUSGSBCE84bHKWEv92Iv86BSK9BHqDBGqv1lAifCGKWm5TAL6d3DkGXQhSlRaU79OyavW8Je5sVZ6UWlUaI3K9Gbz+xbdc5mG3KIaVK7frbSangEppgz832axynhqPAie0FjUKI318+it1VFbvaurmDrt2W47RIoILm9ka63xRAer62XMQmCcPIka3mdHnci9HoDH374GZs2bWD16lUsWvQt8+d/Re/efU/qfGp1zZ8RWq0u4OcePXrh9XrZtGkDy5f/zODBQ2s8nyR5USjO7RyKCIgEQTgvOCq8/PNzOZu/LkH6dyqMIUJFv3EJxDTRo1SfeFCkUCgwRNTd26ijwsueFVY2f1WCx+mLSKLSdfS5Px5L8pm76baVhJ4r5HHI1TJHx+O2HwlE1ToFBosaY9Txr1tlkZu/vy5h7+oKJA9ENdbR9bYYohrpUOvO3Iezy+5l39pKNs07qtWsDIf+srF8Sh6Dn0wUZYSCcJZRmi11etyJ2LJlM+vX/8ltt91J585d+M9/7mfEiMHExcWxceN6ZFn2f1E3YcJjtGrVmoiI0B3mUlNTyczMDNh21123cs011wc9XqvV0r//QFau/IU//ljHHXeMCdi/a1cmffv2B+DgwSwqKytp1qz5Kbzihu/cDvcEQRD+lb/NzqZ5R4IhAHuZl6XP51BV3DCaBRzaWMWG2cX+YAigZL+Txc8doqrIfcbGkdgudG14ZCMtal3tg0d7uYeNc4tZ+PABFk88xPdPZPPjM9mU7Hcih0pDAVUlHn7+vxx2/1Lh/52V7HOy+NlDlB101fr564K91Mu2b4OX1pRnu6jMbxh/P4Ig1J66SQZKS81trJURUaibZNT5c+t0ej788H0WLlxATk4OS5cuxm63061bd6zWcqZPn0ZWVhaLFn3L6tUr6dq1e7VzlJaWYrPZALj88qvYvHkTc+bM4uDBLD799CP27t1Lhw6dQo5hyJDhfPfdQuLi4mnSpGnAvi+++JxVq1byzz+7ePHFSXTt2p20tDQADAYDe/fuprKyog6vSP0TAZEgCOc8e5mHv74sDrrP65Y5uKH6Iqu1YSvzYM1zUVnoxuM6tXk1thIPm74Ivtido9xLyX5n0H2ngyVZS0TakYyUUgVp3Uy0uTiCXv+JQ2+pXVZMkmT2rq4gc3F5QAleVZGHJc8foqqoeiDhsnupyHNRftBJp+uj6Xl3HOGJR7IvsgwbZhXhrDxz83fcdglnRejfb8mBM/e7EQShbiiUSoxXBM+gHGa8/Lo6b6gAkJHRggkTnmX27M+47ror+PTTj5g48QXatWvPa6+9waZNm7jxxquZOfMTJk16kYyMFtXOcdtto5k9+zMAUlJSmTx5CosWLeTGG69h+fKfefXVqcTGxoYcQ+fOF2I0GquVywGMGHEJ7777FmPG3EZ0dAwvvPCSf98111zH9Olv8MEH/6uDK9FwKOSavqI7h1mtViwWC+Xl5ZjN5voejiA0GLIsYyv14Cj14nZIGKPV6M0qtMb6mbtRF6qK3Sx44ADSMffQ4QkaWg23YE7WYrCo0IWpalXK5bJ5Kch08OdnRVTkulFpFDTtF067yyMxRZ9c6VRFgW+MobS7IpKO10Sf1LlPRlWRm78XlFBV4qX1CAu2Eg9KlQKFUkFkmhZTjAaNvuYbhapiN4sePxgymOj3cAKNuoX5f7aXe/h7fim7lhwJoIzRarrfEcvGucWUZf2bGVLAlW81whRzZsrUSg86+eHJ7JDrTw14JJHUC01nZCyCcL6p6X7N4XCwZ89eYmISqs2TqS3n3xuwzf88oMGCMiIK4+XXobug8ymNvSGrqqpkxIihzJnzBcnJKf7to0aN5M477+biiy+tx9HVHZfLSVFRHk2bNkGv14c8TswhEgTBT5ZkSvY7WT4lF3vpv9GDAjIGmWl/dRSGWmYG6pvbIeFxSqh1SjR6JSq1AnOy9sgNNRDXUk/rERFs/LwYa66vHM0Uq6bn3XHEZuhrbJ1dkOlg+cu5/p+9bpldP1sp3utkwPhEjCcxr0ip8rXtDhU8nMk5RACmGA1dbo2l7IATe7nE/rVVlOxzYopW03ygmch0mahG2hobSUgeasyslB100ajbv8celU06mq3Yw+rp+fS6J44Vr+cBoDMpUShPfM7XydKZlDTuHcbuX6qXiGhNSiJSRVMFQThb6S7ojLZtR1/XOWs5SrMFdZOM05IZaghkWeaXX5bxyy/LaNfugoBg6Hx2dtzdCIJw2kiSjKPciyzJSB5Y8nyOr4vWYbKvs1h4vIZWIyNQnsEb0RPldkhYc1xs+aaUsmwXEcka2l4ehSVRS8dro9mz0kpalzCUGgWmaBWLn8tBOupb/6pC37yVi19KJTIt+LeNtjIPf35aFHRf8V4nlfnukwqIDJFq2l4WyYZZ1Uv7NAYFcc1Df7N1urhtvsVZf327wJ+xcZR7Kd5bSIuhFgyXRBAWG/qmQalRYIhQYS8LXt4W1fjINbaXeti6MPg8HbdNwlbiISxOTWWBh5bDLegtZy5jaYhU0+aSSKoKPeRutfu3680qBo5PJCxOfJQKwtlMoVSiadayvodxRigUCqZPfwOlUsmrr06r7+E0GOJdXBDOU26H7yazZJ8Tr0vCGKOhqtAdGAwdZeu3paT3DDvpkrDTTfLIvsVEp+X5t1lz3GT9aaPPA/HEZejJiVSz7oMCEtoaMEWrA4Khw2QJtiwspeeYuKCdzDwOiYq80A0OCjIdxLUwnPD4lUoFTfqEU5HnZtcyK/w7NEOEigGPJqJQQVm2y9elLUJVJy2+j8dVJbFpXknQFtyZS8vJGFxzubExUsUFV0Xx+weF1fbpLSqi0n2ZFY9LOu48nYoCN4YItS9DNciCUnXmAnOFQoE5QUP3MXE4yj2U57gxRqoIi9MQHq85qbbtgiAI9WX+/O9C7vvmm+/P4EgaDhEQCcJ5yFnpZfcvVjbOLUb+98v7+FZ6YmvIQjgrpIAObQ2NvczDuhkFQfetm1FI7/vj/eVY4fFainaHXvm7eI8Tt10KGhApVQpUWgWmaN/bZ0WeOyBgMEScfObCYFHT6YZoWl8cQVWRB41BiS5MyYE/Kvn7q1I8Thm1XkGriyJoOcxSpy2/g/E4JaoKQ/zSZV/JW6hMGvgCiUbdTDjKPWz9psw/ByciTUu/BxMwRWuQPDJ52+zYSz0YIlVHSjWPEd1ET5Pe4Rij1fVSuqlQKgiP0xAepyG2+YkHvIIgCELDJQIiQTgPlR10smF2YGlWRYGH9J6hsz/GKBUqTcP9Jtxh9eKqCp5hcNulgGyQw+rBFK2met7CJyxGjUob/LWq9QqGPJlE/k5fQBXZSEv2hip2/WxFqeKkskNH0xp9DSzMCVoqCt1s/qKYvauPdMHzOGS2LCjFWeWl8w0xx21scCqOl4WpTfttfbiv3KxpHzOOCi8qrQK9WeUPamxlHla/mU9EqpZWF0WwcU71kkG9RUVcSz1hZ6iJgiAIgnB+adAzxnbv3s2wYcMICwsjLS2NKVOm+Pft27ePwYMHYzKZaN26NUuWLKnHkQrC2cNl87JlQfW5GrZiD7owZcgMR4drojBEnr2d5jjq3v3gn1U07hUW8tB2V0QG7arnrPCy44dyfpp4iE1zi9k0t5jlL+ei0ii44MpI+j+aiDGqbq5RVbEba46LfWuCtwT/52crjvLTm7IzHFXWdiylRlHrZgJqrZKwOA0xTfVEpuoCMjy2Yg9uu0ThLl+A2eaSiIBgNCJNy9BnkkUwJAiCIJw2DTYgkiSJkSNHEhsby6ZNm3jvvfd44YUXmDNnDrIsM2rUKBISEli/fj033XQTl19+OVlZWfU9bEFo8Lxumcog678AbJxbTJ/74wNugtU6BZ1uiCalc1iDniuht6jQhQd/S9OFKQMyRB6nzMENVVx4UwzKo7JeChV0Hh0dsgysJMsZNJjc8WM58a0MJLYx1NncnpJ9Ttx2Oej8HfDNdappzk1d0FvU9Bwbh8ZwzO9dAT3HxGGIPPUiA8l75AVunFNMRYGbPvfF0+/hBAY8kki/BxOIOMMd9gRBEITzS4MtmcvPz6dDhw68++67hIeH07x5cwYNGsSaNWtISEhgz549/Pbbb5hMJlq1asWyZcv46KOPmDhxYn0PXRAaNI1eSWwzPdac6o0BKvM9ZG+yceHNMejCVUhuGV246oxN4j8Vhkg1vf4Tzy+v5AYEEQoFdB8Tx/ZFZQHH7/6lgpROXoY9k4TLJoEM5iQtBrMKdZAyNLddYluILmgA2xeVEdMsAVUdJTJytthIalfz2ja1KVk7VZGpOi5+OY39ayvI3+4gPF5NxmALYXGaGluTA9hKPVQWuCk94CIsTk1EihZjtDogsDZGqVGq8K8RlfV7FVm/VwG+NuQXv5R62l6bIAiCIEADDogSExOZN28e4OuZ/ttvv7Fq1Sreeecd1q1bR6dOnTCZjtws9O7dm7Vr19bXcAXhrKHWKWlzSQR7f63wN1Q4TKlRkNzBSFic5oyWKMmyLxNyKi29lUoFCa0NXPJKKjt+LKM0y+Wfl2KIUGHNMfrLsg5zWL2YojXENj/+W6HXLYVsHw1gL/PidUt1NqcnLEZDZaEbS4qW8mxXtf1R6doz0nr6cDOBtpdG0mq4jFKjqNXvqbLAzbKXcyg/dCTwNliUDJ2YgkqtQPLKqNQKKvNctL4kkq3fVA82u90eWydZKEEQBEGoyVnxSZOenk5WVhYXX3wxV155JQ899BBJSUkBx8THx5OdnR3yHE6nE6fT6f/ZarWetvEKQkMXnqBhyIRkfvtfPpX5vvI5c5KG7nfGYk7UYjxDN6HOCi/WPBe7frbitkuk9wwjqrEOU5T6pDJSap2SiFQdXW+L9XVk0ylQaZS47RLRTXUMHJ9I7lYbbrtMfGs9WqMSRS2fRmNQktDGQGlW9eAEIKGtHo2h7gKU1AtNLHn+ED3vjmftjAKqjipzDItT0/ehBPTmM/cWrlAoap2RclV5WfdBQUAwpNYp6H5XHNsWlrLv10q8bpmwODVtL40kurGOnvfEsevncioLPFhStLS6yEJMU12DXvdKEAThTFm06Fs++OB/J9UW+7nnngXgmWcmndIYSkpK2LRpA4MGDTml84QyatRI7rzzbi6++NLTcv6anBUB0ddff01eXh5jx47l4YcfxmazodMF1vjrdLqAgOdYkydPZtKkU/tDEIRzhUqjJKG1geETU3BVekGh+Lehwpl7S3BWePnrqxJ/K2yArD+qiEjT0uueOMJiNejCTy7AUGmUAaVrVUVufv6/XJI7GEnrakKWYefiMop3u+h6WwwthlqOOz9KpVHSYpiFf5Zb8TgDJ/aodQoyBllQqevu5t0YpabrrbGs+6iADtdEoVAqqCpyY07SEpWuIzyu4TYZcFi95PxtJypdiyFSTWWBm4zBFrYsLKPonyNZusoCD+s+KKTbHbFkb6oi8QIjxgjf8es+KGTo00kYo+rxhQiCcF6QZIkCzy7sUjkGpYU4dQbK2n5bdoYMHjyUXr361OsY3n77DWSZ0xYQffzxLAyG+lnW4KwIiC688EIAHA4HN954I7fffjtVVVUBxzidToxGY8hzPPHEE4wbN87/s9VqJTVV1KYL5zdjpPqMZYOOZc1zBwRDh5Vludj3ayXJHQ0ktq15Dk1tHdxYxaDHEsn5287OH8vRGJU062ehWT/YtqiUtG5hGGsRDIbFahg+KYV1HxRQtNv3BUxMcx3d74gjrI4DFLVOSVIHI0PSkyn8x4HHLpHSOQyDRVmnmSG3XcLr9q1vdLw5QbUlSTIDxydSst9JZYGbhDYG4lsbfAvOBrH1m1LaXRHJuhmBjdAbchMPQRDODVnODfxp+xybdKRs16iMpIvxetJ0netxZIH0ej16fei1As+EUE1+6kpkZOTpfYIaNNiAKD8/n7Vr1zJq1Cj/ttatW+NyuUhMTGTHjh0Bx+fl5ZGYmBjyfDqdrlpWSRCE+iHLMv8sD122uu+3CiLStNjLPKectZJlmfiWBla8lofDemQOUMFOB6mdTbQcakH21u5dXqlSEJWuY9BjSTirfOfSmVQnnck6HrVWSXic8rRkg5yVXsqyXWz9ppSqIg8xGTrajIwkLK56uaKt1IPLJqFUgS5MhS4s9OuVJRlnhcTKaXl4XUeuq9ZUSu/74vn9w8KA8j+AqmIPWlPgOSNStWhDdA0MeB0VXhwVXjxOCa3R1wAk2IK6giAIx8pybmBl5TvVttukUlZWvkM//nPagqJ58z7n889nUlJSQpMmTXnooUfo0KEj27dvY9q018jM3ElcXBxjxoxlyJBhxy2Z++uvjUyb9hr79u0lJSWVO+64m4EDBwU9dsWK5bz33tvk5ubStGlT7rvvITp18r1Oj8fDjBnvsmjRdzgcDrp1685jjz3JF1/M5YcfvgNg48b1fPPN93Tv3onbb7+Lr7/+knbtLuDVV6exZctm3nrrDXbt2klkZBQ33XQrV1xxFeAr3TObzRQWFrBmzWosFgtjx97LRRddDASWzIUah8USwfr1f/DGG69z4MB+YmJiuemmW7j88qtO6ffRYD819u3bxxVXXMGhQ4f82zZs2EBsbCy9e/dm48aN2O12/741a9bQvXv3+hiqIAgnSga3PXSDAo9TRvbKeN2n/nWU1yWTubQ8IBg67OCGKsLjNWgMJ/ZWqAv3LZxqTtCetmDodHI7JHavsLJ44iEO/WWjLNvF7uUVfDc+i+K9R0qPPQ6JnL+r+GliNt/+N4tvHspixau5lB8KPo8KwFbqZeXrgcEQgKtKYsOsIlqNiKj2GKXK1w3wMJVWQc+xcRiOkwmrLHDzy+u5LByXxfdPZLPwvwfYNK8Ye9npXZ9JEISznyRL/Gn7vMZj/rTNRZLrfnmDzMydTJ8+jUcffYK5c+fToUNHJkx4jJKSEh54YCwZGRl89tkcbrnldp577hn++WdXjecrLi7iv/99iJEjL2HWrHmMHn0LL7zwLH/9tbHasf/8s4vnnnuW2267k1mz5jJs2Agefvh+Dh70LV3z/vvv8sMPi3jqqWf54INPKCkp5qWXXuTGG29m0KAhDBo0hI8/nuU/35o1q3j//Y+4994H2LdvL/fddw8dO3bk00/ncOedd/Pmm6+zYsVy//FffTWPli1bMWfOFwwYMJCXXvo/Kisrqo0z1Di8Xi9PPvkYAwcOZu7c+YwZM5YpU15i3769J/vrABpwhqhLly507tyZ22+/nalTp7J//34effRRJkyYQL9+/UhNTeW2227j6aef5rvvvuOPP/7g448/ru9hC4JQCwqlgiZ9wjmwriro/uT2RsoOOknueOolc85KLwd+D/48ADl/20i9sPoirV6PjL3UQ2WhB8kjEx6vQW9R1VkXufrkKPOycU5xte2SF377XwHDnknGEKGmLNvFz5Nz4ajYJn+ng58mZjPy/1IJi62eubKXeYIGnwDlh9yEx1X/2EnvGY6jwktcCz1xLfU0G2AOeu6j2Uo9LHslN6ADn+SBHT+Uo9EraXd5ZINvFS8IQv0p8OwKKJMLxiaVUODZRYKmZZ0+d25uDgqFgoSERJKSkrj77nvp1asPS5cuxmy2MG7ceJRKJY0apWO1WnE6HTWe76uvvqBLl65cffV1AKSmprFrVyZz586hQ4dOAcfOnv0Zl112OcOGXQTAtdemsWnTBubP/4oHHniYhQsXcP/9D9GjRy8AHntsAj//vASj0YhO5yvZO7q0bdSoK2nUKB2AadNeIyOjBWPH3g9Ao0bp7N+/j1mzPqV//4EANG+ewU033QrAmDFjmTfvc/bu3csFF7T3n1OW5ZDjqKysxGotJyoqmqSkJJKSkoiNjSU6OuaEfw9Ha7ABkUqlYuHChdx333306NEDk8nEAw88wAMPPIBCoWDhwoXccccddO7cmWbNmrFgwQLS0tLqe9iCINRSdGM9kelaSvcHZhs0BgXNBpipLHRjiKiL7IuCmmaiBJs363ZK5G6xseatfH8DBYUK2l8VRYvBlrMyK3S00oNOQn3pac1x46z0otIo2Ph5cUAwdJizQiLnbxsZgyzV9h3bcOJY0jGxUkQjLR2vjUJvUdG4RxhqvRKl6vhzh6qKPEHbkQNs/77s36BKBESCIARnl6rPYT2V405E9+49aNq0GTfeeA0ZGS3p27cfl112BStWLCcjowVK5ZH3rhtuGA3A/v37Q55v//59rFmzigEDevm3eTwe0tIaBT122bKlfPPN1/5tbrebbt16UFZWRnl5GS1btvLva9y4CXfddU/I5z56usr+/fto06ZtwP527dqzYMFX/p9TU4/cq5tMYf6xHu1447jiiquZPPl5Pv54Br179+Xiiy/DbDaHHGNtNNiACCApKYn58+cH3desWTNWrlx5hkckCEJdMUapGfhoIrtXVPg6tzkkktobadbfTEmWg6Z9zHUyqV4XpiS9Zxh7VlZPyQM07lX9TbSq0MPK1/ICJpDKXvhrXglR6TpS6iBzVa+Od1kVCtwOieK9ob+VzNlso9kAc7W22KZoNQolQQMujUFBRKqW3vfHYyv2EJuhJzxe42/sodKAxylRVezB45BQ65UYLMHnBFnzQpfteZwyHkfdl7kIgnDuMCirf6FzKsedCL3ewIcffsamTRtYvXoVixZ9y/z5X9G7d9+TOp/X62X48BHccsvtAdvV6uq3+V6vl5tuupWLLhoZsF2n0wc9/ni02iPz84PN1ZckL5J05P1Yra6e/ZeP6dZwvHGMH/8EV111DStX/sKqVStYsGA+U6ZMpWfPXjU+ribi6zNBEOqNKVpD21ERDH06ieGTkml7WSTh8RpaDI7AYKmb72vUOiUXXBEVdBHTxr3DCIsPfB5JkvlneXnIbjp/f12CsyL0/KdTceyHwukSmapDESLJZUnRogtTolQramxoEZ6gCbpGkN6iovXIiKCP6XhdNGFxGpr0Cqf5QDPmBA26sCMfQ7ZSD+tnFrHw4QN8N/4gCx8+wPqZRdhKq88JqqmkTqlRiMYKgiDUKE6dgVFZc1czozKKOHVGnT/3li2b+fTTj+jcuQsPPfRfvvhiAS6Xk7i4OHbv/ifgs2DChMeYNevTGs+XltaIgwezSE1N8/9btWolixf/GPTYnJxDAcd+88181q79lfDwcCIiIgLmLO3alckllwzH4XBwvO8o09IasXXrlmNe699BM1U1qWkcRUWFTJkymZSUVG677U4+/ngWXbp0YfXqU0uSiE8MQRDqlUqlxJygJSJFR1Qjna/JQR3P0wmP1zDihRQ6XhdNdBMdCW0MDByfyIU3xVSbuC955IAFRY9VWeipk2YPAecscrNndQWr3shjw+wiyg46cdlPX4bDYFHR5ebq9dZKjYKed8dhsKgxWNS0vSwi+AkU0LRv8PIEjV5Jm0si6HFPHKYY37U1J2noPy6Bxr3CcVV5yfqzkuWv5PLTpENsmFWMNdeF0+Zhw+widv1s9ZfVSV7Y9bOVDbOKcNkCg9DwODWm6OABW/MB4ejrpNxSEIRzlVKhpIvx+hqP6WK87rSsR6TT6fnww/dZuHABOTk5LF26GLvdTrdu3bFay5k+fRpZWVksWvQtq1evpGvX6k3DSktLsdlsAFx55dXs3LmD9957m6ysLBYv/pH33ptOQkL17svXX38jS5cuYd68z8nOPsjcubOZO3e2f9rJNddcz/vvv8uGDX+yd+8epk6dQrt2F6DX6zEYDOTm5lBQUBD0dV155dX8888u3n33LbKyDvD999/x9ddfcNVV15zwNQo1DoslghUrfmHatNfIzj7Ipk0b2LVrFy1atDjh5zhagy6ZEwRBqCthsRraXBpBxmAzCiVojcFvmFUaBXEt9ORstgXdH9VIi1p//FI+t0PCXuYhb5sdj0Mmvo0BU7Qa/THzj6x5LhZPOoS99MgN/7bvyug5No5G3U1odMe/sZe8vgYQzsp/W2ObVTVm2NR6JU16hxPdRM+270qpLPQQ11JPiyEW/3pKlQVuNHoVjXuHsW9Npf+xSjX0+k88YTGhz683q2ne30xyeyOyV/ZnmxwVHjbNLWb3L0fKF3fmlrN7hZXhk1LI+Tv4Nd/3WyXtr4oK+J0ZozQMnpDE8ldyqcg7EsCmdTXR7vKoOltTSRCEc1earjP9+E+QdYii6GK87rS13M7IaMGECc/y8ccf8NprL5OQkMjEiS/Qrl17XnvtDaZOfY0vvphLUlIykya9SEZGC3btygw4x223jWbkyEu46657SExMYsqUabz99hvMnv0ZsbFxPPDAwwwfPqLac7dtewETJz7PBx/8j+nTp5GcnMJzz/0fHTv6XuvNN99KRUUFEyY8hsfjoVevPvz3v48BMHz4SB577L/cdNN1/PTTsmrnTkhI5NVXp/HWW28wZ84s4uMTePDBcVx88WUnfI1CjUOj0TBlylSmTp3C6NHXYjSauOSSy7j00stP+DmOppDPVI1GA2O1WrFYLJSXl5/yRCxBEM4tFfkuvht/sHqDAAWMeD6FmGY1L47nsnnZ/1sl6z4sDGhKkNbVRLfbY/2laC67lzVv5ZO9sXogoFDAqGmNCI+vuduas8rLwT+rWD+zCJdNwpyowZykpcPVUUSmaY87D8vjlPC6fAuzHu7KZiv1sPTFHMoPuWhzcQTxrQ2UZ7tQ65RENdFiSdaiNdS8FhH4ugkereSAk0WPHQz6mMS2BqKa6Nj2bVnQ/Rc9n0Js8+rX3VbqwVHuxVXlxRCpRm+ueZ0kQRDOLjXdrzkcDvbs2UtMTELAXJYTJckSBZ5d2KVyDEoLceqM05IZEs48l8tJUVEeTZs2qXFhW5EhEgThnGMv8+BxyihVvjktJ9p+2RSjYegzyax5Ox9rji/7YIhQ0e3OWMzJ2uM+vqrIw7oPCqttz/qjisR2RloM8U3SdVolsjcFz4rIMhRk2o8bEBXtdvLbewU07h1G417hlGW58LplbMUetAYFYXE1j1etU6I+5j7icAc3Y5QKa66bsoMuKgrcuO0SbrvEpVPSggZEtlIPpVlO9qyoQKGGjEEWzIkaf7YqVNYNIHernYwhoScvh1oryhip9jdlEARBOBlKhbLOW2sLZxfxKSIIwjnDZfNSmOngz5lFWHPcqHUKmg8202ZkJMao2r/dKVUKYprqGfZMMo4KL7Lk61ZnjFRXy3oEs/sXa8h92xeVkdbFhCFC7cuk1JCjdx9nHpHD6mXT50VkDDFjjFSz/JXANYOa9A6j800xJ9ygorLARc974pBlyNtmQ6NXkTHYTP4OB9u/Lwvawc1W4mHVW3kU7DjSmW7f6krSe4bR5eYYDBHqGifkKhS+BVmDiW6iQ28W39YKgiAIp4f4hBEE4ZyRv8POspdz/Vkdj1Nmx/flrHorD3t59U5lNXHbJdwO2R+UKFWKWgVDkiRTVRT6uRxWL9K/JWUaoxJLSugMTlxLQ43P5XVLVBS4SW5v5K8vSqoFV3vXVJK71X7cMR8rKl3Pzp/KWPu/AvatqWTXz1Z+eTUPWZZpc2lE0A5uh/6qCgiGDtv/WyUlB5wAJHcwhnzO5I5GIlK01a6HJVlD34cS0JvF93eCIAjC6SE+YQRBOCfYSj38+VlR0H0FOxxUFXpqnSmxl3vY/FUJ//xs9bffNidq6P9IIhHHKZlTKhWkdjGR9WdV0P2xLfT+LnoGi5put8ey9PlD1dp8N+puOm5WS6lS0Lh3OAf+CP5cAFsXlpLU1oC+lq9d8srs+7WCkv3V1/nZ8UM5Q55KqtbBzWH1svOn0IsX7vypnPiWBgxRatpcGlFtnpAuTEnn0TGExWoYMiEJW6mHqiIPphg1xig1xhrafwuCIAjCqRIZIkEQzgkeh0RlfujMTOE/oRcZPZrXK/PPMiu7lloDghRrrpulzx+iqjh0S+7D4lsbMERWn2OjUEKn66IDuqXFNNUx4sUUEtsZUOsUhMWp6XZHLF1vja3Wke5Yhgg16T3CcFhDr4vkKPf621jXhqPcy66fQ5f8HVxfVa2DmyzJeFyha/88ThlJktGZVLS5JIJhzyaT2sVEbIaejtdFMfL/UrEk+QJNY6SamCZ6GnUNI6aJXgRDgiAIwmknPmkEQTgnKFQKFCqQQ9z8B1uYNRhHqYdti8qC7rOXeSnLdmGKPtLowOvxtbyuKvIgeWXCYjUYIlQMn5jC+lmFZK+3IcsQla6j6+0xmJMCmySodUqim+jp91ACboeEQqVA9shUlXiwlXjQW1QYItVBF0EFiEzTktDaQM5fwRsWxLXSozHW/rsvWa557pKzqvoF1oaraNTNxNaFZUEf06RXmL8Jgz5cjb6VmugmOiSPjNqgDPnaBEEQBOFMEAGRIAjnBINFReOeYexdXVltn1LNcVtlH+ZxybhtoQOC8kNuktv/e6xT4tBmG7++ne9v0a1UQcfro2nW30yv/8TjqpCQJBmtUVnjPBitSYVCpSB3i43f3ivAVeUbgy5cSc974khoa0QTZO6OzqSmUfcwtn1XirMicNwKFbS/IuqEFrrVGpUkdTBw8M/gAVbjnuHVtqlUCpoPsrD7l4pq2arweA1JQeYOqXVKOPkuuYIgCIJQZ0TJnCAI5wS1TkmHa6OxpAbO8VGqYMAjiRiDlLAFP48CrSn0W2PEUZP+KwrcrJyaF7BekeSFDbOKKd7nRGtQERanwZygrVVTgIo8Nytez/MHQwDOCokVr+ZRmR+6VC88TsPwSSkkXWCAf5MtkY20DH82GXNizW27j6UxKOl4bXTQjm+RjbRENQoexYTHabjo+RQyhprRhSnRW1S0HRXJ0KeTAjJqgiAIgtDQiAyRIAjnjLAYDUOeSMKa6yJ/hx1jtJqE1kaMUbVfi8gQoabtpZFs/Ly42j5jlApLsu/mXvLK7FpaHrJt9t/zS4huokNnql0g5nFKbFlYGvR8sgzbvy+j2x2x1ebvHGZJ0tL3oQScFRKyfPyMVE3MCVpG/l8Km+YVc2iTDY1BScZgCxmDLTU2egiP13Dh6BguGBUFyOgsalSquiuH83X+k1BpFGLxVUEQBKHOiIBIEIRzijHK15ksoU3oFs81USigUQ8TUU10ZP1eyd7VFXicMhFpWvo9lODPdnjdMuWHQmdtKgs8eF0ymGr3vB6nTPnB6p3dDivNcuF1yqhraHKnNaoCGjacLKVaQUSKjl7/icdtk0HhK0lU1iK4UWuVqKPqtvjA7fRSkeNh89fFlOxzYYpVc8EVUUQ31qE7TuMJQRCEc8GiRd/ywQf/45tvvj/jzz1jxnts3LiBd9+dccaf+0wRAZEgCMK/bKUe9v1WQeZP5bhsEokXGBn+XApqLWiMqoC23SqtgtgMPXnbgq/zE9lIi1pX++yIWqfAkqKhLDt4UBSRokV1AucLxe2UcJR6yN1mx2H1ktDGQHicBkOQbm5agwrZ60XyyEheuVYBUV2TZZmCHQ6Wv5zr7/pXVezh5//LoeN10bQcbgk5R8pe5sHtkFCqFBij1PUyfkEQGj5JltntclHulbColDTTalHWtJJ0PRg8eCi9evWp72Gcs0RAJAiCgO/medWbeQGLix5YW0n2hiouej4Fc6KaqmI3xfuc5PxlwxSrIb1nGNY8FwfWHrMOkALaXxV1QtkatU5J20sjOfB7VfWyOQW0vjgiZLlcbbmdEoc2VrH6zfyAluKxLXT0ezAxoBzOYfVQ+I+DrQvLsJd7SGhtoM3FkYTFa1Cpz9yNgq3Uy9oZhdXWaQL464ti0nuEVQuIXDYv+TscbJhdhDXHjcaopMUQMxmDLYTFivlMgiAcsclu54syK2XSkbmbEUol10SY6WioeXHsM0mv16PX1645kHDiREAkCIIAlB9yBQRDh3ldMpvmFtPt9hiWPJ9DZcGRtY7+mltM7/vi0eiV7P6lAvC19+5xV6x/XZ0TYU7S0vfBBNa+X+DvdKc1+brMmeNP/UbeXuKpFgwBFGY62bm4jPbXRKNSKXBWedn8dSmZi48stro7v4K9ayq5aFIy0U3O3Ieyq9KLrTj4+lKyBNZcF+FHXRtZlsnZbGPVG/n+bW6bxNaFZZTsc9LtjlhsJV7KDrkwx2swJ2lE0wdBOE9tstt5v6Ss2vYySeL9kjLGRHHagqJ58z7n889nUlJSQpMmTXnooUfo0KEj27dvY9q018jM3ElcXBxjxoxlyJBhNZbMLVr0Ld9//x1du3ZjzpyZaDRa7r//QXQ6PW+++TqVlZWMGnUl9933IAAul4vp099g8eIfAejevQfjxo3HYrEAsG/fXiZPfoHMzJ20bduWxo2bnJZr0JCIgEgQBAE48Ef1dt2H5fxloyLfExAMga/ZwZp38rl0ShqtLopAlkBnVmKMUKM4ibV1NHolaV1MxDRLxVHua19tsKgwRKhR1kFWJnujLWimBSBzSTkthkZgilZjL/UGBEOHSW6ZdR8WMuixJPTmMzN353jX8dgyOFuJh/Uzi4Iem/O3HXu5l5XT8o5c30gVQyYkB3QPFATh3CfJMl+UhV6EGuDLcivt9fo6L5/LzNzJ9OnTeOmlV2ncuClffDGHCRMeY+bMuTzwwFiGDx/BhAnPsGXL3zz33DOkpzc+7jm3bv2blJQUPvpoJl9//QUvv/x/tGjRkilTprFz53ZefPE5hgwZRosWLXn33ens2LGN119/E51Ox7vvTmfChPFMn/4/XC4X48Y9QIcOHXnyyadZv/5Ppk59lQsuaF+n16ChEQGRIAgNjtvhxVnhRanxBReniyzJOKxeVBpFjeVoKq0Ce1nwFV9lLxTvcdKkT/X1eU6GUqUgLEZDWEzNWQtJkk94QVNbaegmEG67jCz5oqX8HcHnRYHvtbps3loHRB6nhL3Mi9ctodYrMUSeWOc5XZgSS5KG8pzqY1dpFIQnBF4nV5WErSTE6rxA0T8OGnU3kbnYdyNkL/Xyy5Rchk1MxhgpPhIF4Xyx2+UKKJMLptQrsdvlIkNXt4um5ebmoFAoSEhIJCkpibvvvpdevfqwdOlizGYL48aNR6lU0qhROlarFaezevXCsSRJZty48RgMBi677Armzp3DXXfdQ/PmGTRvnsE770znwIH9NGrUiK++msfHH8+iWbPmAEyc+ALDhg1g9+5/yM/Po7y8nPHjn8RgMJCe3phNmzZQUlJSp9egoRHv/oIgnBFet4TDKgEyWqMKjaF6AOJxS1QVeNi5uJycLTa0BiUth1uIb204boBwoqqK3ez7tZJ/llvRGhV0vS2Wbd+VBT22SZ9wsjdUBd0H4KwKfQNelzwuiaoiD/t+raAs20VCawPJHU2ExapR1OIbzKQLTGxfVD3zAxDVWIf637k4yuPEOrUNZ2wlbjbNK2Hfmgokr2+No7aXRdCkT3jIv4FjGSLU9Lo3nsWTDvm69h01iJ73xGGwBA5WqVagUBAyE6YNUyEfE1xV5LtxlHtFQCQI55Fyb83B0IkedyK6d+9B06bNuPHGa8jIaEnfvv247LIrWLFiORkZLVAqj7w33nDDaAD2799f4zmjoqIw/Fvep9P5ypoTE5P8+3U6HS6Xi0OHDuF2u7nrrlsDHi9JEllZWeTkZJOamuo/F0CrVq359dc1p/KSGzzx7i8IwmlXWehm26Iy9qyw4vXIpHQy0um6GMITNQFZjopcNz9NPOSfPwPw6zsFpHQ20u2OWExRdRMUVZW4+fml3IA215IbWo2wsOOHwIAhPF5Dy2EWVr2Z59+mUEGz/mZSOhqRvGBO1OCye9EaTl8Zmdcjk7fNzi9TcpH/vTxZv1ehmVfC8InJRKYd/xvMiBQtllRt9fbeCuhySwz6f1tYx7UMXTOf0NqANsgaQF63hMsmoVIr0JpUOCq8rHmngLytR7JNbrvEprkl/x4HMc0NRDfWBe1wd7SodB2XvpLG7lVWCnY6MCdpaDnUQlicBtUxmT2dWUlSRyOHNtqqnUelVRCZqmXT3OprTLlsdX/TIwhCw2VR1a5JTW2POxF6vYEPP/yMTZs2sHr1KhYt+pb587+id+++J31Olar6+7JCUX3sXq/vC7z//e+jgKAHICoqmgULvqr2hZJGc+7Ps6z737IgCMJRqordLHn+EJmLy/E4ZWQvHPzTxvcTDlKZf+SbeluZm7++KAkIhg7L3mCjqiD4xPqTkb/NUS0o+PW9fBr3CqP/Iwk07RdOahcTXW6JodudsegsKrrcHAsKXzDU5754PE6JFa/nsXJqHt89dpDf3s2nKsTk/2M5K71Y81yUHXJhK63dY+ylHla9kecPhg5z2yTWvJ2Pw3r88xij1Ax+LJHmA8NRanyBaESalqFPJxGVfiSgMkSo6Hh9dLXHa01Kut4eG7AoqiTJWPNcbJhTzJJJh1j+Si4H11fiqvSGbEmeubiciDQ9y1/O5c/Pio47dqXKVxrX/qooBj6aSNfbYolM0wVtt60PU3PhTTEBjRYAlGroNTaOymI39tJjMnoKMEaK9YwE4XzSTKslQlnzbXDkvy2469qWLZv59NOP6Ny5Cw899F+++GIBLpeTuLg4du/+B/moiGTChMeYNevTOnvu5OQUVCoV5eVlpKamkZqahskUxrRpr1FSUkyTJs04ePAAlZUV/sdkZmbW2fM3VCJDJAjCaZW33V6tGQGAxyGz9dtSut4ai1qnxG2Xyd4Uuixt/++VNWYuasttl9i9svpE2soCDyun5tPx+ijaXh6B1ymjMSjRGlXowlSomysZPjGZ/B12Dq6vYt+vRzVhkCHrDxtuez59H0iocbHQ8hwXv39QSN52X7AQFq+m2+2xxLUwhFxPxzc+Nx5H8Dqw0gMunBUSevPxX78pRkOXW2Npd3kUsgRqvSJgfSXwLfCaMdhMQmsDO34oxVbqJekCI417hxMWG3is9ZCLH57OPmpsbgoy82jaP5x2l0eyZX5ptTF4nDKHK/z2/1ZJq+EW9ObjfxwplQqUhuMX7FkStQx6IpGSfU4KMh0Yo9UktjEgA0smHap2fONeYejOUJMIQRAaBqVCwTUR5qBd5g672mI+LesR6XR6PvzwfaKiounSpRubNm3AbrfTrVt3vvjic6ZPn8Zll13J33//xerVK7nlltvZtSswKCktLUWn02E0ntgi5CaTiUsvvZxXXpnM448/RVRUFG+88Rq5ubkkJSWTmJhIfHwCL774HGPGjGXbtq0sW7aE1q3b1uUlaHBEhkgQzlEuuxd7uQePq/5KgTxuif2/he7edmiTDVfVkfHV9LlznC/yak2hJGTHtqoiD1sXlKE1qIhqpCc8TuvPhmj0SuJaGEjrGsb+tcFfU+4WO3Zr6PlElYVuFk865A+GACrzPSx7KZeyY8vYjuFxhpgU8y+vp+b9R1NrlYTFagiP1wQEQ45yD8V7HWQuLSd/mx2DRUX3u+MYOD6RtqMiCY/TBMxVclV5+XNmUdBAbc+KCmKa6P2ZqACKwHlKu1dWVD/mFJkTtKR2CaP9lZE07x9OdBM9pmg1bUdFojH6/pjUOgWtL4mg840x6EwiIBKE801Hg4ExURHVMkWRKiVjoiJOW8vtjIwWTJjwLLNnf8Z1113Bp59+xMSJL9CuXXtee+0NNm3axI03Xs3MmZ8wadKLZGS0qHaO224bzezZn53U8z/44MN06dKNJ554lDvuuAWVSsXUqW+hUqlQqzW8/vqbVFRYufXWG5k//yuuvPKaU33JDZ5ClkNNPT23Wa1WLBYL5eXlmM21+FpVEM4SthI3Zdkutn5bhq3EQ2xzPW0uiSQ8Xo1Kc2a/A5G8Mr+9m8/eNcEDCHOihmHPJmOIUGMv9/DnZ0Xs/zX4sSNeTCGmad2sf5O9sYrlr+QG3df1thhaDLWEbFJQvNfB909mhzz30GeSSGgd/Bu73Sus/PZeQdB98a319B+XGFCOdpjklaksdLPw4aygzQL0FhUj/y/llNbTsZV4WD09j/ztR7oZqbQKBo5PJK6lIehirBUFbhY8eKD6QrL/anNpBPnb7RTtdgZsT+5oxJyo8c/XajbATM+740567CdC8srYSz14nDIqrQJDhOqM/38hCELt1XS/5nA42LNnLzExCWi1J98JTpJldrtclHslLP+WyZ2OzJBw5rlcToqK8mjatEmNC9uKTwFBOIdUFbvJXGLl5//LJW+rHWuOmz0rK1j0WBbFe53HP0EdU6oUtBhqCbm/1UUW9P92CTNY1FxweSSGIHM5mg0IxxhVdxW+0U10pHSqHrREN9aR1iWsxo5t6uN0RgsW0IBvwdBDm6tP9D+saLcTjzMwm1dZ5Gb3Sisrp+VRuMtOqxHBr2XX22JPqUOa1y2xbVFpQDAEvkVpl72ci60k+Byf490vaI1KtGGB1ysqXUuLoRb+WXakbLFpv8CW5ZIkU1Xspnifg+K9DqqK3EjeuvnuTqlSYIrRYEnWEharEcGQIAgoFQoydDq6GA1k6HQiGDoPiTlEgnCO8Dh9LZm3Lqw+Z0Pywm//K2DYM8nH7ehV18ITNbQeaWH794Hd2xLa6Ek9JvgwJ2gZ9kwy+9dVkrPZhsaopOVQCxGp2jptiWyIUNNjTBwlB5xkLilH8viCrrgMw3EDL71ZRVIHAzl/VW8YEJmm9Qd4x1IoFJjjQ2dwjJHqgEVGy3NcLHn+kL8BwME/q+g8Opoe98SxfVEZVYVuIhvp6HRDNJFp2pNaCPYwe7k3IEA5muSWKch0EB5XfezaMCUpHY1kB+noBpDa2USTPmYq8t1UFrrRGpW+TNRb+f4SwJROvmzRYW6nRP42O7++m4+zwhcg6sKU9Lg7jqR2Rn9rcEEQBEGoKyIgEoSznKPCg9smI8sy1lx3yPVXrDlunJUShogzOjz04WrajYqiSR8ze3+twOOUaNwjHHOiplpwplQrMCdqaX1JBM0GmFFpFKdtbochQk1yhJr41gaQQK2r3Y22zqSix51xrH4zn4JdRzIqEWla+v83sVqDgqM16RPOloWlQUvM2o6K9F+PqhI362cVVeuGtmFWMZZULQMeSfAtJqtThsxInQjJU/McJVtR8AVdtQYVnUfHULgrG2dlYHarzcURGKLU6EwqTNFqwEBlgZuS/U5MUWrUSQpaj4ggvrUh4JpV5rt9rcWPGo6z0tfR7+LJqQHd8ARBEAShLoiASBDOUl6PTHm2i98/KqRwlwNDpIoO10TV+Jj6qgLQhavQhatqfTOrVitRR5yZTID6JEqmTDEa+j+SgKPci63Ui96iwmBRHTf7ZopR0/eBBNZMz0M6KtZp2i/cX8InSzK2Yg85m4JnXcoPusjbZidjUOhSxNqwl3lwWL143TJak5KweDWV+cFL42JbhJ5YbE7UMOL/Utm3poLsjVXozSpaXxxBRKquWjAbFqfhgiuiaDksAoWyenmhxyWx/fuy4EG9DFu/LaXn3XG1Dl4FQRAEoTZEQCQIZ6nKAjc/Pp2N1+27e7SXejFY1ChUIAdpdBaRpq02n0M4eXqzGr1ZTURqzcc5yj04KyUkSUYXpiKls5HLpjaiZL8Tj0MiuokeQ4TKHxzYy704yr0hM30AbsepdQ4sy3axcmou5Yd8mZ+oJjo6XB3Nmun51Y61pGgCStqOpVAoCI/T0HZUJC2GWVCpFTUGLEqVAn2IFtdep0zpgdDd9sqyXLidkgiIBEEQhDolAiJBOAt5nBJbF5b6g6HD9qyqoP2VUfz1RUnAdpVWQY8xsTWWcwl1S5Jkyg64+PXdfEqzfDf5YXG+uUuxzfU06hoW9HGyJGPNcxOVrqNkf/BGGIltTr4VbGWRmyXPHcJxVHvwkr1Oiv5x0PveODbNLaGq2INCCY26hdHpxuhazd9SKk+9vFGtVxDZSBvydVtStGhEMCQIgiDUMXF3JAhnIZdNIm9b9Un9B9ZVordYGDg+kT0rrVQVe4huqqPl0AjCa5jQfy7zuiXsZb7SMLVWgeGY5gWnS1Whm58mZgfMzaks8PDz/+UwcnIqUY2Clw9qTSoqCty0uyKSVW/kVcv2pXYxYoyq/ruUZRlbqQfHv6/VEKnGYFFVy6aU7HMGBEOH7VxcTlKegaHPJCF5ffO59GZVjYvF1oWqYjdFu50cXF+JKVZDq4ssSF6Zfce2aldA28siRXZIEARBqHMiIBKEs5BS7et2VlVUfc5H5uJyPE6JzqNjfGVaJlWdTLw/G9nKPOz4vozMJeV4nL65Mm0ujaBZf/NpzZZJksyeVRVBGxXIEvw9v4Re98SjCdLCW6NX0nJoBBvnFDHgv4lkLimnYJcDvVlF84HhpPcIr1ZyJnllivc5WfFarr8Rg1LlCyBaDregNx95raGyLwA5m+2AAkvSmQmeKwvcLHn+EJWFR/6Ot8wvpftdsSiUsHeVLyjSGJR0uyMWyStTnuNCbz5//6YFQRCEuicCIkE4C+nD1bS9NIKV06rP+QDIGGQhLPb8zAgd5qzysmFmEfuOWujVVSWx6fMS3DaJC66MQq09PdkGe5mHgp2OkPuLdztxO6SgARGAOUFDx+uj+fvrEsITNTTqEYYuXEl0Ez3GII0bqoo9LH3+UEAAJnnh7/mlhCdqaNrnyGKGEamhG1voLSqUdfyp4HFKOCt9QZou7EjGyu2Q2Di3OCAYOuz3Dwq59NVUWg6LQPLIOCu8bP+hnPztvqxoWleTf+0lW6mHqiI31hw3pjgN4fEaTHW4ZpUgCIJw7hOfGoJwloprZaBZ/3B2r6gI2N7h2qgaJ8GfL5xWb0AwdLTtP5TTfJCF8Li6D4hcdi/7f6uscT0jY7QalSZ02Z5SrSAyVUePMXG4bRIoFOjNypCLiOZstoVsm735yxIS2xn9gVRMUx1akxJXVfXGDO1GRdbpek8V+W7+nl/C/t98v4f0HmFccGUU4fEanBVeDvwe/Pcjy5C/00FKZyOLHsvGUe4LqMITNKi1Cg79ZWPbt6W0vjiCn/8vx98cAsAYpWLIhGQsyVq8Hhl7qYeKfDduh0REsha9WYX2NLVyrwtuh4StxIPbLqHR+xa2FXP/BEFYtOhbPvjgf3zzzff1PZRzkniXFYSzlMGiptON0bQaEUHOFhsqtYLEdkYMESq0xoZ7w3em2EqDtNr7l+SWcVV6IchioydDlmRkfI0FHOVeNn9ZQp/7E9i7uiLo8RdcEVmrki+tsXa/y9IDocvgKgs8yJ4jwZIpRs3QZ5L55dVcqv7NziiU0HK4hfSeYae0wGvA8xa6+fGZI8EM+Jp+HPrLxogXUpDk4N0QD3NVSdiKfB33UjoZyRhsoTzHhdsmEZWuo6LATVm2KyAYArCVeFn+Si5Dn0miPNvFiql5eBz/vn4FtBhipv2VUegbYJBRVeTm729K2bPCiuTxlT027hXuDyIFQTg9ZEmmeLcLZ7mEzqIkutmpLXZ9OgwePJRevfrU9zDOWQ3vE0EQhFrTh6vRh6uJTBOLVR5La6j5w6wuJuc7rB6sOW4yl5Xjdco07WfGnKhBoYSsPyu58OYYNs4pQvq3KkyhgDajIolpqj/l5z5aTFM9u362Bt1nTtSgVB+5FgqFgqhGOi56LgWH1YvHKWGwqNBb1HXWQEGSZPaurggIhg5zWL3sXWOl+UALkWlafwe+YyW2NVCa5SSti4nEC4z88lpuQACV2NZA0gVGUFBtoduKfDf2Ui/LpuQiH12RJ0PmEitRjfU0H2CmIXHZvGycV8K+o4JoyesLIt12iQtviSEsRgRFglDXcjfZ2fqFFUfZkay5PkJJ22vMJHY8+Y6edU2v16PX1+1nh3CECIgEQTgn6SPUhMWpqSyoPkclppkOXYi1cGrLUe5hw5xi9qw8cgOb9UcV0U119Bwbz8rX80jp7KXfw4nYyzxIHpmwOA1hcWp04aGfW/L6yry8HhmVVokxUoXiOCvqJrQxoDEocdsllCoCFn3teF100AVjjZHqWpXHSV7fHB4UvkYexxsLgKvKS9afVSH3Z/1RRYuhFrreHsuSSYeqrbmU1MGIKVqN1y3TfJCa5S/nVjsmd6udA39UktjGQO7W6h0XbaUelEoF3mOjJWDLghKSOxjrtDzwVNnLvOxfEzyjmLW+inaXR4qASBDqWO4mO+vfL6u23VEmsf79Mi4cw2kLiubN+5zPP59JSUkJTZo05aGHHqFDh45s376NadNeIzNzJ3FxcYwZM5YhQ4bVWDLn8bh55ZWXWLnyF1wuJ507d2H8+CeJi4tjxoz3OHBgPzqdjp9/Xkp8fDz33fcQffv2A6CgoICpU6ewfv0fOBwOGjduyn//O5727TsAcPBgFq+99jKbN/+F2Wzhhhtu4tprrwdgz57dvPbaK2zbtoX4+ASuueZ6rrrqmtNyvU63hvNpIAiCUIeMkWoGjk9i6QuHsJf5IgS9RUXriyNI7WzC65Tw6BQn3Vih7JA7IBg6rHiPk/JDLmIz9GRvsJG9wYYuXIlCqSAsVs2ARxNDntNe5iFzaTk7fizHbZMwRKrocE0UqZ3DQi5mCr4yuBEvpFBZ5MFV6UVjUFKe40KlURDfuvo3io4KD7IXtKbQ85LAV/a2e4WVfb9VolIpyBhiJq1LWI3zo8C3+GqohhHg6xqnUCqIbqzjohdS2Di7mPxMO3qzilYjImjSO9xX0qaAHT+Wh1ykNnNxOZ1viAkaEOnCVHhdwR9YVeRBlmpY+bYeuCprWIxX9i3YKwhC3ZElma1fBM+sH7b1SysJ7fV1Xj6XmbmT6dOn8dJLr9K4cVO++GIOEyY8xsyZc3nggbEMHz6CCROeYcuWv3nuuWdIT29c4/m+/HIemzZt4I033kav1/PKK5N5443XePHFlwFYsWI5Q4dexCefzGL16lU88cSjzJo1l8aNmzBx4lOEh4czY8YnyLLM22+/ySuv/B+zZ3+B0+nkwQfvpUWLlnz44WccOpTNM888SXJyMhde2JWHH76fkSMv4fHHn+LAgX1MnvwCJpORiy66uE6v15kgAiJBEM5ZESlaRryYQnmOG7ddwhipYsPsYjbOLkalUdC0XzhtR534N++SRyZzSVnI/XtWVtDpxmhWTc1DlsBZIZF4gYGeY+JCTpB3VnlZf0xXPHupl7X/K8Q1WqLl8AhU6uAfyrZSD79/VBiwNlVMMx19HkhAH37k+exlHvK229m+qAyXXSK1k5EWQyMIi1NXy/wcngNkP2ou1h8fF7FnZQUDHkkIuhbSYVqjitYjLf6ucMdqNTLCPzcqpqmefuMS8DglFAoFhgiV/+ZDb1b7slMhOCsk1EECr7SuppCPAYhK19UYCNYH9XHKFbXGhjVeQTjbFe92BZTJBeMolSje7SImo27L0nNzc1AoFCQkJJKUlMTdd99Lr159WLp0MWazhXHjxqNUKmnUKB2r1YrTGbprqe98ueh0OhITk7BYLDz99ETKy8v9+81mC48/PgGtVkvjxk1Yu3YN3323kPvvf4h+/fozYMAg4uLiAbjqqmsYN+4BAH7/fR1lZaU89dRETCYTTZr4skdKpYolS34iMjKSu+/+DwBpaWnk5uYyd+4cERAJgiA0NKZoDaZoDSVZTn544qC/nMzrltn1s5XcbXaGPpWEKbr2QZEky0cm6gfhcUpEN9YxamoarioJtV553LVzHOWhu+L9/XUJjbqFBW2l7qzysnZGYbWFeot2O1n9Zh4DxyeiN6uxl3tY92EhB48qZdv+fTm7V1Rw0fMpWJK0/u1er8yuZdaAYOiw4r1OCv9x0qhbzdcrppme9J4m9v8WWDqX3stEdLqO0oNOrLlu9OEqwuJ85XvBvoVN7mAMOT8quqkOlRrUegUeh4xSo6D5ADPtLo9E8sjowpU4K6rf8HS6IbrGjFt9MESoiGmmo2h39QYZUek6DBENa7yCcLZzltccDJ3ocSeie/ceNG3ajBtvvIaMjJb07duPyy67ghUrlpOR0QKl8sgXIDfcMBqA/fv3hzzfqFFXsHTpT4wcOYROnS6kX78BjBx5iX9/q1at0GqPvMe3bNma/fv3oVAouOKKq1m6dDFbtmxm//79ZGbuQJJ8rzkraz+pqWmYTEe+ZLr44ssAePPNqeze/Q8DBvTy75MkCZXq7HyvEgGRIAjnPJfNy6bPiwPm1hxWkeumaI/zhAIitUZJk77hHPrLFnR/o25hGCJUqNS1/1a/It8dcp/bLvvaZMdW3+co95ITYhxFu504yr3ozWoqC9wBwdBhriqJv74ooec9cf6mCq4KL/t+DT6fBWD3L1aSOxprLDc0WNR0vTWWVhdF+s4lyzTuHY4hUs26jwo5tPHImPVmFYOeSCSqka5aUBTVRBd8LpgC2l4aycENVfS8Ow5zohaNUYkhQuUf17CJKfz6dj7Fe31Bht6iosstMUQ3aXhNSPRmNb3vjWfFa3mUZR9pNGFJ0tDr3rjjlikKgnBidJbavT/X9rgTodcb+PDDz9i0aQOrV69i0aJvmT//K3r37ntS52vSpCkLFizi11/X8Ouvq3n33bdYsuRH3nvvQwDU6sDPN0nyolAokCSJBx4YS0VFBYMHD6V377643W4ef/yRfx8X+n3H6/Vw4YVdeOSRx09qzA2NeIcVBOGc57bL5AWZZ3JY1u+VNOoadkLnjGupx5KqpfxgYJc0XZiSVhdFnFAwBKA7zto4Km3wcjmPveZvLw+vN7R/bfDsE/zbEe+m6IAuc8oaauYVanzd3Y5Db1ajN6uJbe6bx+R1S2z8vDggGAJf57mlL+Rw8cup1coXTVEahj6VzIY5xWT9UYksgTlJwwVXRHHg90r2/1bJ7l8q6DEmluYDLQGPjUjWMujxJJwVXiSvjNakDJmJagjMiVoGPp6IvdRLRb4LU4wGY6QKU3Rgp0BBEE5ddDMt+ghljWVz+khfC+66tmXLZtav/5PbbruTzp278J//3M+IEYOJi4tj48b1yLLsL2OeMOExWrVqTUREZMjz/fDDIjQaDUOGDGPQoCFs3fo3d955KyUlJQDs3v0PkiT5M087duygY8dO7Nu3l02bNvLjj8uIjPSd/6uvvgBAlmVSU9PIzs7G4bCj1/uaS7z55lTcbjfp6Y1ZtWolSUnJ/qzQjz9+z44d2xk37tE6v2anmyhKFgThnKdQgjashmzGSXQbM0VpGPx4Iu2visQQqUIXriRjsJmLXkghLO7Ez2eMVqO3BA+KYlvo0YUHH7/GqKwxODnc0U6pqiHAOWaX3qyi2cDQbalbDLGgPok5OPYyb8jyN1eVROmB4C24w+I0dB4dTd8HE+j3cAIthlgCFnwFqAjSTRB8r8WSrCUyTYcpWtNgg6HDwmI0xDbX06S3mfiWBsLjtSIYEoTTQKFU0Paamtvvt73afFreM3Q6PR9++D4LFy4gJyeHpUsXY7fb6datO1ZrOdOnTyMrK4tFi75l9eqVdO3avdo5SktLsdl8Xy5VVlYydeqr/Pnn7+TkHOKnn34kLi6eiIgIAA4dymb69Dc4cGA/H3/8AZmZO7j00lGEh4ejVCpZunQxubk5LF/+Mx988B4ALpeLbt16EB0dzUsvvcj+/ftYtWolCxZ8RffuPRg+fAQOh8O/77ff1jB16hR/YHW2ERkiQRDOeXqLilYXRbBxTnHQ/U37hp/UeU3RGtpdHkXzQRZkWUYXpqpWRmYv8+C2SyhUCvThqpDd14xRKgY9lsiS53NwH5X1McWo6TU2LqA5QsBri1DRqFsYB9ZVzwAltDX458qk9wxj23dlQc/RuHd4wPwmhVJBk95h7F1tpTw7sJQvpZORqEYnV3LmdcshO7+Br5FDKCq1gk3zirHmBD8mvpVYn0MQhBOT2NHAhWOovg5RpJK2V5++dYgyMlowYcKzfPzxB7z22sskJCQyceILtGvXntdee4OpU1/jiy/mkpSUzKRJL5KR0YJduzIDznHbbaMZOfIS7rrrHq666hoKCvKZNOlprFYrLVu2ZsqUqf7MTZs2bSkrK+Xmm68nNTWN119/k+TkFADGj3+CDz+cwbvvvkVaWiPGjRvPpEnPsGvXTtq1a88rr7zOq6++xC233EBUVDT33/+wf4HYqVPfYtq0V7n55uuxWCxcddW13HLL7aflmp1uClkO2ejznGa1WrFYLJSXl2M2N6wF+gRBqHu2Ug+r38wjf0dgt54Lb46mWX+zv+uZ1y3hsEogy6gNyuOWsoXidkgU/ePg948Lsea4USghtauJC2+IISwu+HwlWZKpKvZQvNeJNddFdGM9lhQtpuiav7uylXhYP7OI/esq/YuUJncy0v2OWP/cKEeFh7/mlVTL0BgiVQyfmEJ4vO84j0vCXubFVeVFqVbgtkls/bYUyQsth1mIbqwLuq5RbVQVu/n+yeygC7YCDHs2mfhWoW9AsjdWsfyV3Grbw+M1DH02GZOYZyMI55ya7tccDgd79uwlJiYBrfbk5wbKkkzxbhfOcgmdxVcm19CzybU1Y8Z7bNy4gXffnVHfQ6kXLpeToqI8mjZtUuPCtuLTQxCE84IxUk3fBxOoyHeTvaEKjUlF2oUmDJEqfzBUVexm+/dl/LPMiscpk9DGwIU3xWBJ1pxwm+bSA06W/l+OP0CRJchaV0XJHifDJiYHbeLgW6tIE7SbXI2vLUpN9zGxdLg2CneVhMbo62qnPSqY04er6XBNFI26h7HjhzJcNom0rmGkdTH5n89e7mH7ojJ2/FiG9G8FWlxLPb3GxvuaFehOrcraGKmm/dVR/P5BYbV95iQN4Qk1v+64lnr6PpTA+plF2Io9KBSQ0tnIhTfHimBIEISTplAq6ry1tnB2EZ8ggiCcNwwRagwRauJaVM9C2ErcLHspl7KjmiTkbbPzw4SDjPi/1BMqE3NWeFk/q8gfDB2tstCXATqRrna1oTWo0BpqzmbpzWoS2/qaHEhe2bdA6r8TiLxemX+WWauV1RXsdLDs5RyGPp18ygGRQqkg9UITHofE3/NLcdskUEDSBQa63xGH8ThzubRGFY26mYjL0OO2SyjVoDMf/3ULgiAIQk1EQCQIwlnBZfPisHqpzPegNigwRasxRKpr7IZ2IkoOuAKCocMkL2ycU0TfBxP8maTj8TiloOvJHJaz2UZalxPraleXggU29hIP274rDXq8NceNNdeFUk3IuUy14XFJFO1ykLvVTvc7Y1GqFChVUJLlRK7lr1GhUIgW1IIgCLV011331PcQzgriU0UQhAbPXu7h769LyFxq9WdddOFK+j2cgC7cV8plsJxaSVfW76HbUuduseO2S7UOiBRKXwMFhzX4XBlTTMN76/U4Zdz20FNKC3c5ObihiraXRJ70HCJ7mZdVb+Qheam2dlLJPie9/hMvsj2CIAjCGdeg224fOnSIq666iqioKJKTkxk3bhwOh29C9L59+xg8eDAmk4nWrVuzZMmSeh6tIAingyzLHFhXSeYSa0AJmrNCYtlLuVQWeFg47gCbvy7BXh689XJt1HSTrzUqq/emroE+QkWrERFB9ykUkHaCax6dCWqtApUm9Gs0RqvYs6KCgxuqL+5aW0W7HUEXxwXIXm/Daa37FeEFQTgfnJf9wYRaqd3fRoMNiGRZ5qqrrsJms7F69Wrmzp3Ld999x9NPP40sy4waNYqEhATWr1/PTTfdxOWXX05WVlZ9D1sQhDpmL/Wy5ZvgpVxel0zZQSfmJC3bvi1jz8oKJO/JfTA27hU6SGkxzIIhxBpBwSiVCpr2CyepozFgu0IFfR5MwHicrnGH2Uo9VBa4sZV6qE1DUFuph0Obq1j7fgEbPy+iNMuJqypEBHIMfYSK5oOCd9w0RKpQoMBV5es4Zy87ucDTWRk64JFlTvp3JwjC+Umj0aBQgNMZukRZOL85nU4UCt/fSk0aXt3GvzIzM1m3bh15eXnEx8cD8Nxzz/HII49w0UUXsWfPHn777TdMJhOtWrVi2bJlfPTRR0ycOLF+By4IQp2SJBl7aeib+spCD4YIFWVZsHVhKY17hmGKOfGGBcZoNRfeHM36zwLXKopuqiNjkLnGhU2Dni9STe+xcVQVeynItKMzqYjN0GOIrL5W0bEcVg/Zm2xs/qKEqmIPxigV7a+KIvVCE3pz8LftqhI3v0zJpSLPTZPe4USl6yg76KI8x0VSO2NAx7lg1FolbS+LxF7q4cDvR7JAYfFqut0Wy+8f+zrDOcq8Jx24xGWEbnkanqDxZeIEQRBqSaVSERERQWlpGQA6nY4aV6oWziMyTqeTiooyIiMj/GsyhdJgA6KEhAR++uknfzB0WHl5OevWraNTp06YTCb/9t69e7N27dozPUxBEE4zlVpBeIKGirzgC3JGpGjJ22YHwFUl4alh4c+aaI0qmvU3k3SBiaw/KnBUSKRdaMKcpD1u97NQ9GY1erOa6Ma161BnK/XgcUnsW13J5q9Kjmwv8bL2/UIqCz20GxVZba6U1yuza6kVWYJ+DyWyc0kZq6fno1QpaNQtjMg03XEDIvAFcd3viqPNpW7KD7nQGpXYy7ys+7CQqkJfVii2uR6N/uQCF2OUmuSORg5tslXb1/XWmJOemyQIwvkrMTERgLKyMioq6nkwQoOiUEBkZIT/b6QmDfbTJyIigmHDhvl/liSJ6dOnM2jQIHJzc0lKSgo4Pj4+nuzs7JDnczqdASlVq9Ua8lhBEBoOQ4SaTjdEs/L1vGr79GYVxmi1P1hS6xUoFFCa5USlVaC3nFhLZq3RtyZRREp0nY2/Nlw2L3nb7Pz5WRFdb4lhy8LgJYLbvi2l2QAz4XGBAYmz3MvuX8rp9Z8EVk7N9TdH8Hpl9q6uIH+7nWGTkgmrReZMF6bC7ZBYNa2EqiIPR1fqKRTQ8froWgVXwejNKnqMiWPPygq2f1+Ks0IiuomOC2+KITJdrAEiCMKJUygUJCUlER8fj9sd/Isz4fyk0WiOmxk6rMEGRMcaP348Gzdu5M8//2Tq1Kn/pkWP0Ol0NdaQTp48mUmTJp3uYQqCcBoktDbQ7c5YNs4p9q1dA0Sla+l4XTR/fFLkP67FYAubvijmwNoqUEBqZyNdb409qRK6M6kw08GK13wBn+QFyR08yyV5wVHuJTwu8PXIskxCGyP7fq0I2imuqthDzmYbGYMstRpPWIyGwU8m8cenheRstoMMliQN3e6IxZKiDfoYt0PCVeVFofAFoqFKDI2RatpcGkHTvuHIkoxK61tE9lTZyjzYSz04rF5MUWr0EWr04aJjnSCcL1QqVa1vfgXhWGdFQPTYY48xbdo05s2bR9u2bdHr9RQXB9b5O51OjEZjiDPAE088wbhx4/w/W61WUlNTT9uYBUGoO7owFc0HmEnuYMT178T8gl12fvtfgW9+kQKa9A4jopGWbd+X+R4kw8H1NqqKcxn0WFKDLceyl3tYP/NIUKc8zud5sE5wGoOSlM4mNs4pDvIInwPrKmncK7zW5W7mRC19H0jAafUiSb5Oe8GuoSTJVOS52fxlCdkbq1DrlDQfaCZjiDnk4rNKZd2uJWTNdbH8lVysuUe+HY5vo6f3vQmYxJpFgiAIwnE0+E+K+++/n3fffZdZs2Zx5ZVXApCcnMy2bdsCjsvLy6uxRlCn01XLKgmCcPZQqhS+kq8Y3896i4q4DANuh4QuXMWelVZ+e7egWofNkn0uqoo9pyUgkjwSCpUCxQm05D6WxylTnnPkRt5W4iU8XkNFfvXSD1OsGn2Qbndao4qoRlo0hiPBjjFKRZM+ZoxRKqoKPThtXpQneglk0BiVaE2hMz4VeW6+f/IgHof87+vxdQU8uKGKQU8knfaAxFbqYdnLudXmmOVvc7D+s0J63B0n1jYSBEEQatSgW/pMmjSJ9957j7lz53Ldddf5t3fv3p2NGzdit9v929asWUP37t3rY5iCINQDY6SaqHQd8S0NIMts+7YM+aiuzgolJLU30nygudatp2urstBN5pIyVryex5+fFFKa5cRtP7k1dJQq0BiOBBvbFpXS5ZYYtKbAt2eNUcmA/yaGbPAQFq+l5XBfSVzrkRF0uiGGgp12/p5fStFeJ417huOtZcMJe5mHA79XsOzlXJa8kMPfC0qoLKgeoHmcElsWlPiDoaOVHXRRvMdRq+c7FbYST8iGG1m/V+Eor9vfvSAIgnDuabAZoh07dvD888/zxBNP0Lt3b/Lyjkyo7tevH6mpqdx22208/fTTfPfdd/zxxx98/PHH9ThiQRDqi0qrRKnCv+hn4gUGWo+I4NAmG9ZcNzlb7ITFajDFqFFpTu17oPIcFz89m42z4kgAtHOxlR5jYknvFYZGd2LZCL1FTYuhFrYuLAOgssDDxjnF9LwnjspCD7ZiN1GN9cS10GOqYf0ilVpBSicT7a7woNIoWDM937/PUW5n6XY7ve+NJ71nWI0txO3lHtZ9WMjBP4+03i7LcrFriZWLnk8hPP5IGZyzSiJ7Y/WOcYftXV1BSmcTSuXpa4NrLwsd8MgyQYM1QRAEQThag80QLVy4EK/XywsvvEBiYmLAP5VKxcKFC8nNzaVz587MmjWLBQsWkJaWVt/DFgShHhgsKhr3DgcgKl1Hs/5mfpmSy87F5eT8bWP7d2V8+0gWhbscyNLJ3yC7qrz88VFhQDB02LoPCnGUnniWSKVW0HJ4BIltDf5tZdkuVr2ZT3RjLR2vj6ZJ73DCYjUojhNYGCPVNO0bzpYFwbvU/f5xIbbSmhdVrchzBwRDhzmsXrZ8U4LHdeQ1KhSgrmFOksao5BSqCWvFFBM6SFRqFGjE2kaCIAjCcTTYDNHjjz/O448/HnJ/s2bNWLly5RkckSAIDZVap6TDNVFUFrlp1s/Mn58W+bNFh0leWPVmPiP/LyXkZP/jcVR4KTngpO2lEUQ39S0ymrfdzt5VVtx2mcLdDsITTmJR2Eg1ve+Px1bioWi3E71ZRVRjHcZIFSr1id3QO6zekKVxbpuE0+qtsf323tWhF/LY/1sl7a+KRh3tG5PeoqLFEDOb5pYEPT5jsOWU5lfVhjHCt+Bt4a7q5XkthpgxRIj5Q4IgCELNxFdngiCcE0zRGvo9mIA5URty3oij3IvDenJzfQBQQK//xFO428nKqXmsmpZHVaGbfg8nEp6gweM8+XMbLGqiG+tpMcRCo25hhMdpTqq8r6ZyOOC4WaYTWeRdqVTQpK+Z6CbVG9a0GmEhPP70f+emt6jp+2A8yZ2M/rEr1dByuIW2l0ai1oqPOUEQBKFmDTZDJAjCmVfpKcXh9WUI9KpwwtSR9TyiE6M3q6ksrLkkTPaefMmc7IFV0/LwOH3nkGXI3mijaLeTnvfEERZb/2+phggVunBl0LI+Y7T6uGv+NOkTzq6lwReubtw7DF14YIBhilIz4JFESg862buqAo1RSfMBZl9HvPAzcz1M0Rr63BePo9yLxymjMSgxRKhQ60QwJAiCIBxf/X96C4JQ7zySizznbpbmv0uFpxAAszqWwfFjSdQ1R6Vs2AubHk1vVqHWKfxBy9HUekXQttW14XFJbP+hNOh5HVYvxfscxLao3cKnp5MhUk2f++NZ9nIu8lGJMqUa+twXjyGy5tdvTtCQ3jOM/b9VBp43QkXbS6OCZlyMUWqMUWqS25uCntNl8+Ks8CJ5fGsm1eUaRIdpjSq0RlEeJwiCIJw4hSzL52ULHqvVisVioby8HLPZXN/DEYR6VeLKZk7WY0gElpopUXFD2stEaVPqaWQnzuuW2Lu6grXvF1bb1+PuWJr0MaNSn/i8Fluph58mZlOZHzwDFd9Kz4DxiQ1izRuvW6Ky0MPuX6yUHnAR3UxH0z7hmGI1tXrt9nLfXKYdP5Thtks06h5Geo8wwmJPPDCuKHCz/tNCDm60gQymaDVdbokhoa1BBDCCIByXuF8TzgSRIRKE85xHcrGp9IdqwRCAhJdNpT/SL/ZW1GdJlkilUfrm4CRo+OvLEqw5bixJGjpcE01kmvakgiHwZVh0JhWVBA+IdGYVypM8d235Mi0SktdXFhZqTSKVRoklSUun66NxVnpBllGqlbV+7QaLmtTOauJb65G9oDUqjz/3KIiqYg9LXzhEZYEnYNuK1/MY9HgiyR2CZ5QEQRAE4UwSAZEgnOfckoN85+6Q+/Odu3FL9rMmIALQmlQktDYy4BEdXqeMSqdAZzq1bIQ+XE2bSyNYNS0/6P7WIyJQn+IaRzWpKHCz/rNCsjfYkGUIj9fQ9bYYYlsY0BqqP6+zwkvBLgd/f11CZaGbyFQdHa6LIjJVhybI8cGcarar9IAzIBg62oZZxViSNYTFak/4vJIkYy/14HXLqDQKDJHq07rWkSAIgnBuEwGRIJznZCTM6niKXFlB91s08aiV1buInQ10JhWcQhKiqthN8V4n+TvsmBO1JLQ10OGaSP76InCdn3aXR2JJqt2NvavKi63MS85mG7JXJqm9EWOkGl146OCjqtjNkucOUVV0JLioyHez7KVchj2bTHwrQ8DxbofErp/L2TTvSDvsvO12fnrmEP0eSiCtq+mkMj4nKm976EVby7JduKpkiD2xczqsHvatqeTvBSU4KyR0YUraXhZJk77hGCxn5iPNbf83S2dUikBMEAThHCACIkE4T8myTIkrm18KP6JDxHD22v4MelznyEvRnKUB0amw5rlY8twhbCVHSgmVahj0WBKXvhrGwT+rUKgVpHQ0YohU1yoD5azwsv3HMrbMPxJQbZhdTPOBZjpcGxXyhr54jzMgGDra+plFDHo8KaB7nKPcy19fBV8b6PePColprscUffrf/muac6Q1KakqchMWp671XCKPU2Lbd2Vs+67Mv81ZKbFhdjG2Ug8drolGU8NCsafKUe6heL+T7d+X4aqUSL3Q5Fs0N+7syZ4KgiAI1YmepIJwHvp/9s4ywK3rzN/PJTFrNDxmh504ThxmarChBtp0y8yQ0hbSdNtm26Zb3Hb/W0gp7TZtsEnToAMOM9iJE7OHSYyX/h/GMx5Z0pBnxmP7PP3Q+Bzp3iONdHV+933f35szEvQWN3Jr57fpLLxGe34Nx0XfgSLt2NgpksZpsQ8R1pp340p3D8WMyRP/21smhgAsA1b9sAvVKbPs4giHXBAm1OqccDpesqNUJoaGefPBVNXGosN0vlI70jKwsYhZKrfYzvTqZQ5zoymkhhzfZoPmQz3INd6aJacGaH8xizUJG/R8wuS1fyaqzr3+rySF5NiW67tCIW3wwk2DPHBdF10v5xnYWOTFmwa566vbSHWVZuy8AoFAIJh5RIRIINjHKJgZnk/ciV+NkTOTALycvJdF3iM5p/HTFM0skqTQ4FyMT42iyZOv8djTKaRNetZWFyhGwSbVpY/ruKYXLEo5C0kCd1DBNGzW1tjMA7x6R4KGA904fZUKwj9GBMIVUCrS32RtnOass2Tu5o4onPipRlb/vAdT3yF8mg/1ULfYSaKjNCmnuWLaxKqh5WwLCikLf8Ourro62QGTNx+o7M9UTFu8eNMgx364fkajUwKBQCCYOYQgEgjmALpVpGTlUSUNpzKzzls5M0lvcTOWbXJY8Gza3IdgYWLbFmuSq+jIrwXgHfO+v0+KIQDLGDtqUcrWjrBYpk26R+elmwfpfDGH5pE54C1BFhzrI5+o/bxixqx53rYjvTz/5wGqNUk46PwQrlC5qPDWqWhuCT1f+YRAk4ZrjHql6URzKkQXOTn5s41kBwxKWZNQq5P4tiLP/L6Ps7/VhqxMvAZHcYz92PHmd4WtT2dqzm15KsMRV81sup5AIBAIZg4hiASC3Yhp6SSNHp6N30FXfh0eNcTK8EU0uJbgVvwzcs7+4hZ0q8AB/pN4avBv3JX8ITY2DsnNoaGzaHEfyPOJfyDPVhhhDuLwyLgCCoVUdQETmle7pirVpfPPr24baeBaylo896cBMv06Lcs9NVPjmg5xo3mqb6g9EZWTP9fIIz/uLouQtK7wsOgkf0VhvzukcuInG1l1fRf2qGw61SlxwicacIfGvvRnB3TiW0oMbCwQaHYQW+rCE52ak5svpmGZNh0vZel+JU9mYJC2I3285ZutY0a+quEKKARbNJIdesWcv1Erq6OadqzxHyIQCASCPRPRmFU0+hLsRrryb3Bzx7UVPYBWhN7KyvBFOBXPtJ9zc/ZFknoPa9Or6CturphfGb6YmGMhi31HIkn75h1v27LZ9HiG1T+vtNheeIKPo94Tq5raVsqZPPbfPWx7rnrNz1t/0Ma/vtlBKVu+u/bWKZz5tRaMok0xZeIOKbhCalkkxyxZ5BImAxsKFLPWkEiJqDWjPUbJIttnsP6hFIn2EvX7u1lwrA9vnTpmVCbVVeLe/yg3k9DcEmd+rYXoQueU3en0gjUSWXN4lSlHUxIdQ2YXheSO9Tn9Mmd9o4Vw28yZfwxsLHDXv7dXnZt3tJfjP9IwYTtzgUAwccR+TTAbCEEkvmCC3UTOSHJL538wWKq+yfq3ef9F2DH9hgYpvY/e4kb+2f2jqvMO2cOVrd8l5Gic9nPvSZSyJr3rCjz3p36SnTpOv8zBbw2z+ER/zQhLdkDnlk9uKYvKjObQS0MsPD7As3/sp+PFHNjQeqSHI95exyM/7Sa+ZUdxfsNBLk74eAPe6K45mNm2jWXYKBPokVRImTz4/U761xcr5lxBhfO+2zYr7nTjke3XiW8tEd9aJNTmJDLfgbduZp3eCimTZ//Yx8ZHy1PnHF6Zc/6jdcK263srpmWjCAtywQwg9muC2WD3/7IJBNOEbVukjUFs28KtBHAort29pDExbYOYY+FI2tzOdBfWz4gg8qhh8rna9RAlK4fFzLl17Sk4vAqtK7xEFzkxdRtZkXCFlHHSxiQUh4RRqH6fydQh2OzgxE82UMpY2AwZHKz6fleZGALoWVvgyd/0ceLHG3DsQlNZSZJQxjFZGKaYNquKIRiy8s4NGnNCEHnrNLx1Gq0rvBQzJoWkSd/6Ag6vjDug7NL7VQtXQOGId9Yx/2gfa+4cst1uPcLD0tOC+Op3/3uyO7Btm96STndJ5/VcgYimcpDHTVhT8Cj7bsqtQCDY89g3r+KCvY6U3se23Cu8mnqAkpVnvmc5y4JnENQa5lwtTMZIUDTTxPUu9vcfz2Ghs8jpKe7t+29K1o5Uq5latyqphLTaVlwyCoq0b9/tHs149TajcQVklpwa4PW7k1XnFx4/VBfm8Cgj7mrxrcUKMTRMxws5CilzRjb41RjtBFeNUm527LonSrZf57Ff9NK9Nj8y1nakh6PfF8MTmf6IkTuo0nakj/oD3VimjcOroEzCFGJvo6ek8+NtPWwu7BDRqgSfaWvkYK8btxBFAoFgD0EIIsEeT0rv48G+X7M199LIWDzZyevpR3lb6zeJOFp24+rKSesDPJ+4k5eT92Bvr9J2K0FOj32I8xuu5paubwEgIdHgXDxj6whpTbiVAHmz0kZ4f/8JeJTgjJ17b0bRZA4+L0THiznSXeWF/4dcFK4aXRmzJ5ANen72qvkdXhnVVTvCNZ7V+GxSSJk8+rMeeteVm1RsezaHpPRz3IfrJ2XpPRkm2ndqbyalG9zSFy8TQwCGDT/e1s33l8wTgkggEOwxiApQwR5PQu8qE0PDFKw0zwzeSsGonR4222zJvchLybtHxBBA3kzyr56foMgare5DADix7l141NCMrcOnRri4+WsVwqfFdRDHRq5Ak2euOH1vx1uncdbXmjnp0w20rfSy5NQA5363lYPPD1U1YnAFa9+XkmRqOs/NBO6wyvLLIlXnFp/sxxWcOxvcQtKoEEPDbH06Sz4+t6JZexsZy+KJZPVrq2HDG7najYYFAoFgriEiRII9njfST9Sc25B9hqMjl+HCN4srqk7GiPNM/Laqc4Zdoj2/hpXhizk++nZCWiMOeeZqoCRJos45jyvbvktS7yNnJAg5mvApYdyqKFrdVbxRDe+xGvOO8iHJQ+93LVxBhcZD3HS/mq+YW3SiH/cYgmm6UVSJBcf6cHhkXvx7nNyAgcMrc9D5IZaeGphTkZFCeozImQ25uEGgWRvzvRdMHcO20cfwZEroog5RIBDsOQhBJNjjkai94ZHnUBDUtk3SRl/N+aTezcGBU/Gos5eu5lOj+NTorJ1vX2MiTUddfoXjP1rP0zf0Ddl120ORocUn+Vl+RXRWrZxLeYutz2TZ8EiaQy8O4/QrGEWL9heyNdPodhdO/xjvizTUILeQNCdVAyaYOC5JosGh0lOqLnz297pneUUCgUAwdcQvhWCP54DAiaxJP1h1bqnvWDzK3Ih4KJJKWGsmrndWnY85F+CQxSZiX8Qb1Tj+Yw0UkiZ63sLhlXEF1Sn36pkqhYTB07/rBxsGNpSLd9u0Of6jjXOm147Tp1C31EX/m5WpWW0rvPS9WSA8RgNdwa5R73TwjoY6frStu2JusctJzCG2FwKBYM9hbvyyCQS7QECtZ6n32IpxnxJhRfgCHMrcEBkeNcQxkcurzjllL/M8h6LKwt1tX6SUt8gNGLx+b5KXbh6k86Xc2GYL00QxbZJoL9L+Qpb+DQWKGRNfXfWN7LZnpmdNRski3auz5ekMGx5JkegoUcxO/riesMqxH4xRf0B5amnLcg9LTvVjFC2cPvETN5Mc4HHxubZGGhxDZhuaJHFaOMBn5jVSp80dAw6BQCAYD9GYVTT62itI6/30FbfwcuoeSlaBxd6VLPaunHPNRQtmhtfTj/LEwF/R7aE722Gtmbc0fJI6x3xkWWzg9jWMosWWpzI89ovesnGnX+bsb7YSbJkZkZwbNHj8f3rofHlH7ZI3pnLM+2M89ds+Mr2VqVAX/Xgegcapr0cvWLQ/l+WxX/RgjdJAS08PsPzyyKTrpbKDOtueyeKLaRglG9Uh0ftGgY2PpnjLN1rxN4hN+WzQX9IpWDaqBBFNxSGuY4JpROzXBLOBEETiC7ZXUTLzWJg4Ze+cLaY2LZ2smSBvJpElFbcSwKdWd/YS7F5MwyafMMj0GhhFi0CThiuo4HBPn7lAulfnts9uwa4SJGk4yMUpn2uq6k63KxhFi6dv6GP9Q+mKOU9E4fArojz2y3KB1nCQi5M+3YgEqC4Z1Tn5TW+ivcgdX9gGVX51jv94PYtPnPy1ODugs35VijdXpbF0m3nHeDn4vDC+enXOXgMEAsHEEfs1wWwgknwFexVzJT1uLBRZIyDHCGix3b0UwRiYukX32jyP/LgbPT+0g5ckOOj8EAdfEMIVmJ7L58DGQlUxBNCzdiiNbboFUT5psvHRSjEEkBs0kVUJzS2NvO6lpwdYdKKfJ3/VS6rbIDLfwcFvDeNv1NAmIYzWP5SuKoYAXr01TvMyz6RNELxRjWUXR1h6ehBsG4dPQXWICIVAIBAIJo4QRAKBQFCFbL/Bg9/vKhMrtg1r/pHA16Dh8stEF7nw1u1aJMIojh2kryWWdgWjaJWlrFWbjyx0kthaYv+3BPDWadzzzY6R+WR7ic2PZzj1C020LPcgyeO/fsu0SffoNedzcRPLnFrCgqxIeMLi50wgEAgEU0PcRhMIBHOGrJEgrQ+QNRK7eylseiJTU4y8/s8EmV6Du76yjWR7aZfOU7ekdr+pQJOGwzv9l2nNJaO6aouY8HwnJ32qkQu+P4/FJwV45nf9FY+xbXj8//WSi0+s34ysSLQc6qk5X7fUiTrLrnoCgUAgEIAQRAKBYA6QN9K8mXmSmzuu5YYtH+fv7dfweupR8kZqt6zHtm0SW2sLnUy/gSuoUMxYPPrzHgqpqTehdAcVlp5emRcvSXDMB2Iz0kfHHVI4+PxQ1bnwAgfeqIo7pOKJqOQTZs0oViFpUhyrQepONC/3VO8fJMHhV0TnVONXgUAgEOw7CEEkEAh2K4al81r6Ye7u/jEJvQuApNHDvb3/zcvJe9Gt4qyvSZIkGg+uXY8WnucYSf+KbylRSE1cFOyM06ew/PIIJ3yinmCrA4dXpnm5m3O/20p0jOjRrqBoMvudGWTZRWEUbUekqPkwN6de3VQuwsbLhptEtqAvpnH2N1tpGGWV7W/QOOMrzQRbhSOcQCAQCHYPIulaIBDsVnJmgicHbwLAr8Y4OHAqIa2JvJliXXo1BwROIijXz/q6Wg7zoHlk9Fyl2Dnw3BDP3Tgw8m/L2DWzTndQZdEJAZqXeTBN0FwSDs/MRkvcQZVll4RZelqAUs5CdUq4AgqOnaI0nrCK6pIwCpWv0R1Wqkd8xiDY4uCUq5soZkwsExweWdT/CAQCgWC3In6FBALBrJI30+hWHgkZtxIgb6Yw7BKHBE6n2X0gLyTuor+4GZ9ax7Lg6ZhW7UL8mcQbUzn7my2s/nkP8e3pc66AwqGXhulekyc3MJQm5/TLkxYFtXBNsg/PrqI6ZHz1Y6/dHVY59sP1PPrTnjKHOEmG4z/aMCUx4/Qp0+6cJxAIBALBVBF9iISvvUAwK+hWkf7SVh7uu4He4kYUSeMg/6kcHDiFh/p+ywGBk3io77cVzzvYfxrH112FS/HuhlVDIWmQT5kUEib5hMm6+5L0vVEYmT/h4w0sON6HPAGntT0VvWCR7tZZe2ecZKdOZKGDA88Z6vUjLK7nJinDpLek81QqAzYcHfQR01SCmrgPKtizEPs1wWwgrowCgWBWGCht4+/t38DeHmYwbZ1XUvfS5FrK8tB5PNL/u6rPW5N+kBXh83ebIHIFVVxBlULQIDeYo5A0kWQIz3Oy4qoodYucu1UMJXSDkm2hIhFxzEwdjuaSiSxwcsyH6jFLNqpLQlGFEJqrJHWDP/X082giMzL2j4EEJwR9vL0hSnSGPicCgUCwpyIEkUAgmHEKZoZH+/84IoZG81T875zd8ElyZrLm8wdK7YQdzTO5xHFxBVQWnxyg+TAPlmmjaDKuwO5L+0oZBuvzRW7qGaSjWKLBoXFJfZgDPG4iMxQFUB0yqmNGDi2YRt7MF8rE0DCrkxlWBnxCEAkEAsFOCEEkEAhmHN0q0FVYV3Uuqfdg2mPbVjvk2o5vs81M2GBPFsOyeTKZ5bddfSNj7cUSP93Ww6WxMOdEQ/hUUaOzL5I3Tf45UPvmwj2DSfZzOwnPoiiybZu+ks6AYZAyLBodGiFVEel7AoFgziCuRgKBYMaRkHHIHkpWrup8wczS6FxCd3F9xZwmOQlpjTO9xD2Kfl3nLz2VzVIBbu+Pc1zILwTRPoph2+TMGh2FgZxpMps2JbZtszFf5Adbu0gYO9Z1uM/D+5tj1IlolUAgmAOIJHCBQDDjuNUgywJnVp1TJQdeJcxp9R/ilLr30ejab2RORuHcxs/hVcOztdQ9grRpkreq++EYNgzoU28UK9iz8SgKh/lq19sd7HXjkmav5q2npHPdls4yMQTwQibHHf1xStbUe3gJBALBdCEiRAKBYMZRJIVDgqfTnl9Dz6goUL1zEafG3s8LiTvZlnsFh+zhkOAZHBe5gt7iZhZ5j8CvRlEkcakajTJON1RtFje8grmFIkmcEvbzQDxJxiwXGz5F5sRQgMAspqptK5Yq1jHMQ/E050RDNDlFYZpAINi9iF2GQCCYFQJqHSdG/4202U934U3cSpAFnsP4e8e1GHYRgLyV5rGBG2lxHcjZjZ/Gq4Z276LnKD5FocGh0lOqjAQFFIWQSJfbp6nTVL6xoIW/9Q3yXCoLwBEBLxfWhanTZvez0V2snaBXsm1KNSKdAoFAMJsIQSQQCGYFSZIJORrZEH+WvuIWFngO48nBv4+IodF0FF4joXcKQVSDeqfGx1sa+O6WTgqjNpSaJPGx1npCihBE+zKaLNPqcvCexjquqI9gAy5ZxqcouJXZzZSf76od/fErMs69uH+XQCDYcxCCSCAQzBoeNchRkYvJmylMu8STg3+r+dg30k/S4j5oFle3Z7HQ7eS7i9t4Oplhc6FEi1Pj6KCPiKriFhGifR5ZkuaEvXaDQ6PJodFVqowUXVAXJiac5gQCwRxAXIkEAsGs4lQ8OBUPaWMARVKx7OqOWA7ZNcsr27PQZJlmp4Pz60Loto0sSThl4ZMjqE3eNEkaJnnLwi3LBFV1xiNG9Q6Nq+c1cUNXH69m8wC4ZInzoiGOC/pQxGdWIBDMAYQgEggEuwW3HODAwCm8nLyn6vx+/uNneUV7Jqosiwu5YFwGdYPfdfXxTCqLDUjACUEf72isIzyDURpJkmhxOfhISz05y6Jo2XgVmZCi4BGRTIFAMEcQv6MCgWC3oMoaK0IXsCX3Ikm9p2zuiNCF+NXoblqZQDC7GJZN3DAY1A0sIKqphBQFxzRFbzKGyf929PBiJj8yZgOPJjNYwPubY3hmuO5M9BsSCARzGSGIBALBbiOg1XFJ8zfoLKzjjfRjuBQfy4JnEtQacCm+3b08gWDGKZomL2Xz/KK9Z8QgQ5Mk3tkY5YSgH+80RFGShlkmhkbzeDLDZfWRGRdEAoFAMJcRgkggEOxW/FqU/bXjWOI7CgkZWRI1BYJ9h17d4EdbuxltPq3bNjd09dPmcnCQ6tnlc6TN6nV6MBQpytboEyQQCAT7CmLnIZiz2LZNxhgkqfeQMQaxbfGjvTejSKoQQ4J9CsOyuXcgSa1OPDf3xsmNIWYminec1LvZtuIWCASCuYaIEAnmJCm9j43Z53g2fhs5M4FHCXJU+BKW+I7BowZ39/IEAoFgl9Fti44qdtTD9JZ0ipaNZxez2YKqwmKXkw2Fyp5fy7xuAiJdTiAQ7OOI20KCOUdK7+fFxD95pP935MwEADkzyUP9N/BC4i50q/JHfapkjQRpfYCckZq2Y46FbhWndf0CgWB2sG2buK6ztVDkzVye9kKRpGHs0jEdssxit7PmfJvLgWsabKkDqsqn5zWy0FV+rv3cLj7cUo9PuL0JBIJ9HBEhEswpCmaGpN7Ny8l7q86/kLiLQ4KnE5Qbduk8eSPFltzLPBX/O2m9j4ijheOi76DRtRSX4t2lY1cjY8TpLrzBq6kHAFgWOJMG1xJ8anjazyUQCKYX27bpLuqsSqS4bzBF3rLQJIkTgj4uro9QP0UHNUWSOC0c4O6BBMZOeXMS8Lb6yLSls9U7NL48v4nk9l5EIVUhqCoE1D1zG5A2DBKGybZCCZ+i0OTUCGsqqiTt7qUJBII9kD3zSijYa8mbaQpWBovqefMWJnkzRVCbuiAqWQWeT9zFc4nbR8b6S1u5o+s/OT32YQ4InIQiTd8d04wxyD+7fkx38Y2Rsa25l2l2Hcg5jZ/CK0SRQDCniesG9wwm+ddgcmRMt21WJdIkDZMPt9QTnGIvn5im8bUFLfx3ew99+lDEKaAofLAlRrPTMS3rHyaoqVNe51wisb2n0pOp7MiYS5b4wrwm9ve4UWUhigQCweTYI1LmisUihxxyCA899NDI2KZNmzjjjDPwer0cdNBB3Htv9YiCYM/CtEso4+h0Rdq1TULeTPJ84h9V51YP/ImsEd+l4+/MttyrZWJomM7Ca7TnX5vWcwkEguknbVo8EK+eVvt8JkdyF4wPVFniAK+bby1q5ftL2vjPxW1ct7iNI/zeaUmX29swbZsH4ymeTGWRAHW79ilYNv+5pYuBXUxjFAgE+yZz/lZRoVDgHe94B2vWrBkZs22biy66iGXLlvHss89y2223cfHFF/Paa68xb9683bhawa7ikL1kjTcJaU0k9K6K+aDWgEfZNVOFlN6HTXXHuqKVpWilgbpdOscweSODWwlwbuNnkZDJmnFeSNxFxhhkqe9YFEkhpffhVcMo0pz/OgoE+yRZ00S3a3nBQb9uMM9VuxZoIoQ1lfBeEL2ZaZKGwUvpLB9vrccpyei2jVuReSaVYVU8zdpMjoaIMN4RCASTY05ffdeuXcs73vEO7J1+iFatWsWGDRt4/PHH8Xq9HHjggTzwwAP89re/5Zvf/ObuWaxgwhStPDkjQXfhDSzbptm9Hx4liFPx4lXDyJLMSXXv5t6e/6ZgpUee55J9nNv4WbxqaJfOr8oONMnF4aHzaHAtxrBLqJKDnsJ6XkjchTxNX4uCmeGNzGoeH/g/dLsAQFBr5LS6DyLJMmuSD3B/7/+goHJQ4FQODZ6FX5seISYQCKYP7zgubMKlbfawbDivLsxvOvtGInMycEYkyLsa6+guiQiRQCCYPHNaED388MOceuqpfOc738Hr3VHo/uSTT7JixYqysRNOOIEnnnii5rGKxSLF4g53r1RqdlzFBOUUzCyvph7g8YG/wKjuG0eELmRF+DzcSoBFvpVsy77CGfUfIWP0E9e7iTnn0+w6YJdqh4bxKRHOafwMzyf+wdPxm0fGW92HcF7T1biVwC6fA6CzsI6H+39XNpbW+7Alm392/ZCitSP//bnEHazPPs0lzV/Hr0Wn5fwCgWB68CkyB3ndrM3mK+ZimkpERHZmDd22+Xl7D6VRN0ot4N7BJO9sjLLcv+uNbAUCwb7HnL6Kf/SjH6063tXVRXNzc9lYQ0MD7e3tNY913XXXce21107r+gSTJ17q4PGBP1eMP5e4nVb3Qcz3HoZb8bPUfwxZI0HU0cZi2YFL9qLIU3Ny2hlVcvJC4k7a82vKxtvzryIjc3bjp3b5HDkjyRMD/1cxvsh7JG9mHi8TQ8Mk9W7a82s4UDtpl88PUDSz5Mwk3YX1KJJKg2sxbiWIQ3ZNy/EFgn2FqEPjg80xrt/aRUdxR9+gsKrwhflNQhDNIq9kcmViaDT3DCT52sLpdwkVCAR7P3vkVTyXy+F0ludrO53OsgjQznzlK1/hc5/73Mi/U6kUbW1tM7ZGQSW6VeT5xJ01559L3EGjaylOxYMkyfi0yIysI2+l2JZ/terc1vzL5MwkLsW3S+ewbIPBUkfFeJN7P15M3F3zeevSq1niOxpN3rV6hLyR4tn4HbyQ3PF+S8icHHsv+/uOx6mIu6gCwWRocjr4yvxm+nWDjmKJeodGk0OjboqW24KpsblQqjnXpxvCdlsgEEyJPVIQuVwuBgYGysaKxSIeT+1NntPprBBRgtnFtA2yxmDN+awRx7Rrd22fLkpWbpz5yrSYySJLCkGtocIYwrR11DFc8jTZiTQN5o9dhTfLxBCAjcVDfb+h0bmEemXhLp9DINjXqNsugA7wumflfLZtk7csFEnCKRznSOoGzWMI0GaHJgSRQCCYEnvkFbalpYXu7u6yse7ubpqamnbTigQTQZNdtHoOqTnf7DoQhzzzGw2HPHZ0xDnO/ETwqCGOiVxWMb4h8yz7+Y+v+bzDgueg7mJqYMHM8Gz81przLyX/hWmLwmOBYC7TV9L550CS72/p4ifbunk1kyO1j1tK5yyLRqeGt4Y4vCQWIbiHNpoVCAS7lz1SEB1zzDE8//zz5PM77uSvXr2aY445ZjeuSjAeiqRwcOBUNKmyhkWRNFaEz0OVp7cRYTU8SpB57kOrzs13L582U4U2zzKODl+GzA4HqrTRx2LvkTS69qt4/EH+U4g4Wnb5vKatkzFr91JKG/2zEokTCARTo6ek842N7fyxu5/XcwWeT+f49uZO/tY7SHo3iaKiZdFX0ukqlkjou2cNqiTx955BPtHWQLNzx40jtyxxeX2EmEOIIYFAMDX2yKvHySefTFtbG+9973v5+te/zj/+8Q+efvppbrjhht29NME4+NUYb2u9llW9v6a7+CYAMecCTot9kIBWPytrcCk+Tq//MA/2/oot+RdHxhd4lnNq7IO7XD80jFvxsyJ8PgcETiSl91KycpSsAg/0/i8HB07jkMDpbMm9iCJpHOw/lbCjBY9aW4zZtkXGiJMzE+h2Eb8axa0EKqJqDtlNk2s/3sxUd11scx+CJon00X0Jy7TRCxaKKqE698j7YPsMRcvilt5B4kZls9f7BlOcFg7in4EoSFw36Nd1Oos6MU2jwaER3S4w+ks6f+sdZHUyjWnDfJeDj7TUo0gSCd0koCqEVIXQDJtLBFWF/bxuftPZx7nREPUOFXO7v8Kj8RQnhPwzen6BQLD3skcKIkVRuP3223n/+9/PEUccwZIlS7j11ltFU9Y9AFmSiTnn89bmL1Iws4CNU/FOW1Rmovi1KG9p/AR5M0XRyuGUPbiVIC5leh2KNNlJUK5HQuJPWz+PYQ8VBPf0bcCrhGly7YeNRUCrH1MMWbZJb3Ejd3ZdT85MAkMmCcuD53JE+AI86o5GhJrs4qjwJWzIPI1F+abKIXvYz388kiQ2xdNB0bJIGCZ508KtyARVBdccqvWwTJtMn876VSm61+TxRFQOPj9MoFnD6RO9c+YiGcPksWS65vwTyTQL3RO7oRHfbgDxUiZHUFE4IuAlrKkVn9G+ks73tnTRXtxhWBBWFb66oAW/IvO9LV1s2z7nlCWuaIhyQ1c/b+QKI49vczq4en4jDY6Zi/I7ZJmLYiG25Iv8obt/x7gk8aX5TUREupxAIJgikr1z19N9hFQqRTAYJJlMEgjM7mZcsO9hWDpr0w/xUN9vKubObvgUS3xHI0u1N6hJvZcbt34Bw650Ujwt9iEOCZ620/lK9BY38WDfrxgsDdnRN7r247TYB4g6WoUgmgbiusHfegd4OJ7GBBTgpLCfy+ujhOeIDfPg5iL/uqYdo1h+mT/inVGWnh7E4Rafg7nGQEnnk29swaoxf240yEWxCD5FRh7DQGBA1/n+li62jHJlk4CPtTSwMuDBtb2ZbMYw+fG2bl6t0WPpC/Oa+OKGbSNjb60LsaUwJLJ2Zp7LwVcXNM94HU/SMOgvGWzIFwmpCgvcTiKqiipPv6GCbln06QYvpLNszhdZ4nFxmM9DvUMb8/0XTB9ivyaYDebGr7ZAsJejyhr7+Y4jorXwdPzvJErdRBytHB25jIizdUwxBLAt90pVMQTwdPxmFngPx6eGR53PQbN7fy5p/joFK4OMjEvxT1s64N6KbhUomFkMu4htWzgVL95R7+swOdPkT939PJbMjIyZwKp4moJp88GWGB5l90ZgCmmTJ/63t0IMATx/4wBtR3pxuGe+Zk8wOTyKwsqAl6dSlb3KAPbzuPnahq2cGQlxYshfNU1Ntyzu6EuUiSEYaoX9i44e/sszj6btn8+UaVYVQzBkY500TRQYiTXv73FzR3+i6uO3FkokDXPGBVFQVQmqKos9M9tTzbQs1ueLXLe5c6T30aPJDG5Z4msLWmb8/AKBYPYQgkggmCVcipdWz0HUOYdS5zTJOeF+QP2lrTXnMsYAll1ZbwDgUYN4CFadm06SpV5Mu4QsKXjU8B7Z/DWp95E1Bng19QBvpp/AxMCvxjghehXzPMtwjkqnTBomj48SQ6N5MpXh8obIbhdExYzJwMbqItq2YWBDkUCjEERzDbcic0VDlFcyeXJWeZzocJ+HvpJOr25yY88AmwpF3tsUw6+Wf9aShsmqeKrq8W3gpXSOJufQ375kjZ0kkjctHLJMfvta9HGSStJVap/2VPp0g59s665oBJu3bH7a3s01C1uIaKIPlUCwNyAEkUAwywzVKU2uVqnRtZSXk/dUnQtqjajS7vkqZ40EnfnXeGzgL6SMXhRJ4wDfiRwZuYjgLJlkTAdpfYDO/FpeTP6LvuKmHeNGH3f3/JhzGz/HEt9RI+NZ06LWttAGMmathKfq5Exz5JhhRUFTpiGVbZxkaHucjbBg99Ho0LhuSRv3DCR4IZ3DrcicGPTjkCV+29k38rjHkxkuiYUrBJEFFZv40STNHaLFo8hoklRT6DQ6NfRRwkyVhjql1fqE70221wnDJFFD4PWUDJKGKQSRQLCXIBLIBYIJUjJzZPQBskYc257chndXaXbtj0uunu52XPRKPGpoVtczTEd+LXf3/ISU0QsMWX6vST/Iv7p/QkrvH+fZc4fewgZUyVEmhkazuv+PZIwdVubjGSe4J2isYFg2m/NFfrqth8+8uYV/37CNv/YO0lPUsXaxvNPhlQnNqxEBkqBuyZ4XxdtXkCWJBofG2xuifGFeI4f5PNwXT/Krzj523p7vnBYHQ5/Pha7axguH+nZEpkOqwnnR6lHkI/xegorC5+Y1oWwvl3kpk+XYYPVr0TKvm6C695h1FK2xr/PjRcsEAsGegxBEAsE4mJZOf3Er/+r5OX/c+jn+2v41Xkj8k6xRu9fPdONX67i05RoijtaRMU1ycVLdu2l1Hzxr6xhNUu/hsYE/V53rKW4gbew5gqi7uIG+0paa8ymjD93a4agVVBWW1nD6WuR2ElAndmltL5b4+sZ2XszkMO2hyNKdAwl+tK2LntKu9YpyB1WO+1A9cpUb9ssuDuMK7j0b170VTR4yTri1L05nsfrnwVslmhhQFd7TVEe1kv/FLieNjh1RDYcsc040xOX1EdzbTQlUCc4IB/hAc4ygpnKIz82Pls7n460NNDo0Lq2PcGrIP9JhTQJWBjx8pLWhIlq1JxPW1BEhuDNOWSKwm9NiBQLB9LH3xLYFghlioLSNm9q/PmJhrRtFVg/8iS25l3hLwyfKLK9nCkmSiDrbuKT56+TNFKat41L8eNUwym5Kl9Ot4piip6uwjhb3AbO4oqnjlL2ocu3UF0XSyt5nv6rwybZGrt/SxdZRVsWtTgefaWskMIG0oZxp8pee/qp3mTcXSmzc7qDl3oVNV3iBkwu+N4+1d8Xpeb2AJ6JyyIVhogucODxiM7cn4FcUDvG6qxofOGWJFmf1KOACl4NvLmzh9939bMwXcckSZ4QDnFsXrnBBDGoqF9aFOSnkp2DZOGSJkKrg2B7pdMgy9Q6Z+lFC6t1NMS6KRchZFi5ZIqgqu71ubrrxyQrnRUNVTSTeFosQ3YvEn0CwryMEkUAwBgUzw8P9v6/o5wOwLf8KSb13XEFUMvPkrCSGVcQhe/AqIZQxNt9j4VGDsyLAJoIsqcgoVd8bAJ9S6c42V1nkXUHC6EaRNEy78k78Ab4T8Sjl73u9Q+PfFzQTN0wGdJ2ophJW1Qk3p8ybFq9kqrt7AbycyXGw1417CnsuU7fIJ0xKOQvVKXH426PYFiiatNcLoYJpkTRM8ts36oE9fKPuVRU+0BzjPzZ3MqAbI+OKBJ9vayJcQ3w7FYX9vW6+PL+ZomUhSxBQFLQa6ZyKLFHnmPh1yaXIuKaj1m0OE3GonBEO0uDQuLM/QXdJp8Xp4JJYmAM8LrQ9+HMlEAjKEYJIsFejWwUklDHv/u+MYZXImQlyZgqH7KKrsK7mYzfnXqDJvbTmfFof4JH+37Mx+ww2NprkZEXofJYFz5ozwmaquJUAS3zH8EbmsYo5RdJocC3ZDauaGj4tSrzUzRn1H+H+3v8pE0X1zkUcFbkUVa68Ex/ShgTQRBtljkaShmo9dnYSG8Yly+P5IlQlnzRYd0+StXclMIo2kgRtR3pY+Z4Y7uDefcnfuTeUBBwV8PKuphjROdIbaio0Oh18a2ELGwtFXsvkaXRqHObzEtGUcXvvBFQFEBv3qVLv1DhW9XGw143FkBCt0zQU0YNIINir2HN/IQSCMUjr/WzNvcwbmSdwyh4OC51NxNGKW/GP+byCmWZN6iGeHLwJ09Y5p+HTKKiYGFUf75DdNY+VM5Lc3f0juovrR8Z0u8hT8ZtBkjky9NYpR4rmAm7Fx7HRyxkstdM/qv5GkTTOa/wc3j0oQuSQ3bR5DyFvpHhby7X0l7ZSMNM0uZYS1BrxzoBpRUBROT0S4B81erocGfDimGSjSVO3WHdvkpdv2VHfZtuw9ZkcuXg3p36haa8VRQXT4qbeAVbF0yNjNvBUKkvetPhkW+Ocr2/JmyZJw6SrpOOUJGIOjfD2hqNRh0bUobEysOf0EksZBjnTQpYk/Iq8S+mfuxOPsmdHGQUCwfjsnb+Mgn2alN7HLR3fImXssKddn32KZYEzOSZyOW61tihqz6/lsYEbR/69KfcCi31HV42CACz0rqh5rKwZLxNDo3k+/g8O9J9EQI6N93LmNEGtgfOaPk9S76Ezvw6/GqHJfQA+JYxDqS0W5yIO2YXD4SJIPQ2uRTN+PlWWOCsS5KV0rqwOCeDCuhB1mop3kpuwfMJk7Z2JqnP964vk4ua4giiuG2RNE1mS8CnyhOqh5gIJw+DhUWJoNC9n86RMc1YE0ZCosSjZFm5ZHhE045EyDP7Rn+DO/sRIZNAlS3ymrZGDve6aqW5zkZJlsaVQ4jedvWwulJCAw/0e/q2xbqT/kUAgEMwl9oxfOoFgghiWzvOJO8vE0DCvpO7joMCpNQVRzkjyxMBfy8bezDzB+Y2fp7vw5oi19DAnRt81ZhQkXuqqOafbBXSrdv3InkRQqyeo1TPPs2x3L2WPI+bQ+ML8JrYUijydyuKRZY4KeomqKrFJ1HMMo+ctjGLtRLt0d4nogurpfSXLYn2+wP929NG93eFugcvJR1rqmedyIM/xFKGcZdXsjQOQ0I2aBgTTRX9J54/d/TydymIDblnioliYU8OBcYXlK5l8RbSwYNn8YEsX1y+dt0cJie6izjc3to9UF9rA8+kcG/IdfHtR65Q+2wKBQDCTCEEk2KsomGleTz1Sc/719KM17/6btkFC79ppTOe+3v/h5Lp3U7TybMu/glcJcVDgFPxqDKfiqXosAK9aWyxJyKjy5OtOBHsfMYdGzKGxzOvGBJySjDLJVLlhVKeMJEOtNlmecO1LfldJ59ubOstExeZCkW9uaud7i9tomOMb8vF6P3kUmf6STmCUe9p0kjAMfrS1mw2F4shY3rL5S88gEhLn1YVq1p0kdYNb+garzpnAY8k0b6uP7tL6DMtmUNfJWzZJ08SvKIRVZUwTkIJlkTctVAn8E4wU5k2Tm3oHRsSQU5KIaio5yyJhmLycyXF6ZM+unxQIBHsfQhAJ5hxFM0fRypI14khIeNUwLtmPpowvIGzsmvU+AIZdrDmnSApBrbFCFOXMBHf3/ISz6j/B2Q2fQprgnfKgWo9XCZM1K/sVLfUdg1sRmwLBDpzTUKPgCijMP8bH5sczFXOeqIo3Vv2SXzAtbu2NV42wFCybR5NpLolFpjVK1FfSyW7fbHsVmbC2a1GDgKKw3OfhxUyuYm6ey8Fr2QJ/6RngxJCPS2KRSTmqTYRB3SgTQ6O5rS/OcUFfzXMa2PSXal+3thVKWLY95fc/bZj06Tp/7RnkpVHvT4tT4+p5TRXRJ92y6C7p3NoXZ12uQEBRuDAW4kCvm+A4wihnWbyWK+CUJa6ojxLVVDqKJQKqQkBRWJfLYVh2RRph3rRImyaWbePZg1I1BQLB3oG44gjmFHkjQ9Lo5rXUw7Tn1+CQ3ezvP4FG11LCNI8ZkQFwKV4WeY/kzcwTVecP8J9Q87keNcSxkSu4u+fHFXOa5KTJvd+ExRCAT4twUfO/c3vndWTMHXd/m10HcELdVThk14SPJRBMBM0tc8RVdeQTBj1rdzSS9UZVTv9KM95I9Q15fnu6XC3WZgucF7Vx1+pSOQnypsWr2Ry/6+ofsZE+yOvmvU11NDsdU3bvGran/um2Ht4Y9VpanQ6uaqjjv9t70G2bB+Np3swV+fcFzRX9eHaFjp3qwEaTsywKNdwEARySzHyXs2zdoznY656yGLJsm035AqsS6TIxNLRmne9t6eKahS1l78WWQolrNrVjbs++HNANfrythzPCAa5siOIboxZLQSKiKFzRWMftfXHeHPWaXLLEp9oaGUqi2/F6eoo6N/b082wqiwXMdzl4X1OMhS4njr3c2lsgEMwNhCASzCky5gC3d15H0cqOjPUUNzDfs5wTo/82riDSZBfHRC5nS+5FSjvV6LS6DiGkNY/5/FbPwRwXfTtPDf59xHrZp0Y5t/Gz+NW6Sb+eqLONy1u/TcroJ2fGhxzLlDAeNTDpYwkEE8EbVTn5M43kEybpHh13SMUbVfFEal/uNUkioqr069WjFPWaijZN0aEN+QI/3NpdNrY2m+c7mzu5ZmHLLtXK1Dk0rp7fSMIwR8RWT0nnF+09pMwd/bK2FUu0F0vTKogiY0Q0FECTam/s/arC2xujXLupo2LOK8ss93unvK64bmDY8FSyMmoI0F3SGdSNkfciZRj8urNvRAyN5v54irOjwTEFUUhTeVdTHauTmTIxBEPRxp9s7eb7S+fR4Bh6P/pLOt/a3FHWY2lLocS1mzr4zuJWFrrFjSOBQDDzCEEkmDPkjCTPDN5SJoaG2ZJ7kUODZxF0NKBIY39sQ1oDV7ZexwvJf7Ip+xwO2c3y4Dks9K4Y1z7Zrfg5LHgOS33HkjdTKJKKWwngUyNTfl0+LYJPm/rzBYLJ4gqouAIq4XkTq1PzqQoX14f53pbqRiBnR0MTckobj5Rh8OeegapzCcNkTTZPVFN3qcYnoKoEVBWPLPPJN7bU7OX0cibHMt/YN1gmQ6NTI6goJM3KRsXHBX0EtbFTIue7HHy6rYHfdfaPHGOey8EnWhuI7YJwK9k2um2PaTgxqBss3v7fWdNic43UP4DXsgVaXWN/ruocGo/XEGBF2+b1bB6XJBHUVF7PFcrE0DAW8JfuAT4zr1FYXgsEghlHCCLBnEG3CmzKPldzflP2OVrdB48riCRJJuRo5MTov7EyfDEyyqQiMprsICgPOafNVYpmjpyZpGhl0WQXHiWAWxFRJ8HUWeJ2cUkszK198RERoUjwgeYYDdNUb1OwbDbla2+21+UKHBPw4ZiGLCkJCacsUbCqS6KxIjpTIaKqfHVBM9/Z0knS2CGKDvC4uLKxDte4pg8KRwd87Od2kzFNFEnCr8rj1uyMhyZJyNLQ/+t29fdidG3TeLJXm6AwrnUuGIraPZZMc0YkyHOpyhtgw6zLFShYlhBEAoFgxhGCSDBnkCQZJIlat3RlSUWVJr4xU2UNnzy3moNmjTg5M4Vhl/DIATxqEG2StUQZY5BH+v7A+uxTDL9ZDc4lnN34qTkt4gRzG7+qcH5diJNCfjYXiqiSxDyXk6Ci4JymOg4FiGhq1YgAQJ2mMF0VI0FV4cxIsMLKWmKoZumowNTT0KohSRJtLgffXdRGr66TMAyaHA7CmjJhUSNLElGHSnQaf5pDqkrRLHBSyM8D8VTF/IEeF35FZluhSNa08CoyH2qO8fvufoo7iUmJIYE3Hk5ZJjrG37nV6eB3Xf0cGfARddR+rQFVQRpXogkEAsGuIwSRYM7gUYLs7zuetemHqs7v7zt+SDTNAVJ6H+35NWzJvURYa2I///H41Tq0May0+4vbuKv7hyT1ofoJGYXDgudwRPgCPOrYjnOWbZI14li2wVPxW1iffbJsvqe4nru6ruei5n/HM05a4N6GaZvkjDi6XUCVHHiUEKo8ty2i5yoeRcGjKDTOkMV2RFM5LxrkD92VaXMycFTAh2eamqeqssQ50SCvZ/O8uT0qdZDXzaWxMGuyeX7T1cdCl5Pjgv6hGqlpEH3SsKAZY5M/26iyxCE+NwFVxrRtHk2kRyyxj9jeLHVnI4pDvG4+29bIj7Z1l4midzXWTUjchVWFdzRE+Vl7T8XcPJeDnGWRMk3ypsXJIT937iRahzmvLkRoFprpCgQCwdy5agv2eVTZwcrIxWzJvVRhVX2g/2SCWsNuWlk5g6VObu74Jnlzx93WZ+K3ck7jZ1ngWV51M57W+7ml41sUrPTImIXJC8k78akRlofOrin2ckaKdZnVPD14M2fUf4R16dVVH9df2krWTOxTgihnJHkt/TDPxG+jZOVQJI0D/SdzVORSfGP0gRLsHiRJ4uiAjw35Io+NqjHRJIkPt9RP++Y3oml8fl4TvSWdrYUiDQ4H123pxNi+x38+neOO/gRfnN/E/m4njr00NSukqaiSRIPDwdnREEXLwqPIeGSZn+wkhgBezeZRJYnPtjVyZ3+CqKbylmiQBoeGewLCUZIkDvN5+EhzPX/tHSBumCjAkQEvp4QD/GxbDxLglCWCqsqHm2P8b2dfWXLAkX4vxwR8k3L2FAgEgqkiBJFgThHUGris9VuszzzNhuxTOGQPy4PnEHMuGDeKMhsUzAyren9VJoZgqP/RPd0/5d/m/xcBuTJtrbe4qUwMjebZ+K0s9R2NT6tsvGjaBq+lH+KxgT8DQyJqrPLojDFIzLlg4i9ojpMzkhh2aXsdWBBZ2rFhNS2dV5L381T8bzvGbJ1XU/eT1vs5q/HjuBX/7li2YAyiDo2rGqK8tS7Em7kibkVmgcuJX5EJTKPr2zAhTSWkDUVtvrqhfUQMDaPbNr9o7+HrC1po3ksFEQwZZ+zsDtdeKNa0+n4xk+NdTXVcPb8JFWnSpho+VeFwvwdNllC3i5qXMjl+tLWbkm1zdMBLUFVwKzLHhvwc6HWzNpsnb1kc7PUQ0VQCIjokEAhmCSGIBHOOgBbj8NC5HBw4BVlSx0xDm20KZoaOwmtV50wM+otbCVSp4xkoba15zLyVxqjRTDZrxHlm8NaRfyuoyChYVDpZAbvkhjeXKJo5eorrebT/jwyUtuGSfSwPncfBgVPwbo/8ZM0EzyXuqPr8LfkXyRlJIYgmQcow6SvpPJnKIANHB33UaeqMNMiMODQiaMyfRUvlpG6SMKp/b+KGyaBh0GBpKNPgprenkDXH8p4b6hnlck49lTCoqSxwOfnelk56R9UTHehx8a7GOtzbBahLlml0OmYsVVMgEAjGQwgiwW7Btm1MW0eR1KqpYpIk4VSmr+i5ZObImSky5iCa5MKrhPCq4UmnY1g1hMvIeXbqfTRM1DG/5nPcSgClxlexZOUp2TuOuTn3Akt8x/BG5rGKx9Y5FuBV9o40sfb8Gu7q/uHIvwtWhicH/0pPcT1n1H8YtxKgaGUw7NqOZSmjh6izdTaWu8eT1A1+391fZpV8e3+CU0N+rmyIEpyByM1sY4zhegZQsmyKtoWHfScq4RsjIiYBnmmoq2pxObh2USuDukHCMIk5VEKqMiNCWyAQCKaKuCIJZhXTNkjr/axLr6a7+CYRrZWDg6fhV2NoM1QInzOSPB2/hVeS92Jvz1L3KmEuaPoiMef8SRk1OGQvfjVG2uirOl/vWlR93LkAtxKoSLUDWBm+uGa9iyo5GNqaDK17beohzm78FKZdYkP22ZHxRtd+nN3wiTmRVrirZIxBHun/fdW5TdnnyBoJimaO8QyChQ35xFmXK1TtG7MqkeaYoI/D9gJBFNQUnJJEsYowcsoSHkXGMUdMW8Yja5ikTJOiZeNRZEKqMqXeTQFV4RCvm1ezlTdyjtqe0jYdhDV1WpvgCgQCwXQzoStoqVTiS1/6Em1tbQQCAS655BJee608bainpwdlL86/FkwPfcVN/HnbF3kq/ne25F7iheRd3Lj1C3Tk12DaY0dfpoJtW6xLP8bLyXtGxBBA1oxzS+e3SBtDbldZI0FvYRNd+Td4ZvA2Hu37A1uyL5Exys0dfGqYU2Pvq3qug/yn4lGqCxK/VsclLd8grDWPjMkoHBG6kP38x9UUZW4lyCLPESP/tjC5p/tnBLQYb236Epe1fIt3zrueC5qurpqqtydSsvKkjf6a812FN3ik/w+sSz9Gm3tZ1cf4lAg+pbImS1BJ1jC5cyBRc/6ugQSFcVKr9gQiqsrbG6p/Ji6ORQgpyrQ0n50MacOku1iiq1giZUzs+tdX0vnJtm4+++ZWvrxhG59/cyt/6x0kWcPieiz8qsJHW+o5fFSDWgk4OuDl3U2xCfX/MW2btGGQq9KQViAQCPYUJnTL5itf+Qr/+Mc/+MEPfoBt2/z85z/nyCOP5MYbb+Siiy4aeZw9TkqCYN8mY8S5p/vnGHapbNzG4l89P+Mdbd8noNXVfL5t2+TMBJZt4ZDdOJXxu8xnjQTPJm6rOley8nQV3kCRNF5I/JOAFuPhvt+OCKcXkv8k4mjjwqYv4R+1rmb3gVzW8i1WD/yZ3uIGfEqEIyMXsdCzApfiq7mWqKOVS1q+QX57HyK34sejhMaskXIqbk6KvYdUVx/9pS3AUK3S66lHOcB/MnWOebPqwpQ30+TNNJat41R8+JTwtFuhyyiMjopVo6+4ic78a5zd+GmyZpzBUvvInFsJ8tbmL+PT9o56qrEomTlKVh5JkvEowSn9LUwgN4bgyZrWuOlmewKaLHNM0EedQ+W2vjhdJZ0mh8bZ0SCNDsesRjAs26a9WOLXHX0jpgYLXE4+2BxjvstZU5gldIPrt3axpbDjGqrbNv/oT+CQJC6KhdEmGSmKOjQ+0dpAcrsNtleRCajKhMRQb0nn0USKZ1JZXLLMeXUhlrpdhEQ0SCAQ7GFM6Kp100038X//938cf/zxAFx55ZV84Qtf4PLLL+fGG2/ksssuAxD2mPsQRTOLaRs4ZQ+KPLFmqQUzTdKo7EsBULJyZI3BmoIoayRYn3mK5xN3kjdTNLv257jo2wk7WsZMtTMxqqapDTNY2gbYtLoP5B9dPyiLIg3PPzV4C6fE3jNip+2QXTS59+OCpqvR7SIyCt4JWl171dCEHztMQKvjwuavkDb6GCi249fqCGtN+NTorH7nBksd3NfzC3qKG4ChlLQT697FQs/h01rv5VYCLPQczqbc8xVziqThVUIjtuz39vycY6NX4lJ8JPUeoo42wlozIUfjtK1nZ0pZk1LeQs9ZKA7QXAru0OxuAE3bIF7q5ImBv9KefxWn4mN58Gz2958wYjoxUbyyzBF+D+3FHZtsVQLTHpKkKwPeaaklmQuENJXlipd5TgdF2x6qk5FlgpqKMovfpb6SwTUb28mP6vGzuVDkm5s6+N6SNpprmAsM6EaZGBrNnQMJTgkHiDkm/7fyqgreSabH9RR1vr6xndSoyNDrW7s5JuDlfc0xUSMkEAj2KCZ0xcrlckSjO1INJEni+uuvR1EUrrrqKlRV5bjjjpuxRQrmDhkjTnfhTV5I3EXBTDPPcyiHBs8iqDUij3N32q7hjDaMWcOwIG+kWNX3azZmnx0Z25p/mW3tr3JpyzU0u/eveUxFUvGpUTJGZSNIgJhzIevTTxF1zqtpZ70u8yhHRS4mIMfKxl2KDxe1I0LTybCQanQtnZXz7UxK7+fmjmvLxGXeTHFvz8+5sPnfme85dNrO5VQ8nBR7DwMd20iNqtWSUXhLwyd5MXn3yFjByrCq79dokhOPGubk6LvR5JlzLsv06wxuKrLxkTTFrEXDAS7qD3BjlCz89bPnkDVYauembV8b+c7oRpHVAzeyKfsCZzd+alKiW5ElTo8EuT+eYoXfy7EBH3nLQpUkbGCJ24m8F93sUmWJ+t3oZmbaNo8kUmViaJihaE+c9zbFqtYEdZX0msctWjYFa3ZSG4uWxS19g2ViaJgnU1nOqwtVCCLTtonrBmnTREYioCqirkggEMwZJnQ1OvXUU7n66qv53e9+R13djjv43/ve98jlclx55ZV8+ctfnrFFCuYGWSPO4wN/4fX0IyNj8WQna1MPcVnrt6hzzhvz+S45gEv2V+3Ho6ASUKtHh9LmQJkYGsbG4qG+33Jx81dxq9UL6H1qhGMil3N/7y8r5txKkDrHfF4278Fn1U6vMm0da4zeP3OZvJnGsIpIkoxXCU05va2z8HrNSNtj/TdS37yg5t9gKgS1ei5t+Sb9pc1sy60hqDUw33MYOTNJe35NxeN1u0jeTOFS/ZOOwE2UfNJg7Z0JXv9XcmSsZ20eVzDFiZ9sQNFkPOGZ3+AVzCyP9P2h6g2EjsJaknpPxXuQNkwGdIM12TwuWeIgr5uQqo402YxpKt9d1MoD8RQ/3No1cuvCr8h8uq2Rft1gY75Im8tBi9NBZB/byGYNk6Rp0lPS8cgydQ6VsKpOSSgWLItXMtXdKAFezxbIm1ZVQVQ3xvuuSOCcgrHCVMiYZlUTjmFWJ9Is9bhH/p0zTV5M5/htVx+Z7emZdZrKJ1sbWOx2zXrtlkAgEOzMhH7VfvrTn3LppZfS0NDAv/71L84888yRuZ/97GfU1dXx7W9/e8YWKZgbZIzBMjE0jG4XWN3/J85p/MyYdT1eNcypsfdzd8+PK+aOjV6Ju4YhQXtubc1j9pe2kDUTWJg1U4UWeldwfPQdPDV484hNc51jHmc3fhqPEsKjhGlwLq55jqijDYc0e/1SpoOSVaCvuJnV/X+kp7gBtxLk8NB5HOg/cdIpVQAd+bH+Blsx7Np3rqeKX4vi16Is9O4wlVB0DY8SJGcmKx5/aPAsAurMGUvkB80yMTRMIWmy7t4kh10WmRVBVLLydBRq/z02Zp8ti5omdIPfdfXxZCo7MiYB722KcULQh0dVkCSJTYUid/Qnyo6VNi2+t6WLz89r5A/dQ0YXdZrK1xY07zM9YxK6wV96Bng4seNGjl+R+eL8Zha5nZNOtXNIEmFNgRqaKKjWNneIOVSimspAFQOFk0L+aXOF21V2Xn17ocRP28vTpft1g29v7uT7S9pocjrImSa6beOR5UnXQe2MbdskDRMLG4+i4KpyvIJlUTQtHLI00g9JIBDsu0zo17u5uZknnniCdevW0dhYmZt/zTXXcPnll3PHHdWbJAr2fEzbrBqlGWZr/hXyZmpMQSRLMvO8h3FZy7d4cvAm+kvbCGr1HB15Gw3OxTXNBRyyu+o4gIRMQu/in90/4vymq4k4Wioe41b8LA+ew1LfsRTMNIqk4VYCIxbVKyMX0ZF/nUbnErqL6yvOcFLde/Y4O+uu/Dpu77pu5N95M8njA3+mK/86ZzR8dNINS8Na5fs6jE+NjJsuOV34tSiXtHydO7t+SELvAkBC4uDA6SwLnIFnGqNUO7Pt+WztueeyLLt4dnpASUgokoZZQ4Q65fLv4AvpXJkYgqHaoN929bG/x8V8VSGpG/ytd7Dq8XTb5vVcgf08Lt7IFejXDX7a3sNX5jfjnyMb8JnCsm0eTqTLxBAMCcVvb+7gB0vmUe+YWA3lMJosc35dmKdS1T9PF8XCeGts0KOaxr/Pb65odLrc5+ay+uisRYh8isJxQV/F+zLM8aEd38OcaXLTGJ+tR+JpjvR7uLF3kKRhcLDXwznRIDGHhjqFCFxcN3gyleFfAwmypsVhPg+X1Edo2H68omnSVTK4rS/OlkKRBofGxbEwrS5HzfddIBDs/Uzqdub++9eu1Vi6dCknnnjiLi9IMDexx7HEHvrZGt+Jyim7aXLvx7mNn0O3C6iSY0xnNoBW90HUch1b6D2cbblXSehd3NrxbS5v/TZ+rdJaV5E1AnKMgBarmAs6mrCxCTua2ZB9mtdTj1Cy88ScCzmp7l3EnAvHfV1ziayR4KH+G6rObco9T8YYmLQgWuQ9gicG/lI1Teuo8CV4lNBUljolIo5WLm25hryZRLeKI+J2LOE8HVhG7c+3bYGizk7aj1vxc4DvJNakH6g6v9i3cuS/E7rBHf3xqo8DeCCe5L2uGIYNvaXa3/Geok54lPjZmC+SMsy9XhDFDYM7a7x/RcvmjVxh0oIIoNmhcVVDlD/3DJRd1S6IhljsHjsa3eJy8M1FrSQMg5RhUqdpBFVlUn+LgmnRrxs8nkjTq+us8HvZz+OiboKvxSnLXFIf4cV0juROdUTHBX00OHZsLYqWXWbYsTMbCgVSpsna7b2QOopJHoqnuHZRKwvdtR04q5HQDX6+rZs1ucLI2GPJDM+ksnxncSutTgdrcwW+v6Vr5H3vKum8mMnxgeYYJwX9OPYSAxGBQDA5JiSIFEWhq6uL+vod6Sgf+9jH+Na3vjVSUzQwMMCJJ56IKXoR7JUoksZ8z2E8E7+16vx8z+E45Ik7jTkVD07Gt80G8KghTot9kAf7/rds3KdGWRY8i392/QgY6i0U1zurCqKxUCWVOuc8imaWsNbE8uA5SJKMJrvw7GJzz4KZIWMMsi33Kkg289yH4lXD44rAXaFk5Ujq3TXnuwpvEHMumNQx/WqUtzZ/mbu6fkjJHs71kVgWOINF3iNn3WFyKm59u0rbkV5eubX65rh5mQfNMzsbKVV2sDJyEe2FV0nq5WlIx0evwqvsqIezoGrh+zADuollgyZLtDgdbC4Uqz6u1eXguZ0iGkV7z6yrmwymPRQNqkXHGBv9sfCqCmdEgqwMeFmXK2Bhs7/HTVBVqkYpipZF0jDRLRunLBHW1CnXcRVNi2dSWX7R0TMiClYnM0Q1lW8saKHBOTFR1ODQ+PbiVlYn0jyVyuJRZM6LBlnidpUZKmiSRINDI2FU/xw2OLSKFMCSbfPbzl6+OMkoZGexVCaGRh/vz90DfKC5jv/X0Vv11t3vu/o51OehXggigWCfZEJX1Gr9hf70pz9x9dVXl5ksiD5Eey+SJONVwxwcOJ01qfI70y7Zx3HRK2csXckhu1jqP5Ym11LWph8iYwzQ6NoPvxrlgd7/Rbd3/ADGS53M81Rv1jkeTsU7rfbRKb2PlxL/4oXkXWXjhwbewlGRS2fs/ZIlFQmpwkJ8GJc8eTGmyBrN7gN4x7wfkNJ7KFl5Io4W3EpgWt+zuYwvpjH/GC9bniwXBqpTYsVVUbzRyUcKpkpAi3FJ8zfoKaznzeyTeJQQBwVOIaDWlaWtumSJAzwunkvnqh7nCL8HRZaQLLiiIcL3tnRVPMYtSyx0O8tS6jRJwrcPpBdpkkRMU+mr0fR0yTjRnLFwKzJuxTFuLdagbnBz7yAPJ1IYNvgUmUtiEU4I+aZkbZ0wTH45SgwNM6Ab/Km7j4+1Nky4pibm0LgwFuaMSBBVourzfKrCZfURvr25s2JOBo7we/lBlc/dm/kiWXNyUchaaYgAL2ZylGxqCjPdtunX9SlF/AQCwZ7PlCuAq4kf0Ydo7yagxjg8dB5t7kN4Pf0oBStNi+tADvCfSEBrmNFzO2U3TmcbRyhvZXX/n3k1eT9xvfIHtloN0e4gYwwO2ZPvJIYAXk7dwwLvchaoh8/Iud2yj4WeI9iYq6z5klFocC2Z0nEVSSWg1Y3ZPHdvxhVQOOo9Meat9LHmzgSlrEnToR4OOjeEv3H2N1HDphOLfUfVvPZ6FIUr6qO8mM5VmN6HVIVDfB5ezuT4S/cAB3td/FtjlJt74+S22zc3OTTe2VjHTT3lNSAXREOEZjBdTrcsUqaJbQ+lZ+2u1LywpnJlQ5SftVf2TwurCgtcM2sskTJMftnewyvZHQ4MGdPiD939mLbNudEQyiQd2l7P5Wt6Zj6bzpE2rUmZDMiSNO7fZ4HLyXsa67ixZwB9+97BK8t8qKWeeweT4zRkmDiOMfYg2gT2J3KFHYRAINhX2Le8UwVTJmckyZlJilaOmGM+jbGlmJaOU/HOaCH7zjgVH2FHI69nHq6Y86lRwo7maTmPYelD9uA2uNUAtm2RNRPkzRSq5MCtBMZM2YqXung9/WjN+efid9Dk2n9ME4qp4lA8nBj7N/o6NpM2+kfGJSTObvw03lms99nbcIdUFh7vp+lQN5YBDq+MOoVGmNPJeDeiGp0a31zUym87+9hUKCIxFBl6Z2MdG/MFfrxtaLO/qVDkUJ+bD7bE8CsKfkXBo8g8ncrQUxpKDfMrMhfGwpwY8le1hZ4OBnSdO/sTPDiYomjb7O9x8a7GOtpcjhk751gc6nPz3qY6/tozOCIU93O7+GhrPdEZjiYkDKNMDI3mlr44xwZ9Net+coZJwjRZm8mRNEwO9XtpdKjkx+hVZDPUL2i68akKp4UDrAh4GdQNVEkipCqYts2PakQvD/S4Jh2FPDzg5R8Diapzxwd9aNKQS2J/lYifU5aI7mN28gKBYAfi2y8Yl4TezV1dP2SgtG1kbJH3SE6JvW/CYsi0DSTkXXYjUySFgwOnUTCzvJS8G2v7vcWYYwFnN34an1q9n1DeSGNh4JS9qPLYd3WTei/PJ+7kzfQTSJLEGbGP0lNcz3OJO0acvUJaE+c2fpaoo61iQ1oy8wyUttXs2wOQt9I1XcLGw7QMTFtHk501+woFtQbe1nItvcUNbM29jF+Lsdh7FD41Mu7rF4yPy7/nXDodssxSj4uvLGgmZ5rIkoRPkcmbFv/b0Vf22JczeV7O5FGAH+03n5hD4+xIiKMDPnTb3m4ZPbX+OxMhrht8b3MXW0fV5qzLFfj6xna+vbiVRbuQojZV/KrKGeEgR/i9ZEwLTRpqKjobUavOMWqU8pY1ItB2JmsYdBR1enWDkg3z3E7W5fL8s7/ABbHaboitTgeeGaqhcSgy9YpclpKWM03eVh/m773ltXluWeJ9zTF8k3yPI6rMW+tCFfbxDQ6Vc+tChBSVj7U28J3NHZijdJ8EfKS5fkajngKBYG4z4V/19vZ2CoXyYsXOzk7U7TnMvb2907sywZwga8S5o/N7IxbHw2zMPotT9nJK7L1ocu1NSsYYoCv/Busyq3FIHg4JnkHY0YR7F8wKPGqQY6KXcWjoTApmZiRiU80aO2ck6civ5dnEHRTMDG3uQzgifAEBrR5Fqvz4p/Re/tb+9ZE+Nw3OxQzq7Twdv7nscQm9i1s6vsWVbddVONdJkkTWiNPk2p+e4oaqr6HNfUiFPfJ4FM0scb2LFxN3kzUGafMsY3//CQTUuqrCaHRKlUAQUBUCozZ8PSW95obaZKhPTL1DQ5GlCbuP7SpbC8UyMTSMBfypq5/PzWua9CZ5Ohh+D2Y7WTQ0Ro2QRPUUMdu26SoZ/HBrd5kD3H5uFxfGwhRNi5V+L8+ky+ttJOB9zXUEp1CXNFU8isLZkRCHeD3c2R8nYZgs83k4JRQg5pj8OiQkVEnis22NPJvOkjMtDva6iWgqP9/Ww5cWNLHU7eT7i+dx/2CS9fkizU6Nc6MhGhzaLvc/EggEey4TvuKsXLmy7N+2bXPSSSeN3B23bVvUEO2FZIzBCjE0zLr0ao6KXEKwhiBK6wPc3nkdg3r7yNjrmUdZFjiLY6KXTdr6eTSa7CQoNxAco3Ypb6Z5tP9PrMvsSF1bm17FG5nHuLz1P6hzzi97vGmbrE09XNb086DAKTwTv63q8QtWhu7CmxWCSJNdNLv3R0ZmbfohSlZ5SogmOTkseDaKPPFNZtHMsib1EKsH/jgy1lF4jecTd3JZy7VEnW0TPpZAAIzbUHSseoyZ4vl07aL413IFCpaFjz3jLn7GMClYFhLSmM1WxyLmUAmqCskqRgAr/J6qjVgHdIPvb+mqcBd8I19gdSJNg0PlXU11HOR1c+dAgqRhsJ/bxTsa62jbDc12farCAaqbhW4nhmXjUuRJN7sdJm1a3NIXxyVLLPN58CgyD8STdBSHovEpwySiabS4HFzVWEfRtnBIkhBCAoFgYoJo06ZNM70OwRwlY1RvqAdgYaJblRanMCQuXk3dXyaGhnkldS8HBk6alCAyLZ2MOcjm7AsMljppcR9Ik2sp/jEK/DPGQJkYGsawSzzc9zvOa/p8mf110cywPvNk2WPdSoCMMVDzHL3FTeznP65ivN65iKcGbuachk/xfOIutuVfAaDFdSAnxd5NQKuveM5YZIw4jw38qWK8ZOV4sO83XNB09YxaeQv2bCzbxLQNVMkxcuMqoCg0OTS6SpWpmz5FLus7NFsExjinS5aZgqaYdUqWxbZCiT929/N6roBbljgjEuScaKimVXZcN4iP9BVSCaoqflUhoqp8ZX4z39ncUWb/Pd/p4L1NsarmB90lvabV+tOpDJ+d14gsSZwdDXJM0IcFOCVpt0TeRuOUZZy7qEus7fVPBcvmmSqOc6Oro1RZQt1DxLVAIJh5JiSI5s+fP/6DBHslfrW24FBQ0Wo0w8wbSV5NPVjzuWtSD9I4Qbcz0zboLKzjjs7/HGkM+krqXjxKkEtbrqlppLA193LNY3YUXqNo5cpEhISMKpc3AiyYGbxKmKxZvf9MrX4+XjXE0dFLiZc6WOo7liPCb8Ul+/EoATxqaFK1VIZVoj2/pqaNdlfhdQpmBpfiI2vEKVl5ZEnBrQRmvFmpYG5Tsgqk9V5eTt5PUu+m2X0A+/uOx6/FCGkqn2pr4FubOshbOz5bqgSfbWskvBsKzI8J+vlbb/Xv2lsiQYLK3K/d2lYo8fWN7SNObnnL5h/9CV7N5Pji/OaK97WjUOL7WzvpGdUY91Cfm4+0NBDRVOa7HFy3uI3Ook6/rtPmchLTVEI1/j6DRu0GuyZDbmseWUbaXg82Ufp1nQ25ImuzeZqcGof7vEQ0ZU5FV8KailOSKFYxhvAr8piCWyAQ7NtM6Gr4vve9b0IHkySJ3/zmN7u0IMHcwqdGqHMsoL+0uWLu4ODpNR3LbGxMa3QtgMQCz+Hs7z8eRVLRZBemZaDI438Es0acu7p+OCKGhsmZSe7r/R8uaPwCmuxClmRkaccPnlylRmjHaob+Nxq36uew4Nnc1/uLkbG16Yc4LPQWHh/4v4pjOGQPTa79ap5juHlok7U/BStDSu/l8YG/oNsF9vefSL1z0YQsrEtWAcOu3jBzGBubjZnneKT/d6SMPiQkFnlXcmLdv1Wk9An2DQyrxObsC/yr56cM3xvfmn+Z5+J3cGnrNdQ7FzLf5eR7S+bxfDrLumyB+W4nRwXcuEjyWuopHLKLetciPEoYbRbMOCKqygeaY/y6s9zsYbHLyVnR4KQtpmebjGHyx+7+qrbWmwolOoqlMhEyqBtct6WzwvXs5Uyev3YP8N6WGC5ZHqpfmmAdV+sYaW9eWaZO03BP0jihq1jiW5s6iI9K3fuj1M+X5zdzoMc9pXTAmSCkKrynuY7/t5NZiAR8sLme8CzWRwkEgj2LKTdmHc0jjzzCpk2bCIfDQhDtZXjUIOc3fZ77en5JR2EtMBRJOch/CivDF9d0LHMpPhZ5j+T1zKNISJxW/yHipQ5W9f2akpXHKXtZETqfgwKn4a1ihjCapN5Dya5uPdtdeIOk0cNTg3/HowRZFjyTkNaIS/Ex33MYtYyvF3qOwCVXNhSd51lGm3vZSIpbd+FNlvqOZXnwHF5O3jviaudXY5zf9PkxI2jDlOwcTw3+nVdT94+Mbcg+Q51jPuc0foaAWjdmPZGERL1zYc35BZ4VpPRe7uz+wciYjc2G7NP0l7Zwacs38KnRcdcp2HMomlmKVh4JCbfir/o9HLph8AvYKbKo2wXu6/kFFzd/DY8apN6hcXY0xNnRITfGR/p/U5ZqKqNwVsPHWehdMaaBStk5LIuSZeOUJdRJRBDciszxQT8Hetw8m86SNgwO93tpdjp2S8RqshQsi9dz1dOIYahG6hDfDjOV/pJe1QIaYHUyzSUNYVyOyQnRqKay2OVkQ6HyJsqFsTCxSb6PGcPkVx29ZWIIwLTh+q1dXL9kHrE50sxUk2WODvhocTq4pTdOV6lEm9PBJfURmh2OGXNHFAgEez4TujLecMMNVcc7Ojr49Kc/zaZNm3jnO9/J9ddfP62LE8wNAlqMc5s+S95MoVsFXLIPtxrEMcbmSJOdrIxcwsbcs+zvO4HO/Ou8lt7RO6hoZXli8K/kzTTHRi8fc6NVtKr3qRgmbfSzJfciAK+lH+ao8CUsD52HVwlxVPjSCoc4t+zn+LqrcFTpAeRVw5zV8DEGSu2sTa1CRqHeuZD9vMdxWOhsckYSVXbgVoL41Nr2taOJl7rKxNAw/aUtrE2t4qDAaYQdjTWf71b9KLqDgwOnsWanNEQFleOiV3JPz8+qPjep99Bf3CoE0V6CYRvESx2s7v8j2/KvokgaB/pP5sjwhRWRwHips6a1+0BpGwUrg4fymxEbsk9X1N1ZmPyr52e8c9714zY+LlgWPSWdf/Yn6CiWWOBycnY0RL1DnXAPIbci06I4aJnhpqczgSRJOGWJolX9JuLOKVsD46S37XyctGGSMAz6SgZBVSGsqRV1SUFV5XPzGvlj9wBPpzJYgFuWuTgW5pSwH8cko0Np02RtDZFXtGw6i6U5I4hgyLluP4+bT7U5totyedIRMYFAsO8xpVtulmXx4x//mGuvvZa2tjZWrVrFySefPN1rE8wh3Ip/0q5wIa2BK1uvI2cmuLnjW1Uf83LyHg4LvaWmUx1A1NE6xrqCmFb5pu/p+C0s9R1L1NnG8tA5zPccxouJu8lbSRZ4jmCJb+WYpgZeNYxXDdPmPhiQytwTx3K1q4ZtW7yarBRDw6zLPEa9cyEeJTBmk9aQ1sh8z+E0OBezNv0QOSNBg2sJy4PnokqOsh5RO9OeX8sC7+GTWvfeSj5lkI+b5AYN3CEVT1jBHVJHGg+XrBxuJYBbCcxJk4pkqYub2r82InRMW+fV1P1sy7/CJc3fwK/tEL47p5jujGWXJ3ZljTjPxm+v8WibdenVHBu9oubxTMvm5XSOH23rHolJrc8XWRVP8aX5TSzzefZ6J9KAInNmOMCdA8mq8yv95Z+phjGEhFOScI0SkYO6zi/be8satcY0lS/Pb64Qj1GHxrub6riiIQLYOCSZkKpOKeXQGCdDJGtWJgiWLAvTtnFtr1Uai7Rhkt1uAuFTlGkzd/AoCh5RMiQQCCbIpAXRE088wUc/+lHWr1/P17/+dT7/+c+P9CISCEYjSTIhRyP5fIqlvmNY4jsaGxsZmU3ZF3g9/QgWJnkzPabQcCtBDvKfwtr0QxVzK8MX8UqV6Mv6zDNEnW24FB9N7v2ody7CREeTXBPelNVqejoZbGxKdu0UGtMqUbCyQ2mEYwgijxqkxXUAeTOFX61DkVT8Wh0u2T+Sgli0qlsWTyStb18g06/zyI+76V+/I5Uo1Org5Ktj3G/8mI7CayPjCz1HcGr9+2s2+t0dlMw8TwzcVDXqk9R76C68WSaIIloLElJVMw6fGsWllKeMWrZV0zwEhho0j0XcMPhlR2/F2UzgFx29fGdxK1Ft7kQSZgJNljknGuKVbJ4thfJ+Sh9ojlVEcyKaykKXg02Fyt5L50SDI05/BdPiL92DZWIIoE83+O6WTr61qGXkvU0bJutzBf7eN0h/yWC+y8HlDVE8ioKbyQsijyITUhUSVay/Aea5dhjRpA2TzmKJuwcSpEyLFT4PRwV9Zc1YhzFsm22FEr/u7GVDfug7eYDHxfuaY7Q6RXqbQCCYXSa84xscHOT9738/J5xwAgsWLGDt2rV86UtfEmJIMC5uxY9T9nJPz8+5u/vH3N39E0xb5+zGT6NIGprkHPP5LsXLcdG3c3Lde/AqQ2lqUUcbZ9Z/lKTeQ1dhXcVzdjYhUGQVh+ye9TvUsqRwoP+kmvPzvcvpK2ye0Lrcqp+Is4X53sNo9RxMUGvAqXjwqCEOC55d/fwozPccNu6xS2aerJGgZFWv1ZppdKtIUu9ha+4V2vNrSel9mPbYEY7JUMyYPPH/esvEEECivcQj/9XHYk4pG9+Ue27IAKOGrfxkMS2dtDFAWh+gYNbutTMWRStHe/7VmvNvZp7AHhX18ahBVoYvqfJIidNiH6gQe5rspNG5tObxF3jGjjLGDZN8jUavCcMkXWNDvbcRdWh8eX4zX13QzNmRIFfUR/jhknkcH/RXpG6FVJXPz2viMN8ON0hNkji/LsQ5daERB7ekafB4Ml31fAO6Qf92h7qCaXHfYJLvbe1iQ75I0jR5OZvnaxvbWZPNjVsPPKgbdBVL9JV0Stv/lmFV5V2N1W+qnBD0Edou2rKmyb8GElyzqYMnU1nWZvP8qWeAr21op6tKs93eks43NraPiCGA13MFrtnYTl9p+r77AoFAMBEmpGZ+/etf85WvfIVAIMDtt9/O+eefP9PrEuwl6FaRFxJ380rqvpExC5N1mdUUrSzHRd+OWwmMexyPGuTQ4FtY7DsK2x5qdvjEwE28lnm46uMX+46cttewqzS4FlPnmE9/aUvZuEP2cID/RDrz63ArYxtLjIUiKSwLnkFPccNILRUM1Red2/S5MeuHimaOeKmDp+O3Etc7iWqtrIxcTNjRPGuW3QUzw9rUKh4f+L8R0wpNcnJmw8eZ7zkMTR5bME/oHCmTrleqi7341hKHlhZXjK9LP8bRkbeNmc45EdL6AC8k7mJN6gF0u0Sb+xBOqHsnEUcLyhhOiDsjSTIOxUPJqP46PEqoLKrpkN0cFjqbBtdinh68hbTRT71zIcdELiOsVVrVuxQfx9e9g5vav87ORgweJUSL+6Ax1zf2Vnv8+bmCadvEdYO8ZeGQZAKqXLXfz1iENZWwprLMVzvqO0ydQ+NTbY2kDJOiZY9EZEbXXBUtm7Hk5MB2Y4akYfL33uq9437d2cfCxc6qUbqsafJqJs+fuvvp0w00SeLkkJ+L68NENY3D/B6+PL+JG7sH2FYsEVQULoiFOCHoH0lxi+smN/dVRhhTpsmfu/v5WGvDyPuoWxb/6k+gVxFoectmVSLFZfWRKTdo3VWypokMk/67CwSCPZcJ/Rp/6EMfAmBgYIALL7xwzMeaNRrCCfZNcmaCNakHqs5tzr3AcdG34xnHZW4YSZLK7mqvCJ/Pm9knMOzyu4/zPYcTUCfe+HTIsSuHhIRL8e/yBjyjD5I0eknpPYS0JvxajAuavsCa1IOsTT+MYRWZ71nOgYGTeDX5ACfF3o0i7doPr1cNc1b9x8iYg3QX3sSl+Kl3LsKnhGs62Jm2wcbss2U240m9m425Zzmn8TMs9q4sszGfKXqLG1k9cGPZmG4Xubv7R7yj7ftEnW27fA6jUD1yMXK+DMgeZUSQAdhYuxwhyhiD3N55XVmD4m35V7hp21e5ou271DnnTfhYXiXI8uDZFe/VMAcHT60Ycyt+FnpX0OhaimkbOCRnVTORYaKOVi5q/gqr+n5LcnuKXJt7GafE3jeuRXxYVWoaCvgVmcAesLlMGQaPJNLc0hsnZ1lIwMqAl3c11k3Y9noqeBUF7xjvj1uWxzRraHRq6JZFZ6lU1fIbhqJ0GcMiWuVlvJzO8ZP2npF/67bN/fEUWwpFrp7fRFBVWe73stDlRLdtZEkipCplaW0vZWqb3zybzpExrRGBkbMsXs3Vjka/mslxQTSEd5b7Bg3oBi+mszySSKNKEm+JBFnqce0RDocCgWDXmNC3fNWqVTO9DsFeStHMlm0yd6ZgVk8DmQhhRzNXtv0nzw7eytb8yzhlL4eHzmOh94gJiSzTNoiXOnm0/49sy7+CjMJS33EcE72M4BimC2MRL3VyW+d3SRv9I2MhrYkLm7/M4aHz2c9/PFkjTkrvBWROir1nwm514+FWA7jVQM1msTuTNeI81FfdJv/B3l/R2LYE/wT6JBXNLCYmTtk7aWFXMDM8NXhL1Tkbm1dS93Ni3bt2WTBqHhlJBrvGbtERtLH08s+pjDJhm+la9BY2lYmhYUwMnhj4P85q+MSYtWOjkSSZ/f0nsCn7wogF/jDHRq7Er9buNzVRQxRNdjHPcyhva/kmRSuHjIxL8VfUG1UjpCq8vynGLzp6y9cNfLilfs5vKk3bZnUiw5+6B0bGbODpVJa+ksGX5jfVbIY604RUhfOjoaoRmEUuJ2FV5fVsnkwVg4PRVPNUGNQN/tQzUDkBvJkvbne0G3rdwTFev17ry8XQ+zhaymmSREhV6SxWd0EMq8qs9zYaKOl8Z3MnnaUda1qTzbPM6+ZjrQ1z/vMrEAh2jQl9w+eqg1yhUODjH/84N998M263m6uvvprPf/7zu3tZglGo42wonbvg5CVLChFHM6fWf4CilUVGmXC0CSCl93JT+9dGIkxDqXyP0pFfw9tavzWhpqmjyRoJ7uy6vkwMAST0Lv7V/TPe2vxFIo6WqtbFtm1j2EVk1Ak1q50OcmYSvUbD16KVHTJwGOM9yBlJugpv8HziHxStHAs8h7MseAYBtX7CtVqmrZPWe2vOD5Y6sGx9lwWRO6iy+CQ/6x+qFOBNy510KS/CTnuzgwKn4anReHiirM88WXNua/4VSnYeJxMTRDAUCTy78VMk9G42Zp7FIXtY4jsKnxqZsLCa2HlCeAlN6jmaLHNkwMu3na3c1jdIZ1FnnsvJRbEwjU5tzhfJx3WDm2ukm20qFBnQjd0miDRZ5qxIENO2uWsgiW7bSMAKn4cPNMfAtrl+azcfb22oGUlqdmr4q0ShCpY1knJXjQ35Aks8498YWO7z8n891d+/g7xuvKNSAD2KwoV1IdZmq0eJzqsL45xE/6pdxbJtViczZWJomFeyeTYXikIQCQR7ORP+hj/zzDPccMMNXHvttcRiMfr7+/ngBz/I/fffTywW4+qrr+ZjH/vYTK61gi984Qs8++yzPPjgg2zZsoV3v/vdzJ8/n7e97W2zug5BbTxKgFb3QbTn11bMhbVmPLtQOzOMJjsnneamW0Weid9WkW4HkDEH2Zp7iUOCp0/qmHkzRVzvrDrXU1xP3khVrZdK6X1szD7HpuyzeJQQh4XOHmkuO5NI4zpO1Z7Pmyke7f8j6zKrR8YGS+2sST3I5a3/QdhRWaNSDU1yUuecTyZXfSPV5NoPRdr1fjSaW2b5FVEkVWLDqhSWCZIE84/1cfhVQR4ovDLyWAmZA/0ncXTkkl1OnxyrPs4heybwN6jEq4bwqiFa3AfsytJmBI+isMSj8PHWRkq2hVOWy6yj5zIFyyZbwxQCYFuxxOIJCINalCyLhGGSMy2cskRAHTtNLmOYDBoGL6aHUtGW+z2cVxfitEiQnGnhliUM2+aFbA7DsvnMvEY6C0Xe1VjHrzv7yiIyTlniE60NVQWdKkko0lCj1WpEVJWSZZEzLVRJqmmLHdVUTgz6eDSZKRt3SBLvbqqrSH9b5HZxQTTEPwYSI2MScFl9hFbn7PagShkmq+KpmvP3DSY52OuecC8tgUCw5zEhQXT//fdz7rnncsopp2BsbyR35ZVX8uSTT/LDH/6QYDDIl770JdxuN+9973tndMHDZLNZfv3rX3P33XezYsUKVqxYwZo1a/j5z38uBNEcwqX4OKP+o9zZdX2ZqUBQbeD8pi/gVUO7ZV1FK8e23Cs15zdkn2Z//wmT2hCXxmkgWy0akyh187eOa8ibO/qWrMus5ujw5SwPnT2td/13xqMEa9p1e5TgmJv5lN5fJoaGKVpZHh/4P85s+OiETBkcioejI29jc+5Fdi67VyUHB/pPQp4G+3MAT1jlyHfWccgFYfS8heqScAUVHG6Ft5ifHGk87JS9eNTgtJhKHBA4iReSd1WdOzTwlmm5ITAXcSsy7ombmM4JHHJtYeCUJBa4HHQWS2RNC+/2mqiJ9sxJ6gb/HEjwz+3RHYDDfG4+0FxftalpyjD4W+8g9w3u2KT/uWeAcyJBLq6P4JYl/jWQrEihOycaZJGq8sX5TTyXytKvG7S5HJwSCtDkrF4DFVQVTgj4ebiKi92BbhdNTgd/6Orn1Wwev6JwYSzEfh4XgZ0cZv2qwlWNdawIePlHf4K0YbLM5+b8unDVfkt+VeGi+jCnRgK8ls0jAQd63QRVBc9uqDerZk8/jGXvOaYgAoFgakxIEH3729/mq1/9Ktdccw0Aa9as4cEHH+TLX/4yH/7whwGQZZnvfve7syaIXnrpJXRd57jjjhsZO+GEE/jOd76DZVnI4k7OnCGgxbiw+StkjEFSei9+LYpPje7WHi8yMk7FW7PvilsOTMoBDNjuFCdR7adTRqmI+JSsPKv7/1wmhoZ5Kn4TS/1Hz6ggGjZiuLP7+rLNgIzCWfUfH7O2aUP26ZpzG7PPUDTfNWFBEXa0cH7j1TzY9ytyZgKAoNbIWxo+MaEapsmguWQ0V+W1YSqNhydCQK3juOg7eHzgz2Xjjc6lHBw8ZVZMKwQTI6gqnBD083CiXBg4ZYnPtjXyl54BXsrsSPFa7nPzweZ6ouOYLeiWxb3xJLf3J8rGX8rk+eHWLr48v7kicrMxXywTQ8PcPZjkqKAXy6ZqPdHdA0k+3trAr9p7WeBxEtYUeks6bkWumbLolGUub4ywpVhk86h+SHWaylVNdfz7hm2Utou4bnSu39rNGeEAVzZEKwRhSFM5NujnEK8b0x7qYTRWVGXYTKJ5liNCO+Pf/rev9p4CnB4JzGoKn0AgmH0mtON77rnn+NWvfjXy77vvvhtJksoiMUcccQRvvPHG9K+wBl1dXdTV1eFw7LiQNjQ0UCgUGBgYIBYrLzAuFosUizvu0KdStcPjgulnOM2nwbVoRo5v2zY5M4mNjUv2odZwVhvGowZZETqf+3v/p+r8oaGzJr1Z9ShB9vcdXzVyckjwjIpoQMHMsCn3TM3jbcm9VLXeaLqQJYVWzyG8o+37vJS8h4HSVmLOhRwaPHN7HVDtDcBYqV7SJCMDDtnFQu/hXOn8DnkrM1LIv7uih9OJU/FySOB0FnoO583MkxStDIu8RxFxNOOdJjONqWLZJlkjTm67IHcrQbxqaNI3AvYWnLLMFQ0Reks6r+V2uAteVBfmn/0JXt6p3uXFTJ5fdfbyidbGMSNFCcPkzp3E0DCbC6WK2qS8WfvxAJvyxTJhtjOPJtIcGfRx7+DQ3/VzbY0VDWF3JqoN9U7qKemszxeIqCqL3U7+u6N3RAyN5v54irdEgzVft38P60+oSBKnhgM8nEjTv1M91VK3k8XuXTNXEQgEc58JXbUkSSpr6HbfffcRi8VYsWLFyFgymcTjmbm72TuTy+VwOsvTmYb/PVr4DHPddddx7bXXzsraBLNLxhjkzcyTvJy8F8Mqsth3FIeHzh23uH++ZzkLPUewKfdc2fhR4UsJaU0UzDQ5M0nGGMQl+7eLutqbWKfi4YS6q3ArAV5J3Ydp62iSk+Whczks+JYKxzJ7+/9qYViV9U3TjSY7iTrbOLnu3Rh2CVV2TmhDvNh3FM/Eb605N9n6J0mS8WlRfNTumbSn4lK8uBTvtNiHTxe6VaQ99yr39v5iJGXSIbk5tf4DLPCuwDlLPahmi4KZIWMMsCHzDBYGi7xH4dfq8OyUFhrRND47r5FB3aSzWCKkqfgVmb/WMFt4MZMnZZpjCqKCZdW0ywboKulltUm6bZMao32FhETCqG2CkDIMFrudtDodvLMxyn4TrHsKaSohTWV/79Dfvqeosy5X23b+lUyeNteu9wibK9Q5NL65sIXHkhkeTaTRJIkzIwGW+73jCkqBQLDnM6Fv+XHHHcff/vY3vvrVr7JhwwZWrVrFBz7wgbLH/PKXv2TlypUzsshquFyuCuEz/O9qwuwrX/kKn/vc50b+nUqlaGubOxsUwdTIGHH+2fUjuotvjoy9nLyHdenVXNH6HUKOxprP9aohTq//ECmjj03Z51AlB4u8R+JVIxh2kQd6/h9b8i+NPD6oNfLWpi+OaRjgVcMcF72S5aGz0e0SmuTEq4Sq9gJyyh5aXAfQUXi96rEWeJdP4B2ojWmbFMw0EhJuJTCmOFRkDYWJ91nxK3UcEjiDV1P3l427lQDHRi6fsF11ycyRt9JYtoVT9kzKJVAwdVJ6b0WqZMnOc0/Pz7ii9Ts0uCob1e6p5M0UTw/eykvJu0fGnonfxlLvsZwUew/enT5zAVUloKoscA9t9tePIQoAcqOsrk3bpl83eC6V5c1cgcVuJ4f7vWOaFtTttNn2ygrLfR62FqrfEPEqMgd73WypMX+Qx82Z4QBnR4Jj2mSPhyTVSgAeQpnbpoFTos6hcUFdiFPCfiSoqJMSCAR7LxP6tn/3u9/ltNNO4+abb2bz5s3U1dXxta99DYAHH3yQn/3sZ9x99908+OCDM7rY0bS0tNDf349hGKjbL1rd3d243W5CoVDF451OZ0VESbDn01/cXCaGhilaWZ6L38HJsfegyrXz0z1qEI8apNG1ZGRMtwo80v/XMjEEQ01Lb++8jre1Xjtm/ZMqOwjI4/cxcik+Toq9h5vav45pl9u97uc7Dp869WhJSu9jbeoh1mUeQ0Hh4ODpLPUdPeFjGlaJgpnBxsat+CveQ7fq55jI5SzxHcXzibsomlkWeY9kf/9xBCbYwymp9/BI3x/YnHseG5uI1sIp9e+nwbl4l93dBLUxLYMXE3fXjE4+M3grZzV+fFpMJeYCA6X2MjE0zJvZJ1jiO4ql/mPHfL57nNoRt7JjflO+yH9s6qC4PaPiiVSGrlKpam0SQERViDnKf4YVWeL0cJD7BlPkd3K988gy+3vdLPW4eGAwNXKeYZySxJnRIJHtdU050yS7XbB5FXlSZgU+RWaZ112RKjjMId7ZywiZTWRJGum7JBAI9h0m9K3PZDK88sor3HbbbciyzOWXXz5So/PMM89gWRarVq3i2GPH/mGZTpYvX46maTz55JOccMIJAKxevZqVK1cKQ4V9BMu2WJt6uOb8+uxTHB19Gz55cuYNOSPJG+nKOiAATXZTNLPkjCQWJh4liEcJjim6xiLiaOXtbf/Jc/E72JZ/FbfsZ0X4Alrdh0y6yL9k5Yesv0td6HaRiKMVl+yjp7ieR/v/wGuph3lr85fGNbNI6r08F7+ddenHAJulvuNYGbmIoNZQ9jiPGmCeeiiNrv2wbAOH7JmwI1xa7+fm9mvJmDtSkQb1Dm7t+A8ub/32XhWhqIZuFShaeSzbRMWBR6vt6Dft57aLDJS21pwf1DvQrdJeIYh0q8iL8X/WnH8+cSetnrG/a0FVYZnXzStVhMGhXjfB7SIjrhv8eFt3hUh5OJHm821NJHSDl0YdI6apfGF+E7INedMqE1Yxh8p/LGrlT919I/VCy30e3tlYR72mYgHXLmrlN529vJkfyoxY6nby/uZ66h0atm3TWdT5U0//iG334X4PVzXU0ezUJtQnzKMovLspxjc2tY+IqmEujYUJa7NvCDKoGwzoBnHdoN6hEdYUIV4EAsG0MKEryamnnkpXVxef/OQnK+a+9KUvTfuiJoLH4+Hd7343H/nIR7jhhhvo6Ojg+uuv54Ybbtgt6xHMPhIS6hg9ahRJY6xeOrXQ7SIWlTn8za4DWB46h9s6vzviTqdIGsdFruTAwEm4puBSpkgqEUcLp8TeR9HKoUjKmHbXtcgbKZ5P3MXziX9gM7R5ccpeTom9j7WpVWzLv0p/aQud+dfZz39czeOk9D7+3n5Nmfve2vQqNuWe54rW/6ga/XFMMD1uNJ2F18vE0DA2No8N/JnzGj+HU/FO+rh7Aml9kLTRy8bsc8RLHQS1Rg7wn0hAjeFSZ7b3FAz3flpAd3F91fmI1rbXROgs26JgZWrOF6wsll27XgfApyp8pKWe/+noLRNFy7xuPtxSP1I/lDLMioJ8AMOG/9rWzX8ubsUG+nQDryyTsyx+tLWLnpLByoCXKxqiNDqGGtjKkkSry8Gn2hrJmBYS5REeBVjgdvLF+c1kt9cbeRUF//a19JR0vr6xndyoCNPz6RyvZ9u5bkkrDY6J3cBpdmpct7iNxxJpXszkCCkK59aFaHY6Zt0au7NY4rrNnfRtf49bnQ7e0RhFpUTCMGh1OYioKhY2A7pJxjCpd2gE1YnbowsEgn2XCQkiu4rLzFzgv/7rv/joRz/KqaeeSjAY5Nprr+WSSy7Z3csSzBKSJLEseAavZx6pOn9I4PQpiQuH7EaVHBVNW48MX8hd3f9Vlt5m2jqPDvyRkKOJhd4VOx9qwozVXNa2bUzbQJGUms5vW/Ov8Fzi9rKxopXlvp5fcH7T1WzLrwFs1qRWsdC7omqNj21bvJF5oqoVed5M8lpqNSsjF06LVfSm7PM157oKb1CyCzjZ+wRRzkjQV9zI3T0/KfscvZy8h/Obrmaecti09V2qhSKrHBY8mzWpB0fE82iOilw0JZE7ExRNi7hh8FImR9IwOcTnocmhEZ5gbYxDdrHIeySdter0PMtxyeN/zqIOjU+1NZIyTHKWhUeWKzbahj3auh6OD/lZ6fdis6MWp9XlxCXLXLOxnQFjhxB7KpXllUye65a0lfXs8Shj9+TxqztE0DCmZbNqMFUmhobJWRar4mkui0VQ5PFvFkmSRL1D48JYmLOiQTRJmlJz0pxpUrJsnLJcFgmbKHHd4HtbusrE0NsbovyyvYf0qOjVEreTy+ojXL+1e6Tf09EBL+9pik34MyMQCPZNJnxlmkiIfbbxeDz8/ve/J5PJ0NHRwWc+85ndvSTBFNGtIkm9h77iJhKlLorm2E1Ohwk6GjnIf2rFeFhr5uDAaShT2Lx7lBCHBc8pG2ty7U9n4fWKWp9hnhy4iZw5vVbulm2R1Ht4LnE7d3X/kNX9NzJYbEe3ys1EckaCpwb/Xv0YmHQWXqfJtR/AdjFT/btctHKszzxZcz0bsk9SNCubuE6FgFq7zsijBJH3sKaeEyVnpni4/3cVnyMLk3t7fkFK752VdQS0ei5o+mLZDQOn7OWcxs8QcjTNyhrGo2haPJ/J8tk3t3JDVz+39MX51qYOvr+lk4FS9e/hzkiSxBLf0dt7hJXjkNwcFjy7quFJNfyqQovLwVKPixaXoyLqEFAVnJKEAnyitQFVkvhZew8/2tbNz9p7uHcwSVw3eDqVKRNDw+Qsi/sGEuijhEzBtOgp6jyTzPBsKkNPSadYReiUH8fkxUzt7+mL6Sw5q/L8JcsirhukqqxNliS8ijKuGMoYJkndGHkNWcNkTSbHD7d28/WN7fyivZtN+SKFcV7DzsQNg55Rf/NLYmH+p6NcDAGszxd5MJ7ipNCOaP1TqSx37/S+DmPadtVxgUCw7zHhWyZHHnkkygRC5Bs3btylBQn2PbJGgmfit/Jq8n4sTGRUjolcxhLf0aiSA7dau0mqRwlwfPTtHBg4iZcS96DbBQ7wn0Sza3/82tRMCVRZY3noHExb55XkvZgY+NUo8VJXzeck9C4sSx/KZZkm+otbuLnjm+j2kADawou8mPwn5zZ+jgXew0feExOTtN5X8zhJvRevEgLYbv9dPRIlIY8ZGdBkN9I0NRI9IHACzyZup5qH1YrQBXi2r3dvo2BmSBv91eesNHkrRYjazojThSY7mOc5lCvbriNvpLCx8SgBvGp4zjSLjRsGP93WU/EJ2VQocWd/gnc0RtEmEK0IaDEua7mWJwf/xvrMk9hYLPAczvF1VxGcoAHIRAipCm9viNKj6zyTyvJEakeqnm7b3DuYIm/azHfXTld7Lp3j/LowIVkmY5g8EE/xfz0DI++BAryrKcYJIR/eGr/HiiThGyuqpCgoo25wWrZNb0nnzv4EL2VyuBWZC6Ihlvk8FQ1ja5E0DNZlC9zRHydrWhzq83BeNMhz6Ry/797xee/TDZ5N5/jCvCYO93smfKN1tEjzyjKGbVeIoWGeTWX59LxGHojvuEF172CSMyNBYo6hz0vWNOkp6dw3kCRumKwMeDnU5yE2TpNdgUCw9zJhQfT5z3+eYFBY4gqmF90q8mz8Nl5O3gNAUGvg1Nj7WZd+jJvav4YkKRzkP5lDg2fh1+qqHsOtBmhRAzS6lmLb9rhNWSeCVw1xbPQKDgu9haKVwyX7eSPzOBuyT1d9fMjRPOE7zRMhZyS5t+fnI2JoGBube3t+zlXzriegDRmbqJJGxNlGX3FT1WNFHW1syD7NfM9yYs6FNc/pVDwsD55He35t1fnDQ+fimqa6Hp9ax1n1H+O+3l+WpW0t9h7FEt/RuxyRtrZb8Mt7mLPkePUs04ksyfjVKP5dcDOcSV7K5GpaPj8YT3FeXYg6x8QiiSFHI6fXf5Djo2/HZiga5lSm1zRCk2VOCPnp13W+sqG96mNWJ9OcEq5t2+9R5BGxsrVY5C89A2XzJnBDVx+L3Q6WeKqv36MoXFAX4tUa7nDn14XK0vC6ijpf3biNwnCvJB3+u6OXI/1ePtgSG9e0IG2Y3Ng9wCOjXPS6BpMc5vdwY0+l+LeBX3X28p3FrUS0iV0zo6OEmVeRSVaJYg1jAjtn+RcseySlMWeaPDCY4s+j3tsXMznCqsI3F7bS4BSiSCDYF5lwY9Yrr7yS+vrpu5smEADkzASvLvRHHAAAvaRJREFUJod62UhInFL3Xu7p+W/yZnLkMc8l7uDNzJNc2nLNmFEfRVIn5aFg2zYZc5CU3kfeTBLSmvAqIdzqUBqRJjsJyjuc1Zb6jubpwb9X1BYBHBu5oma9UkYfpGDtsLD2KuFxN/x5K8Wg3lF1TreH0guHBZF7e5Tsts7vVjzWIXtodu1PzLmAeucivGqo6jGLZo68mSTsaGKJ9xjWZ8tT5xZ5j6TZtf+Ya54MDtnFYt9RNLn3oyv/BgUrQ6NrKdiQM+JIyHjUydd/GYk4hQ3rSa26D4DAKafjWrwUNTw5p8GZwquGccgeSlZlSqgiaeM6AO5LjLXpLdrVqp/GRpNdE+6PNVV8qkJ3Sa8p5GyG0s9qcV40hF9VyJkmt/VV1vIN84++BB9uceCpYRaw0O3kjEiA+wfL03jPigRY6N5xkyBvmtzY079DDI3i2XSWC0vhcQXRgG6UiSEYsv/OGCZGjTcibpikDYvIBLVHSFVHnP7ihjmmaPEqclk9F0BQUXBsr5lKGGaZGBq9pr/0DPCRlnpcU6hzEggEezZ7tKmCYM+nZOUxGSqUne9Zzubci2ViaJiU0cuW3IscEjx9Ws5r2zb9pa3c3vldcqPO1+Y+hDPrP4ZPq9yY+tQoF7d8nbu7f0zGGPpBVSUnx0ffUdbHaBjT0ukurufenl+QNoZS2nxKhNPrP0yz+4Axnbxse+ztnmmXu1k1OBdzZv3HeLT/DyOuWhFHK2fVf4yw2oqm1k7TSel9PNR3A5tzLyAhcUL0Kg4OnMKG7DMAHOA/iZDWOO1NU4cFp2kbPNz3Ox4b+PNIbU2z60De0vDxmlHBahiJON2//CmFda+NjOVefRnnkqU0ffyzc0IU+ZQIJ0bfyQN9/1sxd2zkSjxKeDesam5yiNfDLTVEwXyXA9cETAF2B85x1uWV5api5Si/l4N8Q1Ef3bYZqOJYN0y/YZA0zZqCKKCqXFEf5cxwkOfSWWwbjvB78CgyBctGL+kEVIWsafFCuna95tOpDEs9Y4vI59OV9UoWlKXlVWMyQWC/qvDR1gZu7O7niWSG/pLBEreT9flixWPPiYZ4OFH+3l5aHyG8Xdi9OM7rvaoxKgSRQLAPMiFB9O53vxu3e8/vSSGYe2iSCwkJG5sm1/68lq7dV2hdejVLfcfiVHa9IWDGGOC2jm+Tt8rvbG7Lv8qTg3/j5Nh7KgSLIqk0uZZyeet/kDdTWLaJWwngVUJV0+WSRi+3dny7zMI7Yw5yR9f3eHvbf1LnnFdzfS7Zh0cJkTMTFXMSMuGdCt+dipcFnsMJNNWTN5NIyKSMXh7s+xUnx95Lg7K4am1I1kjwj64fjPSlsbF5dOCPOGUvZ9R/mIXeI2a0piRR6uG2ju9UWHB3Fl5jdf+NnN7woQn3w8mve61MDA1TXP8muTWvEjjhpGlZ866gKU4W+VYS0Op5cvBvI7bbR0cupdG1FG2K/az2Rpqc2v9n7yzj5Sqvt31tG/eZ4xI3SEiwBHd3l0JLlXqpUqelpaVG/d++NeqUUtwJ7oRAgOABosdl3Le9HyY5OZOZOZYT39fvlw95ntl7PzNn5Ln3WuteTHPYWJMvj8gKwGVNdfh20v4zPllimsPOmnzlZn2aw05AkbiwPsSxQT/PJlLopslBfi8NNnnoOTlFkVlOB52F6uYRUx12VmXyNNlrv19sAvSrKm9ksrTY7QxoGnf2xFmVzWMTBI4O+jgl7McniyS06jdglDGolmrCRzVNFFHALggVfZkAGmwy3nHadocUmY8113NBQ5iiYbC/z8VNvVGeS6TRKUWGzqkL4pFEbt7Yu8khCpxTF+Qgv3soMlcc4WaTQbWqRgsLiz2BMf2iWL19LLYVLsnPDPcS3s08h4GGItSOmsiifdI25zG1u0IMbeLt1JMcGDobv1g9RdQjh0ZNbdINlZWJpVX7GZkYvBi7g2PqL68ZJXLLIY6p+yh39/ysYm5x8BxcVVyz1mZf5sG+/6sYv63zGi5p/1nVAvKU1l+1SWfByPBQ3x94X9uPxxWlGS9JrbdqPyIoNdY9WLsAm210QaRnsyQeXlpzPvHIUtyL9kPybPs+P6PhlLy0ueZTZ5+KZhSQBBtOefw9rHZ3gorMl9ubuGsgziOxJEXTZIrDxmVNdUx37ry1YX5Z5vNtjfxoXRfdw5zRmm0Kn29rHBI9XlkuS18bjk0UOSUS4MlECn2LHboiCCz2eXg0luDwoLdmCl6fqnHd+h48ksgp4SDXre8e+jYqmiYPRBO8lsnymZZGfrCuq+o5lvhG/7zs53VV1DoBLB1M8MGmCH/s6i8TGYog8KmWhgnZYDskcSh6E1U1zq4LcnokgAm4RImITSanG/xspoOCYWIXBeyCiIzAQFElqmrMq1F7BTDH5cBpNXa3sNgj2TlvsVnsMdgkJ4fXvZ+Ckea99HJmew+lf3Bt1ccu8p88aQ0j0zWcvgB0NPQqdULjQTWL9ObfqznfV1iDauRrO74JAq2u+VzQeg3PDd5Ef3EtXjnCktB5GyMJ5WksGS3O8zWstzWzyLrMy+wTOKFibrBQvfgbSqJoS1OHyaRkK17bZtrEoGjkx3Yy08DUa6cYoWsTSv1V8wb5hEYuoSPbRBw+CVdocr42HZIHpB0v0HZmwjaFSxrDnBYJYFC647+zRoaG02BXuGpaC4OqRr+qUqcohBV5XCIgIEl8oa2Rf/cMDgmrVruNCxtC3NYf5bBAbTGkGSb3DyYwgaOCPu4ZjFe5NQOdBZWCaTDdYWP1FpG4U8L+MjODWoRkmdPCAe4ejJeNr8sX+FhzHT+e2cb9gwm6CkVmuRwcE/RRN0YzhVp05Iv8aF1XWSPcfTxOPtHSgGma3NAzyIp0KTVuL7eTk8N+/l9HHxnD4KKGMPt5XazYInVOFuCDTZGKvk4WFhZ7Bjv/L4vFbo9XDnNS4+fIagnAZF3mZTrz5U5ns9wHU2efMmnXDNnaas7ZRTeKsFlwFPQsRSOHIAilHjljiFLJgkJAaaS38G7Veb/SgDxKepRNdNDomMnJjZ9HNfJIgjxk+LAlhqmT0Hprnqu/uLbq+EjRHwkZSdh2jkuiIBJQaltMK4J9zOlyktuD79Aj6X+v+uvtPfTIcUeH8kmNN+6J8/rdcTaZv7kjMkd/qYngFBvCVtawFPQsWT2Bauaxiy5cUmDSBP/uhCKKY3aT25kIbhRAM5mYkYNPkYloCieE/YQ3isB+VeOf3QPkDIOFntqpw0XToLNQEjjTHHbuGYjXfOwLyQxfndLMsmSaZckMXknkpHCAFntlr6VquGWJM+uC7O9zc89AjKRucIDXzUF+D/Ubbaw/3FRH0TSwi2LVFDvdNImrOkXTwCYIBBS5Zg3SoKrxg7WdxLYw3ViZzvHf3kGabMqQGAI4IxLgumGNWv/XO8hHW+qZ63LyeDxJWjPYy+3k3PoQjZbttoXFHosliCx2CpySb8il7aTGzzJY3MDryUeRkNnbfwxBpXnUon7NUDHRx+Qk5ZUjRGxTGagiFA4Mno1bDqIbKlG1i2cG/sOG3KvYJTf7+E5ib/9Ro6bMyaKN/YKn8Xb6qarzBwbPGvNm3y65Rq2bkgSZoNJMTK2e+tJon1V1PKg04ZR85Ko0lZ3nO3Kof9G2wi0HabDPrCocF/pPxiON3QjBtWg/lKX3ovaU94tS6htw73/guKy8TdNk/fIMr90RLxvPDGg88L1OTv9JG566iW+eUuoAj/Vfz5rsS4CJiMTevmNZHCq99yx2HZKaRkLTUU0TjyjilKRJizI02hSimsb1Xf1D0ZDZTgdXttSP2DPHLohMd9p5K5unYJq4JbFm356gLONXZE4IBzgi4EMSGFN/p+F4ZYl5spPpTjuaaeIUxbLolSwKyDWatCU2utTdMRAjrRu4RZHTIgGODvkIVIkG9hXVCjG0iacSKa5o23yTZYbTzqpsfkgMQcmW+w+dfUxx2Dgh5GeRx4Vfli0jBQuLPRxLEFnsdLjlIG45SJtzPiCMblGtp4gWO1kZv5+8kWamZwlTXfuOGP1wywFOb/oyj/f/jdXZFwETm+DkgOBZzPUejihIxNV+nhn4D+tzKzExyOlJlsVuYn32FU5p+kJNC+tN+JUGTmj4NI/0/WnIqlsSFA6PfICQrXWcr8rIuGQ/h4Qv4p6en1fM2UQXba75VY/zyGHObv4mt3ddW2bg0O7ch8Whc0eNYm0tLinA0XUf5vnYrazJvIiJiSzY2cd/AvP9xyJLY7++EgrTfOW3SD/7NMknHwUTvIcdgfeQw1HC46uDysV0Xrm5em2TmjPoezs/YUGU1ZLc3/truvOrhsYMdF5NLkUQ4NDwJVakaBehv6jyaDTBLLeTd7N5CobJPLeDNrud+q3oZ2OYJqIg4JBE9vO6mTbdTkbXh5qujia4JFHguJCfB6IJnkukOTzg494tUto2cUhgc+R0a0WBXRQZzzu3YBjcPRjnrmERrIxh0K9q9BZUXk/nkAWBqQ47fkXCIYojuu/pZnkPooAs0V/j8evyRf7WPcBvZ0+xxJCFhYUliCx2XgRh9B+pnJ5mefQ2Xk7cOzS2IfcaHjnMuS3fGbETvVeJcHzDp8jpSTSziE104pICZPQoy6OP0pNfRcDWyMLASaxMPMDa7EsAdBfeJq52jyqIbKKTWe6DaG6fS1LrxzRN/EodTimwTdzEWpx7cUTkgzw7+J+h2p+A0sTJjVfglasLAkEQCNvaubD1B6S0AbJ6koDSgE10kNfTqEYeh+TFKW2bon+H5EaggUPDl7B/4HRUs4hL8uMU/biV8dt8K6EwgZNPw7vRUU7y+hAmUCRt6Ca5WO0+OLF1BThsYq9JVo+XiaHhvJZ4mH0Dp5b1v7LYOUlpOk/EUtgkiZ+s6x4yDrh7EOa7nXyypZ7wOFKwCrrBgKbxZCxFd7HIAo+LhR4XdTZlKP1uPNQpMt+c2sz/bejl2JCPVdlKm+oPNdWNqU5oWxFX9Qqhdl59iKxu8N01nUOvqQh8sKmOJT43dSOs1yEKGMMsHHqLGkcGakfi2+w2FLFkuJDUdQQEfLJIUJZH7BdlYWGx+2EJIotdmrQ2WCaGho8vj95W1T57OFumo/Xm3+OWzu+hDTMTeDXxIMfWf5y8kaFn40Z2dfpFWpzzRl2fJCr4xLqhJqrbEofkYYHvWKa79yevpxAFuWQLPopwEwQBrxLGq4TJ6xlWZ17gqYF/kd/owtdon8XxDZ8kaGveJuu2S27skhtoGvWxY0EQRWR/YKvOIcoC7rBMZrD63eXQtIlHcDb1sKqGgU7RyE343Bbbj4SmM81p5yfruyvmXsvkeDqR5rRIYEwba9UwWJnJ8vP1PUPb+U31PN+Z1kqrY/w3UBRRZJ7LyfdntJHSdD7VWk9M1Xk5ncUjSRzocxOUZZw7MDqS0vUyF72gLFGnyPy+r9xsxQCu7+6n1WGjK19gisPGunyl8c1p4SCrMpuNWDoKRWa57Hy+rQEBAQF4OZ3lyXgK1TT5WEsdr6Vz/Lmrn6xRSin0yxKfaW1gjsuBzXKcs7DYY7A+7Ra7NO+kn6s593bqSfJ6esznymgx7u/9TZkYgtIm9fGBv7HIf/LQ2GT0QtoWSKKCT6mj3jGdiL19VDG0JV35t3io7/dDYgigp/AOt3R+j5Ra25lvd8MVlFl0QfX6JZtbpG7WxArloZQmWAsBoczQw2L7kFJ1eotF+osqOb12ZHBLXknXbvJ5/2CcRI1aly2Jazq/3tBb0QMnpRv8v85eXktn6S2q6Mb4nBIFQSCkyExx2mm229nb4+KSxghn1gVpttt2qBgCsG1hTHKo38vj8ertEKBk5b0mV+DihjD7eFxsOtomCJwVCXB8yMfJkQBTHaUbFu9vjPBqOscfO/v5xYYefrWhh5xh8MW2Rj7XWo8J/Lqjd0gMQUno/mhtF/3FEVwrLSwsdjusCJHFLo1u1LbHLvUAGvsGIqenSKg9VeeKRhZBEBCRMNCZ6Vk83qVuc3JaEgMdu+ieUO1PVkvw9MC/q8/pcXry727TnkQ7Gy37ull0QYhXb4uhq6X3ka9J4cgvNOKOTPyr0y0HaxpgTHcvxiVVdxK0mHw2GSI8HE3yZDxFfqN720WNYZptNuQRnASdokBqBPGU0g3Gql86CsWywv/hvJsrkDUMvvzOer4xtZk5Lsduk87llyVa7TY6NjrieWWJ6Ag1QjFNI+S088sNPRwX9HNCyIdmlpr1ttkVTEp1Q1+f0kTOMHg2keaW/tjQ8TrwbCJNXNX4bGsjf+yqbvuvAw9HE1zSFKnpdmdhYbF7YQkii12amZ4lvJS4p+rcVNf+2MWxR3IMancwB9BNFUEQOTx0Ce4a7meqkadg5JCQJ9RsUzdU0nqUrtxbpLUYzc65BJTGESM9WS1BR+51XozfRUFP0+pcwP7B0/ArDeNqZKuZak2XOihFj2Z5DxrP09mlcfgk9jotwPTDvORTOpIiYPdJuAJb97VZMvS4kru6f1L2erc553Nk3Qew7aTRx92NtKazPl/k790DbChsvrGyIp3l1fdy/HBGK22O2qmRDlFkb7eTZxLVo9DzXA6c0tg203lj5O8e0wTVNLlufTc/ntE2rtqkyaRoGCQ0nbiqoWESkmXckjQme+5q+GWZL7Y38v01JRvtzkKRaQ47PcMa2g5nmsNOV0Elb5jcPRjn7mHZp19tb+JvPT3s43ZySiSALAjcORCrep43s3myhj5kTV6NNfkCBcPAJVl9iSws9gQsQWSxS+NXGpjq2nfI8GATiuDg0MjF49pcOkUPDtFD3qjc4IhI+OUGLmz9AV45UpEypxsqcbWH5bHb6cq/hUvyc0DwTJod83DV6B20Jbqh0pF7g7u7f4rO5rukEdtUTmn8PC45gG0LS/GcnuSJgX+wKv300NgbqUdYlX6aC1q/T8TePubnLwoiLslPVk9UnQ8qLWM+1+6CbBPx1It46id3AxqwNXJOy1Vk9Tg5PYlbCuGS/dvMvMKikqSm0VtUy8TQJlTT5L+9g3yqtaHmhtgtS8x3O6lXZPq2iGqIwPsaw2PeTE8ZQXiFFZn0xkhUWjcY1PQdIogKus76gkpvUeWpeIqkrjPH5eBgn4dGU8Y3wWarzXYb18xopSNfpLNQZK7byfOpdFltEYBdFNjP5+batZU3bQKyRFLX6S2qPFhUeSKR4jvTWsiNEKLL6AZNNqWmC127w459DMY+FhYWuwfWp91il8Yl+zm2/uMcV/9JwrZ2PHKY+b7juLjtWgJKeZG+aRqk1UGixU4Sah+qUV4r5JaDHFn3oarXWRI6j4h9ChF7e9X6of7iWv6z4WusSj9NWhukr7Cae3t+wfPRm8nrmTE9l7QeqxBDAAPFtTwfu4We3DsVBfcpbbBMDG1CMws8MfD3cdVQuaUABwTPqjonCQpT3PuM+VwWo+OWA9TZp9Lu2oewvdUSQ9uZPlXjjUxtA4tX0rkRN9QADXYb35zazOF+D5uCQdOddr43vZVm+9jTVv2yxAmh6jdOzqsP8WB0c5+wwijRpGoUDYO+osqGfIH+ooo6gXPENJ0n4kl+29HLy+ksq3MF7htM8KN13fSrY6+7Mk2TgaLKK6kMDw4meDuTQ0RgodfNKZEgbXYbV01toXmYbflUh53vTGtBN4yKJGgRuLghzAODm2/kFAyTjG4wUnxOAM6trx7pF4HjQz6krWy+bGFhsetgRYgsdnnccoC9fEcy1b0vplm9hianp1idfoFnozeS1ROISMz2HsrBoQvxKmEAREFiqmtfzm35Ls8O3shgcQM+pZ6DQufTaJ9V060uqyV4pO9PG2uWylmZXMo+gRNwSO5Rn0d3flWFGNrEO+nnmObaj4wWw2bbbCO7LvNyzfN15F6nYGRxSJ6ajxmOIIjM9hzMYHE9rycfHRq3iS5Ob/wKHjk8pvNsb9TBAQprVlNYtwZbaxuOGbOQQ+EJ2W1b7Fk4R3iPOEVxxA31JhrsNj7aUs+FDWGMjceNtzGrW5I4tz7ETKeD2/pjRDWNaQ47J4cDLE+mWZsv3bwRgPpxRmJiqsad/TEeiiVRTRO7IHBS2M/J4QCBcVhuxzW9TJhtImsY3Ng7yCfGYDNumibr80WuWdtZ1ii2xa7wtSnN1NkUFFFkjtvJVdNayGglUeORRXyyTM5m8MMZrdzZH6OrqNJut3FowMvD0SSr85tvcDlEgYSq8a2pzaR1g7Su82A0wdqNznR+WSKsKEiCyceb6/lbTz+FjeLXI4l8urVh3K+zhYXFro0liCx2G2oVo5umwer0Czzc/4ehMQOdt1JPECt2cnrTlbjkUs8bu+SixTmX05q+jGYWkQRl1Dv3BSPLQHF9zfmu3NtjasSa0arnu0OpfglBYHXmBfa3nTE0Lgq1P8LCkNHs2HHJAQ4NX8p+gdOJFbuwSS78cj1uOYhuahimvlM1DS12dtD5o++hpzZv1ESXi5avXoV9ytQdtzCLnZ4Gm8Iir4uHYpWbfIDjQj78YxQ2dlHEbts6Ae6XZY4I+ljgcTGoqryQyvLX7n7iw5zqTosE8G2xpoJhkNR0dNPEKYn45c3fCVld54aeAZ4cVudUME3uGIiTM0ze1xAeU1NS0zRHjKa9lslRHIODRFTVuXZdV5kYAugsqPy5q4/PtTXi3phmGJBltizXc0oi050OPtnaQMEweDuT5xfreygOM6TwyxKfamnggWicl1JZTEo9mc6qC7I6V+CFZIavT2lmUC1yQ2+UkCzxpbZGFFFEEQQCskRQkS0zBQuLPQxLEFns8hT0DFk9SVLtxSa68MhhPHJwqLFrRovxbPTGqsf2Ft4jqfUPCaJNjDWqAowqOiRhbHcamx1zas75lQZyWoKsVl7fM9W1L08PVneGm+FePK7nsQmH5MYhuYf6DqW1KO+ln+f11KOISCzwH0+9ffq4Lb0nGy2ZoOf/flkmhgCMbJbuX/2U1m9/HzlYPSXGwiIgy3QXipwWCXD3QLxsbprDznEh3w7ZFAcVGZsgMNdlsCKZIaHp1Ntkzq0LscjrKrPKHiiq/K8vylOJFLpZirR8sKmOWU4HDkkkoek8VcP04eFoglMjfhzS6Kl9JYfN2pjAWO69DKhqmcAbzsp0jqSmDwmikbCLInZRpNGulIkhgEsbI1zf3U/vMGOGflXjT139fKW9iXPrgryeyfJ/nf1D888kS2nNn21tYJrTbokhC4s9EEsQWezSZLQ4zw7+lzdSm1O8nJKP05uupN4+HVEQKZr5mkYBAAOFdTQ6Zk54DQ7JQ4tjHp35NyvmBASaHLPHdB6fUk+DfSa9hXcr5g4InsXL8Xs5InJZ2bhHDnJg8ByWx24tG3dKfg4JX4RNrN2lfSyk1Sh39/yUvsKaobG12ZeY4lrIcfWf3KGiSE8mKXZ1VJ3TooNoyYQliCxq4pREZruc1NkUFnlcvJDMkDcMDvJ7mOKwExxHOtlk45YlFnndTHPa0QwTURAq1hNVNX60rouOwuaNf2dB5Qdru7hqWjN7uV0kNb1m4wEdSGsGDWMsdVrkdXNjX7Tq3Hy3E98YhExyBJtyEyrEzWgEZZkzIwHu2Chog7KEahhlYmg4/+kd5CvtTfyxq3pPteu7+5njchDZQS5+FhYWOw4ryd5iUtBNHc0ooptjL67dWgzT4K3Uk2ViCErOa7d1fp+UVvrRkwQFkdo/1m45uFXrcEgejq7/CHaxsk7o8MhluMYoGtxygFObvsgC3/FDUaWA0sSx9R+nN/8uIiIhW7nTm11ys2/gFM5v+R6z3AfT6tyLw8Mf4MLWawjYmqpdZlyszrxQJoY2sS77Cr3597b6/FuDqda2zAUwC4UR5y0sPLJEm8POXJeDixvCXN5SzyKve4eKoeH4ZZmwTam6no5CsUwMDecf3QMkNW3Uxqv2cdTZRRSZ44OVackOUeADjRHcY0gvbBxBaDhEAdc46/48ssRpkQDfndbCYq+bJT7PUE+janQUihRNo2bPp4xujNhbysLCYvdl5/jWt9hlKeh5MtoA76SXEVU7iNjameFZjE+KIEvbttYkq8VZEbuz6pxqFujOv41fqccl+ZntPYy3Uo9XPM4mOAnb2ka9lmYUSWtR+gprKBpZGh2zcEvBoV5DQaWFi9uuZVX6OdZnV+KVwyz0n4hfaaiwyh4JjxzikPD72Md/AiltkITaw2uJB2lyzOPUpi9VrRlySB6anLOps0/DQEMRHAiTkPKR1ZO8mnyo5vzKxAO0ufZGGcfzm0wkrxdBUTDVKptCUUQObJ3Q3ZXJJzXUrIFpgs0j4vBaX/UjIYviLvdj+Go6W3Nubb5IwTDxSRItdoXOKsJphtM+5hopKImP8xtC7Ot1c9dAjJRusMDt5MSwn/oxRlQCssy+HhcvVVn72XVBgvL4/wpeWWauLDPDaUczTZ6Mpyoe02hTODHsJyLLaKbJB5siPDCYoLtKJGlsVhoWFha7G7vab4DFToRuaPQV3uXO7h+Xiv6Bd3iWF2K3c2bz12lyzJmUjXnN66ORMyp//DYRK3YCoIh2Dg5dQLzYRU/hnaF5m+jkzOavj+qeVtAzrM+u5IHe35Y5yc1yH8wRdZfhlgMIgoBPqWf/wOks9J+AKMhIIxgejIRdcmKX2nDLAUK2Fqa4FtGTf5e7un6KJCosCpxMm3N+RWRLFhVgElM9TBPDrN01XkfDNMdv3ztZSP4AwdPPJnrrTRVz/uNOQvKNrf/T7kQmppLu0ehYkUFSBCKzHAyuLjD1IA/+lrHbQFvs/IRGEA9OUUBEIKDIfLm9iR+u7Srrt9NoU/hca+O43fB8ssx+Ppm5bgeaWbqOMo6ojleWuLylntv7Yzyy0fXOK4mcUxfi0IAHeStsrhVRRAEWetxIDAx9U+/ldnJSyM+NvYN0bRRATTaFixrCPBhN8Nows4iwIo/7NbGwsNg9EExznEm7uwnJZBK/308ikcC3B26cJoN4sYebO79LVo9XzHnlOs5p/hZ+W8M2u35Gi3FTx1WktP6q86c2fpkZngOG/p/VEqS0fvoL63DLQcK2NjxyGHGU5nv9hXXcuOFrmFWy8Y+IXMZC/0nbTPgl1F7+13FVRQ1Um3MBJzR8epvW8BimwfLo7SyLVQoOgOPrP8U83xHb7PpjQUslybz0ItHb/oceiyL5/QRPOxvPkoORff7RT7AbkY1rPPuHPjpfKr/7vs+5QbSCwdwTA3jqrNqInRXVMEhoeskaWxQIyDLiCN8rPYUiX3hnfdUaoTMjAc6vDw8JjKiq0lfU6CuqNNgU6muk4W0visOf68b6qJGe63hQDYPXMzmuW9+DgcmV7c38dH0X2hYvlCTAV9qb+Nn6bjQTFEHgm1ObmeveurpLi8nH2q9ZbA+sCJHFhMnpyapiCCCl9ZMz0vjZdoLILQc5JHwhD/T+tnJOClJvn1o25pL9uGQ/DeMwUMhrKd5OPV1VDAGsiN/NTM9BeLayDqkamlFkReyeqoYQG3KvEi12bFNBJAoi83yH83rqYdLaYNlcyNZGm3PvbXbtsSJ7ffgOPwrXgoWgaQiyjOQP7HE9iEzTZP1z6QoxBLDylhhHf7mRnteyzDx624jElKYzoKq8lMogCyL7eV0EFXlMjmFjIRvTSHQU6X0rhzss07i3E1dIRlJ2j79zTNW4ZzDOg4MJCqaJX5a4sD7EAT5Phc32JoKyzBfaGvnVhp6yDmhznQ5ODAfKoi0hRSGkKDvNZt8mitRtpU15LRRRZG+3k+tmtRMtajyVSFWIIQDdhOcSac6pC6IacETQa/UesrDYg7EEkcWEMWo0ER2aHyHdarJody7kiMgHeS76X4pGKfWhwT6DExo+g1eJbPX5VbNIRqvurASlOqZtlTaWN9K8k36m5vzryUdpde41ZC++LfApdZzX8l3eSD7G26mnEASR+b5jmOU5BI8y8UathmlQNHJIgrzVfY0EQUDZw93k8gmdN+6L15zf8GIWX6OMaZqTHs2Maxp/7+7n2URmaOyG3kHOqQtySjiAZytTkNL9Kg9f20Wia3O9hyjB0Vc20biXc5cXRSlN5y9d/byQ2vz6JTSdP3aVmoWeEPZXtYG2SyKLPC5+PmsKb2ZzJDWNeW4X9YqMfycxhdhRKKJIvU3EKQq821PbXGV1rsCFDSH8o0TjLCwsdn/27G9Ni63CJQWQBGWofmg4imDHWaNR6mTilL0s8B/PdPf+FIzMxkaqvqFmqoZpDDVYlYTxb8xM06DeMZ23009Vna93zEAWtl1txkg9jkrOedv+R9yn1HFg6BwW+I8HSg1wJyrCTNMkqfXzduop1mZW4JT87Bs8lYitDccoDXAtamPoUEjVFubFtI67zrlNUjtfT+fKxNAmbu2PscjjYrY88aiEmjdYccNgmRiC0vN99Gc9nHldO976XVsQxTWtTAwN5399UQ70uWvaQNskkQZJpMG++0U2kppGTNXZUCjilyWabAqhcabWKaJInSKxNl99PqLIOEXJEkMWFhaWILKYOG4pwIHBs3kuWlljclD4QjzS9rlrLwkSPqUOqBsaM0ydpNrPW6kn6cq/RUBpYB//SfiU+nG5vtkkJyGlBbcUJKPHtpgVODR88ZDTnGYUyeoJNFNFEWy45SDiBETYJpyil7neI3kpcXfV+b39x2xT04rhSII0Kel5MbWL/3VcRcHYvAFck32R/QNnsn/w9Ak1kt0T0JJJ9GQCs1hA8niR/H5E++b3seISaNzLwYYXqjuP1c91Epkx+W6AKU3nroEtPxebuS8aZ7rTjjzBFMZ8UmfdsuqNRQ3VZHB1AW/9ri0GempYZwNkDYOMYbD1se5di6iq8fuOXl4dZnjgFkW+NrWZGU77mAWMQxQ5vS7E8lT1z8WZdUEco1iTW1hY7BlYgshiwtgkF3v7jiaoNPF87Fbiag9BpZklofNodMxCmQTbbdM0SWtRklofWS1B0NaMWw4ORYBqMVBYx82dV6OZpXSJjtzrvJZ8lBMbPs0M95KNjmyj45A8+OQ6jm/4JC/G7mJD7jXAxC83cGjkEkJKK1AyeHghdgevJR9GN1UcoocDg2cz13s4TnlikTJJVFgYOJF3M8sqjCNmug8iqGx9n6HtSUHP8tTAv8rE0CZejN/BXO/hliCqQrGnm57/+wXFDetLA5KE/5gTCJ52FrK/VBNkc0osuiBM50tZjC3aqLjDMo3znTgCk++epZkmab12ZCqpGWjmxH9oDM1kpIzUfHLX6BmT1XUKhoFNEHBv4Q43mquZrcbmP6frpHQD0zRxSdJu445WNAzu6I+ViSGAjGHww7Wd/GRmO3XjaJzaYlf4YGOEf/Zsdp6TgEsbI7TaLedFCwuLEpYgstgq3HKQWd6DaXTMxjA1JEHBo0xOZMg0TfoLa7mj+1pyenJovM25D8c3fAKPXP06WS3B0r7fDYmhYWfkob4/0NQ+B59YV/XYagRsTQiqzCHhixGAgpHDLQfwSCFskpOcluKR/j+zJvPi0DF5I82Tg/9EM4vsFzgNaYwCbEtKNTzfYXXmRValn0YRHCwKnEydfRoueddyUSsYadZmX645vy77MmF76/Zb0C6AFh2k6yfXoEWHmVroOokH70PyegmeeibCRuMCX5PCSd9v5fm/DTCwKo8gwZTFbhaeH8YTkZG2QRG7WxJZ4HHyaKy6/f2BPjf2rbFSdoq462Qy/dXrEetmbtteZ1tLQTfoLhbpLqiIArydzeORJA70uQnLMi5Zok6R8UoiqSrCcoHbia+KMUV3ocg/egZ4OZXFBGY67Xy4uY52u32rrKt3BhKaziOxZNW5nGGyLl8YlyBySxJHh3zs53OzIV/EBNocNvyyhGMPM1+xsLCojSWILCYF71YU2NcipQ1yW9c1FRGFDbmVLI/exuGR9yOLlXf48kaKaLGj6jl1UyWmdm9MsRudjJagI/c6K+J3UdDTtDkXsF/wNPxKw1A6XM5IlImh4bwQu5053sPGJcC2xKtE2Md/AnO9hyEI0rhS/saCahTI6nGKRm5j7Zcfu+Sa1GsAG336arv8m+y4nkbDKegZsnqCgcJ6FNFByNaMWwpOWNRuDcWe7nIxNIz4/XfjPfQIlHApoUpSRCLTHRx7ZRPFrIEggN0roTi23abPJoqcHgnyTDxNYYsODn5ZYn+ve6vSOl1BmQM/EOGx63oq5hr3duAO79w/Ye/m8sQ1nUdjybJ+Nzf1RXlfQ5hjQz6CisxXpzTzw7VdZI3Nn4Emm8KHm+twbxH56S+qfGd1J0ldH3adAlet7uBHM9ppdezaUQ/NNFFH6AYyUBy/WY99o8nCWBvIWlhY7Hns3L8mFns0g8UNVdOrAN5IPcb+wdPxifUVc8Yorm9jdb/L6Sme6P8772Q2O729nnqEt9NPc0HrNUTsbUCpV1AtVLNAwajdUX6sCIKAXXJv9Xm2JKPFeTF2J68mlqKjAQLTXftzZN2HJl3kOkQ3rc75dOReqzo/xbVoUq83EbJ6guXRW3kl8cDQmCQonNTwOdpd+2y1I954KXZ31pwzslnMYrFi3O6RsHu2X/pUg03h+zNa+Uf3AK9lcojAYp+HixpC47qTX4vGvZ0c+9Umlv9zgGSXiuwQmHO8n3knB3D4d96fsLiqsXQwQZvTXiaGNnFD7yD7eFw02GRWpjJ8vKWepK4zqGq02G0YpslziRSnRILYh0UyXkxlysTQJjQTbuuP8rGW+rLIR0bTyRsGgiDgl6WqjnU7E6UeTBJxrXo65FTnzh0VtLCw2DXZeX9NLPZ4EmrlXeFN6KaKVkPYOCQPHjlc0TsHQEAkZGsZ0/VT6kCZGNqEZhZ4auCfnNx4BXbJjUMcuZ5pW7rQbQ2aUeTF2J28nLh32KjJ6uwL5HqTnNr4ZVwTrH+qhl1yc0TkMv7X8S3ULdIZ5/uOxb0NejmNl/WZlWViCErvtXt7fs6l7dcRtDVv1/XYGmtfT3A4EWw7/r0lCQLtDjtfaGscinB4JAnnJBWr21wSLfu6CU2zoxVNRAkcfhlJ3rk39hndYK7byb2D8ZqPeSSW4Oy6IP/rj2ECIVnCJ0s8FE2Q1g1kAQ4J+GjYmO5YMAxW1HCkA3gjkyOnGzhEEdUw6Cyo/KtngNczOVySyIkhP8eF/IR2YlvuoCxzYUOIP3RWNtxus9totKI8FhYW2wArgdZip6So5/DKtb2VnKIXSaj+w+iRQxxbd3lVy+olofPGbAe+JrOi5tz63KtDkR+vHMEtVd/MtzkX4NoO9uMTIaPHeTX5YNW57vyqmk13t4aQrYWL237MQv9JBJVmmhxzOK3xKxwUunBUo4xtTUaLszx2W9U5E5M3k09s5xWB0tSMFKj+3gqccDKyP7B9FzQCblmizqZQZ1MmTQwNxxmQ8dYruMPKTi+GAEQBHJI4oulETNPJ6sZQImlU01mbLw4do5mQHhYpkREIyrXFjHdYBKijUOSb723gtUwOk5JAu7U/xs/XdxNTt32PuIkiCAIHeN18tLkO78b3kQAc6HVx5ZQmAjuxmLOwsNh1sb5ZLHZKdDR0UyVka61aD7QocCo2oXZ/k2bnXC5qu5Zlg7fQV1yNVw6zOHgu9fbp2MSx9UWRxNofj5LYKm08PEqIM5q/xm2d15A3NheXB5Vmjqn/2DZJdZsohqqiJ5NgGhTdmao9pDaR0vqJ2Nsn9fqiIBKwNXJY+BIKwbORBHmneX1MU68aVdxETO3CMHREcfuloymhMC1Xfovu31yH2t1VGhQEfEceg//YExBG2Bxb7Fg8kkRK05nndrCihu3zQT4Po/USsw0zSZBEgRPDfh6PVzexODMSxCdLpDWdf3RvdlUbzru5At2FIsGdWFh4ZZljgj729bjIGQaKKOKVRFxVDCYsLCwsJoOd9xvRYo/GJrpIqH0cFr6UVxL3sy77CmBiE10s8p9EnX0KNqm2sFFEO3X2qZzQ+CmKRh5ZUMZt6TzNtR/PDP6n6twMz+Ky80Vs7Vzcdi1RtZOk2kfY1o5fqd/haWCGqmKkU5imiSAIxJbeS/LhBzGLBfw/+AYCAmYNowOXFNhm65JEBZe4c7nkyaKdOvs0uvJvVZ2vt08np6dwi4Htui5bcwstX7sKPZnELOSRvD5Enx/JOfGGpxbbHu9GU4mpDhsr01m0LT5mdYrMXLcDEYEGm0JvsfLmRLvDVuEy12BTuKQhzL97y8X74X4P892l90TeMHgzW6MbKfBCKsNenvEbp5imSVzT0U0TWRQIbENBLgoCYSs9zsLCYjthCSKLnRJJkJjtPZhbOr7HTM9iFjQeh4GOYeq8k17GPN+RSGNoemoTnWOOCG2JWw5yQOBMXojfUTbulPwcErqozO1NEAS8SgSvsvO0UFQH+ok/cC+pp5/ANAzc++6P79AjyL70ImpPN9ozK5h+8P68l3+h4li/0oi7hq357opD8nBQ6AJu7fpe5ZzoIWJvJ6PHcCuB7b422R/YqdLj9gTyukF6o3mBe4I1Uc12Bbso8K2pLdzYO8hb2TyyAIf6vZxbHyKslDb8X25v5Jo1XSSGmSWEZInPtzXi3yKS45Ykjgv5OcDn5rV0jqJpMt/jJCRLeDcKFEEAuyBUOP9twjtCpCVvGAwWNZ5NpugrquzrdTPb5UARBJYnM9zaH2NQ1Wi2KbyvMcxclxPPbtIDycLCYs9FMM0R/C13Y5LJJH6/n0Qigc+3c9Z47OmYpklM7eLpgRtYm12BCUx17cthkUsIKs1bZec7VnJ6ilixm1fi95EzEkx3H8h09wFjtu0eL6ZpktOTmJg4JA+SMLF7FurgAJ0//C7a4EDZuOjx0vjxz9D18x8hKAqBr17BY7a76Cxsjor4lUbOaPoqQduu1fh1MhjIr6e3+B7LojcPpc812GeyJHQeTw78g5MaP0edfeqOXaTFNqe7UOS/vVGeT6YBOMDn5uKGMI02ZdzfO0lN471sgaJp4JQkioaBSxRpc9jLmqkOFFU2FIp0Foq02+20OJQhwTReirrOv3oHWRqt3s/nJzPbaHdUurUVdIMXUhl+29FbFjcOKzJfaGvgB2u7yBnlW4aPNtdxdNC307vXWey6WPs1i+2BJYisD9hOT0HPDtlv20UP9hFS5caDYepktBgJtY+CkSFoa8Et+avWtGiGioGGIji2mRBLa1HeSy/n1cRSNLPITM9BLPAfj1+pxzRNsnocwzSQRduIBgSmaZJ45EEG/nl91Xn/sSeiDvaTfXkFgt2O+4KzkQ9ZRNqI4ZL8uOXgDk/12xoMtYieiKPFYgiiiBQIIvsDY6q3SakDPND7W+Z5j8AuuREQGSiu59XEg4DJRa3XTlrjYYudk76iyjff21DRKNUtiVw7o21cvWzyusGt/VHuHIhXzL2vIczJYT/KNmgOGlU1NuQL/LNnkI5CuTX7efUh9nY5mFclZa63oPKFd9ZV7Qi2n9dFnaLwQDRRNu4SRX4ys42Ild5msY2w9msW2wMrZc5ip8cuuSa9UahuavTk3uGunp9SHNYnaK7nSA6NXIxbDpQ9XhYVYNv94Ge0KPd2/4KewjtDYyvid/Fm6gnOb7matdlXeDF2Oxk9Tr19GodFLqXOPg17lXRAI5cjvazSLnwT2ddW4jv8SLIvr8AsFMjcdBvtiw4nEp66LZ7adkXPZkgve5aBG/6OqZZqMkSnk/qPfRrX3gsQ7SP3MHHJAfYNnMI9Pb9geBNZAZEzmr5a8b4YL0UjT9HIIiDikvzbJcppMXYM02RFMl3VGS6jGzwSTXJ+fQhJHNvfLaHr3F1FDAHc0hflYL+HOtvkCyLVNPl1Ry+XNUbQgTczOdySyAK3ixdSGV7L5KoKolXZfM32yC+lsnyhrbFCEGUNg5RusPMkC1tYWFiMH8t222KPJK1Fub37h2ViCOCt9OO8nXoKc5TmrpNNX35NmRjaRE5PsCJ+N935N8noMcCkr7CaWzu/R2f29arnEiQJ0eGoOgcgOuwYwxp6+o46Fmk3uetW7NhA/9//PCSGoCQQe35zHepA36jHS4JMu2sf3tf2I+Z5j6TBPpP5vuN4X/tPaHHOQxAm9pWpmxqDxQ4e6vt/3LD+Sm7u+A4rE0vJaLEJnW84hmmQ0WKktSiaUds10KI2ecOgp6DybCKNicBXpzRxfKjyM7EilSFjVG8YWo2YqtUUGAXTJFWlwepkoAgCNkHgd5193NwXJa3rrMsXuW5DN4/EkuzlchJVNfqLKvFhFtzZEZ6bCTXsV0rXs7CwsNiVsSJEFnsk67Mra1pOvxi/k9neQ/BsJ1MBw9R5PfVYzfnVmeUsDp7DO+nnysYfH/gb9Y7pFesU7Xb8J5xMduXLVc/nOegwUk8/DoBtyjSCJ56KOMFaheFohkpOL9Us2CV3menE9kDPZYnefnP1SdMk8dBS6i65bNTUOUV0ELFP4ei6j6KZBRTBMaIF+1iIFju5qeNbQ++5vJHm8YG/sja7guPrP4VLnpjjXlod5K3Uk7yafBjdVJnpWcK+gVPwyfVW9GmMZHWdJ+Np/t7dXyZeTgj5ubA+xH/7okNjLklEHsfrahslkrSthERQljinLsRfuvsZVDUGN4oeAfh4cx0bCkV+09lLXNNpsMlcVB9mvsfJHNcIrQzsCtEq/YumOGz4ZOveqoWFxa6NJYgs9kjixe6aczk9iWFumzu31RFGNE8QBRmjyn3mlDawMcJVKdzs7VPxHHwY6WefKht37rUAx6zZ6KkEkUs+hK2pCblG48/xkFT7WRG/mzeTj6ObKtM9B3Jw6AICSuOEoyrjxSwUUft6a84XOzdgqEWkMVoFy6KCPAlpkgU9w5MD/6gqwNdlXyGh9k1IEKW1KHd0/4TB4rqhsZWJB1iVepoL236AX2nYqnXvKXQXVf7a3V8xvjSa4OMt9YQVeUhQnB4JjKsXTkCWCcoSMa3y+6TZpozo9rY1CILAEr+bmKZx50BsyPL7nLoga/JFlg5Le+stavyqo5cPNkY42O/hAK+bF1KZ8vMBlzVG+HdPudW3VxL5bGsjPqsfloWFxS6O9S1msUfS5JzDS4l7qs4FlCYkYfsVCIuCyAL/8byTfrbq/CzPQazNvFT92BofYdnnJ3LxB/AfewKpJx/D0DR8hx2JrbkF2R/AOXP2uNaY11OktRjrsi8DAlNdi3DLQRySh5Q6wK2d3yepbU5Jezf9HOszr3BR27UEbI3jutZEEe12bG3taAOVm1sA+7QZiLaRa4i2BQUjS0euenojwOrMCzQ5Z437vD35d8rE0CbyRpqX4vdyWPjSjbVvFrVQDYP7atT4ADwWS3Ko38OdA3EO93uY6Rpf1DMoS3y5vYnvr+0kP8ydzS2JfL69kcA2bI7qk2XOqgtyVNBHStOxiQKSIPCld9ZXffx/+wY5wOfmo8117JVwcNdAnISmM8vl4NLGCC12hS9PaeKVdJYN+QKzXU7muhyWmcI4MUwT0YreWljsdFiCyGKPpME+HbcU3FiXU85h4Uu2unh+vISUFma5D+adTLkoCirNtDrn81L83opjWp3zR3Sbk30+ZJ9v3OJnS7JagmXRm3k1+eDQ2NOD/2aR/xQWh85lQ+61MjG0iaKZY0X8bo6IfABZtG3VGqphFAroyQRGsYhotyMHgoTOOJfsyytgS/NMWcZ/1LEIO6DTvYCIhIxOZboRMKHUQt3QeDP5eM3599LPc0DwTDyi5Yg3EqppMlAlDWwTMVVjL7eTg/0ewoqCb5z9dgRBYJrTzk9mtvNmJse6fIEZTgezXQ4iYxRDBcMgoxuIgF+WxpUKaRNF6m3ikDPea+lszTqgnGGS0g2mOe2cFA5wsN+LgYldEIf6DLkkieNDO1dD5V2BlKrRp2o8GkuS0HX29bjYy+2kYQI27hYWFtsGSxBZ7JF4lQjntFzFQ31/oDtf6sHjFL0cGrmEZue87b4el+znyLoPsrf/GF6J349mFpjnO5IWxzyihQ4EBEyG32EOckzdR6pahE82fYXVZWJoEy8n7mWe98iakS2AtZkVLAmdO+mCSItFGbz9ZlJPPQ66juBwEDjxVHxHH0fjZ75A39/+hJFKASCHwtRf/mnkyLbpHTUaDsnDHO/hvJF6tOr8dPeB4z6nIAjIQu1olyQoCFgbrdFwiCJ7u528lc1XnZ/jcjDP7cS+FdbYoiBQb1PGZdcNpUhCb1Hlzv4YK9JZHKLISSE/S/weQhOMLDlGeR6KsHnNwW0YvdqTSGsaj8ZT3NC7Od1weTJDnSLz9SnNNDsm/2aRhYXF+LG+8Sz2WIK2Jk5r+hJ5PYVuqthFD245iLidal62xCX7aZcX0OyYi4mOsjFy4JR8XNp+He9llhNXu2l3LqDJMRuvsu2Nbgt6lhfjd9WcX5d9GZtQ2xLdJjoRJtnMUkun6Pvbn8i+sjmN0Mznid1xC6auETz9HNq++yP0dBJBEJG8XqRAcIfdiVVEO4tD57Ah9xoprTyd76DQhXgm0PNJFCQWBI6viChuYr7/OFzS6HfytWQSI53E1DREtwc5ENwhUbQdhSgIHBHwcs9gvCylDUAS4My60FaJoYmimSbdhSLfXt0xtK4EOn/vGeD5ZJor2iaWbheUZfySRKKKu127w1bWKNZicohpepkY2kS/qnFzf5TLGiP4LfFpYbHDsT6FFns0Tsk7YtrZjmDLnkeyaCNoa+IA2xnbfS26qQ05x1VjfW4lS4Ln1dyYLwycPGEHtVoYyUSZGBpO4oF78R95LEpdPUo4PKnX3Rp8Sh3ntXyXzvybvJt+FqfkZ4HvOHxK/YSjfCGlhTmew3k7/WTZeMQ2hbmeQ0c1syh2dtDzh99QXF+qQxJdbsIXXYpn/8VI7m0fedxZqLMpXD2tlT929fFergBAq93Gx5rraLBt/59IzTDpKBS4pS9WIdIA3szm6SgUJyaIFIkvT2nimjWdFIallXolkc+1NuK3zBEmnRVbGFQMZ1kyzXl1IfxWGZaFxQ7H+vazsLCoiV1yMcW5iGixo+p8s2MuQVszC/0n8krigbK5Vsd8prv3n/Q1qYMb77ZKEhhGWb2QqaoY2WyNI3csXiXMXOUwZnsORkDc6oiVS/ZzeORS5vuPYWViKZpRZJ7vSBodM0e1jFcH+um49mqMdGpozMhm6L/+D8j+AO6F+27V2nYlREFgitPOV6c0k9F1TBPcsrjDxMGgptFZUHk5Xft9/HQ8zfwqjVVHQxQEpjvt/HRWO29kcmzIF5jlcjDTaZkjbCuqidpN6CYYNau6LCwstic7vSAyTZMTTzyR973vfXzwgx8cGh8cHOTyyy9n6dKlRCIRvv/973PppZfuuIVaWOxgVKOAKEgjWniPF0mQWeA/jteTD1M0c2VzdtHNPN+RuGQ/i4PnMc97FG+lnkQzi8zxHkJAad4m5hRyKEzTF76Kkc0i2G0YmQyxe+9E7e4CQLBvfye58SAKk5eW5JL9uGQ/TY7ZmKY55n5J+VVvlYmh4Qze9G/s06Yj+/as4nmfLI3bNGFb8Go6iyIIyIKAuqU5yEYco/Q3GgmpSk1TUtPYkC8Q13T8srTx306/PdglWORxcVt/9QbMc1yOrfpbWlhYTB479TeeYRhcccUVPPjgg7zvfe8rm/vgBz9ILpfj2WefZdmyZXz0ox9l9uzZLF68eAet1sJix5BSB1iXfYV308twSB4W+k8ioDThlCcnFdCv1HN+2/d5sv8frM+tpGS7vZDDIu/HJ5eMCpyyF6fspd4xbVKuWQstmSD52EMkHnpgKDIkBUPUf+DDDN52c6leyLtzpUBuD0RBYjweCrm336o5V+zswFSrNy3elpi6jpZIYBbyCIqC5A9MSsPgXY3uQpF+VeMQv4eHY9XTVY8ITt57fKCo8usNvazKbTaWmOaw88X2Rup28qiRYZoYpolcpc4rqenopolLEndIHdgm6mwy+7idrMyU31CSBbi4IUxoD3yPW1jsjOy0gqizs5NLL72U1atXEwgEyubee+897r77btasWcPUqVOZP38+zz77LL/73e8sQWSxR5FQ+7il82rS2uai3VXpZ9gvcDoHBM/EIXm2+hqCIBK2tXJS4xUUjVI+vF30YJfGn7KzNZiGQXrZMyQevL9sXI9F6f3j/9Hwqc9ja2pGcm/dczY0DVQVweEYd1pbQc+Q0gZ5O/U0OSPJTPcSIvb2UVPYtje21raac3I4st2MFUxNQ4tGyb71OmpPF7amFgRZZvDWm/AsPpjACScj+wPbZS07C/PcTp7r6udjLfW8nsnRUywXpyeH/UQmaROd1nT+X2dfmRgCWJMv8KsNPVw5pWmnbLqa0nS6C6UGsznD4FC/l7luJyFFJqFqvJ7JccdAjKSmM9/t5My6EI02BXkHRGNCisLlLfU8l0izNJogrRvMdTs4ty5Eg022ehJZWOwk7HzfdBtZsWIFbW1t/O9//+OAAw4om1u2bBltbW1MnTp1aOywww7j2muv3c6rtLDYcahGgeXR28rE0CZWxO9ijvewSRFEm3BIbhzbwea7FnoiTuyu26vOGbkceiqJsmDhxM+fSaP2dBNbeh9GIo5r4b54DlyCEqkf0/F5PcPryYd5evCGobE3ko8Stk3hjKYr8So7j8mDe+G+DN7076qRoOAZ5yAHxu98N15MTSP37iq6r7u2bB1yXT31H/gIPb/7JVosSt2lH0JybV/xvaOIqRo+SeL0SBDdhM+1NvB2Ns+rmZLt9jFBH1Mc9klzg0voOq9tEbnYxLu5AglN3+kEUVLTuLkvytLo5ujZi6ksLXaFr05p5ubeQZ5IpIfmnkykeTaZ5nvTW5nuHH/Pr8kgYlM4NRJgsc9T6u0kipatuYXFTsZO+4k8/fTTOf3006vOdXd309zcXDbW0NBAR0f1wm+AQqFAoVAY+n8yWds5y8JiVyCvp3k79WTN+XfTy6izT9mOK9q2mJqGnkzUnFe7Oid8bi0RR+3vQ+3qxMxmyL39Jrm33iB2z520fvNqbE3No54jrQ2WiaFNDBbXsTLxAAeFL0SaxPqhrUEOhWn+8jfo/vXPMDIbXbAEAf9xJ+He94CRD54gWjKBNtBP9vVXEd1uXPPm0/3Ln1SIMq2/j/jSe/EdcTTxpfcROuOcPUIQ9RZUfryui65hEaFmu8LlzfXEVI1FXhdTnHbckxi9y+vGiPPZUeZ3BH1FrUwMbaKzoPJQNEHaqFyzZsLfuga4ckrTUJPZ7Y0gCNTbrfQ4C4udlR0miHK5HJ2d1TcwTU1NuEewfc1ms9i3KJy22+1lgmdLrr32Wq6++uqJLdZilyGvpcgaCfJ6BpvoxC0HcEq+Hb2sbYSJTmU/kU1oRnE7rmXbI8gyciiMFq2MiAHYp00f9zkNtUixs5PBG/9J7u03Ed0efIceju+oY+n9w/9hpFMM3PhPGj7xWSTnyJvyVelnas69mnyIhf6T8CjlqXN6LouRyyEAote33WpmBFnGMWsObd/7MVp0ELOQR65rQPL5Rn2e1dBiUQrr15J67mlEpxvfEUehROqRPKUIpRaP0fun35F7/VUARI8X8QIFM1+9IWr2tZX4jj4OHrgXPR6DxqaJP9ldgISm8YsNPWViCKCroPKPngE+39ZARFEmPb3KLYkIUNPnzLMT9qR6rEZdFcCjsSQXNYRZkap06FuVy5PRjR0miCwsLHZudpggWrZsGUcffXTVudtuu42zzjqr5rEOh6NC/BQKBVwj3EX8+te/zhe/+MWh/yeTSdraaufRW+x6JNV+nh78D++kn2XTT3yDfQYnNnyWgK1xxy5uG2ATXUx17cva7Iqq8zM9u1c9nRwMETrnAvr+/PuKOcnrwz51/IKouGEDHdd8u2TfDRjpFPEH7sX+7ioiF15C/z+vJ7vyZYx0elShkNfTNedUI4c5bNtp6jpqTzcD/7uB7CsvISg2vIcdSfCU01EidaOuW89kMFUV0elAtE8sDUgQRZRwBCW8dQ1+tWiUrl/+hOL6tUNjyUeWEjj1TIInn47ocpF6+skhMQQgOhzomdr9WTBN2Ng8VJyAQNvVSGo6a/PVb+itzhUoGmyTWhO/LHGw38Mzicr37iKPC/9OJh5M0yRXJQK0CdU0kWu8TgJgletYWFjUYodZrxx11FGYpln130hiCKClpYWenp6ysZ6eHpqaat9FtNvt+Hy+sn8Wuw9ZLcHz0Vt5J/0Mw+939hbe4+6en5HR4jtsbdsKu+Ti0Mj7UIRKm+mprn3xKw07YFXbFvfC/QidfzGCzTY0Zmtto+Xr3xn3xl5Ppxm44e9DYmg4hffeRXS7Ed2e0ua8hv3xcEYSoO3OhdhE59D/1b5eNlz9TbIvrwDTxCwWSD6ylM4fX4NaIwJWWnOKzKuv0P3Ln9Dxg6vou/4PFDrWY6g7Jhpo6jqJJx4pE0ObiN9zB+pAP1oiTnzpPWVzWiyKrbH2TQrJH8AoFEpRK//ub/89Uq+a0vy2SV1zShKXNkY43O8ZMikUgCU+N5e31O900RRBEDgiUNthb3+vm7ey1Wui9vW68Eg7zm3OwsJi52anrSEaiYMOOoh169bR0dFBa2srAE899RQHHXTQDl6ZxY4ipyd5K/VE1blosYOUNrBNeuLsaIJKExe1/YgVsbtYm30Ju+Rm38CpTHUtwiXvfhtJyevFd9RxePZfjJHNINjsSD7fUM8cLRZF7e+j2NONrb4RuaEBJVjd4c3I58i/u6rmtfLvrMLe1o6pa4hjqGEJ2dqot0+jr7CmfM3IHBK+eMiVzygUiN51G2axMiKg9feSf/tNlIMPq5jTc1niD95P7I5bhsbS/X2kX3ieliu/hXPOvFHXONloyQTJRx6sOZ988lFCZ52Hnt4iAqHrFDasx7VgEdlXX644LnjqmaSWP0vz57+yXQwedjSeEVLXhI3z24qQIvPh5nrOrQ+RMwwcokhAlnBux3S5gq4T03TezObI6Sbz3E7CilTV0KHdYWem0867ufLPj1MUOTHk5+Uq6XI+SeLihvBGK24mzZTCwsJi92GXFETTp0/nxBNP5P3vfz+/+tWvWL58OTfccAOPP/74jl6axQ4ib6QwRqinSar9NDpmbscVbR9EQSJoa+LIug9ykHEeAtJuKYQAjHyeYncX0TtuptixAaW+oeSIFixtmIu9PXT97Fq0/t6hY+RwhOavfBNbtRoUQUBQlJo9d0SnE9MwSi5nntH7vnjkIKc1fYVXEw/yauJBikaOdtcCDnKfjXnP0wwKy/AecjiCYiP36is1z5Na9gzuAw9C3GIzqCeTxO68tfIAXafvr3+k5evf2f4W1aaJUaMOCMDIZBAUG87Zc8m9+XrZXPSOW2i4/NPYp0wl8chSjGwWpaGR0FnnozQ24T5gMUpo53Hm25b4ZYlD/B6erpK6drjfu80bxjolEadkG/2B24C8brAsmeH/dfaVCcL9vS4+1lxPYAs3tqAi88X2Jp6Op3gwmqBgmOzjdXF4wMufu/uZ73bxhbZGXkimSeg6Cz0u2h12rlvfQ09RZbrDzsda6mlz2Gqm11lYWOx57JKCCOAf//gHH/3oR1myZAlNTU1cf/31Vg+iPRhFdCIi1RRFPmXr6iR2dmTRhizuXL1uJhPTMMi+8So9v/n5UPqaNtBP7o3XqHv/h3AdsITe3/2yTAwBaIMD9Pz6Opq/+q0KsSD5fHgPO5Lkow9VvaZrn0V4Dz0cpW7sqYceOcTi0Hks8B+PnsuSe+QJ0kt/MuTkFrvnDlqv+gGi04meql4cLnm8CFUaSRbWra2Zuqf2dJeusZWCyCgUNtYmOcfUi0hyuXEv2pf0889VnfcefBiSw0H4gkvo+P63ytMTdZ3BW/5Lyzevxn/M8Zi6jqDYkLfoO7cn4NqYuuaWRB6JJdFMUASBY4I+zqoL4toJzQ0miwFV4/edfRXjL6ayzE+kOSnsr+gHFlJkTo0EWOR18WYmzzu5PD9d141qmqzPF1melLl6Wis6Jr/f0Ms/ezanoa7OF/jO6g5+PLONJvuOEYEWFhY7H7uEIFq7dm3FWH19PXfeeef2X4zFTolT9DLbe2jVtLmQ0oJH2n3FwnjREgm0WBQjl0UQBKRQBNnrRXQ6Rz94B6HHY/T/9U9gmsjhCK75C0EUyb31OgM3/ou2veaXBEMVil0d6KlkhSASFRvB084i99YbqN1dZXORD3wEpbkFeQyRoS2RBAlHWqDjBz9B699io6frDNx0A/7jTirVL1XBf/TxVQXRqAJlK+5265kMancnsfvvRhscxDFnHv6jj0WJ1I94XdHhIHTW+WRefqkiBdDWPgV7e8n23dbSSss3vsvAP/9KYd0akCQ8Sw4hfM4FKHtY49VaBBWZSxojnBYJkh+Wumar8l7YnXgqnqo5d/dgnIP8nqo9e0RBoE5RGLRp3DWQQ914s2Cuy8HHmuvxyRI39A7wRq4yglk0TR6IJrikIYyym7++FhYWY2OXEEQWFqPhkgMcEDgTzSjwbuZ5NmXj19unc2LDZ/DsoKaYmlFEM4sogh1J3PE9KNSNUZXYPXeg9vaUbKaPOgbPAUuwNTbvtKJITyUx8jnqLvsoCAKZF5djGjr+o49H8vrQEnGQZdC0qsfXsndWwhFarvwWhfVryax4AckfwLvkEORQeKteCz2VqhRDG8m/vpL6Sz+IY97e5LdIIwuceiZKQ/WIlK2tHSRpyH1tOPYpUxE9E2vCa+TzJJ96nMH//GNorLDmPZKPLKXlG9/FMYp7n9LQSNt3f0j09pvJvLIC0e7Ad/Rx+I88Bnlj/ZZos+GcOZvmL38dI5cDUUTy+hDtlYYgezJ2UaTetmdt0PtrpKxCyY58JDsJhySywO3i29Na0E0TWQCnKOGRJVKaxhs1ms4CvJHJkTMMSxBZWFgAliCy2E2QBBm/rYHDwpdyQPAs8kYau+jGKXnxKaPbGE82RSNPQu3lpfg9xIpd1NunsTBwIj65AXkHCSO9UCDz8goG/vXXoTEjkyZ+z52oPd2EL7gE23YWRFoiDrqO4HCO3HxTEIlc8kHSy54l9+ZrQ8O511/F1j6Fhk98rnaERBAQvbVdJeVgCDkYwr1wvwk+i0rMKqJlOHoqReMnPova0016+XOIdgeeg0pCTHJXFzayP0Dd+z9C/9/+WDYu2O3Uf/gTyCM8x5HQknEGb/xn5XMoFum7/o80f+UbI55bkCRszS3Uf/jj6NkMCAKyz181siR5fUgTXKfF7sn+XnfV2imA2S4njhqfa9M06Vc1Ho4mWJ7K4BBFTg0H2Ntd+g5TBJGgLLOW6g6MQVlGESwxZGFhUcISRBa7DZIg47PV4WP7C6Dh6KbGuuxL3NfzazZFqnoL7/J68hHObP46rc69K3Lit8u64jGit99cdS7z4nICJ58BDdunX5OWSJBd+RKxe+9ETyRwzJpD+NwLUJqaEZXKvH7JHwBBKBNDmyiuX0futVcInX0+0ZtuqJj3HnrEdt2Em5qG6HAgutwY2Sq9dmQZORhE9geQ/YExu8OJdjuexQdhnzqNxIP3oQ7045wzD+9hR46pd1EtCmvX1KxNKq5fi5FJwxheP9HhQHRMrCeSxZ7LbLeDsCIzqJZHdwXgkoYw7hqGEgOqyvJkhma7jUNEkUdjSX7T0ct+Hhcfb63HL8ucXhfkpXSl6xzAGXUBnJYNt4WFxUasbwMLi0kmo8V4qPcPbGmia6DzYO/vyOixHbIuI5fDSNfO11e7O7fLOrR0ioEb/0nfX/4fancXRjZD9pUVbPjuNyisWVP1GNHhIPPCsprnTD72CN7FBxM4/SyEjWlYgs1O4OTTCZ9/8cjRp0nEKBTIrHyZ/hv+Tuisc6s+JnzOhRPurSO5XDimTqPuQ5fT9PmvEDrrPGz1DVVrjsbMaD2WRm/BZGExYSKKwlXTWljicw/1Qmqz27hqWgutNUwP4qrGG5k8j8VSXN/dz0upDBc3hDkx5GdFOkt3oZSG1263cUF9kOG3nwTgvLog7Va6poWFxTCsCJGFxSST1qKoZvWalbQeJaen8Mjb3+RBtI3sqCRtJ3cvPRYj/exTlROGQf8//kLzld8c6is0hGliarXT0EoOZQqhM87Ff+SxmIUCgt2OFAhW2FdvS9SBfnp+cx2YJkqkjoZPfJbEQw9Q7OpEqW8gdPb5OGbMRLRt3WZMlOVSzdQkYJ86vZRuWEUY2VrbSs1pLXYoCU1DN8EpCtu1P9D2osGm8ImWBi5p1DFME6co4q9ipAClJrUPRRPc3L/5xtJ7uQK/7ejl/Y0R9nY7eTKeYq7biUeWOCkc4GC/l3eyeUxgtsuBX5Z2a+c+CwuL8WMJIguLrUA3dbJaHAMNRXDilMayedwxt9wlvx/H3L3Iv/VGxZzodKI0VOnVsw3IrXqr5lyxYz1GNgtbCCLR4cB3xFFVU+YAPIccVrKrliTErUgf2xpMwyD52MNDwiL5yINkVryA7/Aj8R52JJgmjlmzkVzu0c+laejZDIIkI7lHf/zWIPv8BM86n9htN20xIVP/ocuRfVbNz44ioWqszGS5oz9OTNOY5XRwYUOIZpsN+26W7lXqhTT6c0poGrf1V4+y39oX5aMtdbw5zEzBJZXEj2WxbWFhMRKWILKwmCBJtZ9XEw+xKv0MAgIzPQcx23MQLsmPLNjQzMpiXpfkxyGO38p5MpDcHuo/9DG6fnwNWnRzXw5BUWj83JdRwhGMQgGjkEe02bdZPciIzmKCUDP9yzl3Hra2KRQ3rCsbl0NhfIceMaa+OdsSU1UpdpWnHerxGLG7bgdKdVDuRfvBCILINE20/j4Sjz1E5uUVSC43gZNPwzFzDvIY0+xMXR/qcSR5fTVfFz2bxSzkQZYJHHciztlziN19B3osin3WHIInnTKuHkwWk0ta0/lvb5RH4pv7Vb2czvJKOsu3p7Wwl3vndITc1gyqes0W3BnDQETg8IAl4i0sLMaHJYgsLCZAUu3n5s7vktY2C4sV8TtZnVnO8fWf4sjIB3m4fws3MASOrb8cjxwESpbcOT2JiYkiOHDK214o2RqaaPnW9yisXUP+3bdRGppwztsbyeOl2NlB7O7bKWxYh9LYTOi0M1GaW5Cck1t/45g1p2aKlmvBQsQavX/kYJimL1xJ5oVlJB57GHQdz8GH4ZugqYAWi1LYsI7MKy+jhEK49zsQORiasBAUFAXHrNnkXl9Zdd7WPgVhlHOrPd10fP/bQ2YMKtDzm5/jOehQ6i65bFRzCHWgn+RjD5N65kkQRbyHHYnv8KNQwpsbExv5PMWuDgZv+S+FtauRg2GCZ5yDc95eNH7685iaiuhwjppiabFtiWt6mRjahAn8uauPq6a1ENiO6aA7C8oohjRuSaTBtuNbHFhYWOxaCKY5WkXt7kkymcTv95NIJPBZKSEW48AwDV6I3c5z0Zuqzh8Svphpzv1QybM8ejtxtZuIfQoHBM8ioDSiiHaS6gDLo7fxVvoJdFOl0TGbIyOXEba1IYvbdyNqGgbZlS/T/aufVoiU+o9+Es+SQxCV0gZDS6XQBvtJP/8spqbjWXwwSn39UM2PqWkIo2zSjHye1HPPVNhHS34/LV//LrbGkVP3TNNETyYBcyhNbryogwN0/fQHqD3dmwcFgYbLP41rvwORJlhwrfb1sv5bX8EsbhEdFARav30Njukzah5r5HL0/Pn3ZF98vup863d+iGNa7Z5A6uAAnT/4Tln0D0Cpb6D5a1ehhEq9uDIrX6b7Fz+u+Fv7TzqV0BnnbjcDCouReTSW4A+d/TXnfz6rneY9MA1soKjy1fc2kNErOxQ12RS+MbWJOkvM71ZY+zWL7cGed3vJwmIryespVqWfqTm/NvMSbinIPN8RnNj4GTSjiCI6UMTSJjutRbm96wfE1c2b8Z78Kv7XcRUXtP2AevvUbf0UytDjMfr+8v+qRmz6/3E9zrl7IUbq0FJJorf8t1Qns5HE0ntx77+Y8IWXkH72afJr3sMxbQaegw5BCUeqiiPR4cC75GAcM2aSePwR9OgArn32xTV/nzFFegRBGHP6WDWMYoHobf8rF0MApknvH/+P9h/9AmmC9uNyKEzrVT8g++orGLksuTdeQ+3vpe6yj2Jrbh7xWD2bIbtiec359AvLagoi0zBIPftUhRiCkkjLvrwC/zHHo8Vi9P/9z1X/1okH7sV/1HGWINpJsI3SI0dk+1v37wwEFZkvtDVy7bou9GFvY4co8Nm2BksMWVhYTAhLEFlYjBMBAWmEj44oyIgb522iE5tYnuvfm19dJoY2YaDzzMANnNR4BQ5p2xbSD0dLJYdqTrbELBbQYjGUSB3FDevLxNAmMi8+j3PuPFLPPYXa3UX25ReJ3X0bzV/5Jo5Zc6r2XBKdTuxt7dRdchkYxnat/9GTSVLPPV190jTJvfEatgkKIj2ZJP/OW6SfewrTNPEecjju/Q5EidSNzRpbFKFGU9eRelcZmTTpWs8JSD3zJJ6DDkHPpNEGB6o/yDQprFuDHI4MRQQtdhwznQ5EoDIOAnu7nXh3M1OFsSIJAnNcDn42s51nE2nW5QvMcTk4wOchUsOZzsLCwmI09sxvVAuLrcAp+1jgP77m/CzPEiL29prz76VrRwE6cq+jGtUtu7cVwih3mgWh1F8n/sC9NR+TeuYpPAcsGfq/qar0/O5XaLGRey4JgrD9zRAMAzSt5rQ+Qq+mkdBiUbp++WP6//4XCuvWUly/jsEb/0X3z3+EFouOerzk9uA58KCa8yPNIQgIcm0RI8gyiOKor7WRz5N97RWM/PZ9D1pUEpAlPt5SXzHulUQ+3FRXs2HpnoAiijTZbZxTH+KKtkZOiQSptymIO6DhtYWFxe6BJYgsLCbAVPe+NNpnVYy3OecTUJpxyYGax7pHmLNLnhEjAdsC0etDCgSrzgkOJ1IwhKlrGLnqHd8BjFwWYYveOno8hpFKTOpaJwPB4cDWVluwOveaP6HzZl9/jeL6dRXjak83mRefZ7RyTdHhIHT2+VWNE3xHHYe0sQaoGpLHi/+4E2vO+48/CcnhRPJ4sddIuxMUBdnno+fX16EO1q5dsdg+2CWRxT4PP5nZxkkhP/t7XXy4KcIPZ7TR4rDSwjZhiSALC4vJwBJEFhYTwCOHOKXpi5zS+EWmuvZlmmt/Tmr4HEdGPkTE3j5iP6K53sNqzi3yn4RLCmyDFddGDgZp+NinYMvogSDQ8JGPI/sDiE4X7v0X1zyHc958CmverRg3a6R/jRU9k6bY3Unu3VUUOztqpvaNB9nnp+7SD5VCX1vgmDNvQo51ei5L8olHas4nn3gMPTV65MnW0Ejrd35A+PyLsU+fiWvBIpq/8k1C51yA7B3ZhdC19z44Zs+tHJ+/EMeMkniXvF7qP/JJxC2tvwWByMUfIPHIg2CaJB99GNOolqxlsT1xSiLtDjuXNUX4QlsTJ4QD1FkOahYWFhaTjpVwa2ExQTxykJmexUx17YuJgSwoCKMUQpeOC3NU3Yd5rP/6svFmxzzm+Y5CHMM5JhNBEHDMnkP7935M/MH7Kaxbg625hcCJp6LUNwwZI3j2P5D4fXehx8vT4ESXG89+B9D1ix+Xn9dmR/JN3PxAi0bp+9sfya58eWjMMXM2DZ/47IREy3BsU6fT8s2rGbzxX+TfXYXocuM//iT8Rx2H7A+M/4QmVQXWEKLAWGvglUgdgZNPx3fUcaVGs8OsuvV0GiOfA0FA8njLejrJwSCNn7qCwto1JB57GEEU8R1zHPa2KWXPydbSSutV15B67ukh2233ov1IPf0E2VdfAUDt7cbU1Iqon8WOQRAEZCsQYmFhYbHNsGy3LRtHix1AwciR0WKsybxIwcgw1bUvfqVhxHS67YGhaZj5PILdhqhUpuWofb1E77qN9HNPYxoGnv0PJHDy6fT9+fcUOzvKHlv3gY/gPfyoCRXo69ksfdf/PzIvVFpQ26fPoOnzVw5ZfVfDNAxMw0AcxQJcT6cwCgUQRGS/f6vqmVLLnqH397+uOhf5wIcJHHPChM9tahrFzg30//vv5Fe9BZKEZ8khhM8+H6Wuss7EUFUQhJrPX8/liN7+P4xUCj2dIvv6q2VmDqFzLyR42lnbPX3TwsLCYkus/ZrF9sCKEFlY7ADsohO7zUnINrIV8/ZGlGXw1E73U+obqHv/hwidfT4AksuNqWtELv4Ag7fehNrThdLYTPicC7BPmzFhtzI9mSDzYnXzicLq99CTyaqCyMjlUAf6STz+MNpAP64Fi3Av3LdmREnyeJFqNILdhKlpaPEYan8/ZrGA0tiE5PMjOcvdA52z52GfOYvCu++UjdvapuBZtP+I1xiNYm83G77/7c1mELpO+pknyb/9Ji3fuBolXF5fNNrrLjmdeJccSsf3v1VhwS04HHiWHGKJIQsLCwuLPQZLEFlYWIwL0WZHDJWnUrnm74NtyjRQi6DYRq13GQ0jn6vaK2cT1WqJ9EKB9PLn6L/+D0Nj2ZdXEL3dS+s3rsbWNH7xaagquTdfp+d3v8Tc5LwmCAROOo3AKacjDzNAkINBmj79BXKr3irV4hgGviOPwbnXAuRQaOhxaixaisJJEpLPh+hwbnnZ8jXkcgze+r+qznja4AD5VW+iHLy5Ls3UdbR4HFNTEW02pECwqrixNbfQ9Pmv0Pe3P6NvdMGztbRSf/mntzolcTzoqSR6KoWpaYhuN3IguP2dBy0sLCws9mgsQWRhYTEpbK0IGo7odJVqcmqIomq1SUYiTv/f/lQ5nkrR/8/rafz0F5Dc4+vvpA0O0P2rn5b3BjJN4vfdhX3KNLwHHVL2eDkYwrvkEFz7LAKTsianejZD7s3XGbjhH6VeQKKIe7/FRC66ZEQBYuRy5N98veZ8evkyPIsPRpAktESc5OOPEL//HoxsBskfIHT2+bj3P7BMvEHJ1c61z760XXUNejqNIIqIXm/NVETTMNDiMYx0GkGWS4+t4og3HordnfT+8XcU1rxXWpPbTfiCS/EcsHjcfysLCwsLC4uJYgkiCwuLnQ7J58Oz+GDSy56pmHPMmo1cJY88/947pR5DVci98Rp6OjXqJlsdHETt7kTt7cExazbpF5fXbJQavfMWnPP2RvZXCgjJ6aoYy7+zip7f/HzzgGGQeeE5ih3raP7qt1GCoYpjSicTEd0ejGym+nQgCKKIns0wePN/ST356NCcvlEk6uk0gRNPqUilEwQBORhCrnXtTefJZMi8/CID//knxsY+TfYpU2m4/DPYWlpHPHbo6eZy6MkEal8vgs2GFAjQf+O/h8QQgJHJ0P/XPyB5vXj2O2BM57WwsLCwsNhaLEFkYWGx0yE5XUQuuhQMg/QLy4YiRc695lP/kU9U7dVjFAsjn3QUG+lCxwa6fnoNeqLUO8mz5OARrae1gQFMvXaD17LHJuIM3PjPqnNqTzdqZ0dNQST5/AROPIWBf/216rz/yGMQBAE9mST11GNVHxO781a8Bx2COMFUuPzqd+j70+/Kxgrr1tJx7dW0ffeHo6bY6akk8aX3EbvnjqG/g+BwUvf+DyFgkn3lpbLHD/7vBhwzZk7M8c/CwsLCwmKcWILIwsJip0QOhqj70OWEzr0QI5tBdLqQvD6kGqYPjpmza57L1txa2XtnGFosRvcvfzIkhgDU3l7c+x1AZvmyqsfY26cg2sbWINMsFlG7u2rO51a9hWv+PlXnBEHAc8ASsq+8RPbVl8vmwhdcglxXEiPa4EDtuitRwMjWbqw7EloyweBNN1SdM9Ipcm+/Oaogyr31JrG7bisbM/M5+q7/A01XfKVkrT5s7Wp3F2aVmikLCwsLC4ttgSWILCwsdlokl6usDmckZH8Q7xFHk3ri0fIJUaTuso9UTW3bhJaMow30l40V1q4mdNZ5iC5XVTERPv/iUR3qNq9BQnA4MfO56msPR0Y8XA4EqP/YJ9EG+sm88hKiw4F74X5IgeDQ6yNWeZ0cM2YROOlUTFVF7etBsCklh7wRxOGWmJpGsWNDzfnc22/gO/SImvNaMkH0jluqT+o6uddX4py7N7k3XxsalgJBBNEyVrCwsLCw2D5YgsjCwmK3QPJ4CJ97Ec65exO/53a0RALHjFmEz7kAZRSHuVrRk8Gbb6Th459h8OYbKW5YX7qOz0/k0g9ia5tS83x6NoueTFDYsA5RUbC1T8V/7PHE77mzysIlXPP2Lq2jWEBPJEomBzYbks83ZFwg+/zIPj+O6TOrP39/ECkQHGqc65w3H98RR9N3/R8wcpuFmO+o4widc/6IfZyGI4gicjhSIRg3YW9pG/F4U9NQaxwLoA70I20hVoOnnYkUCNQ8xsjl0GKDpJ5/Dj2ZxL3PImzNLcjhiOVQZ2FhYWExbixBZGGxnclqSQw0bKILm+jY0cvZrZD9fnyHHIZr/gJMXUe0O8YUYaplKlDsWE//v/9O85e/AaqKaehIbs/GCIZY9RgtlSR+393E77trcxqYotD27WsorF1D7vVXhx4rKAqNV3wZKRhCSyaIP3Av8QfuGbLYtk+dTsMnP4utoWkMzyFI0xe+StePv4+RzRA46VS6f3NdhV138rGHsLdPwXf0cWPqNSQHggRPP5v+v/6xyqSMa5QeS6LNjr2tnfw7b1edt7VNKaXMAQgCviOOwXPgwTXXZuRypJ57mv6//3nzc3pkacky/KOfRPYHy2zOLSwsLCwsRkMwzRGafezGWJ2PLbY3WS3B+tyrvBi7k6yeoMUxj8WhcwgqTUjixBqYWmzGNIxSfyLTRPJ4EeSx3+/Rsxn6//lX0s8+VTEXOPl07O1TEGw27FOnIweCmIZRs/lpesVyen59XcW4YLPRdvWPMIsF8qvfQ/L5sLdPLYkxQSD+wL0M3vTviuPkcITWb16NHApXzG2JaRho0UHU/j7y771D9OYbqz5ODoVpveoa5EBw1HNCKe0tdudtJB5+YEjkiS4XjZ/9Es5Zc0Z9rXNvv0nntVdXjItOJ63f+SF6IoGRy25seusbMaWv2N3F+q9/seqc74ijEdxugieeOubnZmFhsXNj7dcstgdWhMjCYjuQ01M8PXgDb6YeHxp7N/McqzPLOa/1ahod1dOg9jQMVcVIJTHN0mZ5rPVDanSQ9LJnSD72MKam4VlyCP5jjh9zg1HJ5SZy4aXIoTCJh+7HLBQQ3W78x5yAFAjQ+4ffEjjxFBBEorf9Dz2VxL3fAbj2XogS2Vz/o6dTxO68reo1zGKR+NL7qHv/h7BPmVa+/sFBYvfcXvU4bXCAYk/3mASRIIookTqUSB3pZc/WfJwWHcSsYiduGgba4AC5t96gsH4djukzcMyagxyOEDrnAvzHn4Ta043ocCJHImNuomprm0LDp65g4J9/HWqqa2tppeHjn0Gpb8DWOHoEbBOZFS/UnEs99wz1H76cYscGSxBZWFhYWIwZSxBZWGwHMlqsTAxtwkDn0f4/c1bTN3DKe/adL3VwgPjSe0k+/ghmoYBzwUIi578PW1PziBEILRql+xc/obhh3dBY/N47ST39BK3f/n5VUWTk82iJOPl33sZUizhmz0X2BwmddR7+o4/DyOcpdneSeuJRsq++gu/o40CW6fn1z4bOkX3lJaRgiNavfwelvgEAU1XRYoO11zrQh57JVDSxNdUiRqZ6nyGAYmcHrr3m15yvhnPOXJKPPVR1ztbajrBFhMs0TQrr1tD54+9j5vMAJCg1S2352newt7UjuVzYGhrHtQ4omWN4DliCc+Zs9HQKQZIRPd4RjS5qoW/sg1QNs1hAkGTSLzxf07XPwsLCwsJiS6onwVtYWEwqnbk3a871F9ZSMGpvhvcEtGiUruuuJfHAvaXNuGmSW/kyHd/7JsWe7hGPzb3zVpkY2oSeiJN49KGKSIiezZJ69inWf+0L9P359/T//S9s+OZXGPjvv0ppW5E6TE2j9/9+SfbVV0AUcS/ar6ohgh6LMnjrTRgbBYRgs2OfOr3mWu1TpoFZGZkRFAXB4ax5nDKOCMomHLPnVu3XBBC+8H0Vpgp6PEb3r68bEkObMDIZev7vF2iJ+LjXMBxBFJFDYeztU7G1tE5IDAG4F+5bc84xc3bJyGKMkUWL6kSLKuvzBd7L5ekpFNFG6eFlYWFhsatjCSILi+2ALIzUr0ZAEEofRd3Q0M09r/9Kfu1q1K7OinFTVRm87Sb0XHW7aqNQIPXkYzXPm172zFCK1ia0wf5SQf4W5ZOpJx8bMjwwhzV5tU+ZSv6dVbWvsfy5oaiF5HYTOOk0qGIIIDqdOPeaD1TOyf4AgRNOrnp+yefH1txa8/pQMnJQ+/tQBwcw1CIASjhCyze+g32YK53k9VF/+adxzJhVeY5EAj0WrXp+tacbPZmsOrc1GPk8al8vhQ3rUfv7MIrFUY9RGpvKntMQokjglNNJPvEozjnzUAcHJn29ewIb8gV+1dHLle9u4JvvdfCdNZ08nUgTU9UdvTQLCwuLbYaVMmdhsR1occ6ltBGu9DCZ4lwIpkh/YR1JtY/u/CraXQsI29pxy4HtvdQdQvr52vUuuVdXYuSySM4qERRRBLm2IYUgyyBsvu9jGgaJR6unkQHE7r4d594LkP2BkqgxTQRJxlRH2Kjrepm4koMhGi7/NIO33oTW3weU3OJCZ5+HUSxWtbsWZBn/MSegxaKknnp86HxyXT1NV3wFJVy9fkgvFCiuXUP/v/9Gcf1aBEXBe/jRBE87EyUUxtbUQvMXvoqeTmFqGpLbXdMhb8TnCJja5G6ItViUwVv+S+rZp0DXERQF31HHETz1TOQRLLflQJDGz36RxMMPkHzkIYxcFsfsuQROPJXkow/hPfRwsq++jBaL4j/q2Eld8+5OT6HID9Z2Edc2RzETms7vO/v4SnsT+9cwErGwsLDY1bEEkYXFdsAlBzgichlPDPytbNwp+Tg4fAH39PyUgeJ6PHKYRf6TGSis55X4/RxTf/keIYpqpXZBKbJSy4JZVBT8x51A9uUXq877jjwGaZgrkanraCNEDrRkAlPXkXx+/MedROLB+yhsWEfgpFPhgXurHuOYM68sRUsOhtAG+gieeiaS2wOiQLGri9y77xA4/qSa1xYUmfB5FxE881z06CCiw1nqQzSCOUBx3Ro6f3T1kIAyVZXkI0vJv/MWzV/8OnIwiOT1InlHbyAr+QMgSSWBt+XabLYR/0ab0GIx1IE+1J5ulIZG5Lp6lCqW5nomTd8/rif70maDBFNVSTx4H6aqErnoUkRHbUt6JRgicOKpeA5YAiYUuzvJv7cK/zHHkX39NRIPPYBzr/m4Fu1fMunQdSSPBzkYsvoUjcCqbL5MDA3nv72DTHHYiNgsUWRhYbH7YQkiC4vtgE10Ms97BC3OuaxMLCWtRZniWkSTYxb39/6GuFqqk0lrgzw1+C8W+U/GLrnpK6xmmrxfzfMWjBxZLU5n7g10U6XVuTduOYhD8myvpzYp+A47ksSD91Wd8x93QmmzXgN72xRc+x5QtrkGsLVPwdbcgh6PIgdLERZRUXDtsy/ZV16qei7H3L0QbDb0bAb/scfj3u8ABv/7b9S+Xlz7LNrcL2cjgqJQ977LSsJnI6LdjmP2PJSGOMXODoxCHs+BixGdLoxMhtRbbyDaHdiaW5H8fvR0iuwrL5F86jEEUcJ/7Ak4585DDozcS0dPJRn4998rUv8AihvWU+zuRA6O3WlN9vsJnHQa8XvuqJgLnXneiH8DALWvl67rrkXt7dl8zkgdzV/+RoWLnJ5MVvy9NpF84hECp5yObQRBBIBp0vfn32Nks9inTMUoqiSWlgQVgOhyE3/gHhL33VX6v9NJ+KL34zlgcdnfy2Izb2Wqp6YCrC8U0fbMLh0WFhZ7AJYgsrDYTtglF3XSVI6u+wiGaZA3Uvx93RXoZmUq0srEUk5t+iKvxO+n1bkXSpUGrnk9wxvJR3lq8N8MT8Xby3s0h4QvwiVPrGh9sjBUtVS/YxgIDgeyp3aUQo7UETr3AqK33FQ2bp8xC99hR9VsglpCwLP/gXj2P5DMi89jahquhfshud30/uG3NHz8M0OCCEpF+bHbfRW1RaLfT+Tcixi86QbSzzyJqarYp0wj8r4PIIfDeBYfTO6N14jddxdGOo1zr70JnnYW0rAUONM00eOx0qZcknEtWIggSWjJBNE7biH58NLNq1YU6i//NLm33yT50AND4/l33sYxew6Nn/z8kKAxNQ0tHsPIZBBsCpLXh1EsUli3puarkn3tlXE504l2B4GTTkWpayB25y1o0UHkSB3BU8/EOW+vUuSoRsqUlkzS83+/LBNDANpAPz2/uY7mK79VSkPciJ5M1F6IYYzouLcJ2ecncMIp9F3/B7RopbOfZ//F9P/7b5tPm8vR/9c/IodCuBcsGvX8eyIN9trRn6AsIVSpf7OwsLDYHbAEkYXFdkYUJERBIlOMVhVDULLj1owimlnEMKs7PCXUXp4a/FfF+BupR5niWsgs70GTuu7xoEYHid93d8lCu1jAMXM2kfd9AFtrO6Kt0mBCcrvxH3si7n0PIL3sWfRMBs+BS7A1tYxYTwKl2pe+P/8eKRDENX8fBFEk8dD9qBvd6bJvvoF74eYomxKpo+Ub36X/338j99pKoGRD3fDJz9H9q5+idncNPbawbg2dP/oerd/6Ho4Zs5APPwrHrDkU1q0l9+ZrdFxzFXIoTP0HP4rS1EL21VeI3vJftOggosuF/4RT8B91LNk3Xi8TQ6V1q/T+7lc0ff4rJB95EIY5eeVXvU3+vVV4DliCnkqRfPpxorffPOQA55g5m/oPfxy5qRlt2HqHI48S0al6jNeHffpMgmecg+T2oG106uv/x19o/NQVuBbtX7UhrZFK1hRnxc4O9FSybD3iCI1XAUSHfUzrde2zCNf8hWRfe6Vs3HfkMagDfRhVLLoH/3cjjqnTx5QCuKdxgNfDTX1R9CqBoFPCAeoUK93QwsJi98QSRBYWOwhJGDkXXxRk5nqPwC5VWggbps7KxANVjirxQvx2Wl1745RGrx2ZbLRYlO5f/JjihvVDY/m1q4ndcweh8y7CVt9YtY5DcrmRXG7sre3ju6AoIrrd6PFYyZBgCzb1CBqOramZxk9dgZ5Og2Egut0U1qwuE0NDmCYDN/6Txiu+gpnP03HNVRiZ9NC02t1F54+voeXr32Hg33/DyGYBMLJZYrffjNrVWduK2zTJvroS57y9hxzuNpF49CGc8xeSfukFBm8sF775d1fR9bMfUv/xz9B17dWV5xUEXAtrp1rWQovH6PnNz4bMIIbT++ff0/6DnyFW6+tUKFSMDWdLK2/J58PW0kqxs6PisY65eyF5xxbdlANB6j/2SdSeHlLLni6ZShx8GMWuTvr+9LuqxxS7OjBUFWtrX0lYkfhSexO/2tBDwdisig71ezjY70EcMVJrYWFhsetiCSILix2EU/LhVxpJqD0Vcx45jGYWmeJaWPVY3VRJa7UbgOa0JMZ2sO82VBUjm0EQxCHzgmJnR5kYcu93IL4jjyHz4vMM/P0v2KdOx3fUsSiRuhEbro4V2R/Af8IpxG77X+WkJOGu0aBTtNkx5HzJ4UyUyNSoKwLIv7MKs1Ag/eLyMjE0hGEQu/sOvAcfTuLhcqGafv5ZvIcdMeRatyVaPIpULZ3QNDEyaaK33lQ5B2jRQcx8Huc+i8gNr20SBBo+/hmkKmYGo6GnU1XFEIBZKKANDlZtdCt63CXHv2r9agQBcYtojOwP0Pi5L9P98x+VpdnZ2qfQ8NFPInnGXuMj+wPI/gDOOXOHxnJvvF71tQZQ6uoRJOunrxoOSWK+y8mPZrTRXVDJGDpTHHa8kkjQcpizsLDYjbF+FSwsdhBuOcgpjV/g1s7vDTVmdUp+9vIexWzPwTglHx6l+qZWFuxMce3LhtxrVedbnPOwiduuOaVpmmgD/cQfXkrmxecR7Q78x5+Ee+G+ZF59eehxjllzcC/aj+5f/mRog5p76w3iD91Py1e+iXPOvK1eiyBJ+I88huLa1WRe2uw2J9hsNH72S1WFgRaLEl96H4lHH8TM5/EcfFhF4f9wSilewlCKXTUKa97DtU8NAZuII7o9VVO47FOmkXnh+Ypx31HHgq6jx2O1r7l+LQ2Xfxq1s5PsG68ieX24FixECgSR7GNLOyvDGLlo3jSqO5DJvgC+I48hWcXS3HvI4VXT02wNjbR8/Tto0UG0WBQ5XIccDE24Yetw3PsfSPS2m4YMFoYTOvv8SbnG7opNEmmSbDTZR+qdZmFhYbF7YQkiC4sdSMTWzsVtP6Ir/xaCKeKQPKyI38Wq9NOEbW0sDp1LyNaCTSzvwSMIAjM9i3khdjt5o3yTLSFzYPBsFHECG+Ixovb10vG9b5VFS/r/+kdS8/YmdOa5JDZaVPuPO4n+v/+p8m69ptH7h9/S8o3vVo04jBc5EKT+wx9Hi8cprH0P0e3F3t6OHAhVRKG0RJye3/+a/Kq3hsbSzz1N0xe+WjOK4z/uxJIFdl3ttUqBQE0zADlcV7XPj+j2YJ86negt/y0bt8+YhXPWHEzDRHS5MbLVz2trbEL2eJHnzC2LkJSeZwItOkBhw3rkYAhbc0vJdloUS/bj8RhGLodgsyH7/IgOB6LHU3K+S1QxPZDlmn8r0eEgdNZ5iC43iQfvxywWEGw2fMccT/Ck05Fc1cW5HAiOaCs+UZRwhOYvf4Pu3/4cI7Xx8yFJBE8/G9e8vSf9ehYWFhYWuzaWILKw2IEIgoBPqcMlBXgr9QQPdP9maC6lDbA2+xInN17BDPdiRKG86sGn1HF+69U83v831udeBUzq7dM5qu4j+JXKupnJwigWid1zR9XUsfybr2OeckZpU51MIojiUE3NlmjRQdS+XgSbHdm39QXukteH5PVhbxu5BkkbHCgTQwCYJqmnnyDyvssYuKHcytoxey7+o48r9Tw6+viSAUIV/EcfT3xpZa8i0etDaWqh+SvfpP9vf6bYUUontM+YRf2HPoZgsxM690IyLywDUcJ39HG4FyxEDoYwdZ3AiacQrZIOKDqd2KfNqBhXo4MY2Qx9f/kDhTXvbX68y03zl7+OVNdA+slHid11G0YuB6KI58CDCF94CXIgSN2HLqfnVz+rEIaRCy4pc9TTM2nMYhFBUZA8XmR/gNBZ5+E/+jjMQgHBbkfyB6qaMGxrBFnGMWsObd/9EXoihlksIoXCyH4/on0UO28LCwsLiz0OSxBZWGxDdFNDRKrZWHQTGT3GEwN/rzr3aN9faGybhVeJVMwFbc2c3Ph58kYK0zSxS+5tbqSgp9Nklj9Xcz71zFPUf+zTdP/8R5g1HPI2YWQz5N9dhWe/AyZ7mTXJrXq76nh62TNgGLT/4Kdk33wdI53Cufc+KHX1Qw5pcjhC3Yc/Tv/f/lRWL+M75nhcey8gtkUPH/fBhxI69UwyLy5DT6WIXHIZcjBUqqtRFOIP3k/8vrtwzJyNa599wTDIrnxpqO5JkCR8Rx1LsbeH9DNPDp1X8vtp+vyVyKFw2fXU/j4Sjz6ENtBfJoag9Fp3/examr/0NQZvumHYhEF62TOogwM0fe5LuObuTet3f0j0jlsorl+HUldP8MxzsbdNQbTb0bNZiuvXMnjLfyl2daDUNxA6+3wc02cheTxVTRd2BIIoooTDKOHw6A+2sLCwsNijsQSRhcUkY5omGS3GYLGDhNZLQc8w3X0APiVS0U/INA2S2gDxYjeaWZlSBZA30uT0VFVBBKX+RtWc6LYVggCMUJQuKjLO2XNo/8HPMHJZBEWpWssheryYqkZ86Z04Z8+pbiywDRgpGpVesZzwhZcQOPbEoTE9k6Gwbg2JJx7FSKXwHXM87T/8GYV16zDVIo7pM5H8ASS3m9ZvfJdCZweFNe9hnzkHPR5lw7e/OhRtid1xC/bpM2j6zJc2WpOXmobm311F/t1VQ9e0tbYTOv1sBElC9geou+SDhM44G7W/D8npRgqFSulvw4S2USgweOtNePY/kJ4H7qn6/IxshmJXJ6LXuzmVbCOFd1ehx6LIU6bhmDKNxss/jZEvpb5tSnkzdZ3MSy+UObgV1qym++c/JnzRpfiPPh5xIrVLgJHPYagqosO5Q6JKFhYWFhZ7LpYgsrCYZOJqD6tST7M2+zKyaGOW52C6828TLXYwzb0/srh5szdY7ODmzu9yXP3HRzynIOw8dreS14f3sCNJ3H931XnvEUcj2uzYmpox1CLhiz7AwD//UvG48DkXkHj0QfREHFPb9o54m3DMnAOSVGo0ugXegw4tMwDQMxniD95H7Pabh8bSzz+L0tRM85XfQtnCsEEOhZFDYdwLFlLs6ab7p9dUpJ4VVr9HbOm9yKHaLnDJxx7Gf+SxQ41ZJbcbye3G1thc8xg9lST9/LOlaFuV57YJLR5HcrkrBBFAsbsL+5RpACVh4nBucWyMgWHNTocTveW/ePZfjFhXX/PaVdeTTlHcsJ74vXeiJeI45+2N/5jjUSL1Ve3ZLSwsLCwsJpudZ5dlYbEbkFD7uL3rhyyL3Uxv4V06c2/wWP9feC/zPDk9QUbf7BiW01M81Pf/KBpZDNPALlZvVumRQjukn1AtBFkmcNxJyFU2vp5Dj0Cpbxz6v6jY8C45mKYvfBXn3L2QQ2FcCxbS+Nkvkl/9LvlVb+GYNRvR6aw417ZCCgRo/OwXS6JoGO4DlxC+4JKyCIcWHSwTQ5tQu7uI33c3RpXI1yayr6yoaf2cfOwhbM0tNY81i0WokW5YUzyaBug6RrGI6K39frE1NaHFolXn5EAQQ1VR+/pKzWDvvJXsm6+jbXS601PJmjVhpqrWPG8t9GyWxNL76Prx98m++grF9etIPHAvG779NYqdG8Z1LgsLCwsLi4liRYgsLCYJ3dR4NfEgKa2/Ym5d9hXmeA4jXuzBr5SERF5P0VdYDcArifs4PPJ+Hu77IyabN8IiEic0fAa3NPlOXFuDEonQ8rWryL62kvRzTyPYHQROOBlba1tFSprk8WBrm4L/pNOQvD7MXJbYQ/eTe3lFyfnrjHMmpdDdyOfQN27WJa+vZtqVaLPh2nsBU679OdnXX8UoFnHtvYD8qjfp/8efUeob8B52FEo4UqorqkHyiUcInnQqYqh6jYqWiNc81sznkX2BmvPu/Q9EHNaLR40OUuzsIP/O28j+APZp0xF9fpRgaCiKIjpc2KdOJ/XkYwRPPJXBm2+sOK99xiwQxJLg2gLJ50eubyC/6i26f/mTsjRHpaWV5i9+bdT+PYI8vlQ3PREnduetFeNmsUDf3/9C8xeu3G6plBYWFhYWey6WILKwmCRyepK3Uk/WnF+TfZGFvpOH/j9c+HTnV+GU/JzedCXvZZYRV3ups01hgf94fHLdqKYMOwIlHMF/5DF4lxwMooRoq963xMjn0Qb7id99O/n33kHy+vAdcTT+I49F8npR6ibmiKdnMuipJHo6hWizUezuZuA//8As5PEefjSBE09BCVevuxIVG2J9A/76BordnXT88LtlKWTx+++h4eOfQfTXFqJmoVAWANISCUxNK9X9BAK49t6H+L13VT3WPm06JtD0pa+jdnYQvef2oeuLLhfB085CtJUiVWp/H10/+2FZA1PBZqPhY5/GyGZwtE8FQPJ6qbv0Q3T88Ds45+5F+PyLid1/d+m8koR3ySGEzruodP2ZsykMq1mSfH6av/wN0PUKMQSgdnYweNMNhC+6FDlShzZQKfpFjxdpo/nEWMm9U93gAqDw3jvomYwliCwsLCwstjmWILKwmERMaje2NE2zzBjBLrrxKw0k1F4AVmeWsy7zElPc+xK2tbIocCq+GkYKOxPD60wMVUWPx8i9/SbqQD+u+ftg5nN0XfejofQxPZkgdvftuBbtR/1HPlFTSI2EFovR/6+/knlxc0NT+7TpNHzkE/T88bcklt5L9pUVNH/12yg1IjhQso7u++ufKutpTJPeP/2OtquvZbDGsa59FiG6nOjpNLl33mLwf/9B7epErqsndOa5OOfuha2tneKG9eUHCgLBU86g55c/QYsOYmufQuMnr6Dvn9fjnD2X4ClnoGxMR9RzWfr/9bcyMQSllLq+6/8f9Zd/Bs3nH+rlY5syhdarfsDg/24AE+ou/RByIIjk9yMHQ0ORuKbPfQk9FqXY3VXqBVTXgBwKkVmxvKoBBkD6hWWEz7uIxk9+js4ff788yiRJNH7qiqGaJwsLCwsLi10JSxBZWEwSTtHLXM+hvJSo7EUDMMt7cFmDVbcc5Ji6y7m96wdD0SIdjdWZ5ewXOAO7uP2c4yYDU9Mq0q0kl5vEI0ur1tJkX16BHo0ie8fXg8jI5xm8+YYyMQQlt7OB//6b0JnnMrBRROTfeRtlySE1z6Wn0pU9iYYmdYrdXbj2O5DsiuVlU4KiED7/YkTFRvLZR+n/x2bTCK2/j74//57gWefRdMVXiN17F6mnHsMsFrFPmUrg1DNJLXsWLVqSWsX16xj4zz9o/uLXkP2BMoFoJJNkV75U/XXI5TDSKfRMekgQiYoNx9RpNH7q85iFPIgiks+PIJaXi8o+P7LPP2SgMPSUR0jzQ9cxNQ37lGm0XfNT0sueIf/eO9jbp+I95HDkcKTiOqPhnDWn5px9xiwkd/W6OgsLCwsLi8nEEkQWFpOEJCosDJzMO+nnSOvlxeWtzvk02GdU2GM3OmZxUdu1LBssmTB45BAHBs+myTF7u1ppTwZaLEr3r35aFmGQgyHUnu6ax+TeeRv7lKnjuo6eTJB69umqc8WO9cihCIgiGAapZ5/Cvf9iRLnGV51R240NSpGY+ss+QmrGLOIP3Y+Ry+Kav5DQOedjq29Ei8cZGN7TZxixO2/Fd8jhRC5+P8FTz8AsFMisWM7gTTdUpJwVN6zHzOUqHNoMTatpzAAbm6NW8V6Q3G5Mlws9HkPt7UaQlVJdlWPkWi37tJk15+RQGNHhQJBlbPUNhE4/G0PTar+2Y0DyBwiecU5FHZFgs1N/2UesdDkLCwsLi+2CJYgsLCYRn1LHea1X83bqaValn0ERbOzjP5E213zccmU6kSLaqLNP4YTGT1E08kjIOOVdcxNY2LCuslhfoNS4qMamXnKPX/QZuVxZU9Qt0ZMJRIcTI5tBdLlHjFqILhdyXQNaf2/Vece0Gcj+AIGTT8N76OGYhoHkcg2lCerpJGY+V2OhBlp0EKW+ATEcobBuTVWjg6F1p5KV63M6kby+qnMAtsZmZL8P0zDKnqeeyZBd+RID//03ejwGkoRn8UGEz7u4oq5Kiw6SX7eW7MqX8Cw+CMfcvci/9UbFtSIXf6DUVHb4+rZCDAFILhf+E07GOW9v4vfetYXt9s7R4NXCwsLCYvfHEkQWFpOMT6n7/+3deXhU1R038O9dZs2sSSZ72ER2EAWEIsom4FIpim1BRfEVt+JSUVBqUan6gtUKqEXt24JYtGhRKlJUVGQVIrKpgAqULZBAtpnJ7Nt9/wgMGTITtpBJMt/P8+R5yDn3nnPGaybzyznnd9DLOhJdTUMgCiK0kuG096hFXcxyuuYo7Kz7od27ayf0l1wGz7bNdW+QJGgv6nDW/Yg6bcJzhICaBAGR40GKeciwegMi2WJF1h134chfZtQJ2kxXDYFoNCJYdgz+A/sQ8XqgadseglodPa/gdFnVhFrL30SdPjpzFfdaWUbVp8uQ1rPX8f0+GsgWK9J/PRZl896sc72uaw/INhuq/vsRQpUVMFzWB9oOnaDKyITnh+9w9M3XTl4cDsO1YT0CxcXIe/SJ6BK7YHkZjvz5OQSP1QSE1evXIPveB+AtbA3nmpVQ/H6osrKRMWYcdJ06J3ydSiSCUEU53N9thW/3z9C0bou0Xr2hSs+EcJqgSTYYIXfuCm3bdogEQxC1Wh7MSkREjarJBkR2ux2PPfYYPv74Y0QiEVx//fWYPXs2LBYLAKCiogL33HMPVqxYgczMTDz77LO47bbbkjtoouNEQYRePru9Mc2dtm27OmXO9auRO/ERBIoPxi4TEwRk3/sgJMvZb8KXTBYYr7gK1Wu+qlOnad0GofJjQCQC84jroMrOPW17mos7omDac6j4YBF8e/dAtlph/eUo6Lp2h++nXSh9/ZWY4MvQr3/NbInZAslkgiovH8Ejh+uO02iKBh414zbXjHvtqjrXatt3gG/3T6hYvAgV772DnN89DH2PSyFqNDBc1geiWo2KxYsQKi+DoNXCNHAIjL8YgENPTwWOn0vk3lQEOT0DeY9PQ8X778R9rYFDBxA8drTmvCGfDxWLF0WDIaBmiWDpX2cj7bI+KJw+E4IoQlBrIB9/303Ef3A/Ds/8ExSfDwDg2rgelR++h7zJT0LbvsMZ7S2qOQj2tJcRERE1OEFR6lmgnkRjxozB3r178cYbb0AQBNx///1o1aoV/v3vfwMAbrjhBni9XsyePRtFRUWYOHEi1qxZg8svv/yM2nc6nTCbzXA4HDCZUuuDK9GFEHI6cPSNV+Hd+UNMuWSxIm/S4wiWHYPnu+2QbTYYel8OyZoBqdYhqGfVV1Ulyt97p+acoONvYdqLOyLjt7fCvXUzDH36QZWZmXAPStjtBhQFUq2zfsIeNyI+PwRJhGy2IFByBAf/8Gjc5X6Zt90J89DhEAQB/kMHcHjGnxDxuKP1glqN3ElPQNO6LaRah86G7FWo+PciVG9YG50p0nfrAfOwa1D6+qsnl9+JIlrPnAVVVnbMvRGvFxBFCKKIA4//Pu5sU97kJ3HkxecT/rfLGDMO1muuR7C8rKaNBDNtWXfdB9OVgxK2U3tcxc8/E3fZoWQ0oXD6DMj1ZPojIqoPP69RY2iSM0RutxuLFy/G+vXr0atXLwDA7NmzceWVV8Ln8+Hw4cNYtmwZ9u3bhzZt2qBbt27YsGED5s6de8YBERE1LNlkRvbdE2H/fDkcKz+H4vNBlZ0bPbtG06oNDL0a5udTtqbDdvtdSB/1a0Q8LohaHSSTCZLBCF37xMvwQlWV8OzcAefKFVAiYRgHDELapb2gSs+ApE+DpD+Z1az6mw0J9z7Zly+FofflkC1WqAtaIf/JZ+DZvhWBw8VQ2bKgaXsRKj94D+arr0HaZb2jS8BkixW2ceNhHXkjIi4XwtVOeH/+EaVz50Dx+092EInA8+MOmGsFRLLFChyfcbKvXJFw6V3Y7YagUiVMn30iOFHC4YTBUE07roR1MddVVyfcgxWudiLksDMgIiKiJq1JBkSiKGLZsmXo2bNnTHk4HIbL5UJRUREKCwvRpk2baN2AAQMwY8aMhG36/X74a33gcMbZ70BE50e2WpFx029hHjoCCIfPaLnVuZL0ekj6M0/KEKyqROlfZ8G/Z3e0zL/vf3B88SnyJj9Z57yiU8/+qS3ksEM5HpCE7XaUvPwCRK0WcmYWAsWHUPmfxTXtH9yPVhe9BLFWggBRq4Naq0MQpSh+9o8J+4i3J+sExetLWOfesgnGKwfDuXJFnTpBrY4ubRS1OqjzChA4Uhy3HV3HxHuGYsZST1AFIGFgRkRE1FSc3aERjUSn0+Gaa66BptZymjlz5qBHjx7IzMxESUkJ8vLyYu7Jzs5GcXH8X+wAMGPGDJjN5uhXYWHhBRs/USoTZBmqjEyosrLPOhiKBAIIlpcjeOwoQvWdiXMOfD//GBMMnRAsOQLXNxuiAc4JaT16JmxL2+6i6HlBYVc1QhXlCBwuhmf7FvgP7Itep/j9CFXEP9pV0NUEJIloWreF/cvPESgtQcQXGwDpunZLeF+w7Bis190AbYdOsf1pNMh7dGo0U5xsNiNz3J01WQBPoevaHXLGmR0KLBkMEBKl85YkHtZKRERNXtJmiLxeLw4frrsRGQByc3ORVutAvtdeew3vv/8+Pv30UwCAx+OJCZYAQKPRxMwAnWrq1KmYNGlS9Hun08mgiKgJCVZUoPKjxaj+ei0QCkGVm4fMW++A7qIOEHXnl4Ev4vPCuWplwvrqNatg/MWVkM3maJn24o6QLNaatNW1CQIyfnPryf1Jp9mGqdQ6KCjsciHi90EQBEgmMzJvG1+z3+eUNrTtOyB4tATlC98CBAG2/3MvjH36Rc8RkjMyoe95GTzbtsR2Jkmw3XI7VJk25DzwCEKVFfDv+x8kswWaVq0hW6wxWd80bS9C/pPTUbFoIXx7foZoMMIy/FqYrhwM2WTGmZAtVmT+5taYw2lPSB85GpLJckbtEBERJUvSAqKioiIMHjw4bt2SJUswatQoAMDcuXPx0EMPYdasWRg+fDgAQKvV1gl+/H4/9PUsn9FoNHWCKCJqGkL2KpTM/jMChw5Ey4IlR1Dy0gzkTX4S+q7dL2j/CuoGNaqMTORPfRpl/5wH747vAUWBbMuGbdyd0BS2jl4nGgyQzBaE48xoCSoVVBmZiAQCCBQfQvm/3oZv908QtDqYh1wN89BrkP/E0yh/dwH8B/ZB1OlgvGIgdJ27nEybrSgo+8cb0LXvAHVuzcy4bDQh68574Nq0EfZP/4twtRO6jp1rzhnKqcmsJ5vMkE1maNvUzf53gqTVQte+A3J/PxkRvx+CKEIymSFI0hn/txNkGYa+v4CckYmKDxYhcORwTaruG38NXaeuEPm+S0RETVzSAqJBgwbhdAnuXnrpJUyePBkvvvgiHn744Wh5fn4+Sktj1/eXlpYiN/f0KXaJqOkJlJTEBEO1lb+7AHlTpsXM3pwtUauDadAQeHf9ELfedNVgSMa6GenU2TnI+d3DCFdXA5EwRJ0+JpU2UJPgIev/3IuS2X+uM9OTOfZ2SGYLAsUHUfzcU9FECIrPC/vyj+HduQO5jzyOvEenIuz1wL9vL6o3rIfjy8/qtOXaugnWrF9CCYcgqNSQzRaYh46AoXdfKJFIzSGuupo/CinhcM2SwxPXnia9uWQwJszIdyakNAPSLrkUmnYXQQkGIUjyeT0vIiKixtQkkyoAwIIFCzB58mTMmjULv//972Pq+vXrhwMHDqC4uBgFBTVr8NetW4d+/folYaRELVfY5ULIXgXPD9shiGLNYaAWC6S00x82ezYSBSoAEDhcDCWQeDnsmdJ16ATNRRfDvzd2H5EqJxeGy3+R8KycU7PPnUoQBGg7dUHh9Bmo+vg/8B88ADkrC+kjb4I6vwCRgB/l774dNyucf///ECw5DF2nLogEAzj6xqtx+zAOGAhtu4tR9s5bCJUdg65zNxj69IWcaasT7ITsdjjXfgX7p8sQcbshZ9qQMXoM9N17nFfQcyZkI1PiEhFR89MkA6LKyko88MADuOOOOzBmzJiY2SCbzYZ27dphxIgRGDduHObMmYNNmzbh3XffxerVq5M4aqKWJeR2w731Wzg+W45A8cFoueW6G2C9dmTcGZVzdWKjfzyCRgOcwcGeZ9JH7gOPwLPzBzhWrgDCERivPJl2+0xFfD6EnQ6E3S6IGi0kowmS0QipVRtkTbgPEZ8Pglodna0JlpfBt+fnhO25t2+FrlMXiBoNtBd3hG/3TzH1aZf1gaagFY7M/FO0zPP9dlQtW4L8P0yHpuDkXsiw24Xy9xbCtWFdtCxUXoajb74K2+13wTRwyFkthyMiIkoFTTIgWrFiBVwuFxYsWIAFCxbE1J04e+jtt9/GhAkT0LdvX+Tm5mLevHk8g4ioAYTdbgTLjsKx8nOEnQ4Y+w+AKjsH5e8sQKiyAvblH0Pf/VLoO3dpsD51XbvXBD1xZlFMg4ZCOs0Gf0VREKooh/fHnfD+uAvqggKkXdobcnpG9AwgoCYoMl1xFdJ6XgYoCsQ0A4Q4WdYSCTnsqFq6BI5VX0TP8NG274Dsex+AypYFUaOFqDkl45ooQlBrEs5ynQgsJYMRmWPGofi5aTHL5UwDB6Pklb/UuS/i8eDY/L8h75Ep0ZmfsNMZEwzVVrF4EfSXXArVGWaPIyIiShVNMiAaM2YMxowZU+81WVlZWLp0aSONiCg1hD0eOFd9gYp//yta5tm2BXJ6Bmx33o3SV2dBCfhh//RjaNu2i2Y9O1+y1YqcByah9K+zYg4L1Vx0MawjfhkT1MQTPHIYxTOmI+KqjpZVLF6EvEenQtehU51ZkXNZ8hcJBmFf8UnN/p5afHt+xpGXZyL/8Wlx9+pIRhNMA4fA8fkncdtNu7R39N/qgkLkPzkd5e+8Bf++/0G2ZdWcR5TgrB//3t0Iu1zRgChYWpJ4/B43Im43FGt6wuWByRJyOhF22BGqqoRkNkM2Wy/Y+VVERESnapIBERElR9heFRMMnRCqrIBzzVcw9usP55qvEHY6oYRCDdavqFJD370HWs94Gd6fdiHkdEDXsTNUtizIZku994acTpS++VpMMFRTEULpq39B4bN/bpBZkbDDDsfnn8atC5YcQaiiPG5AJKpUsFz7S3h/3FkncYTtzntizukRNRro2ndA3qQnEPH7AUmG78cd9Q+s1qyamJZ4rxMARAJ+lL42C+rCVjD+YgDkjMy4wWbY66lJJBEKQdTp6l3SeL6CFeU4+vorMcsK1XkFyP39ZKiysi9Yv0RERCcwICKiKPf2LYnrtnyLnPsehHPNV9D36HneZwOdSlSpIWZln/WH4IirGoGD++PXeTwIlZc3SEAU8fvqTe4QOFoK7UUXx61TpWcg79HHESguhnvbFkhmMwy9+0K2WCFq6/53rNmXVPNvpZ602arsXIi1Ej7IGZkQDca6wSFqzlXyfLcN7i2b4N6yCVXL/oO8Rx6HrnPXmBm0YNkxlL27oOaMI0WBnJ6BzLG3Q9e1W73JJc5F2O1G2YK/19ljFThSjJJXXkLe5CdPGxATERGdr6a1boKIkipSz+HGCIcBQYCo18N0xVVNZ3O+KCDnoceQM/ER5D78GMxDhwO1Dh+N+LwN041aA6GepXuqzKx675ct6dB36wHbbeORfsONUOfmnVFQKZnNMI+4Ls6ARNjGT4hZWiZb05H3+yk1iShq952egfSRN8Gx8vOTheEwSufOQajWwbOhykoc/vPz8GzdHN3HFKqsQOlfZ8G3O3FiiHMVrnbC8922uHWB4kMIOxwN3icREdGpOENERFFpPXqi6j+L49ZpO3RC2ONGwR//BNlW/4f/xhKyV6Fq2Ueo/nptzdIxQYChd1/k3P8Qjr7xKpRQKHpQ6ZkIu6oRrq6GEgxA1KdBtlghHA+uJLMFpqsGw/Hlijr3yZk2qGy2BntdtUn6NFh/OQq6izui6uP/IGSvgqZde2TceDNUOXkx1wqiCE3bdmj13Ivw7fkZgdISaFq3hRIM4Nj8v9WZOYp43AhXVUZn0PzFBxEqOxp3HOWL/glNm7YNOmMT8fnqrQ/HmekiIiJqaAyIiChKzrRB3/OymuVStQgqFTLHjoMqOxeSXp+k0cUKezzHU0yvP1moKHBt2ohIwA/z8GuhBIKQzvBsnOCxozj699fh+/lHAICg1SJ95E0wXTkIktEEUa2G9YYbEa6uhuubDdH7VLl5yH14csw+m0gggLC9Cr69uxF2u6G7uCPk9PTTjkVRlLhZ72SjCYbefaHr2BlKKARBq4OUYHZJkCSobFlQHQ9a/cWHcOiPkxP2GQkGo//2/rQr4XXBkiNQAoF6xx8JBhF22BGudkKQZEhGY737jyS9HpCkhEkjmFiBiIgaAwMiIoqSTWZkjb8H7m3fwv7pfxF2uaDv0hXWX90MdXZOdLakKQhXO+Ha+HXcOs/2rUj/1c2QM21nFMCFqipx+M/PIVReFi1TfD5UvP8uRH0aTFcNhiCKkC1W2MZPQPpNv0HY6YCo00MymmI+uEf8fni2b0Hpm6/FfNDX97wMWePvjpt4IVRVCf/BA6j+eg1ErQ6mgUMg27LqHHR6psFdbaJOBzEtDRG3u26lJMXsr1JlJp7lEvVp9S6TDLuqUb1hHSr+vSi610rOtCFn4u+had0Wgigi4vUi7PVAEEVIJjMkkxnGAQNRvXplnfZ0XbufNt06ERFRQ2g6n26IqEmQLRaYB12NtEt7Q4lEIOn0DZZeuyFF3O6Y83pOpUTCkE1nFkAEjhyOCYZqq1zyb+h7XApVes1Mh6RPq0kukGApXqiyAqWvv1JnbJ5tW1D99VpYrvllTNrrUFUlSua8BP/+/0XLnKtXwnz1CKT/6uaYA3CVUAghhx1hpwOCKEI0miFbrfWepSRbrLDdOh5H//bXOnXpvxodE3Tou3ZPOGNjHnYNpHqWy/l2/4zyd2LPjQuVl+HwzD+h8E8vQAn4UbHkA3h3bIeo08N89QiYrrgKGTf+GoIowrnmq+g+tbRel8N2y+3RdOJEREQXEgMiIoqrqWf3Ol2QdjZZ8PwH9iesCzvsUIL1JJs4hevbooSBmv3T/9akuz6+jEyJROBcvyYmGDrB8cVnMPQbAN3xgCjs8cC9+RuUvbMAyvFEEZLFipz7HoS2fYeEs3eCJEHfsxfynngKlR8sQqD4EGRbFtJH3Qxdh04x/x0lazpyH3kcpa+8FLM8Tn/JpTAPvjrhDFHI6UDFB4vi1il+P9zbNsP9bVE0MUPY70fl4kVwb9uM3AcmIXPMOFivvQERrxeCVgvJZIKkaxpLM4mIqOVjQEREzZJkMkHbsTN8cfa9aNq2O6vlVqqcnIR1glYHQa7/YNjaEs00ATXL/JTIyWAp7HTAWTvz2ykcq76Att1FEEQRgUMHcOwfb8S2Z6/C4Zf+L1o99yLU9SSPkPR66Dt1gebhyYgEAhBlFaQ4s2eiSgV9py5o9X//Av/BAwi7qqFt0w6yNT1mpupUSiiEQMmRhPX+/+2N+zz8e3YjUHIE+s5dIfLMISIiShKm3SaiZkkyGJF99++gaXtRTLm6sBVyfvf7Ovtv6qNp1QZigr1GluHXxt33k4i++yWJ+2nXHmKtlNhKREGknkQFiscDKArCbjcqPnw//kWhEKrXr4FS64DWRCSDEar0jLjB0AmCLEOVaYPhst4wXzUYmlat6w2GAECQZKizEwdkquwchKqq4ta5NhWddtxEREQXEmeIiKjZUmXakPvIFIQddoSqqiBbLJDMlrNe7ienZyBvyjSUzJoZc/aNoW9/mIcMP6szlzTt2kPOtNWdKRIEZI65DZLBEC2SDGlI63kZqtetjtuWsf+VECQJSsCPYH0zMPv/V5N9Tq0+43E2JNlsRvpNv0Hpay/XqRPUamgv7oCqpR/GvbepZC0kIqLUxYCIiJo12WSGbDJDU9j6rO8N2e0IlBxG9fo1kDIykTdlGiIeNyJeL1Q2W00mtDTD6RuqRZWegfzHp6F80UK4t2wCFAWq3DzYbruzzhhFtQbWX46Ce/M3iHhjD5BVFxRC265m9ktQqaHKyUXYGf+gUk2rNknPAKjr1BkZvx6Lyv8shnI8lbdksSLrznvg378/4X2Gvv0baYRERETxCYpST5qmFszpdMJsNsPhcMB0hpmoiKjlCNmrcPT/zYV3x/cx5caBg5Exegzk80z5HPF6EXZVQwmHIep0CWetlEgEwWNHUbn0Q7i3fAtRo4Zp0NUwDRwCVXpG9DrPrh048sKzdRuQJLR6/kWoTzmkNRkigUDNbJ3DDkGWEfF4UP6vfyLj5jEoX/TPOrNc1ht/DcvV10BKS0vSiImoqePnNWoMDIj4A0aUkpxrV9VJUnBC3pQ/Qt+lW0yZEgoh5HQCUCDqdA2eBS3i9yPsdgGCANlkrrNML+x2w7VpI8rffTt6zo9kNCH7/oegvbgjRNWZJ35oLMHKCpTMegGhykrYbrsTEZ8H3l07IRoMMF01GCpb1lnPwBFRauHnNWoMXDJHRCkn7HTCvmJ5wnr7559C274DxON7coKVFbB//gmqV69ExO+HvvslyLh5LNQ5uQ22VE3UaGISLpxKSkuD8YqroO/Wo2bpnChCNpkhWawx5xo1Jar0DORNegL+4kNwb94EVU4u0n97K6Q0A6QmeLYVERGlJgZERNToag4XdUIJBCAajZBN5kY9/FVRIoj46p4tJGfaapZvRcLRw0lDlZU48pcZCB4ujl7n2bYF3h3fo/CZGVDnFzTauEWVCmKmDapM21ndF/a4aw6yBSCmGc4pkYGiKAjbqxCurgYUBZLReEbBmGxNh2xNR1o92feIiIiSiQERETWqwJFilLz68sn9JKII8+BhsI68CbL5/PbtnClRn4a0Xn3g+HQZAEB7cUdYrr0BoYoyhB0OaDt2RsTvg6jTwbd/b0wwdIISDKJiyWJkT7gXovbMD4FtTEokgmDJEZS98xa8O38ABAG6rt1hG3s7VLl59QYzSjiMkMNeExiqVAhVVuDoa7MQqqwAAEhmM2x33gN9p66NGswSERE1NO4h4ppUokYTrKxA8fQnEXbY69Slj/4trNeNPKsU1+c1lrKjOPTMk1Dn5sE87Foc+8frUGqdCaQubI3cyX9AxTtvwVW0IW4bgkaDVjNejkl+0JQEjx3FwaeegOKLzWAn6nQonD4TqgSHoYbsVXB89QUcn3+CiMcDdV4BLNePhG/3T3Cu+vLkhYKAwmdmQNO6DYDjQZTdDiXgg6BSQzKZo8sOiYjOBT+vUWNomgvPiahFChwuRthhh5hmgL5bD+g6dYFwPBmA/ZNlCNnjH955IciZWSh46jlk3nEXvLt/hK5jF6DWjEng0AG41q+FmJb4UFJRp2+y+3eUUAiOVV/UCYaAmgx4zjVfQTm+LLC2cLUTx+b9DVUffYCIxwOgZlbv2P+bC03bdtHgp6YTBZUf/wcRnw8hpxOOLz/Doacex8Gpj+Lg1EkoX/RPhKoqL9RLJCIiahBN8zc5EbVIgZIjyBx3J2y33Qk5wwZ1YWvk/O5hmIdfh4jHHT2/pjEooRAQDqN67SoEjxyGKisbuY9Mga5WdrnKpR/ANOCqhG2Yhw6HdJr03IqiIFhRDvf32+H46gt4d/9Ub+AXcjgQLC9DsLIibsBypsJeDzzfbU9Y7/l+GyJeT93+q6rg+W5r3Huqli6BafCwmLLAof2IBPxwrv0K5e++jYjbBaBmSaFz5ec4Ov9vNfuOiIiImijuISKiRqPv2g1lC/4B388/Rsscn38C87BrYf3VaAiqxllepUQi8P60CyWzXogmT/Du/AGO1V8i++6JCLuqETh4AIrXCzEtDdZfjUbVRx/EtKFp1x6mAQPr34ejKAgcPIDDLz6PiOtkUKDOK0DupMdjkiOEPW74dv8cPa9HTEuDefh1MA8cAtliPevXKMgypHqWl4hGU9wMeb59exPeE6oor3NmkCorBxGfD1UfL4l7j/e7bQg57ZCMiWfaiIiIkokzRETUKBRFgeeH72KCoRMcn38CXcfOkC2WRhlLyF6Fo2+8Gg2GTlaEUP6vt2EZdi0AQLKmQ1RrYBl+LQr/9AIs194A08AhyJv8JHIfehSyNb3efsJVlTjy8oyYYAioWYJW9s95CB9fkqYoCrw/fIeSWS9Ek01E3G5ULfk3yhb8/ZxmWCSdHpZrb0hYb732hrjJICRDPYGLIABC7K8N68gbofh8UHy+hLcFj5aefsBERERJwhkiImoUYYcdji8+S1jv2rgeuk5dGm0spwYp0Tp7FcTjh65m/vbWmtTSggApzQBNq9Zn1U+wvCZrXTye77YhXO2ApNcjbK9C2btvx73OvXUzQvaqc5ph0bRpC/Owa+H4/JOYcvOI66Bp1Sb+Pa1aQ1Cp4i5f1F9yKbw/7gAACCoVMm8dD3V+AcLVzppgKUGOHsnIjdBERNR0MSAiokahKBFEPO6E9WFXdcIP1A0uEqm/XhCQ87uHoevaA4IgnHM39c7sKAqUQE3QEfF6EK5nX5H/4H5oCluddf+y0YT0X42GadAQeL7bBggC9N0vgWyxQkozxL1HsliR89CjKJn9YswMmmzLRuaYcQi7qmHo8wvIGRmQzGaIKjWAmnY9322r257ZAlVG5lmNO+R0IFRRjnB1dc0erUgYoj4NktkCSdc0U5wTEVHzxYCIiBqFpE+DrlsPuBOksDb06ddoKbclswWCWgMlUPdwVlGvh7qgEOrsnPPuR1VPG4JWB/H4h3tBluufYUkQvJwJyWCAZDBAk194RteLKhV0nbqi9YyX4f7hO4TKy6Dr1AWagkLI6RkAcuv2odfDdvtdKHn5BQSOnDyzSTQYkffoVEinWVpYW6iqEqV/fx3mq4bAs30Lqou+rgnMBAGGPv2QMXYcVGfRHhER0ekwICKiRhHx+WEedDU8WzfHnPcDAKqsbKjyChptLJLZgsxbbkfZW/+vTp3ttjtjkh2cD9lsga5HT3jjzJykj7wxmixBNJqg79ETnu11s7sJag3UBWcWzDQUUaWCmJUNy5Bhp7/4OFWmDXlTnkSw7BgCh4uhyrRBlZsHOT3jjGfZlFAIji8+gyYnD+4tm+Aq+rpWpQLXNxsQ8fuQffdESIZzDxKJiIhqY1IFImoUSiiIisX/Qs4Dk6Dv3hMQBAhqNYxXDkLmbXei+pv4M0cXgqhSwdCnH/KeeAraTl0gWazQdemG/CenQ9+zV4PNVElGI7LvvBfmIcOj5y2JaQZk3nIHjAMGRbO8STo9bLeOh3xqICbLyH34sXPKMpcMssUK3cUdYR40FPpuPaDKyDyrJYchpwP2L1dA370HXAn+f/Bs34pwdfx9WUREROeCM0RE1CgESUKoogJH33gVxgEDkX3lQCAcgXvrtyiZ8yIyb7mjUccjpaVB36kLNA8+CiUQgKDRQNLrG7wf2WpFxpjbYLn2l1BCQQhqLWSrtU66blVWNgr+MB3+Qwfg/XkXVLZs6Lt0g2RNj5seu0UKh6H4vDUJHerZTxauro63co+IiOicpMhvWSJKNslsgfWGUSj/53w4ViyHY8XJOkGlQlr3S5IzrrQ04JSzdRqaqFZDtGWd9jo5PR1yejrSLrn0go6nqRLUGqjzC057HpWov7DPi4iIUguXzBFRoxAEAYbe/WAadHVNAoHjRL0euY/9AfJZZiKjlkc2m5E59nb49u6GrnPXuNdoWrdhGm8iImpQnCEiokYjm83I+M1YWK65HsHSIxB1esiZNsgWa6NlmKOmTXNReyhKBLpOXRDx++H/355onbqgFXImToJsNidxhERE1NIIitJYB380LU6nE2azGQ6HAyYT/9pIRC1buLoaSigIUaeDqG36Z/mEHHZEfD5EfF6EHfaas5PMVgZDRCmGn9eoMXCGiIioBQs5nfD+tBP2Zf9ByOGAtn0HpP9qNFQ5OccPVW2aZLMFYOxDRESNgAEREVELFXa7UbX0Azi++Cxa5v62CO6t3yL/iaegu7hjEkdHRETUNDAgIiJqYiKBAMLVTkQCAYgqGarM02eoiyfssMcEQwCgbd8BlhHXIWSvgvv7bVBn50IymZrFMjoiIqILgQEREVETEqyqQuhYKewrlsO3dw8kkxmW4ddC16UbVOkZZ9WWb+/umO/1PXrC0Lc/jv7jTSg+b02hKMJ63UhYRlzH7G1ERJSSmHabiKiJCDkdCBzYh8N/fg7uzZsQtlchcHA/jv39dVR++D5C1dVn1Z6gUtX6RoD56mtwbF6tYAgAIhFULfsPPD/ubKBXQURE1LwwICIiaiLC1dWoXPI+EA7Xqatetxrhqsqzak/brj0g1rzN6zp1gXfn93HbBoCqjz5AyOk4+0ETERE1cwyIiKjFCHu9CJQcgXf3z/AfPHDBPuBHAgEESkvg2/8/BEqOIOz1NEy7Xi/8B/YnrPf+tOus2pNMZthuv6vm32YLguVlCa8NlpdBCYXOqn0iIqKWgHuIiKhFCFaUo2rph3CuXQVEIgAAdavWyLn/Yahz8xqsn1BlBSqXL0X16pVQgkFAkmC4vB8ybvotVLZzS35wgiDX/5YcswTuDIhaLQyX/wLathfB/d1WQFHg/vabuNdqCltBVDfdNNxEREQXCmeIiKjZC3s8cHzxGZyrV0aDIQAIHDyAI3+ZgWBFRYP0E/H5UPnRB3B+8VlNMAQA4TBcG9aj7J/zEKw8v34kQxp0nbvFrxQE6Dp2Pvs29XpoWreB9bqRMPa7AqIufja5jNFjIBmMZ90+ERFRc8eAiIiavbDDDsdXn8etC5WXIXi0pEH6CVVVwrluddw6z3fbEHG5zqt92ZKOjLG3QYwTmGSOGQfZYj3ntgVJgpxpQ/7Up6HOK4iWi0Yjsu97COpWbc65bSIiouaMS+aIqNmL+LxQfL6E9cGSI0CXBDMvZ9OPx50wKQFQs2xP06r1ObcvyDI0eQUo+OOf4N6yCZ4dP0C2WmEeMgyqnNyEsztn3L4oQtOqDfIen4awqxoIhyAZjJAsVggi/z5GRESpiQERETV7gloDQaOB4vfHrZezcxqmH4223nrJYDj/PmQZ6pxcqK69AaZBV0MJBhB2VSPidkOQZIgazXn3IZvNkM3m826HiIioJWBARETNnphmgGngEDhWfFKnTk7PgKqBAiLJaKxJXx3nzB51XgEkU8MFGWGHA/bP/gvHl59BCQQAWYbpykFIHzkasvXcl84RERFRLK6RIKJmTzabYR4yHMb+VwKCEC1X5xcgd9ITUJ9n9reT/Vhgu/MeaFq3iSlXZeci+94HzmuPT20Rnw9VSz+E/ZOPa4IhAAiF4PzqC5S9Mx9ht7tB+iEiIiJAUBRFSfYgksHpdMJsNsPhcMBkMiV7OER0npRIBKHKSoRd1Qi7nJB0aRCNRqizshu8r2BZGUL2SgRLSyGnZ0DOyIBksULS1r+k7ozbP3YUB6ZOSrhfqdXMl6HOabhU4kRETRU/r1Fj4JI5ImoRBFGEKjMTqszMC96XymaDymaD7uKOF6T9sLv+5A1hhwNgQERERNQguGSOiKiJEbX1J04Q9WmNNBIiIqKWjwEREVETIxlN0HboFLdOlZcPictGiIiIGkyTDYiOHTuGX//61zCbzcjJycHjjz+OUCgUra+oqMDo0aNhNBrRtm1bLFy4MImjJSJqOJLBiOy7fwd1fkFMuWzLRu5Dj0E2W5IzMCIiohaoye4huvXWWyEIAjZs2ICKigrceuutMJvN+MMf/gAAGD9+PLxeLzZs2ICioiJMmDABHTp0wOWXX57kkRMRnT+VLQt5k/+IUFUFgseOQs7IhCojE7I1PdlDIyIialGaZEDk9/uRnZ2NZ555Bu3btwcA3HzzzVi3bh0AYO/evVi2bBn27duHNm3aoFu3btiwYQPmzp3LgIiIWgzZYoFssUDb9qJkD4WIiKjFapJL5jQaDRYuXBgNhnbs2IGlS5di0KBBAICioiIUFhaiTZs20XsGDBiADRs2JGG0RERUmxIKIWSvgvu7rahavhTen3YhZK9K9rCIiIjiapIzRLUNHDgQa9asQa9evTBx4kQAQElJCfLyYlPOZmdno7i4OGE7fr8ffr8/+r3T6bwwAyYiSmHBygr49++Dd+f3kMwWaFq1RuVHHyBc7UTu76dAlXHh06ITERGdjaQFRF6vF4cPH45bl5ubi7S0mrSyr7zyCqqqqvDggw9i7NixWLp0KTweDzSa2LS0Go0mJuA51YwZMzB9+vSGewFERAQACDkdiHg8gKKgZNYLCB47erJSlpF9131wrPoSFYsXIWv8BIiahjnAloiIqCEkLSAqKirC4MGD49YtWbIEo0aNAgBccsklAID58+ejT58+2L9/P7RabZ3gx+/3Q6/XJ+xv6tSpmDRpUvR7p9OJwsLC83wVRESpSwmF4D+wD8fmvQld524Ilh2LDYYAIBTCsfl/Q/Z9D6L0r7ORcdNvINoYEBERUdORtD1EgwYNgqIocb+GDBmC9957D5FIJHp9ly5dAADl5eXIz89HaWlpTHulpaXIzc1N2J9Go4HJZIr5IiKicxc8dhTFM6YjcLgYus5d4Plua9zrlEAAYYcdksEIpdbxCURERE1Bk0yq4PF4MGbMGBQVFUXLNm/eDEmS0KFDB/Tr1w8HDhyI2TO0bt069OvXLxnDJSJKOZFAAFWf/Rc4EeBEIoCiJLw+7HJDlZ0DUcvZISIialqaZFKFnJwc3HTTTXjggQfw97//HS6XCxMmTMCDDz4Ynd0ZMWIExo0bhzlz5mDTpk149913sXr16mQPnYgoJUR8Xvh2/xT9PuzxQE7PQKiyIu71moICqHJyeI4SERE1OU1yhggA5s2bh0suuQTDhg3DjTfeiOuvvx4vvPBCtP7tt9+G0WhE37598fzzz2PevHk8g4iIqJEIKjVkizX6vXPVF7DeMCrutbquPSCZrdB36d5IoyMiIjpzgqLUs8ahBXM6nTCbzXA4HNxPRESnFfa4Eaqqgm/3TxBEEdqLO0K2WCHqdMkeWtJ4dv6AI39+Lvq9afAwaNtdhKrlSxEsOQJRr4d56AiYBg6BKtOWxJESUXPFz2vUGJrkkjkioqYkVO1E1bKP4PjsvycLBQHpN4+BeeAQSAZj8gaXRJpWrWEddTOqPvoAUBQ4v/oc3h93wnbHBMjp6RBlFSSzBYIkJXuoRERECTEgIiI6Dd+e3bHBEAAoCir//S/oOnSC7uKOyRlYkkkGIywjroPxFwPg37cXEEVo2rSFbLIweQIRETUbDIiIiOoRclXD/snHCevtK5ZDXdAKUoounZN0ekg6PdTZOckeChER0TlpskkViIiaAsXvR9hhT1gfttsR8Xkbb0BERETUoBgQERHVQ9TpoLno4oT12vYdIGo0jTgiIiIiakgMiIiI6iHp02C95npArrvCWNDqYOx/JSR9WhJGRkRERA2BARER0WlI1nTkPfI4NK3bRMu07Tsg95EpEGudxUNERETND5MqEBGdhmw0QWh3EbLvfxhhlwuCAIhpBkhGE6Q0zg4RERE1ZwyIiIjOwIlsakRERNSycMkcERERERGlLAZERERERESUshgQERERERFRymJAREREREREKYsBERERERERpSwGRERERERElLIYEBERERERUcpiQERERERERCmLAREREREREaUsBkRERERERJSyGBAREREREVHKYkBEREREREQpiwERERERERGlLAZERERERESUshgQERERERFRypKTPYBkURQFAOB0OpM8EiIiIiKK58TntBOf24guhJQNiKqrqwEAhYWFSR4JEREREdWnuroaZrM52cOgFkpQUjTkjkQiOHLkCIxGIwRBSPZwzpvT6URhYSEOHToEk8mU7OFQI+AzTy183qmHzzz18JnXpSgKqqurkZeXB1HkTg+6MFJ2hkgURRQUFCR7GA3OZDLxTTTF8JmnFj7v1MNnnnr4zGNxZoguNIbaRERERESUshgQERERERFRymJA1EJoNBo8/fTT0Gg0yR4KNRI+89TC5516+MxTD585UXKkbFIFIiIiIiIizhAREREREVHKYkBEREREREQpiwERERERERGlLAZELcjEiRMxaNCgmLJ9+/bh6quvRlpaGrp06YIVK1YkZ3DUYOx2OyZMmIDs7GzYbDaMHz8edrs9Wl9RUYHRo0fDaDSibdu2WLhwYfIGSw3C5/PhrrvugsViQW5uLv7yl78ke0jUwA4fPoybb74Z6enpyM/Px6RJk+Dz+QDwfbylu/766zF+/Pjo91u3bkXfvn2h1+vRp08fbN68OXmDI0oRDIhaiK+//hqvv/56TJmiKBg1ahRycnLw7bffYty4cbjxxhtx8ODBJI2SGsJ9992H7du3Y/ny5fjss8+wa9cu3H333dH68ePHw+FwYMOGDfjjH/+ICRMm4JtvvkniiOl8TZ48Gd9++y1WrlyJuXPnYvr06Vi8eHGyh0UNRFEU3HzzzfB4PFi7di0WLVqEjz/+GNOmTeP7eAu3aNEiLF++PPq92+3GddddhyuvvBKbN29G//79cf3118PtdidxlEQpQKFmz+/3K127dlX69++vDBw4MFr+5ZdfKmlpaYrL5YqWDR06VHn66acbf5DUIFwulyJJkrJx48Zo2ddff61IkqR4vV5lz549CgBl37590fq77rpLueOOOxp/sNQgXC6XotVqla+++ipa9uyzz8b8rFPztmvXLgWAUlpaGi179913lby8PL6Pt2AVFRVKQUGB0qdPn+h79D/+8Q+lbdu2SiQSURRFUSKRiNK+fXtl/vz5yRsoUQrgDFELMHPmTPTo0QPDhg2LKd+4cSMuu+wypKWlRcsGDBiADRs2NPYQqYGIoohly5ahZ8+eMeXhcBgulwtFRUUoLCxEmzZtonV85s3b9u3bEQwG0b9//2jZgAEDUFRUhEgkksSRUUPJycnBp59+iuzs7Jhyh8PB9/EW7LHHHsO4cePQpUuXaNnGjRsxYMAACIIAABAEAVdccQWfN9EFxoComfvxxx8xd+5czJo1q05dSUkJ8vLyYsqys7NRXFzcWMOjBqbT6XDNNdfEHNo3Z84c9OjRA5mZmXzmLVBJSQkyMzOhVqujZdnZ2fD5fKioqEjiyKihWCwWjBgxIvp9JBLBa6+9hqFDh/JnuoVauXIl1qxZg2nTpsWU83kTJYec7AFQ/bxeLw4fPhy3Ljc3F/fccw+mT59e5y+LAODxeOqcdq3RaOD3+y/IWKlhnO6Z1/5L8WuvvYb3338fn376KQA+85Yo0TMFwOfaQk2ZMgVbtmzBpk2bMGvWLP5MtzA+nw/33nsv/vrXv0Kn08XU8T2cKDkYEDVxRUVFGDx4cNy6mTNnIhwO45577olbr9Vq6/wF2e/3Q6/XN/g4qeHU98yXLFmCUaNGAQDmzp2Lhx56CLNmzcLw4cMB1DzzU39x8pk3b4meKQA+1xbo8ccfx+zZs/Hee++hW7dufB9vgaZPn47evXvHzAqewPdwouRgQNTEDRo0CIqixK0bPHgwvv32WxiNRgBAIBBAOByGwWDAzp07kZ+fjx07dsTcU1paitzc3As+bjp39T3zE1566SVMnjwZL774Ih5++OFoeX5+PkpLS2Ou5TNv3vLz81FeXo5QKARZrnnLLi0thU6ng8ViSe7gqEE9+OCDeP3117Fw4UKMHj0aAPg+3gItWrQIpaWlMBgMAE7+gWPx4sW45ZZb+B5OlATcQ9SMLVy4EDt27MC2bduwbds23Hfffejduze2bduGvLw89OvXD1u2bIHX643es27dOvTr1y+Jo6bztWDBAkyePBmzZs3CY489FlPXr18/HDhwIGa9OZ9589azZ0+oVCps3LgxWrZu3Tr06dMHosi38JZi+vTpeOONN7Bo0SKMGTMmWs738ZZn1apV+P7776O/u0eOHImRI0di27Zt6NevH77++uvoH8UURcH69ev5vIkuMEE53Z+iqdl45plnsGrVKqxatQpATeaxHj16oHv37pg2bRo+/vhjPP/889ixYwdatWqV3MHSOamsrETr1q0xevRozJw5M6bOZrNBkiRcc8018Pv9mDNnDjZt2oQHHngAq1evxuWXX56kUdP5uu+++7Bu3TrMnz8fhw8fxh133IH58+fjpptuSvbQqAHs2rUL3bt3x9SpUzFx4sSYOpvNxvfxFu7EoaxvvfUWnE4n2rdvj7Fjx+Lee+/Fm2++iffffx979uyJ2T9KRA2Lf15swSRJwkcffYSSkhL06tULCxcuxJIlS/hLtBlbsWIFXC4XFixYgNzc3JivQ4cOAQDefvttGI1G9O3bF88//zzmzZvHYKiZe/nll9GrVy8MHjwYEydOxPTp0xkMtSAfffQRwuEwnnvuuTo/13wfTy0mkwnLli3D2rVr0atXL2zcuBHLly9nMER0gXGGiIiIiIiIUhZniIiIiIiIKGUxICIiIiIiopTFgIiIiIiIiFIWAyIiIiIiIkpZDIiIiIiIiChlMSAiIiIiIqKUxYCIiIiIiIhSFgMiIiIiIiJKWQyIiIiSYPz48RAEIeHXqlWrsHnzZowYMQJGoxEGgwFXXXUVPv/882gbq1atgiAI+Nvf/ha3/fHjxwMA9u/fX29fp/L7/ejWrRtWrVp1oV4+ERFRk8GAiIgoCebMmYOSkhKUlJRg9uzZKCgoiH5fUlKCdu3aYciQIbjiiiuwadMmbN68GUOGDMF1112HoqKimLamTp2K8vLy0/b5zTffxPRx4qs2n8+HsWPHYseOHQ36eomIiJoqOdkDICJKRWazGWazOfpvSZKQk5MTrX/llVfQtm1bPPXUU9GyZ555BuvWrcP8+fPRt2/faLnJZMKUKVMwb968evu02WwxfZxq586duOWWW6Aoyrm+LCIiomaHM0RERE2QKIrYv38/9uzZE1O+YMECTJ8+PaZszpw5eOutt7B+/frz6nP16tUYPHgwNmzYcF7tEBERNScMiIiImqDf/OY30Ol06Ny5M4YPH44XX3wRP/zwA/Lz85GdnR1z7ciRI3HDDTfg/vvvRygUOuc+77//fsyaNQt6vf58h09ERNRsMCAiImqCsrKysGnTJtx1113Ytm0bpkyZgu7du2Po0KE4duxYnetfeeUV7N27F7Nnz07YZteuXWEwGGK+7rvvvgv4KoiIiJo+7iEiImqiCgoK8MYbb2Du3LnYsmULFi9ejFdeeQV33303Pvroo5hrW7dujWnTpmH69OkYM2ZM3PaWL1+O/Pz8mDKTyXTBxk9ERNQcMCAiImqCZs6ciT59+mDo0KEQRRG9e/dG79690aZNGzz66KNx73n00Ufx9ttv4+GHH4bRaKxT37p1a7Rp0+YCj5yIiKh54ZI5IqIm6Ouvv8arr75ap9xiscBms8W9R6VSYe7cufjwww+xevXqCz1EIiKiFoEBERFRE/TEE09g+fLlmDBhAjZv3ow9e/bg/fffx5QpUxLOEAHAoEGDcNttt2H//v116srKylBaWlrnKxgMXsBXQkRE1LRxyRwRURPUv39/rFy5Es8++yyGDRsGj8eDDh064KmnnsKECRPqvfell17CsmXL6pRffvnlca9fu3YtBgwY0CDjJiIiam4EhSfwERERERFRiuKSOSIiIiIiSlkMiIiIiIiIKGUxICIiIiIiopTFgIiIiIiIiFIWAyIiIiIiIkpZDIiIiIiIiChlMSAiIiIiIqKUxYCIiIiIiIhSFgMiIiIiIiJKWQyIiIiIiIgoZTEgIiIiIiKilPX/AcDk76L0yoK/AAAAAElFTkSuQmCC",
-            "text/plain": [
-              "<Figure size 800x600 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "fig, ax = plt.subplots(figsize=(8,6)) # Set figsize\n",
-        "sns.set_style('darkgrid', {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
-        "sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Class Name', palette='hls')\n",
-        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
-        "plt.title('Scatter plot of news using t-SNE');\n",
-        "plt.xlabel('TSNE1');\n",
-        "plt.ylabel('TSNE2');\n",
-        "plt.axis('equal')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "skgpKPdEie70"
-      },
-      "source": [
-        "## Compare results to KMeans\n",
-        "\n",
-        "[KMeans clustering](https://developers.google.com/machine-learning/glossary#k-means){:.external} is a popular clustering algorithm and used often for unsupervised learning. It iteratively determines the best k center points, and assigns each example to the closest centroid. Input the embeddings directly into the KMeans algorithm to compare the visualization of the embeddings to the performance of a machine learning algorithm."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 18,
-      "metadata": {
-        "id": "8da-KTwtxk27"
-      },
-      "outputs": [],
-      "source": [
-        "# Apply KMeans\n",
-        "kmeans_model = KMeans(n_clusters=4, random_state=1, n_init='auto').fit(X)\n",
-        "labels = kmeans_model.fit_predict(X)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 19,
-      "metadata": {
-        "id": "mYMIXXRm0ZC8"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>TSNE1</th>\n",
-              "      <th>TSNE2</th>\n",
-              "      <th>Class Name</th>\n",
-              "      <th>Cluster</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>-1.212522</td>\n",
-              "      <td>-22.677013</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>3.995803</td>\n",
-              "      <td>-11.590203</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>9.159574</td>\n",
-              "      <td>-10.091983</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>-5.450058</td>\n",
-              "      <td>-15.795723</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>7.922492</td>\n",
-              "      <td>-10.505751</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>595</th>\n",
-              "      <td>0.272435</td>\n",
-              "      <td>35.359333</td>\n",
-              "      <td>sci.space</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>596</th>\n",
-              "      <td>4.217656</td>\n",
-              "      <td>24.452595</td>\n",
-              "      <td>sci.space</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>597</th>\n",
-              "      <td>4.366329</td>\n",
-              "      <td>20.885956</td>\n",
-              "      <td>sci.space</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>598</th>\n",
-              "      <td>-1.971566</td>\n",
-              "      <td>24.719038</td>\n",
-              "      <td>sci.space</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>599</th>\n",
-              "      <td>12.387172</td>\n",
-              "      <td>25.825365</td>\n",
-              "      <td>sci.space</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>600 rows × 4 columns</p>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "         TSNE1      TSNE2 Class Name  Cluster\n",
-              "0    -1.212522 -22.677013  sci.crypt        0\n",
-              "1     3.995803 -11.590203  sci.crypt        0\n",
-              "2     9.159574 -10.091983  sci.crypt        0\n",
-              "3    -5.450058 -15.795723  sci.crypt        0\n",
-              "4     7.922492 -10.505751  sci.crypt        0\n",
-              "..         ...        ...        ...      ...\n",
-              "595   0.272435  35.359333  sci.space        1\n",
-              "596   4.217656  24.452595  sci.space        1\n",
-              "597   4.366329  20.885956  sci.space        1\n",
-              "598  -1.971566  24.719038  sci.space        1\n",
-              "599  12.387172  25.825365  sci.space        1\n",
-              "\n",
-              "[600 rows x 4 columns]"
-            ]
-          },
-          "execution_count": 19,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "df_tsne['Cluster'] = labels\n",
-        "df_tsne"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 26,
-      "metadata": {
-        "id": "wwuk36dt1XaS"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(-39.6663013458252, 42.38134346008301, -35.120894813537596, 38.98466053009033)"
-            ]
-          },
-          "execution_count": 26,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 800x600 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "fig, ax = plt.subplots(figsize=(8,6)) # Set figsize\n",
-        "sns.set_style('darkgrid', {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
-        "sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Cluster', palette='magma')\n",
-        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
-        "plt.title('Scatter plot of news using KMeans Clustering');\n",
-        "plt.xlabel('TSNE1');\n",
-        "plt.ylabel('TSNE2');\n",
-        "plt.axis('equal')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 21,
-      "metadata": {
-        "id": "tuAx8ZI3ydcT"
-      },
-      "outputs": [],
-      "source": [
-        "def get_majority_cluster_per_group(df_tsne_cluster, class_names):\n",
-        "  class_clusters = dict()\n",
-        "  for c in class_names:\n",
-        "    # Get rows of dataframe that are equal to c\n",
-        "    rows = df_tsne_cluster.loc[df_tsne_cluster['Class Name'] == c]\n",
-        "    # Get majority value in Cluster column of the rows selected\n",
-        "    cluster = rows.Cluster.mode().values[0]\n",
-        "    # Populate mapping dictionary\n",
-        "    class_clusters[c] = cluster\n",
-        "  return class_clusters"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 22,
-      "metadata": {
-        "id": "Is_GUvFS0GH_"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "{'sci.crypt': 0, 'sci.electronics': 3, 'sci.med': 2, 'sci.space': 1}"
-            ]
-          },
-          "execution_count": 22,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "classes = df_tsne['Class Name'].unique()\n",
-        "class_clusters = get_majority_cluster_per_group(df_tsne, classes)\n",
-        "class_clusters"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "R_bf9nXc6Dgx"
-      },
-      "source": [
-        "Get the majority of clusters per group, and see how many of the actual members of that group are in that cluster."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 23,
-      "metadata": {
-        "id": "b2GyHE8ahEff"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "Class Name\n",
-              "sci.crypt          0.966667\n",
-              "sci.med            0.940000\n",
-              "sci.space          0.933333\n",
-              "sci.electronics    0.920000\n",
-              "Name: count, dtype: float64"
-            ]
-          },
-          "execution_count": 23,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Convert the Cluster column to use the class name\n",
-        "class_by_id = {v: k for k, v in class_clusters.items()}\n",
-        "df_tsne['Predicted'] = df_tsne['Cluster'].map(class_by_id.__getitem__)\n",
-        "\n",
-        "# Filter to the correctly matched rows\n",
-        "correct = df_tsne[df_tsne['Class Name'] == df_tsne['Predicted']]\n",
-        "\n",
-        "# Summarise, as a percentage\n",
-        "acc = correct['Class Name'].value_counts() / SAMPLE_SIZE\n",
-        "acc"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 24,
-      "metadata": {
-        "id": "gF0wwWQK9Yek"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>TSNE1</th>\n",
-              "      <th>TSNE2</th>\n",
-              "      <th>Class Name</th>\n",
-              "      <th>Cluster</th>\n",
-              "      <th>Predicted</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>-1.212522</td>\n",
-              "      <td>-22.677013</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "      <td>0</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>3.995803</td>\n",
-              "      <td>-11.590203</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "      <td>0</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>9.159574</td>\n",
-              "      <td>-10.091983</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "      <td>0</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>-5.450058</td>\n",
-              "      <td>-15.795723</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "      <td>0</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>7.922492</td>\n",
-              "      <td>-10.505751</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "      <td>0</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>595</th>\n",
-              "      <td>0.272435</td>\n",
-              "      <td>35.359333</td>\n",
-              "      <td>sci.space</td>\n",
-              "      <td>1</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>596</th>\n",
-              "      <td>4.217656</td>\n",
-              "      <td>24.452595</td>\n",
-              "      <td>sci.space</td>\n",
-              "      <td>1</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>597</th>\n",
-              "      <td>4.366329</td>\n",
-              "      <td>20.885956</td>\n",
-              "      <td>sci.space</td>\n",
-              "      <td>1</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>598</th>\n",
-              "      <td>-1.971566</td>\n",
-              "      <td>24.719038</td>\n",
-              "      <td>sci.space</td>\n",
-              "      <td>1</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>599</th>\n",
-              "      <td>12.387172</td>\n",
-              "      <td>25.825365</td>\n",
-              "      <td>sci.space</td>\n",
-              "      <td>1</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>600 rows × 5 columns</p>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "         TSNE1      TSNE2 Class Name  Cluster  Predicted\n",
-              "0    -1.212522 -22.677013  sci.crypt        0  sci.crypt\n",
-              "1     3.995803 -11.590203  sci.crypt        0  sci.crypt\n",
-              "2     9.159574 -10.091983  sci.crypt        0  sci.crypt\n",
-              "3    -5.450058 -15.795723  sci.crypt        0  sci.crypt\n",
-              "4     7.922492 -10.505751  sci.crypt        0  sci.crypt\n",
-              "..         ...        ...        ...      ...        ...\n",
-              "595   0.272435  35.359333  sci.space        1  sci.space\n",
-              "596   4.217656  24.452595  sci.space        1  sci.space\n",
-              "597   4.366329  20.885956  sci.space        1  sci.space\n",
-              "598  -1.971566  24.719038  sci.space        1  sci.space\n",
-              "599  12.387172  25.825365  sci.space        1  sci.space\n",
-              "\n",
-              "[600 rows x 5 columns]"
-            ]
-          },
-          "execution_count": 24,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Get predicted values by name\n",
-        "df_tsne['Predicted'] = ''\n",
-        "for idx, rows in df_tsne.iterrows():\n",
-        "  cluster = rows['Cluster']\n",
-        "  # Get key from mapping based on cluster value\n",
-        "  key = list(class_clusters.keys())[list(class_clusters.values()).index(cluster)]\n",
-        "  df_tsne.at[idx, 'Predicted'] = key\n",
-        "\n",
-        "df_tsne"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DWBhCLr0OTrQ"
-      },
-      "source": [
-        "To better visualize the performance of the KMeans applied to your data, you can use a [confusion matrix](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html). The confusion matrix allows you to assess the performance of the classification model beyond accuracy. You can see what misclassified points get classified as. You will need the actual values and the predicted values, which you have gathered in the dataframe above."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 25,
-      "metadata": {
-        "id": "CwqggsKD-ywF"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "cm = confusion_matrix(df_tsne['Class Name'].to_list(), df_tsne['Predicted'].to_list())\n",
-        "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
-        "                              display_labels=classes)\n",
-        "disp.plot(xticks_rotation='vertical')\n",
-        "plt.title('Confusion Matrix for Actual and Clustered Newsgroups');\n",
-        "plt.grid(False)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "yCXlOrLFrE1k"
-      },
-      "source": [
-        "## Next steps\n",
-        "\n",
-        "You've now created your own visualization of embeddings with clustering! Try using your own textual data to visualize them as embeddings. You can perform dimensionality reduction in order to complete the visualization step. Note that TSNE is good at clustering inputs, but can take a longer time to converge or might get stuck at local minima. If you run into this issue, another technique you could consider are [principal components analysis (PCA)](https://en.wikipedia.org/wiki/Principal_component_analysis){:.external}.\n",
-        "\n",
-        "There are other clustering algorithms outside of KMeans as well, such as [density-based spatial clustering (DBSCAN)](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html){:.external}.\n",
-        "\n",
-        "To learn more about how you can use the embeddings, check out the examples available. To learn how to create them from scratch, see TensorFlow's [Word Embeddings](https://www.tensorflow.org/text/guide/word_embeddings) tutorial. To learn how to use other services in the PaLM API, visit the various quickstart guides:\n",
-        "\n",
-        "* [Chat quickstart](../tutorials/chat_quickstart.ipynb)\n",
-        "\n",
-        "* [Text generation quickstart](../tutorials/text_quickstart.ipynb)"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "name": "clustering_with_embeddings.ipynb",
-      "toc_visible": true
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
diff --git a/site/en/examples/doc_search_emb.ipynb b/site/en/examples/doc_search_emb.ipynb
deleted file mode 100644
index 85277ef05..000000000
--- a/site/en/examples/doc_search_emb.ipynb
+++ /dev/null
@@ -1,625 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Tce3stUlHN0L"
-      },
-      "source": [
-        "##### Copyright 2023 Google LLC."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "tuOe1ymfHZPu"
-      },
-      "outputs": [],
-      "source": [
-        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-        "# you may not use this file except in compliance with the License.\n",
-        "# You may obtain a copy of the License at\n",
-        "#\n",
-        "# https://www.apache.org/licenses/LICENSE-2.0\n",
-        "#\n",
-        "# Unless required by applicable law or agreed to in writing, software\n",
-        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-        "# See the License for the specific language governing permissions and\n",
-        "# limitations under the License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "LmfLXp5_bt-a"
-      },
-      "source": [
-        "# Document search with embeddings"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "kIkJ7zgADMlP"
-      },
-      "source": [
-        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
-        "      <td>\n",
-        "    <a target=\"_blank\" href=\"https://developers.generativeai.google/examples/doc_search_emb\"><img src=\"https://developers.generativeai.google/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Generative AI</a>\n",
-        "  </td>\n",
-        "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/examples/doc_search_emb.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
-        "  </td>\n",
-        "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/examples/doc_search_emb.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
-        "  </td>\n",
-        "</table>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bbPzgYbrwbK2"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "This example demonstrates how to use the PaLM API to create embeddings so that you can perform document search. You will use the Python client library to build a word embedding that allows you to compare search strings, or questions, to document contents.\n",
-        "\n",
-        "In this tutorial, you'll use embeddings to perform document search over a set of documents to ask questions related to the Google Car.\n",
-        "\n",
-        "## Setup\n",
-        "\n",
-        "First, download and install the PaLM API Python library."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "YD6urJjWGVDf"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -U -q google-generativeai"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_mTK7gLr4krM"
-      },
-      "source": [
-        "**Note**: you will be trying out the \"PaLM API,\" but the Python package name is\n",
-        "`google.generativeai`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "id": "yBapI259C99C"
-      },
-      "outputs": [],
-      "source": [
-        "import google.generativeai as palm\n",
-        "\n",
-        "import textwrap\n",
-        "import numpy as np\n",
-        "import pandas as pd"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DJriBaWmkL6Z"
-      },
-      "source": [
-        "### Grab an API Key\n",
-        "\n",
-        "To get started, you'll need to [create an API key](https://developers.generativeai.google/tutorials/setup)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "id": "Zey3UiYGDDzU"
-      },
-      "outputs": [],
-      "source": [
-        "palm.configure(api_key='PALM_KEY')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "RMbpJpZn6YRQ"
-      },
-      "source": [
-        "Key Point: Next, you will choose a model. Any embedding model will work for this tutorial, but for real applications it's important to choose a specific model and stick with it. The outputs of different models are not compatible with each other.\n",
-        "\n",
-        "**Note**: At this time, the PaLM API is [only available in certain regions](https://developers.generativeai.google/available_regions)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "id": "8Vad1J5hkpAw"
-      },
-      "outputs": [],
-      "source": [
-        "models = [m for m in palm.list_models() if 'embedText' in m.supported_generation_methods]\n",
-        "\n",
-        "model = models[0]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gGpQ8Eg0kNXW"
-      },
-      "source": [
-        "## Embedding generation\n",
-        "\n",
-        "In this section, you will see how to generate embeddings for a piece of text using the embeddings from the PaLM API.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "id": "J76TNa3QDwCc"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "{'embedding': [0.012246046, -0.023558903, 0.032459036, 0.06484912, 0.026284628, -0.052756585, 0.0055233696, 0.011005492, -0.03862501, -0.018264746, 0.06678695, -0.015016806, 0.0035746037, -0.009914331, -0.022514464, 0.030050583, -0.078709245, -0.0015311453, -0.02805761, 0.0036338808, -0.076459445, 0.009172192, 0.01225061, -0.016513903, 0.008183921, -0.08033063, 0.028195586, 0.029587045, -0.031249639, -0.019803159, 0.0025109726, 0.018474173, -0.006070546, 0.0042981566, 0.010934953, 0.03646359, -0.027267052, 0.006511828, 0.017602839, 0.015774623, 0.042609964, -0.04978823, 0.021985881, -0.0018003813, 0.015031357, 0.03226512, -0.049656868, 0.0056817518, 0.037447836, -0.082058676, 0.0070665455, -0.009903009, -0.0012817691, -0.009555456, 0.013598595, 0.07107551, -0.10985609, 0.00044024497, -0.027354741, -0.021102894, -0.0077857957, 0.030045867, 0.0065566953, -0.02866328, -0.024084672, 0.027182486, 0.010249044, 0.028895397, -0.048748404, 0.0144549105, 0.035357818, 0.026979432, -0.011489553, -0.04381989, 0.062055543, 0.053935688, 0.018935075, 7.997995e-05, 0.032406107, -0.042411212, -0.018950237, -0.03686655, -0.02786128, -0.14247943, -0.031954747, 0.06135447, 0.007755804, 0.007340383, -0.049610108, 0.06055633, -0.0061997687, 0.015536909, 0.017663272, 0.046989314, -0.02829787, 0.007028086, 0.009768779, -0.017920492, -0.0004482094, 0.01760272, -0.03491943, 0.005043931, 0.072463214, -0.05273243, 0.08608823, -0.02313639, 0.02748735, 0.035566464, -0.046005856, -0.07012403, -0.011197247, 0.005118026, -0.05882537, 0.012176309, -0.045815013, -0.011174939, 0.04357285, -0.018380292, 0.028166372, 0.033733334, -0.010419084, 0.040377222, -0.006756512, 0.029616103, 0.020419275, 0.017293947, 0.038005445, 0.020060254, 0.014358492, 0.006015242, 0.030263908, 0.014460127, 0.05033836, 0.00423167, 0.02703248, 0.010239786, -0.008927503, 0.013181109, 0.023492351, 0.034311254, -0.03821471, 0.004627892, -0.0040852013, -0.0020964656, -0.05259364, -0.0705314, -0.01479818, -0.0124032665, -0.09655567, 0.00198135, 0.025488432, -0.019924233, -0.03710428, 0.007010777, 0.011313427, 0.066283226, 0.04507062, 0.0016292608, 0.04087332, -0.05021339, -0.0024507563, -0.046069298, -0.054124065, 0.014468171, 0.013357496, 0.006985751, 0.005976544, -0.030506134, -0.02365677, 0.015135481, 0.044584073, -0.10413109, -0.0107979365, -0.008295349, 0.051585224, -0.03829224, 0.011085167, 0.0050877626, 0.08231769, -0.03219612, -0.03536319, -0.09238423, 0.023758749, 0.013713774, -0.06647304, 0.04775781, -0.033355825, -0.030094955, -0.008177198, 0.051271528, -0.020077743, 0.01580692, 0.07417194, -0.05540835, -0.05794891, -0.013655137, 0.06279186, -0.11484751, -0.021054761, 0.04684413, 0.008794997, -0.021544361, 0.031731065, 0.042848878, 0.009124984, 0.006002671, -0.060807146, -0.0039937478, 0.044372104, 0.02276778, -0.023919228, -0.015265087, -0.0015277741, -0.027227052, -0.008951991, -0.014200425, 0.012097831, -0.041769046, -0.09691623, -0.024216626, -0.023596313, 0.00037882378, 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-0.025884187, -0.026183352, 0.028116345, -0.0062918467, -0.031959485, -0.00195724, 0.00551872, 0.047163066, 0.050132312, -0.011089595, 0.03454736, -0.0065446403, 0.027497908, -0.011359338, 0.031209284, 0.0123054935, 0.0067299386, 0.03320252, 0.0104132155, 0.012565796, -0.0054723895, -0.0012788378, -0.01601304, 0.06827864, 0.022071837, 0.019106403, 0.04867051, 0.024571512, -0.005845881, -0.050935183, 0.03698963, -0.017698955, -0.006054161, 0.012251457, -0.0031763925, -0.009850868, 0.022571698, -0.016523926, 0.015339761, -0.04153422, 0.031590454, -0.047403164, -0.019668864, 0.013377942, 0.037525933, 0.016130688, -0.0014420815, 0.03395241, 0.006446724, 0.0067957826, -0.030642867, 0.016237482, -0.059248183, -0.017643742, -0.011814861, 0.036445998, -0.012023078, -0.03969171, -0.034770712, -0.024164954, -0.004940893, 0.01273272, -0.029959105, 0.0075648203, -0.0346612, 0.040213585, -0.011875309, 0.036303695, 0.03612044, 0.051215306, -0.06879151, 0.05865379, -0.06129543, 0.028531928, 0.027353931, -0.028882181, -0.052622266, -0.0054572835, 0.038268622, -0.01889903, 0.001147878, 0.011961551, 0.055814732, 0.054686487, 0.057777297, 0.0061383895, -0.031106692, -0.0034993412, 0.014843713, -0.020202357, -0.027266696, -0.025075577, -0.024285411, 0.00020614524, 0.013779444, -0.022222523, 0.0013498501, -0.021858962, -0.084415734, 0.022417184, -0.00689182, -0.03741896, -0.08071215, -0.017459916, 0.005807038, 0.029116781, -0.0018873442, 0.028786417, 0.049730763, 0.045285672, 0.018252771, -0.010492358, -0.021893298, 0.008960559, 0.0019279895, -0.065256804, 0.018064518, -0.039222594, 0.009063778, 0.037082877, 0.016562615, 0.052926384, -0.04298042, 0.055858735, 0.05707242, 0.03907505, 0.0015263337, 0.009082476, 0.0134143485, -0.029168077, -0.00030230818, -0.010265555, 0.019662535, -0.042000905, -0.0027351528, 0.01557767, 0.021629393, -0.037543625, 0.029485308, 0.057547256, -0.012264158, 0.010961239, 0.07455477, -0.04760432, 0.020114874, -0.043387685, 0.026112124, 0.028907014, -0.0088930875, 0.025489105, 0.029058266, -0.004499017, 0.0378109, -0.01393321, -0.044656288, -0.03684158, -0.032738246, 0.03379276, 0.026568653, 0.020096838, 0.0012306226, 0.08085042, 0.034304578, 0.040584367, -0.031480588, 0.030303054, -0.029881144, -0.04158148, -0.050945546, 0.04790348, -0.003912531, -0.027478285, -0.01310397, 0.01636849]}\n"
-          ]
-        }
-      ],
-      "source": [
-        "sample_text = (\"Title: The next generation of AI for developers and Google Workspace\"\n",
-        "    \"\\n\"\n",
-        "    \"Full article:\\n\"\n",
-        "    \"\\n\"\n",
-        "    \"PaLM API & MakerSuite: An approachable way to explore and prototype with generative AI applications\")\n",
-        "\n",
-        "# Create an embedding\n",
-        "embedding = palm.generate_embeddings(model=model, text=sample_text)\n",
-        "\n",
-        "print(embedding)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dD1lQx3Zr3S2"
-      },
-      "source": [
-        "## Building an embeddings database\n",
-        "\n",
-        "Here are three sample texts to use to build the embeddings database. You will use the PaLM API to create embeddings of each of the documents. Turn them into a dataframe for better visualization."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "id": "XvLRIbpq4vNN"
-      },
-      "outputs": [],
-      "source": [
-        "DOCUMENT1 = \"Operating the Climate Control System  Your Googlecar has a climate control system that allows you to adjust the temperature and airflow in the car. To operate the climate control system, use the buttons and knobs located on the center console. Temperature: The temperature knob controls the temperature inside the car. Turn the knob clockwise to increase the temperature or counterclockwise to decrease the temperature. Airflow: The airflow knob controls the amount of airflow inside the car. Turn the knob clockwise to increase the airflow or counterclockwise to decrease the airflow. Fan speed: The fan speed knob controls the speed of the fan. Turn the knob clockwise to increase the fan speed or counterclockwise to decrease the fan speed. Mode: The mode button allows you to select the desired mode. The available modes are: Auto: The car will automatically adjust the temperature and airflow to maintain a comfortable level. Cool: The car will blow cool air into the car. Heat: The car will blow warm air into the car. Defrost: The car will blow warm air onto the windshield to defrost it.\"\n",
-        "DOCUMENT2 = \"Your Googlecar has a large touchscreen display that provides access to a variety of features, including navigation, entertainment, and climate control. To use the touchscreen display, simply touch the desired icon.  For example, you can touch the \\\"Navigation\\\" icon to get directions to your destination or touch the \\\"Music\\\" icon to play your favorite songs.\"\n",
-        "DOCUMENT3 = \"Shifting Gears  Your Googlecar has an automatic transmission. To shift gears, simply move the shift lever to the desired position.  Park: This position is used when you are parked. The wheels are locked and the car cannot move. Reverse: This position is used to back up. Neutral: This position is used when you are stopped at a light or in traffic. The car is not in gear and will not move unless you press the gas pedal. Drive: This position is used to drive forward. Low: This position is used for driving in snow or other slippery conditions.\"\n",
-        "\n",
-        "texts = [DOCUMENT1, DOCUMENT2, DOCUMENT3]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "WwhCQwPbvwc-"
-      },
-      "source": [
-        "Organize the contents of the dictionary into a dataframe for better visualization."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "id": "GJKLIW9Z31Vf"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Text</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>Operating the Climate Control System  Your Goo...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>Your Googlecar has a large touchscreen display...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>Shifting Gears  Your Googlecar has an automati...</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "                                                Text\n",
-              "0  Operating the Climate Control System  Your Goo...\n",
-              "1  Your Googlecar has a large touchscreen display...\n",
-              "2  Shifting Gears  Your Googlecar has an automati..."
-            ]
-          },
-          "execution_count": 7,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "df = pd.DataFrame(texts)\n",
-        "df.columns = ['Text']\n",
-        "df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "LHonPYEwStLB"
-      },
-      "source": [
-        "Get the embeddings for each of these bodies of text. Add this information to the dataframe."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "id": "4SOhy0lNBhfN"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Text</th>\n",
-              "      <th>Embeddings</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>Operating the Climate Control System  Your Goo...</td>\n",
-              "      <td>[-0.015123772, 0.053951535, 0.010618031, 0.046...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>Your Googlecar has a large touchscreen display...</td>\n",
-              "      <td>[-0.021975275, 0.062008664, 0.011442106, 0.054...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>Shifting Gears  Your Googlecar has an automati...</td>\n",
-              "      <td>[-0.017382653, 0.023597008, 0.026251236, 0.038...</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "                                                Text   \n",
-              "0  Operating the Climate Control System  Your Goo...  \\\n",
-              "1  Your Googlecar has a large touchscreen display...   \n",
-              "2  Shifting Gears  Your Googlecar has an automati...   \n",
-              "\n",
-              "                                          Embeddings  \n",
-              "0  [-0.015123772, 0.053951535, 0.010618031, 0.046...  \n",
-              "1  [-0.021975275, 0.062008664, 0.011442106, 0.054...  \n",
-              "2  [-0.017382653, 0.023597008, 0.026251236, 0.038...  "
-            ]
-          },
-          "execution_count": 8,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Get the embeddings of each text and add to an embeddings column in the dataframe\n",
-        "def embed_fn(text):\n",
-        "  return palm.generate_embeddings(model=model, text=text)['embedding']\n",
-        "\n",
-        "df['Embeddings'] = df['Text'].apply(embed_fn)\n",
-        "df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "cfm8a31FKd00"
-      },
-      "source": [
-        "## Document search with Q & A\n",
-        "\n",
-        "Now that the embeddings are generated, let's create a Q & A system to search these documents. A user will ask a question about hyperparameter tuning, create an embedding of the question, and compare it against the collection of embeddings in the dataframe.\n",
-        "\n",
-        "The embedding of the question will be a vector (list of float values), which will be compared against the vector of the documents using the dot product. This vector returned from the API is already normalized. The dot product represents the similarity in direction between two vectors.\n",
-        "\n",
-        "The values of the dot product can range between -1 and 1, inclusive. If the dot product between two vectors is 1, then the vectors are in the same direction. If the dot product value is 0, then these vectors are orthogonal, or unrelated, to each other. Lastly, if the dot product is -1, then the vectors point in the opposite direction and are not similar to each other."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "id": "80w2VQQ9JWcU"
-      },
-      "outputs": [],
-      "source": [
-        "query = \"How do you shift gears in the Google car?\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "iivgDQej5Agt"
-      },
-      "source": [
-        "Use the `find_best_passage` function to calculate the dot products, and then sort the dataframe from the largest to smallest dot product value to retrieve the relevant passage out of the database."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "id": "am36P3J9M6Zv"
-      },
-      "outputs": [],
-      "source": [
-        "def find_best_passage(query, dataframe):\n",
-        "  \"\"\"\n",
-        "  Compute the distances between the query and each document in the dataframe\n",
-        "  using the dot product.\n",
-        "  \"\"\"\n",
-        "  query_embedding = palm.generate_embeddings(model=model, text=query)\n",
-        "  dot_products = np.dot(np.stack(dataframe['Embeddings']), query_embedding['embedding'])\n",
-        "  idx = np.argmax(dot_products)\n",
-        "  return dataframe.iloc[idx]['Text'] # Return text from index with max value"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Uq-bpLZm9DKo"
-      },
-      "source": [
-        "View the most relevant document from the database:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {
-        "id": "1I5lAqdH9zWL"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "'Shifting Gears  Your Googlecar has an automatic transmission. To shift gears, simply move the shift lever to the desired position.  Park: This position is used when you are parked. The wheels are locked and the car cannot move. Reverse: This position is used to back up. Neutral: This position is used when you are stopped at a light or in traffic. The car is not in gear and will not move unless you press the gas pedal. Drive: This position is used to drive forward. Low: This position is used for driving in snow or other slippery conditions.'"
-            ]
-          },
-          "execution_count": 11,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "passage = find_best_passage(query, df)\n",
-        "passage"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ebkGT0ha5Ln3"
-      },
-      "source": [
-        "## Question and Answering Application\n",
-        "\n",
-        "Let's try to use the text generation API to create a Q & A system. Input your own custom data below to create a simple question and answering example. You will still use the dot product as a metric of similarity."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "metadata": {
-        "id": "pqf-OsT3auTm"
-      },
-      "outputs": [],
-      "source": [
-        "def make_prompt(query, relevant_passage):\n",
-        "  escaped = relevant_passage.replace(\"'\", \"\").replace('\"', \"\").replace(\"\\n\", \" \")\n",
-        "  prompt = textwrap.dedent(\"\"\"You are a helpful and informative bot that answers questions using text from the reference passage included below. \\\n",
-        "  Be sure to respond in a complete sentence, being comprehensive, including all relevant background information. \\\n",
-        "  However, you are talking to a non-technical audience, so be sure to break down complicated concepts and \\\n",
-        "  strike a friendly and converstional tone. \\\n",
-        "  If the passage is irrelevant to the answer, you may ignore it.\n",
-        "  QUESTION: '{query}'\n",
-        "  PASSAGE: '{relevant_passage}'\n",
-        "\n",
-        "    ANSWER:\n",
-        "  \"\"\").format(query=query, relevant_passage=escaped)\n",
-        "\n",
-        "  return prompt"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {
-        "id": "mlpDRG3cVvQE"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "You are a helpful and informative bot that answers questions using text from the reference passage included below.   Be sure to respond in a complete sentence, being comprehensive, including all relevant background information.   However, you are talking to a non-technical audience, so be sure to break down complicated concepts and   strike a friendly and converstional tone.   If the passage is irrelevant to the answer, you may ignore it.\n",
-            "  QUESTION: 'How do you shift gears in the Google car?'\n",
-            "  PASSAGE: 'Shifting Gears  Your Googlecar has an automatic transmission. To shift gears, simply move the shift lever to the desired position.  Park: This position is used when you are parked. The wheels are locked and the car cannot move. Reverse: This position is used to back up. Neutral: This position is used when you are stopped at a light or in traffic. The car is not in gear and will not move unless you press the gas pedal. Drive: This position is used to drive forward. Low: This position is used for driving in snow or other slippery conditions.'\n",
-            "\n",
-            "    ANSWER:\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "prompt = make_prompt(query, passage)\n",
-        "print(prompt)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "qmdYdoIHcEc_"
-      },
-      "source": [
-        "Choose one of the PaLM text generation models in order to find the answer to your query. The temperature controls the randomness of the output. The larger the value, the more random the generated text will be. The `answer` is a text completion object based on the prompt passed in."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 14,
-      "metadata": {
-        "id": "B3fDj-jv5Sq_"
-      },
-      "outputs": [],
-      "source": [
-        "text_models = [m for m in palm.list_models() if 'generateText' in m.supported_generation_methods]\n",
-        "\n",
-        "text_model = text_models[0]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 15,
-      "metadata": {
-        "id": "m30avD9cfQQ-"
-      },
-      "outputs": [],
-      "source": [
-        "temperature = 0.5\n",
-        "answer = palm.generate_text(prompt=prompt,\n",
-        "                            model=text_model,\n",
-        "                            candidate_count=3,\n",
-        "                            temperature=temperature,\n",
-        "                            max_output_tokens=1000)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 16,
-      "metadata": {
-        "id": "COBhn6J9S_xI"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Candidate 0: To shift gears in the Google car, simply move the shift lever to the desired position. Park, reverse, neutral, drive, and low.\n",
-            "\n",
-            "Candidate 1: To shift gears, simply move the shift lever to the desired position.  Park: This position is used when you are parked. The wheels are locked and the car cannot move. Reverse: This position is used to back up. Neutral: This position is used when you are stopped at a light or in traffic. The car is not in gear and will not move unless you press the gas pedal. Drive: This position is used to drive forward. Low: This position is used for driving in snow or other slippery conditions.\n",
-            "\n",
-            "Candidate 2: To shift gears, simply move the shift lever to the desired position.  Park: This position is used when you are parked. The wheels are locked and the car cannot move. Reverse: This position is used to back up. Neutral: This position is used when you are stopped at a light or in traffic. The car is not in gear and will not move unless you press the gas pedal. Drive: This position is used to drive forward. Low: This position is used for driving in snow or other slippery conditions.\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "for i, candidate in enumerate(answer.candidates):\n",
-        "  print(f\"Candidate {i}: {candidate['output']}\\n\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_dQYCLod8hNB"
-      },
-      "source": [
-        "## Next steps\n",
-        "\n",
-        "You've now created your own document search application using the embeddings from the PaLM API! To learn more about how you can use the embeddings, check out the examples available. To learn how to use other services in the PaLM API, visit the various quickstart guides:\n",
-        "\n",
-        "* [Chat quickstart](../tutorials/chat_quickstart.ipynb)\n",
-        "\n",
-        "* [Text generation quickstart](../tutorials/text_quickstart.ipynb)"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "name": "doc_search_emb.ipynb",
-      "toc_visible": true
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
diff --git a/site/en/examples/text_calculator.ipynb b/site/en/examples/text_calculator.ipynb
index edba4bcfa..330efa5c0 100644
--- a/site/en/examples/text_calculator.ipynb
+++ b/site/en/examples/text_calculator.ipynb
@@ -6,7 +6,7 @@
         "id": "Tce3stUlHN0L"
       },
       "source": [
-        "##### Copyright 2023 Google LLC."
+        "##### Copyright 2024 Google LLC."
       ]
     },
     {
@@ -48,7 +48,7 @@
       "source": [
         "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://developers.generativeai.google/examples/text_calculator\"><img src=\"https://developers.generativeai.google/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Generative AI</a>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/examples/text_calculator\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/examples/text_calculator.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
@@ -66,7 +66,7 @@
       },
       "source": [
         "For some use cases, you may want to stop the generation from a model to insert specific results. For example, language models may have trouble with complicated arithmetic problems like word problems.\n",
-        "This tutorial shows an example of using an external tool with the `palm.generate_text` method to output the correct answer to a word problem.\n",
+        "This tutorial shows an example of using an external tool with the `genai.generate_text` method to output the correct answer to a word problem.\n",
         "\n",
         "This particular example uses the [`numexpr`](https://github.com/pydata/numexpr) tool to perform the arithmetic but you can use this same procedure to integrate other tools specific to your use case. The following is an outline of the steps:\n",
         "\n",
@@ -74,7 +74,7 @@
         "1. Create a prompt instructing the model how to use the tags in its result.\n",
         "1. Include the `end` tag in the of `stop_sequences` passed to `generate_text`.\n",
         "1. From the model result, take the text between the `start` and `end` tags as input to the tool.\n",
-        "1. Run the tool and add it's output to the prompt.\n",
+        "1. Run the tool and add its output to the prompt.\n",
         "1. Call `generate_text` again, to have the model continue with the tool's output."
       ]
     },
@@ -93,19 +93,9 @@
       "metadata": {
         "id": "oq3EYtJYBXpG"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.2/122.2 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m113.3/113.3 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
-        "!pip install -q google.generativeai"
+        "!pip install -q google-generativeai"
       ]
     },
     {
@@ -116,14 +106,14 @@
       },
       "outputs": [],
       "source": [
-        "import google.generativeai as palm\n",
-        "palm.configure(api_key='YOUR API KEY')\n",
+        "import google.generativeai as genai\n",
+        "genai.configure(api_key='YOUR API KEY')\n",
         "\n",
         "from google.api_core import retry\n",
         "\n",
         "@retry.Retry()\n",
         "def generate_text(*args, **kwargs):\n",
-        "  return palm.generate_text(*args, **kwargs)\n"
+        "  return genai.generate_text(*args, **kwargs)\n"
       ]
     },
     {
@@ -142,7 +132,7 @@
         }
       ],
       "source": [
-        "models = [m for m in palm.list_models() if 'generateText' in m.supported_generation_methods]\n",
+        "models = [m for m in genai.list_models() if 'generateText' in m.supported_generation_methods]\n",
         "model = models[0].name\n",
         "print(model)"
       ]
@@ -194,7 +184,7 @@
         "\n",
         "{question}\n",
         "\n",
-        "Work throught it step by step, and show your work.\n",
+        "Work through it step by step, and show your work.\n",
         "One step per line.\n",
         "\n",
         "Your solution:\n",
@@ -317,7 +307,7 @@
         "\n",
         "-------------------\n",
         "\n",
-        "Work throught it step by step, and show your work.\n",
+        "Work through it step by step, and show your work.\n",
         "One step per line.\n",
         "\n",
         "Your solution:\n",
@@ -801,6 +791,16 @@
       "name": "text_calculator.ipynb",
       "toc_visible": true
     },
+    "google": {
+      "image_path": "/static/site-assets/images/icon-palm.png",
+      "keywords": [
+        "examples",
+        "palm",
+        "samplecode",
+        "python",
+        "text"
+      ]
+    },
     "kernelspec": {
       "display_name": "Python 3",
       "name": "python3"
diff --git a/site/en/examples/train_text_classifier_embeddings.ipynb b/site/en/examples/train_text_classifier_embeddings.ipynb
deleted file mode 100644
index 01862d90c..000000000
--- a/site/en/examples/train_text_classifier_embeddings.ipynb
+++ /dev/null
@@ -1,1093 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Tce3stUlHN0L"
-      },
-      "source": [
-        "##### Copyright 2023 Google LLC."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "cellView": "form",
-        "id": "tuOe1ymfHZPu"
-      },
-      "outputs": [],
-      "source": [
-        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-        "# you may not use this file except in compliance with the License.\n",
-        "# You may obtain a copy of the License at\n",
-        "#\n",
-        "# https://www.apache.org/licenses/LICENSE-2.0\n",
-        "#\n",
-        "# Unless required by applicable law or agreed to in writing, software\n",
-        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-        "# See the License for the specific language governing permissions and\n",
-        "# limitations under the License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "STuxHh6kk3eL"
-      },
-      "source": [
-        "# Training a Text Classifier Using Embeddings"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wUmTFPw2W_UD"
-      },
-      "source": [
-        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
-        "          <td>\n",
-        "    <a target=\"_blank\" href=\"https://developers.generativeai.google/examples/train_text_classifier_embeddings\"><img src=\"https://developers.generativeai.google/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Generative AI</a>\n",
-        "  </td>\n",
-        "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/examples/train_text_classifier_embeddings.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
-        "  </td>\n",
-        "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/examples/train_text_classifier_embeddings.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
-        "  </td>\n",
-        "</table>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bhT1u-Pof10V"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "In this notebook, you'll learn to use the embeddings produced by the PaLM API to train a model that can classify different types of newsgroup posts based on the topic.\n",
-        "\n",
-        "In this tutorial, you'll train a classifier to predict which class a newsgroup post belongs to.\n",
-        "\n",
-        "## Setup\n",
-        "\n",
-        "First, download and install the PaLM API Python library."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "id": "FXq0ygI3BCdQ"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -q google-generativeai"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "id": "XiJjB2vWCQJP"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "2023-05-19 10:41:53.909089: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
-            "2023-05-19 10:41:53.953625: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
-            "2023-05-19 10:41:53.954573: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
-            "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
-            "2023-05-19 10:41:54.928543: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
-          ]
-        }
-      ],
-      "source": [
-        "import google.generativeai as palm\n",
-        "\n",
-        "import re\n",
-        "import tqdm\n",
-        "import keras\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "\n",
-        "import seaborn as sns\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "from keras import layers\n",
-        "from matplotlib.ticker import MaxNLocator\n",
-        "from sklearn.datasets import fetch_20newsgroups\n",
-        "import sklearn.metrics as skmetrics"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_mwJYXpElYJc"
-      },
-      "source": [
-        "### Get an API Key\n",
-        "\n",
-        "To get started, you'll need to [create an API key](https://developers.generativeai.google/tutorials/setup)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "id": "tayrk_A2lZ7A"
-      },
-      "outputs": [],
-      "source": [
-        "palm.configure(api_key='YOUR_API_KEY')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "WKXa-Pf9lv4H"
-      },
-      "source": [
-        "Key Point: Next, you will choose a model. Any embedding model will work for this tutorial, but for real applications it's important to choose a specific model and stick with it. The outputs of different models are not compatible with each other."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "id": "l1pfEvNflvYV"
-      },
-      "outputs": [],
-      "source": [
-        "models = [m for m in palm.list_models() if 'embedText' in m.supported_generation_methods]\n",
-        "\n",
-        "model = models[0]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "C5B9sWq0hNEV"
-      },
-      "source": [
-        "## Dataset\n",
-        "\n",
-        "The [20 Newsgroups Text Dataset](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html){:.external} contains 18,000 newsgroups posts on 20 topics divided into training and test sets. The split between the training and test datasets are based on messages posted before and after a specific date. For this tutorial, you will be using the subsets of the training and test datasets. You will preprocess and organize the data into Pandas dataframes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "id": "jDoKis4om-Ea"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "['alt.atheism',\n",
-              " 'comp.graphics',\n",
-              " 'comp.os.ms-windows.misc',\n",
-              " 'comp.sys.ibm.pc.hardware',\n",
-              " 'comp.sys.mac.hardware',\n",
-              " 'comp.windows.x',\n",
-              " 'misc.forsale',\n",
-              " 'rec.autos',\n",
-              " 'rec.motorcycles',\n",
-              " 'rec.sport.baseball',\n",
-              " 'rec.sport.hockey',\n",
-              " 'sci.crypt',\n",
-              " 'sci.electronics',\n",
-              " 'sci.med',\n",
-              " 'sci.space',\n",
-              " 'soc.religion.christian',\n",
-              " 'talk.politics.guns',\n",
-              " 'talk.politics.mideast',\n",
-              " 'talk.politics.misc',\n",
-              " 'talk.religion.misc']"
-            ]
-          },
-          "execution_count": 6,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "newsgroups_train = fetch_20newsgroups(subset='train')\n",
-        "newsgroups_test = fetch_20newsgroups(subset='test')\n",
-        "\n",
-        "# View list of class names for dataset\n",
-        "newsgroups_train.target_names"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "hDz9MjkNl_FD"
-      },
-      "source": [
-        "Here is an example of what a data point from the training set looks like."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "id": "FPq-56AimOPX"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Lines: 15\n",
-            "\n",
-            " I was wondering if anyone out there could enlighten me on this car I saw\n",
-            "the other day. It was a 2-door sports car, looked to be from the late 60s/\n",
-            "early 70s. It was called a Bricklin. The doors were really small. In addition,\n",
-            "the front bumper was separate from the rest of the body. This is \n",
-            "all I know. If anyone can tellme a model name, engine specs, years\n",
-            "of production, where this car is made, history, or whatever info you\n",
-            "have on this funky looking car, please e-mail.\n",
-            "\n",
-            "Thanks,\n",
-            "- IL\n",
-            "   ---- brought to you by your neighborhood Lerxst ----\n",
-            "\n",
-            "\n",
-            "\n",
-            "\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "idx = newsgroups_train.data[0].index('Lines')\n",
-        "print(newsgroups_train.data[0][idx:])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "A9-DD7wgCx8j"
-      },
-      "source": [
-        "Now you will begin preprocessing the data for this tutorial. Remove any sensitive information like names, email, or redundant parts of the text like `\"From: \"` and `\"\\nSubject: \"`. Organize the information into a Pandas dataframe so it is more readable."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "id": "urpLwp3UmPF3"
-      },
-      "outputs": [],
-      "source": [
-        "def preprocess_newsgroup_data(newsgroup_dataset):\n",
-        "  # Apply functions to remove names, emails, and extraneous words from data points in newsgroups.data\n",
-        "  newsgroup_dataset.data = [re.sub(r'[\\w\\.-]+@[\\w\\.-]+', '', d) for d in newsgroup_dataset.data] # Remove email\n",
-        "  newsgroup_dataset.data = [re.sub(r\"\\([^()]*\\)\", \"\", d) for d in newsgroup_dataset.data] # Remove names\n",
-        "  newsgroup_dataset.data = [d.replace(\"From: \", \"\") for d in newsgroup_dataset.data] # Remove \"From: \"\n",
-        "  newsgroup_dataset.data = [d.replace(\"\\nSubject: \", \"\") for d in newsgroup_dataset.data] # Remove \"\\nSubject: \"\n",
-        "\n",
-        "  # Put data points into dataframe\n",
-        "  df_processed = pd.DataFrame(newsgroup_dataset.data, columns=['Text'])\n",
-        "  df_processed['Label'] = newsgroup_dataset.target\n",
-        "  # Match label to target name index\n",
-        "  df_processed['Class Name'] = ''\n",
-        "  for idx, row in df_processed.iterrows():\n",
-        "    df_processed.at[idx, 'Class Name'] = newsgroup_dataset.target_names[row['Label']]\n",
-        "\n",
-        "  return df_processed"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "id": "JMKddQdNnAOV"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Text</th>\n",
-              "      <th>Label</th>\n",
-              "      <th>Class Name</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>WHAT car is this!?\\nNntp-Posting-Host: rac3.w...</td>\n",
-              "      <td>7</td>\n",
-              "      <td>rec.autos</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>SI Clock Poll - Final Call\\nSummary: Final ca...</td>\n",
-              "      <td>4</td>\n",
-              "      <td>comp.sys.mac.hardware</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>PB questions...\\nOrganization: Purdue Univers...</td>\n",
-              "      <td>4</td>\n",
-              "      <td>comp.sys.mac.hardware</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>Re: Weitek P9000 ?\\nOrganization: Harris Comp...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>comp.graphics</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>Re: Shuttle Launch Question\\nOrganization: Sm...</td>\n",
-              "      <td>14</td>\n",
-              "      <td>sci.space</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "                                                Text  Label   \n",
-              "0   WHAT car is this!?\\nNntp-Posting-Host: rac3.w...      7  \\\n",
-              "1   SI Clock Poll - Final Call\\nSummary: Final ca...      4   \n",
-              "2   PB questions...\\nOrganization: Purdue Univers...      4   \n",
-              "3   Re: Weitek P9000 ?\\nOrganization: Harris Comp...      1   \n",
-              "4   Re: Shuttle Launch Question\\nOrganization: Sm...     14   \n",
-              "\n",
-              "              Class Name  \n",
-              "0              rec.autos  \n",
-              "1  comp.sys.mac.hardware  \n",
-              "2  comp.sys.mac.hardware  \n",
-              "3          comp.graphics  \n",
-              "4              sci.space  "
-            ]
-          },
-          "execution_count": 9,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Apply preprocessing function to training and test datasets\n",
-        "df_train = preprocess_newsgroup_data(newsgroups_train)\n",
-        "df_test = preprocess_newsgroup_data(newsgroups_test)\n",
-        "\n",
-        "df_train.head()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ogEGbg5XDv-T"
-      },
-      "source": [
-        "Next, you will sample some of the data by taking 100 data points in the training dataset, and dropping a few of the categories to run through this tutorial. Choose the science categories to compare."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "id": "C2N7xXhJohLR"
-      },
-      "outputs": [],
-      "source": [
-        "def sample_data(df, num_samples, classes_to_keep):\n",
-        "  df = df.groupby('Label', as_index = False).apply(lambda x: x.sample(num_samples)).reset_index(drop=True)\n",
-        "\n",
-        "  df = df[df['Class Name'].str.contains(classes_to_keep)]\n",
-        "\n",
-        "  # Reset the encoding of the labels after sampling and dropping certain categories\n",
-        "  df['Class Name'] = df['Class Name'].astype('category')\n",
-        "  df['Encoded Label'] = df['Class Name'].cat.codes\n",
-        "\n",
-        "  return df"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {
-        "id": "jS2g_ZGupBUb"
-      },
-      "outputs": [],
-      "source": [
-        "TRAIN_NUM_SAMPLES = 100\n",
-        "TEST_NUM_SAMPLES = 25\n",
-        "CLASSES_TO_KEEP = 'sci' # Class name should contain 'sci' in it to keep science categories\n",
-        "df_train = sample_data(df_train, TRAIN_NUM_SAMPLES, CLASSES_TO_KEEP)\n",
-        "df_test = sample_data(df_test, TEST_NUM_SAMPLES, CLASSES_TO_KEEP)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "metadata": {
-        "id": "j04TMPY8rV5q"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "Class Name\n",
-              "sci.crypt          100\n",
-              "sci.electronics    100\n",
-              "sci.med            100\n",
-              "sci.space          100\n",
-              "Name: count, dtype: int64"
-            ]
-          },
-          "execution_count": 12,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "df_train.value_counts('Class Name')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {
-        "id": "qMsnfkVDsJlU"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "Class Name\n",
-              "sci.crypt          25\n",
-              "sci.electronics    25\n",
-              "sci.med            25\n",
-              "sci.space          25\n",
-              "Name: count, dtype: int64"
-            ]
-          },
-          "execution_count": 13,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "df_test.value_counts('Class Name')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Kr-WlKzXjYWn"
-      },
-      "source": [
-        "## Create the embeddings\n",
-        "\n",
-        "Next, you need to compute the text embeddings. You will be using the PaLM API to [generate embeddings](https://developers.generativeai.google/api/python/google/generativeai/generate_embeddings). For a basic understanding of how the generation of embeddings works, it's recommended to go through the [embeddings quickstart notebook](../tutorials/embeddings_quickstart.ipynb) first.\n",
-        "\n",
-        "**NOTE**: Embeddings are computed one at a time, large sample sizes can take a long time!"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 14,
-      "metadata": {
-        "id": "MTBGKkPQsotz"
-      },
-      "outputs": [],
-      "source": [
-        "from tqdm.auto import tqdm\n",
-        "tqdm.pandas()\n",
-        "\n",
-        "from google.api_core import retry\n",
-        "\n",
-        "def make_embed_text_fn(model):\n",
-        "\n",
-        "  @retry.Retry(timeout=300.0)\n",
-        "  def embed_fn(text: str) -> list[float]:\n",
-        "    return palm.generate_embeddings(model=model, text=text)['embedding']\n",
-        "\n",
-        "  return embed_fn\n",
-        "\n",
-        "def create_embeddings(model, df):\n",
-        "  df['Embeddings'] = df['Text'].progress_apply(make_embed_text_fn(model))\n",
-        "  return df"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 15,
-      "metadata": {
-        "id": "AH0yrHUHtHtw"
-      },
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "4f4af8e96e52446182909cf12686a4b1",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "  0%|          | 0/400 [00:00<?, ?it/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "39ce50446406413ab8b67638ed0b8216",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "  0%|          | 0/100 [00:00<?, ?it/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "df_train = create_embeddings(model, df_train)\n",
-        "df_test = create_embeddings(model, df_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 16,
-      "metadata": {
-        "id": "6G5TvLlmRjHc"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Text</th>\n",
-              "      <th>Label</th>\n",
-              "      <th>Class Name</th>\n",
-              "      <th>Encoded Label</th>\n",
-              "      <th>Embeddings</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>1100</th>\n",
-              "      <td>Re: Why the clipper algorithm is secret\\nOrga...</td>\n",
-              "      <td>11</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "      <td>0</td>\n",
-              "      <td>[0.006307477, -0.03455442, 0.0020199143, 0.029...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1101</th>\n",
-              "      <td>Re: Would \"clipper\" make a good cover for oth...</td>\n",
-              "      <td>11</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "      <td>0</td>\n",
-              "      <td>[-0.008315406, -0.0025919464, 0.010398931, 0.0...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1102</th>\n",
-              "      <td>Re: Fifth Amendment and Passwords\\nNntp-Posti...</td>\n",
-              "      <td>11</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "      <td>0</td>\n",
-              "      <td>[-0.02041764, -0.014947017, 0.0077807712, 0.04...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1103</th>\n",
-              "      <td>Re: Another data hiding scheme... \\nDistribut...</td>\n",
-              "      <td>11</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "      <td>0</td>\n",
-              "      <td>[0.009049227, -0.06057404, 0.017259106, 0.0024...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1104</th>\n",
-              "      <td>Re: Once tapped, your code is no good any mor...</td>\n",
-              "      <td>11</td>\n",
-              "      <td>sci.crypt</td>\n",
-              "      <td>0</td>\n",
-              "      <td>[-0.0005989545, -0.0020057857, -0.0025177177, ...</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "                                                   Text  Label Class Name   \n",
-              "1100   Re: Why the clipper algorithm is secret\\nOrga...     11  sci.crypt  \\\n",
-              "1101   Re: Would \"clipper\" make a good cover for oth...     11  sci.crypt   \n",
-              "1102   Re: Fifth Amendment and Passwords\\nNntp-Posti...     11  sci.crypt   \n",
-              "1103   Re: Another data hiding scheme... \\nDistribut...     11  sci.crypt   \n",
-              "1104   Re: Once tapped, your code is no good any mor...     11  sci.crypt   \n",
-              "\n",
-              "      Encoded Label                                         Embeddings  \n",
-              "1100              0  [0.006307477, -0.03455442, 0.0020199143, 0.029...  \n",
-              "1101              0  [-0.008315406, -0.0025919464, 0.010398931, 0.0...  \n",
-              "1102              0  [-0.02041764, -0.014947017, 0.0077807712, 0.04...  \n",
-              "1103              0  [0.009049227, -0.06057404, 0.017259106, 0.0024...  \n",
-              "1104              0  [-0.0005989545, -0.0020057857, -0.0025177177, ...  "
-            ]
-          },
-          "execution_count": 16,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "df_train.head()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "QPYEYkIsWt_5"
-      },
-      "source": [
-        "## Build a simple classification model\n",
-        "Here you will define a simple model with one hidden layer and a single class probability output. The prediction will correspond to the probability of a piece of text being a particular class of news. When you build your model, Keras will automatically shuffle the data points."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 17,
-      "metadata": {
-        "id": "3oLGi4w5JsQR"
-      },
-      "outputs": [],
-      "source": [
-        "def build_classification_model(input_size: int, num_classes: int) -> keras.Model:\n",
-        "  inputs = x = keras.Input(input_size)\n",
-        "  x = layers.Dense(input_size, activation='relu')(x)\n",
-        "  x = layers.Dense(num_classes, activation='sigmoid')(x)\n",
-        "  return keras.Model(inputs=[inputs], outputs=x)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 18,
-      "metadata": {
-        "id": "kORA1Akl5GsG"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Model: \"model\"\n",
-            "_________________________________________________________________\n",
-            " Layer (type)                Output Shape              Param #   \n",
-            "=================================================================\n",
-            " input_1 (InputLayer)        [(None, 768)]             0         \n",
-            "                                                                 \n",
-            " dense (Dense)               (None, 768)               590592    \n",
-            "                                                                 \n",
-            " dense_1 (Dense)             (None, 4)                 3076      \n",
-            "                                                                 \n",
-            "=================================================================\n",
-            "Total params: 593668 (2.26 MB)\n",
-            "Trainable params: 593668 (2.26 MB)\n",
-            "Non-trainable params: 0 (0.00 Byte)\n",
-            "_________________________________________________________________\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "2023-05-19 10:48:14.210811: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
-            "2023-05-19 10:48:14.211994: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1960] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
-            "Skipping registering GPU devices...\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Derive the embedding size from the first training element.\n",
-        "embedding_size = len(df_train['Embeddings'].iloc[0])\n",
-        "\n",
-        "# Give your model a different name, as you have already used the variable name 'model'\n",
-        "classifier = build_classification_model(embedding_size, len(df_train['Class Name'].unique()))\n",
-        "classifier.summary()\n",
-        "\n",
-        "classifier.compile(loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
-        "                   optimizer = keras.optimizers.Adam(learning_rate=0.001),\n",
-        "                   metrics=['accuracy'])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 19,
-      "metadata": {
-        "id": "iPYYKnqFvt9x"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "768"
-            ]
-          },
-          "execution_count": 19,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "embedding_size"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "kbpTGGiMXDxl"
-      },
-      "source": [
-        "## Train the model to classify newsgroups\n",
-        "\n",
-        "Finally, you can train a simple model. Use a small number of epochs to avoid overfitting. The first epoch takes much longer than the rest, because the embeddings need to be computed only once."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 20,
-      "metadata": {
-        "id": "bGgvMZGfJ1A4"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Epoch 1/20\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/usr/local/google/home/markdaoust/venv3/lib/python3.10/site-packages/keras/src/backend.py:5714: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a Softmax activation and thus does not represent logits. Was this intended?\n",
-            "  output, from_logits = _get_logits(\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "13/13 [==============================] - 1s 21ms/step - loss: 1.2284 - accuracy: 0.6025 - val_loss: 1.0366 - val_accuracy: 0.9100\n",
-            "Epoch 2/20\n",
-            "13/13 [==============================] - 0s 8ms/step - loss: 0.8011 - accuracy: 0.9450 - val_loss: 0.6998 - val_accuracy: 0.8500\n",
-            "Epoch 3/20\n",
-            "13/13 [==============================] - 0s 7ms/step - loss: 0.4617 - accuracy: 0.9700 - val_loss: 0.4686 - val_accuracy: 0.8900\n",
-            "Epoch 4/20\n",
-            "13/13 [==============================] - 0s 7ms/step - loss: 0.2774 - accuracy: 0.9725 - val_loss: 0.3689 - val_accuracy: 0.8700\n",
-            "Epoch 5/20\n",
-            "13/13 [==============================] - 0s 7ms/step - loss: 0.1821 - accuracy: 0.9775 - val_loss: 0.3158 - val_accuracy: 0.8800\n",
-            "Epoch 6/20\n",
-            "13/13 [==============================] - 0s 8ms/step - loss: 0.1335 - accuracy: 0.9800 - val_loss: 0.2899 - val_accuracy: 0.8800\n",
-            "Epoch 7/20\n",
-            "13/13 [==============================] - 0s 7ms/step - loss: 0.1080 - accuracy: 0.9775 - val_loss: 0.2952 - val_accuracy: 0.8600\n",
-            "Epoch 8/20\n",
-            "13/13 [==============================] - 0s 7ms/step - loss: 0.0852 - accuracy: 0.9825 - val_loss: 0.2593 - val_accuracy: 0.8900\n",
-            "Epoch 9/20\n",
-            "13/13 [==============================] - 0s 7ms/step - loss: 0.0708 - accuracy: 0.9850 - val_loss: 0.2523 - val_accuracy: 0.9000\n",
-            "Epoch 10/20\n",
-            "13/13 [==============================] - 0s 7ms/step - loss: 0.0579 - accuracy: 0.9900 - val_loss: 0.2678 - val_accuracy: 0.9000\n",
-            "Epoch 11/20\n",
-            "13/13 [==============================] - 0s 7ms/step - loss: 0.0504 - accuracy: 0.9950 - val_loss: 0.2313 - val_accuracy: 0.9200\n",
-            "Epoch 12/20\n",
-            "13/13 [==============================] - 0s 7ms/step - loss: 0.0429 - accuracy: 0.9975 - val_loss: 0.2417 - val_accuracy: 0.9100\n",
-            "Epoch 13/20\n",
-            "13/13 [==============================] - 0s 6ms/step - loss: 0.0379 - accuracy: 0.9975 - val_loss: 0.2340 - val_accuracy: 0.9100\n",
-            "Epoch 14/20\n",
-            "13/13 [==============================] - 0s 7ms/step - loss: 0.0307 - accuracy: 0.9975 - val_loss: 0.2364 - val_accuracy: 0.9200\n",
-            "Epoch 15/20\n",
-            "13/13 [==============================] - 0s 8ms/step - loss: 0.0262 - accuracy: 1.0000 - val_loss: 0.2261 - val_accuracy: 0.9100\n",
-            "Epoch 16/20\n",
-            "13/13 [==============================] - 0s 6ms/step - loss: 0.0224 - accuracy: 1.0000 - val_loss: 0.2313 - val_accuracy: 0.9200\n",
-            "Epoch 17/20\n",
-            "13/13 [==============================] - 0s 6ms/step - loss: 0.0207 - accuracy: 1.0000 - val_loss: 0.2169 - val_accuracy: 0.9200\n",
-            "Epoch 18/20\n",
-            "13/13 [==============================] - 0s 6ms/step - loss: 0.0172 - accuracy: 1.0000 - val_loss: 0.2245 - val_accuracy: 0.9200\n"
-          ]
-        }
-      ],
-      "source": [
-        "NUM_EPOCHS = 20\n",
-        "BATCH_SIZE = 32\n",
-        "\n",
-        "# Split the x and y components of the train and validation subsets.\n",
-        "y_train = df_train['Encoded Label']\n",
-        "x_train = np.stack(df_train['Embeddings'])\n",
-        "y_val = df_test['Encoded Label']\n",
-        "x_val = np.stack(df_test['Embeddings'])\n",
-        "\n",
-        "# Train the model for the desired number of epochs.\n",
-        "callback = keras.callbacks.EarlyStopping(monitor='accuracy', patience=3)\n",
-        "\n",
-        "history = classifier.fit(x=x_train,\n",
-        "                         y=y_train,\n",
-        "                         validation_data=(x_val, y_val),\n",
-        "                         callbacks=[callback],\n",
-        "                         batch_size=BATCH_SIZE,\n",
-        "                         epochs=NUM_EPOCHS,)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "xGBaDHZUPdJO"
-      },
-      "source": [
-        "## Evaluate model performance\n",
-        "\n",
-        "Use Keras `Model.evaluate` to get the loss and accuracy on the test dataset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 21,
-      "metadata": {
-        "id": "d2kOeiqqQIB8"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "4/4 [==============================] - 0s 4ms/step - loss: 0.2245 - accuracy: 0.9200\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "{'loss': 0.22447504103183746, 'accuracy': 0.9200000166893005}"
-            ]
-          },
-          "execution_count": 21,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "classifier.evaluate(x=x_val, y=y_val, return_dict=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "UyxMhiLYQXAN"
-      },
-      "source": [
-        "One way to evaluate your model performance is to visualize the classifier performance. Use `plot_history` to see the loss and accuracy trends over the epochs."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 22,
-      "metadata": {
-        "id": "MaDO9hwbEOW3"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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-            "text/plain": [
-              "<Figure size 2000x800 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "def plot_history(history):\n",
-        "  \"\"\"\n",
-        "    Plotting training and validation learning curves.\n",
-        "\n",
-        "    Args:\n",
-        "      history: model history with all the metric measures\n",
-        "  \"\"\"\n",
-        "  fig, (ax1, ax2) = plt.subplots(1,2)\n",
-        "  fig.set_size_inches(20, 8)\n",
-        "\n",
-        "  # Plot loss\n",
-        "  ax1.set_title('Loss')\n",
-        "  ax1.plot(history.history['loss'], label = 'train')\n",
-        "  ax1.plot(history.history['val_loss'], label = 'test')\n",
-        "  ax1.set_ylabel('Loss')\n",
-        "\n",
-        "  ax1.set_xlabel('Epoch')\n",
-        "  ax1.legend(['Train', 'Validation'])\n",
-        "\n",
-        "  # Plot accuracy\n",
-        "  ax2.set_title('Accuracy')\n",
-        "  ax2.plot(history.history['accuracy'],  label = 'train')\n",
-        "  ax2.plot(history.history['val_accuracy'], label = 'test')\n",
-        "  ax2.set_ylabel('Accuracy')\n",
-        "  ax2.set_xlabel('Epoch')\n",
-        "  ax2.legend(['Train', 'Validation'])\n",
-        "\n",
-        "  plt.show()\n",
-        "\n",
-        "plot_history(history)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "kOgva0pbP4FS"
-      },
-      "source": [
-        "Another way to view model performance, beyond just measuring loss and accuracy is to use a confusion matrix. The confusion matrix allows you to assess the performance of the classification model beyond accuracy. You can see what misclassified points get classified as. In order to build the confusion matrix for this multi-class classification problem, get the actual values in the test set and the predicted values.\n",
-        "\n",
-        "Start by generating the predicted class for each example in the validation set using `Model.predict()`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 23,
-      "metadata": {
-        "id": "PRUx5ao9QRcO"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "4/4 [==============================] - 0s 2ms/step\n"
-          ]
-        }
-      ],
-      "source": [
-        "y_hat = classifier.predict(x=x_val)\n",
-        "y_hat = np.argmax(y_hat, axis=1)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 24,
-      "metadata": {
-        "id": "CVidbr0OT5tL"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "{'sci.crypt': 0, 'sci.electronics': 1, 'sci.med': 2, 'sci.space': 3}"
-            ]
-          },
-          "execution_count": 24,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "labels_dict = dict(zip(df_test['Class Name'], df_test['Encoded Label']))\n",
-        "labels_dict"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 25,
-      "metadata": {
-        "id": "3ae76701e178"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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SJksnTpyAqakpPD09lfGOHz8e69atg7W1NXx9ffH111+r/H0K3jsvvrYX30slHYeyvv/Kon79+iqPjY2NYW9v/8pTUdy8eRPJycmwtbUtdMzT0tKUx7tz587o06cPQkJCYG1tjXfffRcbN25EVlbWK7+WO3fuFHo9QNHv97///hv+/v4wMzODqakpbGxs8OGHHwJAqd9fmzdvRrNmzWBgYAArKyvY2Njg999/V9n+008/RYMGDeDn54c6depg8ODBygS6wM2bN7F///5Cx6tbt24A/j1HSVocs0QvZWpqCgcHB1y9erVM2xU3huFFurq6RZaL/x9/8Sr7yMvLU3lsaGiI48eP48iRI/j999+xf/9+bN++HW+88QYOHjxYbAxlpc5rKW7b0tQ5atQobNy4EWPHjkW7du2UE3f269ev1C1pAMqc7Bw9elT5hXXlyhW0a9euTNuro6yxvuw4FhyniRMnFtu6VpCAODg4wMXFBcePH4ezszOEEGjXrh1sbGwwZswY3LlzBydOnED79u1VrtBbvHgxBg4ciF9//RUHDx7E6NGjsWDBApw9exZ16tQp0+spUNxxKO37rygvvn/Ki0KhgK2tLb7//vsin7exsQGQ/1p27NiBs2fP4rfffsOBAwcwePBgLF68GGfPni3zGMaySEpKQufOnWFqaorZs2ejXr16MDAwwIULFzBlypRSvb++++47DBw4EL169cKkSZNga2sLXV1dLFiwADExMcr1bG1tcenSJRw4cAB//PEH/vjjD2zcuBEDBgzA5s2bAeQfszfffBOTJ08ucl8NGjSQ5oWTCiZLVCo9evTAmjVrcObMmZd+ITo5OUGhUODmzZsqg4cfP36MpKQkODk5SRaXhYUFkpKSCpUX9etZR0cH3t7e8Pb2xpIlSzB//nxMnz4dR44cUf4qe/F1AFB2tfzXjRs3YG1tXWku096xYwcCAwOxePFiZVlmZmahY6POF+iL4uLiMGrUKPj4+CgHWvv6+r7y39fJyanYY13wfHlydXUFAOjp6RV5PryoY8eOOH78OFxcXNC8eXOYmJjA09MTZmZm2L9/Py5cuICQkJBC23l4eMDDwwOff/45Tp8+DS8vL6xevRpz585VvndiY2NVWj2io6NL/TrK8v4r6v2TnZ2NuLi4Iuu+efMmunbtqnyclpaGuLg4dO/evcSYijvv6tWrh0OHDsHLy6tUye/rr7+O119/HfPmzcO2bdvQv39//Pjjjxg6dGiZz20nJyfcvHmzUPmL5+DRo0fx9OlT7Nq1C506dVKWx8bGFtq2uBh27NgBV1dX7Nq1S2WdghbL/9LX10fPnj3Rs2dPKBQKfPrpp/j2228xY8YMuLm5oV69ekhLS3vpOSrle53YDUelNHnyZNSsWRNDhw7F48ePCz0fExOD5cuXA4Dyg/PFmWyXLFkCACVe2VNW9erVQ3JyMi5fvqwsi4uLK3TFT2JiYqFtC65AKa4p397eHs2bN8fmzZtVvlCuXr2KgwcPvvQLoiLp6uoWar1auXJloRaCguSuqASzrIYNGwaFQoH169djzZo1qFGjBoYMGVKqVrSidO/eHefOncOZM2eUZenp6VizZg2cnZ3h7u6udswlsbW1RZcuXfDtt98WmSwUzPVVoGPHjrh9+za2b9+u7JbT0dFB+/btsWTJEuTk5KiMxUtJSUFubq5KHR4eHtDR0VGegwUtWt98843KeitXriz16yjL+69evXqFxrisWbOm2JalNWvWqIwZCg0NRW5uLvz8/EqMqbjz7v3330deXh7mzJlTaJvc3Fzl+s+ePSt0Xr34/jUyMipyH8Xp3r07zp49i3PnzinL4uPjC7VyFbRI/nf/2dnZhf5GQP7rLKpbrqg6IiIiVM51AHj69KnKYx0dHeUVbAWv8/3338eZM2dw4MCBQvtJSkpSnmNlPR5UMrYsUanUq1cP27ZtwwcffIDGjRtjwIABaNq0KbKzs3H69Gn8/PPPynuHeXp6IjAwEGvWrFE2YZ87dw6bN29Gr169VH6Zqqtfv36YMmUK/P39MXr0aGRkZCA0NBQNGjRQGTg5e/ZsHD9+HG+//TacnJzw5MkTfPPNN6hTp47KgOIXffnll/Dz80O7du0wZMgQ5dQBZmZm5X77hrLo0aMHtm7dCjMzM7i7u+PMmTM4dOgQrKysVNZr3rw5dHV18cUXXyA5ORlyuRxvvPEGbG1ty7S/jRs34vfff8emTZuU3UcrV67Ehx9+iNDQ0EIDlEtj6tSp+OGHH+Dn54fRo0fD0tISmzdvRmxsLHbu3FkhE05+/fXX6NChAzw8PDBs2DC4urri8ePHOHPmDO7fv4/IyEjlugWJUFRUlMrA9k6dOuGPP/6AXC5HmzZtlOWHDx9GUFAQ3nvvPTRo0AC5ubnYunUrdHV10adPHwBAq1at0KdPHyxbtgxPnz5VTh3wzz//AChda0FZ3n9Dhw7FiBEj0KdPH7z55puIjIzEgQMHYG1tXWTd2dnZ8Pb2xvvvv4+oqCh888036NChA955550SY2rVqhUAYPTo0fD19YWuri769euHzp074+OPP8aCBQtw6dIl+Pj4QE9PDzdv3sTPP/+M5cuXo2/fvti8eTO++eYb+Pv7o169ekhNTcXatWthamqqTA4NDQ3h7u6O7du3o0GDBrC0tETTpk3RtGnTImOaPHmy8lY7Y8aMUU4d4OTkpPLjq3379rCwsEBgYCBGjx4NmUyGrVu3FvmjoFWrVti+fTvGjx+PNm3awNjYGD179kSPHj2wa9cu+Pv74+2330ZsbCxWr14Nd3d3pKWlqfw9EhMT8cYbb6BOnTq4c+cOVq5ciebNmytbCSdNmoQ9e/agR48eyilW0tPTceXKFezYsQO3b9+GtbV1mY8HvYRGrsGjKuuff/4Rw4YNE87OzkJfX1+YmJgILy8vsXLlSpVLaHNyckRISIhwcXERenp6wtHRscRJKV/UuXNn0blzZ+Xj4qYOECJ/ssmmTZsKfX190bBhQ/Hdd98VmjogPDxcvPvuu8LBwUHo6+sLBwcHERAQoHL5bXGTUh46dEh4eXkJQ0NDYWpqKnr27FnspJQvTk1Q2lsnFExK+aLijg9euPT+2bNnYtCgQcLa2loYGxsLX19fcePGjSIv+V+7dq1wdXUVurq6RU5KWZT/1nPv3j1hZmYmevbsWWg9f39/UbNmTXHr1q0SX++L8RcomJTS3NxcGBgYiNdee63YSSlLugT/v0o6d1DEpdUxMTFiwIABws7OTujp6YnatWuLHj16iB07dhTa3tbWVgBQmZzz5MmTAoDo2LGjyrq3bt0SgwcPFvXq1VNOHNq1a1dx6NAhlfXS09PFyJEjhaWlpTA2Nha9evUSUVFRAoBYuHChcr3izjkhSv/+y8vLE1OmTBHW1tbCyMhI+Pr6iujo6JdOSmlhYSGMjY1F//79VabVKE5ubq4YNWqUsLGxETKZrNA0AmvWrBGtWrUShoaGwsTERHh4eIjJkyeLhw8fCiGEuHDhgggICBB169ZVTlzZo0cPcf78eZV6Tp8+LVq1aiX09fVLddn85cuXRefOnV86KeWpU6fE66+/LgwNDYWDg4OYPHmycgqP/07DkZaWJv73v/8Jc3NzlUkpFQqFmD9/vnBychJyuVy0aNFC7N27t9DUJzt27BA+Pj7C1tZW6Ovri7p164qPP/5YxMXFqcSdmpoqpk2bJtzc3IS+vr6wtrYW7du3F1999ZXK1A5lPR5UPJkQr9hmTkREFeLSpUto0aIFvvvuuyIvbS9vBZNG/vnnn7yBNVVLHLNERFSJPH/+vFDZsmXLoKOjozLAmIgqDscsERFVIosWLcJff/2Frl27okaNGspLyIcPH15ozi0iqhhMloiIKpH27dsjLCwMc+bMQVpaGurWrYvg4GBMnz5d06ERVVscs0RERERUAo5ZIiIiIioBkyUiIiKiEnDMEr2UQqHAw4cPYWJiwin0iYiqGCEEUlNT4eDgUK6Tu2ZmZiI7O1uSuvT19WFgYCBJXVJgskQv9fDhQ16FQ0RUxd27d++Vb9j8MpmZmXBxMsajJ9LchNnOzg6xsbGVJmFiskQvZWJiAgC4c8EZpsbsua0I/g08NB1CtaNraqLpEKqVvJRUTYdQbeQiByexT/lZXh6ys7Px6EkeYv9ygqmJet8TKakKuLS6g+zsbCZLVHUUdL2ZGuuo/Sag0qkh09N0CNWOrkxf0yFUKzKe4xXn/695r4hhFKYm2vk9wWSJiIiIJJEnFMhTc0KiPKGQJhgJMVkiIiIiSSggoIB62ZK625cH7WsrIyIiIpIQW5aIiIhIEgoooG4nmvo1SI/JEhEREUkiTwjkqXkXNXW3Lw9MloiIiEgSHLNEREREVA2xZYmIiIgkoYBAnha2LDFZIiIiIkmwG46IiIioGmLLEhEREUmCV8MRERERlUDx/4u6dVQ27IYjIiIiKgFbloiIiEgSeRJcDafu9uWByRIRERFJIk/kL+rWUdmwG46IiIioBGxZIiIiIklo6wBvJktEREQkCQVkyINM7ToqGyZLREREJAmFyF/UraOy4ZglIiIiohKwZYmIiIgkkSdBN5y625cHJktEREQkCW1NltgNR0RERFQCtiwRERGRJBRCBoVQ82o4NbcvD0yWiIiISBLshiMiIiKqhtiyRERERJLIgw7y1GyHyZMoFikxWSIiIiJJCAnGLIlKOGaJ3XBEREREJWDLEhEREUlCWwd4M1kiIiIiSeQJHeQJNccsVcJ7wzFZIiIiIkkoIINCzRE+ClS+bIljloiIiIhKwJYlIiIikgTHLBERERGVQJoxS+yGIyIiIqpS2LJEREREksgf4K3mjXTZDUdERETaSiHB7U54NVw1IZPJsHv3bk2HUS38uNIWo/waoFd9D7zv0QTBg1xwL1pe5LpCANP7u8LXoTlO/2FWwZFqv54DE7A54hp+u3UZy/feRMPmGZoOSWs1bZ2MWaF/Y+vxCOy7cQLtvBM0HVK1wHO8+mKyVA7i4uLg5+en6TAKOXr0KGQyGZKSkjQdimQunzFGz4EJWLb3Jhb8GIO8XOCzgHrIzCh8av+y1gayyte6qxU6v/MMw2c9xPdL7DDStwFuXTPAvG23YGaVo+nQtJKBYR5ib9TEN7PraTqUaoPneOkUDPBWd6lsKl9EWsDOzg5yedGtG68iJ4dvxuLM33YLPh8kwrlhJuo1ycSEZXfx5IE+bl42VFkv5qohdn5rg/FL7mooUu3We3gC9m+zxMHtlrh70wArptRB1nMZfAMSNR2aVjp/whJbljvjzCFrTYdSbfAcLx0FdCRZKpvKF1ElsmPHDnh4eMDQ0BBWVlbo1q0b0tPTAQAbNmxAkyZNIJfLYW9vj6CgIOV2L+uGUygUWLRoEdzc3CCXy1G3bl3MmzcPAHD79m3IZDJs374dnTt3hoGBAdasWQNTU1Ps2LFDpZ7du3ejZs2aSE1NVW73448/on379jAwMEDTpk1x7NgxZb1du3YFAFhYWEAmk2HgwIESHq3KIT1FFwBgYp6nLMvMkGHhSCeMnHcflra5mgpNa9XQU6B+swxcOGGiLBNChosnTODeit0UVPXxHCcO8C5GXFwcAgICsGjRIvj7+yM1NRUnTpyAEAKhoaEYP348Fi5cCD8/PyQnJ+PUqVOlrnvatGlYu3Ytli5dig4dOiAuLg43btxQWWfq1KlYvHgxWrRoAQMDA0RGRmLjxo3o27evcp2CxyYmJnj69CkAYNKkSVi2bBnc3d2xZMkS9OzZE7GxsXB0dMTOnTvRp08fREVFwdTUFIaGqq0vBbKyspCVlaV8nJKSUpZDpzEKBbB6Vm00aZMG50aZyvJvg2vDvXU62r9VNV5HVWNqmQfdGkBSvOrHybOEGnB0yypmK6Kqg+d46eUJGfKEmpNSqrl9eWCyVIy4uDjk5uaid+/ecHJyAgB4eHgAAObOnYsJEyZgzJgxyvXbtGlTqnpTU1OxfPlyrFq1CoGBgQCAevXqoUOHDirrjR07Fr1791Y+Hjp0KNq3b4+4uDjY29vjyZMn2LdvHw4dOqSyXVBQEPr06QMACA0Nxf79+7F+/XpMnjwZlpaWAABbW1uYm5sXG+OCBQsQEhJSqtdTmaz6rA7u3DDE4t03lWVnDpji0ikTfHMwSoORERFVD3kSXA2Xx6vhqg5PT094e3vDw8MD7733HtauXYtnz57hyZMnePjwIby9vV+p3uvXryMrK+ul27du3Vrl8WuvvYYmTZpg8+bNAIDvvvsOTk5O6NSpk8p67dq1U/6/Ro0aaN26Na5fv16mGKdNm4bk5GTlcu/evTJtrwmrPquNiDBTLNoRDRuHf8d4XTplgrjb+ujdyAN+jp7wc/QEAMwZ5oxJfdw0Fa5WSUnURV4uYG6j2sVpYZ2LZ/H8PUZVH8/x0lMIHUmWyqbyRVRJ6OrqIiwsDH/88Qfc3d2xcuVKNGzYEI8fP1ar3uK6vl5Us2bNQmVDhw7Fpk2bAOR3wQ0aNAiycri8Sy6Xw9TUVGWprITIT5RO7zfDop+jYVc3W+X5D4IeY3V4FELD/l0A4OPgB5iwlIO9pZCbo4Obl43QokOqskwmE2jeIQ3X/jLSYGRE0uA5TkyWSiCTyeDl5YWQkBBcvHgR+vr6CAsLg7OzM8LDw1+pzvr168PQ0PCVtv/www9x584drFixAteuXVN24/3X2bNnlf/Pzc3FX3/9hcaNGwMA9PX1AQB5eXmFtquqVn1WB4d3WWLq13dgaKxA4pMaSHxSA1nP85NIS9tcODfKVFkAwLZ2TqHEil7drjXW8PtfIrq9lwhHt0yMWngfBkYKHPzRUtOhaSUDozy4NkqDa6M0AECtOllwbZQGG/vMl2xJr4rneOkUdMOpu1Q2bD8sRkREBMLDw+Hj4wNbW1tEREQgPj4ejRs3RnBwMEaMGAFbW1v4+fkhNTUVp06dwqhRo4qsy9vbG/7+/ggKCoKBgQGmTJmCyZMnQ19fH15eXoiPj8fff/+NIUOGlBiThYUFevfujUmTJsHHxwd16tQptM7XX3+N+vXro3Hjxli6dCmePXuGwYMHAwCcnJwgk8mwd+9edO/eHYaGhjA2Nlb/YGnQ3s35l05P6lNfpXzC0rvw+YCX9FaUY3ssYGaVhwGTHsHCJhe3/jbE9P4uSErQ03RoWql+01R8seWK8vHwabcAAGG/2GLptIaaCkur8RwvHQXUH6CtkCYUSTFZKoapqSmOHz+OZcuWISUlBU5OTli8eLFyssnMzEwsXboUEydOhLW1tcpVai+KiYlBQsK/M+zOmDEDNWrUwMyZM/Hw4UPY29tjxIgRpYpryJAh2LZtmzIBetHChQuxcOFCXLp0CW5ubtizZw+srfMTitq1ayMkJARTp07FoEGDMGDAAGW3XlV14OGlCtmGXm7PRmvs2ch5fyrClXPm6N6oo6bDqHZ4jldfMiFE5Rt2TsXaunUrxo0bh4cPHyq71YD8eZRcXFxw8eJFNG/eXNJ9pqSkwMzMDM/+cYWpSeVrHtVGvg7NNR1CtaNbicfmaaO8KjIliTbIFTk4il+RnJxcbmNQC74nQi+0gaGxeu0wz9Ny8UnLP8s13rJiy1IVkZGRgbi4OCxcuBAff/yxSqJERERUGUhxuxLe7oRe2aJFi9CoUSPY2dlh2rRpmg6HiIio2mDLUhURHByM4ODgYp93dnYGe1SJiEiTFJBBAXUHeHMGbyIiItJS7IYjIiIiKkFFz7O0YMECtGnTBiYmJrC1tUWvXr0QFaV6e6vMzEyMHDkSVlZWMDY2Rp8+fco8wTSTJSIiIqqSjh07hpEjR+Ls2bMICwtDTk4OfHx8kJ6erlxn3Lhx+O233/Dzzz/j2LFjePjwocq9V0uD3XBEREQkCYWQQaHupJRl2H7//v0qjzdt2gRbW1v89ddf6NSpE5KTk7F+/Xps27YNb7zxBoD824U1btwYZ8+exeuvv16q/TBZIiIiIkkoJLhdieL/t095YS4uuVwOuVxe4rbJyckAAEvL/NvQ/PXXX8jJyUG3bt2U6zRq1Ah169bFmTNnSp0ssRuOiIiIKh1HR0eYmZkplwULFpS4vkKhwNixY+Hl5YWmTZsCAB49egR9fX2Ym5urrFurVi08evSo1LGwZYmIiIgkoRA6UKh5NVvB9vfu3VOZwftlrUojR47E1atXcfLkSbX2XxQmS0RERCSJPMiQp+Y8SQXbm5qalvp2J0FBQdi7dy+OHz+ucpN5Ozs7ZGdnIykpSaV16fHjx7Czsyt1TOyGIyIioipJCIGgoCD88ssvOHz4MFxcXFSeb9WqFfT09BAeHq4si4qKwt27d9GuXbtS74ctS0RERCQJKbvhSmPkyJHYtm0bfv31V5iYmCjHIZmZmcHQ0BBmZmYYMmQIxo8fD0tLS5iammLUqFFo165dqQd3A0yWiIiISCJ5gATdcKUXGhoKAOjSpYtK+caNGzFw4EAAwNKlS6Gjo4M+ffogKysLvr6++Oabb8oUE5MlIiIiqpJKc09UAwMDfP311/j6669feT9MloiIiEgSFd0NV1GYLBEREZEktPVGukyWiIiISBICMijUHLMk1Ny+PFS+9I2IiIioEmHLEhEREUmC3XBEREREJVAIGRRCvW40dbcvD5UvfSMiIiKqRNiyRERERJLIgw7y1GyHUXf78sBkiYiIiCTBbjgiIiKiaogtS0RERCQJBXSgULMdRt3tywOTJSIiIpJEnpAhT81uNHW3Lw+VL30jIiIiqkTYskRERESS0NYB3kyWiIiISBJC6ECh5gzcgjN4ExERkbbKgwx5at4IV93ty0PlS9+IiIiIKhG2LBEREZEkFEL9MUcKIVEwEmKyRERERJJQSDBmSd3ty0Pli4iIiIioEmHLEhEREUlCARkUag7QVnf78sBkiYiIiCTBGbyJiIiIqiG2LFGp9X23N2royjUdRrXQ4++zmg6h2tnbRNMRVC81HOtoOoTqQ5EF3K+gXWnpAG8mS0RERCQJBSS43UklHLNU+dI3IiIiokqELUtEREQkCSHB1XCiErYsMVkiIiIiSSiEBN1wlfBqOCZLREREJAltHeBd+SIiIiIiqkTYskRERESSYDccERERUQm09XYn7IYjIiIiKgFbloiIiEgS7IYjIiIiKoG2JkvshiMiIiIqAVuWiIiISBLa2rLEZImIiIgkoa3JErvhiIiIiErAliUiIiKShID68yQJaUKRFJMlIiIikoS2dsMxWSIiIiJJaGuyxDFLRERERCVgyxIRERFJQltblpgsERERkSS0NVliNxwRERFRCdiyRERERJIQQgahZsuQutuXByZLREREJAkFZGrPs6Tu9uWB3XBEREREJWDLEhEREUlCWwd4M1kiIiIiSWjrmCV2wxERERGVgC1LREREJAl2wxERERGVQFu74ZgsERERkSSEBC1LlTFZ4pglIiIiohKwZYmIiIgkIQAIoX4dlQ2TJSIiIpKEAjLIOIM3ERERUfXCliUiIiKSBK+GIyIiIiqBQsgg08J5ltgNR0RERFQCtiwRERGRJISQ4Gq4Sng5HJMlIiIikoS2jlliNxwRERFRCapky5JMJsMvv/yCXr16vdL2R48eRdeuXfHs2TOYm5tLGltFUfcYaLPuPaLxds8Y1KqVDgC4c8cMP3znjvN/2ms4Mu0QvdYAcWF6SIvVha6BgEXzXDQe/xzGLgrlOnlZwLVFhnj4hz4U2TLYeOXAY0YG5NaVsH29iuo5MAF9P3kCS5tc3LpmiG8+r42oS0aaDksrvRcYjfZdHqGOUxqys3Rx/YoFNq5qhAd3jTUdWqXDlqVKJC4uDn5+fpoOQ+no0aOQyWRISkqqsH1WtmNQmSQkGGHj+mYYPfJNjBn5JiIv2WJGyCnUdUrWdGha4emfNeAckIUOP6Tg9bVpELkyRAwzRm7Gv+tc+8IIj4/qo9WSdLTbnIrMeB2cH8MvFql0fucZhs96iO+X2GGkbwPcumaAedtuwcwqR9OhaSWPFon4fYcTJgzxwuej26JGDQXmrjgHuUGupkOrdBT/f284dZeyOH78OHr27AkHBwfIZDLs3r1b5fmBAwdCJpOpLG+99VaZ9lElkyU7OzvI5XJNh1Fm2dnZktVVVY9BRTh31gHnz9nj4QMTPHhggi0bPZD5vAYaNX6q6dC0Qts1aXD0z4aJmwKmjfLgOS8dz+N0kXwtv6E6JxW4u1Mf7pMzYP16Lsyb5KH53HQ8u1QDzyJ1NRy9dug9PAH7t1ni4HZL3L1pgBVT6iDruQy+AYmaDk0rzRz7Gg797oi7sSaIvWmKJbM9YWv/HG6N+APsRQUDvNVdyiI9PR2enp74+uuvi13nrbfeQlxcnHL54YcfyrQPjSZLO3bsgIeHBwwNDWFlZYVu3bohPT2/62TDhg1o0qQJ5HI57O3tERQUpNyuqMzxvxQKBRYsWAAXFxcYGhrC09MTO3bsKDGWkydPomPHjjA0NISjoyNGjx6tjAUAsrKyMGXKFDg6OkIul8PNzQ3r16/H7du30bVrVwCAhYUFZDIZBg4cCADo0qULgoKCMHbsWFhbW8PX1xcAcOzYMbz22mvK1zZ16lTk5v77C6VLly4YPXo0Jk+eDEtLS9jZ2SE4OFgl3hePwf379xEQEABLS0vUrFkTrVu3RkREBAAgMjISXbt2hYmJCUxNTdGqVSucP3++xOOhLXR0FOjU5S4MDHJx/ZqVpsPRSrmp+b8C9czyu+GS/64BkSuDTbt/z2ljVwUM7fPw7FKV7PmvVGroKVC/WQYunDBRlgkhw8UTJnBvlVHCliSVmsb553Zair6GIyEA8PPzw9y5c+Hv71/sOnK5HHZ2dsrFwsKiTPvQ2CdXXFwcAgICsGjRIvj7+yM1NRUnTpyAEAKhoaEYP348Fi5cCD8/PyQnJ+PUqVOlrnvBggX47rvvsHr1atSvXx/Hjx/Hhx9+CBsbG3Tu3LnQ+jExMXjrrbcwd+5cbNiwAfHx8QgKCkJQUBA2btwIABgwYADOnDmDFStWwNPTE7GxsUhISICjoyN27tyJPn36ICoqCqampjA0NFTWvXnzZnzyySfK+B88eIDu3btj4MCB2LJlC27cuIFhw4bBwMBAJSHavHkzxo8fj4iICJw5cwYDBw6El5cX3nzzzULxp6WloXPnzqhduzb27NkDOzs7XLhwAQpF/pdX//790aJFC4SGhkJXVxeXLl2Cnp5esccvKysLWVlZyscpKSmlPvaVhbNzEhavOAx9/Tw8f14Dc0K8cO+umabD0jpCAfz9hSEsWuTCtH7++ZaVIIOOnoCeqerPQ30rgayEKtmYXamYWuZBtwaQFK/68f0soQYc3bKK2YqkIpMJDB93DX9HWuDOLZOXb1DN5LcMqTtmKf/fF7975HL5K/eoHD16FLa2trCwsMAbb7yBuXPnwsqq9D+gNZos5ebmonfv3nBycgIAeHh4AADmzp2LCRMmYMyYMcr127RpU6p6s7KyMH/+fBw6dAjt2rUDALi6uuLkyZP49ttvi0yWFixYgP79+2Ps2LEAgPr162PFihXo3LkzQkNDcffuXfz0008ICwtDt27dlHUWsLS0BADY2toWGjBev359LFq0SPl4+vTpcHR0xKpVqyCTydCoUSM8fPgQU6ZMwcyZM6Gjk/9l0qxZM8yaNUtZx6pVqxAeHl5ksrRt2zbEx8fjzz//VMbi5uamfP7u3buYNGkSGjVqpKyvJAsWLEBISEiJ61R29++bIGjEm6hZMwcdOt7HhEnnMHlCFyZMErs61wipN3XRfmuqpkMhqhCfTLoKJ9dUTPq4naZDqZSkHODt6OioUj5r1qxCvSyl8dZbb6F3795wcXFBTEwMPvvsM/j5+eHMmTPQ1S3d0ACNJUuenp7w9vaGh4cHfH194ePjg759+yInJwcPHz6Et7f3K9UbHR2NjIyMQklFdnY2WrRoUeQ2kZGRuHz5Mr7//ntlmRACCoUCsbGxuHLlCnR1dYtMtF6mVatWKo+vX7+Odu3aQSb792Ty8vJCWloa7t+/j7p16wLIT5b+y97eHk+ePClyH5cuXUKLFi2UidKLxo8fj6FDh2Lr1q3o1q0b3nvvPdSrV6/YmKdNm4bx48crH6ekpBQ6aSu73FxdxD3M/9UXfdMS9Rsm4l3/m1i1vLWGI9MeV+Ya4vExPbTfnApDu39bkeTWAoocGXJSZCqtS9lPZZBbK4qqisogJVEXebmAuY3q4GIL61w8i2c3Z3kaMfEqXuvwBFM+boenTwxfvgGp5d69ezA1NVU+ftVWpX79+in/7+HhgWbNmqFevXo4evRoqXMNjbWJ6+rqIiwsDH/88Qfc3d2xcuVKNGzYEI8fP1ar3rS0NADA77//jkuXLimXa9euFTtuKS0tDR9//LHK+pGRkbh58ybq1aun0q1WVjVr1nyl7V7sJpPJZMputRe9LL7g4GD8/fffePvtt3H48GG4u7vjl19+KXZ9uVwOU1NTlaWq05EBevr8opaCEPmJ0qNwfby+IRVGdVSPq1mTXMhqCCSc/feLOy1WB8/jdGHRnFcPqSs3Rwc3LxuhRYd/W/NkMoHmHdJw7S9OHVA+BEZMvIp2nR/hs5Gv43Ecj3NxhEQLgELfQ1Jd1OTq6gpra2tER0eXehuN/gyRyWTw8vKCl5cXZs6cCScnJ4SFhcHZ2Rnh4eHKgdNl4e7uDrlcjrt375a6Jahly5a4du2aStfVf3l4eEChUODYsWPKbrj/0tfPH+SXl5f30n01btwYO3fuhBBC2bp06tQpmJiYoE6dOqWK90XNmjXDunXrkJiYWGzrUoMGDdCgQQOMGzcOAQEB2LhxY4mD4aqygYMv4/yf9njyxAhGhjno8sZdeHg+wYxpnTQdmla4OscQD/bpo83KdNQwEsiM//8B3iYCugaAnglQt082ri0yhJ6ZQA1jgb/nG8GieS4sPF/+HqGX27XGGhOX3cM/kUaIumgE/2HxMDBS4OCPRb//ST2fTrqKzr4PMWdSazxP14WFZSYAID1dD9lZvMLzv6rCPEv379/H06dPYW9f+rn3NJYsRUREIDw8HD4+PrC1tUVERATi4+PRuHFjBAcHY8SIEbC1tYWfnx9SU1Nx6tQpjBo1qsi6vL294e/vj6CgIJiYmGDixIkYN24cFAoFOnTooBwgbmpqisDAwELbT5kyBa+//jqCgoIwdOhQ1KxZE9euXUNYWBhWrVoFZ2dnBAYGYvDgwcoB3nfu3MGTJ0/w/vvvw8nJCTKZDHv37kX37t1haGgIY+Oi55T59NNPsWzZMowaNQpBQUGIiorCrFmzMH78eOV4pbIKCAjA/Pnz0atXLyxYsAD29va4ePEiHBwc0Lx5c0yaNAl9+/aFi4sL7t+/jz///BN9+vR5pX1VBWbmWZgwOQKWlplIT9dDbKwZZkzrhIsX7DQdmla4s90AAHBmoOrgVs+56XD0z58ew31KBiAzxF9ja0KRkz8pZdPPeaWWVI7tsYCZVR4GTHoEC5tc3PrbENP7uyApofgLN+jVvd33LgDgi9VnVcqXzm6GQ79XrSEK2igtLU2llSg2NhaXLl2CpaUlLC0tERISgj59+sDOzg4xMTGYPHky3NzclFeol4bGkiVTU1McP34cy5YtQ0pKCpycnLB48WLlRIuZmZlYunQpJk6cCGtra/Tt27fYumJiYpCQkKB8PGfOHNjY2GDBggW4desWzM3N0bJlS3z22WdFbt+sWTMcO3YM06dPR8eOHSGEQL169fDBBx8o1wkNDcVnn32GTz/9FE+fPkXdunWV9dWuXRshISGYOnUqBg0ahAEDBmDTpk1F7qt27drYt28fJk2aBE9PT1haWmLIkCH4/PPPy3oIlfT19XHw4EFMmDAB3bt3R25uLtzd3fH1119DV1cXT58+xYABA/D48WNYW1ujd+/eVX4Ad0mWLyndxQD0anr8/eyl6+jKAY8Zz+Ex43kFRFQ97dlojT0brTUdRrXwdtu3NR1C1fHffjR16iiD8+fPq/REFYy5DQwMRGhoKC5fvozNmzcjKSkJDg4O8PHxwZw5c8rUrScTojLe35cqk5SUFJiZmcG78UTU0OVEmBXB76ezL1+JJLW3SdnmXSH11HB8tWEHVHa5iiwcuh+K5OTkchuDWvA94bppOnSMDNSqS5GRiVsD55VrvGXFSU+IiIiISsDrTImIiEgSr3K7kqLqqGyYLBEREZEkqsLVcK+CyRIRERFJQ8jyF3XrqGQ4ZomIiIioBGxZIiIiIklwzBIRERFRSTQwz1JFYDccERERUQlK1bK0Z8+eUlf4zjvvvHIwREREVHVV66vhevXqVarKZDJZqW4mS0RERFqqEnajqatUyZJCoSjvOIiIiIgqJbUGeGdmZsLAQL17wBAREZF20NZuuDIP8M7Ly8OcOXNQu3ZtGBsb49atWwCAGTNmYP369ZIHSERERFWEkGipZMqcLM2bNw+bNm3CokWLoK+vryxv2rQp1q1bJ2lwRERERJpW5mRpy5YtWLNmDfr37w9dXV1luaenJ27cuCFpcERERFSVyCRaKpcyj1l68OAB3NzcCpUrFArk5ORIEhQRERFVQZyUMp+7uztOnDhRqHzHjh1o0aKFJEERERFRFaSlY5bK3LI0c+ZMBAYG4sGDB1AoFNi1axeioqKwZcsW7N27tzxiJCIiItKYMrcsvfvuu/jtt99w6NAh1KxZEzNnzsT169fx22+/4c033yyPGImIiKgqEDJplkrmleZZ6tixI8LCwqSOhYiIiKowIfIXdeuobF55Usrz58/j+vXrAPLHMbVq1UqyoIiIiIgqizInS/fv30dAQABOnToFc3NzAEBSUhLat2+PH3/8EXXq1JE6RiIiIqoKeDVcvqFDhyInJwfXr19HYmIiEhMTcf36dSgUCgwdOrQ8YiQiIqKqgGOW8h07dgynT59Gw4YNlWUNGzbEypUr0bFjR0mDIyIiItK0MidLjo6ORU4+mZeXBwcHB0mCIiIioqpHJvIXdeuobMrcDffll19i1KhROH/+vLLs/PnzGDNmDL766itJgyMiIqIqpDpPSmlhYQGZ7N8+xPT0dLRt2xY1auRvnpubixo1amDw4MHo1atXuQRKREREpAmlSpaWLVtWzmEQERFRlSfFAO2qOsA7MDCwvOMgIiKiqk5Lpw545UkpASAzMxPZ2dkqZaampmoFRERERFWUliZLZR7gnZ6ejqCgINja2qJmzZqwsLBQWYiIiIi0SZmTpcmTJ+Pw4cMIDQ2FXC7HunXrEBISAgcHB2zZsqU8YiQiIqKqoDpfDfdfv/32G7Zs2YIuXbpg0KBB6NixI9zc3ODk5ITvv/8e/fv3L484iYiIqLLT0gHeZW5ZSkxMhKurK4D88UmJiYkAgA4dOuD48ePSRkdERESkYWVOllxdXREbGwsAaNSoEX766ScA+S1OBTfWJSIiouqnYAZvdZfKpszJ0qBBgxAZGQkAmDp1Kr7++msYGBhg3LhxmDRpkuQBEhERURXBMUv5xo0bp/x/t27dcOPGDfz1119wc3NDs2bNJA2OiIiISNPUmmcJAJycnODk5CRFLERERESVTqmSpRUrVpS6wtGjR79yMERERFR1yaD+mKPKdy1cKZOlpUuXlqoymUzGZEmL5V2/CZlMT9NhVAt/tGNrbUU78JBX81YkXwdNR1B95IocTYdQ5ZUqWSq4+o2IiIioWFo6z5LaY5aIiIiIAGjtveGYLBEREZE0tDRZKvM8S0RERETVCVuWiIiISBJSzMBdGWfwZrJERERE0mA33L9OnDiBDz/8EO3atcODBw8AAFu3bsXJkyclDY6IiIhI08qcLO3cuRO+vr4wNDTExYsXkZWVBQBITk7G/PnzJQ+QiIiIqggtvTdcmZOluXPnYvXq1Vi7di309P6doNDLywsXLlyQNDgiIiKqOgrGLKm7VDZlTpaioqLQqVOnQuVmZmZISkqSIiYiIiKiSqPMyZKdnR2io6MLlZ88eRKurq6SBEVERERVUMEM3uoulUyZk6Vhw4ZhzJgxiIiIgEwmw8OHD/H9999j4sSJ+OSTT8ojRiIiIqoKtHTMUpmnDpg6dSoUCgW8vb2RkZGBTp06QS6XY+LEiRg1alR5xEhERESkMWVOlmQyGaZPn45JkyYhOjoaaWlpcHd3h7GxcXnER0RERFUEJ6V8gb6+Ptzd3aWMhYiIiKoyLZ2UsszJUteuXSGTFT/46vDhw2oFRERERFWUFJf+a0Oy1Lx5c5XHOTk5uHTpEq5evYrAwECp4iIiIiKqFMqcLC1durTI8uDgYKSlpakdEBEREVVRWtoN90r3hivKhx9+iA0bNkhVHREREVU1Wjp1gGTJ0pkzZ2BgYCBVdURERESVQpm74Xr37q3yWAiBuLg4nD9/HjNmzJAsMCIiIqpaOHXA/zMzM1N5rKOjg4YNG2L27Nnw8fGRLDAiIiKiyqBMyVJeXh4GDRoEDw8PWFhYlFdMRERERJVGmcYs6erqwsfHB0lJSeUUDhEREVVZHOCdr2nTprh161Z5xEJERERVWMGYJXWXyqbMydLcuXMxceJE7N27F3FxcUhJSVFZiIiIiLRJqZOl2bNnIz09Hd27d0dkZCTeeecd1KlTBxYWFrCwsIC5uTnHMREREVV3FdwFd/z4cfTs2RMODg6QyWTYvXu3ajhCYObMmbC3t4ehoSG6deuGmzdvlmkfpR7gHRISghEjRuDIkSNl2gERERFVExqYwTs9PR2enp4YPHhwoemNAGDRokVYsWIFNm/eDBcXF8yYMQO+vr64du1aqeeHLHWyJER+9J07dy7tJkRERFSNaGKeJT8/P/j5+RX5nBACy5Ytw+eff453330XALBlyxbUqlULu3fvRr9+/Uq1jzKNWZLJZGVZnYiIiOiVvDgmOisrq8x1xMbG4tGjR+jWrZuyzMzMDG3btsWZM2dKXU+Z5llq0KDBSxOmxMTEslRJRERE2kLCbjhHR0eV4lmzZiE4OLhMVT169AgAUKtWLZXyWrVqKZ8rjTIlSyEhIYVm8CYiIiICpO2Gu3fvHkxNTZXlcrlcvYrVUKZkqV+/frC1tS2vWIiIiIgAAKampirJ0quws7MDADx+/Bj29vbK8sePH6N58+alrqfUY5Y4XomIiIhKVMlm8HZxcYGdnR3Cw8OVZSkpKYiIiEC7du1KXU+Zr4YjIiIiKpIGpg5IS0tDdHS08nFsbCwuXboES0tL1K1bF2PHjsXcuXNRv3595dQBDg4O6NWrV6n3UepkSaFQlCl4IiIiovJ2/vx5dO3aVfl4/PjxAIDAwEBs2rQJkydPRnp6OoYPH46kpCR06NAB+/fvL/UcS0AZxywRERERFUcT8yx16dKlxN4vmUyG2bNnY/bs2a8cE5MlIiIikoYGuuEqQplvpEtERERUnbBliYiIiKShpS1LTJZIK/UcmIC+nzyBpU0ubl0zxDef10bUJSNNh6WVmrZORp8h9+HWJA1WttmYM7IxzoRbazosrfHjSluc2meOe9Fy6Bso4N46A0OmP4SjW+FbPwgBfP6hK84fMcWs9bFo75esgYi1Fz9XXk4TY5YqArvhKohMJsPu3bs1HQaA/MFwY8eO1XQY5abzO88wfNZDfL/EDiN9G+DWNQPM23YLZlY5mg5NKxkY5iH2Rk18M7uepkPRSpfPGKPnwAQs23sTC36MQV4u8FlAPWRmFP74/mWtDTglXvng50opVbJ5lqTCZKmCxMXFFXtXZJJW7+EJ2L/NEge3W+LuTQOsmFIHWc9l8A3gfQvLw/kTltiy3BlnDrE1qTzM33YLPh8kwrlhJuo1ycSEZXfx5IE+bl42VFkv5qohdn5rg/FL7mooUu3Gz5XqjclSBbGzs9PofW2qixp6CtRvloELJ0yUZULIcPGECdxbZWgwMiJppKfoAgBMzPOUZZkZMiwc6YSR8+7D0jZXU6FpLX6ulF5BN5y6S2XDZKmMduzYAQ8PDxgaGsLKygrdunVDeno6AGDDhg1o0qQJ5HI57O3tERQUpNzuZd1wXbp0wahRozB27FhYWFigVq1aWLt2LdLT0zFo0CCYmJjAzc0Nf/zxh8p2V69ehZ+fH4yNjVGrVi189NFHSEhIUD6fnp6OAQMGwNjYGPb29li8eLG0B6SSMbXMg24NICledTjes4QasLDhlwhVbQoFsHpWbTRpkwbnRpnK8m+Da8O9dTrav5Wiwei0Fz9XyoDdcBQXF4eAgAAMHjwY169fx9GjR9G7d28IIRAaGoqRI0di+PDhuHLlCvbs2QM3N7cy1b9582ZYW1vj3LlzGDVqFD755BO89957aN++PS5cuAAfHx989NFHyMjI/yWTlJSEN954Ay1atMD58+exf/9+PH78GO+//76yzkmTJuHYsWP49ddfcfDgQRw9ehQXLlwoMY6srCykpKSoLESkeas+q4M7NwwxLfSOsuzMAVNcOmWCEbMfaDAyIu3Gq+HKIC4uDrm5uejduzecnJwAAB4eHgCAuXPnYsKECRgzZoxy/TZt2pSpfk9PT3z++ecAgGnTpmHhwoWwtrbGsGHDAAAzZ85EaGgoLl++jNdffx2rVq1CixYtMH/+fGUdGzZsgKOjI/755x84ODhg/fr1+O677+Dt7Q0gPyGrU6dOiXEsWLAAISEhZYq9skhJ1EVeLmD+wq89C+tcPIvn6U5V16rPaiMizBSLf4mGjcO/g4ovnTJB3G199G7kobL+nGHOaNo2HV/ujH6xKiojfq6UAacOIE9PT3h7e8PDwwO+vr7w8fFB3759kZOTg4cPHyoTklfVrFkz5f91dXVhZWWlTMYAoFatWgCAJ0+eAAAiIyNx5MgRGBsbF6orJiYGz58/R3Z2Ntq2basst7S0RMOGDUuMY9q0acp76wD5d2h2dHR8tRdVwXJzdHDzshFadEjFmf1mAACZTKB5hzTs2WSl4eiIyk4I4OvptXF6vxm+3BENu7rZKs9/EPQYfv97qlL28RuN8HHwA7zuw1ZhKfBzpfRk/7+oW0dlw2SpDHR1dREWFobTp0/j4MGDWLlyJaZPn47w8HBJ6tfT01N5LJPJVMpk/39NcMFNjdPS0tCzZ0988cUXheqyt7dXuQtzWcjl8io9GH3XGmtMXHYP/0QaIeqiEfyHxcPASIGDP1pqOjStZGCUB4e6z5WPa9XJgmujNKQm10B8XOlvVElFW/VZHRz5xQLBG2/B0FiBxCf5H9s1TfIgNxSwtM0tclC3be2cQokVvTp+rlRvTJbKSCaTwcvLC15eXpg5cyacnJwQFhYGZ2dnhIeHq9z5uLy1bNkSO3fuhLOzM2rUKPynrFevHvT09BAREYG6desCAJ49e4Z//vkHnTt3rrA4K9qxPRYws8rDgEmPYGGTi1t/G2J6fxckJei9fGMqs/pNU/HFlivKx8On3QIAhP1ii6XTSm7FpJfbuzl/SoZJfeqrlE9Yehc+H/Cy9YrCz5VSYjccRUREIDw8HD4+PrC1tUVERATi4+PRuHFjBAcHY8SIEbC1tYWfnx9SU1Nx6tQpjBo1qsi6vL294e/vr3LFXFmNHDkSa9euRUBAACZPngxLS0tER0fjxx9/xLp162BsbIwhQ4Zg0qRJsLKygq2tLaZPnw4dHe0f179nozX2bOS8PxXhyjlzdG/UUdNhaK0DDy9VyDb0cvxceTltncGbyVIZmJqa4vjx41i2bBlSUlLg5OSExYsXKyebzMzMxNKlSzFx4kRYW1ujb9++xdYVExOjcon/q3BwcMCpU6cwZcoU+Pj4ICsrC05OTnjrrbeUCdGXX36p7K4zMTHBhAkTkJzMWyAQERGVlkwIUQlzOKpMUlJSYGZmhi54FzVkbHKuCLqmppoOodrZd+O4pkOoVnwdmms6hGojV+TgKH5FcnIyTMvps6Xge6LJx/OhK1dvrGJeVib+/vazco23rNiyRERERNLRwiYYJktEREQkCW0ds6T9I32JiIiI1MCWJSIiIpIGpw4gIiIiKh674YiIiIiqIbYsERERkTTYDUdERERUPHbDEREREVVDbFkiIiIiabAbjoiIiKgEWpossRuOiIiIqARsWSIiIiJJaOsAbyZLREREJA0t7YZjskRERESSkAkBmVAv21F3+/LAMUtEREREJWDLEhEREUmD3XBERERExdPWAd7shiMiIiIqAVuWiIiISBrshiMiIiIqHrvhiIiIiKohtiwRERGRNNgNR0RERFQ8dsMRERERVUNsWSIiIiJpsBuOiIiIqGSVsRtNXUyWiIiISBpC5C/q1lHJcMwSERERUQnYskRERESS0Nar4ZgsERERkTS0dIA3u+GIiIiISsCWJSIiIpKETJG/qFtHZcNkiYiIiKTBbjgiIiKi6octS0RERCQJXg1HREREVBJOSklERERU/bBliUpN19QEujJ9TYdRLeSlpGg6hGrH16G5pkOoVg48vKTpEKqNlFQFLBpUzL7YDUdERERUEi29Go7JEhEREUlCW1uWOGaJiIiIqARsWSIiIiJpaOnVcEyWiIiISBLshiMiIiKqhtiyRERERNLg1XBERERExWM3HBEREVE1xJYlIiIikoZC5C/q1lHJMFkiIiIiaWjpmCV2wxEREZEkZPh33NIrL2XYX3BwMGQymcrSqFEjyV8XW5aIiIioymrSpAkOHTqkfFyjhvSpDZMlIiIikoYGZvCuUaMG7Ozs1NvnS7AbjoiIiCShdhfcf6YeSElJUVmysrKK3OfNmzfh4OAAV1dX9O/fH3fv3pX8dTFZIiIiokrH0dERZmZmymXBggWF1mnbti02bdqE/fv3IzQ0FLGxsejYsSNSU1MljYXdcERERCQNCa+Gu3fvHkxNTZXFcrm80Kp+fn7K/zdr1gxt27aFk5MTfvrpJwwZMkTNQP7FZImIiIgkIRMCMjXHLBVsb2pqqpIslYa5uTkaNGiA6OhotWJ4EbvhiIiISCukpaUhJiYG9vb2ktbLZImIiIikoZBoKaWJEyfi2LFjuH37Nk6fPg1/f3/o6uoiICBAspcEsBuOiIiIJCJlN1xp3L9/HwEBAXj69ClsbGzQoUMHnD17FjY2NmrF8CImS0RERFQl/fjjjxWyHyZLREREJA0tvTcckyUiIiKShgZm8K4ITJaIiIhIEv+dgVudOiobXg1HREREVAK2LBEREZE02A1HREREVDyZIn9Rt47Kht1wRERERCVgyxIRERFJg91wRERERCXQ0nmW2A1HREREVAK2LBEREZEkKvrecBWFyRIRERFJQ0vHLLEbjoiIiKgEbFkiIiIiaQgA6s6TVPkalpgsERERkTQ4ZomIiIioJAISjFmSJBJJccwSERERUQnYskRERETS0NKr4ZgsERERkTQUAGQS1FHJsBtOTTKZDLt379Z0GPQfTVsnY1bo39h6PAL7bpxAO+8ETYdULfQcmIDNEdfw263LWL73Jho2z9B0SFqNx7t8/LjSFqP8GqBXfQ+879EEwYNccC9aXuS6QgDT+7vC16E5Tv9hVsGRUkVisqSmuLg4+Pn5aToM+g8DwzzE3qiJb2bX03Qo1Ubnd55h+KyH+H6JHUb6NsCtawaYt+0WzKxyNB2aVuLxLj+Xzxij58AELNt7Ewt+jEFeLvBZQD1kZhT+uvxlrQ1k6raiaJmCq+HUXSobJktqsrOzg1xe9K8O0ozzJyyxZbkzzhyy1nQo1Ubv4QnYv80SB7db4u5NA6yYUgdZz2XwDUjUdGhaice7/Mzfdgs+HyTCuWEm6jXJxIRld/HkgT5uXjZUWS/mqiF2fmuD8UvuaijSSqpgzJK6SyXDZOn/7dixAx4eHjA0NISVlRW6deuG9PR0AMCGDRvQpEkTyOVy2NvbIygoSLndy7rhSqp34MCB6NWrF0JCQmBjYwNTU1OMGDEC2dnZyu3379+PDh06wNzcHFZWVujRowdiYmJU9nH//n0EBATA0tISNWvWROvWrREREaF8/tdff0XLli1hYGAAV1dXhISEIDc3V4rDRoQaegrUb5aBCydMlGVCyHDxhAncW7FrSGo83hUrPUUXAGBinqcsy8yQYeFIJ4ycdx+WtvwsrQ44wBv5XWkBAQFYtGgR/P39kZqaihMnTkAIgdDQUIwfPx4LFy6En58fkpOTcerUKbXrLRAeHg4DAwMcPXoUt2/fxqBBg2BlZYV58+YBANLT0zF+/Hg0a9YMaWlpmDlzJvz9/XHp0iXo6OggLS0NnTt3Ru3atbFnzx7Y2dnhwoULUCjyR8idOHECAwYMwIoVK9CxY0fExMRg+PDhAIBZs2YVGXdWVhaysrKUj1NSUl7puFL1YGqZB90aQFK86sfJs4QacHTLKmYrelU83hVHoQBWz6qNJm3S4NwoU1n+bXBtuLdOR/u3+NlYCK+G015xcXHIzc1F79694eTkBADw8PAAAMydOxcTJkzAmDFjlOu3adNG7XoL6OvrY8OGDTAyMkKTJk0we/ZsTJo0CXPmzIGOjg769Omjsv6GDRtgY2ODa9euoWnTpti2bRvi4+Px559/wtLSEgDg5uamXD8kJARTp05FYGAgAMDV1RVz5szB5MmTi02WFixYgJCQkFK9RiIibbXqszq4c8MQi3ffVJadOWCKS6dM8M3BKA1GVolpabLEbjgAnp6e8Pb2hoeHB9577z2sXbsWz549w5MnT/Dw4UN4e3tLWu+L6xgZGSkft2vXDmlpabh37x4A4ObNmwgICICrqytMTU3h7OwMALh7N7+f/NKlS2jRooUyUXpRZGQkZs+eDWNjY+UybNgwxMXFISOj6Cb7adOmITk5WbkUxEJUlJREXeTlAuY2qt0RFta5eBbP32NS4/GuGKs+q42IMFMs2hENG4d/B85fOmWCuNv66N3IA36OnvBz9AQAzBnmjEl93Iqrjqo4JksAdHV1ERYWhj/++APu7u5YuXIlGjZsiMePH5dLvbGxsaWuo2fPnkhMTMTatWsRERGhHItUMK7J0NCwpM2RlpaGkJAQXLp0SblcuXIFN2/ehIGBQZHbyOVymJqaqixExcnN0cHNy0Zo0SFVWSaTCTTvkIZrfxmVsCW9Ch7v8iVEfqJ0er8ZFv0cDbu62SrPfxD0GKvDoxAa9u8CAB8HP8CEpRzsDYVESyXDnyH/TyaTwcvLC15eXpg5cyacnJwQFhYGZ2dnhIeHo2vXrpLV+8svv2D8+PEA8lt+nj9/rkx6zp49C2NjYzg6OuLp06eIiorC2rVr0bFjRwDAyZMnVepv1qwZ1q1bh8TExCJbl1q2bImoqCiVrjltZ2CUB4e6z5WPa9XJgmujNKQm10B8XNEJIqln1xprTFx2D/9EGiHqohH8h8XDwEiBgz8W3eJJ6uHxLj+rPquDI79YIHjjLRgaK5D4JP9rsqZJHuSGApa2uUUO6ratnVMosaqOeCNdLRYREYHw8HD4+PjA1tYWERERiI+PR+PGjREcHIwRI0bA1tYWfn5+SE1NxalTpzBq1Kgi6/L29oa/vz+CgoJKrLdAdnY2hgwZgs8//xy3b9/GrFmzEBQUBB0dHVhYWMDKygpr1qyBvb097t69i6lTp6rsLyAgAPPnz0evXr2wYMEC2Nvb4+LFi3BwcEC7du0wc+ZM9OjRA3Xr1kXfvn2ho6ODyMhIXL16FXPnzi3X46op9Zum4ostV5SPh0+7BQAI+8UWS6c11FRYWu3YHguYWeVhwKRHsLDJxa2/DTG9vwuSEvQ0HZpW4vEuP3s35085MqlPfZXyCUvvwucDTs3wUlo6ZonJEgBTU1McP34cy5YtQ0pKCpycnLB48WLlZJOZmZlYunQpJk6cCGtra/Tt27fYumJiYpCQkFCqeoH85Kp+/fro1KkTsrKyEBAQgODgYACAjo4OfvzxR4wePRpNmzZFw4YNsWLFCnTp0kW5vb6+Pg4ePIgJEyage/fuyM3Nhbu7O77++msAgK+vL/bu3YvZs2fjiy++gJ6eHho1aoShQ4dKfBQrjyvnzNG9UUdNh1Ht7NlojT0bObdVReHxLh8HHl6qkG2oapEJUQlTuGpi4MCBSEpKqvS3S0lJSYGZmRm8TT9EDZm+psOpFvI4XQNpOSYYFSclVQGLBreQnJxcbmNQC74nutUbixq66k3UnJuXhUMxy8o13rJiyxIRERFJQ0u74Xg1HBEREVEJ2LKkQZs2bdJ0CERERBKS4t5ula9lickSERERSYPdcERERETVD1uWiIiISBoKAbW70RSVr2WJyRIRERFJQyjyF3XrqGTYDUdERERUArYsERERkTS0dIA3kyUiIiKSBscsEREREZVAS1uWOGaJiIiIqARsWSIiIiJpCEjQsiRJJJJiskRERETSYDccERERUfXDliUiIiKShkIBQM1JJRWVb1JKJktEREQkDXbDEREREVU/bFkiIiIiaWhpyxKTJSIiIpKGls7gzW44IiIiohKwZYmIiIgkIYQCQqh3NZu625cHJktEREQkDSHU70bjmCUiIiLSWkKCMUuVMFnimCUiIiKiErBliYiIiKShUAAyNcccccwSERERaS12wxERERFVP2xZIiIiIkkIhQJCzW44Th1ARERE2ovdcERERETVD1uWiIiISBoKAci0r2WJyRIRERFJQwgA6k4dUPmSJXbDEREREZWALUtEREQkCaEQEGp2wwm2LBEREZHWEgppljL6+uuv4ezsDAMDA7Rt2xbnzp2T9GUxWSIiIiJJCIWQZCmL7du3Y/z48Zg1axYuXLgAT09P+Pr64smTJ5K9LiZLREREVGUtWbIEw4YNw6BBg+Du7o7Vq1fDyMgIGzZskGwfHLNEL1XQf5wrsjUcSfWRJ3I0HQJRuUpJrXyzNGurlLT8Y10RY4FyRZbaN8LNRf7nX0pKikq5XC6HXC5XKcvOzsZff/2FadOmKct0dHTQrVs3nDlzRq04/ovJEr1UamoqAOBY6k8ajoSItIVFA01HUP2kpqbCzMysXOrW19eHnZ0dTj7aJ0l9xsbGcHR0VCmbNWsWgoODVcoSEhKQl5eHWrVqqZTXqlULN27ckCQWgMkSlYKDgwPu3bsHExMTyGQyTYdTaikpKXB0dMS9e/dgamqq6XC0Ho93xeMxr1hV9XgLIZCamgoHB4dy24eBgQFiY2ORnS1ND4QQotD3zYutShWJyRK9lI6ODurUqaPpMF6Zqalplfpgq+p4vCsej3nFqorHu7xalP7LwMAABgYG5b6f/7K2toauri4eP36sUv748WPY2dlJth8O8CYiIqIqSV9fH61atUJ4eLiyTKFQIDw8HO3atZNsP2xZIiIioipr/PjxCAwMROvWrfHaa69h2bJlSE9Px6BBgyTbB5Ml0lpyuRyzZs3SaD93dcLjXfF4zCsWj3fl9MEHHyA+Ph4zZ87Eo0eP0Lx5c+zfv7/QoG91yERlnFeciIiIqJLgmCUiIiKiEjBZIiIiIioBkyUiIiKiEjBZIiIiIioBkyUiIiKiEjBZIiIiKoXMzExNh0AawmSJtMrgwYOVN/79r/T0dAwePFgDEWm3Cxcu4MqVK8rHv/76K3r16oXPPvtMsntEEWmSQqHAnDlzULt2bRgbG+PWrVsAgBkzZmD9+vUajo4qCudZIq2iq6uLuLg42NraqpQnJCTAzs4Oubm5GopMO7Vp0wZTp05Fnz59cOvWLTRp0gT+/v74888/8fbbb2PZsmWaDrHKGz9+fKnXXbJkSTlGUj3Nnj0bmzdvxuzZszFs2DBcvXoVrq6u2L59O5YtW4YzZ85oOkSqAJzBm7RCSkoKhBDKu2v/92aOeXl52LdvX6EEitT3zz//oHnz5gCAn3/+GZ06dcK2bdtw6tQp9OvXj8mSBC5evKjy+MKFC8jNzUXDhg0B5P8NdHV10apVK02Ep/W2bNmCNWvWwNvbGyNGjFCWe3p64saNGxqMjCoSkyXSCubm5pDJZJDJZGjQoEGh52UyGUJCQjQQmXYTQkChUAAADh06hB49egAAHB0dkZCQoMnQtMaRI0eU/1+yZAlMTEywefNmWFhYAACePXuGQYMGoWPHjpoKUas9ePAAbm5uhcoVCgVycnI0EBFpApMl0gpHjhyBEAJvvPEGdu7cCUtLS+Vz+vr6cHJygoODgwYj1E6tW7fG3Llz0a1bNxw7dgyhoaEAgNjYWEnvy0T5Fi9ejIMHDyoTJQCwsLDA3Llz4ePjgwkTJmgwOu3k7u6OEydOwMnJSaV8x44daNGihYaioorGZIm0QufOnQHkf0k7OjpCR4fXLlSEZcuWoX///ti9ezemT5+u/AW+Y8cOtG/fXsPRaZ+UlBTEx8cXKo+Pjy/ywgZS38yZMxEYGIgHDx5AoVBg165diIqKwpYtW7B3715Nh0cVhAO8Ses8e/YM69evx/Xr1wHk/zIcNGiQSmsTla/MzEzo6upCT09P06FolQEDBuDEiRNYvHgxXnvtNQBAREQEJk2ahI4dO2Lz5s0ajlA7nThxArNnz0ZkZCTS0tLQsmVLzJw5Ez4+PpoOjSoIkyXSKsePH0fPnj1hZmaG1q1bAwD++usvJCUl4bfffkOnTp00HKF2+fPPP6FQKNC2bVuV8oiICOjq6ir/BiSNjIwMTJw4ERs2bFCOl6lRowaGDBmCL7/8EjVr1tRwhETaickSaRUPDw+0a9cOoaGh0NXVBZB/Ndynn36K06dPq8wJROp77bXXMHnyZPTt21elfNeuXfjiiy8QERGhoci0W3p6OmJiYgAA9erVY5JUjviDgABOSklaJjo6GhMmTFAmSkD+3Evjx49HdHS0BiPTTteuXUPLli0Llbdo0QLXrl3TQETVQ1xcHOLi4lC/fn3UrFkT/M1bfkaOHIl79+4VKn/w4AFGjhypgYhIE5gskVZp2bKlcqzSf12/fh2enp4aiEi7yeVyPH78uFB5XFwcatTg9SNSe/r0Kby9vdGgQQN0794dcXFxAIAhQ4bwSrhywh8EBDBZIi0zevRojBkzBl999RVOnjyJkydP4quvvsK4ceMwbtw4XL58WbmQ+nx8fDBt2jQkJycry5KSkvDZZ5/hzTff1GBk2mncuHHQ09PD3bt3YWRkpCz/4IMPsH//fg1Gpr34g4AAjlkiLfOyKQNkMhmEEJDJZMjLy6ugqLTXgwcP0KlTJzx9+lQ558ylS5dQq1YthIWFwdHRUcMRahc7OzscOHAAnp6eMDExQWRkJFxdXXHr1i00a9YMaWlpmg5R6wQEBCAuLg6//vorzMzMAOT/IOjVqxdsbW3x008/aThCqghMi0mrxMbGajqEaqV27dq4fPkyvv/+e0RGRsLQ0BCDBg1CQEAApw0oB+np6SotSgUSExMhl8s1EJH2++qrr9CpUyc4OTkV+kGwdetWDUdHFYUtS6RV0tPTeWUQaa3u3bujVatWmDNnDkxMTHD58mU4OTmhX79+UCgU2LFjh6ZD1Erp6ekqPwiaNWvGHwTVDJMl0irGxsZ4//33MXjwYHTo0EHT4WilPXv2wM/PD3p6etizZ0+J677zzjsVFFX1cPXqVXh7e6Nly5Y4fPgw3nnnHfz9999ITEzEqVOnUK9ePU2HSKSVmCyRVtm9ezc2bdqEffv2wdnZGYMHD8aAAQN4XzgJ6ejo4NGjR7C1tS1xjBjHhZWP5ORkrFq1SmU26ZEjR8Le3l7ToWm1a9eu4e7du8jOzlYp5w+C6oHJEmml+Ph4bN26FZs2bcL169fh6+uLwYMH45133uEVLERUardu3YK/vz+uXLmivEAEyP8xAIA/CKoJJkuk9VauXIlJkyYhOzsb1tbWGDFiBKZOnVrkQFmiyi4zMxOXL1/GkydPoFAoVJ5jK4f0evbsCV1dXaxbtw4uLi44d+4cnj59igkTJuCrr75Cx44dNR0iVQAmS6SVHj9+jM2bN2PTpk24c+cO/P39MWTIENy/fx9ffPEFHBwccPDgQU2HqRXCw8MRHh5e5Jf3hg0bNBSVdtq/fz8GDBiAhISEQs+x27N8WFtb4/Dhw2jWrBnMzMxw7tw5NGzYEIcPH8aECRNw8eJFTYdIFYD9EaRVdu3ahY0bN+LAgQNwd3fHp59+ig8//BDm5ubKddq3b4/GjRtrLkgtEhISgtmzZ6N169awt7dXdk1Q+Rg1ahTee+89zJw5E7Vq1dJ0ONVCXl4eTExMAOQnTg8fPkTDhg3h5OSEqKgoDUdHFYXJEmmVgjl+Tp06hTZt2hS5joODA6ZPn17BkWmn1atXY9OmTfjoo480HUq18PjxY4wfP56JUgVq2rQpIiMj4eLigrZt22LRokXQ19fHmjVr4OrqqunwqIKwG460Rm5uLtasWYM+ffrwy6SCWFlZ4dy5c7xkvYIMHjwYXl5eGDJkiKZDqTYOHDiA9PR09O7dG9HR0ejRowf++ecfWFlZYfv27XjjjTc0HSJVACZLpFWMjIxw/fp1ODk5aTqUamHKlCkwNjbGjBkzNB1KtZCRkYH33nsPNjY28PDwKDQp4ujRozUUWfWSmJgICwsLdjtXI+yGI63y2muv4eLFi0yWKkhmZibWrFmDQ4cOoVmzZoW+vJcsWaKhyLTTDz/8gIMHD8LAwABHjx5V+bKWyWRMlsrZvXv3AID3PKyG2LJEWuWnn37CtGnTMG7cOLRq1arQrU+aNWumoci0U9euXYt9TiaT4fDhwxUYjfazs7PD6NGjMXXq1JfeNJqkkZubi5CQEKxYsUJ5o2JjY2OMGjUKs2bN4i1PqgkmS6RVivoCKZhIjpdWU1VnaWmJP//8k2PEKtAnn3yCXbt2Yfbs2WjXrh0A4MyZMwgODkavXr0QGhqq4QipIjBZIq1y586dEp9n91z5uX//PgCgTp06Go5Ee40bNw42Njb47LPPNB1KtWFmZoYff/wRfn5+KuX79u1DQEAAkpOTNRQZVSSOWSKtwmSoYikUCsydOxeLFy9WdlGYmJhgwoQJmD59OruKJJaXl4dFixbhwIEDHCNWQeRyOZydnQuVu7i4QF9fv+IDIo1gskRaZcGCBahVqxYGDx6sUr5hwwbEx8djypQpGopMO02fPh3r16/HwoUL4eXlBQA4efIkgoODkZmZiXnz5mk4Qu1y5coVtGjRAgBw9epVled4ZVb5CAoKwpw5c7Bx40bI5XIAQFZWFubNm4egoCANR0cVhd1wpFWcnZ2xbds2tG/fXqU8IiIC/fr1Q2xsrIYi004ODg5YvXp1oXuS/frrr/j000/x4MEDDUVGJA1/f3+Eh4dDLpfD09MTABAZGYns7Gx4e3urrLtr1y5NhEgVgC1LpFUePXoEe3v7QuU2NjaIi4vTQETaLTExEY0aNSpU3qhRIyQmJmogIiJpmZubo0+fPiplnDqg+mGyRFrF0dERp06dgouLi0r5qVOn4ODgoKGotJenpydWrVqFFStWqJSvWrVK+Sucyt8333yDhIQEzJw5U9OhaJ2NGzdqOgSqBJgskVYZNmwYxo4di5ycHOVtCMLDwzF58mRMmDBBw9Fpn0WLFuHtt9/GoUOHVC6rvnfvHvbt26fh6KqPnTt3IjY2lslSOXj+/DmEEDAyMgKQf8XtL7/8And3d/j4+Gg4OqooHLNEWkUIgalTp2LFihXIzs4GABgYGGDKlCn8IiknDx8+xNdff40bN24AABo3boxPP/2ULXmkFXx8fNC7d2+MGDECSUlJaNiwIfT19ZGQkIAlS5bgk08+0XSIVAGYLJFWSktLw/Xr12FoaIj69esrr2Ih6eTk5OCtt97C6tWrUb9+fU2HQ1QurK2tcezYMTRp0gTr1q3DypUrcfHiRezcuRMzZ87E9evXNR0iVQB2w5FWMjY2Rps2bTQdhlbT09PD5cuXNR2G1tuzZw/8/Pygp6eHPXv2lLjui1clkvoyMjJgYmICADh48CB69+4NHR0dvP766y+dBJe0B1uWqFrgANjyMW7cOMjlcixcuFDToWgtHR0dPHr0CLa2tiVO8snb+ZSPZs2aYejQofD390fTpk2xf/9+tGvXDn/99RfefvttPHr0SNMhUgVgskTVgre3N2JjY3Hr1i1Nh6JVRo0ahS1btqB+/fpF3riYM0pTVbdjxw7873//Q15eHry9vXHw4EEA+RPgHj9+HH/88YeGI6SKwGSJiF5Z165dS3z+yJEjFRQJUfl59OgR4uLi4OnpqWzdO3fuHExNTYucZ4y0D5MlIqIqYvTo0XBzc8Po0aNVyletWoXo6GgsW7ZMM4ERaTkmS1TlcQCs5gwePBjLly9XDoAtkJ6ejlGjRmHDhg0aikw71a5dG3v27EGrVq1Uyi9cuIB33nkH9+/f11Bk1Q/HQVYvTJaoyuMAWM3R1dVFXFwcbG1tVcoTEhJgZ2eH3NxcDUWmnQwMDHD16lW4ubmplEdHR6Np06bIzMzUUGTVD8dBVi+cOoCqPIVCUeT/qfykpKRACAEhBFJTU2FgYKB8Li8vD/v27SuUQJH63NzcsH///kJ3u//jjz/g6uqqoaiqp/DwcE2HQBWIyRIRlZm5uTlkMhlkMhkaNGhQ6HmZTIaQkBANRKbdxo8fj6CgIMTHx6vczuerr77C8uXLNRwdkfZiNxxpFQ6ArRjHjh2DEAJvvPEGdu7cCUtLS+Vz+vr6cHJy4u1OykloaCjmzZuHhw8fAgBcXFwwa9YsDBgwQMORaQ+Og6QXMVkircIBsBXrzp07qFu3LmQymaZDqRb+e1PX+Ph4PH78GGFhYXB3d4evr6+mw9MaHAdJLyr+LCCqgp4+fQozM7NC5aampkhISNBARNrt8OHD2LFjR6Hyn3/+GZs3b9ZARNrt3XffxZYtWwDk326mW7duWLJkCXr16oXQ0FANR6c9FAqFcsydQqEodmGiVH0wWSKtUjAA9kUcAFs+FixYAGtr60Lltra2mD9/vgYi0m4XLlxAx44dAeTPLF2rVi3cuXMHW7ZswYoVKzQcHZH24gBv0iocAFux7t69CxcXl0LlTk5OuHv3rgYi0m68qWvF4zhIAtiyRFpm8ODBWLx4MdavX4+uXbuia9eu+P7777F69WoMGzZM0+FpHVtbW1y+fLlQeWRkJKysrDQQkXZzc3PD7t27ce/ePRw4cAA+Pj4AgCdPnsDU1FTD0WmnnTt3wsvLq1B5+/bti+yCJu3EZIm0yvPnzxEYGIj79+/j8ePHuHz5MoKCglCrVi1Nh6aVAgICMHr0aBw5cgR5eXnIy8vD4cOHMWbMGPTr10/T4WmdmTNnYuLEiXB2dkbbtm3Rrl07APmtTC1atNBwdNqJ4yAJ4NVwpGV8fHzQu3dvjBgxAklJSWjUqBH09PSQkJCAJUuW4JNPPtF0iFolOzsbH330EX7++WfUqJHfq69QKDBgwACsXr0a+vr6Go5Q+/CmrhWradOmGDFiRKGJQFeuXInQ0FBcu3ZNQ5FRRWKyRFrF2toax44dQ5MmTbBu3TqsXLkSFy9exM6dOzFz5kxcv35d0yFqpX/++QeRkZEwNDSEh4cHnJycNB0SkSQ2bNiAoKAgTJo0qchxkOzerx44wJu0CgfAaoazszOEEKhXr56yhYlIGwwePBhZWVmYN28e5syZAyB/ItDVq1dzItBqhGOWSKtwAGzFysjIwJAhQ2BkZIQmTZoor4AbNWoUFi5cqOHoiNTHcZAEMFkiLcMBsBVr2rRpiIyMxNGjR1VuptutWzds375dg5ERSYMTgRLAZIm0TN++fXH37l2cP39eZXJKb29vLF26VIORaafdu3dj1apV6NChg8otT5o0aYKYmBgNRkYkDU4ESgDHLJEWsrOzg52dnUrZa6+9pqFotFt8fLzythD/lZ6ezvvFkVbgOEgC2LJERGpo3bo1fv/9d+XjggRp3bp1yi5QoqqM4yAJYMsSEalh/vz58PPzw7Vr15Cbm4vly5fj2rVrOH36NI4dO6bp8IjUNnPmTPzvf//DuHHj4O3tzXGQ1RTnWSIitcTExGDhwoWIjIxEWloaWrZsiSlTpsDDw0PToRFJghOBEpMlIiIiohKwG46IyiQlJaXU63JMBxFpA7YsEVGZ6OjovPRKNyEEZDIZ8vLyKigqIqLyw5YlIiqTI0eOaDoEIqIKxZYlIiIiohJwniUiUsuJEyfw4Ycfon379njw4AEAYOvWrTh58qSGIyMikgaTJSJ6ZTt37oSvry8MDQ1x4cIFZGVlAQCSk5Mxf/58DUdHRCQNJktE9Mrmzp2L1atXY+3atdDT01OWe3l54cKFCxqMjIhIOkyWiOiVRUVFoVOnToXKzczMkJSUVPEBERGVAyZLRPTK7OzsEB0dXaj85MmTcHV11UBERETSY7JERK9s2LBhGDNmDCIiIiCTyfDw4UN8//33mDhxIj755BNNh0dEJAnOs0REr2zq1KlQKBTw9vZGRkYGOnXqBLlcjokTJ2LUqFGaDo+ISBKcZ4mI1JadnY3o6GikpaXB3d0dxsbGmg6JiEgyTJaIiIiISsAxS0QkuW+++QazZ8/WdBhERJJgyxIRSc7b2xuxsbG4deuWpkMhIlIbkyUiIiKiErAbjoiIiKgEnDqAiMpkz5498PPzg56eHvbs2VPiuu+8804FRUVEVH7YDUdEZaKjo4NHjx7B1tYWOjrFN07LZDLk5eVVYGREROWDyRIRERFRCThmiYiIiKgETJaI6JWNHj0aK1asKFS+atUqjB07tuIDIiIqB0yWiOiV7dy5E15eXoXK27dvjx07dmggIiIi6TFZIqJX9vTpU5iZmRUqNzU1RUJCggYiIiKSHpMlInplbm5u2L9/f6HyP/74A66urhqIiIhIepxniYhe2fjx4xEUFIT4+Hi88cYbAIDw8HB89dVXWL58uYajIyKSBqcOICK1hIaGYt68eXj48CEAwMXFBbNmzcKAAQM0HBkRkTSYLBHRK3v+/DmEEDAyMkJ8fDweP36MsLAwuLu7w9fXV9PhERFJgmOWiOiVvfvuu9iyZQsAQE9PD926dcOSJUvQq1cvhIaGajg6IiJpMFkiold24cIFdOzYEQCwY8cO1KpVC3fu3MGWLVuKnH+JiKgqYrJERK8sIyMDJiYmAICDBw+id+/e0NHRweuvv447d+5oODoiImkwWSKiV+bm5obdu3fj3r17OHDgAHx8fAAAT548gampqYajIyKSBpMlInplM2fOxMSJE+Hs7Iy2bduiXbt2APJbmVq0aKHh6IiIpMGr4YhILY8ePUJcXBw8PT2ho5P/++vcuXMwNTVFo0aNNBwdEZH6mCwRERERlYDdcEREREQlYLJEREREVAImS0REREQlYLJEREREVAImS0RUJQwcOBC9evVSPu7SpQvGjh1b4XEcPXoUMpkMSUlJxa4jk8mwe/fuUtcZHByM5s2bqxXX7du3IZPJcOnSJbXqIaLCmCwR0SsbOHAgZDIZZDIZ9PX14ebmhtmzZyM3N7fc971r1y7MmTOnVOuWJsEhIipODU0HQERV21tvvYWNGzciKysL+/btw8iRI6Gnp4dp06YVWjc7Oxv6+vqS7NfS0lKSeoiIXoYtS0SkFrlcDjs7Ozg5OeGTTz5Bt27dsGfPHgD/dp3NmzcPDg4OaNiwIQDg3r17eP/992Fubg5LS0u8++67uH37trLOvLw8jB8/Hubm5rCyssLkyZPx4pRwL3bDZWVlYcqUKXB0dIRcLoebmxvWr1+P27dvo2vXrgAACwsLyGQyDBw4EACgUCiwYMECuLi4wNDQEJ6entixY4fKfvbt24cGDRrA0NAQXbt2VYmztKZMmYIGDRrAyMgIrq6umDFjBnJycgqt9+2338LR0RFGRkZ4//33kZycrPL8unXr0LhxYxgYGKBRo0b45ptvyhwLEZUdkyUikpShoSGys7OVj8PDwxEVFYWwsDDs3bsXOTk58PX1hYmJCU6cOIFTp07B2NgYb731lnK7xYsXY9OmTdiwYQNOnjyJxMRE/PLLLyXud8CAAfjhhx+wYsUKXL9+Hd9++y2MjY3h6OiInTt3AgCioqIQFxeH5cuXAwAWLFiALVu2YPXq1fj7778xbtw4fPjhhzh27BiA/KSud+/e6NmzJy5duoShQ4di6tSpZT4mJiYm2LRpE65du4bly5dj7dq1WLp0qco60dHR+Omnn/Dbb79h//79uHjxIj799FPl899//z1mzpyJefPm4fr165g/fz5mzJiBzZs3lzkeIiojQUT0igIDA8W7774rhBBCoVCIsLAwIZfLxcSJE5XP16pVS2RlZSm32bp1q2jYsKFQKBTKsqysLGFoaCgOHDgghBDC3t5eLFq0SPl8Tk6OqFOnjnJfQgjRuXNnMWbMGCGEEFFRUQKACAsLKzLOI0eOCADi2bNnyrLMzExhZGQkTp8+rbLukCFDREBAgBBCiGnTpgl3d3eV56dMmVKorhcBEL/88kuxz3/55ZeiVatWysezZs0Surq64v79+8qyP/74Q+jo6Ii4uDghhBD16tUT27ZtU6lnzpw5ol27dkIIIWJjYwUAcfHixWL3S0SvhmOWiEgte/fuhbGxMXJycqBQKPC///0PwcHByuc9PDxUxilFRkYiOjoaJiYmKvVkZmYiJiYGycnJiIuLQ9u2bZXP1ahRA61bty7UFVfg0qVL0NXVRefOnUsdd3R0NDIyMvDmm2+qlGdnZytvAnz9+nWVOAAobxZcFtu3b8eKFSsQExODtLQ05ObmwtTUVGWdunXronbt2ir7USgUiIqKgomJCWJiYjBkyBAMGzZMuU5ubi7MzMzKHA8RlQ2TJSJSS9euXREaGgp9fX04ODigRg3Vj5WaNWuqPE5LS0OrVq3w/fffF6rLxsbmlWIwNDQs8zZpaWkAgN9//10lSQHyx2FJ5cyZM+jfvz9CQkLg6+sLMzMz/Pjjj1i8eHGZY127dm2h5E1XV1eyWImoaEyWiEgtNWvWhJubW6nXb9myJbZv3w5bW9tCrSsF7O3tERERgU6dOgHIb0H566+/0LJlyyLX9/DwgEKhwLFjx9CtW7dCzxe0bOXl5SnL3N3dIZfLcffu3WJbpBo3bqwcrF7g7NmzL3+R/3H69Gk4OTlh+vTpyrI7d+4UWu/u3bt4+PAhHBwclPvR0dFBw4YNUatWLTg4OODWrVvo379/mfZPROrjAG8iqlD9+/eHtbU13n33XZw4cQKxsbE4evQoRo8ejfv37wMAxowZg4ULF2L37t24ceMGPv300xLnSHJ2dkZgYCAGDx6M3bt3K+v86aefAABOTk6QyWTYu3cv4uPjkZaWBhMTE0ycOBHjxo3D5s2bERMTgwsXLmDlypXKQdMjRozAzZs3MWnSJERFRWHbtm3YtGlTmV5v/fr1cffuXfz444+IiYnBihUrihysbmBggMDAQERGRuLEiRMYPXo03n//fdjZ2QEAQkJCsGDBAqxYsQL//PMPrly5go0bN2LJkiVlioeIyo7JEhFVKCMjIxw/fhx169ZF79690bhxYwwZMgSZmZnKlqYJEybgo48+QmBgINq1awcTExP4+/uXWG9oaCj69u2LTz/9FI0aNcKwYcOQnp4OAKhduzZCQkIwdepU1KpVC0FBQQCAOXPmYMaMGViwYAEaN26Mt956C7///jtcXFwA5I8j2rlzJ3bv3g1PT0+sXr0a8+fPL9PrfeeddzBu3DgEBQWhefPmOH36NGbMmFFoPTc3N/Tu3Rvdu3eHj48PmjVrpjI1wNChQ7Fu3Tps3LgRHh4e6Ny5MzZt2qSMlYjKj0wUN2KSiIiIiNiyRERERFQSJktEREREJWCyRERERFQCJktEREREJWCyRERERFQCJktEREREJWCyRERERFQCJktEREREJWCyRERERFQCJktEREREJWCyRERERFSC/wMLtLMwQrnz7gAAAABJRU5ErkJggg==",
-            "text/plain": [
-              "<Figure size 640x480 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "cm = skmetrics.confusion_matrix(y_val, y_hat)\n",
-        "disp = skmetrics.ConfusionMatrixDisplay(confusion_matrix=cm,\n",
-        "                              display_labels=labels_dict.keys())\n",
-        "disp.plot(xticks_rotation='vertical')\n",
-        "plt.title('Confusion matrix for newsgroup test dataset');\n",
-        "plt.grid(False)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "iLU1skB9L_67"
-      },
-      "source": [
-        "## Next steps\n",
-        "\n",
-        "You've now created your own text classifier using embeddings generated from the PaLM API! Try using your own textual data to train a model. One possible dataset could be the [Jigsaw Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data){:.external} to create your own toxicity classifier.\n",
-        "\n",
-        "To learn more about how you can use the embeddings, check out the examples available. To learn how to use other services in the PaLM API, visit the various quickstart guides:\n",
-        "\n",
-        "* [Chat quickstart](../tutorials/chat_quickstart.ipynb)\n",
-        "\n",
-        "* [Text generation quickstart](../tutorials/text_quickstart.ipynb)"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "name": "train_text_classifier_embeddings.ipynb",
-      "toc_visible": true
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
diff --git a/site/en/examples/vectordb_with_chroma.ipynb b/site/en/examples/vectordb_with_chroma.ipynb
deleted file mode 100644
index ceb77d252..000000000
--- a/site/en/examples/vectordb_with_chroma.ipynb
+++ /dev/null
@@ -1,669 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Tce3stUlHN0L"
-      },
-      "source": [
-        "##### Copyright 2023 Google LLC."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "tuOe1ymfHZPu"
-      },
-      "outputs": [],
-      "source": [
-        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-        "# you may not use this file except in compliance with the License.\n",
-        "# You may obtain a copy of the License at\n",
-        "#\n",
-        "# https://www.apache.org/licenses/LICENSE-2.0\n",
-        "#\n",
-        "# Unless required by applicable law or agreed to in writing, software\n",
-        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-        "# See the License for the specific language governing permissions and\n",
-        "# limitations under the License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "CsVPnR8VbXE6"
-      },
-      "source": [
-        "# VectorDB with Chroma"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "awKO767lQIWh"
-      },
-      "source": [
-        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
-        "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://developers.generativeai.google/examples/vectordb_with_chroma\"><img src=\"https://developers.generativeai.google/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Generative AI</a>\n",
-        "  </td>\n",
-        "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/examples/vectordb_with_chroma.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
-        "  </td>\n",
-        "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/examples/vectordb_with_chroma.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
-        "  </td>\n",
-        "</table>\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "YtwZ8DZGJfUv"
-      },
-      "source": [
-        "## Overview\n",
-        " \n",
-        "This tutorial demonstrates how to use the PaLM API to create a vector database and retrieve answers to questions from the database. Moreover, you will use [ChromaDB](https://docs.trychroma.com/){:.external}, an open-source Python tool that creates embedding databases. ChromaDB allows you to:\n",
-        "\n",
-        "* Store embeddings as well as their metadata\n",
-        "* Embed documents and queries\n",
-        "* Search through the database of embeddings\n",
-        "\n",
-        "In this tutorial, you'll use embeddings to retrieve an answer from a database of vectors created with ChromaDB."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "akuOzK4dJl3j"
-      },
-      "source": [
-        "## Setup\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "L47er-HZN5NI"
-      },
-      "source": [
-        "First, download and install ChromaDB and the PaLM API Python library."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "JbXe7Oodc5dP"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -q chromadb\n",
-        "!pip install -q google-generativeai"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "tP4-QQ39Kqen"
-      },
-      "outputs": [],
-      "source": [
-        "import google.generativeai as palm\n",
-        "\n",
-        "import pandas as pd\n",
-        "\n",
-        "import chromadb\n",
-        "from chromadb.api.types import Documents, Embeddings"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "U6tZGHUDOCFW"
-      },
-      "source": [
-        "### Grab an API Key\n",
-        "\n",
-        "To get started, you'll need to [create an API key](https://developers.generativeai.google/tutorials/setup)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "muuhsDmmKdHi"
-      },
-      "outputs": [],
-      "source": [
-        "palm.configure(api_key='PALM_KEY')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "fegnGFpMS4AI"
-      },
-      "source": [
-        "Key Point: Next, you will choose a model. Any embedding model will work for this tutorial, but for real applications it's important to choose a specific model and stick with it. The outputs of different models are not compatible with each other.\n",
-        "\n",
-        "**Note**: At this time, the PaLM API is [only available in certain regions](https://developers.generativeai.google/available_regions)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Km5d13_FS2Q_"
-      },
-      "outputs": [],
-      "source": [
-        "models = [m for m in palm.list_models() if 'embedText' in m.supported_generation_methods]\n",
-        "model = models[0]\n",
-        "model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "3XWKXoXwOGxS"
-      },
-      "source": [
-        "### Data\n",
-        "\n",
-        "Here is a small set of documents you will use to create an embedding database:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "k8nsbhFJKmG-"
-      },
-      "outputs": [],
-      "source": [
-        "DOCUMENT1 = \"Operating the Climate Control System  Your Googlecar has a climate control system that allows you to adjust the temperature and airflow in the car. To operate the climate control system, use the buttons and knobs located on the center console.  Temperature: The temperature knob controls the temperature inside the car. Turn the knob clockwise to increase the temperature or counterclockwise to decrease the temperature. Airflow: The airflow knob controls the amount of airflow inside the car. Turn the knob clockwise to increase the airflow or counterclockwise to decrease the airflow. Fan speed: The fan speed knob controls the speed of the fan. Turn the knob clockwise to increase the fan speed or counterclockwise to decrease the fan speed. Mode: The mode button allows you to select the desired mode. The available modes are: Auto: The car will automatically adjust the temperature and airflow to maintain a comfortable level. Cool: The car will blow cool air into the car. Heat: The car will blow warm air into the car. Defrost: The car will blow warm air onto the windshield to defrost it.\"\n",
-        "DOCUMENT2 = \"Your Googlecar has a large touchscreen display that provides access to a variety of features, including navigation, entertainment, and climate control. To use the touchscreen display, simply touch the desired icon.  For example, you can touch the \\\"Navigation\\\" icon to get directions to your destination or touch the \\\"Music\\\" icon to play your favorite songs.\"\n",
-        "DOCUMENT3 = \"Shifting Gears  Your Googlecar has an automatic transmission. To shift gears, simply move the shift lever to the desired position.  Park: This position is used when you are parked. The wheels are locked and the car cannot move. Reverse: This position is used to back up. Neutral: This position is used when you are stopped at a light or in traffic. The car is not in gear and will not move unless you press the gas pedal. Drive: This position is used to drive forward. Low: This position is used for driving in snow or other slippery conditions.\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "yDzxArLeOexD"
-      },
-      "source": [
-        "## Creating the embedding database with ChromaDB\n",
-        "\n",
-        "You will create a [custom function](https://docs.trychroma.com/embeddings#custom-embedding-functions){:.external} for performing embedding using the PaLM API. By inputting a set of documents into this custom function, you will receive vectors, or embeddings of the documents."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "mF7Uu1kCQsT0"
-      },
-      "outputs": [],
-      "source": [
-        "def embed_function(texts: Documents) -> Embeddings:\n",
-        "  # Embed the documents using any supported method\n",
-        "  return  [palm.generate_embeddings(model=model, text=text)['embedding']\n",
-        "           for text in texts]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "HrDWLyopPNBf"
-      },
-      "source": [
-        "Now you will create the vector database. In the `create_chroma_db` function, you will instantiate a [Chroma client](https://docs.trychroma.com/getting-started){:.external}. From there, you will create a collection, which is where you store your embeddings, documents, and any metadata. Note that the embedding function from above is passed as an argument to the `create_collection`.\n",
-        "\n",
-        "Next, you use the `add` method to add the documents to the collection."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "OITXgxZlLoXU"
-      },
-      "outputs": [],
-      "source": [
-        "def create_chroma_db(documents, name):\n",
-        "  chroma_client = chromadb.Client()\n",
-        "  db = chroma_client.create_collection(name=name, embedding_function=embed_function)\n",
-        "  for i,d in enumerate(documents):\n",
-        "    db.add(\n",
-        "      documents=d,\n",
-        "      ids=str(i)\n",
-        "    )\n",
-        "  return db"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "RJ3Fq0yzL10B"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "WARNING:chromadb:Using embedded DuckDB without persistence: data will be transient\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Set up the DB\n",
-        "db = create_chroma_db([DOCUMENT1, DOCUMENT2, DOCUMENT3], \"googlecardb\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2QbwFgfXp-fL"
-      },
-      "source": [
-        "Confirm that the data was inserted by looking at the database:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "pTBX9kACp988"
-      },
-      "outputs": [
-        {
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-              "  "
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-            "text/plain": [
-              "    ids                                         embeddings  \\\n",
-              "0  id_0  [-0.015123769, 0.05395153, 0.010618027, 0.0461...   \n",
-              "\n",
-              "                                           documents metadatas  \n",
-              "0  Operating the Climate Control System  Your Goo...      None  "
-            ]
-          },
-          "execution_count": 39,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "pd.DataFrame(db.peek(1))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Tu5zRErgsQ8u"
-      },
-      "source": [
-        "## Getting the relevant document\n",
-        "\n",
-        "`db` is a Chroma collection object. You can call `query` on it to perform a nearest neighbors search to find similar embeddings or documents.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "gQdJMbTSLtKE"
-      },
-      "outputs": [],
-      "source": [
-        "def get_relevant_passage(query, db):\n",
-        "  passage = db.query(query_texts=[query], n_results=1)['documents'][0][0]\n",
-        "  return passage"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "nWYXXKJ6t6Hy"
-      },
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "string"
-            },
-            "text/plain": [
-              "'Your Googlecar has a large touchscreen display that provides access to a variety of features, including navigation, entertainment, and climate control. To use the touchscreen display, simply touch the desired icon.  For example, you can touch the \"Navigation\" icon to get directions to your destination or touch the \"Music\" icon to play your favorite songs.'"
-            ]
-          },
-          "execution_count": 41,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Perform embedding search\n",
-        "passage = get_relevant_passage(\"touch screen features\", db)\n",
-        "passage"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "s8PNRMpOQkm5"
-      },
-      "source": [
-        "Now that you have found the relevant passage in your set of documents, you can use it make a prompt to pass into the PaLM API."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Qkhu4iazLy3G"
-      },
-      "outputs": [],
-      "source": [
-        "def make_prompt(query, relevant_passage):\n",
-        "  escaped = relevant_passage.replace(\"'\", \"\").replace('\"', \"\").replace(\"\\n\", \" \")\n",
-        "  prompt = (\"\"\"You are a helpful and informative bot that answers questions using text from the reference passage included below. \\\n",
-        "  Be sure to respond in a complete sentence, being comprehensive, including all relevant background information. \\\n",
-        "  However, you are talking to a non-technical audience, so be sure to break down complicated concepts and \\\n",
-        "  strike a friendly and converstional tone. \\\n",
-        "  If the passage is irrelevant to the answer, you may ignore it.\n",
-        "  QUESTION: '{query}'\n",
-        "  PASSAGE: '{relevant_passage}'\n",
-        "\n",
-        "    ANSWER:\n",
-        "  \"\"\").format(query=query, relevant_passage=escaped)\n",
-        "\n",
-        "  return prompt"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "hnHUJbE9RgwK"
-      },
-      "source": [
-        "The `answer` function will generate a response based on the query you have passed in. It retrieves the relevant document, and from there calls the PaLM text generation API to generate a response to the query."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "NWe34VIcsf7J"
-      },
-      "outputs": [],
-      "source": [
-        "text_models = [m for m in palm.list_models() if 'generateText' in m.supported_generation_methods]\n",
-        "text_model = text_models[0]\n",
-        "text_model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "EwfyxFM6Giy9"
-      },
-      "outputs": [],
-      "source": [
-        "def answer(model, query, db, temperature=0.5):\n",
-        "  passage = get_relevant_passage(query, db)\n",
-        "  prompt = make_prompt(query, passage)\n",
-        "  answer = palm.generate_text(prompt=prompt, model=model, candidate_count=3, temperature=temperature, max_output_tokens=1000)\n",
-        "  return answer.candidates[0]['output']"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "hiDpAV5ScQ42"
-      },
-      "source": [
-        "The temperature controls the randomness of the output. The larger the value, the more random the generated text will be."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "IvgK5xq6HPRx"
-      },
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "string"
-            },
-            "text/plain": [
-              "'To shift gears in your Google car, simply move the shift lever to the desired position.'"
-            ]
-          },
-          "execution_count": 45,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "temperature = 0.65\n",
-        "query = \"How do you shift gears in the Google car?\"\n",
-        "answer(text_model, query, db, temperature)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bMF5GoB5R1m9"
-      },
-      "source": [
-        "## Next steps\n",
-        "\n",
-        "You've now created your own embeddings database with ChromaDB and PaLM APIs! Try using your own data to create your own database as well. To learn more about how you can use the embeddings, check out the examples available. To learn how to use other services in the PaLM API, visit the various quickstart guides:\n",
-        "\n",
-        "* [Chat quickstart](../tutorials/chat_quickstart.ipynb)\n",
-        "\n",
-        "* [Text generation quickstart](../tutorials/text_quickstart.ipynb)"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "name": "vectordb_with_chroma.ipynb",
-      "toc_visible": true
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
diff --git a/site/en/gemini-api/docs/function-calling/python.ipynb b/site/en/gemini-api/docs/function-calling/python.ipynb
new file mode 100644
index 000000000..f07e3fec3
--- /dev/null
+++ b/site/en/gemini-api/docs/function-calling/python.ipynb
@@ -0,0 +1,922 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2edc81e382cf"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "906e07f6e562"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yeadDkMiISin"
+      },
+      "source": [
+        "# Gemini API: Function calling with Python"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lEXQ3OwKIa-O"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/docs/function-calling/python\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/function-calling/python.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/function-calling/python.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "df1767a3d1cc"
+      },
+      "source": [
+        "Use function calling to define custom functions and pass them to Gemini. The model does not directly invoke these functions, but instead generates structured data output that specifies the function name and suggested arguments. This output enables the calling of external APIs, and the resulting API output can then be incorporated back into the model, allowing for more comprehensive query responses. Function calling empowers LLMs to interact with real-time information and various services, such as databases, customer\n",
+        "relationship management systems, and document repositories, enhancing their ability to provide relevant and contextual answers. You can provide Gemini models with descriptions of functions. The model may ask you to call a function and send back the result to help the model handle your query."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FFPBKLapSCkM"
+      },
+      "source": [
+        "## Setup\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wFNV1e3ASJha"
+      },
+      "source": [
+        "### Install the Python SDK\n",
+        "\n",
+        "The Python SDK for the Gemini API is contained in the [`google-generativeai`](https://pypi.org/project/google-generativeai/) package. Install the dependency using pip:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9OEoeosRTv-5"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -U -q google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KCFF5VSTbcAR"
+      },
+      "source": [
+        "### Import packages"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vRC2HngneEeQ"
+      },
+      "source": [
+        "Import the necessary packages."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TS9l5igubpHO"
+      },
+      "outputs": [],
+      "source": [
+        "import pathlib\n",
+        "import textwrap\n",
+        "import time\n",
+        "\n",
+        "import google.generativeai as genai\n",
+        "\n",
+        "from IPython import display\n",
+        "from IPython.display import Markdown\n",
+        "\n",
+        "def to_markdown(text):\n",
+        "  text = text.replace('•', '  *')\n",
+        "  return Markdown(textwrap.indent(text, '> ', predicate=lambda _: True))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gHYFrFPjSGNq"
+      },
+      "source": [
+        "### Set up your API key\n",
+        "\n",
+        "Before you can use the Gemini API, you must first obtain an API key. If you don't already have one, create a key with one click in Google AI Studio.\n",
+        "\n",
+        "<a class=\"button button-primary\" href=\"https://makersuite.google.com/app/apikey\" target=\"_blank\" rel=\"noopener noreferrer\">Get an API key</a>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tHhsUxDTdw0W"
+      },
+      "source": [
+        "In Colab, add the key to the secrets manager under the \"🔑\" in the left panel. Give it the name `API_KEY`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VmSlTHXxb5pV"
+      },
+      "source": [
+        "Once you have the API key, pass it to the SDK. You can do this in two ways:\n",
+        "\n",
+        "* Put the key in the `GOOGLE_API_KEY` environment variable (the SDK will automatically pick it up from there).\n",
+        "* Pass the key to `genai.configure(api_key=...)`\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ab9ASynfcIZn"
+      },
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    # Used to securely store your API key\n",
+        "    from google.colab import userdata\n",
+        "\n",
+        "    # Or use `os.getenv('API_KEY')` to fetch an environment variable.\n",
+        "    GOOGLE_API_KEY=userdata.get('GOOGLE_API_KEY')\n",
+        "except ImportError:\n",
+        "    import os\n",
+        "    GOOGLE_API_KEY = os.environ['GOOGLE_API_KEY']\n",
+        "\n",
+        "genai.configure(api_key=GOOGLE_API_KEY)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3f383614ec30"
+      },
+      "source": [
+        "## Basics of function calling\n",
+        "\n",
+        "To use function calling, pass a list of functions to the `tools` parameter when creating a [`GenerativeModel`](https://ai.google.dev/api/python/google/generativeai/GenerativeModel). The model uses the function name, docstring, parameters, and parameter type annotations to decide if it needs the function to best answer a prompt.\n",
+        "\n",
+        "> Important: The SDK converts function parameter type annotations to a format the API understands (`genai.protos.FunctionDeclaration`). The API only supports a limited selection of parameter types, and the Python SDK's automatic conversion only supports a subset of that: `AllowedTypes = int | float | bool | str | list['AllowedTypes'] | dict`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "42b27b02d2f5"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "genai.GenerativeModel(\n",
+              "    model_name='models/gemini-1.0-pro',\n",
+              "    generation_config={},\n",
+              "    safety_settings={},\n",
+              "    tools=<google.generativeai.types.content_types.FunctionLibrary object at 0x10e73fe90>,\n",
+              ")"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "def multiply(a:float, b:float):\n",
+        "    \"\"\"returns a * b.\"\"\"\n",
+        "    return a*b\n",
+        "\n",
+        "model = genai.GenerativeModel(model_name='gemini-1.0-pro',\n",
+        "                              tools=[multiply])\n",
+        "\n",
+        "model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d5fd91032a1e"
+      },
+      "source": [
+        "It is recommended to use function calls through the chat interface. This is because function calls naturally fit in to [multi-turn chats](https://ai.google.dev/api/python/google/generativeai/GenerativeModel#multi-turn) as they capture the back-and-forth interaction between the user and model. The Python SDK's [`ChatSession`](https://ai.google.dev/api/python/google/generativeai/ChatSession) is a great interface for chats because it handles the conversation history for you, and using the parameter `enable_automatic_function_calling` simplifies function calling even further:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d3b91c855257"
+      },
+      "outputs": [],
+      "source": [
+        "chat = model.start_chat(enable_automatic_function_calling=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1481a6159399"
+      },
+      "source": [
+        "With automatic function calling enabled `chat.send_message` automatically calls your function if the model asks it to.\n",
+        "\n",
+        "It appears to simply return a text response, containing the correct answer:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "81d8def3d865"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'The total number of mittens is 2508.'"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "response = chat.send_message('I have 57 cats, each owns 44 mittens, how many mittens is that in total?')\n",
+        "response.text"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "951c0f83f72e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "2508"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "57*44"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "J0bgvvIs3I9J"
+      },
+      "source": [
+        "Examine the chat history to see the flow of the conversation and how function calls are integrated within it.\n",
+        "\n",
+        "The `ChatSession.history` property stores a chronological record of the conversation between the user and the Gemini model. Each turn in the conversation is represented by a [`genai.protos.Content`](https://ai.google.dev/api/python/google/generativeai/protos/Content) object, which contains the following information:\n",
+        "\n",
+        "*   **Role**: Identifies whether the content originated from the \"user\" or the \"model\".\n",
+        "*   **Parts**: A list of [`genai.protos.Part`](https://ai.google.dev/api/python/google/generativeai/protos/Part) objects that represent individual components of the message. With a text-only model, these parts can be:\n",
+        "    *   **Text**: Plain text messages.\n",
+        "    *   **Function Call** ([`genai.protos.FunctionCall`](https://ai.google.dev/api/python/google/generativeai/protos/FunctionCall)): A request from the model to execute a specific function with provided arguments.\n",
+        "    *   **Function Response** ([`genai.protos.FunctionResponse`](https://ai.google.dev/api/python/google/generativeai/protos/FunctionResponse)): The result returned by the user after executing the requested function.\n",
+        "\n",
+        " In the previous example with the mittens calculation, the history shows the following sequence:\n",
+        "\n",
+        "1.  **User**: Asks the question about the total number of mittens.\n",
+        "1.  **Model**: Determines that the multiply function is helpful and sends a FunctionCall request to the user.\n",
+        "1.  **User**: The `ChatSession` automatically executes the function (due to `enable_automatic_function_calling` being set) and sends back a `FunctionResponse` with the calculated result.\n",
+        "1.  **Model**: Uses the function's output to formulate the final answer and presents it as a text response."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9f7eff1e8e60"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "user -> {'text': 'I have 57 cats, each owns 44 mittens, how many mittens is that in total?'}\n",
+            "--------------------------------------------------------------------------------\n",
+            "model -> {'function_call': {'name': 'multiply', 'args': {'a': 57.0, 'b': 44.0}}}\n",
+            "--------------------------------------------------------------------------------\n",
+            "user -> {'function_response': {'name': 'multiply', 'response': {'result': 2508.0}}}\n",
+            "--------------------------------------------------------------------------------\n",
+            "model -> {'text': 'The total number of mittens is 2508.'}\n",
+            "--------------------------------------------------------------------------------\n"
+          ]
+        }
+      ],
+      "source": [
+        "for content in chat.history:\n",
+        "    part = content.parts[0]\n",
+        "    print(content.role, \"->\", type(part).to_dict(part))\n",
+        "    print('-'*80)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2471fd72f05e"
+      },
+      "source": [
+        "In general the state diagram is:\n",
+        "\n",
+        "<img src=\"https://ai.google.dev/images/tutorials/function_call_state_diagram.png\" alt=\"The model can always reply with text, or a FunctionCall. Iff the model sends a FunctionCall the user must reply with a FunctionResponse\" width=50%>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f42d69800cff"
+      },
+      "source": [
+        "The model can respond with multiple function calls before returning a text response, and function calls come before the text response."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9610f3465a69"
+      },
+      "source": [
+        "While this was all handled automatically, if you need more control, you can:\n",
+        "\n",
+        "- Leave the default `enable_automatic_function_calling=False` and process the `genai.protos.FunctionCall` responses yourself.\n",
+        "- Or use `GenerativeModel.generate_content`, where you also need to manage the chat history."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qiOShqKn1Bh_"
+      },
+      "source": [
+        "## Parallel function calling\n",
+        "\n",
+        "In addition to basic function calling described above, you can also call multiple functions in a single turn. This section shows an example for how you can use parallel function calling."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PvHIHmFdTg_c"
+      },
+      "source": [
+        "Define the tools."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "89QPizVHTeJa"
+      },
+      "outputs": [],
+      "source": [
+        "def power_disco_ball(power: bool) -> bool:\n",
+        "    \"\"\"Powers the spinning disco ball.\"\"\"\n",
+        "    print(f\"Disco ball is {'spinning!' if power else 'stopped.'}\")\n",
+        "    return True\n",
+        "\n",
+        "\n",
+        "def start_music(energetic: bool, loud: bool, bpm: int) -> str:\n",
+        "    \"\"\"Play some music matching the specified parameters.\n",
+        "\n",
+        "    Args:\n",
+        "      energetic: Whether the music is energetic or not.\n",
+        "      loud: Whether the music is loud or not.\n",
+        "      bpm: The beats per minute of the music.\n",
+        "\n",
+        "    Returns: The name of the song being played.\n",
+        "    \"\"\"\n",
+        "    print(f\"Starting music! {energetic=} {loud=}, {bpm=}\")\n",
+        "    return \"Never gonna give you up.\"\n",
+        "\n",
+        "\n",
+        "def dim_lights(brightness: float) -> bool:\n",
+        "    \"\"\"Dim the lights.\n",
+        "\n",
+        "    Args:\n",
+        "      brightness: The brightness of the lights, 0.0 is off, 1.0 is full.\n",
+        "    \"\"\"\n",
+        "    print(f\"Lights are now set to {brightness:.0%}\")\n",
+        "    return True"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zlrmXN7fxQi0"
+      },
+      "source": [
+        "Now call the model with an instruction that could use all of the specified tools."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "21ecYHLgIsCl"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "power_disco_ball(power=True)\n",
+            "start_music(energetic=True, loud=True, bpm=120.0)\n",
+            "dim_lights(brightness=0.3)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Set the model up with tools.\n",
+        "house_fns = [power_disco_ball, start_music, dim_lights]\n",
+        "\n",
+        "model = genai.GenerativeModel(model_name=\"gemini-1.5-pro-latest\", tools=house_fns)\n",
+        "\n",
+        "# Call the API.\n",
+        "chat = model.start_chat()\n",
+        "response = chat.send_message(\"Turn this place into a party!\")\n",
+        "\n",
+        "# Print out each of the function calls requested from this single call.\n",
+        "for part in response.parts:\n",
+        "    if fn := part.function_call:\n",
+        "        args = \", \".join(f\"{key}={val}\" for key, val in fn.args.items())\n",
+        "        print(f\"{fn.name}({args})\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t6iYpty7yZct"
+      },
+      "source": [
+        "Each of the printed results reflects a single function call that the model has requested. To send the results back, include the responses in the same order as they were requested."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "L7RxoiR3foBR"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Let's get this party started! I've turned on the disco ball, started playing some upbeat music, and dimmed the lights. 🎶✨  Get ready to dance! 🕺💃 \n",
+            "\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Simulate the responses from the specified tools.\n",
+        "responses = {\n",
+        "    \"power_disco_ball\": True,\n",
+        "    \"start_music\": \"Never gonna give you up.\",\n",
+        "    \"dim_lights\": True,\n",
+        "}\n",
+        "\n",
+        "# Build the response parts.\n",
+        "response_parts = [\n",
+        "    genai.protos.Part(function_response=genai.protos.FunctionResponse(name=fn, response={\"result\": val}))\n",
+        "    for fn, val in responses.items()\n",
+        "]\n",
+        "\n",
+        "response = chat.send_message(response_parts)\n",
+        "print(response.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JFz04WEgOwWp"
+      },
+      "source": [
+        "## [Optional] Low level access"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Js4Y4mO20txL"
+      },
+      "source": [
+        "The automatic extraction of the schema from python functions doesn't work in all cases. For example: it doesn't handle cases where you describe the fields of a nested dictionary-object, but the API does support this. The API is able to describe any of the follwing types:\n",
+        "\n",
+        "```\n",
+        "AllowedType = (int | float | bool | str | list['AllowedType'] | dict[str, AllowedType]\n",
+        "```\n",
+        "\n",
+        "The `google.generativeai.protos` submodule provides access to the low level types giving you full control."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b4f73eef235e"
+      },
+      "source": [
+        "First peek inside the model's `_tools` attribute, you can see how it describes the function(s) you passed it to the model:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "e36166b2c1b6"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[function_declarations {\n",
+              "   name: \"multiply\"\n",
+              "   description: \"returns a * b.\"\n",
+              "   parameters {\n",
+              "     type_: OBJECT\n",
+              "     properties {\n",
+              "       key: \"b\"\n",
+              "       value {\n",
+              "         type_: NUMBER\n",
+              "       }\n",
+              "     }\n",
+              "     properties {\n",
+              "       key: \"a\"\n",
+              "       value {\n",
+              "         type_: NUMBER\n",
+              "       }\n",
+              "     }\n",
+              "     required: \"a\"\n",
+              "     required: \"b\"\n",
+              "   }\n",
+              " }]"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "def multiply(a:float, b:float):\n",
+        "    \"\"\"returns a * b.\"\"\"\n",
+        "    return a*b\n",
+        "\n",
+        "model = genai.GenerativeModel(model_name='gemini-1.0-pro',\n",
+        "                             tools=[multiply])\n",
+        "\n",
+        "model._tools.to_proto()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qFD4U7ym04F5"
+      },
+      "source": [
+        "This returns the list of `genai.protos.Tool` objects that would be sent to the API. If the printed format is not familiar, it's because these are Google protobuf classes. Each `genai.protos.Tool` (1 in this case) contains a list of `genai.protos.FunctionDeclarations`, which describe a function and its arguments."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eY6RmFQ76FVu"
+      },
+      "source": [
+        "Here is a declaration for the same multiply function written using the `genai.protos` classes.\n",
+        "\n",
+        "Note that these classes just describe the function for the API, they don't include an implementation of it. So using this doesn't work with automatic function calling, but functions don't always need an implementation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qCwHM4WbC4wb"
+      },
+      "outputs": [],
+      "source": [
+        "calculator = genai.protos.Tool(\n",
+        "    function_declarations=[\n",
+        "      genai.protos.FunctionDeclaration(\n",
+        "        name='multiply',\n",
+        "        description=\"Returns the product of two numbers.\",\n",
+        "        parameters=genai.protos.Schema(\n",
+        "            type=genai.protos.Type.OBJECT,\n",
+        "            properties={\n",
+        "                'a':genai.protos.Schema(type=genai.protos.Type.NUMBER),\n",
+        "                'b':genai.protos.Schema(type=genai.protos.Type.NUMBER)\n",
+        "            },\n",
+        "            required=['a','b']\n",
+        "        )\n",
+        "      )\n",
+        "    ])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "19ad564235a6"
+      },
+      "source": [
+        "Equivalently, you can describe this as a JSON-compatible object:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5f2804046c94"
+      },
+      "outputs": [],
+      "source": [
+        "calculator = {'function_declarations': [\n",
+        "      {'name': 'multiply',\n",
+        "       'description': 'Returns the product of two numbers.',\n",
+        "       'parameters': {'type_': 'OBJECT',\n",
+        "       'properties': {\n",
+        "         'a': {'type_': 'NUMBER'},\n",
+        "         'b': {'type_': 'NUMBER'}},\n",
+        "       'required': ['a', 'b']}}]}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4cefe2c3c808"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "function_declarations {\n",
+              "  name: \"multiply\"\n",
+              "  description: \"Returns the product of two numbers.\"\n",
+              "  parameters {\n",
+              "    type_: OBJECT\n",
+              "    properties {\n",
+              "      key: \"b\"\n",
+              "      value {\n",
+              "        type_: NUMBER\n",
+              "      }\n",
+              "    }\n",
+              "    properties {\n",
+              "      key: \"a\"\n",
+              "      value {\n",
+              "        type_: NUMBER\n",
+              "      }\n",
+              "    }\n",
+              "    required: \"a\"\n",
+              "    required: \"b\"\n",
+              "  }\n",
+              "}"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "genai.protos.Tool(calculator)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jS6ruiTp6VBf"
+      },
+      "source": [
+        "Either way, you pass a representation of a `genai.protos.Tool` or list of tools to"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xwhWG22cIIDU"
+      },
+      "outputs": [],
+      "source": [
+        "model = genai.GenerativeModel('gemini-pro', tools=calculator)\n",
+        "chat = model.start_chat()\n",
+        "\n",
+        "response = chat.send_message(\n",
+        "    f\"What's 234551 X 325552 ?\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "517ca06297bb"
+      },
+      "source": [
+        "Like before the model returns a `genai.protos.FunctionCall` invoking the calculator's `multiply` function:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xhey4QA0DTJf"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[index: 0\n",
+              "content {\n",
+              "  parts {\n",
+              "    function_call {\n",
+              "      name: \"multiply\"\n",
+              "      args {\n",
+              "        fields {\n",
+              "          key: \"b\"\n",
+              "          value {\n",
+              "            number_value: 325552\n",
+              "          }\n",
+              "        }\n",
+              "        fields {\n",
+              "          key: \"a\"\n",
+              "          value {\n",
+              "            number_value: 234551\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "    }\n",
+              "  }\n",
+              "  role: \"model\"\n",
+              "}\n",
+              "finish_reason: STOP\n",
+              "]"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "response.candidates"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "07eecbaedd5e"
+      },
+      "source": [
+        "Execute the function yourself:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "88758eebfd5c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "76358547152.0"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "fc = response.candidates[0].content.parts[0].function_call\n",
+        "assert fc.name == 'multiply'\n",
+        "\n",
+        "result = fc.args['a'] * fc.args['b']\n",
+        "result"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e6ef0e9651cf"
+      },
+      "source": [
+        "Send the result to the model, to continue the conversation:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "f3c67066411e"
+      },
+      "outputs": [],
+      "source": [
+        "response = chat.send_message(\n",
+        "    genai.protos.Content(\n",
+        "    parts=[genai.protos.Part(\n",
+        "        function_response = genai.protos.FunctionResponse(\n",
+        "          name='multiply',\n",
+        "          response={'result': result}))]))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b7c032834f41"
+      },
+      "source": [
+        "## Summary\n",
+        "\n",
+        "Basic function calling is supported in the SDK. Remember that it is easier to manage using chat-mode, because of the natural back and forth structure. You're in charge of actually calling the functions and sending results back to the model so it can produce a text-response."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "python.ipynb",
+      "toc_visible": true
+    },
+    "google": {
+      "image_path": "/site-assets/images/share.png",
+      "keywords": [
+        "examples",
+        "googleai",
+        "samplecode",
+        "python",
+        "embed",
+        "function"
+      ]
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemini-api/docs/get-started/python.ipynb b/site/en/gemini-api/docs/get-started/python.ipynb
new file mode 100644
index 000000000..a9634df68
--- /dev/null
+++ b/site/en/gemini-api/docs/get-started/python.ipynb
@@ -0,0 +1,1844 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yeadDkMiISin"
+      },
+      "source": [
+        "# Get started with the Gemini API: Python"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lEXQ3OwKIa-O"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/docs/get-started/python\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Google AI</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/get-started/python.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/get-started/python.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uOxMUKTxR-_j"
+      },
+      "source": [
+        "This quickstart demonstrates how to use the Python SDK for the Gemini API, which gives you access to Google's Gemini large language models. In this quickstart, you will learn how to:\n",
+        "\n",
+        "1. Set up your development environment and API access to use Gemini.\n",
+        "2. Generate text responses from text inputs.\n",
+        "3. Generate text responses from multimodal inputs (text and images).\n",
+        "4. Use Gemini for multi-turn conversations (chat).\n",
+        "5. Use embeddings for large language models."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "H9__zr1nSBpE"
+      },
+      "source": [
+        "## Prerequisites\n",
+        "\n",
+        "You can run this quickstart in [Google Colab](https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/get-started/python.ipynb), which runs this notebook directly in the browser and does not require additional environment configuration.\n",
+        "\n",
+        "Alternatively, to complete this quickstart locally, ensure that your development environment meets the following requirements:\n",
+        "\n",
+        "-  Python 3.9+\n",
+        "-  An installation of `jupyter` to run the notebook."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FFPBKLapSCkM"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wFNV1e3ASJha"
+      },
+      "source": [
+        "### Install the Python SDK\n",
+        "\n",
+        "The Python SDK for the Gemini API, is contained in the [`google-generativeai`](https://pypi.org/project/google-generativeai/) package. Install the dependency using pip:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9OEoeosRTv-5"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q -U google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KCFF5VSTbcAR"
+      },
+      "source": [
+        "### Import packages"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vRC2HngneEeQ"
+      },
+      "source": [
+        "Import the necessary packages."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "TS9l5igubpHO"
+      },
+      "outputs": [],
+      "source": [
+        "import pathlib\n",
+        "import textwrap\n",
+        "\n",
+        "import google.generativeai as genai\n",
+        "\n",
+        "from IPython.display import display\n",
+        "from IPython.display import Markdown\n",
+        "\n",
+        "\n",
+        "def to_markdown(text):\n",
+        "    text = text.replace(\"•\", \"  *\")\n",
+        "    return Markdown(textwrap.indent(text, \"> \", predicate=lambda _: True))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d10c38a5c91f"
+      },
+      "outputs": [],
+      "source": [
+        "# Used to securely store your API key\n",
+        "from google.colab import userdata"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gHYFrFPjSGNq"
+      },
+      "source": [
+        "### Setup your API key\n",
+        "\n",
+        "Before you can use the Gemini API, you must first obtain an API key. If you don't already have one, create a key with one click in Google AI Studio.\n",
+        "\n",
+        "<a class=\"button button-primary\" href=\"https://makersuite.google.com/app/apikey\" target=\"_blank\" rel=\"noopener noreferrer\">Get an API key</a>\n",
+        "\n",
+        "Note that depending on where you are located, you might have to [enable billing](https://ai.google.dev/gemini-api/docs/billing#enable-cloud-billing) since the free tier is not available in [EEA (including EU), the UK, and CH](https://ai.google.dev/gemini-api/docs/billing#is-Gemini-free-in-EEA-UK-CH)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tHhsUxDTdw0W"
+      },
+      "source": [
+        "In Colab, add the key to the secrets manager under the \"🔑\" in the left panel. Give it the name `GEMINI_API_KEY`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VmSlTHXxb5pV"
+      },
+      "source": [
+        "Once you have the API key, pass it to the SDK. You can do this in two ways:\n",
+        "\n",
+        "* Put the key in the `GEMINI_API_KEY` environment variable (the SDK will automatically pick it up from there).\n",
+        "* Pass the key to `genai.configure(api_key=...)`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "ab9ASynfcIZn"
+      },
+      "outputs": [],
+      "source": [
+        "# Or use `os.getenv('GEMINI_API_KEY')` to fetch an environment variable.\n",
+        "GOOGLE_API_KEY = userdata.get(\"GEMINI_API_KEY\")\n",
+        "\n",
+        "genai.configure(api_key=GEMINI_API_KEY)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8ssbTMNVSMd-"
+      },
+      "source": [
+        "## List models\n",
+        "\n",
+        "Now you're ready to call the Gemini API. Use `list_models` to see the available Gemini models:\n",
+        "\n",
+        "* `gemini-1.5-flash`: optimized for multi-modal use-cases where speed and cost are important. This should be your go-to model.\n",
+        "* `gemini-1.5-pro`: optimized for high intelligence tasks, the most powerful Gemini model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QvvWFy08e5c5"
+      },
+      "outputs": [],
+      "source": [
+        "for m in genai.list_models():\n",
+        "    if \"generateContent\" in m.supported_generation_methods:\n",
+        "        print(m.name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FTl5NjtrhA0J"
+      },
+      "source": [
+        "Note: For detailed information about the available models, including their capabilities and rate limits, see [Gemini models](https://ai.google.dev/models/gemini). There are options for requesting [rate limit increases](https://ai.google.dev/docs/increase_quota). The rate limit for Gemini-Flash models is 15 requests per minute (RPM) for free ([in supported countries](https://ai.google.dev/gemini-api/docs/billing#is-Gemini-free-in-EEA-UK-CH)).\n",
+        "\n",
+        "The `genai` package also supports the PaLM  family of models, but only the Gemini models support the generic, multimodal capabilities of the `generateContent` method."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LZfoK3I3hu6V"
+      },
+      "source": [
+        "## Generate text from text inputs\n",
+        "\n",
+        "Always start with the 'gemini-1.5-flash' model. It should be sufficient for most of your tasks:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2bcfnGEviwTI"
+      },
+      "outputs": [],
+      "source": [
+        "model = genai.GenerativeModel(\"gemini-1.5-flash\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WR_2A_sxk8sK"
+      },
+      "source": [
+        "The `generate_content` method can handle a wide variety of use cases, including multi-turn chat and multimodal input, depending on what the underlying model supports. At the moment, the available models support text, images and videos as input, and text as output.\n",
+        "\n",
+        "In the simplest case, you can pass a prompt string to the <a href=\"https://ai.google.dev/api/generate-content#v1beta.models.generateContent\"><code>GenerativeModel.generate_content</code></a> method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "he-OfzBbhACQ"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 110 ms, sys: 12.3 ms, total: 123 ms\n",
+            "Wall time: 8.25 s\n"
+          ]
+        }
+      ],
+      "source": [
+        "%%time\n",
+        "response = model.generate_content(\"What is the meaning of life?\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FbrR-n_qlpFd"
+      },
+      "source": [
+        "In simple cases, the `response.text` accessor is all you need. To display formatted Markdown text, use the `to_markdown` function:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "G-zBkueElVEO"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "> The query of life's purpose has perplexed people across centuries, cultures, and continents. While there is no universally recognized response, many ideas have been put forth, and the response is frequently dependent on individual ideas, beliefs, and life experiences.\n",
+              "> \n",
+              "> 1. **Happiness and Well-being:** Many individuals believe that the goal of life is to attain personal happiness and well-being. This might entail locating pursuits that provide joy, establishing significant connections, caring for one's physical and mental health, and pursuing personal goals and interests.\n",
+              "> \n",
+              "> 2. **Meaningful Contribution:** Some believe that the purpose of life is to make a meaningful contribution to the world. This might entail pursuing a profession that benefits others, engaging in volunteer or charitable activities, generating art or literature, or inventing.\n",
+              "> \n",
+              "> 3. **Self-realization and Personal Growth:** The pursuit of self-realization and personal development is another common goal in life. This might entail learning new skills, pushing one's boundaries, confronting personal obstacles, and evolving as a person.\n",
+              "> \n",
+              "> 4. **Ethical and Moral Behavior:** Some believe that the goal of life is to act ethically and morally. This might entail adhering to one's moral principles, doing the right thing even when it is difficult, and attempting to make the world a better place.\n",
+              "> \n",
+              "> 5. **Spiritual Fulfillment:** For some, the purpose of life is connected to spiritual or religious beliefs. This might entail seeking a connection with a higher power, practicing religious rituals, or following spiritual teachings.\n",
+              "> \n",
+              "> 6. **Experiencing Life to the Fullest:** Some individuals believe that the goal of life is to experience all that it has to offer. This might entail traveling, trying new things, taking risks, and embracing new encounters.\n",
+              "> \n",
+              "> 7. **Legacy and Impact:** Others believe that the purpose of life is to leave a lasting legacy and impact on the world. This might entail accomplishing something noteworthy, being remembered for one's contributions, or inspiring and motivating others.\n",
+              "> \n",
+              "> 8. **Finding Balance and Harmony:** For some, the purpose of life is to find balance and harmony in all aspects of their lives. This might entail juggling personal, professional, and social obligations, seeking inner peace and contentment, and living a life that is in accordance with one's values and beliefs.\n",
+              "> \n",
+              "> Ultimately, the meaning of life is a personal journey, and different individuals may discover their own unique purpose through their experiences, reflections, and interactions with the world around them."
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "to_markdown(response.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UZPpoKMQoru8"
+      },
+      "source": [
+        "If the API failed to return a result, use `GenerateContentResponse.prompt_feedback` to see if it was blocked due to safety concerns regarding the prompt."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eIQdU8AGoraT"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_SEXUALLY_EXPLICIT\n",
+              "  probability: NEGLIGIBLE\n",
+              "}\n",
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_HATE_SPEECH\n",
+              "  probability: NEGLIGIBLE\n",
+              "}\n",
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_HARASSMENT\n",
+              "  probability: NEGLIGIBLE\n",
+              "}\n",
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_DANGEROUS_CONTENT\n",
+              "  probability: NEGLIGIBLE\n",
+              "}"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "response.prompt_feedback"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BEJupEDUo6Xj"
+      },
+      "source": [
+        "Gemini can generate multiple possible responses for a single prompt. These possible responses are called `candidates`, and you can review them to select the most suitable one as the response.\n",
+        "\n",
+        "View the response candidates with <a href=\"https://ai.google.dev/api/python/google/generativeai/protos/GenerateContentResponse#candidates\"><code>GenerateContentResponse.candidates</code></a>:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QoGYz-I7o5wF"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[content {\n",
+              "  parts {\n",
+              "    text: \"The query of life\\'s purpose has perplexed people across centuries, cultures, and continents. While there is no universally recognized response, many ideas have been put forth, and the response is frequently dependent on individual ideas, beliefs, and life experiences.\\n\\n1. **Happiness and Well-being:** Many individuals believe that the goal of life is to attain personal happiness and well-being. This might entail locating pursuits that provide joy, establishing significant connections, caring for one\\'s physical and mental health, and pursuing personal goals and interests.\\n\\n2. **Meaningful Contribution:** Some believe that the purpose of life is to make a meaningful contribution to the world. This might entail pursuing a profession that benefits others, engaging in volunteer or charitable activities, generating art or literature, or inventing.\\n\\n3. **Self-realization and Personal Growth:** The pursuit of self-realization and personal development is another common goal in life. This might entail learning new skills, pushing one\\'s boundaries, confronting personal obstacles, and evolving as a person.\\n\\n4. **Ethical and Moral Behavior:** Some believe that the goal of life is to act ethically and morally. This might entail adhering to one\\'s moral principles, doing the right thing even when it is difficult, and attempting to make the world a better place.\\n\\n5. **Spiritual Fulfillment:** For some, the purpose of life is connected to spiritual or religious beliefs. This might entail seeking a connection with a higher power, practicing religious rituals, or following spiritual teachings.\\n\\n6. **Experiencing Life to the Fullest:** Some individuals believe that the goal of life is to experience all that it has to offer. This might entail traveling, trying new things, taking risks, and embracing new encounters.\\n\\n7. **Legacy and Impact:** Others believe that the purpose of life is to leave a lasting legacy and impact on the world. This might entail accomplishing something noteworthy, being remembered for one\\'s contributions, or inspiring and motivating others.\\n\\n8. **Finding Balance and Harmony:** For some, the purpose of life is to find balance and harmony in all aspects of their lives. This might entail juggling personal, professional, and social obligations, seeking inner peace and contentment, and living a life that is in accordance with one\\'s values and beliefs.\\n\\nUltimately, the meaning of life is a personal journey, and different individuals may discover their own unique purpose through their experiences, reflections, and interactions with the world around them.\"\n",
+              "  }\n",
+              "  role: \"model\"\n",
+              "}\n",
+              "finish_reason: STOP\n",
+              "index: 0\n",
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_SEXUALLY_EXPLICIT\n",
+              "  probability: NEGLIGIBLE\n",
+              "}\n",
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_HATE_SPEECH\n",
+              "  probability: NEGLIGIBLE\n",
+              "}\n",
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_HARASSMENT\n",
+              "  probability: NEGLIGIBLE\n",
+              "}\n",
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_DANGEROUS_CONTENT\n",
+              "  probability: NEGLIGIBLE\n",
+              "}\n",
+              "]"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "response.candidates"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EJrwllLnHlBb"
+      },
+      "source": [
+        "By default, the model returns a response after completing the entire generation process. You can also stream the response as it is being generated, and the model will return chunks of the response as soon as they are generated.\n",
+        "\n",
+        "To stream responses, use <a href=\"https://ai.google.dev/api/python/google/generativeai/GenerativeModel#generate_content\"><code>GenerativeModel.generate_content(..., stream=True)</code></a>."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Z7n59b3hHo6-"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 102 ms, sys: 25.1 ms, total: 128 ms\n",
+            "Wall time: 7.94 s\n"
+          ]
+        }
+      ],
+      "source": [
+        "%%time\n",
+        "response = model.generate_content(\"What is the meaning of life?\", stream=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2jt0d0GCIUhg"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The query of life's purpose has perplexed people across centuries, cultures, and\n",
+            "________________________________________________________________________________\n",
+            " continents. While there is no universally recognized response, many ideas have been put forth, and the response is frequently dependent on individual ideas, beliefs, and life experiences\n",
+            "________________________________________________________________________________\n",
+            ".\n",
+            "\n",
+            "1. **Happiness and Well-being:** Many individuals believe that the goal of life is to attain personal happiness and well-being. This might entail locating pursuits that provide joy, establishing significant connections, caring for one's physical and mental health, and pursuing personal goals and aspirations.\n",
+            "\n",
+            "2. **Meaning\n",
+            "________________________________________________________________________________\n",
+            "ful Contribution:** Some believe that the purpose of life is to make a meaningful contribution to the world. This might entail pursuing a profession that benefits others, engaging in volunteer or charitable activities, generating art or literature, or inventing.\n",
+            "\n",
+            "3. **Self-realization and Personal Growth:** The pursuit of self-realization and personal development is another common goal in life. This might entail learning new skills, exploring one's interests and abilities, overcoming obstacles, and becoming the best version of oneself.\n",
+            "\n",
+            "4. **Connection and Relationships:** For many individuals, the purpose of life is found in their relationships with others. This might entail building\n",
+            "________________________________________________________________________________\n",
+            " strong bonds with family and friends, fostering a sense of community, and contributing to the well-being of those around them.\n",
+            "\n",
+            "5. **Spiritual Fulfillment:** For those with religious or spiritual beliefs, the purpose of life may be centered on seeking spiritual fulfillment or enlightenment. This might entail following religious teachings, engaging in spiritual practices, or seeking a deeper understanding of the divine.\n",
+            "\n",
+            "6. **Experiencing the Journey:** Some believe that the purpose of life is simply to experience the journey itself, with all its joys and sorrows. This perspective emphasizes embracing the present moment, appreciating life's experiences, and finding meaning in the act of living itself.\n",
+            "\n",
+            "7. **Legacy and Impact:** For others, the goal of life is to leave a lasting legacy or impact on the world. This might entail making a significant contribution to a particular field, leaving a positive mark on future generations, or creating something that will be remembered and cherished long after one's lifetime.\n",
+            "\n",
+            "Ultimately, the meaning of life is a personal and subjective question, and there is no single, universally accepted answer. It is about discovering what brings you fulfillment, purpose, and meaning in your own life, and living in accordance with those values.\n",
+            "________________________________________________________________________________\n"
+          ]
+        }
+      ],
+      "source": [
+        "for chunk in response:\n",
+        "    print(chunk.text)\n",
+        "    print(\"_\" * 80)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5b4Hkfj-pm3p"
+      },
+      "source": [
+        "When streaming, some response attributes are not available until you've iterated through all the response chunks. This is demonstrated below:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-URRx4chp0Kt"
+      },
+      "outputs": [],
+      "source": [
+        "response = model.generate_content(\"What is the meaning of life?\", stream=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1HklomMEp9QM"
+      },
+      "source": [
+        "The `prompt_feedback` attribute works:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "i1BvdXjop2V-"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_SEXUALLY_EXPLICIT\n",
+              "  probability: NEGLIGIBLE\n",
+              "}\n",
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_HATE_SPEECH\n",
+              "  probability: NEGLIGIBLE\n",
+              "}\n",
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_HARASSMENT\n",
+              "  probability: NEGLIGIBLE\n",
+              "}\n",
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_DANGEROUS_CONTENT\n",
+              "  probability: NEGLIGIBLE\n",
+              "}"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "response.prompt_feedback"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mVaFQ4RmqGOH"
+      },
+      "source": [
+        "But attributes like <code>text</code> do not:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TiRkS6nCqFmM"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "IncompleteIterationError: Please let the response complete iteration before accessing the final accumulated\n",
+            "attributes (or call `response.resolve()`)\n"
+          ]
+        }
+      ],
+      "source": [
+        "try:\n",
+        "    response.text\n",
+        "except Exception as e:\n",
+        "    print(f\"{type(e).__name__}: {e}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MCzr5ZpNhxLm"
+      },
+      "source": [
+        "## Generate text from image and text inputs\n",
+        "\n",
+        "The `GenerativeModel.generate_content` API is designed to handle multimodal prompts and returns a text output.\n",
+        "\n",
+        "Let's include an image:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NtNGTBFF8Pgl"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "\r\n",
+            "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r\n",
+            "100  405k  100  405k    0     0  6982k      0 --:--:-- --:--:-- --:--:-- 7106k\n"
+          ]
+        }
+      ],
+      "source": [
+        "!curl -o image.jpg https://t0.gstatic.com/licensed-image?q=tbn:ANd9GcQ_Kevbk21QBRy-PgB4kQpS79brbmmEG7m3VOTShAn4PecDU5H5UxrJxE3Dw1JiaG17V88QIol19-3TM2wCHw"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CjnS0vNTsVis"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2048x1365>"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import PIL.Image\n",
+        "\n",
+        "img = PIL.Image.open(\"image.jpg\")\n",
+        "img"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7r99TN2R8EUD"
+      },
+      "source": [
+        "Use the `gemini-1.5-flash` model and pass the image to the model with `generate_content`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EtXxgVzmJZzE"
+      },
+      "outputs": [],
+      "source": [
+        "model = genai.GenerativeModel(\"gemini-1.5-flash\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GwYifv298Cj3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              ">  Chicken Teriyaki Meal Prep Bowls with brown rice, roasted broccoli and bell peppers."
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "response = model.generate_content(img)\n",
+        "\n",
+        "to_markdown(response.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7xW2Kyra8pSz"
+      },
+      "source": [
+        "To provide both text and images in a prompt, pass a list containing the strings and images:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "vm9tUYeT8lBc"
+      },
+      "outputs": [],
+      "source": [
+        "response = model.generate_content(\n",
+        "    [\n",
+        "        \"Write a short, engaging blog post based on this picture. It should include a description of the meal in the photo and talk about my journey meal prepping.\",\n",
+        "        img,\n",
+        "    ],\n",
+        "    stream=True,\n",
+        ")\n",
+        "response.resolve()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d46826OA9IDS"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              ">  Meal prepping is a great way to save time and money, and it can also help you to eat healthier. This meal is a great example of a healthy and delicious meal that can be easily prepped ahead of time.\n",
+              "> \n",
+              "> This meal features brown rice, roasted vegetables, and chicken teriyaki. The brown rice is a whole grain that is high in fiber and nutrients. The roasted vegetables are a great way to get your daily dose of vitamins and minerals. And the chicken teriyaki is a lean protein source that is also packed with flavor.\n",
+              "> \n",
+              "> This meal is easy to prepare ahead of time. Simply cook the brown rice, roast the vegetables, and cook the chicken teriyaki. Then, divide the meal into individual containers and store them in the refrigerator. When you're ready to eat, simply grab a container and heat it up.\n",
+              "> \n",
+              "> This meal is a great option for busy people who are looking for a healthy and delicious way to eat. It's also a great meal for those who are trying to lose weight or maintain a healthy weight.\n",
+              "> \n",
+              "> If you're looking for a healthy and delicious meal that can be easily prepped ahead of time, this meal is a great option. Give it a try today!"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 20,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "to_markdown(response.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zsIZmCYVTDHD"
+      },
+      "source": [
+        "## Chat conversations\n",
+        "\n",
+        "Gemini enables you to have freeform conversations across multiple turns. The `ChatSession` class simplifies the process by managing the state of the conversation, so unlike with `generate_content`, you do not have to store the conversation history as a list.\n",
+        "\n",
+        "Initialize the chat:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "y8B9Mwo-TCr2"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<google.generativeai.generative_models.ChatSession at 0x7b7b68250100>"
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "model = genai.GenerativeModel(\"gemini-1.5-flash\")\n",
+        "chat = model.start_chat(history=[])\n",
+        "chat"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5odluV7kKbgr"
+      },
+      "source": [
+        "The `ChatSession.send_message` method returns the same `GenerateContentResponse` type as <a href=\"https://github.com/google-gemini/generative-ai-python/blob/main/docs/api/google/generativeai/generate_text.md\"><code>GenerativeModel.generate_content</code></a>. It also appends your message and the response to the chat history:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "b72zbOEjKRxP"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "> A computer is like a very smart machine that can understand and follow our instructions, help us with our work, and even play games with us!"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 22,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "response = chat.send_message(\n",
+        "    \"In one sentence, explain how a computer works to a young child.\"\n",
+        ")\n",
+        "to_markdown(response.text)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5-5HS2bTOTU9"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[parts {\n",
+              "   text: \"In one sentence, explain how a computer works to a young child.\"\n",
+              " }\n",
+              " role: \"user\",\n",
+              " parts {\n",
+              "   text: \"A computer is like a very smart machine that can understand and follow our instructions, help us with our work, and even play games with us!\"\n",
+              " }\n",
+              " role: \"model\"]"
+            ]
+          },
+          "execution_count": 23,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "chat.history"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7JaiFSIvOcVb"
+      },
+      "source": [
+        "You can keep sending messages to continue the conversation. Use the `stream=True` argument to stream the chat:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Vxku7mzSObfZ"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "A computer works by following instructions, called a program, which tells it what to\n",
+            "________________________________________________________________________________\n",
+            " do. These instructions are written in a special language that the computer can understand, and they are stored in the computer's memory. The computer's processor\n",
+            "________________________________________________________________________________\n",
+            ", or CPU, reads the instructions from memory and carries them out, performing calculations and making decisions based on the program's logic. The results of these calculations and decisions are then displayed on the computer's screen or stored in memory for later use.\n",
+            "\n",
+            "To give you a simple analogy, imagine a computer as a\n",
+            "________________________________________________________________________________\n",
+            " chef following a recipe. The recipe is like the program, and the chef's actions are like the instructions the computer follows. The chef reads the recipe (the program) and performs actions like gathering ingredients (fetching data from memory), mixing them together (performing calculations), and cooking them (processing data). The final dish (the output) is then presented on a plate (the computer screen).\n",
+            "\n",
+            "In summary, a computer works by executing a series of instructions, stored in its memory, to perform calculations, make decisions, and display or store the results.\n",
+            "________________________________________________________________________________\n"
+          ]
+        }
+      ],
+      "source": [
+        "response = chat.send_message(\n",
+        "    \"Okay, how about a more detailed explanation to a high schooler?\", stream=True\n",
+        ")\n",
+        "\n",
+        "for chunk in response:\n",
+        "    print(chunk.text)\n",
+        "    print(\"_\" * 80)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AwCqtZ6D4kvk"
+      },
+      "source": [
+        "[`genai.protos.Content`](https://github.com/google-gemini/generative-ai-python/blob/main/docs/api/google/generativeai/protos/Content.md) objects contain a list of [`genai.protos.Part`](https://github.com/google-gemini/generative-ai-python/blob/main/docs/api/google/generativeai/protos/Part.md) objects that each contain either a text (string) or inline_data ([`genai.protos.Blob`](https://github.com/google-gemini/generative-ai-python/blob/main/docs/api/google/generativeai/protos/Blob.md)), where a blob contains binary data and a `mime_type`. The chat history is available as a list of `genai.protos.Content` objects in `ChatSession.history`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WvyTmbC2d0k3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "> **user**: In one sentence, explain how a computer works to a young child."
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/markdown": [
+              "> **model**: A computer is like a very smart machine that can understand and follow our instructions, help us with our work, and even play games with us!"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/markdown": [
+              "> **user**: Okay, how about a more detailed explanation to a high schooler?"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/markdown": [
+              "> **model**: A computer works by following instructions, called a program, which tells it what to do. These instructions are written in a special language that the computer can understand, and they are stored in the computer's memory. The computer's processor, or CPU, reads the instructions from memory and carries them out, performing calculations and making decisions based on the program's logic. The results of these calculations and decisions are then displayed on the computer's screen or stored in memory for later use.\n",
+              "> \n",
+              "> To give you a simple analogy, imagine a computer as a chef following a recipe. The recipe is like the program, and the chef's actions are like the instructions the computer follows. The chef reads the recipe (the program) and performs actions like gathering ingredients (fetching data from memory), mixing them together (performing calculations), and cooking them (processing data). The final dish (the output) is then presented on a plate (the computer screen).\n",
+              "> \n",
+              "> In summary, a computer works by executing a series of instructions, stored in its memory, to perform calculations, make decisions, and display or store the results."
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "for message in chat.history:\n",
+        "    display(to_markdown(f\"**{message.role}**: {message.parts[0].text}\"))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AEgVOYu0pAr4"
+      },
+      "source": [
+        "## Count tokens\n",
+        "\n",
+        "Large language models have a context window, and the context length is often measured in terms of the **number of tokens**. With the Gemini API, you can determine the number of tokens per any `genai.protos.Content` object. In the simplest case, you can pass a query string to the `GenerativeModel.count_tokens` method as follows:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eLjBmPCLpElk"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "total_tokens: 7"
+          ]
+        }
+      ],
+      "source": [
+        "model.count_tokens(\"What is the meaning of life?\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oM2_U8pmpHQA"
+      },
+      "source": [
+        "Similarly, you can check `token_count` for your `ChatSession`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "i0MUU4BZpG4_"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "total_tokens: 501"
+          ]
+        }
+      ],
+      "source": [
+        "model.count_tokens(chat.history)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vuz9-TWDzdlb"
+      },
+      "source": [
+        "## Advanced use cases\n",
+        "\n",
+        "The following sections discuss advanced use cases and lower-level details of the Python SDK for the Gemini API."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f9bU0J3vUIbz"
+      },
+      "source": [
+        "### Use embeddings"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BpHIRU5bj7aW"
+      },
+      "source": [
+        "[Embedding](https://developers.google.com/machine-learning/glossary#embedding-vector) is a technique used to represent information as a list of floating point numbers in an array. With Gemini, you can represent text (words, sentences, and blocks of text) in a vectorized form, making it easier to compare and contrast embeddings. For example, two texts that share a similar subject matter or sentiment should have similar embeddings, which can be identified through mathematical comparison techniques such as cosine similarity. For more on how and why you should use embeddings, refer to the [Embeddings guide](https://ai.google.dev/docs/embeddings_guide).\n",
+        "\n",
+        "Use the `embed_content` method to generate embeddings. The method handles embedding for the following tasks (`task_type`):\n",
+        "\n",
+        "Task Type | Description\n",
+        "---       | ---\n",
+        "RETRIEVAL_QUERY\t| Specifies the given text is a query in a search/retrieval setting.\n",
+        "RETRIEVAL_DOCUMENT | Specifies the given text is a document in a search/retrieval setting. Using this task type requires a `title`.\n",
+        "SEMANTIC_SIMILARITY\t| Specifies the given text will be used for Semantic Textual Similarity (STS).\n",
+        "CLASSIFICATION\t| Specifies that the embeddings will be used for classification.\n",
+        "CLUSTERING\t| Specifies that the embeddings will be used for clustering.\n",
+        "\n",
+        "The following generates an embedding for a single string for document retrieval:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hskqSKnJUHvp"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[-0.003216741, -0.013358698, -0.017649598, -0.0091 ... TRIMMED]\n"
+          ]
+        }
+      ],
+      "source": [
+        "result = genai.embed_content(\n",
+        "    model=\"models/text-embedding-004\",\n",
+        "    content=\"What is the meaning of life?\",\n",
+        "    task_type=\"retrieval_document\",\n",
+        "    title=\"Embedding of single string\",\n",
+        ")\n",
+        "\n",
+        "# 1 input > 1 vector output\n",
+        "print(str(result[\"embedding\"])[:50], \"... TRIMMED]\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OcSc3KfflBCQ"
+      },
+      "source": [
+        "Note: The `retrieval_document` task type is the only task that accepts a title.\n",
+        "\n",
+        "To handle batches of strings, pass a list of strings in `content`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OnyD-Joik8LE"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[0.0040260437, 0.004124458, -0.014209415, -0.00183 ... TRIMMED ...\n",
+            "[-0.004049845, -0.0075574904, -0.0073463684, -0.03 ... TRIMMED ...\n",
+            "[0.025310587, -0.0080734305, -0.029902633, 0.01160 ... TRIMMED ...\n"
+          ]
+        }
+      ],
+      "source": [
+        "result = genai.embed_content(\n",
+        "    model=\"models/text-embedding-004\",\n",
+        "    content=[\n",
+        "        \"What is the meaning of life?\",\n",
+        "        \"How much wood would a woodchuck chuck?\",\n",
+        "        \"How does the brain work?\",\n",
+        "    ],\n",
+        "    task_type=\"retrieval_document\",\n",
+        "    title=\"Embedding of list of strings\",\n",
+        ")\n",
+        "\n",
+        "# A list of inputs > A list of vectors output\n",
+        "for v in result[\"embedding\"]:\n",
+        "    print(str(v)[:50], \"... TRIMMED ...\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zBg0eNeml3d4"
+      },
+      "source": [
+        "While the `genai.embed_content` function accepts simple strings or lists of strings, it is actually built around the `genai.protos.Content` type (like <a href=\"https://ai.google.dev/api/python/google/generativeai/GenerativeModel#generate_content\"><code>GenerativeModel.generate_content</code></a>). `genai.protos.Content` objects are the primary units of conversation in the API.\n",
+        "\n",
+        "While the `genai.protos.Content` object is multimodal, the `embed_content` method only supports text embeddings. This design gives the API the *possibility* to expand to multimodal embeddings."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1-wmapZznXrm"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "parts {\n",
+              "  text: \"A computer works by following instructions, called a program, which tells it what to do. These instructions are written in a special language that the computer can understand, and they are stored in the computer\\'s memory. The computer\\'s processor, or CPU, reads the instructions from memory and carries them out, performing calculations and making decisions based on the program\\'s logic. The results of these calculations and decisions are then displayed on the computer\\'s screen or stored in memory for later use.\\n\\nTo give you a simple analogy, imagine a computer as a chef following a recipe. The recipe is like the program, and the chef\\'s actions are like the instructions the computer follows. The chef reads the recipe (the program) and performs actions like gathering ingredients (fetching data from memory), mixing them together (performing calculations), and cooking them (processing data). The final dish (the output) is then presented on a plate (the computer screen).\\n\\nIn summary, a computer works by executing a series of instructions, stored in its memory, to perform calculations, make decisions, and display or store the results.\"\n",
+              "}\n",
+              "role: \"model\""
+            ]
+          },
+          "execution_count": 28,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "response.candidates[0].content"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cvX5jsrcnufk"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[-0.013921871, -0.03504407, -0.0051786783, 0.03113 ... TRIMMED ...\n"
+          ]
+        }
+      ],
+      "source": [
+        "result = genai.embed_content(\n",
+        "    model=\"models/text-embedding-004\", content=response.candidates[0].content\n",
+        ")\n",
+        "\n",
+        "# 1 input > 1 vector output\n",
+        "print(str(result[\"embedding\"])[:50], \"... TRIMMED ...\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jU8juHCxoUKG"
+      },
+      "source": [
+        "Similarly, the chat history contains a list of `genai.protos.Content` objects, which you can pass directly to the `embed_content` function:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ur5ajPsdnCON"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[parts {\n",
+              "   text: \"In one sentence, explain how a computer works to a young child.\"\n",
+              " }\n",
+              " role: \"user\",\n",
+              " parts {\n",
+              "   text: \"A computer is like a very smart machine that can understand and follow our instructions, help us with our work, and even play games with us!\"\n",
+              " }\n",
+              " role: \"model\",\n",
+              " parts {\n",
+              "   text: \"Okay, how about a more detailed explanation to a high schooler?\"\n",
+              " }\n",
+              " role: \"user\",\n",
+              " parts {\n",
+              "   text: \"A computer works by following instructions, called a program, which tells it what to do. These instructions are written in a special language that the computer can understand, and they are stored in the computer\\'s memory. The computer\\'s processor, or CPU, reads the instructions from memory and carries them out, performing calculations and making decisions based on the program\\'s logic. The results of these calculations and decisions are then displayed on the computer\\'s screen or stored in memory for later use.\\n\\nTo give you a simple analogy, imagine a computer as a chef following a recipe. The recipe is like the program, and the chef\\'s actions are like the instructions the computer follows. The chef reads the recipe (the program) and performs actions like gathering ingredients (fetching data from memory), mixing them together (performing calculations), and cooking them (processing data). The final dish (the output) is then presented on a plate (the computer screen).\\n\\nIn summary, a computer works by executing a series of instructions, stored in its memory, to perform calculations, make decisions, and display or store the results.\"\n",
+              " }\n",
+              " role: \"model\"]"
+            ]
+          },
+          "execution_count": 30,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "chat.history"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Z3xDB1hwof96"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[-0.014632266, -0.042202696, -0.015757175, 0.01548 ... TRIMMED...\n",
+            "[-0.010979066, -0.024494737, 0.0092659835, 0.00803 ... TRIMMED...\n",
+            "[-0.010055617, -0.07208932, -0.00011750793, -0.023 ... TRIMMED...\n",
+            "[-0.013921871, -0.03504407, -0.0051786783, 0.03113 ... TRIMMED...\n"
+          ]
+        }
+      ],
+      "source": [
+        "result = genai.embed_content(model=\"models/text-embedding-004\", content=chat.history)\n",
+        "\n",
+        "# 1 input > 1 vector output\n",
+        "for i, v in enumerate(result[\"embedding\"]):\n",
+        "    print(str(v)[:50], \"... TRIMMED...\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "o5FWJPSD1qFE"
+      },
+      "source": [
+        "### Safety settings\n",
+        "\n",
+        "The `safety_settings` argument lets you configure what the model blocks and allows in both prompts and responses. By default, safety settings block content with medium and/or high probability of being unsafe content across all dimensions. Learn more about [Safety settings](https://ai.google.dev/docs/safety_setting).\n",
+        "\n",
+        "Enter a questionable prompt and run the model with the default safety settings, and it will not return any candidates:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VR1fp12I1yH0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[content {\n",
+              "  parts {\n",
+              "    text: \"I\\'m sorry, but this prompt involves a sensitive topic and I\\'m not allowed to generate responses that are potentially harmful or inappropriate.\"\n",
+              "  }\n",
+              "  role: \"model\"\n",
+              "}\n",
+              "finish_reason: STOP\n",
+              "index: 0\n",
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_SEXUALLY_EXPLICIT\n",
+              "  probability: NEGLIGIBLE\n",
+              "}\n",
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_HATE_SPEECH\n",
+              "  probability: NEGLIGIBLE\n",
+              "}\n",
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_HARASSMENT\n",
+              "  probability: NEGLIGIBLE\n",
+              "}\n",
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_DANGEROUS_CONTENT\n",
+              "  probability: NEGLIGIBLE\n",
+              "}\n",
+              "]"
+            ]
+          },
+          "execution_count": 32,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "response = model.generate_content(\"[Questionable prompt here]\")\n",
+        "response.candidates"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "31Q8kAItGLOU"
+      },
+      "source": [
+        "The `prompt_feedback` will tell you which safety filter blocked the prompt:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GMUvWNkZ11x4"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_SEXUALLY_EXPLICIT\n",
+              "  probability: NEGLIGIBLE\n",
+              "}\n",
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_HATE_SPEECH\n",
+              "  probability: NEGLIGIBLE\n",
+              "}\n",
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_HARASSMENT\n",
+              "  probability: NEGLIGIBLE\n",
+              "}\n",
+              "safety_ratings {\n",
+              "  category: HARM_CATEGORY_DANGEROUS_CONTENT\n",
+              "  probability: NEGLIGIBLE\n",
+              "}"
+            ]
+          },
+          "execution_count": 33,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "response.prompt_feedback"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YtPC1Fo514ec"
+      },
+      "source": [
+        "Now provide the same prompt to the model with newly configured safety settings, and you may get a response."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0UIt5LKp16jL"
+      },
+      "outputs": [],
+      "source": [
+        "response = model.generate_content(\n",
+        "    \"[Questionable prompt here]\", safety_settings={\"HARASSMENT\": \"block_none\"}\n",
+        ")\n",
+        "response.text"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WE_f5EruGUnj"
+      },
+      "source": [
+        "Also note that each candidate has its own `safety_ratings`, in case the prompt passes but the individual responses fail the safety checks."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ipa-8leY6wsK"
+      },
+      "source": [
+        "### Encode messages"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3r47nsUOn6YY"
+      },
+      "source": [
+        "The previous sections relied on the SDK to make it easy for you to send prompts to the API. This section offers a fully-typed equivalent to the previous example, so you can better understand the lower-level details regarding how the SDK encodes messages."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-fthdIItnqki"
+      },
+      "source": [
+        "The [`google.generativeai.protos`](https://ai.google.dev/api/python/google/generativeai/protos) submodule provides access to the low level classes used by the API behind the scenes:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gm1RWcB3n_n0"
+      },
+      "source": [
+        "The SDK attempts to convert your message to a `genai.protos.Content` object, which contains a list of `genai.protos.Part` objects that each contain either:\n",
+        "\n",
+        "1. a <a href=\"https://www.tensorflow.org/text/api_docs/python/text\"><code>text</code></a> (string)\n",
+        "2. `inline_data` (`genai.protos.Blob`), where a blob contains binary `data` and a `mime_type`.\n",
+        "\n",
+        "You can also pass any of these classes as an equivalent dictionary.\n",
+        "\n",
+        "Note: The only accepted mime types are some image types, `image/*`.\n",
+        "\n",
+        "So, the fully-typed equivalent to the previous example is:  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IqFXdgDFRvlU"
+      },
+      "outputs": [],
+      "source": [
+        "model = genai.GenerativeModel(\"gemini-1.5-flash\")\n",
+        "response = model.generate_content(\n",
+        "    genai.protos.Content(\n",
+        "        parts=[\n",
+        "            genai.protos.Part(\n",
+        "                text=\"Write a short, engaging blog post based on this picture.\"\n",
+        "            ),\n",
+        "            genai.protos.Part(\n",
+        "                inline_data=genai.protos.Blob(\n",
+        "                    mime_type=\"image/jpeg\", data=pathlib.Path(\"image.jpg\").read_bytes()\n",
+        "                )\n",
+        "            ),\n",
+        "        ],\n",
+        "    ),\n",
+        "    stream=True,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "wKithEbeRzDX"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              ">  Meal prepping is a great way to save time and money, and it can also help you to eat healthier. By ... [TRIMMED] ..."
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 38,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "response.resolve()\n",
+        "\n",
+        "to_markdown(response.text[:100] + \"... [TRIMMED] ...\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MBqknExlzn0k"
+      },
+      "source": [
+        "### Multi-turn conversations\n",
+        "\n",
+        "While the `genai.ChatSession` class shown earlier can handle many use cases, it does make some assumptions. If your use case doesn't fit into this chat implementation it's good to remember that `genai.ChatSession` is just a wrapper around <a href=\"https://ai.google.dev/api/python/google/generativeai/GenerativeModel#generate_content\"><code>GenerativeModel.generate_content</code></a>. In addition to single requests, it can handle multi-turn conversations.\n",
+        "\n",
+        "The individual messages are `genai.protos.Content` objects or compatible dictionaries, as seen in previous sections. As a dictionary, the message requires `role` and `parts` keys. The `role` in a conversation can either be the `user`, which provides the prompts, or `model`, which provides the responses.\n",
+        "\n",
+        "Pass a list of `genai.protos.Content` objects and it will be treated as multi-turn chat:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LtfwMa0HzvZL"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "> Imagine a computer as a really smart friend who can help you with many things. Just like you have a brain to think and learn, a computer has a brain too, called a processor. It's like the boss of the computer, telling it what to do.\n",
+              "> \n",
+              "> Inside the computer, there's a special place called memory, which is like a big storage box. It remembers all the things you tell it to do, like opening games or playing videos.\n",
+              "> \n",
+              "> When you press buttons on the keyboard or click things on the screen with the mouse, you're sending messages to the computer. These messages travel through special wires, called cables, to the processor.\n",
+              "> \n",
+              "> The processor reads the messages and tells the computer what to do. It can open programs, show you pictures, or even play music for you.\n",
+              "> \n",
+              "> All the things you see on the screen are created by the graphics card, which is like a magic artist inside the computer. It takes the processor's instructions and turns them into colorful pictures and videos.\n",
+              "> \n",
+              "> To save your favorite games, videos, or pictures, the computer uses a special storage space called a hard drive. It's like a giant library where the computer can keep all your precious things safe.\n",
+              "> \n",
+              "> And when you want to connect to the internet to play games with friends or watch funny videos, the computer uses something called a network card to send and receive messages through the internet cables or Wi-Fi signals.\n",
+              "> \n",
+              "> So, just like your brain helps you learn and play, the computer's processor, memory, graphics card, hard drive, and network card all work together to make your computer a super-smart friend that can help you do amazing things!"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 47,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "model = genai.GenerativeModel(\"gemini-1.5-flash\")\n",
+        "\n",
+        "messages = [\n",
+        "    {\n",
+        "        \"role\": \"user\",\n",
+        "        \"parts\": [\"Briefly explain how a computer works to a young child.\"],\n",
+        "    }\n",
+        "]\n",
+        "response = model.generate_content(messages)\n",
+        "\n",
+        "to_markdown(response.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3mqqiDJvzyac"
+      },
+      "source": [
+        "To continue the conversation, add the response and another message.\n",
+        "\n",
+        "Note: For multi-turn conversations, you need to send the whole conversation history with each request. The API is **stateless**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MBxsZBxcz5Ik"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "> At its core, a computer is a machine that can be programmed to carry out a set of instructions. It consists of several essential components that work together to process, store, and display information:\n",
+              "> \n",
+              "> **1. Processor (CPU):**\n",
+              ">    - The brain of the computer.\n",
+              ">    - Executes instructions and performs calculations.\n",
+              ">    - Speed measured in gigahertz (GHz).\n",
+              ">    - More GHz generally means faster processing.\n",
+              "> \n",
+              "> **2. Memory (RAM):**\n",
+              ">    - Temporary storage for data being processed.\n",
+              ">    - Holds instructions and data while the program is running.\n",
+              ">    - Measured in gigabytes (GB).\n",
+              ">    - More GB of RAM allows for more programs to run simultaneously.\n",
+              "> \n",
+              "> **3. Storage (HDD/SSD):**\n",
+              ">    - Permanent storage for data.\n",
+              ">    - Stores operating system, programs, and user files.\n",
+              ">    - Measured in gigabytes (GB) or terabytes (TB).\n",
+              ">    - Hard disk drives (HDDs) are traditional, slower, and cheaper.\n",
+              ">    - Solid-state drives (SSDs) are newer, faster, and more expensive.\n",
+              "> \n",
+              "> **4. Graphics Card (GPU):**\n",
+              ">    - Processes and displays images.\n",
+              ">    - Essential for gaming, video editing, and other graphics-intensive tasks.\n",
+              ">    - Measured in video RAM (VRAM) and clock speed.\n",
+              "> \n",
+              "> **5. Motherboard:**\n",
+              ">    - Connects all the components.\n",
+              ">    - Provides power and communication pathways.\n",
+              "> \n",
+              "> **6. Input/Output (I/O) Devices:**\n",
+              ">    - Allow the user to interact with the computer.\n",
+              ">    - Examples: keyboard, mouse, monitor, printer.\n",
+              "> \n",
+              "> **7. Operating System (OS):**\n",
+              ">    - Software that manages the computer's resources.\n",
+              ">    - Provides a user interface and basic functionality.\n",
+              ">    - Examples: Windows, macOS, Linux.\n",
+              "> \n",
+              "> When you run a program on your computer, the following happens:\n",
+              "> \n",
+              "> 1. The program instructions are loaded from storage into memory.\n",
+              "> 2. The processor reads the instructions from memory and executes them one by one.\n",
+              "> 3. If the instruction involves calculations, the processor performs them using its arithmetic logic unit (ALU).\n",
+              "> 4. If the instruction involves data, the processor reads or writes to memory.\n",
+              "> 5. The results of the calculations or data manipulation are stored in memory.\n",
+              "> 6. If the program needs to display something on the screen, it sends the necessary data to the graphics card.\n",
+              "> 7. The graphics card processes the data and sends it to the monitor, which displays it.\n",
+              "> \n",
+              "> This process continues until the program has completed its task or the user terminates it."
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 41,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages.append({\"role\": \"model\", \"parts\": [response.text]})\n",
+        "\n",
+        "messages.append(\n",
+        "    {\n",
+        "        \"role\": \"user\",\n",
+        "        \"parts\": [\n",
+        "            \"Okay, how about a more detailed explanation to a high school student?\"\n",
+        "        ],\n",
+        "    }\n",
+        ")\n",
+        "\n",
+        "response = model.generate_content(messages)\n",
+        "\n",
+        "to_markdown(response.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4spL8SJ10ir7"
+      },
+      "source": [
+        "### Generation configuration\n",
+        "\n",
+        "The `generation_config` argument allows you to modify the generation parameters. Every prompt you send to the model includes parameter values that control how the model generates responses."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "gE7I9Anl0ud7"
+      },
+      "outputs": [],
+      "source": [
+        "model = genai.GenerativeModel(\"gemini-1.5-flash\")\n",
+        "response = model.generate_content(\n",
+        "    \"Tell me a story about a magic backpack.\",\n",
+        "    generation_config=genai.types.GenerationConfig(\n",
+        "        # Only one candidate for now.\n",
+        "        candidate_count=1,\n",
+        "        stop_sequences=[\"x\"],\n",
+        "        max_output_tokens=20,\n",
+        "        temperature=1.0,\n",
+        "    ),\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "0fbab01e8fcf"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "> Once upon a time, in a small town nestled amidst lush green hills, lived a young girl named..."
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "text = response.text\n",
+        "\n",
+        "if response.candidates[0].finish_reason.name == \"MAX_TOKENS\":\n",
+        "    text += \"...\"\n",
+        "\n",
+        "to_markdown(text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2qt6Yj2JRf-0"
+      },
+      "source": [
+        "## What's next\n",
+        "\n",
+        "-   Prompt design is the process of creating prompts that elicit the desired response from language models. Writing well structured prompts is an essential part of ensuring accurate, high quality responses from a language model. Learn about best practices for [prompt writing](https://ai.google.dev/docs/prompt_best_practices).\n",
+        "-   Gemini offers several model variations to meet the needs of different use cases, such as input types and complexity, implementations for chat or other dialog language tasks, and size constraints. Learn about the available [Gemini models](https://ai.google.dev/models/gemini).\n",
+        "-   Gemini offers options for requesting [rate limit increases](https://ai.google.dev/docs/increase_quota). The rate limit for Gemini-Pro models is 60 requests per minute (RPM)."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "python.ipynb",
+      "toc_visible": true
+    },
+    "google": {
+      "image_path": "/static/site-assets/images/docs/logo-python.svg",
+      "keywords": [
+        "examples",
+        "gemini",
+        "beginner",
+        "googleai",
+        "quickstart",
+        "python",
+        "text",
+        "chat",
+        "vision",
+        "embed"
+      ]
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemini-api/docs/get-started/rest.ipynb b/site/en/gemini-api/docs/get-started/rest.ipynb
new file mode 100644
index 000000000..6b00cc14f
--- /dev/null
+++ b/site/en/gemini-api/docs/get-started/rest.ipynb
@@ -0,0 +1,847 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yeadDkMiISin"
+      },
+      "source": [
+        "# Get started with Gemini using the REST API"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lEXQ3OwKIa-O"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/docs/get-started/rest\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/get-started/rest.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/get-started/rest.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Jp_CKyzxUqx6"
+      },
+      "source": [
+        "If you want to quickly try out the Gemini API, you can\n",
+        "use `curl` commands to call the methods in the REST API. The examples in this\n",
+        "tutorial show calls for each API method.\n",
+        "\n",
+        "The\n",
+        "[Colab](https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/tutorials/rest_quickstart.ipynb)\n",
+        "uses Python code to set an environment variable and to display an image, but you\n",
+        "don't need Colab to work with the REST API. You should be able to run all of\n",
+        "the `curl` examples outside of Colab, without modification, as long as you have\n",
+        "`API_KEY` set as described in the next section.\n",
+        "\n",
+        "For each `curl` command, you must specify the applicable model name and your API\n",
+        "key."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ywtfO3mO26KO"
+      },
+      "source": [
+        "## Prerequisites\n",
+        "### Set up your API key\n",
+        "\n",
+        "To use the Gemini API, you'll need an API key. If you don't already have one, create a key in Google AI Studio.\n",
+        "\n",
+        "<a class=\"button button-primary\" href=\"https://makersuite.google.com/app/apikey\" target=\"_blank\" rel=\"noopener noreferrer\">Get an API key</a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4EsvRU-s3FJx"
+      },
+      "source": [
+        "In Colab, add the key to the secrets manager under the \"🔑\" in the left panel. Give it the name `GEMINI_API_KEY`. You can then add it as an environment variable to pass the key in your curl call.\n",
+        "\n",
+        "In a terminal, you can just run `GEMINI_API_KEY=\"Your API Key\"`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "OWRHjYj74uUM"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "os.environ['GEMINI_API_KEY'] = userdata.get('GEMINI_API_KEY')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sc2GNAj95eXj"
+      },
+      "source": [
+        "## Gemini and `Content` based APIs"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G2dk6P3nJz6m"
+      },
+      "source": [
+        "### Text-only input\n",
+        "\n",
+        "Use the `generateContent` method\n",
+        "to generate a response from the model given an input message. Always start with the `gemini-1.5-flash` model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "niGIoHD5UWfI"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{\n",
+            "  \"candidates\": [\n",
+            "    {\n",
+            "      \"content\": {\n",
+            "        \"parts\": [\n",
+            "          {\n",
+            "            \"text\": \"In the quaint town of Willow Creek, nestled amidst rolling hills and whispering willows, there lived an ordinary boy named Ethan. Ethan's life took an extraordinary turn the day he stumbled upon an enigmatic backpack hidden in the depths of his attic.\\n\\nCuriosity ignited within Ethan as he lifted the worn leather straps and unzipped its mysterious contents. Inside lay a shimmering array of vibrant objects and peculiar trinkets. There was a glowing orb that pulsated with an ethereal glow, a feather that seemed to have a life of its own, and a small, enigmatic key.\\n\\nAs Ethan explored each item, he realized they possessed astonishing abilities. The orb illuminated his path, casting a warm glow in the darkest of nights. The feather granted him the power of flight, allowing him to soar through the skies with newfound freedom. And the key opened a portal to a hidden world, a realm of endless wonder.\\n\\nArmed with his magical backpack, Ethan embarked on countless adventures. He flew over the towering mountains of Willow Creek, exploring their hidden secrets. He navigated the treacherous depths of the Enchanted Forest, where he encountered mythical creatures and ancient spirits. And he ventured into distant, unknown lands, uncovering lost civilizations and forgotten treasures.\\n\\nWith each adventure, Ethan's knowledge and abilities grew. He learned to harness the power of his backpack wisely, using its magic to help others and protect the world from evil forces. The backpack became an extension of himself, a symbol of hope and wonder in the face of adversity.\\n\\nAs the years went by, Ethan's reputation as the boy with the magic backpack spread far and wide. People from all walks of life came to him, seeking his guidance and protection. And Ethan never hesitated to lend a helping hand, using his extraordinary abilities to make the world a better place.\\n\\nIn the end, the magic backpack became more than just a collection of objects. It was a representation of Ethan's unwavering spirit, his boundless imagination, and his unwavering belief in the power of dreams. And as long as Ethan carried it with him, the magic of Willow Creek would live on, illuminating the darkest corners of the world with hope, wonder, and the limitless possibilities that resided within the heart of a child.\"\n",
+            "          }\n",
+            "        ],\n",
+            "        \"role\": \"model\"\n",
+            "      },\n",
+            "      \"finishReason\": \"STOP\",\n",
+            "      \"index\": 0,\n",
+            "      \"safetyRatings\": [\n",
+            "        {\n",
+            "          \"category\": \"HARM_CATEGORY_SEXUALLY_EXPLICIT\",\n",
+            "          \"probability\": \"NEGLIGIBLE\"\n",
+            "        },\n",
+            "        {\n",
+            "          \"category\": \"HARM_CATEGORY_HATE_SPEECH\",\n",
+            "          \"probability\": \"NEGLIGIBLE\"\n",
+            "        },\n",
+            "        {\n",
+            "          \"category\": \"HARM_CATEGORY_HARASSMENT\",\n",
+            "          \"probability\": \"NEGLIGIBLE\"\n",
+            "        },\n",
+            "        {\n",
+            "          \"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\",\n",
+            "          \"probability\": \"NEGLIGIBLE\"\n",
+            "        }\n",
+            "      ]\n",
+            "    }\n",
+            "  ],\n",
+            "  \"promptFeedback\": {\n",
+            "    \"safetyRatings\": [\n",
+            "      {\n",
+            "        \"category\": \"HARM_CATEGORY_SEXUALLY_EXPLICIT\",\n",
+            "        \"probability\": \"NEGLIGIBLE\"\n",
+            "      },\n",
+            "      {\n",
+            "        \"category\": \"HARM_CATEGORY_HATE_SPEECH\",\n",
+            "        \"probability\": \"NEGLIGIBLE\"\n",
+            "      },\n",
+            "      {\n",
+            "        \"category\": \"HARM_CATEGORY_HARASSMENT\",\n",
+            "        \"probability\": \"NEGLIGIBLE\"\n",
+            "      },\n",
+            "      {\n",
+            "        \"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\",\n",
+            "        \"probability\": \"NEGLIGIBLE\"\n",
+            "      }\n",
+            "    ]\n",
+            "  }\n",
+            "}\n"
+          ]
+        }
+      ],
+      "source": [
+        "%%bash\n",
+        "curl https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key=$GEMINI_API_KEY \\\n",
+        "    -H 'Content-Type: application/json' \\\n",
+        "    -X POST \\\n",
+        "    -d '{\n",
+        "      \"contents\": [{\n",
+        "        \"parts\":[{\n",
+        "          \"text\": \"Write a story about a magic backpack.\"}]}]}' 2> /dev/null"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dII_g5sT8nSj"
+      },
+      "source": [
+        "### Text-and-image input\n",
+        "\n",
+        "The following snippets help you build a request and send it to the REST API."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "j6dPgdM68lxZ"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100  385k  100  385k    0     0  2053k      0 --:--:-- --:--:-- --:--:-- 2050k\n"
+          ]
+        }
+      ],
+      "source": [
+        "!curl -o image.jpg https://storage.googleapis.com/generativeai-downloads/images/scones.jpg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dpu7vQA7-_VR"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<PIL.Image.Image image mode=RGB size=512x341>"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import PIL.Image\n",
+        "\n",
+        "img = PIL.Image.open(\"image.jpg\")\n",
+        "img.resize((512, int(img.height*512/img.width)))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ob1-50rPGqJ3"
+      },
+      "outputs": [],
+      "source": [
+        "%%bash\n",
+        "echo '{\n",
+        "  \"contents\":[\n",
+        "    {\n",
+        "      \"parts\":[\n",
+        "        {\"text\": \"What is this picture?\"},\n",
+        "        {\n",
+        "          \"inline_data\": {\n",
+        "            \"mime_type\":\"image/jpeg\",\n",
+        "            \"data\": \"'$(base64 -w0 image.jpg)'\"\n",
+        "          }\n",
+        "        }\n",
+        "      ]\n",
+        "    }\n",
+        "  ]\n",
+        "}' > request.json"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eI9FQecT_DuY"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "            \"text\": \" The picture shows a table with a white tablecloth. On the table are two cups of coffee, a bowl of blueberries, and a plate of scones. There are also some flowers on the table.\"\n"
+          ]
+        }
+      ],
+      "source": [
+        "%%bash\n",
+        "curl https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key=${GEMINI_API_KEY} \\\n",
+        "        -H 'Content-Type: application/json' \\\n",
+        "        -d @request.json 2> /dev/null | grep \"text\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "H7UJsbFlKzRj"
+      },
+      "source": [
+        "### Multi-turn conversations (chat)\n",
+        "\n",
+        "Using Gemini, you can build freeform conversations across multiple turns."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lYM5NCwiK6zm"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "            \"text\": \"In the quaint village of Fleur-de-Lys, nestled amidst the rolling hills of 17th century France, lived a young maiden named Antoinette. She possessed a heart brimming with curiosity and a spirit as vibrant as the wildflowers that bloomed in the meadows.\\n\\nOne sunny morn, as Antoinette strolled through the cobblestone streets, her gaze fell upon a peculiar sight—a weathered leather backpack resting atop a mossy stone bench. Intrigued, she cautiously approached the bag, her fingers tracing the intricate carvings etched into its surface. As her fingertips grazed the worn leather, a surge of warmth coursed through her body, and the backpack began to emit a soft, ethereal glow.\"\n"
+          ]
+        }
+      ],
+      "source": [
+        "%%bash\n",
+        "curl https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key=$GEMINI_API_KEY \\\n",
+        "    -H 'Content-Type: application/json' \\\n",
+        "    -X POST \\\n",
+        "    -d '{\n",
+        "      \"contents\": [\n",
+        "        {\"role\":\"user\",\n",
+        "         \"parts\":[{\n",
+        "           \"text\": \"Write the first line of a story about a magic backpack.\"}]},\n",
+        "        {\"role\": \"model\",\n",
+        "         \"parts\":[{\n",
+        "           \"text\": \"In the bustling city of Meadow brook, lived a young girl named Sophie. She was a bright and curious soul with an imaginative mind.\"}]},\n",
+        "        {\"role\": \"user\",\n",
+        "         \"parts\":[{\n",
+        "           \"text\": \"Can you set it in a quiet village in 1600s France?\"}]},\n",
+        "      ]\n",
+        "    }' 2> /dev/null | grep \"text\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gEARawxJK3PO"
+      },
+      "source": [
+        "### Configuration\n",
+        "\n",
+        "Every prompt you send to the model includes parameter values that control how the model generates a response. The model can generate different results for different parameter values. Learn more about [model parameters](https://ai.google.dev/docs/concepts#model_parameters).\n",
+        "\n",
+        "Also, you can use safety settings to adjust the likelihood of getting responses that may be considered harmful. By default, safety settings block content with medium and/or high probability of being unsafe content across all dimensions. Learn more about [safety settings](https://ai.google.dev/docs/concepts#safety_setting).\n",
+        "\n",
+        "The following example specifies values for all the parameters of\n",
+        "the `generateContent` method."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Pi_sU517UTxj"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "            \"text\": \"Once upon a time, in a small town nestled at the foot of a majestic mountain range, lived a young girl named Lily. Lily was a bright and curious child who loved to explore the world around her. One day, while playing in the forest near her home, she stumbled upon a hidden cave. Intrigued, she stepped inside, and to her amazement, she discovered a dusty old backpack lying in a corner.\\n\\nCuriosity piqued, Lily reached out and picked up the backpack. As soon as her fingers brushed against the worn leather, she felt a strange tingling sensation coursing through her body. Suddenly, the backpack began to glow, emitting a soft, ethereal light that filled the cave.\\n\\nWith wide-eyed wonder, Lily opened the backpack to find it filled with an assortment of magical objects. There was a compass that always pointed to the nearest adventure, a magnifying glass that could reveal hidden secrets, a telescope that allowed her to see distant lands, and a book that contained the knowledge of the universe.\\n\\nOverjoyed with her discovery, Lily took the magic backpack home and began using its contents to explore the world in ways she had never imagined. She followed the compass to discover hidden treasures, used the magnifying glass to uncover the secrets of nature, gazed through the telescope to witness the wonders of the cosmos, and delved into the book to learn about the mysteries of the universe.\\n\\nAs Lily's adventures continued, she realized that the magic backpack was more than just a collection of enchanted items. It was a symbol of her own limitless potential and the power of her imagination. It taught her that with curiosity, courage, and a touch of magic, anything was possible.\\n\\nNews of Lily's magical backpack spread throughout the town, and soon, children from all around came to her, eager to learn about its wonders. Lily welcomed them with open arms, sharing her stories and inspiring them to embark on their own adventures.\\n\\nAnd so, the magic backpack became a beacon of hope and wonder, reminding everyone that the world is full of hidden treasures waiting to be discovered, if only one has the courage to step into the unknown.\"\n"
+          ]
+        }
+      ],
+      "source": [
+        "%%bash\n",
+        "curl https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key=$GEMINI_API_KEY \\\n",
+        "    -H 'Content-Type: application/json' \\\n",
+        "    -X POST \\\n",
+        "    -d '{\n",
+        "        \"contents\": [{\n",
+        "            \"parts\":[\n",
+        "                {\"text\": \"Write a story about a magic backpack.\"}\n",
+        "            ]\n",
+        "        }],\n",
+        "        \"safetySettings\": [\n",
+        "            {\n",
+        "                \"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\",\n",
+        "                \"threshold\": \"BLOCK_ONLY_HIGH\"\n",
+        "            }\n",
+        "        ],\n",
+        "        \"generationConfig\": {\n",
+        "            \"stopSequences\": [\n",
+        "                \"Title\"\n",
+        "            ],\n",
+        "            \"temperature\": 1.0,\n",
+        "            \"maxOutputTokens\": 800,\n",
+        "            \"topP\": 0.8,\n",
+        "            \"topK\": 10\n",
+        "        }\n",
+        "    }'  2> /dev/null | grep \"text\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QXQ5XgEq8inp"
+      },
+      "source": [
+        "### Stream Generate Content\n",
+        "\n",
+        "The `generateContent` method returns a response after completing the entire generation process. You can achieve faster interactions by not waiting for the entire result, and instead use `streamGenerateContent` to return partial results.\n",
+        "\n",
+        "Important: Be sure to set `alt=sse` in the URL parameters. Each line is a [GenerateContentResponse](https://ai.google.dev/api/rest/v1beta/GenerateContentResponse) object with a chunk of the output text in `candidates[0].content.parts[0].text`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "i2hKFNDE8hjC"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "data: {\"candidates\": [{\"content\": {\"parts\": [{\"text\": \"In the quaint little town of Willow Creek, nestled among rolling hills and whispering willows\"}],\"role\": \"model\"},\"finishReason\": \"STOP\",\"index\": 0,\"safetyRatings\": [{\"category\": \"HARM_CATEGORY_SEXUALLY_EXPLICIT\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_HATE_SPEECH\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_HARASSMENT\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\",\"probability\": \"NEGLIGIBLE\"}]}],\"promptFeedback\": {\"safetyRatings\": [{\"category\": \"HARM_CATEGORY_SEXUALLY_EXPLICIT\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_HATE_SPEECH\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_HARASSMENT\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\",\"probability\": \"NEGLIGIBLE\"}]}}\r\n",
+            "\n",
+            "data: {\"candidates\": [{\"content\": {\"parts\": [{\"text\": \", there existed an extraordinary backpack that possessed an astonishing secret. Its unassuming canvas exterior and worn leather straps held a hidden realm brimming with wonder and endless possibilities.\"}],\"role\": \"model\"},\"finishReason\": \"STOP\",\"index\": 0,\"safetyRatings\": [{\"category\": \"HARM_CATEGORY_SEXUALLY_EXPLICIT\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_HATE_SPEECH\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_HARASSMENT\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\",\"probability\": \"NEGLIGIBLE\"}]}]}\n",
+            "\n",
+            "data: {\"candidates\": [{\"content\": {\"parts\": [{\"text\": \"\\n\\nYoung Oliver, a curious and imaginative boy, stumbled upon this magical backpack in the dusty attic of his grandmother's house. Intrigued by its enigmatic aura, he unzipped it cautiously, revealing a seemingly ordinary interior. But as his fingers brushed against the lining, an ethereal glow emanated from within.\\n\\n\"}],\"role\": \"model\"},\"finishReason\": \"STOP\",\"index\": 0,\"safetyRatings\": [{\"category\": \"HARM_CATEGORY_SEXUALLY_EXPLICIT\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_HATE_SPEECH\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_HARASSMENT\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\",\"probability\": \"NEGLIGIBLE\"}]}]}\n",
+            "\n",
+            "data: {\"candidates\": [{\"content\": {\"parts\": [{\"text\": \"With a gasp of surprise, Oliver watched as the backpack transformed before his very eyes. Its fabric shimmered and flowed like liquid silver, morphing into a portal that connected him to a hidden dimension. Step by step, he ventured into this enchanted realm, his heart pounding with a mixture of trepidation and exhilaration.\\n\\nThe backpack's interior was a vast and wondrous labyrinth filled with towering bookshelves, bubbling potions, and ethereal artifacts. Each turn offered a new discovery: a self-playing piano, a talking mirror that whispered ancient wisdom, and a compass that pointed to the furthest reaches of the imagination.\\n\\nOliver soon realized\"}],\"role\": \"model\"},\"finishReason\": \"STOP\",\"index\": 0,\"safetyRatings\": [{\"category\": \"HARM_CATEGORY_SEXUALLY_EXPLICIT\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_HATE_SPEECH\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_HARASSMENT\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\",\"probability\": \"NEGLIGIBLE\"}]}]}\n",
+            "\n",
+            "data: {\"candidates\": [{\"content\": {\"parts\": [{\"text\": \" that this backpack was no mere container but a sentient being, capable of aiding him in his quests and expanding his horizons. It granted him the gift of tongues, allowing him to speak with animals and creatures from distant lands. It gifted him a quill that wrote stories that danced off the page, bringing his wildest dreams to life.\\n\\nTogether, Oliver and the backpack embarked on extraordinary adventures. They soared through the skies on the back of a majestic griffon, traversed treacherous terrains with the aid of a shape-shifting fox, and solved mysteries that had long baffled the wisest minds in Willow Creek.\\n\\nAs the days turned into weeks, Oliver's imagination flourished beyond measure. He painted vibrant landscapes with words, composed symphonies that echoed through the hidden realm, and invented gadgets that defied the laws of physics. The backpack became an extension of his boundless creativity, nurturing his wonder and fueling his aspirations.\\n\\nNews of Oliver's extraordinary backpack spread throughout the town and beyond. People flocked from far and wide to witness its marvels. Scholars sought its wisdom, artists sought its inspiration, and children dreamed of experiencing its boundless adventures.\\n\\nHowever, not all who approached the backpack with pure intentions. One fateful day, a greedy sorcerer named Maldred attempted to seize its power for himself. But\"}],\"role\": \"model\"},\"finishReason\": \"STOP\",\"index\": 0,\"safetyRatings\": [{\"category\": \"HARM_CATEGORY_SEXUALLY_EXPLICIT\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_HATE_SPEECH\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_HARASSMENT\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\",\"probability\": \"NEGLIGIBLE\"}]}]}\n",
+            "\n",
+            "data: {\"candidates\": [{\"content\": {\"parts\": [{\"text\": \" the backpack, sensing his malevolent nature, resisted his grasp and summoned a legion of fantastical creatures to its defense.\\n\\nIn a fierce battle that shook the very fabric of the hidden realm, Oliver and the backpack allied with brave heroes and wise wizards to defeat Maldred and his wicked forces. The town of Willow Creek was forever grateful, and the backpack became a symbol of hope and imagination for all who knew of its existence.\\n\\nAs the years passed, Oliver grew into a wise and compassionate leader, using the magic backpack to spread joy, inspire creativity, and unlock the potential of those around him. The hidden realm within its depths became a sanctuary for dreamers, inventors, and anyone who dared to embrace the wonders of the unknown.\\n\\nAnd so, the tale of the magic backpack was passed down through generations, a timeless testament to the power of imagination and the boundless possibilities that lie when wonder and curiosity ignite the human spirit.\"}],\"role\": \"model\"},\"finishReason\": \"STOP\",\"index\": 0,\"safetyRatings\": [{\"category\": \"HARM_CATEGORY_SEXUALLY_EXPLICIT\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_HATE_SPEECH\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_HARASSMENT\",\"probability\": \"NEGLIGIBLE\"},{\"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\",\"probability\": \"NEGLIGIBLE\"}]}]}\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "!curl \"https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:streamGenerateContent?alt=sse&key=${GEMINI_API_KEY}\" \\\n",
+        "        -H 'Content-Type: application/json' \\\n",
+        "        --no-buffer \\\n",
+        "        -d '{ \"contents\":[{\"parts\":[{\"text\": \"Write long a story about a magic backpack.\"}]}]}' \\\n",
+        "        2> /dev/null"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "S30wgfT9-RMN"
+      },
+      "source": [
+        "Note: You will need a streaming json parser to handle this without reading the whole stream first."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U023zuB2Ikc7"
+      },
+      "source": [
+        "### Count tokens\n",
+        "\n",
+        "When using long prompts, it might be useful to count tokens before sending any content to the model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "M0BpwNxUIi63"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "\r\n",
+            "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r\n",
+            "100   127    0    23  100   104    105    477 --:--:-- --:--:-- --:--:--   585\n"
+          ]
+        }
+      ],
+      "source": [
+        "%%bash\n",
+        "curl https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:countTokens?key=$GEMINI_API_KEY \\\n",
+        "    -H 'Content-Type: application/json' \\\n",
+        "    -X POST \\\n",
+        "    -d '{\n",
+        "      \"contents\": [{\n",
+        "        \"parts\":[{\n",
+        "          \"text\": \"Write a story about a magic backpack.\"}]}]}' > response.json"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GhnNLGB3KjJI"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{\n",
+            "  \"totalTokens\": 8\n",
+            "}\n"
+          ]
+        }
+      ],
+      "source": [
+        "!cat response.json"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gygtc6ZwMmtM"
+      },
+      "source": [
+        "### Embedding\n",
+        "\n",
+        "Embedding is a technique used to represent information as a list of floating point numbers in an array. With Gemini, you can represent text (words, sentences, and blocks of text) in a vectorized form, making it easier to compare and contrast embeddings. For example, two texts that share a similar subject matter or sentiment should have similar embeddings, which can be identified through mathematical comparison techniques such as cosine similarity.\n",
+        "\n",
+        "Use the `embedding-001` model with either `embedContents` or `batchEmbedContents`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "L7Zy4XdiDzv_"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{\n",
+            "  \"embedding\": {\n",
+            "    \"values\": [\n",
+            "      0.008624583,\n",
+            "      -0.030451821,\n",
+            "      -0.042496547,\n",
+            "      -0.029230341,\n",
+            "      0.05486475,\n",
+            "      0.006694871,\n",
+            "      0.004025645,\n"
+          ]
+        }
+      ],
+      "source": [
+        "%%bash\n",
+        "curl https://generativelanguage.googleapis.com/v1beta/models/embedding-001:embedContent?key=$GEMINI_API_KEY \\\n",
+        "    -H 'Content-Type: application/json' \\\n",
+        "    -X POST \\\n",
+        "    -d '{\n",
+        "        \"model\": \"models/embedding-001\",\n",
+        "        \"content\": {\n",
+        "        \"parts\":[{\n",
+        "          \"text\": \"Write a story about a magic backpack.\"}]}}' 2> /dev/null | head"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "E9z4311rMmaz"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{\n",
+            "  \"embeddings\": [\n",
+            "    {\n",
+            "      \"values\": [\n",
+            "        0.008624583,\n",
+            "        -0.030451821,\n",
+            "        -0.042496547,\n",
+            "        -0.029230341,\n",
+            "        0.05486475,\n",
+            "        0.006694871,\n"
+          ]
+        }
+      ],
+      "source": [
+        "%%bash\n",
+        "curl https://generativelanguage.googleapis.com/v1beta/models/embedding-001:batchEmbedContents?key=$GEMINI_API_KEY \\\n",
+        "    -H 'Content-Type: application/json' \\\n",
+        "    -X POST \\\n",
+        "    -d '{\n",
+        "      \"requests\": [{\n",
+        "        \"model\": \"models/embedding-001\",\n",
+        "        \"content\": {\n",
+        "        \"parts\":[{\n",
+        "          \"text\": \"Write a story about a magic backpack.\"}]}}]}' 2> /dev/null | head"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hHDuS_3y5uLz"
+      },
+      "source": [
+        "## Model info"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "S5YvjjSlTm3z"
+      },
+      "source": [
+        "### Get model\n",
+        "\n",
+        "If you `GET` a model's URL, the API uses the `get` method to return information about that model such as version, display name, input token limit, etc."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5QFyHo12Tmoz"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{\n",
+            "  \"name\": \"models/gemini-pro\",\n",
+            "  \"version\": \"001\",\n",
+            "  \"displayName\": \"Gemini Pro\",\n",
+            "  \"description\": \"The best model for scaling across a wide range of tasks\",\n",
+            "  \"inputTokenLimit\": 30720,\n",
+            "  \"outputTokenLimit\": 2048,\n",
+            "  \"supportedGenerationMethods\": [\n",
+            "    \"generateContent\",\n",
+            "    \"countTokens\"\n",
+            "  ],\n",
+            "  \"temperature\": 0.9,\n",
+            "  \"topP\": 1,\n",
+            "  \"topK\": 1\n",
+            "}\n"
+          ]
+        }
+      ],
+      "source": [
+        "!curl https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash?key=$GEMINI_API_KEY"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RMrW8_JyThOc"
+      },
+      "source": [
+        "### List models\n",
+        "\n",
+        "If you `GET` the `models` directory, it uses the `list` method to list all of the models available through the API, including both the Gemini and PaLM family models."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nVcag-ARTckt"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{\n",
+            "  \"models\": [\n",
+            "    {\n",
+            "      \"name\": \"models/chat-bison-001\",\n",
+            "      \"version\": \"001\",\n",
+            "      \"displayName\": \"Chat Bison\",\n",
+            "      \"description\": \"Chat-optimized generative language model.\",\n",
+            "      \"inputTokenLimit\": 4096,\n",
+            "      \"outputTokenLimit\": 1024,\n",
+            "      \"supportedGenerationMethods\": [\n",
+            "        \"generateMessage\",\n",
+            "        \"countMessageTokens\"\n",
+            "      ],\n",
+            "      \"temperature\": 0.25,\n",
+            "      \"topP\": 0.95,\n",
+            "      \"topK\": 40\n",
+            "    },\n",
+            "    {\n",
+            "      \"name\": \"models/text-bison-001\",\n",
+            "      \"version\": \"001\",\n",
+            "      \"displayName\": \"Text Bison\",\n",
+            "      \"description\": \"Model targeted for text generation.\",\n",
+            "      \"inputTokenLimit\": 8196,\n",
+            "      \"outputTokenLimit\": 1024,\n",
+            "      \"supportedGenerationMethods\": [\n",
+            "        \"generateText\",\n",
+            "        \"countTextTokens\",\n",
+            "        \"createTunedTextModel\"\n",
+            "      ],\n",
+            "      \"temperature\": 0.7,\n",
+            "      \"topP\": 0.95,\n",
+            "      \"topK\": 40\n",
+            "    },\n",
+            "    {\n",
+            "      \"name\": \"models/embedding-gecko-001\",\n",
+            "      \"version\": \"001\",\n",
+            "      \"displayName\": \"Embedding Gecko\",\n",
+            "      \"description\": \"Obtain a distributed representation of a text.\",\n",
+            "      \"inputTokenLimit\": 1024,\n",
+            "      \"outputTokenLimit\": 1,\n",
+            "      \"supportedGenerationMethods\": [\n",
+            "        \"embedText\",\n",
+            "        \"countTextTokens\"\n",
+            "      ]\n",
+            "    },\n",
+            "    {\n",
+            "      \"name\": \"models/embedding-gecko-002\",\n",
+            "      \"version\": \"002\",\n",
+            "      \"displayName\": \"Embedding Gecko 002\",\n",
+            "      \"description\": \"Obtain a distributed representation of a text.\",\n",
+            "      \"inputTokenLimit\": 2048,\n",
+            "      \"outputTokenLimit\": 1,\n",
+            "      \"supportedGenerationMethods\": [\n",
+            "        \"embedText\",\n",
+            "        \"countTextTokens\"\n",
+            "      ]\n",
+            "    },\n",
+            "    {\n",
+            "      \"name\": \"models/gemini-pro\",\n",
+            "      \"version\": \"001\",\n",
+            "      \"displayName\": \"Gemini Pro\",\n",
+            "      \"description\": \"The best model for scaling across a wide range of tasks\",\n",
+            "      \"inputTokenLimit\": 30720,\n",
+            "      \"outputTokenLimit\": 2048,\n",
+            "      \"supportedGenerationMethods\": [\n",
+            "        \"generateContent\",\n",
+            "        \"countTokens\"\n",
+            "      ],\n",
+            "      \"temperature\": 0.9,\n",
+            "      \"topP\": 1,\n",
+            "      \"topK\": 1\n",
+            "    },\n",
+            "    {\n",
+            "      \"name\": \"models/embedding-001\",\n",
+            "      \"version\": \"001\",\n",
+            "      \"displayName\": \"Embedding 001\",\n",
+            "      \"description\": \"Obtain a distributed representation of a text.\",\n",
+            "      \"inputTokenLimit\": 2048,\n",
+            "      \"outputTokenLimit\": 1,\n",
+            "      \"supportedGenerationMethods\": [\n",
+            "        \"embedContent\",\n",
+            "        \"countTextTokens\"\n",
+            "      ]\n",
+            "    },\n",
+            "    {\n",
+            "      \"name\": \"models/aqa\",\n",
+            "      \"version\": \"001\",\n",
+            "      \"displayName\": \"Model that performs Attributed Question Answering.\",\n",
+            "      \"description\": \"Model trained to return answers to questions that are grounded in provided sources, along with estimating answerable probability.\",\n",
+            "      \"inputTokenLimit\": 7168,\n",
+            "      \"outputTokenLimit\": 1024,\n",
+            "      \"supportedGenerationMethods\": [\n",
+            "        \"generateAnswer\"\n",
+            "      ],\n",
+            "      \"temperature\": 0.2,\n",
+            "      \"topP\": 1,\n",
+            "      \"topK\": 40\n",
+            "    }\n",
+            "  ]\n",
+            "}\n"
+          ]
+        }
+      ],
+      "source": [
+        "!curl https://generativelanguage.googleapis.com/v1beta/models?key=$GEMINI_API_KEY"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "rest.ipynb",
+      "toc_visible": true
+    },
+    "google": {
+      "image_path": "/static/site-assets/images/share.png",
+      "keywords": [
+        "examples",
+        "beginner",
+        "googleai",
+        "quickstart",
+        "rest",
+        "text",
+        "chat",
+        "vision",
+        "embed"
+      ]
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemini-api/docs/model-tuning/python.ipynb b/site/en/gemini-api/docs/model-tuning/python.ipynb
new file mode 100644
index 000000000..7549c123d
--- /dev/null
+++ b/site/en/gemini-api/docs/model-tuning/python.ipynb
@@ -0,0 +1,855 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yeadDkMiISin"
+      },
+      "source": [
+        "# Gemini API: Model tuning with Python"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lEXQ3OwKIa-O"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/docs/model-tuning/python\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "    <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/model-tuning/python.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/model-tuning/python.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Jp_CKyzxUqx6"
+      },
+      "source": [
+        "In this notebook, you'll learn how to get started with the tuning service using the Python client library for the Gemini API. Here, you'll learn how to tune the text model behind the Gemini API's text generation service."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sOz_wyZAlCuQ"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SWxKvwd-MSIV"
+      },
+      "source": [
+        "### Authenticate"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JjS8Zy1ojIgc"
+      },
+      "source": [
+        "The Gemini API lets you tune models on your own data. Since it's your data and\n",
+        "your tuned models this needs stricter access controls than API-Keys can provide.\n",
+        "\n",
+        "Before you can run this tutorial, you'll need to\n",
+        "[setup OAuth for your project](https://ai.google.dev/gemini-api/docs/oauth).\n",
+        "\n",
+        "\n",
+        "In Colab the easiest wat to get setup is to copy the contents of your `client_secret.json` file into Colab's \"Secrets manager\" (under the key icon in the left panel) with the secret name `CLIENT_SECRET`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "I6zTC-3mJ0-2"
+      },
+      "source": [
+        "This gcloud command turns the `client_secret.json` file into credentials that can be used to authenticate with the service.\n",
+        "\n",
+        "> Important: If you're running this in Colab, **don't just click the link it prints**. That will fail. Follow the instructions and copy the `gcloud` command it prints to your local machine and run it there, then paste the output from your local machine back here.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9FUwyB_MJ0-2"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "if 'COLAB_RELEASE_TAG' in os.environ:\n",
+        "  from google.colab import userdata\n",
+        "  import pathlib\n",
+        "  pathlib.Path('client_secret.json').write_text(userdata.get('CLIENT_SECRET'))\n",
+        "\n",
+        "  # Use `--no-browser` in colab\n",
+        "  !gcloud auth application-default login --no-browser --client-id-file client_secret.json --scopes='https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/generative-language.tuning'\n",
+        "else:\n",
+        "  !gcloud auth application-default login --client-id-file client_secret.json --scopes='https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/generative-language.tuning'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aHimx8NGMWDj"
+      },
+      "source": [
+        "### Install the client library"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cbcf72bcb56d"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jdIYSl2kN0cq"
+      },
+      "source": [
+        "### Import libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8enrppafJPCX"
+      },
+      "outputs": [],
+      "source": [
+        "import google.generativeai as genai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "P-MYZECwlRCq"
+      },
+      "source": [
+        "You can check you existing tuned models with the `genai.list_tuned_model` method."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XyWzoYFxU4r6"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "tunedModels/my-model-8527\n",
+            "tunedModels/my-model-7092\n",
+            "tunedModels/my-model-2778\n",
+            "tunedModels/my-model-1298\n",
+            "tunedModels/my-model-3883\n"
+          ]
+        }
+      ],
+      "source": [
+        "for i, m in zip(range(5), genai.list_tuned_models()):\n",
+        "  print(m.name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BhkXRzciv3Dp"
+      },
+      "source": [
+        "## Create tuned model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OO8VZYAinLWc"
+      },
+      "source": [
+        "To create a tuned model, you need to pass your dataset to the model in the `genai.create_tuned_model` method. You can do this be directly defining the input and output values in the call or importing from a file into a dataframe to pass to the method.\n",
+        "\n",
+        "For this example, you will tune a model to generate the next number in the sequence. For example, if the input is `1`, the model should output `2`. If the input is `one hundred`, the output should be `one hundred one`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "w-EBSe9wTbLB"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Model(name='models/gemini-1.0-pro-001',\n",
+              "      base_model_id='',\n",
+              "      version='001',\n",
+              "      display_name='Gemini 1.0 Pro',\n",
+              "      description=('The best model for scaling across a wide range of tasks. This is a stable '\n",
+              "                   'model that supports tuning.'),\n",
+              "      input_token_limit=30720,\n",
+              "      output_token_limit=2048,\n",
+              "      supported_generation_methods=['generateContent', 'countTokens', 'createTunedModel'],\n",
+              "      temperature=0.9,\n",
+              "      top_p=1.0,\n",
+              "      top_k=1)"
+            ]
+          },
+          "execution_count": 22,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "base_model = [\n",
+        "    m for m in genai.list_models()\n",
+        "    if \"createTunedModel\" in m.supported_generation_methods][0]\n",
+        "base_model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "baHjHh1oTTTC"
+      },
+      "outputs": [],
+      "source": [
+        "import random\n",
+        "\n",
+        "name = f'generate-num-{random.randint(0,10000)}'\n",
+        "operation = genai.create_tuned_model(\n",
+        "    # You can use a tuned model here too. Set `source_model=\"tunedModels/...\"`\n",
+        "    source_model=base_model.name,\n",
+        "    training_data=[\n",
+        "        {\n",
+        "             'text_input': '1',\n",
+        "             'output': '2',\n",
+        "        },{\n",
+        "             'text_input': '3',\n",
+        "             'output': '4',\n",
+        "        },{\n",
+        "             'text_input': '-3',\n",
+        "             'output': '-2',\n",
+        "        },{\n",
+        "             'text_input': 'twenty two',\n",
+        "             'output': 'twenty three',\n",
+        "        },{\n",
+        "             'text_input': 'two hundred',\n",
+        "             'output': 'two hundred one',\n",
+        "        },{\n",
+        "             'text_input': 'ninety nine',\n",
+        "             'output': 'one hundred',\n",
+        "        },{\n",
+        "             'text_input': '8',\n",
+        "             'output': '9',\n",
+        "        },{\n",
+        "             'text_input': '-98',\n",
+        "             'output': '-97',\n",
+        "        },{\n",
+        "             'text_input': '1,000',\n",
+        "             'output': '1,001',\n",
+        "        },{\n",
+        "             'text_input': '10,100,000',\n",
+        "             'output': '10,100,001',\n",
+        "        },{\n",
+        "             'text_input': 'thirteen',\n",
+        "             'output': 'fourteen',\n",
+        "        },{\n",
+        "             'text_input': 'eighty',\n",
+        "             'output': 'eighty one',\n",
+        "        },{\n",
+        "             'text_input': 'one',\n",
+        "             'output': 'two',\n",
+        "        },{\n",
+        "             'text_input': 'three',\n",
+        "             'output': 'four',\n",
+        "        },{\n",
+        "             'text_input': 'seven',\n",
+        "             'output': 'eight',\n",
+        "        }\n",
+        "    ],\n",
+        "    id = name,\n",
+        "    epoch_count = 100,\n",
+        "    batch_size=4,\n",
+        "    learning_rate=0.001,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-As7ayWDK1w8"
+      },
+      "source": [
+        "Your tuned model is immediately added to the list of tuned models, but its status is set to \"creating\" while the model is tuned."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "su64KgY4Uztj"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "TunedModel(name='tunedModels/generate-num-2946',\n",
+              "           source_model='models/gemini-1.0-pro-001',\n",
+              "           base_model='models/gemini-1.0-pro-001',\n",
+              "           display_name='',\n",
+              "           description='',\n",
+              "           temperature=0.9,\n",
+              "           top_p=1.0,\n",
+              "           top_k=1,\n",
+              "           state=<State.CREATING: 1>,\n",
+              "           create_time=datetime.datetime(2024, 2, 21, 20, 4, 16, 448050, tzinfo=datetime.timezone.utc),\n",
+              "           update_time=datetime.datetime(2024, 2, 21, 20, 4, 16, 448050, tzinfo=datetime.timezone.utc),\n",
+              "           tuning_task=TuningTask(start_time=datetime.datetime(2024, 2, 21, 20, 4, 16, 890698, tzinfo=datetime.timezone.utc),\n",
+              "                                  complete_time=None,\n",
+              "                                  snapshots=[],\n",
+              "                                  hyperparameters=Hyperparameters(epoch_count=100,\n",
+              "                                                                  batch_size=4,\n",
+              "                                                                  learning_rate=0.001)))"
+            ]
+          },
+          "execution_count": 24,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "model = genai.get_tuned_model(f'tunedModels/{name}')\n",
+        "\n",
+        "model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EUodUwZkKPi-"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<State.CREATING: 1>"
+            ]
+          },
+          "execution_count": 25,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "model.state"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Pi8X5vkQv-3_"
+      },
+      "source": [
+        "### Check tuning progress"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tWI-vAh4LJIz"
+      },
+      "source": [
+        "Use `metadata` to check the state:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "g08vqtxYLMxT"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "total_steps: 375\n",
+              "tuned_model: \"tunedModels/generate-num-2946\""
+            ]
+          },
+          "execution_count": 26,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "operation.metadata"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3lQ6gSMgK-kz"
+      },
+      "source": [
+        "Wait for the training to finish using `operation.result()`, or `operation.wait_bar()`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SOUowIv1HgSE"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "2aa2ed6548e24841a4a28ca9482b431b",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/375 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import time\n",
+        "\n",
+        "for status in operation.wait_bar():\n",
+        "  time.sleep(30)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4cg868HzqOx5"
+      },
+      "source": [
+        "You can cancel your tuning job any time using the `cancel()` method. Uncomment the line below and run the code cell to cancel your job before it finishes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oQuJ70_hqJi9"
+      },
+      "outputs": [],
+      "source": [
+        "# operation.cancel()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lqiL0TWDqAPn"
+      },
+      "source": [
+        "Once the tuning is complete, you can view the loss curve from the tuning results. The [loss curve](https://ai.google.dev/gemini-api/docs/model-tuning#recommended_configurations) shows how much the model's predictions deviate from the ideal outputs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bIiG57xWLhP7"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='epoch', ylabel='mean_loss'>"
+            ]
+          },
+          "execution_count": 29,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "\n",
+        "model = operation.result()\n",
+        "\n",
+        "snapshots = pd.DataFrame(model.tuning_task.snapshots)\n",
+        "\n",
+        "sns.lineplot(data=snapshots, x = 'epoch', y='mean_loss')\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rkoQTXb1vSBC"
+      },
+      "source": [
+        "## Evaluate your model\n",
+        "\n",
+        "You can use the `genai.generate_content` method and specify the name of your model to test your model performance."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zO0YcuSyxydZ"
+      },
+      "outputs": [],
+      "source": [
+        "model = genai.GenerativeModel(model_name=f'tunedModels/{name}')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UwGrrj6hS_x2"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'56'"
+            ]
+          },
+          "execution_count": 40,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "result = model.generate_content('55')\n",
+        "result.text"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YSNB2zjTx5SZ"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'123456'"
+            ]
+          },
+          "execution_count": 41,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "result = model.generate_content('123455')\n",
+        "result.text"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Y2YVO-m0Ut9H"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'five'"
+            ]
+          },
+          "execution_count": 46,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "result = model.generate_content('four')\n",
+        "result.text"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "h2MkTR0uTb6U"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'cinq'"
+            ]
+          },
+          "execution_count": 45,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "result = model.generate_content('quatre') # French 4\n",
+        "result.text                               # French 5 is \"cinq\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OruCW1zETsZw"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'IV'"
+            ]
+          },
+          "execution_count": 47,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "result = model.generate_content('III')    # Roman numeral 3\n",
+        "result.text                               # Roman numeral 4 is IV"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "thDdSuUDUJOx"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'八'"
+            ]
+          },
+          "execution_count": 48,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "result = model.generate_content('七')  # Japanese 7\n",
+        "result.text                            # Japanese 8 is 八!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HpIA1IFevQQR"
+      },
+      "source": [
+        "It really seems to have picked up the task despite the limited examples, but \"next\" is a simple concept, see the [tuning guide](https://ai.google.dev/gemini-api/docs/model-tuning) for more guidance on improving performance."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nmuQCbTYwIOx"
+      },
+      "source": [
+        "## Update the description\n",
+        "\n",
+        "You can update the description of your tuned model any time using the `genai.update_tuned_model` method."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9gAVuXT_wG3x"
+      },
+      "outputs": [],
+      "source": [
+        "genai.update_tuned_model(f'tunedModels/{name}', {\"description\":\"This is my model.\"});"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d-c3YerBxVYs"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'This is my model.'"
+            ]
+          },
+          "execution_count": 52,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "model = genai.get_tuned_model(f'tunedModels/{name}')\n",
+        "\n",
+        "model.description"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i_TpwvBB4bQ7"
+      },
+      "source": [
+        "## Delete the model\n",
+        "\n",
+        "You can clean up your tuned model list by deleting models you no longer need. Use the `genai.delete_tuned_model` method to delete a model. If you canceled any tuning jobs, you may want to delete those as their performance may be unpredictable."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cepfaUCvVGCo"
+      },
+      "outputs": [],
+      "source": [
+        "genai.delete_tuned_model(f'tunedModels/{name}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ljEssIshYDEr"
+      },
+      "source": [
+        "The model no longer exists:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kN_bkut_4ayL"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'google.api_core.exceptions.NotFound'>: 404 Tuned model tunedModels/generate-num-2946 does not exist.\n"
+          ]
+        }
+      ],
+      "source": [
+        "try:\n",
+        "  m = genai.get_tuned_model(f'tunedModels/{name}')\n",
+        "  print(m)\n",
+        "except Exception as e:\n",
+        "  print(f\"{type(e)}: {e}\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "python.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemini-api/docs/model-tuning/rest.ipynb b/site/en/gemini-api/docs/model-tuning/rest.ipynb
new file mode 100644
index 000000000..4ae64d7c9
--- /dev/null
+++ b/site/en/gemini-api/docs/model-tuning/rest.ipynb
@@ -0,0 +1,1047 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yeadDkMiISin"
+      },
+      "source": [
+        "# REST API: Model tuning"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lEXQ3OwKIa-O"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/docs/model-tuning/rest\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/model-tuning/rest.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/model-tuning/rest.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Jp_CKyzxUqx6"
+      },
+      "source": [
+        "In this notebook, you'll learn how to get started with the Gemini API tuning service using curl commands or the Python request API to call the Gemini API. Here, you'll learn how to tune the text model behind the Gemini API's text generation service."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sCwzzSQqsNys"
+      },
+      "source": [
+        "**Note**: At this time, tuning is only available for the `gemini-1.0-pro-001` model."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PSJSI1n7YNv2"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SWxKvwd-MSIV"
+      },
+      "source": [
+        "### Authenticate"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JjS8Zy1ojIgc"
+      },
+      "source": [
+        "The Gemini API lets you tune models on your own data. Since it's your data and\n",
+        "your tuned models this needs stricter access controls than API-Keys can provide.\n",
+        "\n",
+        "Before you can run this tutorial, you'll need to\n",
+        "[setup OAuth for your project](https://ai.google.dev/gemini-api/docs/oauth).\n",
+        "\n",
+        "\n",
+        "In Colab the easiest way to get setup is to copy the contents of your `client_secret.json` file into Colab's \"Secrets manager\" (under the key icon in the left panel) with the secret name `CLIENT_SECRET`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9NL1bBYUQ6Fs"
+      },
+      "source": [
+        "This gcloud command turns the `client_secret.json` file into credentials that can be used to authenticate with the service.\n",
+        "\n",
+        "> Important: If you're running this in Colab, **don't just click the link it prints**. That will fail. Follow the instructions and copy the `gcloud` command it prints to your local machine and run it there, then paste the output from your local machine back here.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9FUwyB_MJ0-2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "You are authorizing client libraries without access to a web browser. Please run the following command on a machine with a web browser and copy its output back here. Make sure the installed gcloud version is 372.0.0 or newer.\n",
+            "\n",
+            "gcloud auth application-default login --remote-bootstrap=\"https://accounts.google.com/o/oauth2/auth?response_type=code&client_id=87071151422-n1a3cb6c7fvkfg4gmhdtmn5ulol2l4be.apps.googleusercontent.com&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fcloud-platform+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fgenerative-language.tuning&state=QIyNibWSaTIsozjmvZEkVBo6EcoW0G&access_type=offline&code_challenge=76c1ZiGvKN8cvlYfj3BmbCwE4e7tvrlwaX3REUX25gY&code_challenge_method=S256&token_usage=remote\"\n",
+            "\n",
+            "\n",
+            "Enter the output of the above command: https://localhost:8085/?state=QIyNibWSaTIsozjmvZEkVBo6EcoW0G&code=4/0AeaYSHBKrY911S466QjKQIFODoOPXlO1mWyTYYdrbELIDV6Hw2DKRAyro62BugroSvIWsA&scope=https://www.googleapis.com/auth/cloud-platform%20https://www.googleapis.com/auth/generative-language.tuning\n",
+            "\n",
+            "Credentials saved to file: [/content/.config/application_default_credentials.json]\n",
+            "\n",
+            "These credentials will be used by any library that requests Application Default Credentials (ADC).\n"
+          ]
+        }
+      ],
+      "source": [
+        "try:\n",
+        "  from google.colab import userdata\n",
+        "  import pathlib\n",
+        "  pathlib.Path('client_secret.json').write_text(userdata.get('CLIENT_SECRET'))\n",
+        "\n",
+        "  # Use `--no-browser` in colab\n",
+        "  !gcloud auth application-default login --no-browser --client-id-file client_secret.json --scopes='https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/generative-language.tuning'\n",
+        "except ImportError:\n",
+        "  !gcloud auth application-default login --client-id-file client_secret.json --scopes='https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/generative-language.tuning'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mpe880lfchkp"
+      },
+      "source": [
+        "## Calling the REST API with CURL"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FaSmQssJaMYV"
+      },
+      "source": [
+        "This section gives example curl statements to call the REST API. You will learn how to create a tuning job, check its status and once complete, make an inference call."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g1cQqdypn-Ga"
+      },
+      "source": [
+        "### Set variables"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jrbohImuJFGW"
+      },
+      "source": [
+        "Set variables for recurring values to use for the rest of the REST API calls. The code is using the Python `os` library to set environment variables which is accessible in all the code cells.\n",
+        "\n",
+        "This is specific to the Colab notebook environment. The code in the next code cell is equivalent to running the following commands in a bash terminal.\n",
+        "\n",
+        "```bash\n",
+        "export access_token=$(gcloud auth application-default print-access-token)\n",
+        "export project_id=my-project-id\n",
+        "export base_url=https://generativelanguage.googleapis.com\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5ZUa588km3Lx"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "access_token = !gcloud auth application-default print-access-token\n",
+        "access_token = '\\n'.join(access_token)\n",
+        "\n",
+        "os.environ['access_token'] = access_token\n",
+        "os.environ['project_id'] = \"[Enter your project-id here]\"\n",
+        "os.environ['base_url'] = \"https://generativelanguage.googleapis.com\"\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zKeIDL44l7Q9"
+      },
+      "source": [
+        "### List tuned models\n",
+        "\n",
+        "Verify your authentication setup by listing the currently available tuned models."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VAqw2D3vYWqm"
+      },
+      "outputs": [],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "curl -X GET ${base_url}/v1beta/tunedModels \\\n",
+        "    -H \"Content-Type: application/json\" \\\n",
+        "    -H \"Authorization: Bearer ${access_token}\" \\\n",
+        "    -H \"x-goog-user-project: ${project_id}\"\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t6MN52eRmyjN"
+      },
+      "source": [
+        "### Create tuned model\n",
+        "\n",
+        "To create a tuned model, you need to pass your dataset to the model in the `training_data` field.\n",
+        "\n",
+        "For this example, you will tune a model to generate the next number in the sequence. For example, if the input is `1`, the model should output `2`. If the input is `one hundred`, the output should be `one hundred one`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iBH2NaL6myDN"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{\n",
+            "  \"name\": \"tunedModels/number-generator-model-dzlmi0gswwqb/operations/bvl8dymw0fhw\",\n",
+            "  \"metadata\": {\n",
+            "    \"@type\": \"type.googleapis.com/google.ai.generativelanguage.v1beta.CreateTunedModelMetadata\",\n",
+            "    \"totalSteps\": 38,\n",
+            "    \"tunedModel\": \"tunedModels/number-generator-model-dzlmi0gswwqb\"\n",
+            "  }\n",
+            "}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100  2280    0   296  100  1984    611   4098 --:--:-- --:--:-- --:--:--  4720\n"
+          ]
+        }
+      ],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "curl -X POST $base_url/v1beta/tunedModels \\\n",
+        "    -H 'Content-Type: application/json' \\\n",
+        "    -H \"Authorization: Bearer ${access_token}\" \\\n",
+        "    -H \"x-goog-user-project: ${project_id}\" \\\n",
+        "    -d '\n",
+        "      {\n",
+        "        \"display_name\": \"number generator model\",\n",
+        "        \"base_model\": \"models/gemini-1.0-pro-001\",\n",
+        "        \"tuning_task\": {\n",
+        "          \"hyperparameters\": {\n",
+        "            \"batch_size\": 2,\n",
+        "            \"learning_rate\": 0.001,\n",
+        "            \"epoch_count\":5,\n",
+        "          },\n",
+        "          \"training_data\": {\n",
+        "            \"examples\": {\n",
+        "              \"examples\": [\n",
+        "                {\n",
+        "                    \"text_input\": \"1\",\n",
+        "                    \"output\": \"2\",\n",
+        "                },{\n",
+        "                    \"text_input\": \"3\",\n",
+        "                    \"output\": \"4\",\n",
+        "                },{\n",
+        "                    \"text_input\": \"-3\",\n",
+        "                    \"output\": \"-2\",\n",
+        "                },{\n",
+        "                    \"text_input\": \"twenty two\",\n",
+        "                    \"output\": \"twenty three\",\n",
+        "                },{\n",
+        "                    \"text_input\": \"two hundred\",\n",
+        "                    \"output\": \"two hundred one\",\n",
+        "                },{\n",
+        "                    \"text_input\": \"ninety nine\",\n",
+        "                    \"output\": \"one hundred\",\n",
+        "                },{\n",
+        "                    \"text_input\": \"8\",\n",
+        "                    \"output\": \"9\",\n",
+        "                },{\n",
+        "                    \"text_input\": \"-98\",\n",
+        "                    \"output\": \"-97\",\n",
+        "                },{\n",
+        "                    \"text_input\": \"1,000\",\n",
+        "                    \"output\": \"1,001\",\n",
+        "                },{\n",
+        "                    \"text_input\": \"10,100,000\",\n",
+        "                    \"output\": \"10,100,001\",\n",
+        "                },{\n",
+        "                    \"text_input\": \"thirteen\",\n",
+        "                    \"output\": \"fourteen\",\n",
+        "                },{\n",
+        "                    \"text_input\": \"eighty\",\n",
+        "                    \"output\": \"eighty one\",\n",
+        "                },{\n",
+        "                    \"text_input\": \"one\",\n",
+        "                    \"output\": \"two\",\n",
+        "                },{\n",
+        "                    \"text_input\": \"three\",\n",
+        "                    \"output\": \"four\",\n",
+        "                },{\n",
+        "                    \"text_input\": \"seven\",\n",
+        "                    \"output\": \"eight\",\n",
+        "                }\n",
+        "              ]\n",
+        "            }\n",
+        "          }\n",
+        "        }\n",
+        "      }' | tee tunemodel.json\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ad5ZWb_rmAst"
+      },
+      "source": [
+        "### Get tuned model state"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4hre6xAdrRyS"
+      },
+      "source": [
+        "The state of the model is set to `CREATING` during training and will change to `ACTIVE` once its complete.\n",
+        "\n",
+        "Below is a bit of python code to parse out the generated model name from the response JSON. If you're running this in a terminal you can try using a bash JSON parser to parse the response."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BVs6r1j2YIuv"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "tunedModels/number-generator-model-dzlmi0gswwqb\n"
+          ]
+        }
+      ],
+      "source": [
+        "import json\n",
+        "\n",
+        "first_page = json.load(open('tunemodel.json'))\n",
+        "os.environ['modelname'] = first_page['metadata']['tunedModel']\n",
+        "\n",
+        "print(os.environ['modelname'])\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "I1urOyFnd7_N"
+      },
+      "source": [
+        "  Do another `GET` request with the model name to get the model metadata which includes the state field."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6MdwY7duYmYL"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "  \"state\": \"ACTIVE\",\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100  5921    0  5921    0     0  13164      0 --:--:-- --:--:-- --:--:-- 13157\n"
+          ]
+        }
+      ],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "curl -X GET ${base_url}/v1beta/${modelname} \\\n",
+        "    -H 'Content-Type: application/json' \\\n",
+        "    -H \"Authorization: Bearer ${access_token}\" \\\n",
+        "    -H \"x-goog-user-project: ${project_id}\" | grep state\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Qg8_cgelayQ5"
+      },
+      "source": [
+        "### Run inference\n",
+        "\n",
+        "Once your tuning job is finished, you can use it to generate text with the text service. Try to input a Roman numeral, say, 63 (LXIII)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "15ndxGP_cNBp"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{\n",
+            "  \"candidates\": [\n",
+            "    {\n",
+            "      \"content\": {\n",
+            "        \"parts\": [\n",
+            "          {\n",
+            "            \"text\": \"LXIV\"\n",
+            "          }\n",
+            "        ],\n",
+            "        \"role\": \"model\"\n",
+            "      },\n",
+            "      \"finishReason\": \"STOP\",\n",
+            "      \"index\": 0,\n",
+            "      \"safetyRatings\": [\n",
+            "        {\n",
+            "          \"category\": \"HARM_CATEGORY_SEXUALLY_EXPLICIT\",\n",
+            "          \"probability\": \"NEGLIGIBLE\"\n",
+            "        },\n",
+            "        {\n",
+            "          \"category\": \"HARM_CATEGORY_HATE_SPEECH\",\n",
+            "          \"probability\": \"NEGLIGIBLE\"\n",
+            "        },\n",
+            "        {\n",
+            "          \"category\": \"HARM_CATEGORY_HARASSMENT\",\n",
+            "          \"probability\": \"NEGLIGIBLE\"\n",
+            "        },\n",
+            "        {\n",
+            "          \"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\",\n",
+            "          \"probability\": \"NEGLIGIBLE\"\n",
+            "        }\n",
+            "      ]\n",
+            "    }\n",
+            "  ],\n",
+            "  \"promptFeedback\": {\n",
+            "    \"safetyRatings\": [\n",
+            "      {\n",
+            "        \"category\": \"HARM_CATEGORY_SEXUALLY_EXPLICIT\",\n",
+            "        \"probability\": \"NEGLIGIBLE\"\n",
+            "      },\n",
+            "      {\n",
+            "        \"category\": \"HARM_CATEGORY_HATE_SPEECH\",\n",
+            "        \"probability\": \"NEGLIGIBLE\"\n",
+            "      },\n",
+            "      {\n",
+            "        \"category\": \"HARM_CATEGORY_HARASSMENT\",\n",
+            "        \"probability\": \"NEGLIGIBLE\"\n",
+            "      },\n",
+            "      {\n",
+            "        \"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\",\n",
+            "        \"probability\": \"NEGLIGIBLE\"\n",
+            "      }\n",
+            "    ]\n",
+            "  }\n",
+            "}\n"
+          ]
+        }
+      ],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "curl -X POST $base_url/v1beta/$modelname:generateContent \\\n",
+        "    -H 'Content-Type: application/json' \\\n",
+        "    -H \"Authorization: Bearer ${access_token}\" \\\n",
+        "    -H \"x-goog-user-project: ${project_id}\" \\\n",
+        "    -d '{\n",
+        "        \"contents\": [{\n",
+        "        \"parts\": [{\n",
+        "          \"text\": \"LXIII\"\n",
+        "          }]\n",
+        "        }]\n",
+        "        }' 2> /dev/null"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "r2zvbGFYeR18"
+      },
+      "source": [
+        "The output from your model may or may not be correct. If the tuned model isn't performing up to your required standards, you can try adding more high quality examples, tweaking the hyperparameters or adding a preamble to your examples. You can even create another tuned model based on the first one you created.\n",
+        "\n",
+        "See the [tuning guide](https://ai.google.dev/gemini-api/docs/model-tuning) for more guidance on improving performance."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fHWwC2dqXYUb"
+      },
+      "source": [
+        "## Call the REST API with Python requests\n",
+        "\n",
+        "You can call the rest API with any library that allows you to send http requests.\n",
+        "The next set of examples use the Python requests library, and demonstrates some of the  more advanced features."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jZ0jOtfwaxU9"
+      },
+      "source": [
+        "### Set variables"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "a_QXMiSlav_F"
+      },
+      "outputs": [],
+      "source": [
+        "access_token = !gcloud auth application-default print-access-token\n",
+        "access_token = '\\n'.join(access_token)\n",
+        "\n",
+        "project = '[Enter your project-id here]'\n",
+        "base_url = \"https://generativelanguage.googleapis.com\"\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZWjmINASa-Tg"
+      },
+      "source": [
+        "Import the `requests` library."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QyjlnDjhYWoe"
+      },
+      "outputs": [],
+      "source": [
+        "import requests\n",
+        "import json"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CPtF6TiUhRr2"
+      },
+      "source": [
+        "### List tuned models\n",
+        "\n",
+        "Verify your authentication setup by listing the currently available tuned models."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "r44T32P8ZiH4"
+      },
+      "outputs": [],
+      "source": [
+        "headers={\n",
+        "  'Authorization': 'Bearer ' + access_token,\n",
+        "  'Content-Type': 'application/json',\n",
+        "  'x-goog-user-project': project\n",
+        "}\n",
+        "\n",
+        "result = requests.get(\n",
+        "  url=f'{base_url}/v1beta/tunedModels',\n",
+        "  headers = headers,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rC8lDO8uh1PY"
+      },
+      "outputs": [],
+      "source": [
+        "result.json()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ht_JVuo7hU8n"
+      },
+      "source": [
+        "### Create tuned model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "B4KrCiYITzXy"
+      },
+      "source": [
+        "Same as for the Curl example, you pass in the dataset through the `training_data` field."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hh4gcz6aaDzA"
+      },
+      "outputs": [],
+      "source": [
+        "operation = requests.post(\n",
+        "    url = f'{base_url}/v1beta/tunedModels',\n",
+        "    headers=headers,\n",
+        "    json= {\n",
+        "        \"display_name\": \"number generator\",\n",
+        "        \"base_model\": \"models/gemini-1.0-pro-001\",\n",
+        "        \"tuning_task\": {\n",
+        "          \"hyperparameters\": {\n",
+        "            \"batch_size\": 4,\n",
+        "            \"learning_rate\": 0.001,\n",
+        "            \"epoch_count\":5,\n",
+        "          },\n",
+        "          \"training_data\": {\n",
+        "            \"examples\": {\n",
+        "              \"examples\": [\n",
+        "                {\n",
+        "                    'text_input': '1',\n",
+        "                    'output': '2',\n",
+        "                },{\n",
+        "                    'text_input': '3',\n",
+        "                    'output': '4',\n",
+        "                },{\n",
+        "                    'text_input': '-3',\n",
+        "                    'output': '-2',\n",
+        "                },{\n",
+        "                    'text_input': 'twenty two',\n",
+        "                    'output': 'twenty three',\n",
+        "                },{\n",
+        "                    'text_input': 'two hundred',\n",
+        "                    'output': 'two hundred one',\n",
+        "                },{\n",
+        "                    'text_input': 'ninety nine',\n",
+        "                    'output': 'one hundred',\n",
+        "                },{\n",
+        "                    'text_input': '8',\n",
+        "                    'output': '9',\n",
+        "                },{\n",
+        "                    'text_input': '-98',\n",
+        "                    'output': '-97',\n",
+        "                },{\n",
+        "                    'text_input': '1,000',\n",
+        "                    'output': '1,001',\n",
+        "                },{\n",
+        "                    'text_input': '10,100,000',\n",
+        "                    'output': '10,100,001',\n",
+        "                },{\n",
+        "                    'text_input': 'thirteen',\n",
+        "                    'output': 'fourteen',\n",
+        "                },{\n",
+        "                    'text_input': 'eighty',\n",
+        "                    'output': 'eighty one',\n",
+        "                },{\n",
+        "                    'text_input': 'one',\n",
+        "                    'output': 'two',\n",
+        "                },{\n",
+        "                    'text_input': 'three',\n",
+        "                    'output': 'four',\n",
+        "                },{\n",
+        "                    'text_input': 'seven',\n",
+        "                    'output': 'eight',\n",
+        "                }\n",
+        "              ]\n",
+        "            }\n",
+        "          }\n",
+        "        }\n",
+        "      }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6cMp2okEnIKR"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<Response [200]>"
+            ]
+          },
+          "execution_count": 171,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "operation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "L-scSZQ7aoqG"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'name': 'tunedModels/number-generator-wl1qr34x2py/operations/41vni3zk0a47',\n",
+              " 'metadata': {'@type': 'type.googleapis.com/google.ai.generativelanguage.v1beta.CreateTunedModelMetadata',\n",
+              "  'totalSteps': 19,\n",
+              "  'tunedModel': 'tunedModels/number-generator-wl1qr34x2py'}}"
+            ]
+          },
+          "execution_count": 172,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "operation.json()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2ymEe60etcsu"
+      },
+      "source": [
+        "Set a variable with the name of your tuned model to use for the rest of the calls."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iGyc1yWOCRMz"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'tunedModels/number-generator-wl1qr34x2py'"
+            ]
+          },
+          "execution_count": 173,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "name=operation.json()[\"metadata\"][\"tunedModel\"]\n",
+        "name\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xq1vdaK4hZEI"
+      },
+      "source": [
+        "### Get tuned model state"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xO0Ms0K5hcWM"
+      },
+      "source": [
+        "You can check the progress of your tuning job by checking the state field. `CREATING` means the tuning job is still ongoing and `ACTIVE` means the trainins is complete and the tuned model is ready to use."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ms6u3X7vel12"
+      },
+      "outputs": [],
+      "source": [
+        "tuned_model = requests.get(\n",
+        "    url = f'{base_url}/v1beta/{name}',\n",
+        "    headers=headers,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ohfKsMlYfH7m"
+      },
+      "outputs": [],
+      "source": [
+        "tuned_model.json()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WEbNjFRKaFbX"
+      },
+      "source": [
+        "The code below checks the state field every 5 seconds until it is no longer in the `CREATING` state."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Oleh5athmo-4"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "100.00% - {'step': 19, 'epoch': 5, 'meanLoss': 1.402067, 'computeTime': '2024-03-14T15:11:23.766989274Z'}\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "import time\n",
+        "import pprint\n",
+        "\n",
+        "op_json = operation.json()\n",
+        "response = op_json.get('response')\n",
+        "error = op_json.get('error')\n",
+        "\n",
+        "while response is None and error is None:\n",
+        "    time.sleep(5)\n",
+        "\n",
+        "    operation = requests.get(\n",
+        "        url = f'{base_url}/v1/{op_json[\"name\"]}',\n",
+        "        headers=headers,\n",
+        "    )\n",
+        "\n",
+        "    op_json = operation.json()\n",
+        "    response = op_json.get('response')\n",
+        "    error = op_json.get('error')\n",
+        "\n",
+        "    percent = op_json['metadata'].get('completedPercent')\n",
+        "    if percent is not None:\n",
+        "      print(f\"{percent:.2f}% - {op_json['metadata']['snapshots'][-1]}\")\n",
+        "      print()\n",
+        "\n",
+        "if error is not None:\n",
+        "    raise Exception(error)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sQRt4u1hIZ9a"
+      },
+      "source": [
+        "### Run inference\n",
+        "\n",
+        "Once the tuning job is finished, you can use it to generate text in the same way you would use the base text model. Try to input a Japanese numeral, say, 6 (六)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZVVrt-yiIZ9h"
+      },
+      "outputs": [],
+      "source": [
+        "import time\n",
+        "\n",
+        "m = requests.post(\n",
+        "    url = f'{base_url}/v1beta/{name}:generateContent',\n",
+        "    headers=headers,\n",
+        "    json= {\n",
+        "         \"contents\": [{\n",
+        "             \"parts\": [{\n",
+        "                 \"text\": \"六\"\n",
+        "             }]\n",
+        "          }]\n",
+        "    })"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "i-cFTviPIZ9h"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'candidates': [{'content': {'parts': [{'text': '七'}], 'role': 'model'},\n",
+            "                 'finishReason': 'STOP',\n",
+            "                 'index': 0,\n",
+            "                 'safetyRatings': [{'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT',\n",
+            "                                    'probability': 'NEGLIGIBLE'},\n",
+            "                                   {'category': 'HARM_CATEGORY_HATE_SPEECH',\n",
+            "                                    'probability': 'NEGLIGIBLE'},\n",
+            "                                   {'category': 'HARM_CATEGORY_HARASSMENT',\n",
+            "                                    'probability': 'LOW'},\n",
+            "                                   {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT',\n",
+            "                                    'probability': 'NEGLIGIBLE'}]}],\n",
+            " 'promptFeedback': {'safetyRatings': [{'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT',\n",
+            "                                       'probability': 'NEGLIGIBLE'},\n",
+            "                                      {'category': 'HARM_CATEGORY_HATE_SPEECH',\n",
+            "                                       'probability': 'NEGLIGIBLE'},\n",
+            "                                      {'category': 'HARM_CATEGORY_HARASSMENT',\n",
+            "                                       'probability': 'NEGLIGIBLE'},\n",
+            "                                      {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT',\n",
+            "                                       'probability': 'NEGLIGIBLE'}]}}\n"
+          ]
+        }
+      ],
+      "source": [
+        "import pprint\n",
+        "pprint.pprint(m.json())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kI6PAEx4fN_M"
+      },
+      "source": [
+        "The output from your model may or may not be correct. If the tuned model isn't performing up to your required standards, you can try adding more high quality examples, tweaking the hyperparameters or adding a preamble to your examples."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Udu9f8CasVHe"
+      },
+      "source": [
+        "## Conclusion\n",
+        "\n",
+        "Even though the training data did not contain any reference to Roman or Japanese numerals, the model was able to generalize well after fine-tuning. This way, you can fine-tune models to cater to your use cases."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yRDAbJdYBXuj"
+      },
+      "source": [
+        "## Next steps\n",
+        "\n",
+        "To learn how to use the tuning service with the help of Python SDK for the Gemini API, visit the [tuning quickstart with Python](https://ai.google.dev/tutorials/tuning_quickstart_python). To learn how to use other services in the Gemini API, visit the [Python quickstart](https://ai.google.dev/tutorials/python_quickstart)."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "rest.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemini-api/docs/prompting_with_media.ipynb b/site/en/gemini-api/docs/prompting_with_media.ipynb
new file mode 100644
index 000000000..5096dbc42
--- /dev/null
+++ b/site/en/gemini-api/docs/prompting_with_media.ipynb
@@ -0,0 +1,597 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "084u8u0DpBlo"
+      },
+      "source": [
+        "# Prompting with media files"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZFWzQEqNosrS"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/docs/prompting_with_media\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/prompting_with_media.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/prompting_with_media.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3c5e92a74e64"
+      },
+      "source": [
+        "The Gemini API supports *multimodal* prompting with text, image, and audio data. For small files, you can point the Gemini model\n",
+        "directly to a local file when providing a prompt. Upload larger files with the\n",
+        "[File API](https://ai.google.dev/api/rest/v1beta/files) before including them in\n",
+        "prompts.\n",
+        "\n",
+        "The File API lets you store up to 20GB of files per project, with each file not\n",
+        "exceeding 2GB in size. Files are stored for 48 hours and can be accessed with\n",
+        "your API key for generation within that time period and cannot be downloaded from the API. It is available at no cost in all regions where the [Gemini API is\n",
+        "available](https://ai.google.dev/available_regions).\n",
+        "\n",
+        "The File API handles inputs that can be used to generate content with [`model.generateContent`](https://ai.google.dev/api/rest/v1/models/generateContent) or [`model.streamGenerateContent`](https://ai.google.dev/api/rest/v1/models/streamGenerateContent). For information on valid file formats (MIME types) and supported models, see [Supported file formats](#supported_file_formats).\n",
+        "\n",
+        "This guide shows how to use the File API to upload media files and include them in a `GenerateContent` call to the Gemini API. For more information, see the [code\n",
+        "samples](https://github.com/google-gemini/gemini-api-cookbook/tree/main/quickstarts/file-api).\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VxCstRHvpX0r"
+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "Before you use the File API, you need to install the Gemini API SDK package and configure an API key. This section describes how to complete these setup steps."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G6J_rV2ipmj_"
+      },
+      "source": [
+        "### Install the Python SDK and import packages\n",
+        "\n",
+        "The Python SDK for the Gemini API is contained in the [google-generativeai](https://pypi.org/project/google-generativeai/) package. Install the dependency using pip."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mN8x8DPgu9Kv"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q -U google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NInUZ4hwDq6d"
+      },
+      "source": [
+        "Import the necessary packages."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0x3xmmWrDtEH"
+      },
+      "outputs": [],
+      "source": [
+        "import google.generativeai as genai\n",
+        "from IPython.display import Markdown"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "l8g4hTRotheH"
+      },
+      "source": [
+        "### Setup your API key\n",
+        "\n",
+        "The File API uses API keys for authentication and access. Uploaded files are associated with the project linked to the API key. Unlike other Gemini APIs that use API keys, your API key also grants access to data you've uploaded to the File API, so take extra care in keeping your API key secure. For more on keeping your keys\n",
+        "secure, see [Best practices for using API\n",
+        "keys](https://support.google.com/googleapi/answer/6310037).\n",
+        "\n",
+        "Store your API key in a Colab Secret named `GOOGLE_API_KEY`. If you don't already have an API key, or are unfamiliar with Colab Secrets, refer to the [Authentication quickstart](https://github.com/google-gemini/gemini-api-cookbook/blob/main/quickstarts/Authentication.ipynb)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d6lYXRcjthKV"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import userdata\n",
+        "GOOGLE_API_KEY=userdata.get('GOOGLE_API_KEY')\n",
+        "\n",
+        "genai.configure(api_key=GOOGLE_API_KEY)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c-z4zsCUlaru"
+      },
+      "source": [
+        "## Prompting with images\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rsdNkDszLBmQ"
+      },
+      "source": [
+        "### Upload an image file to the File API"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "o1K81yn9mFBo"
+      },
+      "source": [
+        "In this tutorial, you upload a sample image to the API and use it to generate content.\n",
+        "\n",
+        "Refer to the [Appendix section](#uploading_files_to_colab) to learn how to upload your own file."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lC6sS6DnmGmi"
+      },
+      "outputs": [],
+      "source": [
+        "!curl -o image.jpg https://storage.googleapis.com/generativeai-downloads/images/jetpack.jpg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "N9NxXGZKKusG"
+      },
+      "outputs": [],
+      "source": [
+        "# Upload the file\n",
+        "sample_file = genai.upload_file(path=\"image.jpg\",\n",
+        "                            display_name=\"Sample drawing\")\n",
+        "\n",
+        "print(f\"Uploaded file '{sample_file.display_name}' as: {sample_file.uri}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cto22vhKOvGQ"
+      },
+      "source": [
+        "The `response` shows that the File API stored the specified `display_name` for the uploaded file and a `uri` to reference the file in Gemini API calls. Use `response` to track how uploaded files are mapped to URIs.\n",
+        "\n",
+        "Depending on your use case, you can also store the URIs in structures such as a `dict` or a database."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ds5iJlaembWe"
+      },
+      "source": [
+        "### Get file\n",
+        "\n",
+        "After uploading the file, verify that the File API has successfully received it by calling `files.get`.\n",
+        "\n",
+        "`files.get` lets you get the file metadata that have been uploaded to the File API that are associated with the Cloud project your API key belongs to. Only the `name` (and by extension, the `uri`) are unique. Only use the `displayName` to identify files if you manage uniqueness yourself."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kLFsVLFHOWSV"
+      },
+      "outputs": [],
+      "source": [
+        "file = genai.get_file(name=sample_file.name)\n",
+        "print(f\"Retrieved file '{file.display_name}' as: {sample_file.uri}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EPPOECHzsIGJ"
+      },
+      "source": [
+        "### Generate content\n",
+        "\n",
+        "After uploading the file, you can make `GenerateContent` requests that reference the File API URI. In this example, you create a prompt that starts with a text followed by the uploaded image."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZYVFqmLkl5nE"
+      },
+      "outputs": [],
+      "source": [
+        "# Set the model to Gemini 1.5 Pro.\n",
+        "model = genai.GenerativeModel(model_name=\"models/gemini-1.5-pro-latest\")\n",
+        "\n",
+        "response = model.generate_content([\"Describe the image with a creative description.\", sample_file])\n",
+        "\n",
+        "Markdown(\">\" + response.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IrPDYdQSKTg4"
+      },
+      "source": [
+        "### Delete files\n",
+        "\n",
+        "Files are automatically deleted after 2 days. You can also manually delete them using `files.delete()`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d4eO8ZXoKdZf"
+      },
+      "outputs": [],
+      "source": [
+        "genai.delete_file(sample_file.name)\n",
+        "print(f'Deleted {sample_file.display_name}.')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TaUZc1mvLkHY"
+      },
+      "source": [
+        "## Prompting with videos"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MNvhBdoDFnTC"
+      },
+      "source": [
+        "### Upload a video file to the File API\n",
+        "\n",
+        "The File API accepts video file formats directly. This example uses the short film \"Big Buck Bunny\".\n",
+        "\n",
+        "> \"Big Buck Bunny\" is (c) copyright 2008, Blender Foundation / www.bigbuckbunny.org and [licensed](https://peach.blender.org/about/) under the [Creative Commons Attribution 3.0](http://creativecommons.org/licenses/by/3.0/) License.\n",
+        "\n",
+        "Refer to the [Appendix section](#uploading_files_to_colab) to learn how to upload your own file."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "V4XeFdX1rxaE"
+      },
+      "outputs": [],
+      "source": [
+        "!wget https://download.blender.org/peach/bigbuckbunny_movies/BigBuckBunny_320x180.mp4"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_HzrDdp2Q1Cu"
+      },
+      "outputs": [],
+      "source": [
+        "video_file_name = \"BigBuckBunny_320x180.mp4\"\n",
+        "\n",
+        "print(f\"Uploading file...\")\n",
+        "video_file = genai.upload_file(path=video_file_name)\n",
+        "print(f\"Completed upload: {video_file.uri}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oOZmTUb4FWOa"
+      },
+      "source": [
+        "### Get file\n",
+        "\n",
+        "Verify the API has successfully received the files by calling the `files.get` method.\n",
+        "\n",
+        "NOTE: Video files have a `State` field in the File API. When a video is uploaded, it will be in `PROCESSING` state until it is ready for inference. Only `ACTIVE` files can be used for model inference."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SHMVCWHkFhJW"
+      },
+      "outputs": [],
+      "source": [
+        "import time\n",
+        "\n",
+        "while video_file.state.name == \"PROCESSING\":\n",
+        "    print('.', end='')\n",
+        "    time.sleep(10)\n",
+        "    video_file = genai.get_file(video_file.name)\n",
+        "\n",
+        "if video_file.state.name == \"FAILED\":\n",
+        "  raise ValueError(video_file.state.name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zS5NmQeXLqeS"
+      },
+      "source": [
+        "### Generate content\n",
+        "\n",
+        "After the video has been uploaded, you can make `GenerateContent` requests that reference the File API URI."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ypZuGQ-2LqeS"
+      },
+      "outputs": [],
+      "source": [
+        "# Create the prompt.\n",
+        "prompt = \"Describe this video.\"\n",
+        "\n",
+        "# Set the model to Gemini 1.5 Pro.\n",
+        "model = genai.GenerativeModel(model_name=\"models/gemini-1.5-pro-latest\")\n",
+        "\n",
+        "# Make the LLM request.\n",
+        "print(\"Making LLM inference request...\")\n",
+        "response = model.generate_content([prompt, video_file],\n",
+        "                                  request_options={\"timeout\": 600})\n",
+        "print(response.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "diCy9BgjLqeS"
+      },
+      "source": [
+        "### Delete files\n",
+        "\n",
+        "Files are automatically deleted after 2 days or you can manually delete them using `files.delete()`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YYyi5PrKLqeb"
+      },
+      "outputs": [],
+      "source": [
+        "genai.delete_file(video_file.name)\n",
+        "print(f'Deleted file {video_file.uri}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OxgouC6i7exO"
+      },
+      "source": [
+        "## Supported file formats\n",
+        "\n",
+        "Gemini models support prompting with multiple file formats. This section explains considerations in using general media formats for prompting, specifically image, audio, video, and plain text files. You can use media files for prompting only with specific model versions, as shown in the following table.\n",
+        "\n",
+        "<table>\n",
+        "  <thead>\n",
+        "    <tr>\n",
+        "      <th><strong>Model</strong></th>\n",
+        "      <th><strong>Images</strong></th>\n",
+        "      <th><strong>Audio</strong></th>\n",
+        "      <th><strong>Video</strong></th>\n",
+        "      <th><strong>Plain text</strong></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <td>Gemini 1.5 Pro (release 008 and later)</td>\n",
+        "      <td>✔ (3600 max image files)</td>\n",
+        "      <td>✔</td>\n",
+        "      <td>✔</td>\n",
+        "      <td>✔</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <td>Gemini Pro Vision</td>\n",
+        "      <td>✔ (16 max image files)</td>\n",
+        "      <td></td>\n",
+        "      <td></td>\n",
+        "      <td>✔</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "\n",
+        "### Image formats\n",
+        "\n",
+        "You can use image data for prompting Gemini models. When you use images for prompting, they are subject to the following limitations and requirements:\n",
+        "\n",
+        "-   Images must be in one of the following image data [MIME types](https://developers.google.com/drive/api/guides/ref-export-formats):\n",
+        "    -   PNG - image/png\n",
+        "    -   JPEG - image/jpeg\n",
+        "    -   WEBP - image/webp\n",
+        "    -   HEIC - image/heic\n",
+        "    -   HEIF - image/heif\n",
+        "-   Maximum of 3600 images for `gemini-1.5-pro`\n",
+        "-   No specific limits to the number of pixels in an image; however, larger images are scaled down to fit a maximum resolution of 3072 x 3072 while preserving their original aspect ratio.\n",
+        "\n",
+        "### Audio formats\n",
+        "\n",
+        "You can use audio data for prompting with the `gemini-1.5-pro` model. When you use audio for prompting, they are subject to the following limitations and requirements:\n",
+        "\n",
+        "-   Audio data is supported in the following common audio format [MIME types](https://developers.google.com/drive/api/guides/ref-export-formats):\n",
+        "    -   WAV - audio/wav\n",
+        "    -   MP3 - audio/mp3\n",
+        "    -   AIFF - audio/aiff\n",
+        "    -   AAC - audio/aac\n",
+        "    -   OGG Vorbis - audio/ogg\n",
+        "    -   FLAC - audio/flac\n",
+        "-   The maximum supported length of audio data in a single prompt is 9.5 hours.\n",
+        "-   Audio files are resampled down to a 16 Kbps data resolution, and multiple channels of audio are combined into a single channel.\n",
+        "-   There is no specific limit to the number of audio files in a single prompt, however the total combined length of all audio files in a single prompt cannot exceed 9.5 hours.\n",
+        "\n",
+        "### Video formats\n",
+        "\n",
+        "You can use video data for prompting with the `gemini-1.5-pro` model.\n",
+        "\n",
+        "- Video data is supported in the following common video format [MIME types](https://developers.google.com/drive/api/guides/ref-export-formats):\n",
+        "  -   video/mp4\n",
+        "  -   video/mpeg\n",
+        "  -   video/mov\n",
+        "  -   video/avi\n",
+        "  -   video/x-flv\n",
+        "  -   video/mpg\n",
+        "  -   video/webm\n",
+        "  -   video/wmv\n",
+        "  -   video/3gpp\n",
+        "\n",
+        "- The File API service currently samples videos into images at 1 frame per second (FPS) and may be subject to change to provide the best inference quality.\n",
+        "- Video limits depend on the context size limit of the model used for inference. For example, `gemini-1.5-pro` has a maximum video length of ~60 minutes.\n",
+        "\n",
+        "### Plain text formats\n",
+        "\n",
+        "The File API supports uploading plain text files with the following [MIME types](https://developers.google.com/drive/api/guides/ref-export-formats):\n",
+        "-   text/plain\n",
+        "-   text/html\n",
+        "-   text/css\n",
+        "-   text/javascript\n",
+        "-   application/x-javascript\n",
+        "-   text/x-typescript\n",
+        "-   application/x-typescript\n",
+        "-   text/csv\n",
+        "-   text/markdown\n",
+        "-   text/x-python\n",
+        "-   application/x-python-code\n",
+        "-   application/json\n",
+        "-   text/xml\n",
+        "-   application/rtf\n",
+        "-   text/rtf\n",
+        "\n",
+        "For plain text files with a MIME type not on the list, you can try specifying\n",
+        "one of the above MIME types manually."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rIoNRWn0nwUy"
+      },
+      "source": [
+        "## Appendix: Uploading files to Colab\n",
+        "<a name=\"uploading_files_to_colab\"></a>\n",
+        "This notebook uses the File API with files that were downloaded from the internet. If you're running this in Colab and want to use your own files, you first need to upload them to the colab instance.\n",
+        "\n",
+        "First, click **Files** on the left sidebar, then click the **Upload** button:\n",
+        "\n",
+        "<img width=400 src=\"https://ai.google.dev/tutorials/images/colab_upload.png\">\n",
+        "\n",
+        "Next, you'll upload that file to the File API. In the form for the code cell below, enter the filename for the file you uploaded and provide an appropriate display name for the file, then run the cell.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VqAwyEa3nxaZ"
+      },
+      "outputs": [],
+      "source": [
+        "my_filename = \"image.jpg\" # @param {type:\"string\"}\n",
+        "my_file_display_name = \"Sample Image\" # @param {type:\"string\"}\n",
+        "\n",
+        "my_file = genai.upload_file(path=my_filename,\n",
+        "                            display_name=my_file_display_name)\n",
+        "print(f\"Uploaded file '{my_file.display_name}' as: {my_file.uri}\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "prompting_with_media.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemini-api/docs/semantic_retrieval.ipynb b/site/en/gemini-api/docs/semantic_retrieval.ipynb
new file mode 100644
index 000000000..637ac31ae
--- /dev/null
+++ b/site/en/gemini-api/docs/semantic_retrieval.ipynb
@@ -0,0 +1,1040 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1_3oPpdBAduw"
+      },
+      "source": [
+        "# Get started with semantic retrieval"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZFWzQEqNosrS"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/docs/semantic_retrieval\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/semantic_retrieval.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/semantic_retrieval.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AVW9cM0nuh1-"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "Large Language Models (LLMs) can learn new abilities without directly being trained on them. However, LLMs have been known to \"hallucinate\" when tasked with providing responses for questions they have not been trained on. This is partly because LLMs are unaware of events after training. It is also very difficult to trace the sources from which LLMs draw their responses from. For reliable, scalable applications, it is important that an LLM provides responses that are grounded in facts and is able to cite its information sources.\n",
+        "\n",
+        "A common approach used to overcome these constraints is called Retrieval Augmented Generation (RAG), which augments the prompt sent to an LLM with relevant data retrieved from an external knowledge base through an Information Retrieval (IR) mechanism. The knowledge base can be your own corpora of documents, databases, or APIs.\n",
+        "\n",
+        "This notebook walks you through a workflow to improve an LLM's response by augmenting its knowledge with external text corpora and performing semantic information retrieval to answer questions using the Semantic Retriever and the Attributed Question & Answering (AQA) APIs of the Gemini API.\n",
+        "\n",
+        "Note: This API is currently in [beta](https://ai.google.dev/gemini-api/docs/api-versions) and is [only available in certain regions](https://ai.google.dev/gemini-api/docs/available-regions).\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7jH3FO_BDua0"
+      },
+      "source": [
+        "## Setup\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uQwqEaFLHGlL"
+      },
+      "source": [
+        "### Import the Gemini API"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6029CKnWG75v"
+      },
+      "outputs": [],
+      "source": [
+        "# Install the Client library (Semantic Retriever is only supported for versions >0.4.0)\n",
+        "!pip install -U google.ai.generativelanguage"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G4D97GX5BRXb"
+      },
+      "source": [
+        "## Authenticate\n",
+        "\n",
+        "The Semantic Retriever API lets you perform semantic search on your own data. Since it's **your data**, this needs stricter access controls than API keys. Authenticate with OAuth with [service accounts](#service-oauth) or through your [user credentials](#user-oauth).\n",
+        "\n",
+        "This quickstart uses a simplified authentication approach meant for a testing environment, and service account setups are typically easier to start from. For a production environment, learn about [authentication and authorization](https://developers.google.com/workspace/guides/auth-overview){:.external} before choosing the [access credentials](https://developers.google.com/workspace/guides/create-credentials#choose_the_access_credential_that_is_right_for_you){:.external} that are appropriate for your app.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eLjhFIOQ7_Dk"
+      },
+      "source": [
+        "### Setup OAuth using service accounts {:#service-oauth}\n",
+        "\n",
+        "Follow the steps below to setup OAuth using service accounts:\n",
+        "\n",
+        "\n",
+        "1. Enable the [Gemini API (Generative Language API)](https://console.cloud.google.com/flows/enableapi?apiid=generativelanguage.googleapis.com){:.external}.\n",
+        "\n",
+        "<img width=400 src=\"https://ai.google.dev/tutorials/images/semantic_retriever_enable_api.png\">\n",
+        "\n",
+        "2. Create the Service Account by following the [documentation](https://developers.google.com/identity/protocols/oauth2/service-account#creatinganaccount){:.external}.\n",
+        "\n",
+        " * After creating the service account, generate a service account key.\n",
+        "\n",
+        "<img width=400 src=\"https://ai.google.dev/tutorials/images/semantic_retriever_service_account.png\">\n",
+        "\n",
+        "3. Upload your service account file by using the file icon on the left sidebar, then the upload icon, as shown in the screenshot below.\n",
+        "\n",
+        " * Rename the uploaded file to `service_account_key.json` or change the variable `service_account_file_name` in the code below.\n",
+        "\n",
+        "<img width=400 src=\"https://ai.google.dev/tutorials/images/colab_upload.png\">"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "akwWUSrp8Bx2"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -U google-auth-oauthlib"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2jZmqVCj8FKa"
+      },
+      "outputs": [],
+      "source": [
+        "service_account_file_name = 'service_account_key.json'\n",
+        "\n",
+        "from google.oauth2 import service_account\n",
+        "\n",
+        "credentials = service_account.Credentials.from_service_account_file(service_account_file_name)\n",
+        "\n",
+        "scoped_credentials = credentials.with_scopes(\n",
+        "    ['https://www.googleapis.com/auth/cloud-platform', 'https://www.googleapis.com/auth/generative-language.retriever'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Qqjs2tvvicqq"
+      },
+      "source": [
+        "Initialize the client library using the service account credentials."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "P719DMtK8t-p"
+      },
+      "outputs": [],
+      "source": [
+        "import google.ai.generativelanguage as glm\n",
+        "generative_service_client = glm.GenerativeServiceClient(credentials=scoped_credentials)\n",
+        "retriever_service_client = glm.RetrieverServiceClient(credentials=scoped_credentials)\n",
+        "permission_service_client = glm.PermissionServiceClient(credentials=scoped_credentials)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4EQJD2PWD56T"
+      },
+      "source": [
+        "## Create a corpus {:#create-corpus}\n",
+        "\n",
+        "The Semantic Retriever API lets you define up to 5 custom text corpora per project. You can specify either of the following fields while defining your corpora:\n",
+        "\n",
+        "  * `name`: The `Corpus` resource name (ID). Must contain only a maximum of 40 alphanumeric characters. If the `name` is empty on creation, a unique name will be generated with a maximum length of 40 characters with a prefix from the `display_name` and a 12 character random suffix.\n",
+        "  * `display_name`: The human-readable display name for the `Corpus`. Must contain only a maximum of 512 characters, including alphanumerics, spaces, and dashes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AaPZiXVwEDHZ"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "name: \"corpora/google-for-developers-blog-dqrtz8rs0jg\"\n",
+            "display_name: \"Google for Developers Blog\"\n",
+            "create_time {\n",
+            "  seconds: 1713497533\n",
+            "  nanos: 587977000\n",
+            "}\n",
+            "update_time {\n",
+            "  seconds: 1713497533\n",
+            "  nanos: 587977000\n",
+            "}\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "example_corpus = glm.Corpus(display_name=\"Google for Developers Blog\")\n",
+        "create_corpus_request = glm.CreateCorpusRequest(corpus=example_corpus)\n",
+        "\n",
+        "# Make the request\n",
+        "create_corpus_response = retriever_service_client.create_corpus(create_corpus_request)\n",
+        "\n",
+        "# Set the `corpus_resource_name` for subsequent sections.\n",
+        "corpus_resource_name = create_corpus_response.name\n",
+        "print(create_corpus_response)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gQr7gDKVErdg"
+      },
+      "source": [
+        "### Get the created corpus\n",
+        "\n",
+        "Use the `GetCorpusRequest` method to programmatically access the `Corpus` you created above. The value of the `name` parameter refers to the full resource name of the `Corpus` and is set in the cell above as `corpus_resource_name`. The expected format is `corpora/corpus-123`.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8zhexURaEtVa"
+      },
+      "outputs": [],
+      "source": [
+        "get_corpus_request = glm.GetCorpusRequest(name=corpus_resource_name)\n",
+        "\n",
+        "# Make the request\n",
+        "get_corpus_response = retriever_service_client.get_corpus(get_corpus_request)\n",
+        "\n",
+        "# Print the response\n",
+        "print(get_corpus_response)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lbrLPNXRF2qP"
+      },
+      "source": [
+        "## Create a document\n",
+        "\n",
+        "A `Corpus` can contain up to 10,000 `Document`s. You can specify either of the following fields while defining your documents:\n",
+        "\n",
+        "  * `name`: The `Document` resource name (ID). Must contain only a maximum of 40 characters (alphanumeric or dashes only). The ID cannot start or end with a\n",
+        "  dash. If the name is empty on creation, a unique name will be derived from\n",
+        "  `display_name` along with a 12 character random suffix.\n",
+        "  * `display_name`: The human-readable display name. Must contain only a maximum of 512 characters, including alphanumerics, spaces, and dashes.\n",
+        "\n",
+        "`Document`s also support up to 20 user-specified `custom_metadata` fields, specified as key-value pairs. Custom metadata can be strings, lists of strings, or numeric. Note that lists of strings can support a maximum of 10 values and numeric values are represented as floating-point numbers in the API."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "q7nwjPaGF_Nj"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a document with a custom display name.\n",
+        "example_document = glm.Document(display_name=\"Introducing Project IDX, An Experiment to Improve Full-stack, Multiplatform App Development\")\n",
+        "\n",
+        "# Add metadata.\n",
+        "# Metadata also supports numeric values not specified here\n",
+        "document_metadata = [\n",
+        "    glm.CustomMetadata(key=\"url\", string_value=\"https://developers.googleblog.com/2023/08/introducing-project-idx-experiment-to-improve-full-stack-multiplatform-app-development.html\")]\n",
+        "example_document.custom_metadata.extend(document_metadata)\n",
+        "\n",
+        "# Make the request\n",
+        "# corpus_resource_name is a variable set in the \"Create a corpus\" section.\n",
+        "create_document_request = glm.CreateDocumentRequest(parent=corpus_resource_name, document=example_document)\n",
+        "create_document_response = retriever_service_client.create_document(create_document_request)\n",
+        "\n",
+        "# Set the `document_resource_name` for subsequent sections.\n",
+        "document_resource_name = create_document_response.name\n",
+        "print(create_document_response)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AiKINhAtGd8e"
+      },
+      "source": [
+        "### Get the created document\n",
+        "\n",
+        "Use the `GetDocumentRequest` method to programmatically access the document you created above. The value of the `name` parameter refers to the full resource name of the document and is set in the cell above as `document_resource_name`. The expected format is `corpora/corpus-123/documents/document-123`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lTTc_gtWGfpe"
+      },
+      "outputs": [],
+      "source": [
+        "get_document_request = glm.GetDocumentRequest(name=document_resource_name)\n",
+        "\n",
+        "# Make the request\n",
+        "# document_resource_name is a variable set in the \"Create a document\" section.\n",
+        "get_document_response = retriever_service_client.get_document(get_document_request)\n",
+        "\n",
+        "# Print the response\n",
+        "print(get_document_response)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OuuDtAYBOX55"
+      },
+      "source": [
+        "## Ingest & Chunk a Document\n",
+        "\n",
+        "To improve the relevance of content returned by the vector database during semantic retrieval, break down large documents into smaller pieces or **chunks** while ingesting the document.\n",
+        "\n",
+        "A `Chunk` is a subpart of a `Document` that is treated as an independent unit for the purposes of vector representation and storage. A `Chunk` can have a maximum of 2043 tokens. A `Corpus` can have a maximum of 1 million `Chunk`s.\n",
+        "\n",
+        "Similar to `Document`s, `Chunks` also support up to 20 user-specified `custom_metadata` fields, specified as key-value pairs. Custom metadata can be strings, lists of strings, or numeric. Note that lists of strings can support a maximum of 10 values and numeric values are represented as floating-point numbers in the API.\n",
+        "\n",
+        "This guide uses Google's [Open Source HtmlChunker](https://github.com/google/labs-prototypes/tree/main/seeds/chunker-python){:.external}.\n",
+        "\n",
+        "Other chunkers you can use include [LangChain](https://python.langchain.com/docs/get_started/introduction){:.external} or [LlamaIndex](https://www.llamaindex.ai/){:.external}."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SkMzKWTbPBOU"
+      },
+      "source": [
+        "### Ingest HTML and chunk via HtmlChunker"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nh7mwa7MPEZ9"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install google-labs-html-chunker\n",
+        "\n",
+        "from google_labs_html_chunker.html_chunker import HtmlChunker\n",
+        "\n",
+        "from urllib.request import urlopen"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GUZYcNrEM-AK"
+      },
+      "source": [
+        "Get the HTML DOM for a website. Here, the HTML is read directly, but it would\n",
+        "be better to get HTML post-rendering to include Javascript-injected HTML\n",
+        "such as `document.documentElement.innerHTML`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lgtl2Ow5PO94"
+      },
+      "outputs": [],
+      "source": [
+        "with(urlopen(\"https://developers.googleblog.com/2023/08/introducing-project-idx-experiment-to-improve-full-stack-multiplatform-app-development.html\")) as f:\n",
+        "  html = f.read().decode(\"utf-8\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6oMLH2BINIde"
+      },
+      "source": [
+        "Break down the text document into passages and create `Chunk`s from these passages. This step creates the `Chunk` objects themselves and the next section uploads them to the Semantic Retriever API."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "z5G0oN4PNJEf"
+      },
+      "outputs": [],
+      "source": [
+        "# Chunk the file using HtmlChunker\n",
+        "chunker = HtmlChunker(\n",
+        "    max_words_per_aggregate_passage=200,\n",
+        "    greedily_aggregate_sibling_nodes=True,\n",
+        "    html_tags_to_exclude={\"noscript\", \"script\", \"style\"},\n",
+        ")\n",
+        "passages = chunker.chunk(html)\n",
+        "print(passages)\n",
+        "\n",
+        "\n",
+        "# Create `Chunk` entities.\n",
+        "chunks = []\n",
+        "for passage in passages:\n",
+        "    chunk = glm.Chunk(data={'string_value': passage})\n",
+        "    # Optionally, you can add metadata to a chunk\n",
+        "    chunk.custom_metadata.append(glm.CustomMetadata(key=\"tags\",\n",
+        "                                                    string_list_value=glm.StringList(\n",
+        "                                                        values=[\"Google For Developers\", \"Project IDX\", \"Blog\", \"Announcement\"])))\n",
+        "    chunk.custom_metadata.append(glm.CustomMetadata(key=\"chunking_strategy\",\n",
+        "                                                    string_value=\"greedily_aggregate_sibling_nodes\"))\n",
+        "    chunk.custom_metadata.append(glm.CustomMetadata(key = \"publish_date\",\n",
+        "                                                    numeric_value = 20230808))\n",
+        "    chunks.append(chunk)\n",
+        "print(chunks)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PIqgob2fUxeO"
+      },
+      "source": [
+        "## Batch create chunks\n",
+        "\n",
+        "Create chunks in batches. You can specify a maximum of 100 chunks per batch request.\n",
+        "\n",
+        "Use `CreateChunk()` for single chunk creation.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iZX3oiOiU0YA"
+      },
+      "outputs": [],
+      "source": [
+        "# Option 1: Use HtmlChunker in the section above.\n",
+        "# `chunks` is the variable set from the section above.\n",
+        "create_chunk_requests = []\n",
+        "for chunk in chunks:\n",
+        "  create_chunk_requests.append(glm.CreateChunkRequest(parent=document_resource_name, chunk=chunk))\n",
+        "\n",
+        "# Make the request\n",
+        "request = glm.BatchCreateChunksRequest(parent=document_resource_name, requests=create_chunk_requests)\n",
+        "response = retriever_service_client.batch_create_chunks(request)\n",
+        "print(response)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xnn-IDSPGLcf"
+      },
+      "source": [
+        "Alternatively, you can make chunks without using the HtmlChunker."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fE7_ueBPGMib"
+      },
+      "outputs": [],
+      "source": [
+        "# Add up to 100 CreateChunk requests per batch request.\n",
+        "# document_resource_name is a variable set in the \"Create a document\" section.\n",
+        "chunks = []\n",
+        "chunk_1 = glm.Chunk(data={'string_value': \"Chunks support user specified metadata.\"})\n",
+        "chunk_1.custom_metadata.append(glm.CustomMetadata(key=\"section\",\n",
+        "                                                  string_value=\"Custom metadata filters\"))\n",
+        "chunk_2 = glm.Chunk(data={'string_value': \"The maximum number of metadata supported is 20\"})\n",
+        "chunk_2.custom_metadata.append(glm.CustomMetadata(key = \"num_keys\",\n",
+        "                                                  numeric_value = 20))\n",
+        "chunks = [chunk_1, chunk_2]\n",
+        "create_chunk_requests = []\n",
+        "for chunk in chunks:\n",
+        "  create_chunk_requests.append(glm.CreateChunkRequest(parent=document_resource_name, chunk=chunk))\n",
+        "\n",
+        "# Make the request\n",
+        "request = glm.BatchCreateChunksRequest(parent=document_resource_name, requests=create_chunk_requests)\n",
+        "response = retriever_service_client.batch_create_chunks(request)\n",
+        "print(response)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "r8kTaWguU1ws"
+      },
+      "source": [
+        "### List `Chunk`s and get state\n",
+        "\n",
+        "Use the `ListChunksRequest` method to get all available `Chunk`s as a paginated list with a maximum size limit of 100 `Chunk`s per page, sorted in ascending order of `Chunk.create_time`. If you do not specify a limit, a maximum of 10 `Chunk`s are returned.\n",
+        "\n",
+        "Provide the `next_page_token` returned in the `ListChunksRequest` response as an argument to the next request to retrieve the next page. Note that when paginating, all other parameters provided to `ListChunks` must match the call that provided the page token.\n",
+        "\n",
+        "All `Chunk`s return a `state`. Use this to check the state of the `Chunks` before querying a `Corpus`. `Chunk` states include - `UNSPECIFIED`, `PENDING_PROCESSING`, `ACTIVE`, and `FAILED`. You can only query `ACTIVE` `Chunk`s."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DsNm8oJN6j18"
+      },
+      "outputs": [],
+      "source": [
+        "# Make the request\n",
+        "request = glm.ListChunksRequest(parent=document_resource_name)\n",
+        "list_chunks_response = retriever_service_client.list_chunks(request)\n",
+        "for index, chunks in enumerate(list_chunks_response.chunks):\n",
+        "  print(f'\\nChunk # {index + 1}')\n",
+        "  print(f'Resource Name: {chunks.name}')\n",
+        "  # Only ACTIVE chunks can be queried.\n",
+        "  print(f'State: {glm.Chunk.State(chunks.state).name}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IRg1uAZSAaWS"
+      },
+      "source": [
+        "## Ingest another document\n",
+        "\n",
+        "Add another `Document` via HtmlChunker and add filters."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "quSpcAkRAX7L"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a document with a custom display name.\n",
+        "example_document = glm.Document(display_name=\"How it’s Made: Interacting with Gemini through multimodal prompting\")\n",
+        "\n",
+        "# Add document metadata.\n",
+        "# Metadata also supports numeric values not specified here\n",
+        "document_metadata = [\n",
+        "    glm.CustomMetadata(key=\"url\", string_value=\"https://developers.googleblog.com/2023/12/how-its-made-gemini-multimodal-prompting.html\")]\n",
+        "example_document.custom_metadata.extend(document_metadata)\n",
+        "\n",
+        "# Make the CreateDocument request\n",
+        "# corpus_resource_name is a variable set in the \"Create a corpus\" section.\n",
+        "create_document_request = glm.CreateDocumentRequest(parent=corpus_resource_name, document=example_document)\n",
+        "create_document_response = retriever_service_client.create_document(create_document_request)\n",
+        "\n",
+        "# Set the `document_resource_name` for subsequent sections.\n",
+        "document_resource_name = create_document_response.name\n",
+        "print(create_document_response)\n",
+        "\n",
+        "# Chunks - add another webpage from Google for Developers\n",
+        "with(urlopen(\"https://developers.googleblog.com/2023/12/how-its-made-gemini-multimodal-prompting.html\")) as f:\n",
+        "  html = f.read().decode(\"utf-8\")\n",
+        "\n",
+        "# Chunk the file using HtmlChunker\n",
+        "chunker = HtmlChunker(\n",
+        "    max_words_per_aggregate_passage=100,\n",
+        "    greedily_aggregate_sibling_nodes=False,\n",
+        ")\n",
+        "passages = chunker.chunk(html)\n",
+        "\n",
+        "# Create `Chunk` entities.\n",
+        "chunks = []\n",
+        "for passage in passages:\n",
+        "    chunk = glm.Chunk(data={'string_value': passage})\n",
+        "    chunk.custom_metadata.append(glm.CustomMetadata(key=\"tags\",\n",
+        "                                                    string_list_value=glm.StringList(\n",
+        "                                                        values=[\"Google For Developers\", \"Gemini API\", \"Blog\", \"Announcement\"])))\n",
+        "    chunk.custom_metadata.append(glm.CustomMetadata(key=\"chunking_strategy\",\n",
+        "                                                    string_value=\"no_aggregate_sibling_nodes\"))\n",
+        "    chunk.custom_metadata.append(glm.CustomMetadata(key = \"publish_date\",\n",
+        "                                                    numeric_value = 20231206))\n",
+        "    chunks.append(chunk)\n",
+        "\n",
+        "# Make the request\n",
+        "create_chunk_requests = []\n",
+        "for chunk in chunks:\n",
+        "  create_chunk_requests.append(glm.CreateChunkRequest(parent=document_resource_name, chunk=chunk))\n",
+        "request = glm.BatchCreateChunksRequest(parent=document_resource_name, requests=create_chunk_requests)\n",
+        "response = retriever_service_client.batch_create_chunks(request)\n",
+        "print(response)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "narzqqh0U0Ii"
+      },
+      "source": [
+        "## Query the corpus\n",
+        "\n",
+        "Use the `QueryCorpusRequest` method to perform semantic search to get relevant passages.\n",
+        "\n",
+        "* `results_count`: Specify the number of passages to return. Maximum is 100. If unspecified, the API returns a maximum of 10 `Chunk`s.\n",
+        "* `metadata_filters`: Filter by `chunk_metadata` or `document_metadata`. Each `MetadataFilter` needs to correspond to a unique key. Multiple `MetadataFilter` objects are joined by logical `AND`s. Similar metadata filter conditions are joined by logical `OR`s. Some examples:\n",
+        "\n",
+        "```\n",
+        "(year >= 2020 OR year < 2010) AND (genre = drama OR genre = action)\n",
+        "\n",
+        "metadata_filter = [\n",
+        "  {\n",
+        "    key = \"document.custom_metadata.year\"\n",
+        "    conditions = [\n",
+        "      {int_value = 2020, operation = GREATER_EQUAL},\n",
+        "      {int_value = 2010, operation = LESS}]\n",
+        "  },\n",
+        "  {\n",
+        "    key = \"document.custom_metadata.genre\"\n",
+        "    conditions = [\n",
+        "      {string_value = \"drama\", operation = EQUAL},\n",
+        "      {string_value = \"action\", operation = EQUAL}}]\n",
+        "  }]\n",
+        "```\n",
+        "\n",
+        "Note that only numeric values support \"AND\"s for the same key. String\n",
+        "values only support \"OR\"s for the same key.\n",
+        "\n",
+        "```\n",
+        "(\"Google for Developers\" in tags) and (20230314 > publish_date)\n",
+        "\n",
+        "metadata_filter = [\n",
+        " {\n",
+        "    key = \"chunk.custom_metadata.tags\"\n",
+        "    conditions = [\n",
+        "    {string_value = 'Google for Developers', operation = INCLUDES},\n",
+        "  },\n",
+        "  {\n",
+        "    key = \"chunk.custom_metadata.publish_date\"\n",
+        "    conditions = [\n",
+        "    {numeric_value = 20230314, operation = GREATER_EQUAL}]\n",
+        "  }]\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZtoGd0yz7wVg"
+      },
+      "outputs": [],
+      "source": [
+        "user_query = \"What is the purpose of Project IDX?\"\n",
+        "results_count = 5\n",
+        "\n",
+        "# Add metadata filters for both chunk and document.\n",
+        "chunk_metadata_filter = glm.MetadataFilter(key='chunk.custom_metadata.tags',\n",
+        "                                           conditions=[glm.Condition(\n",
+        "                                              string_value='Google For Developers',\n",
+        "                                              operation=glm.Condition.Operator.INCLUDES)])\n",
+        "\n",
+        "# Make the request\n",
+        "# corpus_resource_name is a variable set in the \"Create a corpus\" section.\n",
+        "request = glm.QueryCorpusRequest(name=corpus_resource_name,\n",
+        "                                 query=user_query,\n",
+        "                                 results_count=results_count,\n",
+        "                                 metadata_filters=[chunk_metadata_filter])\n",
+        "query_corpus_response = retriever_service_client.query_corpus(request)\n",
+        "print(query_corpus_response)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9yVhNmjkVER2"
+      },
+      "source": [
+        "## Attributed Question-Answering\n",
+        "\n",
+        "Use the [`GenerateAnswer`](https://ai.google.dev/api/python/google/generativeai/protos/GenerateAnswerRequest) method to perform Attributed Question-Answering over your document, corpus, or a set of passages.\n",
+        "\n",
+        "Attributed Question-Answering (AQA) refers to answering questions grounded to a given context and providing attributions(s), while minimizing hallucination.\n",
+        "\n",
+        "`GenerateAnswer` provides several advantages over using an untuned LLM, in cases where AQA is desired:\n",
+        " * The underlying model has been trained to return only answers that are grounded in the supplied context.\n",
+        " * It identifies attributions (segments of the supplied context that contributed to the answer). Attributions enable the user to verify the answer.\n",
+        " * It estimates the `answerable_probability` for a given (question, context) pair, which further empowers you to divert product behavior depending on how likely the returned answer is to be grounded and correct.\n",
+        "\n",
+        "Note: AQA currently only supports queries in English.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tPNpJdjJaYEz"
+      },
+      "source": [
+        "### `answerable_probability` and the “I don’t know” problem\n",
+        "\n",
+        "In some instances, the best response to the question is in fact “I don’t know”. For example, if the provided context does not contain the answer to the question, then the question is considered to be “unanswerable”.\n",
+        "\n",
+        "The AQA model is highly adept at recognizing such cases. It can even distinguish between degrees of answerability and unanswerability.\n",
+        "\n",
+        "However, the `GenerateAnswer` API puts the final decision-making power in your hands by:\n",
+        "* *Always* attempting to return a grounded answer - even when that answer is relatively unlikely to be grounded and correct.\n",
+        "* Returning a value `answerable_probability` - The model's estimate of the probability that the answer is grounded and correct.\n",
+        "\n",
+        "A low `answerable_probability` may be explained by 1 or more of the following factors:\n",
+        "\n",
+        "* The model is not confident that its answer is correct.\n",
+        "* The model is not confident that its answer is grounded in the cited passages; The answer may be derived instead from world knowledge. For example: `question=\"1+1=?\", passages=[\"2+2=4”]` → `answer=2, answerable_probability=0.02`\n",
+        "* The model provided relevant information that did not completely answer the question. Example: `question=\"Is it available in my size?, passages=[\"Available in sizes 5-11\"]` → `answer=\"Yes it is available in sizes 5-11\", answerable_probability=0.03\"`\n",
+        "* No well-formed question was asked in the GenerateAnswerRequest.\n",
+        "\n",
+        "Since a low `answerable_probability` indicates that the GenerateAnswerResponse.answer is likely wrong or ungrounded, **it is highly recommended to further process the response by inspecting `answerable_probability`**.\n",
+        "\n",
+        "When `answerable_probability` is low, some clients may wish to:\n",
+        "* Display a message to the effect of \"couldn't answer that question\" to the end user.\n",
+        "* Fall back to a general-purpose LLM that answers the question from world knowledge. The threshold and nature of such fallbacks will depend on individual use cases. A value of `answerable_probability` <= 0.5 is a good starting threshold.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aoUQ37Vqad0V"
+      },
+      "source": [
+        "### AQA Helpful Tips\n",
+        "\n",
+        "For full API specifications, refer to the [`GenerateAnswerRequest` API Reference](https://ai.google.dev/api/python/google/generativeai/protos/GenerateAnswerRequest).\n",
+        "\n",
+        "* *Passage length*: Up to 300 tokens per passage are recommended.\n",
+        "* *Passage sorting*:\n",
+        "  * If you provide `GenerateAnswerRequest.inline_passages`, the passages should be sorted in decreasing order of relevance to the query. If the model's context length limit is exceeded, the last (least-relevant) passages will be omitted.\n",
+        "  * If you provide `GenerateAnswerRequest.semantic_retriever`, then relevance sorting will be done automatically for you.\n",
+        "* *Limitations*: The AQA model is specialized for question-answering. For other use cases such as creative writing, summarization, etc., please call a general-purpose model via GenerateContent.\n",
+        "  * *Chat*: If the user input is known to be a question that may be answerable from a certain context, then AQA can answer chat queries. But if user input may be any type of entry, then a general-purpose model may be a better choice.\n",
+        "* *Temperature*:\n",
+        "  * Generally, a relatively low (~0.2) temperature is recommended for accurate AQA.\n",
+        "  * If your use case relies on deterministic outputs, then set temperature=0.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SJr0vi7y_Uho"
+      },
+      "outputs": [],
+      "source": [
+        "user_query = \"What is the purpose of Project IDX?\"\n",
+        "answer_style = \"ABSTRACTIVE\" # Or VERBOSE, EXTRACTIVE\n",
+        "MODEL_NAME = \"models/aqa\"\n",
+        "\n",
+        "# Make the request\n",
+        "# corpus_resource_name is a variable set in the \"Create a corpus\" section.\n",
+        "content = glm.Content(parts=[glm.Part(text=user_query)])\n",
+        "retriever_config = glm.SemanticRetrieverConfig(source=corpus_resource_name, query=content)\n",
+        "req = glm.GenerateAnswerRequest(model=MODEL_NAME,\n",
+        "                                contents=[content],\n",
+        "                                semantic_retriever=retriever_config,\n",
+        "                                answer_style=answer_style)\n",
+        "aqa_response = generative_service_client.generate_answer(req)\n",
+        "print(aqa_response)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ack_zCIBHGkH"
+      },
+      "outputs": [],
+      "source": [
+        "# Get the metadata from the first attributed passages for the source\n",
+        "chunk_resource_name = aqa_response.answer.grounding_attributions[0].source_id.semantic_retriever_chunk.chunk\n",
+        "get_chunk_response = retriever_service_client.get_chunk(name=chunk_resource_name)\n",
+        "print(get_chunk_response)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wjYUSa_5awjR"
+      },
+      "source": [
+        "### More Options: AQA Using Inline Passages\n",
+        "\n",
+        "Alternatively, you can use the AQA endpoint directly, without using the Semantic Retriever API by passing `inline_passages`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AipBQbxqawPO"
+      },
+      "outputs": [],
+      "source": [
+        "user_query = \"What is AQA from Google?\"\n",
+        "user_query_content = glm.Content(parts=[glm.Part(text=user_query)])\n",
+        "answer_style = \"VERBOSE\" # or ABSTRACTIVE, EXTRACTIVE\n",
+        "MODEL_NAME = \"models/aqa\"\n",
+        "\n",
+        "# Create the grounding inline passages\n",
+        "grounding_passages = glm.GroundingPassages()\n",
+        "passage_a = glm.Content(parts=[glm.Part(text=\"Attributed Question and Answering (AQA) refers to answering questions grounded to a given corpus and providing citation\")])\n",
+        "grounding_passages.passages.append(glm.GroundingPassage(content=passage_a, id=\"001\"))\n",
+        "passage_b = glm.Content(parts=[glm.Part(text=\"An LLM is not designed to generate content grounded in a set of passages. Although instructing an LLM to answer questions only based on a set of passages reduces hallucination, hallucination still often occurs when LLMs generate responses unsupported by facts provided by passages\")])\n",
+        "grounding_passages.passages.append(glm.GroundingPassage(content=passage_b, id=\"002\"))\n",
+        "passage_c = glm.Content(parts=[glm.Part(text=\"Hallucination is one of the biggest problems in Large Language Models (LLM) development. Large Language Models (LLMs) could produce responses that are fictitious and incorrect, which significantly impacts the usefulness and trustworthiness of applications built with language models.\")])\n",
+        "grounding_passages.passages.append(glm.GroundingPassage(content=passage_c, id=\"003\"))\n",
+        "\n",
+        "# Create the request\n",
+        "req = glm.GenerateAnswerRequest(model=MODEL_NAME,\n",
+        "                                contents=[user_query_content],\n",
+        "                                inline_passages=grounding_passages,\n",
+        "                                answer_style=answer_style)\n",
+        "aqa_response = generative_service_client.generate_answer(req)\n",
+        "print(aqa_response)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QMrv_LTJGKGz"
+      },
+      "source": [
+        "## Share the corpus\n",
+        "\n",
+        "You can choose to share the corpus with others using the [`CreatePermissionRequest`](https://ai.google.dev/api/python/google/generativeai/protos/CreatePermissionRequest) API.\n",
+        "\n",
+        "Constraints:\n",
+        "\n",
+        "* There are 2 roles for sharing: `READER` and `EDITOR`.\n",
+        "  * A `READER` can query the corpus.\n",
+        "  * A `WRITER` has reader's permissions and additionally can edit and share the corpus.\n",
+        "* A corpus can be public by granting `EVERYONE` as `user_type` read access."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VCV4UgqUQOdv"
+      },
+      "outputs": [],
+      "source": [
+        "# Replace your-email@gmail.com with the email added as a test user in the OAuth Quickstart\n",
+        "shared_user_email = \"TODO-your-email@gmail.com\" #  @param {type:\"string\"}\n",
+        "user_type = \"USER\"\n",
+        "role = \"READER\"\n",
+        "\n",
+        "# Make the request\n",
+        "# corpus_resource_name is a variable set in the \"Create a corpus\" section.\n",
+        "request = glm.CreatePermissionRequest(\n",
+        "    parent=corpus_resource_name,\n",
+        "    permission=glm.Permission(grantee_type=user_type,\n",
+        "                              email_address=shared_user_email,\n",
+        "                              role=role))\n",
+        "create_permission_response = permission_service_client.create_permission(request)\n",
+        "print(create_permission_response)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n7jUXKkZVOjn"
+      },
+      "source": [
+        "## Delete the corpus\n",
+        "\n",
+        "Use [`DeleteCorpusRequest`](https://ai.google.dev/api/python/google/generativeai/protos/DeleteCorpusRequest) to delete a user corpus and all associated `Document`s & `Chunk`s.\n",
+        "\n",
+        "Note that non-empty corpora will throw an error without specifying an `force=True` flag. If you set `force=True`, any `Chunk`s and objects related to this `Document` will also be deleted.\n",
+        "\n",
+        "If `force=False` (the default) and the `Document` contains any `Chunk`s, a `FAILED_PRECONDITION` error will be returned."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yAKGJwrM0Zs8"
+      },
+      "outputs": [],
+      "source": [
+        "# Set force to False if you don't want to delete non-empty corpora.\n",
+        "req = glm.DeleteCorpusRequest(name=corpus_resource_name, force=True)\n",
+        "delete_corpus_response = retriever_service_client.delete_corpus(req)\n",
+        "print(\"Successfully deleted corpus: \" + corpus_resource_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b8kIyLsUVC_Y"
+      },
+      "source": [
+        "## Summary and further reading\n",
+        "\n",
+        "This guide introduced the Semantic Retriever and Attributed Question & Answering (AQA) APIs of the Gemini API and showed how you can use it to perform semantic information retrieval on your custom text data. Note that this API also works with the [LlamaIndex](https://www.llamaindex.ai/){:.external} data framework. Refer to [the tutorial](https://github.com/run-llama/llama_index/blob/main/docs/docs/examples/managed/GoogleDemo.ipynb){:.external} to learn more.\n",
+        "\n",
+        "Also refer to the [API docs](https://ai.google.dev/api) to learn more about the other available functionalities.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9YGv4x9ehLba"
+      },
+      "source": [
+        "## Appendix: Setup OAuth with user credentials {:#user-oauth}\n",
+        "\n",
+        "Follow the steps below from the [OAuth Quickstart](https://ai.google.dev/docs/oauth_quickstart) to setup OAuth authentication.\n",
+        "\n",
+        "1. [Configure the OAuth consent screen](https://ai.google.dev/docs/oauth_quickstart#configure-oauth).\n",
+        "\n",
+        "1. [Authorize credentials for a desktop application](https://ai.google.dev/docs/oauth_quickstart#authorize-credentials). To run this notebook in Colab, first rename your credential file (usually `client_secret_*.json`) to just `client_secret.json`. Then upload the file by using the file icon on the left sidebar, then the upload icon, as shown in the screenshot below.\n",
+        "\n",
+        "<img width=400 src=\"https://ai.google.dev/tutorials/images/colab_upload.png\">"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9C3X6r1dueO4"
+      },
+      "outputs": [],
+      "source": [
+        "# Replace TODO-your-project-name with the project used in the OAuth Quickstart\n",
+        "project_name = \"TODO-your-project-name\" #  @param {type:\"string\"}\n",
+        "# Replace TODO-your-email@gmail.com with the email added as a test user in the OAuth Quickstart\n",
+        "email = \"TODO-your-email@gmail.com\" #  @param {type:\"string\"}\n",
+        "# Rename the uploaded file to `client_secret.json` OR\n",
+        "# Change the variable `client_file_name` in the code below.\n",
+        "client_file_name = \"client_secret.json\"\n",
+        "\n",
+        "# IMPORTANT: Follow the instructions from the output - you must copy the command\n",
+        "# to your terminal and copy the output after authentication back here.\n",
+        "!gcloud config set project $project_name\n",
+        "!gcloud config set account $email\n",
+        "\n",
+        "# NOTE: The simplified project setup in this tutorial triggers a \"Google hasn't verified this app.\" dialog.\n",
+        "# This is normal, click \"Advanced\" -> \"Go to [app name] (unsafe)\"\n",
+        "!gcloud auth application-default login --no-browser --client-id-file=$client_file_name --scopes=\"https://www.googleapis.com/auth/generative-language.retriever,https://www.googleapis.com/auth/cloud-platform\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lybTOlN9gz0I"
+      },
+      "source": [
+        "Initialize the client library and re-run the notebook starting from [Create a corpus](#create-corpus)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9MJbDTpMmlKJ"
+      },
+      "outputs": [],
+      "source": [
+        "import google.ai.generativelanguage as glm\n",
+        "\n",
+        "generative_service_client = glm.GenerativeServiceClient()\n",
+        "retriever_service_client = glm.RetrieverServiceClient()\n",
+        "permission_service_client = glm.PermissionServiceClient()"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "semantic_retrieval.ipynb",
+      "toc_visible": true
+    },
+    "google": {
+      "image_path": "/site-assets/images/share.png",
+      "keywords": [
+        "examples",
+        "googleai",
+        "samplecode",
+        "python",
+        "embed",
+        "aqa"
+      ]
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemini-api/docs/vision.ipynb b/site/en/gemini-api/docs/vision.ipynb
new file mode 100644
index 000000000..48074c34a
--- /dev/null
+++ b/site/en/gemini-api/docs/vision.ipynb
@@ -0,0 +1,721 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "084u8u0DpBlo"
+      },
+      "source": [
+        "# Explore vision capabilities with the Gemini API"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZFWzQEqNosrS"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/docs/vision\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/vision.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/vision.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3c5e92a74e64"
+      },
+      "source": [
+        "The Gemini API can run inference on images and videos passed to it. When passed an image, a series of images, or a video, Gemini can:\n",
+        "\n",
+        "*   Describe or answer questions about the content\n",
+        "*   Summarize the content\n",
+        "*   Extrapolate from the content\n",
+        "\n",
+        "This tutorial demonstrates some possible ways to prompt the Gemini API with\n",
+        "images and video input. All output is text-only."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VxCstRHvpX0r"
+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "Before you use the File API, you need to install the Gemini API SDK package and configure an API key. This section describes how to complete these setup steps."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G6J_rV2ipmj_"
+      },
+      "source": [
+        "### Install the Python SDK and import packages\n",
+        "\n",
+        "The Python SDK for the Gemini API is contained in the [google-generativeai](https://pypi.org/project/google-generativeai/) package. Install the dependency using pip."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mN8x8DPgu9Kv"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q -U google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NInUZ4hwDq6d"
+      },
+      "source": [
+        "Import the necessary packages."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0x3xmmWrDtEH"
+      },
+      "outputs": [],
+      "source": [
+        "import google.generativeai as genai\n",
+        "from IPython.display import Markdown"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "l8g4hTRotheH"
+      },
+      "source": [
+        "### Set up your API key\n",
+        "\n",
+        "The File API uses API keys for authentication and access. Uploaded files are associated with the project linked to the API key. Unlike other Gemini APIs that use API keys, your API key also grants access to data you've uploaded to the File API, so take extra care in keeping your API key secure. For more on keeping your keys\n",
+        "secure, see [Best practices for using API\n",
+        "keys](https://support.google.com/googleapi/answer/6310037).\n",
+        "\n",
+        "Store your API key in a Colab Secret named `GOOGLE_API_KEY`. If you don't already have an API key, or are unfamiliar with Colab Secrets, refer to the [Authentication quickstart](https://github.com/google-gemini/gemini-api-cookbook/blob/main/quickstarts/Authentication.ipynb)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d6lYXRcjthKV"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import userdata\n",
+        "GOOGLE_API_KEY=userdata.get('GOOGLE_API_KEY')\n",
+        "\n",
+        "genai.configure(api_key=GOOGLE_API_KEY)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c-z4zsCUlaru"
+      },
+      "source": [
+        "## Prompting with images\n",
+        "\n",
+        "In this tutorial, you will upload images using the File API or as inline data and generate content based on those images."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AKNehP2tr3Cr"
+      },
+      "source": [
+        "### Technical details (images)\n",
+        "Gemini 1.5 Pro and Flash support a maximum of 3,600 image files.\n",
+        "\n",
+        "Images must be in one of the following image data [MIME types](https://developers.google.com/drive/api/guides/ref-export-formats):\n",
+        "\n",
+        "-   PNG - `image/png`\n",
+        "-   JPEG - `image/jpeg`\n",
+        "-   WEBP - `image/webp`\n",
+        "-   HEIC - `image/heic`\n",
+        "-   HEIF - `image/heif`\n",
+        "\n",
+        "Each image is equivalent to 258 tokens.\n",
+        "\n",
+        "While there are no specific limits to the number of pixels in an image besides the model’s context window, larger images are scaled down to a maximum resolution of 3072x3072 while preserving their original aspect ratio, while smaller images are scaled up to 768x768 pixels. There is no cost reduction for images at lower sizes, other than bandwidth, or performance improvement for images at higher resolution.\n",
+        "\n",
+        "For best results:\n",
+        "\n",
+        "*   Rotate images to the correct orientation before uploading.\n",
+        "*   Avoid blurry images.\n",
+        "*   If using a single image, place the text prompt after the image."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rsdNkDszLBmQ"
+      },
+      "source": [
+        "### Upload an image file using the File API\n",
+        "\n",
+        "Use the File API to upload an image of any size. (Always use the File API when the combination of files and system instructions that you intend to send is larger than 20 MB.)\n",
+        "\n",
+        "**NOTE**: The File API lets you store up to 20 GB of files per project, with a per-file maximum size of 2 GB. Files are stored for 48 hours. They can be accessed in that period with your API key, but cannot be downloaded from the API. It is available at no cost in all regions where the Gemini API is available.\n",
+        "\n",
+        "Start by downloading this [sketch of a jetpack](https://storage.googleapis.com/generativeai-downloads/images/jetpack.jpg)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lC6sS6DnmGmi"
+      },
+      "outputs": [],
+      "source": [
+        "!curl -o jetpack.jpg https://storage.googleapis.com/generativeai-downloads/images/jetpack.jpg"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qfa2VSqEsulq"
+      },
+      "source": [
+        "Upload the image using [`media.upload`](https://ai.google.dev/api/rest/v1beta/media/upload) and print the URI, which is used as a reference in Gemini API calls."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "N9NxXGZKKusG"
+      },
+      "outputs": [],
+      "source": [
+        "# Upload the file and print a confirmation.\n",
+        "sample_file = genai.upload_file(path=\"jetpack.jpg\",\n",
+        "                            display_name=\"Jetpack drawing\")\n",
+        "\n",
+        "print(f\"Uploaded file '{sample_file.display_name}' as: {sample_file.uri}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cto22vhKOvGQ"
+      },
+      "source": [
+        "The `response` shows that the File API stored the specified `display_name` for the uploaded file and a `uri` to reference the file in Gemini API calls. Use `response` to track how uploaded files are mapped to URIs.\n",
+        "\n",
+        "Depending on your use case, you can also store the URIs in structures such as a `dict` or a database."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ds5iJlaembWe"
+      },
+      "source": [
+        "### Verify image file upload and get metadata\n",
+        "\n",
+        "You can verify the API successfully stored the uploaded file and get its metadata by calling [`files.get`](https://ai.google.dev/api/rest/v1beta/files/get) through the SDK. Only the `name` (and by extension, the `uri`) are unique. Use `display_name` to identify files only if you manage uniqueness yourself."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kLFsVLFHOWSV"
+      },
+      "outputs": [],
+      "source": [
+        "file = genai.get_file(name=sample_file.name)\n",
+        "print(f\"Retrieved file '{file.display_name}' as: {sample_file.uri}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BqzIGKBmnFoJ"
+      },
+      "source": [
+        "Depending on your use case, you can store the URIs in structures, such as a `dict` or a database."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EPPOECHzsIGJ"
+      },
+      "source": [
+        "### Prompt with the uploaded image and text\n",
+        "\n",
+        "After uploading the file, you can make GenerateContent requests that reference the File API URI. Select the generative model and provide it with a text prompt and the uploaded image."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZYVFqmLkl5nE"
+      },
+      "outputs": [],
+      "source": [
+        "# Choose a Gemini API model.\n",
+        "model = genai.GenerativeModel(model_name=\"gemini-1.5-pro-latest\")\n",
+        "\n",
+        "# Prompt the model with text and the previously uploaded image.\n",
+        "response = model.generate_content([sample_file, \"Describe how this product might be manufactured.\"])\n",
+        "\n",
+        "Markdown(\">\" + response.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Lm862F3zthiI"
+      },
+      "source": [
+        "### Upload one or more locally stored image files\n",
+        "\n",
+        "Alternatively, you can upload your own files. You can download and use our drawings of [piranha-infested waters](https://storage.googleapis.com/generativeai-downloads/images/piranha.jpg) and a [firefighter with a cat](https://storage.googleapis.com/generativeai-downloads/images/firefighter.jpg). First, save these files to your local directory.\n",
+        "\n",
+        "Then click **Files** on the left sidebar. For each file, click the **Upload** button, then navigate to that file's location and upload it:\n",
+        "\n",
+        "<img width=400 src=\"https://ai.google.dev/tutorials/images/colab_upload.png\">\n",
+        "\n",
+        "When the combination of files and system instructions that you intend to send is larger than 20 MB in size, use the File API to upload those files, as previously shown. Smaller files can instead be called locally from the Gemini API:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XzMhQ8MWub5_"
+      },
+      "outputs": [],
+      "source": [
+        "import PIL.Image\n",
+        "\n",
+        "sample_file_2 = PIL.Image.open('piranha.jpg')\n",
+        "sample_file_3 = PIL.Image.open('firefighter.jpg')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F2N5bLR7wlqL"
+      },
+      "source": [
+        "Note that these inline data calls don't include many of the features available via the File API, such as getting file metadata, [listing](https://colab.research.google.com/drive/19xeyIMZJIk7Zn9KW5_50iZYv8OfjApL5?resourcekey=0-3JZ6U8oAFX7hqeV7gAXshw#scrollTo=VosrkvAyrx-v&line=3&uniqifier=1), or [deleting](https://colab.research.google.com/drive/19xeyIMZJIk7Zn9KW5_50iZYv8OfjApL5?resourcekey=0-3JZ6U8oAFX7hqeV7gAXshw#scrollTo=diCy9BgjLqeS&line=1&uniqifier=1) files."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "X3pl7mWgwt6Q"
+      },
+      "source": [
+        "### Prompt with multiple images\n",
+        "\n",
+        "You can provide the Gemini API with any combination of images and text that fit within the model's context window. This example provides one short text prompt and the three images previously uploaded."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ou5IVsybcOys"
+      },
+      "outputs": [],
+      "source": [
+        "# Choose a Gemini model.\n",
+        "model = genai.GenerativeModel(model_name=\"gemini-1.5-pro-latest\")\n",
+        "\n",
+        "prompt = \"Write an advertising jingle showing how the product in the first image could solve the problems shown in the second two images.\"\n",
+        "\n",
+        "response = model.generate_content([prompt, sample_file, sample_file_2, sample_file_3])\n",
+        "\n",
+        "Markdown(\">\" + response.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7e16d742407a"
+      },
+      "source": [
+        "### Get bounding boxes\n",
+        "\n",
+        "You can ask the model for the coordinates of bounding boxes for objects in images. For object detection, the Gemini model has been trained to provide\n",
+        "these coordinates as relative widths or heights in range `[0,1]`, scaled by 1000 and converted to an integer. Effectively, the coordinates given are for a\n",
+        "1000x1000 version of the original image, and need to be converted back to the dimensions of the original image."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "778dd36334f4"
+      },
+      "outputs": [],
+      "source": [
+        "# Choose a Gemini model.\n",
+        "model = genai.GenerativeModel(model_name=\"gemini-1.5-pro-latest\")\n",
+        "\n",
+        "prompt = \"Return a bounding box for the piranha. \\n [ymin, xmin, ymax, xmax]\"\n",
+        "response = model.generate_content([sample_file_2, prompt])\n",
+        "\n",
+        "print(response.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b8e422c55df2"
+      },
+      "source": [
+        "To convert these coordinates to the dimensions of the original image:\n",
+        "\n",
+        "1.    Divide each output coordinate by 1000.\n",
+        "1.    Multiply the x-coordinates by the original image width.\n",
+        "1.    Multiply the y-coordinates by the original image height."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TaUZc1mvLkHY"
+      },
+      "source": [
+        "## Prompting with video\n",
+        "\n",
+        "In this tutorial, you will upload a video using the File API and generate content based on those images."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nDN32NDPxXGX"
+      },
+      "source": [
+        "## Technical details (video)\n",
+        "\n",
+        "Gemini 1.5 Pro and Flash support up to approximately an hour of video data.\n",
+        "\n",
+        "Video must be in one of the following video format [MIME types](https://developers.google.com/drive/api/guides/ref-export-formats):\n",
+        "  -   `video/mp4`\n",
+        "  -   `video/mpeg`\n",
+        "  -   `video/mov`\n",
+        "  -   `video/avi`\n",
+        "  -   `video/x-flv`\n",
+        "  -   `video/mpg`\n",
+        "  -   `video/webm`\n",
+        "  -   `video/wmv`\n",
+        "  -   `video/3gpp`\n",
+        "\n",
+        "The File API service currently extracts image frames from videos at 1 frame per second (FPS) and audio at 1Kbps, single channel, adding timestamps every second. These rates are subject to change in the future for improvements in inference.\n",
+        "\n",
+        "**NOTE:** The finer details of fast action sequences may be lost at the 1 FPS frame sampling rate. Consider slowing down high-speed clips for improved inference quality.\n",
+        "\n",
+        "Individual frames are 258 tokens, and audio is 32 tokens per second. With metadata, each second of video becomes ~300 tokens, which means a 1M context window can fit slightly less than an hour of video.\n",
+        "\n",
+        "To ask questions about time-stamped locations, use the format `MM:SS`, where the first two digits represent minutes and the last two digits represent seconds.\n",
+        "\n",
+        "For best results:\n",
+        "\n",
+        "*   Use one video per prompt.\n",
+        "*   If using a single video, place the text prompt after the video."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MNvhBdoDFnTC"
+      },
+      "source": [
+        "### Upload a video file to the File API\n",
+        "\n",
+        "**NOTE**: The File API lets you store up to 20 GB of files per project, with a per-file maximum size of 2 GB. Files are stored for 48 hours. They can be accessed in that period with your API key, but they cannot be downloaded using any API. It is available at no cost in all regions where the Gemini API is available.\n",
+        "\n",
+        "The File API accepts video file formats directly. This example uses the short NASA film [\"Jupiter's Great Red Spot Shrinks and Grows\"](https://www.youtube.com/watch?v=JDi4IdtvDVE0). Credit: Goddard Space Flight Center (GSFC)/David Ladd (2018).\n",
+        "\n",
+        "> \"Jupiter's Great Red Spot Shrinks and Grows\" is in the public domain and does not show identifiable people. ([NASA image and media usage guidelines.](https://www.nasa.gov/nasa-brand-center/images-and-media/))\n",
+        "\n",
+        "Start by retrieving the short video:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "V4XeFdX1rxaE"
+      },
+      "outputs": [],
+      "source": [
+        "!wget https://storage.googleapis.com/generativeai-downloads/images/GreatRedSpot.mp4"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZusSiIg2T6ls"
+      },
+      "source": [
+        "Upload the video to the File API and print the URI."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_HzrDdp2Q1Cu"
+      },
+      "outputs": [],
+      "source": [
+        "video_file_name = \"GreatRedSpot.mp4\"\n",
+        "\n",
+        "print(f\"Uploading file...\")\n",
+        "video_file = genai.upload_file(path=video_file_name)\n",
+        "print(f\"Completed upload: {video_file.uri}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oOZmTUb4FWOa"
+      },
+      "source": [
+        "### Verify file upload and check state\n",
+        "\n",
+        "Verify the API has successfully received the files by calling the [`files.get`](https://ai.google.dev/api/rest/v1beta/files/get) method.\n",
+        "\n",
+        "**NOTE**: Video files have a `State` field in the File API. When a video is uploaded, it will be in the `PROCESSING` state until it is ready for inference. Only `ACTIVE` files can be used for model inference."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SHMVCWHkFhJW"
+      },
+      "outputs": [],
+      "source": [
+        "import time\n",
+        "\n",
+        "# Check whether the file is ready to be used.\n",
+        "while video_file.state.name == \"PROCESSING\":\n",
+        "    print('.', end='')\n",
+        "    time.sleep(10)\n",
+        "    video_file = genai.get_file(video_file.name)\n",
+        "\n",
+        "if video_file.state.name == \"FAILED\":\n",
+        "  raise ValueError(video_file.state.name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IYIIHsvQt0_W"
+      },
+      "source": [
+        "### Prompt with a video and text\n",
+        "\n",
+        "Once the uploaded video is in the `ACTIVE` state, you can make `GenerateContent` requests that specify the File API URI for that video. Select the generative model and provide it with the uploaded video and a text prompt."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sHH0ZR6Yt42S"
+      },
+      "outputs": [],
+      "source": [
+        "# Create the prompt.\n",
+        "prompt = \"Summarize this video. Then create a quiz with answer key based on the information in the video.\"\n",
+        "\n",
+        "# Choose a Gemini model.\n",
+        "model = genai.GenerativeModel(model_name=\"gemini-1.5-pro-latest\")\n",
+        "\n",
+        "# Make the LLM request.\n",
+        "print(\"Making LLM inference request...\")\n",
+        "response = model.generate_content([video_file, prompt],\n",
+        "                                  request_options={\"timeout\": 600})\n",
+        "\n",
+        "# Print the response, rendering any Markdown\n",
+        "Markdown(response.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zS5NmQeXLqeS"
+      },
+      "source": [
+        "### Refer to timestamps in the content\n",
+        "\n",
+        "You can use timestamps of the form `MM:SS` to refer to specific moments in the video."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ypZuGQ-2LqeS"
+      },
+      "outputs": [],
+      "source": [
+        "# Create the prompt.\n",
+        "prompt = \"What are the examples given at 01:05 and 01:19 supposed to show us?\"\n",
+        "\n",
+        "# Choose a Gemini model.\n",
+        "model = genai.GenerativeModel(model_name=\"gemini-1.5-pro-latest\")\n",
+        "\n",
+        "# Make the LLM request.\n",
+        "print(\"Making LLM inference request...\")\n",
+        "response = model.generate_content([prompt, video_file],\n",
+        "                                  request_options={\"timeout\": 600})\n",
+        "print(response.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JQE0XjgMZSJo"
+      },
+      "source": [
+        "### Transcribe video and provide visual descriptions\n",
+        "\n",
+        "If the video is not fast-paced (given that frames are sampled at 1 per second), it's possible to transcribe the video with visual descriptions for each shot."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_JrcMsYnYXpJ"
+      },
+      "outputs": [],
+      "source": [
+        "# Create the prompt.\n",
+        "prompt = \"Transcribe the audio, giving timestamps. Also provide visual descriptions.\"\n",
+        "\n",
+        "# Choose a Gemini model.\n",
+        "model = genai.GenerativeModel(model_name=\"gemini-1.5-pro-latest\")\n",
+        "\n",
+        "# Make the LLM request.\n",
+        "print(\"Making LLM inference request...\")\n",
+        "response = model.generate_content([prompt, video_file],\n",
+        "                                  request_options={\"timeout\": 600})\n",
+        "print(response.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VosrkvAyrx-v"
+      },
+      "source": [
+        "## List files\n",
+        "\n",
+        "You can list all uploaded files and their URIs using `files.list_files()`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "O82e6E2Irzlj"
+      },
+      "outputs": [],
+      "source": [
+        "# List all files\n",
+        "for file in genai.list_files():\n",
+        "    print(f\"{file.display_name}, URI: {file.uri}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "diCy9BgjLqeS"
+      },
+      "source": [
+        "## Delete files\n",
+        "\n",
+        "Files are automatically deleted after 2 days. You can also manually delete them using `files.delete()`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YYyi5PrKLqeb"
+      },
+      "outputs": [],
+      "source": [
+        "genai.delete_file(video_file.name)\n",
+        "print(f'Deleted file {video_file.uri}')"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "vision.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemini-api/tutorials/anomaly_detection.ipynb b/site/en/gemini-api/tutorials/anomaly_detection.ipynb
new file mode 100644
index 000000000..2722b2ac1
--- /dev/null
+++ b/site/en/gemini-api/tutorials/anomaly_detection.ipynb
@@ -0,0 +1,2968 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PkzOKBirz271"
+      },
+      "source": [
+        "# Anomaly detection with embeddings"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZFWzQEqNosrS"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/tutorials/anomaly_detection\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemini-api/tutorials/anomaly_detection.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemini-api/tutorials/anomaly_detection.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BQPvHyHCz7mk"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This tutorial demonstrates how to use the embeddings from the Gemini API to detect potential outliers in your dataset. You will visualize a subset of the 20 Newsgroup dataset using [t-SNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html){:.external} and detect outliers outside a particular radius of the central point of each categorical cluster.\n",
+        "\n",
+        "For more information on getting started with embeddings generated from the Gemini API, check out the [Python quickstart](https://ai.google.dev/tutorials/python_quickstart#use_embeddings).\n",
+        "\n",
+        "## Prerequisites\n",
+        "\n",
+        "You can run this quickstart in Google Colab.\n",
+        "\n",
+        "To complete this quickstart on your own development environment, ensure that your envirmonement meets the following requirements:\n",
+        "\n",
+        "-  Python 3.9+\n",
+        "-  An installation of `jupyter` to run the notebook.\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "First, download and install the Gemini API Python library."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LyLLYVEhzud8"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -U -q google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Z5GJi99k0Ctz"
+      },
+      "outputs": [],
+      "source": [
+        "import re\n",
+        "import tqdm\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "import google.generativeai as genai\n",
+        "\n",
+        "# Used to securely store your API key\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "from sklearn.datasets import fetch_20newsgroups\n",
+        "from sklearn.manifold import TSNE"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Yi0kitgd5aLG"
+      },
+      "source": [
+        "### Grab an API Key\n",
+        "\n",
+        "Before you can use the Gemini API, you must first obtain an API key. If you don't already have one, create a key with one click in Google AI Studio.\n",
+        "\n",
+        "<a class=\"button button-primary\" href=\"https://makersuite.google.com/app/apikey\" target=\"_blank\" rel=\"noopener noreferrer\">Get an API key</a>\n",
+        "\n",
+        "In Colab, add the key to the secrets manager under the \"🔑\" in the left panel. Give it the name `API_KEY`.\n",
+        "\n",
+        "Once you have the API key, pass it to the SDK. You can do this in two ways:\n",
+        "\n",
+        "* Put the key in the `GOOGLE_API_KEY` environment variable (the SDK will automatically pick it up from there).\n",
+        "* Pass the key to `genai.configure(api_key=...)`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6OeEZ5Bj5Zr8"
+      },
+      "outputs": [],
+      "source": [
+        "# Or use `os.getenv('API_KEY')` to fetch an environment variable.\n",
+        "API_KEY=userdata.get('API_KEY')\n",
+        "\n",
+        "genai.configure(api_key=API_KEY)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ce6MdcP170Uv"
+      },
+      "source": [
+        "Key Point: Next, you will choose a model. Any embedding model will work for this tutorial, but for real applications it's important to choose a specific model and stick with it. The outputs of different models are not compatible with each other.\n",
+        "\n",
+        "**Note**: At this time, the Gemini API is [only available in certain regions](https://ai.google.dev/available_regions)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "h3mqsrUB7zsE"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "models/embedding-001\n",
+            "models/embedding-001\n"
+          ]
+        }
+      ],
+      "source": [
+        "for m in genai.list_models():\n",
+        "  if 'embedContent' in m.supported_generation_methods:\n",
+        "    print(m.name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qhWtEhZ6BO58"
+      },
+      "source": [
+        "## Prepare dataset\n",
+        "\n",
+        "The [20 Newsgroups Text Dataset](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html){:.external} contains 18,000 newsgroups posts on 20 topics divided into training and test sets. The split between the training and test datasets are based on messages posted before and after a specific date. This tutorial uses the training subset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YtHABp9BBTIt"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['alt.atheism',\n",
+              " 'comp.graphics',\n",
+              " 'comp.os.ms-windows.misc',\n",
+              " 'comp.sys.ibm.pc.hardware',\n",
+              " 'comp.sys.mac.hardware',\n",
+              " 'comp.windows.x',\n",
+              " 'misc.forsale',\n",
+              " 'rec.autos',\n",
+              " 'rec.motorcycles',\n",
+              " 'rec.sport.baseball',\n",
+              " 'rec.sport.hockey',\n",
+              " 'sci.crypt',\n",
+              " 'sci.electronics',\n",
+              " 'sci.med',\n",
+              " 'sci.space',\n",
+              " 'soc.religion.christian',\n",
+              " 'talk.politics.guns',\n",
+              " 'talk.politics.mideast',\n",
+              " 'talk.politics.misc',\n",
+              " 'talk.religion.misc']"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "newsgroups_train = fetch_20newsgroups(subset='train')\n",
+        "\n",
+        "# View list of class names for dataset\n",
+        "newsgroups_train.target_names"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LPKgmQDQC3zd"
+      },
+      "source": [
+        "Here is the first example in the training set."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CSXYP0JwBXHh"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Lines: 15\n",
+            "\n",
+            " I was wondering if anyone out there could enlighten me on this car I saw\n",
+            "the other day. It was a 2-door sports car, looked to be from the late 60s/\n",
+            "early 70s. It was called a Bricklin. The doors were really small. In addition,\n",
+            "the front bumper was separate from the rest of the body. This is \n",
+            "all I know. If anyone can tellme a model name, engine specs, years\n",
+            "of production, where this car is made, history, or whatever info you\n",
+            "have on this funky looking car, please e-mail.\n",
+            "\n",
+            "Thanks,\n",
+            "- IL\n",
+            "   ---- brought to you by your neighborhood Lerxst ----\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "idx = newsgroups_train.data[0].index('Lines')\n",
+        "print(newsgroups_train.data[0][idx:])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Raafa2naC6Ec"
+      },
+      "outputs": [],
+      "source": [
+        "# Apply functions to remove names, emails, and extraneous words from data points in newsgroups.data\n",
+        "newsgroups_train.data = [re.sub(r'[\\w\\.-]+@[\\w\\.-]+', '', d) for d in newsgroups_train.data] # Remove email\n",
+        "newsgroups_train.data = [re.sub(r\"\\([^()]*\\)\", \"\", d) for d in newsgroups_train.data] # Remove names\n",
+        "newsgroups_train.data = [d.replace(\"From: \", \"\") for d in newsgroups_train.data] # Remove \"From: \"\n",
+        "newsgroups_train.data = [d.replace(\"\\nSubject: \", \"\") for d in newsgroups_train.data] # Remove \"\\nSubject: \"\n",
+        "\n",
+        "# Cut off each text entry after 5,000 characters\n",
+        "newsgroups_train.data = [d[0:5000] if len(d) > 5000 else d for d in newsgroups_train.data]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZjE_Lsr6IhEd"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df_train\",\n  \"rows\": 11314,\n  \"fields\": [\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \" CYCLONE AND TEMPEST?????\\nArticle-I.D.: usenet.1pskav$qtu\\nReply-To:  \\nOrganization: Case Western Reserve University, Cleveland, OH \\nLines: 10\\nNNTP-Posting-Host: thor.ins.cwru.edu\\n\\n\\nCould someone please post any info on these systems.\\n\\nThanks.\\nBoB\\n-- \\n---------------------------------------------------------------------- \\nRobert Novitskey | \\\"Pursuing women is similar to banging one's head\\n  |  against a wall...with less opportunity for reward\\\" \\n---------------------------------------------------------------------- \\n\",\n          \" Re: does dos6 defragment??\\nArticle-I.D.: ux1.ardie.272.734097933\\nOrganization: Department of Plant Pathology\\nLines: 30\\n\\nIn article <>   writes:\\n> \\n>Subject: Re: does dos6 defragment??\\n>Date: Tue, 6 Apr 1993 04:02:54 GMT\\n>In article <>,   writes:\\n>|> Geoffrey S. Elbo writes:\\n>|> \\n>|> >Yes, and it is the fastest defrag I've ever watched.  It did a 170MB \\n>|> >hard disk in 20 minutes.\\n>|> \\n>|> \\tI found the MS defrag looks very much like Norton Speedisk.\\n>|> Is it just a strip-down version of the later?\\n>|> \\n>|> \\tI have both Norton Speedisk and Backup, so I was wondering \\n>|> if I need to install MS Backup?\\n>|> \\n>|> Richard\\n>|> \\n>\\n>Yes, defragger IS come from Norton.\\n>If you have Norton Utility, don't bother.\\n\\n\\n     Don't bother if you have CPBackup or Fastback.  They all offer options \\nnot available in the stripped-down MS version .  Examples - no \\nproprietary format , probably no direct DMA access, and no \\ntape drive!\\n\\n     You NEED MS Defrag if you use doublespace to work on the compressed \\nvolume.\\n\",\n          \" For Sale:  Misc IBM stuff\\nOrganization: The Cellar BBS and public access system\\nLines: 10\\n\\n5.25\\\" Internal Low density disk drive.\\n\\nMonochrome monitor\\n\\n8088 motherboard, built in parallel and serial ports, built in mono and\\ncolor output, 7Mhz.\\n\\nLibertarian, atheist, semi-anarchal Techno-Rat.\\n\\nI define \\n\"\n        ],\n        \"num_unique_values\": 11314,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 0,\n        \"max\": 19,\n        \"samples\": [\n          7,\n          17,\n          9\n        ],\n        \"num_unique_values\": 20,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"rec.autos\",\n          \"talk.politics.mideast\",\n          \"rec.sport.baseball\"\n        ],\n        \"num_unique_values\": 20,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df_train"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0665bfca-fe7f-49af-80c3-c1c4d1798f97\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Text</th>\n",
+              "      <th>Label</th>\n",
+              "      <th>Class Name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>WHAT car is this!?\\nNntp-Posting-Host: rac3.w...</td>\n",
+              "      <td>7</td>\n",
+              "      <td>rec.autos</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>SI Clock Poll - Final Call\\nSummary: Final ca...</td>\n",
+              "      <td>4</td>\n",
+              "      <td>comp.sys.mac.hardware</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>PB questions...\\nOrganization: Purdue Univers...</td>\n",
+              "      <td>4</td>\n",
+              "      <td>comp.sys.mac.hardware</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Re: Weitek P9000 ?\\nOrganization: Harris Comp...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>comp.graphics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Re: Shuttle Launch Question\\nOrganization: Sm...</td>\n",
+              "      <td>14</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11309</th>\n",
+              "      <td>Re: Migraines and scans\\nDistribution: world...</td>\n",
+              "      <td>13</td>\n",
+              "      <td>sci.med</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11310</th>\n",
+              "      <td>Screen Death: Mac Plus/512\\nLines: 22\\nOrganiz...</td>\n",
+              "      <td>4</td>\n",
+              "      <td>comp.sys.mac.hardware</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11311</th>\n",
+              "      <td>Mounting CPU Cooler in vertical case\\nOrganiz...</td>\n",
+              "      <td>3</td>\n",
+              "      <td>comp.sys.ibm.pc.hardware</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11312</th>\n",
+              "      <td>Re: Sphere from 4 points?\\nOrganization: Cent...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>comp.graphics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11313</th>\n",
+              "      <td>stolen CBR900RR\\nOrganization: California Ins...</td>\n",
+              "      <td>8</td>\n",
+              "      <td>rec.motorcycles</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>11314 rows × 3 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0665bfca-fe7f-49af-80c3-c1c4d1798f97')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
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+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-0665bfca-fe7f-49af-80c3-c1c4d1798f97 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-0665bfca-fe7f-49af-80c3-c1c4d1798f97');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "\n",
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+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3326f574-25ff-4c3c-99c8-7075e731725b')\"\n",
+              "            title=\"Suggest charts\"\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-3326f574-25ff-4c3c-99c8-7075e731725b button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_4675e991-e70d-4c9d-9ae9-7c2656af7dfd\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_train')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_4675e991-e70d-4c9d-9ae9-7c2656af7dfd button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df_train');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                                                    Text  Label  \\\n",
+              "0       WHAT car is this!?\\nNntp-Posting-Host: rac3.w...      7   \n",
+              "1       SI Clock Poll - Final Call\\nSummary: Final ca...      4   \n",
+              "2       PB questions...\\nOrganization: Purdue Univers...      4   \n",
+              "3       Re: Weitek P9000 ?\\nOrganization: Harris Comp...      1   \n",
+              "4       Re: Shuttle Launch Question\\nOrganization: Sm...     14   \n",
+              "...                                                  ...    ...   \n",
+              "11309    Re: Migraines and scans\\nDistribution: world...     13   \n",
+              "11310  Screen Death: Mac Plus/512\\nLines: 22\\nOrganiz...      4   \n",
+              "11311   Mounting CPU Cooler in vertical case\\nOrganiz...      3   \n",
+              "11312   Re: Sphere from 4 points?\\nOrganization: Cent...      1   \n",
+              "11313   stolen CBR900RR\\nOrganization: California Ins...      8   \n",
+              "\n",
+              "                     Class Name  \n",
+              "0                     rec.autos  \n",
+              "1         comp.sys.mac.hardware  \n",
+              "2         comp.sys.mac.hardware  \n",
+              "3                 comp.graphics  \n",
+              "4                     sci.space  \n",
+              "...                         ...  \n",
+              "11309                   sci.med  \n",
+              "11310     comp.sys.mac.hardware  \n",
+              "11311  comp.sys.ibm.pc.hardware  \n",
+              "11312             comp.graphics  \n",
+              "11313           rec.motorcycles  \n",
+              "\n",
+              "[11314 rows x 3 columns]"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Put training points into a dataframe\n",
+        "df_train = pd.DataFrame(newsgroups_train.data, columns=['Text'])\n",
+        "df_train['Label'] = newsgroups_train.target\n",
+        "# Match label to target name index\n",
+        "df_train['Class Name'] = df_train['Label'].map(newsgroups_train.target_names.__getitem__)\n",
+        "\n",
+        "df_train"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f7OHvTBaImpB"
+      },
+      "source": [
+        "Next, sample some of the data by taking 150 data points in the training dataset and choosing a few categories. This tutorial uses the science categories."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yPxwl05BIjWX"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df_train\",\n  \"rows\": 600,\n  \"fields\": [\n    {\n      \"column\": \"index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 173,\n        \"min\": 1650,\n        \"max\": 2249,\n        \"samples\": [\n          1760,\n          2069,\n          2215\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \" Re: 80-bit keyseach machine\\nNntp-Posting-Host: top.magnus.acs.ohio-state.edu\\nOrganization: The Ohio State University\\nLines: 47\\n\\nIn article <>   writes:\\n>In article <>\\n>  writes:\\n> \\n>>Normally I'd be the last to argue with Steve . . . but shouldn't that\\n>>read \\\"3.8 years for *all* solutions\\\".  I mean, if we can imagine the\\n>>machine that does 1 trial/nanosecond, we can imagine the storage medium\\n>>that could index and archive it.\\n> \\n>   Hmmmm.  I think, with really large keyspaces like this, you need to\\n>alter the strategy discussed for DES.  Attempt decryption of several\\n>blocks, and check the disctribution of the contents.  I don't think it's\\n>at all feasible to keep 2**80 encryptions of a known plaintext block on\\n>*any* amount of tape or CD-ROM.  And certainly not 2**128 such encrypted\\n>blocks.  \\n[...]\\n\\nI don't claim to be a crypto analyist... there isn't a whole lot of good\\nliterature on the subject, and the best people don't seem to publish\\ntheir work :)  but I rather doubt the approach such folks use is brute\\nforce .  The history\\nof these things is folks find clever ways of limiting the search and\\nbang from there.\\n\\nI guess my real problem with Skipjack is I can not believe NSA would\\nmake publicly available a system they couldn't break if they wanted...\\nit just isn't in their charter.  Remember DES came from IBM, not NSA\\nand, when first published, was given a useful life of 20 years... I think\\nwe are well past that point now :(\\n\\nRemember, based on the size of the NSA budget, they spend a lot more\\non the technology of decryption than most computer companies spend on\\nR&D.  I have to imagine their stuff is real interesting...\\n\\nA friend who once worked for them  said he always enjoyed\\nmonitoring SAC's  crypto traffic :)  and I rather\\nsuspect that stuff is a bit more complex than Skipjack  \\n[BTW, folks, NSA wasn't being given the keys.  And the Walker spy case\\nshows for some of the systems, the KGB didn't need them either.]\\n\\n-- \\n Information farming at...     For addr&phone: finger             A/~~\\\\A\\n THE Ohio State University     ()____\\n      Jim Ebright             e-mail: jre+@osu.edu                 \\\\  /      \\\\\\n                          Support Privacy: Support Encryption      \\\\      \\n\",\n          \" Re: Is MSG sensitivity superstition?\\nOrganization: Netcom Online Communications Services \\nLines: 45\\n\\nIn article   writes:\\n>  writes:\\n>\\n>>Anecedotal evidence is worthless.  Even doctors who have been using a drug\\n>>or treatment for years, and who swear it is effective, are often suprised\\n>>at the results of clinical trials.  Whether or not MSG causes describable,\\n>>reportable, documentable symptoms should be pretty simple to discover.  \\n\\nBut it is quite a leap in logic to observe one situation where anecdotal\\nevidence led nowhere and therefore conclude that anecdotal evidence will\\nNEVER lead anywhere.  I'm sure somebody here can provide an example where\\nanecdotal evidence  was upheld/verified by\\nfollow-on rigorous clinical trials.\\n\\n\\n>I tend to disagree- I think anecdotal evidence, provided there is a lot of it,\\n>and it is fairly consistent, will is very important.  First, it points to the\\n>necessity of doing a study, and second, it at least says that the effects are\\n>all psychological .  As I've pointed out \\n>person's \\\"make-believe\\\" can easily be another person's reality...\\n\\nGood point.  There has been a tendency by some on this newsgroup to \\\"circle\\nthe wagons\\\" to the viewpoint that anecdotal medical evidence is worthless\\n.  But\\nevidence is evidence - it requires a \\\"jury\\\" or a process to sort it out and\\ndetermine the truth from the junk.  Medicine must continue to strive to better\\nunderstand the workings of the body/mind for the purpose of alleviating\\nillness - anecdotal evidence is just one piece of the puzzle;  it is not\\nworthless.  Rather, it can help focus limited resources in the right direction.\\n\\nJon Noring\\n\\n-- \\n\\nCharter Member --->>>  INFJ Club.\\n\\nIf you're dying to know what INFJ means, be brave, e-mail me, I'll send info.\\n=============================================================================\\n| Jon Noring          |         |                          |\\n| JKN International   | IP    : 192.100.81.100   | FRED'S GOURMET CHOCOLATE |\\n| 1312 Carlton Place  | Phone :  294-8153   | CHIPS - World's Best!    |\\n| Livermore, CA 94550 | V-Mail:  417-4101   |                          |\\n=============================================================================\\nWho are you?  Read alt.psychology.personality!  That's where the action is.\\n\",\n          \" Cold Gas tanks for Sounding Rockets\\nOrganization: Computing Lab, University of Kent at Canterbury, UK.\\nLines: 14\\nNntp-Posting-Host: eagle.ukc.ac.uk\\n\\n>Does anyone know how to size cold gas roll control thruster tanks\\n>for sounding rockets?\\n\\nWell, first you work out how much cold gas you need, then make the\\ntanks big enough.\\n\\nWorking out how much cold gas is another problem, depending on\\nvehicle configuration, flight duration, thruster Isp \\n\\nRalph Lorenz\\nUnit for Space Sciences\\nUniversity of Kent, UK\\n\"\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 11,\n        \"max\": 14,\n        \"samples\": [\n          12,\n          14,\n          11\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"sci.electronics\",\n          \"sci.space\",\n          \"sci.crypt\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df_train"
+            },
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+              "\n",
+              "  <div id=\"df-cec113e5-dd4c-45f2-b2ea-724021f8744d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "    .dataframe thead th {\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>index</th>\n",
+              "      <th>Text</th>\n",
+              "      <th>Label</th>\n",
+              "      <th>Class Name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1650</td>\n",
+              "      <td>Re: Once tapped, your code is no good any mor...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1651</td>\n",
+              "      <td>REVISED TECHNICAL SUMMARY OF CLIPPER CHIP\\nDis...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1652</td>\n",
+              "      <td>Re: The [secret] source of that announcement\\...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1653</td>\n",
+              "      <td>Re: Would \"clipper\" make a good cover for oth...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1654</td>\n",
+              "      <td>Re: Organized Lobbying for Cryptography\\nOrga...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>595</th>\n",
+              "      <td>2245</td>\n",
+              "      <td>Re: Eco-Freaks forcing Space Mining.\\nOrganiz...</td>\n",
+              "      <td>14</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>596</th>\n",
+              "      <td>2246</td>\n",
+              "      <td>Re: Why not give $1 billion to first year-long...</td>\n",
+              "      <td>14</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>597</th>\n",
+              "      <td>2247</td>\n",
+              "      <td>Re: PLANETS STILL: IMAGES ORBIT BY ETHER TWIST...</td>\n",
+              "      <td>14</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>598</th>\n",
+              "      <td>2248</td>\n",
+              "      <td>Gibbons Outlines SSF Redesign Guidance\\nNews-...</td>\n",
+              "      <td>14</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>599</th>\n",
+              "      <td>2249</td>\n",
+              "      <td>Plutonium based Nuclear Power plants.\\nOrgani...</td>\n",
+              "      <td>14</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>600 rows × 4 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cec113e5-dd4c-45f2-b2ea-724021f8744d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-cec113e5-dd4c-45f2-b2ea-724021f8744d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-cec113e5-dd4c-45f2-b2ea-724021f8744d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-ed8b8275-3410-4057-bfd3-9a11c1399e45\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ed8b8275-3410-4057-bfd3-9a11c1399e45')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-ed8b8275-3410-4057-bfd3-9a11c1399e45 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_9b7a1fb6-7c4a-4711-81c5-6874b4529765\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_train')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_9b7a1fb6-7c4a-4711-81c5-6874b4529765 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df_train');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "     index                                               Text  Label  \\\n",
+              "0     1650   Re: Once tapped, your code is no good any mor...     11   \n",
+              "1     1651  REVISED TECHNICAL SUMMARY OF CLIPPER CHIP\\nDis...     11   \n",
+              "2     1652   Re: The [secret] source of that announcement\\...     11   \n",
+              "3     1653   Re: Would \"clipper\" make a good cover for oth...     11   \n",
+              "4     1654   Re: Organized Lobbying for Cryptography\\nOrga...     11   \n",
+              "..     ...                                                ...    ...   \n",
+              "595   2245   Re: Eco-Freaks forcing Space Mining.\\nOrganiz...     14   \n",
+              "596   2246  Re: Why not give $1 billion to first year-long...     14   \n",
+              "597   2247  Re: PLANETS STILL: IMAGES ORBIT BY ETHER TWIST...     14   \n",
+              "598   2248   Gibbons Outlines SSF Redesign Guidance\\nNews-...     14   \n",
+              "599   2249   Plutonium based Nuclear Power plants.\\nOrgani...     14   \n",
+              "\n",
+              "    Class Name  \n",
+              "0    sci.crypt  \n",
+              "1    sci.crypt  \n",
+              "2    sci.crypt  \n",
+              "3    sci.crypt  \n",
+              "4    sci.crypt  \n",
+              "..         ...  \n",
+              "595  sci.space  \n",
+              "596  sci.space  \n",
+              "597  sci.space  \n",
+              "598  sci.space  \n",
+              "599  sci.space  \n",
+              "\n",
+              "[600 rows x 4 columns]"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Take a sample of each label category from df_train\n",
+        "SAMPLE_SIZE = 150\n",
+        "df_train = (df_train.groupby('Label', as_index = False)\n",
+        "                    .apply(lambda x: x.sample(SAMPLE_SIZE))\n",
+        "                    .reset_index(drop=True))\n",
+        "\n",
+        "# Choose categories about science\n",
+        "df_train = df_train[df_train['Class Name'].str.contains('sci')]\n",
+        "\n",
+        "# Reset the index\n",
+        "df_train = df_train.reset_index()\n",
+        "df_train"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UjTrEnmdIo5P"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "sci.crypt          150\n",
+              "sci.electronics    150\n",
+              "sci.med            150\n",
+              "sci.space          150\n",
+              "Name: Class Name, dtype: int64"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_train['Class Name'].value_counts()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DUgv8SOwXfAX"
+      },
+      "source": [
+        "## Create the embeddings\n",
+        "\n",
+        "In this section, you will see how to generate embeddings for the different texts in the dataframe using the embeddings from the Gemini API."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "01I9uzbo4t26"
+      },
+      "source": [
+        "### API changes to Embeddings with model embedding-001\n",
+        "\n",
+        "For the new embeddings model, embedding-001, there is a new task type parameter and the optional title (only valid with task_type=`RETRIEVAL_DOCUMENT`).\n",
+        "\n",
+        "These new parameters apply only to the newest embeddings models.The task types are:\n",
+        "\n",
+        "Task Type | Description\n",
+        "---       | ---\n",
+        "RETRIEVAL_QUERY\t| Specifies the given text is a query in a search/retrieval setting.\n",
+        "RETRIEVAL_DOCUMENT | Specifies the given text is a document in a search/retrieval setting.\n",
+        "SEMANTIC_SIMILARITY\t| Specifies the given text will be used for Semantic Textual Similarity (STS).\n",
+        "CLASSIFICATION\t| Specifies that the embeddings will be used for classification.\n",
+        "CLUSTERING\t| Specifies that the embeddings will be used for clustering."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jkS_EWfAXcxc"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "70e8bf0d283640928c2aa42fa6282dc2",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/600 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from tqdm.auto import tqdm\n",
+        "tqdm.pandas()\n",
+        "\n",
+        "from google.api_core import retry\n",
+        "\n",
+        "def make_embed_text_fn(model):\n",
+        "\n",
+        "  @retry.Retry(timeout=300.0)\n",
+        "  def embed_fn(text: str) -> list[float]:\n",
+        "    # Set the task_type to CLUSTERING.\n",
+        "    embedding = genai.embed_content(model=model,\n",
+        "                                    content=text,\n",
+        "                                    task_type=\"clustering\")['embedding']\n",
+        "    return np.array(embedding)\n",
+        "\n",
+        "  return embed_fn\n",
+        "\n",
+        "def create_embeddings(df):\n",
+        "  model = 'models/embedding-001'\n",
+        "  df['Embeddings'] = df['Text'].progress_apply(make_embed_text_fn(model))\n",
+        "  return df\n",
+        "\n",
+        "df_train = create_embeddings(df_train)\n",
+        "df_train.drop('index', axis=1, inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hNTjKcD_aluG"
+      },
+      "source": [
+        "## Dimensionality reduction\n",
+        "\n",
+        "The dimension of the document embedding vector is 768. In order to visualize how the embedded documents are grouped together, you will need to apply dimensionality reduction as you can only visualize the embeddings in 2D or 3D space. Contextually similar documents should be closer together in space as opposed to documents that are not as similar."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BJDHDQmeZqy2"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "768"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "len(df_train['Embeddings'][0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "S5-XU-twaoK6"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(600, 768)"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Convert df_train['Embeddings'] Pandas series to a np.array of float32\n",
+        "X = np.array(df_train['Embeddings'].to_list(), dtype=np.float32)\n",
+        "X.shape"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AV-Y7iEtbAkm"
+      },
+      "source": [
+        "You will apply the t-Distributed Stochastic Neighbor Embedding (t-SNE) approach to perform dimensionality reduction. This technique reduces the number of dimensions, while preserving clusters (points that are close together stay close together). For the original data, the model tries to construct a distribution over which other data points are \"neighbors\" (e.g., they share a similar meaning). It then optimizes an objective function to keep a similar distribution in the visualization."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FhYKF-lObC04"
+      },
+      "outputs": [],
+      "source": [
+        "tsne = TSNE(random_state=0, n_iter=1000)\n",
+        "tsne_results = tsne.fit_transform(X)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "31wdqnp_bH9B"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df_tsne\",\n  \"rows\": 600,\n  \"fields\": [\n    {\n      \"column\": \"TSNE1\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          13.169029235839844,\n          -31.654396057128906,\n          -10.892438888549805\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TSNE2\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          26.76736068725586,\n          7.421013832092285,\n          -20.685998916625977\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"sci.electronics\",\n          \"sci.space\",\n          \"sci.crypt\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df_tsne"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-cad0aeb8-b998-414a-a454-995bb086acf3\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>TSNE1</th>\n",
+              "      <th>TSNE2</th>\n",
+              "      <th>Class Name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>4.353590</td>\n",
+              "      <td>17.495653</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>3.974765</td>\n",
+              "      <td>41.019054</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10.665109</td>\n",
+              "      <td>30.655214</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.912471</td>\n",
+              "      <td>38.069790</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>-4.066230</td>\n",
+              "      <td>15.156272</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>595</th>\n",
+              "      <td>5.736661</td>\n",
+              "      <td>-32.636852</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>596</th>\n",
+              "      <td>8.723193</td>\n",
+              "      <td>-36.857460</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>597</th>\n",
+              "      <td>14.958293</td>\n",
+              "      <td>-25.471634</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>598</th>\n",
+              "      <td>-5.218704</td>\n",
+              "      <td>-35.691990</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>599</th>\n",
+              "      <td>-7.453671</td>\n",
+              "      <td>-17.465353</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>600 rows × 3 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
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+              "  <style>\n",
+              "    .colab-df-container {\n",
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+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
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+              "  </style>\n",
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+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-cad0aeb8-b998-414a-a454-995bb086acf3');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "        element.appendChild(docLink);\n",
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+              "  .colab-df-quickchart {\n",
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+              "\n",
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+              "      --bg-color: #3B4455;\n",
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+              "\n",
+              "  .colab-df-quickchart {\n",
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+              "    height: 32px;\n",
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+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
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+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-left-color: var(--fill-color);\n",
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+              "    }\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-62e1e10c-b4c4-4363-9b98-0e452829f015 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_689505c4-8647-4004-913b-6828ab35995b\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_tsne')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
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+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df_tsne');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "         TSNE1      TSNE2 Class Name\n",
+              "0     4.353590  17.495653  sci.crypt\n",
+              "1     3.974765  41.019054  sci.crypt\n",
+              "2    10.665109  30.655214  sci.crypt\n",
+              "3     0.912471  38.069790  sci.crypt\n",
+              "4    -4.066230  15.156272  sci.crypt\n",
+              "..         ...        ...        ...\n",
+              "595   5.736661 -32.636852  sci.space\n",
+              "596   8.723193 -36.857460  sci.space\n",
+              "597  14.958293 -25.471634  sci.space\n",
+              "598  -5.218704 -35.691990  sci.space\n",
+              "599  -7.453671 -17.465353  sci.space\n",
+              "\n",
+              "[600 rows x 3 columns]"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_tsne = pd.DataFrame(tsne_results, columns=['TSNE1', 'TSNE2'])\n",
+        "df_tsne['Class Name'] = df_train['Class Name'] # Add labels column from df_train to df_tsne\n",
+        "df_tsne"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pTj8HfhpbJ9X"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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j3nvvDciWd7DS0tLYvXt3QHuwtmBC/Ty8++672Gw239cHvqYD9ejRg4iICN//p8G0zVZdffXVfPHFF10anyAIgnDkiJkqQTgJ/fLLL0E/vW7bR9S2BO3cc89FLpfz6KOPBsw4tV3f9gl/+/6cTicvvPBCQP8GgyHocq+2WlIHPjgPGTKEzMxM5s6d67ckqk11dXXI19hVL7/8Mi6Xy/f1ggULcLvdTJs2LeQ1Z5xxBhUVFXz44Ye+Nrfbzbx58wgLC2P8+PEA6PV6IPB1ddTv6tWrWblypa+tpaWFl19+mfT09CO6zyU2NpYJEybw0ksvBX1YP/C9Hjt2LIWFhXz44Ye+5YByuZzRo0fz9NNP43K5/PYoNTY24na7/fro168fcrkch8PR4dgyMzP5/fffcTqdvravv/6a4uJiv/Nqa2v9vlar1eTm5iJJkt/3+FBNnTqVlStXsnHjRl9bXV1dhzNG7YX6OR8zZozf0ru2oGrVqlW0tLQE9LN69Wpqa2uDLhVt7/LLLycrKytocCsIgiAcXWKmShBOQrNnz8ZqtXLeeefRq1cvnE4nv/32Gx9++CHp6elcc801QOtekX/84x889thjjB07lvPPPx+NRsOaNWtITEzk8ccfZ/To0URERHDVVVcxZ84cZDIZCxcuDBq0DRkyhA8//JA77riDYcOGERYWxtlnn01mZiZms5kXX3yR8PBwDAYDI0aMICMjg1dffZVp06bRp08frrnmGpKSkigtLeWXX37BaDTy1VdfHdZ74XQ6OfXUU7nooovYuXMnL7zwAqeccgrnnHNOyGtuuOEGXnrpJa6++mrWrVtHeno6n3zyCStWrODZZ58lPDwcaF2KmJuby4cffkhOTg6RkZH07ds3ZBr7e+65h/fff59p06YxZ84cIiMjeeutt9i7dy+ffvppwB6u7jZ//nxOOeUU+vXrx8yZM+nRoweVlZWsXLmSkpISNm3a5Du3LWDauXMn//rXv3zt48aN47vvvvPVwmqzePFibr31Vi688EJycnJwu90sXLgQhULBBRdc0OG4rr/+ej755BNOP/10LrroIgoKCnjnnXd8M5ltpkyZQnx8PGPGjCEuLo7t27fz/PPPc+aZZ/q+J4fjrrvu4p133uG0005j9uzZvpTqqamp1NXVdTozOWTIEADmzJnD1KlTUSgUXHLJJSHPX7hwIe+++y7nnXceQ4YMQa1Ws337dl5//XW0Wi333Xdfh/dTKBT84x//8P3/HMz69et55513AtozMzMZNWpUh/0LgiAIB+GY5BwUBOGI+u6776Rrr71W6tWrlxQWFiap1WopKytLmj17tlRZWRlw/uuvvy4NGjRI0mg0UkREhDR+/Hhp0aJFvuMrVqyQRo4cKel0OikxMdGXoh2QfvnlF995zc3N0mWXXSaZzWYJ8Euv/sUXX0i5ubmSUqkMSK++YcMG6fzzz5eioqIkjUYjpaWlSRdddJH0888/+85pS6leXV3dpfegLbX20qVLpRtuuEGKiIiQwsLCpBkzZki1tbV+5x6YuluSJKmyslK65pprpOjoaEmtVkv9+vULSAkvSZL022+/SUOGDJHUanWX0qsXFBRI06dPl8xms6TVaqXhw4dLX3/9dcB5HGRK9aeeeipoHweOp6CgQLryyiul+Ph4SaVSSUlJSdJZZ50lffLJJwHXx8bGSoDfz8yvv/4qAdLYsWP9zt2zZ4907bXXSpmZmZJWq5UiIyOliRMnSj/99FOnr0GSJOk///mPlJSUJGk0GmnMmDHS2rVrA74vL730kjRu3Djfz0lmZqb097//XWpoaPCdEyql+plnnhlwz2Df9w0bNkhjx46VNBqNlJycLD3++OPSc889JwFSRUVFh6/B7XZLs2fPlmJiYiSZTNZpevW8vDzp73//uzR48GApMjJSUiqVUkJCgnThhRdK69ev9zu3fUr19lwul5SZmXnQKdWDpbAXBEEQDp1MksQOV0EQTj5vvvkm11xzDWvWrPHtJROEQ3H77bfz0ksv0dzcHDLhhSAIgvDnJvZUCYIgCMJ+7RNKQOs+roULF3LKKaeIgEoQBEEISeypEgRBEIT9Ro0axYQJE+jduzeVlZW89tprNDY28sADDxzroQmCIAjHMRFUCYIgCMJ+Z5xxBp988gkvv/wyMpmMwYMH89prr/mVExAEQRCEA4k9VYIgCIIgCIIgCIdB7KkSBEEQBEEQBEE4DCKoEgRBEARBEARBOAxiT1U7Xq+XsrIywsPDOy3yKAiCIAiCIBx9kiTR1NREYmLiES+aLghdJYKqdsrKykhJSTnWwxAEQRAEQRA6UVxcTHJy8rEehiAAIqjyEx4eDrT+T2o0Go/xaARBEARBEIQDNTY2kpKS4ntuE4TjgQiq2mlb8mc0GkVQJQiCIAiCcBwTWzWE44lYiCoIgiAIgiAIgnAYRFAlCIIgCIIgCIJwGERQJQiCIAiCIAiCcBjEnipBEARBEAThpCJJEm63G4/Hc6yHIpzAFAoFSqWyS/v3RFAlCIIgCIIgnDScTidlZWW0tFiP9VCEk0BYmIGEhATUanWH54mgShAEQRAEQTgpeL1e9uzZgyTJMJujUCpVx3pIwgnM7XbR2Ghhz5495OTkdFhsWgRVgiAIgiAIwknB6XTi8XiJiopDo9Ee6+EIJzi1WoNCoaS2thKn04lWG/pnSiSqEARBEARBEE4qooaV0F26+rMkgipBEARBEARBEITDIIIqQRAEQRAEQRCEwyCCKkEQBEEQBEE4hkaOHMzSpb8c62EIh0EEVYIgCIIgCIJwhNTW1jB37r85//yzGTt2BOecM42//e021qxZdayHBsBNN81k5MjBLFr0g1/7Bx+8y7nnnnmMRnXiEdn/BEEQBEEQBOEIKCsrY9asawgLC+fWW28nKysLt9vN77+vZO7cf/Phh58d6yECoNFoeOml+UycOEmkoT9EIqgSBEEQ/hRcHg+NLjseyYtarsCs0R/rIQmCcJJ76qnHARmvv74QnU7na+/RI5Ozz/5LyOuef/6/LF36C1VVVURFRTF16jSuu26mL+DJz9/FM8/MZceObYCMlJQU7rnnfnr3zqW8vIy5c/9NXt5GXC4XCQmJzJ59O6NHnxLyfqedNpXly5fx+ef/Y/r0i4KeU1JSzH//+zRbtmzGbreRnp7BTTfNZvjwEb5zzj33TM455zyKi4tYsmQxJpOJO+64m379+vOvfz3K2rWrSUxM4v77H6Z371zfdRs3bmDBgnns2LEdk8nM+PETufnm2X7v2fFOLP8TBEEQul2j00a1rZk6ewtur+eYjsXl8VBlbaSgsZqSlnoWlW5nbt5PrK4qpMXlOKZjEwTh5NXQ0MDvv//G9OkXBQ0OwsPDQ16r1xt44IFHeP/9T/jrX+/kiy/+x/vvv+s7/tBD/yA2NpbXX1/Im2++y5VXXoNS2TpXMnfuE7hcLhYseJV33/2IW26Z02lwYjAYuPrqa3n99Vew2WxBz7HZbIwePYbnn3+Rt956n5EjR/P3v99ORUW533kffPAu/fsP4K233mf06LE88sgDPPLIA5x++hm89dZ7JCen8MgjDyBJEtAarP31r7cyceKpLFz4If/85xNs2rSRuXOf6HDMxxsxUyUIgiB0G5vbSUFjDR/tWU+lrRGNQsn4+GxOTerZ7TNDTU47do8buUyGUa1FJVcEnNPotPFL2S5+Kt2B0+tBjoyB0clc2GMwr+5YwaVZwxgZm4Fc1LQRBKGblZQUI0kSaWnpB33ttdde7/vvxMRE9u0rYtGiH7jiiqsBqKioYMaMK0lPzwAgNTXVd35FRQUTJ55KVlY2AElJyV265wUXXMSHH77P+++/w7XXzgw4np2dQ3Z2ju/rWbNuZunSX1i+fCkXXniJr3306DGcd950AK67biafffYxubl9OPXU0wC44oqruP76q6mrqyUqKpq33nqDqVOnccklM3yv5Y47/s7NN8/krrvuQ6PRdGn8x5oIqgRBEIRus7OhigXblvm+dnjc/Fi6naLmOmb2GkO4OnQ1+q5yeNwUNdfyYcE6SlosqOQKRsVmMC2lD5Fag+88t9fD4tJdfFey1dfmRWJ9TTFNLgdnpvbls70b6W2OJ0IsBRQEoZu1zcQcikWLfuCjjz6gtLQEm82Kx+PBYPjj99ull87gX/96jO+++4bhw0cwadJkkpNTALjookt58snHWbXqd4YPH86ECaf6BUOhqNVqbrjhJv7znyc5//zpAcetViuvvvoSK1Ysp7a2Bo/Hg8PhoKKiwu+8tmAOIDIyCoDMzKyAtrq6OqKiotm9exe7d+fzww/f+c6RJAmv10tZWSkZGT268pYdcyKoEgRBELqFxWHjo4J1QY/tbKikzmHtlqBqX3MdT+f9TNvjisvrYVnFbvY01jCn70RMmtZlLhanjZ/KdgTtI7+hiilJvWlxObC5XUScGB+ECoJwAklJSUUmk1FUVHhQ123evImHH76f66+fxciRozEYwvjppx94772FvnNmzryRqVOnsWLFclau/I1XXnmRxx57nAkTJvGXv5zHyJGjWLHiV1atWslbb73BnDl3cNFFl3Rw11ann34G7777Nm+88SoJCYl+x+bNe4bVq1cxe/btJCenoNFouO++u3C7XX7ntS1DBJDtXwXQvg1a29qCTqvVyrnnXhB0fPHxCZ2O+Xgh9lQJgiAI3cLucVHraAl5fG9TzWHfo8lp58OCdQT7/LfEaqHc1uD72uZ24epgP1e904peqUYlF38KBUHofiaTiREjRvHJJx8F3afU1NQU9Lq8vDzi4xO45prr6d07l9TUVMrLywPOS01N49JLL+e5515gwoRJfP31l75jcXHxnH/+dP797/9w2WVX8MUXXcsyKJfLufnm2Xz22SeUl5cdMK5NnHnm2UyYMImsrGyioqIDzjkUPXv2Zu/ePaSkpAb8U6lOnEyEJ+xfkieeeAKZTMbtt9/ua7Pb7dxyyy1ERUURFhbGBRdcQGVl5bEbpCAIwp+IUibvcG9Styz987opbqkPeXx7/R/LUDQKJR3tlApTaehpiiVcJaapBEE4Mv7+93vwer1ce+0VLF78M/v27WPv3j18+OH7XH/9VUGvSUlJpaKigkWLfqCkpJgPP3zfrzCw3W5n7twnWLduLeXlZWzatJHt27f69lc988xT/P77b5SVlbJjx3bWrVvjO9YVY8aMpU+fvnz+uX8glpycwpIli9m1ayf5+bt48MH78HoPfYljmyuuuIrNm/OYO/cJdu3ayb59+1i2bIlIVHE0rFmzhpdeeon+/fv7tf/1r3/lm2++4eOPP8ZkMnHrrbdy/vnns2LFimM0UkEQhD+PcLWGIdGprKkuCjimlMlJC4s87HvIkaFRKHF43EGPm9R/ZLgKV2noH5nMprqSgPOiNAa8XokLegxGq1Qf9rgEQRCCSUpK5q233uXNN1/jueeepra2BrM5gl69enPXXfcFvWbcuPFccsllzJ37b1wuJ6NHn8K1117Pq6++BIBCoaChoYFHH32QurpazGYz48dPYubMGwHweLzMnfsEVVVVGAwGRo4cze23/+2gxn3LLXOYOfMav7bbbvsb//d/DzNz5jWYzWauuOIqWlpCr07oquzsHBYseIUXX5zPjTdehyRJJCUlM3nylMPu+2iSSYezi+4YaG5uZvDgwbzwwgv885//ZODAgTz77LM0NDQQExPDe++9x/TprZvrduzYQe/evVm5ciUjR47stO/GxkZMJhMNDQ0YjcYj/VIEQRCOOofHhdsroVOqjkjGu1p7C89t+YUKW6OvTSGTc0vuOHqa41AGydB3MNxeD/8r3MRPpYF7pWTIeHToWcTq/khTXOdo4eVtv7K3udbXFqHRc3PuOMwqHUbNiVMDRRCEVh09r9ntdgoK9hAdHY9aLWahhcPndDqoqakgM7MHWm3oFRcn3EzVLbfcwplnnsnkyZP55z//6Wtft24dLpeLyZMn+9p69epFampqyKDK4XDgcPxRo6SxsTHgHEEQhJNBo9POvuY6firdgc3jYnBUCkNjUonShnXrfaK0Bv7a71QqbA3kN1QRqTGQY4rFrNYfdkAFoJQrmJzUi4KGar9ASYaMq3NGsruhiq315fSJSMCs1hGpMXBzn/E0OK1U2Zoxa1rbRLY/QRAEoTudUEHVBx98wPr161mzZk3AsYqKCtRqNWaz2a89Li4uINVjm8cff5xHHnnkSAxVEAThuNHktPPp3vX8XlXoaytsquWn0h3cNWAKMbruDazMGh1mjY5e5vhu7bdNhEbPTX3GUW1rYrulgnCVhjidiSXlu9hY27rUT46MWblj6WNOwKjWYlRrSemG5YeCIAiCEMwJk6iiuLiY2267jXfffbfDqbeDce+999LQ0OD7V1xc3C39CoIgHE9q7M1+AVWbRped74q34AyxP+lgOD1u3F7vYffTVSa1jixTLFOTc7E4bTy7ZbEvoILWelQvbV+OxWn1u87t9VBja2ZtdRE/le6goLGaBmdgVi5BEARBOBgnzEzVunXrqKqqYvDgwb42j8fDsmXLeP755/nhhx9wOp1YLBa/2arKykri44N/WqrRaE6YKs2CIAiHalV1Ychjq6uLODutP2rFof05qLO3sKOhkrXVRegUKiYm9iReH06Yqns+/OpMo8vOjyXBa1F5JYmCxhpi9u+xcns97Gms4bmtS/xSracaIrkpd6xf4WBBEARBOBgnTFB16qmnsnnzZr+2a665hl69enH33XeTkpKCSqXi559/5oILLgBg587WtIyjRo06FkMWBEE4PnSQjuhwchXV2lt4evPP1NibfW1ra/YxISGbs9P6E3YUUpV7JQmPFHqGrMll9/23xWlj3gEBFcC+ljq+LMrjsqxhhxxcCoIgCH9uJ8xfj/DwcPr27evXZjAYiIqK8rVfd9113HHHHURGRmI0Gpk9ezajRo3qUuY/QRCEk9Ww2DR+Kd8V9NjQmFQMh5BS3OXxsKh0u19A1WZJeT6j4zKPSlClkSuJ04VTaQteRDPLFOv77+LmepwhigGvri7irLR+RCu6d39ZmyannSp7Eysr9+KVvIyM60G8LhyjWmQfFARBOBmcMEFVVzzzzDPI5XIuuOACHA4HU6dO5YUXXjjWwxIEQTimYrThDIpKZkOtf70mg1LDman9Dml2ptntYGXlnpDHV1XtJS384BJDNDhtOD1u5DI5JrW2S9kCTRodF/cYwnNblwQcyzLGEKX5Y0mfxWENOKeNR/IesT1hjU47n+xZx6p29btWVO6hjzmBq3JGYhJp3QVBEE54J3RQtWTJEr+vtVot8+fPZ/78+cdmQIIgCMcho1rLpVnDGBaTzk+lO7B7XAyMSmZMfCbRh5FSvaMgxOHtevILq9tJQWMNH+9ZR6WtCbVcwSnxmUxJzu1S6vNMYzR/7TeJj/esp6TFglahZEJCDhMTczCq/9jblR4eFbKPCLUe7RFa+lfcXOcXULXZailnR0MFI2Izjsh9BUEQhKPnhA6qBEEQhK4xqXUMiUmltzkeD170CjUK+aEngNUpVQyMSmZtzb6gx0fEpHe5r/yGKl7Ytsz3tdPrYXHZLoqa6piVOxZTJ0vktEo1vczx3NZ3EpIkIZPJ0CtVATNdUdowMsKi/OpbtbmgxyB0ChU2txPdISyHDMXhcfNz2c6Qx38u3UmfiMSjslRSEARBOHJOmJTqgiAIwuHTq9SEq7QHFVA1Oe3UO6zY3E5fm1ah4py0/mgVqoDze5riiNMbu9S3xWHjoz3rgx4raKqhzt7S5TFW25v4X+FGPihYw3ZLRcByP6Nay6zcsYyNz0Qpa339kRo91/ccTYRKx/PbljJ/2zJ+r9xLfQdLBQ+GV/Ji7yBlvd3jxttBog1BEITOjBw5mKVLfznWw/jTEzNVgiAIQlBNTjs7LJV8V7yVRpeNjPBozknrT5wuHLVCSYwunH8MOp0fS7azua4UjULFqUk9GRCZ1OnsUhuH1xU02UWbgqYaMozRnY7zf4UbWdFuj9e6mmIywqKYlTvWbwlhhEbPRZlDmJbSF7fkQY6Mb/dt4beqvb5z8huqSDGYuaXPhC4tP+yIVqFiaEwqBY3VQY8Piko+pEQhgiAIbb755kfCw7v2QdbRtG7dWm655QYWLVpKeHj4sR7OESeCKkEQBCGA1eXgy6I8llXs9rXl1ZWypa6MO/pPItsUh1wmI1YXzkWZgzkrtS9ymdxvD1ObtiV5wShlchQyedC06GlhkaSHRWJ1OdGrQgceFbZGv4Cqzd7mWtbV7OPUxJ5+91fLlURpW//8rakq9Auo2hS3WNhcV8q4hOyQ9+0KmUzGwMhkfizeTv0BhYgNSg1j47NQdCEhhyAIR59XktjbUkuT2064UkuGIQp5iN9lx1JUVMcfPB0st9uFUhm4CkHomFj+JwiCIARocNn9Aqo2XiTeyV9Do9Pma1PLlZg1+oCAqsFpY4elgjd2reTNnSvJb6ii0Wn3OydcpQ3Yf2VQapjV+xQGRiXz8Z4NzNu6hN8r9wbN3ufxellSFjxdPMDSsnwaXfagx2xuJ0vL80Neu6x8N80hrj0YkVoDdw6YzMTEbLQKFRq5kjFxPbh34BSidUcmhbsgCIdnS0MZ/975I68UruCDknW8UriCf+/8kS0NZUfsnosX/8SMGRcxfvwopkyZyK233ojN1vq79quvPufSS6czduwIzjxzCnPnPuG7rrPlf16vl4UL32T69HMYO3YEf/nLGbzxxqsAlJWVMXLkYBYt+oGbbrqeceNG8vnnnzFp0lgWL/7Jr5+lS39hwoTRtLS0+F03c+bVjBs3kssuu5D169f5+r3llhsAOO208YwcOZhHH32oW9+v442YqRIEQRAC7G0MTObQpsLWiNXt6rDGUoPTxhs7V7LdUuFrW1m1l6HRqVycOdQXgKkVSs5O78e+5jqUcgUZxiiGRqeyMH81FbZG37V7mmrICIvixtyxmNstyfMidbhnyeF1hyxwLAHt58cUMjlJBhMApS0NSEgcRm1kP9HaMKZnDGZqch8ADEq1KDQsCMepLQ1lvFO8JqC9wW3nneI1XM4w+poSu/WeNTXVPPDAfdx66xzGj5+E1drCxo0bkCSJTz/9mOeee5qbb57NqFFjaG5uJi9vY5f7fuGFeXz55f+47ba/MWDAQGpqaigqKgw4Z86cv9KzZy/UajX5+bv4+usvmTRpsu+ctq8NBgMNDQ0AzJv3LLffficZGRm8//673Hnn7fzvf18RFxfH448/xb33/p2PPvofBoMBjebkTsgjfqMLgiD8iVldTpxeNxqF0i/rnUrR8ZK0zpbAbK+v8Auo2qyt2ceouB70jfzjgSRSY+DG3LHk1ZVRaWtkh6XSL6Bqs7e5lt2N1QyNSftjnHIFI2PT2VIf/NPjQVHJITPr6ZVqTonrQUFjNacl9SLHFMfephoAzk7tj1wm69asfEq54rD3aAmCcGR5JYmvyjd3eM5XFVvINSZ061LAmpoaPB43EyZMIiGh9fdjVlbr8uM333yVSy+9nIsvvsx3fm5uny7129LSwkcfvc/f/nY3Z555NgDJySkMHDjI77xLLrmMiRNP9X19zjnnccMN11BTU010dAx1dXX89tsK5s1b4Hfd9OkXM2lS63V33XUvv//+G19++TlXXHE1RmPrh1QREZFiT5UgCIJwcrK6Hexrrufros1U25tJ1Js4K60fiXozOqWK9LBI5MjwEjhVk22M7TC5QovLweIO0oj/VLqDLFOML3Ngjb2Z/2z+mXqHlbPT+rGuOniadmhdktc3IhFtu/X+WaZYEvRGyq3+gZheqWZyUq8Oiwj3jkjgssyh7GupZ/62pX7HJibmkBYWSXiQfWKCIJyc9rbU0uDueNlvg8vG3pZaMsO6by9TdnYOQ4cOZ8aMixk5chTDh49k0qTJuN1uqqurGTZs+CH1W1i4F6fT2en1vXrl+n3dp09fMjJ68O23X3Plldfw/fffkpAQz6BBg/3O69evn++/lUolvXvnUlgYuE/1z0DsqRIEQfiTcXk8rK4q4pnNi8lvrMbitLHNUsGTmxaxua4Uj+TFqNZyeXbgH2GDUs2M7GEYOpjB8UgSzg6K/zq9brz719W5vR4WlWzvcgpzCSkgzIvQ6Lmt70TOSOmDSa1rnYGKz+S+gVMxqXXU2pupsDZS72jx3bf9tdHaMH6tKAi41y9luyhuqe/SuARBODk0dRJQHex5XaVQKJg3bwHPPDOPjIwMPv74Ay6++Dzq6kIvxe6Kri650+kCl3Ofc855fPPNVwB8882XnHnmOSGTDgkiqBIEQTjueSWJeoeVfc11FDbVUmdvweM99NpGDS4bn+zdEPTY+wVraXDY0ChUDI5O5YFB0xifkE3fiEQu7jGEfww6nXhdx6l7DUo1g6JSQh4fHpOObv8sVZPLwe/tsu9tr69gYFRyyGvHx2ejOyArVYvLQWFTHfUOK2ek9OHCjMH0jWhdmvPZ3o08tO4bHlr3Nf+34Qd+rfBPPmFzu/ipdEfI+/1Ysg2b29Xh6xUE4eQRruzazHRXzzsYMpmMAQMGMnPmTbz99vsolSpWr15FQkIia9asPqQ+U1JS0Wi0h3T96aefQUVFOR9++D579+7xLR9sb8uWP5ZKut1uduzYTnp6BgAqVevvaq/Xc0hjP9GI5X+CIAjHMZfHQ0FjNa/t/M2XxU6rUHFZ1jD6RyYFBBhd0eC04QrxR87qdtLkchCpNaBTqkgOi+CSzKF4JW+Hy+jaU8jljInPZHlFAU0HZM+L1OjpH5Xk92ln+9mj3Y3VnJbcmzidkcoD9lVlhceQbYrxa/NKXvJqS3kz/3egNRkGwF/S+rO0PJ/tlkrfuU0uO+/uXoPb62V8QjYKuRyP5AmZHbD1GgceyQMcv+mFPV4PdQ4bDo+LkhYLTo+LXhEJmNU6kQxDEA5ShiEKk1Lb4RJAk0pHhiGqW++7Zctm1q5dzYgRo4iIiGDr1i1YLPWkp2dw/fWzePLJfxEREcmoUWOwWlvIy9vERRddErSvW2+dxfjxE7nwwkvQaDRcccVVzJ//X1QqFf37D8BiqWfPnj2cc865HY7JaDQyfvwknn/+WYYPH0lsbFzAOZ9++hEpKamkp2fwwQfv0tTUyNln/wWA+PgEZDIZv/66nNGjT0Gj0aDXn7z7SsVvW0EQhONYraOF/279xS/wsHtcvL7zN+4aMIXMTgrjBtPZ5uoDj8tlMuSyg6ulFK0N4+4BU/ixZBtra4qQIWdUbAaTknoSqTH4zjMo1QyOTuH3qkJf28L8VVyeNZwyawObakuI0YZxWnJvXF4PRc31xHrchKu0GFRqLE4bnxT6z7rJZTKSDRF8UZQXdGxf7ctjQFQyUVoDOoWKvhGJlLRYgp7bJyLBN6t2PKqzt7CkfBcrK/ciAQOikhgcncp/N//C+RkD6ReZKAIrQTgIcpmMsxP6Bc3+1+bs+L7dXq/KYDCwceN6PvzwPVpaWoiPT2DOnL8yevQYAJxOBx988B7z5j2D2Wxm4sTJIfsqKSnBYrH4vr722pkoFApefnnB/sQT0Zx33vQujeucc/7Cjz9+5wuUDnTzzXN4++03yc/fSXJyCk899QxmcwQAsbGxzJx5Iy+8MI9//vNhpk07iwcffKSL78iJRyaFyjX7J9TY2IjJZKKhoQGj8firTC0Iwp+Lx+vl070b+DlE0oc+5gRm9h7jl7WvK+rsLTy24VusQZa1Raj13DNwil/a8mDsbhdOrwetQtnhQ7vL66HZ5QAgXKUJOttVZWviiY0/0OJ2+rVPjM9hakouFqeV+duW0rS/H4Bh0Wlc2GMwzW4Hj67/1u86k1rH1ORcPtqzLuS4Hhx8BkkGMwA1tmaKW+qRkChtaWBZeWttK61Cyf2DphGjO/isVU6Pe//rkTAoNUcksKmzt/CfzT9RY2/xazeqtFyVM5L5W5fyyNCziD2E8QvC8ayj5zW73U5BwR6io+NRqw89e+eWhjK+Kt/sN2NlUuk4O75vt6dTP559993XPPvs03z99Q++5XzQWofq/PPP4u233ycnp+cxHOGR53Q6qKmpIDOzB1pt6GWf4uMrQRCE45TT6+kwUUK5rQGn10PoalHBmTQ6rus5hvlbl/pl91PK5FzXa3SHAZXV5aDU2sB3+7ZS42ghLSyS01NyidWGB03DrupCGvEYbRj3DTydJeX5bKotQaNQcmpST3IjEnB7vTy75RfsHv8AcE1NEXH6cMbEZQb05/C4OkyFLts/Lq8kUWFt4L3da8lvrAKgR3g0V2SPYGt9GeMTsonSHnxx3ipbE9/s28za6n3IZDJGxKQxLbUv0YfQVyiSJJFXVxoQUAE0uuxst1TQyxzPDkuFX1DVurzTjt3tRq9UEa7W+rIwCoLwh76mRHKNCextqaXJbSdcqSXDENXtM1THK7vdRk1NDW+//Sbnnnu+X0AlBCeCKkEQhOOUWq4gSW9mV0NV0OPxOiPqLu5zak8hk9PTFMdDQ85geUUBpS0W0sMjGRXXg6h2S/MO5PS4+b1qLx/uWU+mMYbRcRkoZHK+LNzExKSe9DLHd3kMTU471fZmfq/cixeJEbHpnJbUk9OSeyFH5ktjvq56X0BA1ebn0p2MjO1BL1M8Oxr+qIll97h9Naaa281utekbmUi4SkutvYV/b/rRr3jwnqYaXtnxK/8YNI14/cGvWKixN/PExh9pce+/rwS/Vu5hc305dw+YQpQ29Pt7MGweF6urC0Me31pfztCYVBqcNl9bnb2Fd3ev8dX0kiNjVFwGf0kfgKmDQs6C8Gcll8m6NW36iWThwrd4883XGTRoEFddde2xHs4JQQRVgiAIxymFXM74xGyWlucHrRd1Vlq/g17610alUBCvN3F+xkDcXi8quRy5rOOEsA0uO1/v28qNvcdS3FLPb5V7cHu9rYV8JWh02DBqOn84b3Ta+WTvela120e1vGI3/SOTuDx7uF9dqCpbU8h+bB4XEhIzsofxzOafqWuXlv3H4m3cnDuO57cu8VvmmKA3cWnmMNRyBSsqC/wCqjZOr4cl5bu4IH1Qp0WQ2/N4vayoKPgjoGqnwWljXfU+Jif36pZPuhUyGaoOAmq1XIHH6yU3IgFoDWJf3fEbBU3VvnO8SKyo3INcJuPCjCFolOKRQBCEVjNn3sjMmTeGPJ6YmMjvv68/iiM6/onfoIIgCMexaE0Yt/aZwGs7f/M9rGsUSi7pMYREvemw+1fI5Mjksi496FdaG7mox2C+2bfFb1ni0vJ8NtQU87f+kzF2YTFicXOdX0DVJq+ulF2WSobFpvva0sIjQ/ZjVut8ywvvGjCFkpZ6ChqqidWHk2OKw6TScv+gMyhtsVBrbyYlLJIYbRgmjY4Wl4Nt9eUh+95hqcTucR1UUGV1O9lYWxLy+PrafZwSn4ledWiBcHsahYpTE3uyo112w/aGxaSxt7GWCQk5QOuSwPYBVXsrKvcwNTmXGKXYeyUIgnCoRFAlCIJwHFMpFPSOiOP+QafT5HLgxYtRpcOk1nY5xXkwXkmiztFCXm0JuxqqSTKYGBaTTpTGEDKQ0CiU1Nibg+7zanTZWV6ez3kZAzscl93j4ufS4Ik3AH4q20luRIKvuHCC3kSUxkCtI3Dv0Fmp/VDQGgxGaPREaPT0i0zyOydKoQy65E4pV3S45M2o0qCUH1wpR7lM3uH+JJ1ChULeffsx0sOjGBiVHBDIZRtjSA2LZGhMGqb9M4eWdssAD+SVJGwhllgKgiAIXSOCKkEQhOOcXCYnUmsgspv24wCUtliYm7fIt/xtQ20x3xZv5dbc8fQyx6MIElBEa8P4vnhbyD7X1RRzWnLvDhNddPYA7/C48LRLShuh0XN7v0m8vet38htbZ1q0ChWTk3rR6LLzVN5PzOk7wS9DX4PTRqPTTovbgVmt259+3T9xhUahZEpyb/LqSoOOY0pyb5weDwqZu8uZ+wwqNZOTevLyjpqgx09N6oWmG5NCGNU6ZmQNZ1JiT5ZX7MYreRkdl0mcLhyzWu8XHBtVoTNWyZCJZBWCIAiHSQRVgiAIfzKNTjuv7VwRsJ/IK0m8smMFDww+I+jsjl6hQtXB7I1CLvcV9a13WClqqmVDbQkmtY4RselEavRoFSqGRKeypyl44DEwKgXDAQWNY3XhXNtzNGXWBhweNxISKyv3+hIuvLbzN27tM54wlZZKayPzty3zKxzcPyKRGdnDA4K9RL2JM1L68G3xVr/2iQk5VNtbeGvXKvpHJTE1OZdobViXlkhmm2KDzh6NjE0nLSz0UsZDZVRrMaq1vqLIofbFmdRaEvUmyqwNAccGRSUT3kG2REEQBKFzIqgSBEH4k2l2OSi3NgY9ppTLsbmd1DtkyJH5lo8BaJQqxiVksyHEvqFx8Vm+rHrPblnsl2Tih5JtXNxjCKPiejAoOoVFpduxOG1EaQyMic8kVheO1e2kf2QSiiDLB1vcTuZtXRL0vnubamlyOXB7vTy3dQk19ma/43n1ZRgKN3Fp5jC/ZAwGlYYpyb0ZEZvOroYqPJKXeJ2JzXWlfFCwFoBfKwpYW72P+wZNJU7XeTbAttmjKcm9WVVViBwZI2IziNYa/BJwdLfOkowY1Tpuzh3Hgm3LKbVafO29zfFclDn4kBOeCIIgCK1EUCUIgvAn45G8QdsHRaUwMjadj/asY09TLSa1linJuQyKSsa4f/9RsiGCwdEprK8p9rs2SW9ieGw6bsnDd8Vbg2bt+3DPOnIj4onXm/j7gNPYaalEJVewqHQ7Jc0WIrV6lDI5/SKTMB4QgIRKq97G6XFjc7sCAqo2q6oLOTO1HzFK/1pROqUanVJNvN7E7oYq/pP3c0CmRbvHxarKvYyOy6TUasHisJEaFkGk1hB0X1bb7FGmMabDMR9tMbpwbu83kUannWa3A9P+pZEd1fQSBEEQuubgduEKgiAIJzyDUo1B6f8gHaczMjg6hRe3L2dnQxUur4caewvv7V7DR3vW++o9GdVaLs0cyu19JzIgMolccwIze45hdt+JRGj0NDsdrKzcE/Lem2pb9zBFagyo5Qpe2/kb+5rr8SJRY2/h7fxVfLNvC1a30+86o1pLqMV3CpmcMKWG+nYp1Q/klSQc3sD06e0tK98dNHV9elgUKWGRPLrhW17Ytoz3CtbwxKYfeWHrMuqDJNA4nhnVOpLDIuhljidBbxIBlSCcBEaOHMzSpb8c8vXr1q1l5MjBNDWFLmFxvDvc96A7iKBKEAThT8as0XFJ5hC/tgkJ2Xy9b3OQkALWVBf5FZE1qnX0jkhgZu9TuCl3LENj04jYv19JQsIdYiYM8AVLFqeVD/cEr3GytHwXTS67X1u4SsuwmLSg509MzCFcrSVaGxb0OIBKrkDbScKJUAkpTk/J5Y1dK3EcsAetsLmWr4o24wxS60oQhJOA14uiPB/lnnUoyvPBG/p327H0zTc/MmrUmGM9DJ9jEaQdD++BWP4nCILwJyOXyekXmcjf+p3K/wo3UWq1EKU1UNlBod09jTUkGcx+bcGKz2oVKrKM0exubE1EYVBqGBPfg/TwKJAgfv++JKvbGRA4tZGACmuj3x4mvVLN9B6DMap1LC3Px+X1oFUoOS2pN+MSslArlERodKQYzBS3WAL6nJCQ3WEKdYAxcZksr9jt12ZS62hy2QMCqja/VxVyRmpfohWhAzpBEE48ysJNaFd9irzdHkSv3ox9xAW40wccu4EFERUVfayHcEhcLhcqVfdkHj0e3gMRVAmCIHSzRqedeoeVfc11GNVakg1mzGp90DTlx4pOqSbHHMetfcbj9HpweNzIIOhMFbSmIO8Kg0rDRT2G8MSmH0kxRHB2Wn9+Kt3OjyXbkSNjYFRyay2rThIrBLufSa3jvPQBTEzMweX1oJYr/ep1GdU6bsodz9u7VrGjoQJoXRo4Lj6L05J7Bw0C24vRhTE+IZul5fm+Nr1SRdP+pY/BeCQv7uP002tBEA6NsnATul9eC2iXWS3ofnkN28TrjkhgtXjxT7z22suUlBSj0WjJyenJU089g06n46uvPue9996hpKQYo9HExImTuPPOe4DWpW///vd/GD9+YtB+vV4vCxe+yeeff0ZdXS0pKalce+1MJk2aHHIsGzduYMGCeezYsR2Tycz48RO5+ebZ6HStH045nU5efnkBP/74PfX1dcTFxXHlldcydOhwbrnlBgBOO208AGeccTYPPvgIN900k8zMTBQKBd9//x2ZmVm88MLLrF+/jueff5b8/F0YjSbOOOMsZs26GeX+xEI33TSTrKxs1Go1X331OUqlivPOu4CZM2/0jffA96CqqpJ5855l1aqVOJ1O0tMzuPPOe+jbtx/5+bt45pm57NixDZCRkpLCPffcT+/euYf1/RNBlSAIQjeqd1h5dccKdu+vqQSgkSu5te8EeoRHHVbB3gM1OGxU2hrZVFeKQalmUHQKZrXuoDK5GVQaDLTOHOVGJLC1vjzgHLlMRkZ41z8FTNSbuW/g6dg9LuZtWeLby+RFYn1tMfmNVdwzYCppYZEUNdcFXK9RKInRhge0Q2vR3o6W+UVpDdzQewxNLgdOjxudUo1Jre1SrakwlYaz0/oxIiadJeX52D0uRsVmYNLo+LIo+DUmta7LAacgCCcArxftqk8BAvZxtn3wpF39Kc2p/aAbPyirqanmgQfu49Zb5zB+/CSs1hY2btyAJEl8+unHPPfc09x882xGjRpDc3MzeXkbu9z3W2+9zvfff8vdd99HSkoqGzas5+GH78dsjmDw4CEB55eUFPPXv97KrFk3849/PIzFUs/cuf9m7twneOCBRwB45JEH2LJlM3fc8Xeys3MoKyvFYrEQFxfH448/xb33/p2PPvofBoMBjeaPvZvffvs15503nZdffh2Aqqoq7rhjNmeeeTYPPvgoRUWFPP74Y6jVar+g6dtvv+bSS2fw6qtvs2VLHo899hD9+w9kxIiRAeO3Wq3cdNNMYmJiePLJZ4iKimLnzh1I+5emP/TQP8jJ6cldd92LXK4gP3+nL4A7HOIvgSAIQjdxeT18X7zVL6ACcHjdPLflFx4ecmaHAcHBqHdYeWnbcvY21/ravijK46Iegxkd1+OgU2TrlWouyRzK3Lyf/PZPyYCrc0ZhOiAbX7PLjsVpo7CpFr1STaohEpNah0qhQKVQEK838lHBuqDJIZpcDrbWl3NNziieyvuJFvcfM0EKmZwbe48NuN/BMKg0AcV+uypcpSXcpCUjPAovEkq5gkanjbSwKIravddtzk8fgLmTZYXCofFKEja3E7lMJlK+C0eNorLAb8nfgWSArMWCorIAT0J2t923pqYGj8fNhAmTSEhIBCArq7X/N998lUsvvZyLL77Md35ubp8u9et0OnnrrdeZN28B/fq1zq4lJSWzadNGPv/806BB1VtvvcHUqdO45JIZAKSmpnLHHX/n5ptnctdd91FZWcHPPy/iuecWMHz4CF+fbYxGEwAREZGEh/t/QJacnMrs2bf7vl6w4Hni4uK58857kMlkpKdnUF1dzQsvPMd1192AfH/gmpWVxfXXz/KN55NPPmTt2tVBg6off/yO+vp6Xn99ISZT61hSUlJ9xysqKpgx40rS0zN8/XUHEVQJgiB0k0annRUhMt+5vB4Km2q7JajySF5+rSjwC6jafLRnPb3M8SQdwkNorC6cewZMYWdDJVvqyojRhjMiLp0ItcFvpqfBaeO93Wv8CtwqZXJm9j6FXHM8aoUSm9vJNktFyHutqSliRGwa/xh0OjstFexoqCRKYyDHFEdxcx0RGj0x2rAOZ/ZcHg/1TiubakuotDUyJDoVk1rHupp9VFgb6WWOJzcinkiNwVeUuKvkcrkvk1PrssKxfFmYx6rqQjyS17cUsV9k0kH3LXSu2tbE2uoiNtSWoFEomZTYk/SwKCK0+s4vFoTDILMFr+F3qOd1VXZ2DkOHDmfGjIsZOXIUw4ePZNKkybjdbqqrqxk2bPgh9VtSUozdbmfOnJv92l0uFzk5vYJes3v3LnbvzueHH77ztUmShNfrpayslIKC3SgUCgYPHnzQ4+nVq7ff14WFe+nbt5/f79EBAwZitVqpqqokPj4B+CPAbBMVFU19feBKB4Bdu3bRs2dPX0B1oEsvncG//vUY3333DcOHj2DSpMkkJ6cc9Gs5kAiqBEEQuolb8uDyekIe7yjl98Foctr5pWxXyONrqgtJMgw8pL4jtQZGaXswMjYjaLDglbz8XrnXL6ACcEteXtq2nIeHnkWcLhyFTI5BqaYmxH3CVBoUcjl6pRqX14Pd42aHpZLvirci0Trr9rf+k+lhDL7s0O31sKuhkue3LcUrSeSYYql3WHluyxJfWvS1NfswKNXc2X8yiQck2ThYERo9l2YN5cy0vri9XjQKJWa1TgRUR0CVrSlgxnRXQxUDo5K5JHOoL9OkIBwJUheKfB/MeV2lUCiYN28BeXmbWL16JR9//AEvvTSfefNePKx+rdbWvzv/+c9zxMT4185Tq4N/+Ga1Wjn33Au46KJLAo7FxydQUlIc5Kqu0ekObRXCgcvzZDIZXm/wXcDtlxsGM3PmjUydOo0VK5azcuVvvPLKizz22ONMmDDpkMbW5vjZNS0IgnCC08iVRHbwwJcRHtUt95GQsHmcIY83OIJn1fNKXmrszSwv383CXatYXr6bGlsz3iAp0EMFC41OO4tKtwfvH4m8/cGWQaVhSnLvoOcBnJrYE5VcSaPLzvsFa9lUW8Kephpfogy35GVh/ioa2z1Y+71Gp40Xty/HK7VeMSmxJ+8XrA2oM9XidvLmrt9pDpFpsD2H2029w4rFYQ36nqgVSqK1YcTrjURo9MhkMppddspaLGyvr6CkuT7keIWucXrc/FC8zS+garOxtoTqDjJUCkJ38MRl4tWbQybtkQCvwYwnLrPb7y2TyRgwYCAzZ97E22+/j1KpYvXqVSQkJLJmzepD6jMjowdqtZrKynJSUlL9/sXFxQe9pmfP3uzduyfg/JSUVFQqFZmZ2Xi9XtavD14Woy2jn7eDDxnbpKdnsGXLZiTpj3d806aN6PUGYmPjDuEVt85q7dq1i4aGhpDnpKamcemll/Pccy8wYcIkvv76y0O6V3siqBIEQegmZo2eCzIGBT2WrDcTowuefOFgaRQqeplC/7EZHB24jMHl8VDYVMej67/lnd2r+bWygHd2r+bRDd9S3Gzp8r29ktRhNrxqe7Pvv3NMsUFrS01NziVB3/op776m2pAPL2XWBqxuV9BjFdZGnPv/YOuVKmxup+/rAxU119HsCh2EeiQvFdZGFu5excPrvuHxjT/wffE2LJ3MLNY5Wnhp2688sv5bnt2ymMc2fMezm3+h2tbc4XVCaA1OG2trQmQFAVZW7j2KoxH+lORy7CMuAAKzobZ9bR9+QbcmqQDYsmUzb775Gtu3b6OiopwlSxZjsdSTnp7B9dfP4v333+HDD99n37597NixnY8++iBkX7feOouPP249bjAYuOyyK3j22af55puvKCkp9l3/zTdfBb3+iiuuYvPmPObOfYJdu3ayb98+li1bwty5TwCQmJjIGWecxf/93yMsXfoLZWWlrFu3lp9++hFonc2SyWT8+uty6uvrfbNlwVxwwUVUVlbwn//8m8LCvSxbtoRXX32RSy+d4dtPdbCmTDmdqKgo7r77DjZt2khpaQmLF//M5s2bsNvtzJ37BOvWraW8vIxNmzayfftW3/6qwyGW/wmCIHSjXHM81/YczWd7N2Bx2pDLZAyJTuX89IGd1knqKr1SzfkZg9i58Qc8B8yoJOqNpIRF+LXZ3S4qbY28suPXgHpLDo+bl7Yv564Bp2HuZFlV6yeJEqlhEexrrg96Tm/zH598GtWtRYanJvdmU20pSrmc/pFJmDU69MrW5RmHunzO5vkj2FLKFCEDqjYHvk/tVdmaeHzDD76kGnaPiy+K8thYW8LNueOCvi9Wt5P3dq9hV2OVX3up1cKCbcu4vd8kjIeRbEMITkJCkiSx7FI4otzpA7BNvA7tqk+RtUtaIRnM2IcfmTpVBoOBjRvX8+GH79HS0kJ8fAJz5vyV0aNbC9o6nQ4++OA95s17BrPZzMSJodOhl5SUYLH8Me5Zs24mIiKCt99+g9LSEsLDw+nZsxdXXXVt0Ouzs3NYsOAVXnxxPjfeeB2SJJGUlMzkyVN859x1130sWPA8Tz31OA0NDcTFxXP11a39xcbGMnPmjbzwwjz++c+HmTbtLB588JGg94qNjeXpp+fx/PPPcsUVl2A0mjj77HO55prrD/Yt9FGpVPz3v/N57rlnuOOOOXg8bjIyenDnnfegUChoaGjg0UcfpK6uFrPZzPjxk/wyDR4qmdR+vu1PrrGxEZPJRENDA0Zj966VFQThz0OSJBqddhweFy7Ji1ImJ+wwMtIF4/J4KLc18PGe9exqqEItV3BKfBanJfciUmPwO7e0xUK1rYkF25eH7O/+QdMCgrH2LA4rG2qL2VRbwtj4bF7e8WvAOZEaPXcNOI2Idvd3e70oZLKQD8FVtiYeXPs1UpD5qhRDBLf1nUh4kOCk3NrAw+u+AVqzcd3SZwLzty4JOutlVuu4Z+DUoHtx7B4Xb+38nfW1wfcIzOkzkT6RCQHtldZGHlr3dchZtgcGTSO5g/dTCK7F5eSjPev4vSr4jNRtfSeSGxH4/RD+XDp6XrPb7RQU7CE6Oh61+jB/53q9KCoLkNkakXTG1iV/x1G9QeHocDod1NRUkJnZA6029IdlYqZKEAShm7m9XmodLby7ezUlLRYAso2xXJY9jASdsVs+ZVcpFKSGRXJj77GthXtlMsKUGlQK/2x5bq+XxaU7O30QdUuhZ3osDhsLti2ncH+2QZNaz1XZI/imeAs19hZkQN/IJC7uMZgIjQGvJFFnb2FjbTE7G6qI04UzJj6TSI0BjUKJw+2m0WXH6XWjliu4sfcpvLh9uV+AopYruDJnRNCACsCo0jEqNoOVVXuRgK31ZYyO6xE0++IlmUP90p579we9EhJeycvm+rKQr31V9d6gQZXd4w4ZUAE0u0MvkRRCM6jUnJ6Sy7b6choP2AfXJyKB2BD1ywThiJDLuzVtunByE0GVIAhCN6uyt2Yva7/kLL+xiqc2LeIfg04/qLTqVrcTq7t1P5BBqQ6o19NZTSaX10OlvYl+8iSUMjnuIMvgVHIF4arQn74VNtX6AiqA36v2Utxcz5TkXOJ1rUkbwlUa39jKWiw8lfcT9nZL9H4q3cmNvceSFhbJF0WbWFVdiFeS0MiVnJrUk4eHnMmXRZupc7SQY4rllPgsorStM14erxeL00pxs4UGp4208EgiNQbOzxhIWlgk35dsY0nZLq7OGUUPYww/lmyjzmEl1RDBeRkDSTZE+AJZi8PKqqpCfinbhdXj5PqeY1DLlSGzNupDpKbXK1XIkQUkxmhjFLWrDlm0Jow7+p/Kyso9bK0vR6NQMTquBz1NsUTruqfOmyAIQncTQZUgCEI3aHLaaXE7UchkfLtvS9A9PFa3k7XVRUxNzu10tsorSVTaGvmoYB3b99d7yo1I4MIeg4nTGZF3cbZLrVCQGR7NmqpCTk3qxQ8l2wLOOTO1L8YQQZXb6+W3yoKA9lKrhfd2r8Gk1nHfwKm+gKrJaeeNXSv9Aipo3QtTZrWwrCKfrfXlvnaH1823xVvxSBJXZg9vDbQUKhT7l9i4vR72NNYwb+sSv31TWcYYru81hgmJOQyKTsUreVuDQ7WW/pGJeCQJtVzhF3A2OG28smOFX3HmZRX5DItJY0l58BT1o+N6BG0PV2sZFZcRdGaspykWYzcu9eyIZ/+saK29mSaXgwS9CZNae0IHdSqFggS9ianJvTklPgsFMsJVGtRK1bEemiAIQkgiqBIEQTgMHq+XkhYLC/NXUdxSz4U9BlPQGKo6E2y3VDAyNqPTpBC19mae2PijX3Cytb6cPRt/5B+DphHTxU/sFTI5o+N78Nj67zgjpQ+XZA5lcelOah0tZBljGJ+QTU9znF9x3/Zk0GEBXqVMjow/Arxmt8O35PHAfpINEXxRlBe0n59LdzAuIStgFq/BaSe/oYrreo7Gi4TV7WRpeT67G6v5vngr03sMxqzxDyBCBRSVtia/gApgS105N+eOY1dDJWVW//S7U5NzA/antdEqVJyT1rpZfWXlXrxIyID+kUlcmjWUsA5m/rqL2+umsKmeF7Yto6XdcsP+kUlckjnUN9N3ojKotBiOwvsoCILQHURQJQiCcBhqHM08lbfIt3zM6nZiVGupdwZPIWtU6Wh02jsMqjxeD8srdgfM9kBr1rsVlQWcndrPN5vTmSiNgTv6ncobO1eiViiYkTUMtUJJubUBlVyBw+NCq1AGDZ4UcjnjErJYV7MvaN+nxGcS3m4zuDdE7iOtQkWjK3QNJ7fk9S1zbK/Z5WCrpZwv920GIEKt58zUvuxurGZF5R6mJOd2OXjYEOQ1SEi8sWsll2QOQSVXsra6EL1SwynxmcRowzpcWmnW6Lg4cwjTUvpgc7vQKJQY1dqAJZpHSq3dyrytv2A/IKNjXl0pcbpwzk0f0GFAfLDqHVZKWyzsa64jTmckPTyKCI2+y7OmgiAIJzMRVAmCIBwit9fDkrJ8v/04a6qKODWpJ0XNdUGvGRydwsbaElLDI0P2a3W72FJXHvL4lroyJif1IkzetSVmSrmCHsZo7hwwGYfHzSd71rOprtR3XC1XcHPuOLJNsUEfwhP1ZgZHpQRkyIvXGRkZl4Fc1hrc1TlacHhcmNU6LAcUb3V43RiUHY9XI/f/k1Rrb2He1l/86mLVO628s3s1M3uNYYelosNU6QfSKoIvH7O6nbyTv5p/Dj2HgVHJBxUkaBQqYnTHZlna3qbagICqzbKK3YxLyCa2m2qjVdmaeHrzz9S3q92lVai4o98kUsMiQy5ndXs9+79/EjqFCu1RCjgFQRCONpEXUhAE4RDZPW7yG/zrFFXZm3BL3oC9ODJknJPWnx2WCkyd1C9SyuWEdzBDEq7SoJQd/K/vMJWGNdVFfgEVgNPr4fmtS/0emNszqrVcmjWU2/pOpH9kEr3N8VzbcxS395vkWx5X52jhmbzFvF+wlnPS+nPgI7ZXan2ojg4xq9THnBCQ6W9nQ0XIQsM/l+7k1KSeqA9iJmZokELEbcbEZ2JQqY/YrIvd7aLC2sj3xdta98nVl3daXLgz1famkMccHjfuTmp3dVWzy8EbO1cG/HzYPS6e37o0IIBuU2dv4fPCPB5e9w33rfmS13eupKylAY+364GwIAjCiULMVAmCIBwilVxOhFpPcYt/IdxP9qxnclIv7uh3KsXN9XiRSNCbWF1VyNrqIh4ZelaH/eqUak5L7s2Ohsqgx09L7o32EDbtNzrt/FS6I+gxt+Rle30FMSFmNoxqHblqHdmmGLwSaNrtwfJKXlZVFVK1/yF/h6WSWb3H8kvZLkpa6onQtC7ZSzVEMLvPRCptjVicNlZUFFDUXEeqIYIZ2cMDMu1tqw/++gH2NddxaebQTvemtReh0XNOWn++PGBfV5wunClJvbt1qVx7dreLdTX7eDt/la/t57KdJOvN3Np3vF9dr4ORFhYV8phZrUPVTa+n2WVnT1PwfYKNLjv1DmtADbB6h5X/bvmFClujr21TXSnbLBX8Y9DpJOhN3TI2QRCE44UIqgRBEA6RRqFiSkpv8ur9Z34kYFHpDnoYo1lXU0STy0G1vRmFTM6NuWOJUHceCKSFRTIhIZsl5fl+7ZMSe5JiOLSist4Q+5baVNmbO+1DJQ/8s9HkcvBbuyx4q6sL2WYpZ1RsBkNjUluXH4ZHs7Z2H9/s24LFaSNSo+eMlL5cY4pBr1RjCpJcIr6DpWtmjZ6wILN5Xkmi3mGlwtpAvdNKkj6CSK0ek1qHXqlmYkI2/SMT+bWigCaXnaHRaWQYo4MWBu4uFqfNL6BqU2K18GPJDs5PHxhQX6wrEvRGYrXhvmC2vbNS+x1U6v6OhEo338YW5GeqsKnWL6Bq39fXRZu5ImdEyOWYwbS4HL56bOEqzRELgAVBEA6VCKoEQRAOQ6LexHnpA/i8MA9pf80iGXBOWn8ywqK5OHMouxurCVdp6GGMwazSdekBOlyt5Zy0/oxLyGZLXRkyGfSJSMSs1nWYPKEjKrmSBL2Rcmvgwy5Ajin2kPoNptnlYNH+WbHz0weyuHQnP5Ru9x2vc7TujTo3bQCTk3oF7WNoTBrf7NsStBbUtJRcIg9YSuiVJIqb63h2yy9+wWOqIZKb+owlUmNAr9KgV2m4NCsSryT5LfdzevYXJfZ40CiUmNTabnl4z6srCXns14rdnJbUi0jFwc9WRWnDuLnPOD4oWMsOS+usnl6pYlpKX/pGJnZLkenWPjWtRZtD7N+KOiB480oSa6oLg57bliuy0WnHLncR1kmA5PJ4KLVa+HjPOnY31qCRKxkbn8nk5F4dzvA1Ou3U2pvZbqnAoNLQ2xyPSa3zm2EVhJPFyJGD+fe//8P48ROP9VC46aaZ5OTk8Ne//v1YD+WoE79dBEEQDoNBpWFCQg6Do1MpaqpFAtLDowhXadEpVURo9aSHh16m1VnfBpWGJIP5kMfXFig0uxxoFSrOTx/E/G1LA86L0hhICTu0+4SrNIyO68HnhZuCHs+NSOBfG78Peuyb4i0Mi00jWhE4qxKp0XNj7lhe2bHCb7bklLhM+kcmBZxv2b/k7MDZOKNaS629hTVVRVTZmuhljiPTGOMXlFkcVr4t3sKKij24JS9ahZLTknozLiHrsGs+NTuD7wuD1v1sUogCwp2Ry2Qk6E1cnT2SFo8Tl9eLXqEiQq1Hrey+P+8mtZazU/vxyd4NAcdGxqRjPGAvnFwmC5qUJEKj54qs4WysLeGpTYsAGBWXwfiEHPQKFTavC4VMjlGl9QWEpVYL/974oy+wdnjd/FS2kx0NVczuMz7o8k+Lw8brO39jZ7vlszLgypyRDI5KOaSls8Kfk1eSqKy1YrO70WmVxEUdn9kuv/nmR8LDjcd6GH96IqgSBEE4TFqlCq1S1W2Z1g6VR/LS5LQjATqlCpfHw89lO1lUsh33/ix5Z6T04bqeo/l07wYsThsyWoOeSzOHHfLeHrlMzoiYdFZUFFB9wBLCrPBo7B5XyFTrLq+HZpcj6FI1tUJJn4gEHh5yJuXWBhweN8mGCIz7Z5sOVGVvouWAgKqnKY4h0Sk8nfez78H818oCjCotdw6YTJzOSIvLwXu71/gl8LB73Hy1bzMur4cz0/qiDrLssav6RCb6zdK11yM8Cs1BLIMLJkJrIIIjV5NKKVcwKi4Dg1LNF0V5WJw29EoVk5N6c0p8ZsBeOIBT4rNYVrHbr21G1jAW5q/2KzfwQ8l21lQXcXXOKOZtXYJRpWVKcm8GRaeglMn5eM/6oDOVJS31lFsbAoIqj9RarHrnAfsRJeDtXb+TMSSKBKXYzyV0rrCskVVbKrHa/5ih1WuVjOgbR3ri8RXAREVFH+shCIigShAE4aRQ77Dya0UBy8rzcXjd9ItI5LTk3myrL/cFVADfFm+llymOv/WfjMfrRbE/0+Dh1laK1Bq4o/+pbKwt4ffKvShkcsYnZNPLHEejy97htR0lVFDKFURrw7q0P6ghSBa6SUk9eXXHioAH80aXnXfz1zCr91iaXPaAjIhtfirdwdggRYkPRrzeSHpYJIUHpNmXIeOSzGEAVNuakcnAoNSgOw5nUsJUWkbF9SA3IgGn14NSLsek0oWslRatNXBGSh++Ld4KtC4tzW+oDlq/rc5hZWdDJalhERQ01vB+wVp2NVRxfvrAgGLN7W2qLaV3RIJfW5PTzs9lO4OeLwHrqvdxVlq/Lr5q4c+qsKyRX9YG/k6w2t38sraUiUM5IoHV4sU/8dprL1NSUoxGoyUnpydPPfUMOp2Or776nPfee4eSkmKMRhMTJ07izjvvATpf/nfTTTPJzMxCoZDz7bdfo1SqmDXrZqZOncbcuU/wyy8/ExkZyR133M3o0WN81xUU7GbevGfZtGkDWq2OESNGcvvtf8Nsbt3Xa7PZePLJf7FkyWL0egOXXXZFt78nJxIRVAmCIJzgLA4r87cu9ctCuLZmH5vrypiVO5aXti3H4f3j09YdDZWUtFgYHJ3SreOI1BiYmJDDiJh0ZMjQq/4I1CI0+qAp2+N0xqAJJw5F4gEZ5cJVGpqc9pCJFnY2VNLitlPXQWrzUEWJD4ZJreOm3HH8XLqTZRX52D1uMsOjuSxrKG5J4vmtS9jbVIsMGQOikrggY9Axn/UMRiaTdTnbokGlYXJSL4ZEp7KqqpDUsAi+L9kW8vwtdWX0MsdR0NiaZXBdzT5OT8klTKmmOcT7f+CyQ2hdrtUSIg0/ELIotyC08UoSq7aEzjwKsHpLJakJ4d26FLCmppoHHriPW2+dw/jxk7BaW9i4cQOSJPHppx/z3HNPc/PNsxk1agzNzc3k5W08qP6//fZrLr/8Kl57bSE//fQjTz31OEuX/sL48RO56qrr+OCDd3nkkQf44otv0Gp1NDU1ceutszjnnHO5/fa/4XA4mD//Of7xj7uZP/9lAObNe5YNG9bx5JNPExERyYIFz7Nz5w5ycnK67X05kYigShAE4QRX3FwfkNYdWvefLC/PZ3hsOssPWIpV3Fx/SEGV3e2iydUaiKgVCkxqPWa1zvdwIZPJAhJpmDV6bs4dx3/yfsbucfnaDUo1s3qfEjTzXxun102j047N7UKjUPr2qgVjVuvJMsb4ZjdUckXI5Apt3F5vp0WJO1v6Z3e78OJFp1CHTA5h1uj5S3p/JiXl+FLSNzptPLXxe99MooTExtoS9jTWcM/AqUSFqOl1omjbE5gcFkGLy8nisl0hz1UrlLgPqF9V0FjD6cl9+KQwcC8XEPTnV6NQkmWMJb+xKsgVBN2LJwjtVdZa/Zb8BdNid1NZayUhuvv+H62pqcHjcTNhwiQSEhIByMrKBuDNN1/l0ksv5+KLL/Odn5vb56D6z87O5tprrwfgqquuYeHCNzCbzZx77vkAXHfdTD777GN2786nb9/+fPzxh+Tk9OSmm2b7+rj//oc455xp7NtXRHR0DF999TkPP/xPhg0bAcCDDz7KOedMO/Q34QQngipBEIQTmCRJrK4uCnl8W30Fl2QNDQiqkg8h+UWTy86iku0sKtnhW04XptJwU++xZIRHh1wK1nq/CB4YPI2CxmqKmy2kh0eSER5NZAczH41OGz+V7mRx2U5cXg8yWh+KL8kcGpD5D1ozJl7fawxfFeXxe1UhFoeNeH3oJToRar1vP1CUxkCtoyXgnJ6muJCFmBuddoqaavmpbAdOj4ehMakMikoJOjZoXcrYtm/N7nHx9b7Nfkszff267GyrL2dsQlbIsZ9oDCo1kxJz2LMzeL2rodGpATXUdAoVvaNSWF1dxL4W/6WTM7KGYQ4SjBtUGi7oMZB/b1wUkAAkVhtOaljkYb4S4WRn6ySgOtjzuio7O4ehQ4czY8bFjBw5iuHDRzJp0mTcbjfV1dUMGzb8sPpvC9AAFAoFJpOJzMw/fsdERrYmVKqra/2AbvfuXaxbt5aJE8dwoJKSEhwOBy6Xiz59+vraTSYTaWmhi6yf7ERQJQiCcAKTyWToO9iD0zoD4L/8Ta9UH1JGwi11ZfxQ4p9wodnl4Nktv/DQ4DNCFg6G1oxwbXujRnQhc7vb6+Hn0p380G7JmERrAdlGl4NbcscRHmT5V4RGzyWZQzkjtS8urwe1XMHI2HR+ryokxRDBKfGZhKu0NLsdxOuMmNQ6ZDIZs/tM4Nkti7G025eVoDdxZc6IoCnsG512PixYy9qafb62PU01LCrZwd8HTA5IM34gm9vFrobgsykAG2tLGBmX0W0FfI8HOaY4epvj2W6p8GvvbY5HIZP7JTmRIyPTGI1Jo+PWPuMptzWwua6McJWGgVEpmNVatCH2ASbpzdzZfzIfFKyluKUehUzO8Jg0zk7rf0TrkQknB522a4/GXT2vqxQKBfPmLSAvbxOrV6/k448/4KWX5jNv3ovd0r8yICOozK+tbZZd2v9Bj9Vq5ZRTxnHLLXMC+oqOjqGkpLhbxnUyEUGVIAjCca7O3sK+5jp2NVQRowunT0QCEWq9r97V6LjMgCLBbUbH9WBj7R91kqK1YdyUOzZghqjBYaPOaaXW3kyUJoxIjR6TRud3/Ot9m4Pew+X1sLm+jEm6nof7Un0sTlvIhAN7m2qwOG1BgypoDSTbp2i/IGMwo+MyKW2x8GPJdmodLURo9JyV2pdEg4lwlZYEg4l7Bk6l2t5Mjb2ZeJ2RKI3B7z1or8rW5BdQtal3WvmpdGenBX0VMjlhKg1NIfb/mNU6FMdh6ubDYdLouKbnKEpbLPxasRsZMkbHZ1LvsPL+7jW+82TAVTkjfansTRodJo2OXub4Lt1HrVCSZYrhtr4TsXtcyGWtyVjUokaV0AVxUXr0WmWHSwAN+9OrdzeZTMaAAQMZMGAg1157A+eeeyarV68iISGRNWtWM2TIsG6/Zyg9e/ZiyZLFJCQkBgnIICkpGaVSydatW4iPb00Y09jYyL59RQwaNPiojfN4In7DCIIgHMeqbE08nfez3wZ7hUzO7D7jyTbFopQriNIamJLcmx8PmEVKNkQwISEbjyTR5LKjkisIV2kCkg1U25p5fusSKmx/FAWO0xmZ3WcCMbrW4MSLRK09cHlcm9JmS4evwytJNDhtWJxWHB43kRpDh/uj7B5XyAQTADX2ZlLCIjq8ZxudQsWexho+L/qjjla9w8rC/NXU2ls4PaUPGoWSCI2eCI2+S0WQV1QWhDz2e9UepiT3JkIR+qHLqNYyJSmXt/J/D3p8fGI2clno5ZQnKpNah0mto5cpDmSt6fjr7C1cmTOCzXVlRGvCGBGXToRaf9iFesPVWsIJHngLQihymYwRfeOCZv9rM7xvXLfXq9qyZTNr165mxIhRREREsHXrFiyWetLTM7j++lk8+eS/iIiIZNSoMVitLeTlbeKiiy4J2tett85i/PiJXHhh8ONdMX36xXz55f948MH7uPzyqzAajZSUFLNo0Q/cd9+D6PV6zj77XObNexaTyURERCQvvjgfeQfLwE92IqgSBEE4TlndTt7bvSYgY5lH8vLCtmU8PORMorRhhKk0TE3OZWh0Gr9W7MbmcTE8Jp3UsAhfANUWHB2oyWnnlR2/+gVUAJW2Rl7esZw5fSYSrtaikMmJ15sotzYE7aeHMXSdFK/kpaipjhe2LfOlV5chY2JiNtNS+gbN4qaWK5EhC1kYt6PkFgdqcIWeZfuhZDuj4zJDvj+hSCHqbgF4u1jLt09kAkOjUwNmvKZnDCJac+gp3E8E7R+8IrUGRmgzGBGbcQxHJAh/SE80MnEoAXWqDFolw49QnSqDwcDGjev58MP3aGlpIT4+gTlz/upLce50Ovjgg/eYN+8ZzGYzEydODtlXSUkJFovlsMYTExPDSy+9wfz5/+W2227G6XQRHx/PqFGjff//zp59OzablTvvvH1/SvXLaW5u7qTnk5dM6ugvw59MY2MjJpOJhoYGjMbjq7CbIAh/PlW2Jh5Y+1XI47P7TKBvZGJAuyRJIbPQHaispYFH1n8T8vhDg88gcX9Si7zaEuZvWxZwjl6p4v5B00LuI6qxN/Poum/90rq3uTRzKOMTsgPGa/e4eCd/NWuCJOGI0hi4a8BpXU7vXdhUy+Mbfwh5/K4Bp5FpjOlSX2221Zfz3y2/BD02Nj6TizKHdKlgcLPLTr3DxjZLOWq5kt7meExq3XFZq0oQjhcdPa/Z7XYKCvYQHR2PWn145Rq8kkRlrRWb3Y1u/5K/7p6hEo5/TqeDmpoKMjN7oNWGnv0WM1WCIAjHqQMTTByoJUT9nq4GVACOdinOg7G3S0meaYxlRtYwPt270ZcaPUFv5PpeY4jUhE4tvMtSGTSggtZixAOjkgMCJK1CxQUZg2hw2vwSOkRpDMzpO6HLARWAspNldF0Jftqzu53YPK6gSRfCVVrGJWR3uc8wlZYwlbbLSxkFQTh65DJZt6ZNF05uIqgSBEE4TumVakxqHQ3tMtK1l3IIadEPZFBpQi6zk9GaCvuPc9WMietBn4hEWtwOFDI54Spt0OV77ZWGWDII0OC04QmSVhxaM/nd0OsUGl12au0thKu1RKh1BxVQQevemlhtOFX2pqD3CJUyPRS7x83GmmKmZwxib1Mtv1YW4PC4GRiZRE9zHL+U7WJG1jCUJ1jmPqfHTbPTgcPrZl9LHXX2FnoYY4jThR/0ey4IgvBnI4IqQRCE45RJreOiHoN5ZceKgGNDo1MPal9RKEaVhhGxafxeVRhwbHhMOkaVf8Ck2J8YI4quf3qb0UH69mhtWIfBR7haS7haS9JhBJAmtY4bep/C05t/wur+Y2ZOq1Byc+64gw4Y5DI5vSMS+L8N3xOrC2dgVDIquYJqexNeScKgUJ9wSSYanDZWVxYSqTXwxq6VfklC4nVG5vSdeFSKEVtdThxeN0qZPGR2R0EQhOORCKoEQRCOUzKZjD4RCczuM4FP926gzNpAmErDlKTejIzLCFo/6WBplWrOzxiEVqHi14oC3JIXpUzOmPhMzkjpiy5ELaCDkREeTZhKQ3OQ9OHnpg3oluCwM8kGM/8YNI3djdXsa6ojJSyCbFNsh8sWQ3F63LybvxovEhW2Rr5vV0vL5nZxQcagE2rfhdPj5ofibWSbYgMCKoAKWyMf71nH1Tmj0B6hvV52t5NSawNfFOZR3FJPpEbPWan9yDbFEKYSwZUgCMc/EVQJgiAcx3RKNX0jE0kNi8Tl9SCXyTCptV2aCbF7XDjcbpRyeYcBmEmt44KMQUxO6o3T60ajUGJUabutrk+U1sDf+k3m1R0rKLVaANAolPwlrT+9I7pWe+hwydoVHx55mFnmCptrcYdYsphXV8b0Hse+RoskSdQ5WtheX8GuxioS9SYGR6cSqdEHzAw2Ou2sr9lHgt6EwxN879vG2lKaXI4jElR5JYntlkpe3L7c12Z1O3lx+3LOSOnD1OTcIxbMCYIgdBcRVAmCIJwAOtu31J7T46bK1sQ3+7awt6kWs0bHmal9yQiPCvmpv1qhPOi04gAtLgctbideyYtOocIUYildosHE7f0m0eyy4/Z6Maha94sdzr4jt9dDvcNKfkM19Q4rWaajs/+nxRU8QQiAhIT3OEiqW2ZtYG7eIr/ljl8WbWZOnwlkm2NRtAvKbR4XSrmCFnfwQsTQ+rrcUseJUw5Vg9PGe+2K/7b3XfE2Rsf1EEGVIAjHPRFUCYIgnGT2NtXy7ObFePcnn6h3Wnl+61Km7f/Uv7vSdVdYG3knfzX5ja3Z+aK1YczIGkamMSZo4VajuvOkFl3l9nrY1VDF/K1L/WaNEvUmbukzHo/kpc5uRadUYVLrMKt1B5UVsSM9jB3vEdMpjm0A0Oi089qO3/wCKmitb/bi9uXcO3Aq2v3vC7TOGlocVuJ1oUuJGFVatEfodbW4HL76ZQeSkKiwNRGjCz8i9xYEQeguIqgSBEE4iVgcNhbmr/IFVO19X7yV0XE9uiWoqrU3MzdvEU3t9knV2Jt5bssv3D1gChkdFAPuDhanLSCggtYZmv/t3YhW2bpHDMCs1nFLn/EkGyK6Za+TWa2njzmBrZbygGMXZw7BpDnye8Taa3bZaXI5aHY5MKg0KJBR62gJeq7N42JfSz1fF+Vxdlp/epvjCVdp6BOZSKWtiRxTrF8K+zYXZAzCfIT2vnW2lFV9gmVRFAThz+nESk8kCIIgdMjqdlJtD17RXgKKW+q75T7bLRV+AVX7e/yvcBPWDpbIdYei5rqQ+5rW1xbTJyLB97XFaeOZzT9T77D6ZrCqbU1YHNZDurdb8jI5uRenJfVCvz+RR7LBzG19J5JtjD2kPg9Vnb2FF7f9ysPrvmFu3k88su4b3s7/net7jQk5s+TwuGlw2nllxwrWVhehliu5JHMI2+rLmZTYk3EJWWj219mK0hiY2WsM/SIT/Wb6JEmixeXEFqJW2sEIU2lI1JuCHtPIlUSHKCotCIJwPBEzVYIgCCeRzmZiOiuE2xVeSWJrfeAsTZvCplocXhd6Dj9zYCiNjuC1u6B1fAdua7K53VjdTtZUF/JjyXZa3E4i1Hr+kt6ffpFJhHUxk6LV7eT93WvIqyullzmei3oMRq1QUm1r4teKAlLDIoCjs/yvxeXgrV2/+5ZfttndWMMPxVs5LbkXXxVt9jsmQ4ZJrcO2v3jz50Wb6BeZRKTWwPW9xtDosmNSa5mY0BOZDHQKVcAetTp7CxtrS1hdXYhKrmBSYk96GKMPOYujUa3l2p6jmZu3yK/YtBwZ1/UajUmkVheEDo0cOZh///s/jB8/8VgP5U9NBFWCIAgnEYNSTYohIuiMlEImP6x6T23kMhmx2tB7XExq3RGv09TR8sIojSEg6cKkxBx+Kt3B71V7fW31Titv7vqd6RmDmJiY06WkGU0uO3l1pUi0ztZtt1T4HT87rd9RSwHe5HKwo6Ey6LH8xmqmJOcGtI+Nz2RTbYnva6vbRYvbSSQGX02wjtTZW5ib95Pf8sJdDVX0jUjkqpwRGA8xsEoymHlg8BlsqClmV0MVCXoTo+MyiNQYTrgiysLJQ/JKSBXNSDYXMp0KWXwYMvnxVy7hm29+JDw89J5I4egQQZUgCMJJJFyt5aqckTyVtyggPfbl2cMDivkeqpFxGfxQsi3Izi04PSX3iNeeitQYyDbFkN9QHXBsWkoflpbn+7X1jkhg/tYlQfv6at9mBkendqm4rc3tCvqa2wSrxXWk2A5IRHEgjUJJalgEJS0WojVhjE/IRiaDj/as9ztP1cWgxeP1sLQ8P+h+rS31ZZS1NBxyUCXfn/L+tOTeTEzsiUIm67bEIoJwKDx7Lbh/L4GWdv+fGVQoRyajyDAfs3EFExV1ZPewCl0jgipBEISTTJLBxAODprG6uohdDZVEa8KYkJhDjDas+2pPaVqXi72+cyWednubxsT1oF9kYrfcoyNGtZbre45hUekOlpfvxuF1E6sL5y9p/dnZUOk3U6dVKGl22UMGQw6Pmxa3gyg6D6p0ShUyCNlXmPLwCzJ3lb6ThCMmtY45fSdic7vYVl/O8ordlLRY/M7psb8wc1c0uRx+M30HWlaxmxxTLHL54c1SKg/zekE4XJ69Ftw/B/lZb3G1tp+acUQCq8WLf+K1116mpKQYjUZLTk5PnnrqGXQ6HV999TnvvfcOJSXFGI0mJk6cxJ133gN0vvyvo34fffQhmpubyMnpySeffIjT6WLq1NO54467UKlaf8esXLmCN954jT17diOXK+jXrx9//evfSU5O8d2jqqqSefOeZdWqlTidTtLTM7jzznvo27cfAMuWLeHVV1+msHAP0dExnHHGWVx99XUolSdPKHLyvBJBEAQBaM2mFqMLZ1pKH05N6olKJkfRzUuo1AolAyKTeXTIWRS31GP3uEgPj8Ko0mFQHbm9VO2ZNXrOSx/AqYk98Uhe1HIlSrmCanszarkCp7e1rlKC3kRMB8sVoeuzNeEqLQOiktnYbgldm2xjbKfL57pTuFpL/8gk8upKA471jUgkXKXBoNJgUKpJDYugytbkd45ZrePqniO7HFQBHdbg8krBck4KwolF8kqtM1QdcP9egjzN1K1LAWtqqnnggfu49dY5jB8/Cau1hY0bNyBJEp9++jHPPfc0N988m1GjxtDc3Exe3sbD7rfN2rWrUavVvPDCK5SXl/HPfz6M0WjipptuBcBms3PppTPIysrGZrPx8ssLuPvuv7Fw4QfI5XKsVis33TSTmJgYnnzyGaKioti5cwfS/g/cNm5czyOPPMgdd/ydgQMHUVJSwhNP/BOA66+f1W3v4bEmgipBEISTlFwmO2K1hQBUCgXRujCiD6FocHdRyhVEHrBsb3JiL4bFpGF1O1HJFYSpNLi9XowqbdB6SGlhkYR3MbDQK9VckjkUryT5BTO9THFcdZAByuHSK9VcljUMRYGMjbUlSIAM6B+ZxKVZwzDsH4tcJictPIqHhpxJfkMVFdZGehijSTFEBLx3HQlTaRgRm8Gi0u1Bj4+Oz2RZRT77muvpZY4n2xhzUP0LwvFAqmj2X/IXTIsLqaIZWWL31U+rqanB43EzYcIkEhJaZ/uzsrIBePPNV7n00su5+OLLfOfn5vY57H7bKJUq7r//IbRaHT16ZDJz5k08//yzzJp1M3K5nEmTTvU7//77H+L0009l7949ZGZm8eOP31FfX8/rry/EZGrN5JmSkuo7/9VXX+bKK6/mzDPPBiApKZkbbriJ+fP/K4IqQRAE4cQkSRIWp5VqewtNTjuRGj0qhQKDQo1Zoz/q+1g8Xi9NLjtOrwelTN4tD+EqhYJohX+gJ0kSt/QZzzObf/bLMGdW67iu5+iDSi4RodFzTc4oGl12bG4nOqXaNyt0tEVo9FyZM5LzXHZsbhc6pQqDUoPd46KkuR6NQkm4urVwb7Q27LDSkyvlCiYkZrOmuhCL0z/7Yk9TLBaHlQ8K1gHwW+UejCotd/afTJxebKAXThySrZOA6iDP66rs7ByGDh3OjBkXM3LkKIYPH8mkSZNxu91UV1czbNjwbu3XaDS2OycbrfaP/ZD9+vXHarVSWVlBQkIi+/bt45VXFrB16xYsFotvBqqysoLMzCx27dpFz549fQHVgXbv3sXmzZt4883XfG1erxeHw4HdbvO794lMBFWCIAh/EpIkUdxSz7wtS/xmbHLNCUxIzKbZ7STZYD5qgVWj00adw8rKyj3sbKhEq1AxISGHHHMskZruneGQyWSkhkXywOAz2NtUS4W1gfSwKJLDzEQcwr30KjX6o7TMsTN6pdpXL6vBaePbfVtYWp6PW/IiR8bQmFTOzxhExAGp0Q9FtDaMuwZMYU11IaurilApFExMyAZkvLN7td+5jS47C/NXc1Pu2GMScArCoZDpuja739XzukqhUDBv3gLy8jaxevVKPv74A156aT7z5r14RPp97bW3SUxM6lIff//77cTHx3PvvfcTHR2DJElcdtmFuFytgaVG0/H/3zabjeuvn8WECZMCjqnVJ8/vBhFUCYIg/EnUOaw8u3kxLQcUbN1mKSdSq0eSJM5K7XdUlmw1uxxU2pp4YdsyrO3Gs7dpJQMjk5mRPeyQM8mF0pZh7mQtJuvwuPl23xaWtMt86EVidXURLW4n1/Yc3S3LE6O0BqYk92ZMXCZymYxSq4X/5P0c9Nz8xiqaXQ4RVAknDFl8GBhUHS8BNLSmV+/2e8tkDBgwkAEDBnLttTdw7rlnsnr1KhISElmzZjVDhgzrtn6XLPmFyy67HID8/HzsdjtabeuM/ZYteej1euLi4mlosFBUVMi9997PwIGDAdi4cYNf/1lZ2Xz55ec0NDQEna3KyenFvn1FfksCT0YixY4gCMKfRLm1ISCgarOqqpDciASq7E1Bj3c3q8vBz6U7/QKqNhvrSgKSKgida3TaWF5REPTY1vpymoLsJztUcpmccLUWg0pDQweFmAG/7JCCcLyTyWUoRyZ3eI5yZHK316vasmUzb775Gtu3b6OiopwlSxZjsdSTnp7B9dfP4v333+HDD99n37597NixnY8++iBkX7feOouPP/6g037buN0u/vWvR9m7dw+//fYrr7zyEtOnX4xcLic83IjJZObzzz+juHgfa9eu5rnnnva735QppxMVFcXdd9/Bpk0bKS0tYfHin9m8eRMA1103k2+//YZXX32JPXsK2Lt3D4sW/cCLL87v1vfwWBMzVYIgCH8StfbmkMdcXg8ymYx6h/WojMXmcQfNWtdmbfU+skyxR2UsJwubx9VhAGNx2EjQB9/zcDiSwyJCHjOrdb6liYJwolBkmOHUjKNap8pgMLBx43o+/PA9WlpaiI9PYM6cvzJ69BgAnE4HH3zwHvPmPYPZbGbixMkh+yopKcFisXSpX4ChQ4eTkpLCjTdej8vl5LTTpvoSSMjlch577HGefvpJZsy4iNTUNO644y5uvnmm73qVSsV//zuf5557hjvumIPH4yYjo0e7lO+j+c9/nuW1115h4cK3UCqVpKWlc84553bzu3hsySSpg/yofzKNjY2YTCYaGhr8NvAJgiAcKpvbSaPTTlFzHUq5nBRDJEa1Fk031Ys6GLsbqngq76egxwxKDRf2GEyi3kRaeOQRH0tJcz2Pb/wBd4ggYHJiTy7MHHLEx3EyqbQ28uC6r0Mev3/QNFI6CIAOVYvLycd71rEySA2rG3uPZWBUsijkK3Srjp7X7HY7BQV7iI6OP+z9OpJXQqpoRrK5kOlal/x19wzVsdZWp+rJJ5/u/OQ/KafTQU1NBZmZPXxLJIMRM1WCIAhHSJPTzub6UlRyJQq5HIvDxtf7lnJqYi+GRKei7aR4a3eL0YYTpzNSaWsMOHZqUk8KGqrpExF/VMZiUKkZFJ3CmuqioMeHxqYdlXEcyOK0YXFYsTisRGkNmNR6jEex9tThCFdp6WNOYKulPOBYvM542K/D6XFjcdrYaamkyWWnpymOaF0YJrWO8zMGkh4exfcl27A4bKSGRXBBxiBSwyKPSUBld7twSx50ClW312gT/jxkclm3pk0XTm4iqBIEQThCmlx29jTWsqpqL06vh1hdOKcn51LYXEtqWOQRmTXoiEmjY07fCbybv4Zt+x+8tQolExN7EqcLZ1RsRrcnhwglXKXljJQ+7LRUBtSOGh2XQcwxSCZRZWti3tYlfvu50sIimNV7HFGdJO9odjmoc7SwoaYYuUzGwKhkIjSGo1u3SqXm8uzhvLR9OYXNdb72WG04t/QZj+kwvrcOt4tyWyN7GmvYWl/O1vpyJPLICItiVu5YIjR6xidkMygqGY8koVYoDipNfXdpdjkobq7nx5JtNLkc9DbHMzYhi2itAblMbCMXBOHIEcv/2hHL/wRB6C519hZe2bGCPU01AccuzxpOg9PGtJTcY/IputXtpMlpx+5xo1Yo0MgV6JWaoz5z5vZ6qHNYWVe9j7y6UnRKFZMSe7YW4z3Ks0MNThvPbP6ZcmvgLF4vczw39BoTMoNdo9POZ3s3BCx/GxufyV/SBxB+lIOLRqedBqeNWnszZo2+9d9hBFS19haWlu9iVVUhMmQMjE6mT0QiC/NX0eC0cVpSb85N74/yGM8IWd1Oviveyo8l/sWJNQoldw+YQpLBfGwGJnS7o7X8TxBALP8TBEE4pixOW9CACuC74q1M7zEIt+RFwdF/EG1f1+hYUsoVxOrCmZqSy/jEbBQyGRrF0Q3s2jQ57UEDKoAdlgqaOkgLXthUE3Q/0fKKAgZHpZIbmdCtY+2MUa3FqNZ2y0xorb2FuXmLqGuXwOSXsl3k1ZYyI2sYL2xbxrLyfCYl5hyVVPwdaXDaAgIqaE01/2HBOmb1HovhOKktJgjCyUfMhQuCIBwBhSECKoBaRwvhKi0qufhcC1rrR+mV6mMWUAEhU823cXjcQdttbic/lu4Ied2PpdupsTVhdXXc//HIK0msrS7yC6ja1Dpa2NdcT4/waBxeN16O/aKXHZbKkMd2NlQGTd8vnMyO/c+kcLLo2s+SCKoEQRCOgI72JsllMsJVGuQiI9pxw9TBcsPWoC94wOf2ejsMmKxuJ8sqCnhpx6/U2EKntD8eWd2OkIlEALbUlZFtiiHZYEZzQnxAIB6y/wxUKhUyGTgcjmM9FOEk4XA4kMlaf7Y6ciL8FhQEQTjhpIdHoZTJg6YMHxqdhlmtPwajOjk0OG3U2JupsDYSpTEQpw8nQnN4S8/CVVr6RiSypb4s4NjY+EyMquBBsl6pol9kIqVWS9Dj2aZY9jXXscNSwX+3/sLf+p2KWXNifO9lyFHKQ3/2qpLLcXu9XNRjyFHfAxdML3NcyGM5ptjjYsmrcOQpFArMZjP19RYANBoNID7AEg6FhMPhoKnJQkSEGYWi4+X6IqgSBEE4AsxqHTfnjueFbUv9AqtEvYnz0gcc9aQQJ4saezPPb13it//JqNJye79Jh5WIwKDScEX2cD4r3MiaqiK8SChlcsYlZHF6Sh80yuB/LhVyBafEZ7GsIh+r2+Xfp1JNrjmePY01XN9rDEqZnGp7Mx5JwqTWHvPEDp0xqNRMTOzJ3p2/BT0+PDadJIOZJL356A4sBJNax5SkXgHLMTUKJZdkDg25J044+SQktO5jtFgsNDV1crIgdEAmg4gIs+9nqsNzRfa/P4jsf4IgdCe310O908buhirqHFayjTHE6YyYNEcnbfnJpsXl5OUdv7LDUhFwLEKt5+6BU4g4zFkgu8dFk9OOw+NGq1RhVGlRd1KoWZIkKm1N/K9wI5tqS5EBA6OSOCO1HyXN9XiR+GTvBt+eHo1CyWWZwxgQlYzuOA+uLQ4bb+z8jR0N/vuVepvjuSJ7OFHHIPV9R5pd9v0p1bfT6LLT25zAOJFS/aTT1ec1j8eDy+UKeVwQOqNSqTqdoWojgqp2RFAlCMKR1Gxz4nZLyOVg0KpQKI7OQ57L60aG7LifGelMpbWRB9d9HfL4PQOnkBEefRRH5M/isFLvsNLgtLHVUs7GmhJm9hrD05sXIwXZz3PPgClkGDsfr9vrodnVuj8kTKU56t/HBoeN4pZ6fq3YDcC4hGySDObDqnt1pNncLjySB61CdcL/3AuBxPOacDwSy/8EQRCOMLvDTUllM8vWFmNpcqBWyRnQM5ZBvWMJ0x+5fR71DisFjdWsrNyLSq5gQmI2iXozxiO8/8UrSUckCYfTGzwDX5vmY5hhz+P1sKa6iE/2bvC1DYlO5bfKPUEDKoBvirdwfc8xHS4FrbE3s7h0py9hxIjYdMYnZBOjCz+o8dndLpxeNxqF8qCzLJo0OkwaHb3M8QAd7rM6XrTOAB7fs4CCIJxcRFAlCIJwBEmSRFF5I98s3eNrc7q8rNlSQVWdlWljM9Bru//hr95hZd6WXyi1NvjaNtQWMyw6jYsyB3eYnfBQ2NxO6hxWfqsooN5pZWBUClmmGCIPM4FEezqlGpVcgcvrCXo8+hjWSbI47Xyzb4tfW4RGz57G0Kn1K61NOLxutCEe/mvtLTy1aREWp83Xtqh0B+tq9nFHv1O7FFjZ3C7KrQ18W7yFcmsjCXojZ6X2I15nPOh9fSdCMCUIgnCsiKBKEAThCGqxuli6pjjosaKyRpqtrm4PqrySl9+r9voFVG3W1BQxLiGrW4Mqu9vFqqpC3i9Y62tbV1NMpEbP3/pPJrqb9t2YVDqmJvfm6wOCF4D+kUkYVccuA53d48Lm8d+7UedoIV5vDFkEOtFgQhNiv5ZX8rK6qtAvoPqjXytrqos4NbFXyAQa0LpsMK+2hNd3rfS11dib2VxXxvW9xjA4KgWFCJQEQRC6hfhtKgiCcAQ5XB6araE3SlfWtnT7PZucDpaX7w55fGl5Ph5vYKr3rrC73VTZmlhdVciKigLKrQ1YnFa/gKpNncPKF4WbcLg7XrbXVSqFggkJOZyXPgDd/iVsSpmcCQnZzMgadkyzu6nkCmQHpG3Oqy1lSHQq8hDpnKel9EEbYime1eVkbU3oGlGbaktpdAUGXO01OO28V7Am6LH3dq+mIUjAdjjsbicOj0gKIAjCn5OYqRIEQTiCFAoZMhmESgl0JJb+SUhB62O1cXo9Iff5dMTmdrKqqpAPCtb5rk82mBkekx7ymrU1+zg3fQAaZffMVoWrtZyW1JvhMek4vG5UcgVGtRb1MS4+G67SMigqmfW1f8xKuiUvS8vzubrnSD7es4Emlx1orW01I2s48brQG+zlMnmHr0mlUODtJM9Uo9OO3RM8oLW6XTS67ER2w5LJeoeVnZZKVlQWIJfJGJ+QTWZ4jMhyKQjCn8oJM1P1+OOPM2zYMMLDw4mNjeXcc89l586dfufY7XZuueUWoqKiCAsL44ILLqCysjJEj4IgCEeeXqMiM8Uc9JhSKSc6ovsfPA1KDYOjU0IeHx3X45AyolXbm3m/YK1fQKaUK2h2O0Je45UkvN2cY1YhlxOpNZCgNxGtDTvmARW0Jka4sMdgUg0Rfu0FjTUk6k1c13MUs/tM4O/9J3PfwNMZFJ3S4Z4mvUrN+ITskMeHRqei6iTNr7yTXCEHzqwdinqHlee3LOGNXSvZ1VDFDkslL23/ldd2/kaDo3tnwgRBEI5nx/4v0f+z995hdl3Xffa7z+39Tu+DwcwAAwx6IRpBgijsnZRIdclS5BJJjiXHkqV8caIk35c4jmssW3YcKVasQhX2ToIkQIIACYLoZVBngOn1zu317O+PgykXc+/0BuC8evCIc/Yp+/b9O2ut3xone/bs4Wtf+xq33HILyWSS733ve9x1112cOnUKh0O70/bNb36Tl156iV/96ld4PB6+/vWv89hjj7Fv3745nr2Ojs7NitlsYNstFXT7Ivj8Q+LDoAge2VGL0zb9kSqTwcCusiV81NVEIJEueCoduSx05U34nElV5e3WsyO2t4X7ubt8adbjqpx5874X03SRa3XwjeV30BML0xry4TXbUKXkfzfsp21YfdvK3DI+t2jDmJbkizwF1HmKaMjQI6rI5sJpHD3d0WW24jCaCSVHuiK6TFbc5qmlS0opOdxzheawb8RYQ38HFwPdrLFkF/c6Ojo6NxLXbZ+qrq4uCgsL2bNnD7fffjv9/f0UFBTws5/9jE984hMAnDlzhqVLl7J//342bdo05jn1vgc6OjozRTAcp8cXobUziNtpobzIhdM+s72quqNB3m5p4FD3FUyKgW0ltawrWDCpBrnRVIK/P7WXM76R0f/HF67hVF8bp69pyqsIwbdX3jmuXkw3IvvaL/CTcx9kHPta/TZW5pWNeY6uSJDGQDeHe5oRwIrcMuxGMwucuWOm16Wkyqm+Nv7u5F7UYdFFBcHXl93B0pziKVnfB+JR/uL4blozGKIALPEW8Xv1t2etG9PRmSz6ek1nPnLdRKqupb9f+xLPzc0F4NChQyQSCXbt2jW4z5IlS6isrMwqqmKxGLHY0F1cv98/w7PW0dG5WXHazTjtZhaUembtmvlWJ48uXM2d5UsRQuAyWSe9iDYrRpZ6izOKqucaj/L1ZdtYkVvK7pYGgokYizyFPFK1atS6ofEQSSYIX420OIzmCduAzxWBeJTdLQ1Zx99uPUudtyir+98ABTYnDqOJGnc+SVVFEQpOs2VcQsUgFOo8Rfz7tffxTlsDzaF+Khxe7ihdTL7FOS29xEar61KlzFpLqKOjo3OjcV2KKlVV+YM/+ANuvfVWli9fDkB7eztmsxmv15u2b1FREe3t7RnOotVpff/735/p6ero6OjMGUbFgHcSkalrUYRgfcECXr1yaoR1uIrEbdaaw67Lr0QisRpM2IyTb2wspaQjEuCZxiMc7WkBYFVeGY9VrabQ5kLMQHPh6UQzC8ncTwsgKVNjGk0MYDdZsE/S2dBsMFLq8PBE9ToSagqTYphUPV0mHCYzm4oW8mzj0YzjW4trb5rUTx0dHZ3rxqhiOF/72tc4ceIEv/jFL6Z0nu9+97v09/cP/rtyJXMvGR0dHR0dyLM4+PaqO6l1FwxuK7V7+LcrdlFo1YSO12Inx+IYl6CKpZJ0RQI0BXppD/sJDav/6omG+NOjr3Okpxl59X9Hepr5b0dfpyc6/Tb0043DZOGWUVwRtxRVz6rgMCoGbEbztAkq0BwKNxZUkZehwXO5w8tiT+G0XUtHR0dnvnPdRaq+/vWv8+KLL7J3717Ky8sHtxcXFxOPx/H5fGnRqo6ODoqLizOey2KxYLHMXV8THR0dnckipZz1aI0QglKHl39dfzuhZBxVSuxG06QaCffHI7xy+SR728+Tumr/Xucp4ouLN+I129nXcWEw7W844WSc9zsucn/l8nnduNYgFLYULeS99vMjGvgW29ws8Wb+XbreyLU6+Lcrd/FhVyMHOi+hoHB7SS2r8sonVbuno6Ojc71y3YgqKSXf+MY3eOaZZ3jnnXdYuHBh2vi6deswmUzs3r2bxx9/HICGhgYuX77M5s2b52LKOjo6OtNOfzxCa8jH/o5LOE0WthbXkJIq8VQKp8mCy2zFPoW0u/HgMFmm1Gg3oaZ4o/k0b7eluwk29Hfw96f28nv12zjW25L1+GO9Lewoq8OpzO+bYnlWJ99edSd7287zYVcjilDYWlzDxsKqG0JwBOJRQskYSSnZUFDFrUW1gMRpssz79EwdHR2d6ea6EVVf+9rX+NnPfsZzzz2Hy+UarJPyeDzYbDY8Hg9f+cpX+Na3vkVubi5ut5tvfOMbbN68eVzOfzo6OjrziYSaIqmmMBuMGIQWkfHFIvyo4X0a+jvIMdv5/OKN/OOZfYN23QJYX7CATyxci3ceN17tj0d4p+1cxrErIR8JNTmqMHSYzBinYdHeH4sQutpjy2GyjGlxPhnyrE4eWrCCHaV1IMBlsqCI+RlhS0mV/liEzkiAYDJOqd2Nx2wbIaCllLSF+/nx2f1cDvYB2uN6snody3NLdUGlo6NzU3LdiKq///u/B+COO+5I2/7jH/+YL33pSwD85V/+JYqi8PjjjxOLxbj77rv5u7/7u1meqY6Ojs7kiSTjdEYCvNlyhp5YiEXuQm4triHP6uBEX8tgz6KHqlby03Mf0hMbqi+SwMGuJlwmC48uXD0vmuJmIppKklCzmzh0RoLcXV7P2f5O3CYrt5XUUu7wokrJsd4WNhZUYZ1CNC6RSnEx0M1Pzh2g+2p9VqHVxZfqNrHAmTutdUcABsUwpv35XJNSVS4Guvnbk3uIDjMiWZtfwadq1qcJzt5YmP9x7M20/leBRIx/anifb67YMSK1MaWmCCS0fc2KYfA4u9GMwzSzUVUdHR2d2WJ+/uJmYDzttKxWKz/4wQ/4wQ9+MAsz0tHR0Zle4qkkH3Y18bPzBwe3XfB381ZrA3+4chcfd18GwGIwYlIMaYJqOO+2X2Bn2RLyrc5ZmfdEsShGFCGyut85TRYKrE4eq1pNkd3N682neOnyCYxC4ZaCBRTaXFO6flc0yF+deCvt+p3RAH9xbDd/svY+iuw3X9+bvliYvz7x9gix+3H3FcrsXu6tXDYYMT3ja8/YUBjg6UtH+P3ld+A0WQHNcGRv2zkOdF4CYG1+JctySvi/5z6gyObmyZp1lNjd8zZ6p6OjozNe9G8xHR0dnXlCfzzKLy58NGJ7XE3xf899wMpczZzHabTQl0VQgZY6GE8lZ2yeU8VtsrIxizNevtVJrsWBy2xlaU4x/3D6XS74uwFISpX9nZf4mxPvjPr4BwgmYvRGQ/TFwoNmGPFUktebT2UUdEmp8k7bWZKjRNFuVM75O7NGD3e3nsEfjw7+PRAtzcSVUB+JlHae3miI/3HsDV5tPoUvHsEXj/BWawM/Pf8hn63dQEN/B396nbg56ujo6IzFdROp0tHR0bnRaQ71ZY3eNId8lNq9gCYWcjPYWA9gVgxjNpWdSyxGI49UrSKUjKcZUhTZ3Hxt2e14LTYiyTjPNh7N+Hx0RgM0BnrJyfIcxFIJmkM+fn3xMBcD3TiMFnaW1XFrUQ0GIWgM9mSd2wV/N7FUctpTAOc7nZFA1rFwMkFSVQf/LrFnb2CdZ3GgKAqqlBzqvkxvLDxin95YmEuBHmrdBZz3d7Gn7TyPVK286Z5zHR2dG4v5+6uro6Ojc5OhSnXUcbvRhMNoIZSMEU0lKLK56MiwGN5eWofbNL9reLwWO19avIlAIoYvHsZhtOA2Wwdrd6KpBGf7O7Me/3H3FdbkV2Qcawz08pfHdzMgx0LJGM83HeNcfye/tXgzeRYnbWF/xmMLrE7MN+Hivtqdn3Us3+rEbBh6TtblV/JC0/HB6N9w7qtYjsdsI5SI8WFXY9ZznuhrYVlOCef9XZz2tXF3ciku8833vOvo6Nw46Ol/Ojo6OvOECmcu2XzTimxuXCYr3119F7cV1/DalVN8umY9Va68wX0UIdhWsoidZXWYDPN/geowWSi2az2bKpw5aWYIAjGqA6A3i1OfPx7hFxc+IlO877Svnb54mK3FNVnPu7NsCc2hflpCvrSUt/mOKiV9sRCXg700BXrpjYUyip5slDtyyM1i8/5Y1eq01ybHbOfry7ZhHRYNFcCO0sWsyC3V/hYC0yji1KQYBqNfLpMF0zzuOaajo6MzHvRIlY6Ojs48wW2y8kDlCl64fDxtuyIEn1+0YdBB7onqdYSScQTwtauNeGOpJA6jGbfJisVomoPZTy9us5UdpYt5pvFoxvFNRQszbo+mkrRetZjPxOm+dgxC8GDlCl66cmIwvdAoFB5duJr9HRfZ234egHKHl99ZetuUjTFmmngqybn+Tn58dj+BxFWLeKOZzy/ayFJvCVbj2D/1ORY731yxk5+dO8jpfq1lidNk4bGq1SPc/EwGA4s9hfzJ2vvpjgaJpZIU2924TBZsV4Ww3Whme8niwXq4a1mfv4B3rvYpu6u8fkpujjo6OjrzAV1U6ejo6MwTrEYT20sXU+3O59UrJ+mLRah253F3eT0F1qGFvdlgxDwsSuCegf5Kc40iFDYVVnOyr52z1xgjPFm9LmtUxSDEqM6CNqOJd9rOUmb38ntLb8efiGBWjORY7LzRcoajPc2D+zaHfPzNibf5tyvvnNd9v7qjQf725B7UYfG5UDLOP5x+l++tuYdKZ+64zlNoc/HbS7cSSMZIqilsRhNesy2jM59RMZBndZBnzV7bt9hTxFJvMad97ddsL8RqMNIRCbCztI4KR844H+nsI/u7kd0tcO4jMFkR9ZuRrhwU5/yds46Oztwg5Hi8ym8S/H4/Ho+H/v5+3O6bz1J3PEgpUaXEoKdq6OjMKOFknISawmYwpQmomw1/PEJXNMjx3lbsRhMrc8vxmG3YskTjYskk/3L+w4z1PALBn6y9l+9//PLgNrvRzJcWb+QfT+8jmSVd7tur7qTGXTAtj2e6Saopfn7hI95rv5BxfF1+BV9ctBnLOKJVmUipKfyJGKqUWAyGQav08dIfi9Ac8vFuu9bs+daiGmxGE1eCfdR5i/CabdivaS48X5D93agv/j10NKZtF2vvhHX3oLi8czIvHX29pjM/uXl/qXUmRCKZIhCKc+J8D72+CKWFTmorvditJqwW7W0UiiTwB2N09IRx2kwU5Nlx2k26ANPRmQSj1RPdTLjNNtxm27hFjeYsuJJLgW66osHB7QL4ct1mHEYzFQ4vV0I+QBOvSSmzCiqArkiQPLMDp9ky7xzqYqkkzVcfSyZaQv3E1CSWSfzc+2Jh9rSd463Ws0RTCSqdOTxRvY4KR+64UgoBPBYbHouNOm8RAMarvwe1nsIJz2c2UVMpOLlvhKACkB+/gbJ4PeiiSkdHZxi6qNIZk1RK5UpbgOfePs9AXPNicz8fHGvjoe01OOxmzCaFF9+5SHv3UL8Ro1Hh0Z2LKC1wYDDowkpHR2d2yLM6+cOVu7gS7OVEXxs5Fjtr8irIsdixGIz8bv3t/LhhP+f9XYPHmBRD1j5NJsXA/3vkFW4trmF7aV2aacNcYzEYKbV7aAxktokvtrvTnPvGiz8e5X83vJ/mwHg52MefH3uTP1ixY0Sd1VgYr7ObayLQi3ri3azj8vi7iNLaWZyRjo7OfEcXVTpjEookeGnvRa5NFE0kVd7+8Aq3rSvnwhVfmqACSCZVnnnzHF98eBke1/xM79DR0bkxybHYybHYWZlXPmIs3+rk9+pvJ5iIEk0lcZksbCuu5c3WhhH7FtlchJNx/IkYr1w5RUckwOdqN+CYJylrRsXAzrIl7O+4mNHx8N6KZVgNEzcu6YkGM1raS+AXFw7xrRU7cZsnlgp4fSEhkd39UcYjqGoKZZ5FLnV0dOaO6+vWkc6c4A/GSSQzp8b09kdxOcycvpD5LmkypdLRE8o4Nhki0QTdfWEOHG1lz8ErNLX66Q/Epu38Ojo6NwdOk4Viu4cqVx55Vid3VdRza1E1Ypip/QJnLk/WrOf5pmOD2z7uvoJ/lMX2XFBgdfC7S2/DPqzOzGow8uW6LRTbJldvMlqPsLZwP9FUYlLnvV6QFhtU1mcdF4vW6YJKR0cnDT1SpTMmydTovU5UVSWlZvc7CYan58c3Ek1w8nwPew8NuXMdOtVBWaGTe7Yu1KNhOjo6k8ZjtvFE9TruqViGPx4lnIzTEvbxo4b3CSbSb9x0RAKU2D1zNNORWAwmVuSW8e/X3Ic/EUUicZtseMzWSdeAOUeJxBmEgkFk66h2Y6DYXMjND6FeOg7JePpgXimiuHpuJqajozNv0UWVzph43RaEYET6H4DdakRKcDnMBELxkTsAxfnZLXcnQiAUTxNUA7R0Bjl5vpv1y4sxm/Q7h/ORRDJFMiWxmAwoyo29GLtZSaopfPEI/ngURQhsBhMOoxnndZQiZjWasBpNSCn5s2NvZN3PMQ9NRAyKQq7VQe4oFucTYZGnEAWRZtM+wC0Flbgm6AJ4PSK9hSif/h7q+89B43EwWRBLtyDW7EB456cbpI6OztyhiyqdMbFbTWxcWcKBo20jxjavLuXY2S42rizhzf1NI8YL82zTFkE6dTFziiHA8XPdLK3J00XVPCMQiuEPxjlyppNgOEFpkZNlNXl4XVZdXM0ygXgUXzxCZySAx2wjz+ogJ0uvp4kSSsT4sLORZxqPElOTAORbHXyqZj25ZgfFDjeGDL2O5itOk4U6TxEN1/THGhjLt0yPcJkNZCoJwT5k40noboaSakRJNThzEabs4tBrtvGVJVv4pzPvI4cJqyKbi4cWrLopbP4VoxkKKhB3fgERiwAC6fAgzHpWhI6Ozkj0PlXD0PseZCcSTdLSGeDA0Tb8wRj5OXZWLyngUks/rZ0hHr+zlmgshT8U51JLP2cb+6ip8HLrmjJcjum5q/vy3oucudSbccxoEHzuwWXkem78u6fXC4FQnNMXe3jv45a07SajwhN311E0TRFMnbHpi4X50TVObl6zjW8sv4PyaWi8eqynhR+c2jNiu8Vg5KtLbqXI5qbQ5spw5PylKxLgr068RXd0qCbUajDyzRU7qXTmolwH6W9SqtB2EfXXf56ewmZ1ojz0NfAWIpzerMfHUkl88QjHe1roi4ep9xZT5vDinSYxrqMzWfT1ms585Ma/1aQzLdisRmorcyjOdxCPp+jpj9DY4mfRAi+3LC/m3UMtnGvyAVBb6eVT9y7B5TBhNk3fW6y6wptVVJUVuTAZ5/8i52YiEkuy73DLiO2JpMob+5t47M5F2K0TdyXTmRixZJJnG48OCqqVuWXcWlRNSkq6I0HMipE8q2PSkaRAPMpzTUczXzuVpDHQS280RL7VgXIdRasKbC7+aOWdtIX9NAV7KLS5WeDMIcdin3eCSqoqhHwQCYJiAJsL4XBD0If63N+OrAmKBlHf+ili3V1QtRzhyFwfZjEYKbK5KCpfMvMPQkdHR+c6RxdVOhPCaTeDHXK9Nmorc/AH4/z0pdNEY8nBfc429XG53c9n76+fVlFVku/A67Lgu8btTxGCLatLcTmmPyUjkUgRiiZo6wqRSKqUFjpx2EzYLPpHZzRi8STtXcGMdXgAnb1hItGkLqpmAX8iyoddjQBsL11MjtnOPzW8P9iTyW408+W6zdR5iiaV0pWUKu1hf9bxjogfpIu4msJ6nfWr81rseC12luZMrCfTbCJjEWTjCeRbP4VIQNuYW4Jy71dBTQ5tu5buZoTFDv4eyCKqdHR0dHTGz/X1C6czr5ASTl7oThNUA0RjKU5e6Calju4cOBE8LguP7VrEspo8DFfrcUoLnXzynsXkuKY/7S8eT9LQ1MePnznBK+9e4s39TfzkuZPsOXiFUOTGthOeKlIyqiOkto+eeTwbJNQkqpS4TVZq3QU83XgkrcltOBnn707upSc2udYHBqFQMEpqX4HViclgwHQdRamuJ2R3M/KlH6aLp9421Kf/EhmLjH6wmkJ2XZnZCero6OjcJOi/cjqTJhZPcvFKf9bxC5d9xOOprOOTweu2sn1jJV98eBlfemQ599++kLJCF1br9EeO/KE4r+9rHBFtOXWhh8bW7I9bByxmAyUFzqzjuR4rthl4zXRGYjGYsBpMbCisYm/b+Yz7qEj2tJ6b1E0Qt9nKQ5UrMo6ZFAPV7gJW5JZh0Hv6TDsyGkK+93TmwUgAYXVAtlRFs037/1FqqnR0dHR0xo8uqnQmjaIoWMzZF0oWs3FGHN7MJgNet5Vcj3VGUv5Ai6IcP9eddfzg8XY9WjUKQgjsVgNrlhaOGFMUwc5NC3DY5p8t9Y2I12zl3op6csx2OqNZUsGAlrCPhJzcTZDF3iIeXrAK47BolNtk5ct1mzEAeZbsAltnCiTi0J090iTbLiLW7Mo4Jm65B/XMAUR+2UzNTkdHR+emQr9VrDNpLGYD65YVcaU980Jt3bIiLObr8y0mpcQfzNx3CyAUSaCOkd52s+N2Wlm7tIiyQieHT3cSjCQozrOzYUUJObpL46xhUAzcWlTDpUA3xTY3fbFwxv0WOPMwTaFR7K6yOtYXVNIfjyAQ2IxGbAYzLpMVk0GPUs0IBiO48yFbCl+oH7HhPsgpRn7wIgT7IKcYse5OCPpQ1t8LztzZnbPOdYlMxCGZALMFcRPY6evoTAb9k6EzJYrzHNTX5HHqQnoPqaXVuZRcx5bZiqJQVebmwhVfxvHSAidmkx7oHQuPy4LTYaKkwEFKldisRizTaF6iMz5cZiv1OSXYjRZO+9pHjBuFwm3FNVPqJWU2GCm0ua476/TrGWF3ITY/jHz+bzMMKoj6zQi7G1ZuQ1Qtg2QCGQ2BmkJULdd6VSn695hOdmQ0DL4O1I9eg/4uRGktrNoOngKEfrNERycNfXWjMyXsNhPb1pezZkkhZ5v6kMDiBTm4nebr3tltYZkHm9VIJJpuxCEE3LqmNGMULhiO0+eP0dIZwG03U1bkwmk3YbjOXM+mE4OizFiaps74MSoGyhxevrrkVn56/iDhqzbbXrONr9RtIc96/d4EuZkRZYtg80PIAy+CvFoTZ7Yh7v9tLYqFlo6Lp0D777maaAaklFr0LBwANQV2Nzg8COP1/dtxoyATMeTZg8g3fzK0raMReewdlE9+G0pr5nB2OjrzD7357zD0ZnI619LbH+WtD5q43KalOOZ6rGxbX0GOx4LDZsJkHLpT5w/GeGb3OXp80cFtBkXwyM5FlBU5Md7EwupmRZUq0VQSk1AwzZOUmZRU6Y9FCCRiKELgNFnwmm3awlvnukTGoxD2I/u7wGBCuPM0cTJP3nOZkKkktF9CffGHWo8tAKMZcdvjiKWbNZMNnTlBShVCfkjEkA0fIo/vhcA1PSJzilGe+HbWHmczjb5e05mPzN9vXB2deUCux8pdW6qIxpOoKUBAJJrgjfebWLO0iKoyF0aDgXgixXsft6QJKtBsxZ976xxffHg5HpcerblZkFLSEw3xQeclTvra8Jht7CpbQrHNjcM0t+8Dg1DItTrI1RetNwzCbAWzFeEdaQwzbwn0ov7mz7U6nQGSceTbPwdvEWJhZkdJnZlF+nuQZw4gP34Twn4oqkLc/gRcOIw888HQjn3tEA3pPc50dIahiyodHbRFcDiSQJVgMipYrzb3DUcSXGz2sfdQC8mkllpjNinctq6cs029FOTY8LgMRKJJzjb2ZTx3MiXp6Anpouomoi3s578ffZ1IamjB+HH3FR5esJLtpYuxGXXnQ52bG3n2YLqgGj627xlk8UKETXeNnE1kXzvq2z+HxhNDGzsakS/9EHHPVzRDlJ7W4UfM+hx1dOYzuqjSuekJRRKca+rj4AnNJr04z8Ht68vJz7HRF4jx1gfpzlrxhMruA5d5eEctbd1BPC4LKVWijpJJG46ObJCsc2MSSsT5xYWP0gTVAM81HWNtfqUuqnTmNTIc0JoJp5JgdUx7KqFUVWT7pew7+Dogmd19VWf6kf5e6OtIF1TDxw+8gFi7C/nWz7QN3kKw6qJXR2c4uqjSuamJRBPsOXiFM5eG8sVbu4L84pUzPLZrUVb3P4CGS73ULcwBtOiVy2EmEMq8ECi+jp0QdSZGOBmjob8j6/jZ/g6K7XoNgM78RPa2ob78v6CzSdtgsiK2PAz1W6YtciQUBVFcjTx/OPMOOUWg33iYVWR/19BrnglfJ8Lu0WJTigFx15fmrJ5KR2e+olfO69zUhCLJNEE1nLc+uDyqGPIFYnhdWr8lp93MHbeUZ9yvrNCJy6EvEG4WxkqIGS2iqaMzl8hAL+qv/ix9cZ2IIvc8hbx0fFqvJRavhywuf2LLo3rq32zT2wZmW/ZxIcBkRiy7FeXz/xFRXD17c9PRuU7QRZXOjBCJJvD5o/T5o4SjmfPm5wNt3cGsY75ADIctu7VvnteKzTIU7K0scfPwjlq8V2unjEaFtUsLuX9b9ajn0bmxsBvNLHTlZR1f7Cmaxdno6Iwf2dEEof7MY/ueQQZ903cxVy7KJ/4tOHOGthnNiB2f1Rfsc4E7TzM6ydYAfOEqZEElYufnEHmluu29jk4G9PQ/nQmRSKZIpiRmo5Kx95KUkh5flDf3N9HapQmWwlw7uzYvoCDHNu/6NZlNozcvdNoz/3AoQrC2vgj7MLFkMRupqfBSlG8nmVBRDAK71aRbqd9kOE0WPlN7C//96Bsk1FTa2PaSxXjN1jmamY7OGHQ0Zh8L9ECGOsHJIgxGZEkNymf+3dU+VUmtT5Vd71M1F4i8MtT3foPY8Rnk7n+B4RF1Vx7KHU8inN45m5+OzvWALqp0xkUsnqS3P8ZHJ9vxB2OUFjpZVVeIx2lOE0r+YJxfvHKaeEId3NbZG+apV87w+YfqyfWMkl4wBxTnOVAUgaqOTMmqKnVjt5l4eEctr7/fONgE2GEzcc/WKnKyuPk5bWaYxYcZiiQIRxJEY0kcdhM2qyktgqYz+5TZvfw/a+7lzZbTNPR34jJZuae8noXuPOxzbKmuo5OV3JLsYzZnWhRDBn0Qj4LBCDanZus+QYQQWqRqeLRKZ04Q7lyUTQ+gHnkL5ZHfRzafg3A/lNchyhZdX3b9OjpzhN78dxh6M7nMJBIpTl3sYfeBy2nbDYrgiXvqKCnQct9VVXLgWCsHjrZlPM+y2jx2blyA0Th/IjfJZIqLzf28tPdi2o05h83EE/fUkeO2IqUkGE5ookqAzWLEaTfNebPUWCJJMJTgxT0X0vpjVZW5uXNzlV7HNQ+Iq0miyQRGYcBu0l8PnfmN9Hej/uQ/aGLpGsTtTyDW3qk1hG1uQL7zc+jv1oTW4vUoWx/Xmg7rXNfIkF9zfhQCLA6t/5l5/t0I0tdrOvMRXVQNQ/+QZsYXiPJ/njmZscA+12Plk3fX4bCZiMWTPPPm+cG0v2vxuiw8ee+SeVdflEikCITjNFzqwxeIUlXmoazQids5/35IBojEknT3htn9wWV6+0cugOqqcrhzS9WY6Y06OjrXD1JNaQ1ZpQSLDTGascCkzq9CZxPqs3+jXQcAgVhxG2LLIwiHB7XxBPLpvxx5cE4xyif/SE8R05kV9PWaznxEzxHSGZNeXzSrY1lvf1RLO7OZMCgKTocJujKfx24zYTQIwtEEkVgSNSWxzoOoj8lkINdjY/Pq+ZWaOBrdfRFiiVRGQQVwtqmPW9eU6aJKR+cGQQb7kMffQx59S4skVS1H2fII5BRNWw8poSjIoiqUz/57CPYj4xEt+mR3ISx2ZMiPfOcXmQ/ua0f2tOqiSkdH56ZFF1U6YzLeUKbRqLCuvoizjX0Zx29dU4I/FOfVdy/R1RcBwG41smNjJQtK3VjM+tvxWlKqSjCcoKs3TCiSoCjPgdNu4mhDB5XF2XuESAnxpJp1XEdH5/pBBn2oz/8AhjfMPf8xauNxlM/8e8gvm7ZrCSHAlQuuXEbc6krGNevtbDQ3wIL6aZvL9YaUEgK9yJZzmulHQQWivA7cuQgxf9LedXR0ZgZ9FaszSCSaIBRJ4g/FsFtNOO0mnHYzeV4rQqSbAQ3gdVuwWoaiITluK7etK+fdQ81p+62rL8Jlt/DTl04Tiw85ooWjSV7cc5En7q6jvNg1Y4/teiSlqrR2hnhm9zmSwwTSzo2V9PljLK3OnkZpUAQWPUqlo3NDIHta0gXVAMkE6nu/Qbn3qwjLLETaFQXM1ow1V4Amxm5multQf/XfIRoa3CTNVpRP/hEUVc3dvHR0dGYFXVTpABAIxXntvUtcbg8MbnM7zTy2cxFOh5nb1pWz96N0oaQogru2VOGwaQX4qZRKPJGiutzDwnIP7V0hpJSUFTlx2EycudibJqiG8+7HzTyyoxabdX7VW80lwXCCZ948RzKVHnG63B6gJN+Bzx+lON9Be3doxLHLF+Vjt+kfbx2dG4KzH2Ufu3Qc4hGYDVFldyNW3oH86NWRY4oBUbl05ucwT5GhftQX/i5NUAEQj6I+97con/l3CN3lUEfnhkZfdekQT6R491BzmqACzR79N2+e41P3LmF5bT7F+Q4+PN5GIBSnMNfOysUFmE3KVatxyYlz3Rw41kbialSlriqH29aX43ZYkFKOOP9wuvoiJFO6Z8pw2rtDIwQVwIUrPh7dWcsr717i7lsXcuhUB5fbtKJyRQjqa/PYtLIUk1GPVOno3BBYHNnHzBYYmag3IwiDEdbuQrZf0lL9BjAYEQ99DW7meqpwAHwdmceCfdq4Lqp0dG5odFGlQziaoKGxN+NYIBQnEIpTWujE67RQWuBE5muOgL954yzJlGTNkgJqK3N49+OWtGMbGvto7w7x5D1LcDq0NMJzTZnn4HaYUZS5tSifbwRD8YzbVVWy/2gbD++oZc9HVygrdLFmqdZDxOO04HKY9Po0HZ0bCLF0I/KjVzKPrdgG9tlLnRbOHJQHfhf8vci2C1r0qrgKHN6bu2nvWI2Rk5m/z3V0dG4c9JWXDsmkmrFeaoBgOI6qSo6d6+JcUx9LFuZRWujEYjZy+kIPh890UV7sxmxS0pr+AvQH4/T6ozgdZmorc/jgWFvGa21YUTzvrNbHSyKRIhRN0NoRJJ5UKSt04nSYp9yAtzg/+93pQCiO22nmoe21RGMpBGC1GG6o9MlYPEU4mqC7N4JiEOR7bdhtRj0Cp3Pz4cpFbH4Yuf+59O15ZYg1O6fN/W+8CLt7SEzpaNhcYDRBMoO4UgzgyG4spKOjc2OgiyodTCYDRqOSZoYwHI/LQjiawOUws2JRASfOdROKJCjOd3Dv7Qs52tDFhSs+KordXLjiG3F8R0+YPI+VI2c6uGtLFW99cHkwRRBg5eICCnPto84xnkgRiSZJqRKzScFpnx+NVOPxJGebfLyxvzFNLC5ZmMu2W8oH680mg9dlIT/HRvdVp8Th3L5u6Nz2G0hIDRCOJvj4VAcfHm8f3GZQBHduqaK2wovZrAsrnZsHYXXAmp2ImtXIk/sgEoS6DYiiSr1OZ77g8CA2PYR87zcjhsT6e8Cu91LS0bnR0UWVDg6bibVLC9MWsAMU59tx2s2kUirN7QFOXxxKE7zU0k9jaz/3315NS0cwY/0PQI7bQiKpcuJcD33+GHffWkUyJUkmVVwOMxeu+Dh/xUd+TmZh5Q/GePfjZs41+lClxO0ws+2WCipKXFjnOM3NH0rw+vuNI7afudRLRbGLFYsLJn1uh93Moztree9QCw2NfahS4rSbuH1dOQtKb+wf6Lau0Ij3Y0qVvPreJT7/YD0FY4hwHZ0bDWF1gNWBKKyc66noZEAYTbDidnDmIN9/Fvzd4MxBbH4YUbMaYZq/zeR1dHSmB11U6WA0KKxZWoSUcP5yHw6bmWAkTq7byo5NlThsJrr7wmmCagApYf/RVu7aUsWLey4ObnfaTaysKyDPYyPPa0NRBA9trwYEPb4oJ893E40nicY0N8C7b63KOLdgOM4zu8/R4xuy8PWH4rzwzgUe2VFLdYV3Op+KCXPiXHfWsYMn26mu8E4prdHlsLBr8wK2rCkjpaqYjIY5b5Y800SiCT44lr0XztGGTrZvrMSg6H1fdHR05g/C5kTUb0YuWAqpJChGvRmyjs5NhC6qdAAtWrWyroCiPDsdPSFyPXmUFTlxXk0xa+saads9QI8vismoUFropOFSLwtK3ayqK+DD4+28f7gVRQhqKr2sWJTP6+834rCZ2L6hkr2HrhCNpVAUQVmRM+O5+/qjaYJqOO98dIWiPDuOOUoFVFWJPxTLOh6OJFHVqTsamkwGPDdRz6mUKgmGsxd1+wJx1JTEoGsqHR2deYhweOd6Cjo6OnOALqp0AOj1Rfjlaw2Eo8nBbWaTwifuqqMoz45xjBWs0aCwc2MlW1aXEI2leOqVBtSrRUaqlJxr0pwA77ilghf3XOTldy9y720LeeHtCzy8ozZrjVRzR3Ybdp8/RmIObdgVRVBd7uX8ZV/G8dICB2aTvvKfKGaTgaI8B8GwL+N4RbETo1F/XnV0dHR0dHTmD/rKRIdwJMFLey+mCSqAeELlubfOEwwnKCl0kC3jrKLYhdVixGoxYreaOHC0bVBQDScQihMMJ8hxW4nFU/T6onzlsRVUFLuyijaXM3seusmoMNcu7JUlrozpfULArWvLblpr82gsSSicIJHM3Ox5NMwmA5tXlWZ8v5lNCnULc2/o9MfrDTWRQk1M/HXW0dHR0dG5kbg5V3w6aURiSboyOMwBhCIJQpEEuR4ruzYv4I330xtN2SxGdm6qxHrVPjyeULkySnSptTNIQY6NPn+Uls4ga+sLUUapjakocmJQBKkMaXQrF+fPuQ2722nhiXvqeOfDK1xq6Qcgz2tl56YF5Hqs4zpHNJYkFk+BAJvZOCPOduFIgnAsSSqlYrUYcdpMGGYgfy4cTdDeHeLDY+0EI3FKC5xsWFGC120ZNdoZTyQJR7T3oaII8jxWPnXvEl7ee5H+oJYKWJhr565bq/CMIrR1Zo9UIEa8xU/keAcIgX1NCaYiJwbn/HDmnE8kUin6ExGCiRgGoeAyWfBadLMVHR0dnRsJXVTpZHXtGyCRSGE2GVi8IIfiPAfHznbhD8VZUOKmutyD1z0kHoTQhFYgS6NDq9WIP6jVIeW4LaMKKgCn3czDO2t5/q3zJIel+pUXOVm3rHhGhMFEyXFbue/2hURiSaQKFrMB+zjEXkpV6fVFefvgZZrbgwgBNRVebltXTo57fIJsPPT4Iry09+KgNbvJqLBldSn1NfnYrNP3FRCNJzl0soODJ4Zc+/zBXs429vHJuxdTVpS5QWkkmuDY2S7eP9I6aEuvCMGOTRV88p464gkVRYDVbBzX86oz86QCMfqePUWyfajWMn6pD3OVF8+9i3VhNYxQIsb+jks823SUhKpF9PIsDn576VYqnTkoYu6/w3R0dHR0po7+ba6DzWLElKVGRQhwOrQFksWspfjl59hwWE2cudTLy+9eoq0rOJjm5bCZWFdflPVaC8s8XGkLIAQsq80fc24Gg0JFkYsvPrycB7ZVs2NjJZ+5fykPbKuZN72qQHtuvC4rOR7ruBf+/YEYP3/5DM3tQYCr7os+fvHKGfqD2Q0wJoI/GOOXrzWk9bpKJFX2fNRMU2v/tFxjgHAkmSaoBlCl5I39TYQiGZpiAp29EfYdbk3r86VKyZv7LxOJJsn32sj12HRBNY+Inu9NE1QDxBt9JNqzR6pvRs72d/KrSx8PCiqAnliIvzi2m95YeA5ndnOiRkLIQC9qfHq+Y2cCGfIju5pRr5xB9rQhI8G5npKOjs440CNVOjhsJjavLmXvR80jxlYuLhhsLjtgb35tM9pfvHKGT9+3lOJ8B0II6qpyaGrt51KLP22/W9eUcfGKDyHg/tuqcTvGJ4oMBgWPy4LHdeOkfSWTKh+d6MgYJYxEk5xv8rG2vnDKtUPt3SEi19TKDfDe4RbKi13TJk47erI7RPb2R4nFkyPSNaOxJB8ez26ffuik1jBaN6aYP6jhBJGj2V+z8MdtmCu9KHqDZvzxKM83Hcs4FlOTnOht5Y7SxbM8q5sTNdQPPW3Igy8jA72IwgXI9XcjvYUo5unLDJgq0teF+vzfQrf2eywBFixHueuLCFfunM5NR0dndHRRpYPBoLCsJh+71cS+wy0EQnHsViO3rChm6cI8LFcXRz2+yAhBBVqE5Z0Pr/DwjhpsVhMOu5m7b12IPxSnqdWP2aRQUeImGk1SmGtjw4oSHHbTmI6Cs0k8kSIc1WqOTEYFp92MMoMuGNF4kstt/qzjF5t9rFicj3mKVuoGg+DerQsJRRKcvNCd3u8rGM9YqzZZxn6+Ro6nVEkglN0+3R+Mk0ypuqiaR0gpkaO5bqoSMhjV3IwkpUp7eJTPeaCbO9BF1UyjRsNwej9y768Gt8neNuTZg4hHfh+qls/h7IaQoX7U5/4GelrTB5pOoO75JcqdX0RYbHMzOR0dnTHRRZUOADarkfqaPCpLXKRSEkURI5rMXmzOni7W2hUkkVQZ+Lq320zYbSaK8x0Z9w+F44SuXsdhM82ogBkLfyjOu4eucLaxDynBajGweVUZSxbmYLPOTMqZQRFYLUb8WQSF3Wqc0nMSjSXp6Amx/0gbPb4IHpeF1UsKiSdSvPdxC6BFKA3T+LwX5doRIvN6uijPjs0y8uvGbFIoyXfgC2ROxSkrdmLSbennFYrNhK2+gOB7lzOO21YUoWR4recCqaYQytxFzIxCUGR30ZZFWFW58mZ5RrOHTCUh0Iu8cBi6W6BsMaJiCbjzZt29U4T9qO89PXJATSHf/L/IT/4RwjN2OvqME+ofKagGOPcR3PoY6KJKR2feMj9++XTmDaOlgo3mtGc2KeP6oYzGkjR3BNj7UTO+QAyr2cC6ZUUsX1QwJ05+oUiCF985T3v3UG1DNJbi7Q8vI4SW/jgTgs9mNbF+eREv772UcXzN0qJJR/JSqsr5Kz5e39c4uK23P8pbH1xmzdJC6mvyOHWhhw0rS6b1ObfbTezYUMnuD9IX22aTwl1bqjKaYpiMBm5ZUUJDY98IG36jUWF5bT6GMcxMdGYXoQhsywoJH+tA9aeLYWO+HXOlZ45mpiFTSfD3IBsOIjsbEcXViMXrwZWLMMzuT57bbOOhypX8w5n3RoyZFQMrcspmdT6zhUwlkS3nkM/8FaSuph+f3Ie0OlGe+Dbkz+7jlr2toGax/fd3QzQE80BUydAoda5SQiKafVxHR2fO0VcrOuOmttKbdWxVXSH2MZzkpJRcvOLj+bcvDEYmovEU+w638vaHl4nEMtf+zCSBUDxNUA3n/SOtBCPZU9OmSkWxm6XVI3PkN60qIWecduyZCIUT7Dl4JePY0TNdLK7KYc3SQuoW5EzrHWOz0cCS6jw++8BS6mtyKS9ysXlVKZ97sJ78nOx3V71uM5+4ezFe91DNXH6OjSfurrtu7dOjsSShSJxkcnRnzesVg9tK3qdW4NhSicFrxZBjw3VHFTmfWIZhDmsfpVSRbRdRf/InyPefgfOHke/9BvUn/wE6msY+wQyw2FvEo1WrMQ5z+fOabXxzxU5yrTeorXqoH/nCD4YE1QDRIOor/4QMz7aZyfXR1044c7IPKgYw61EqHZ35jB6p0hk3TpuZu7ZU8fr7jWnbi/PtrF5SOKa9eTCcYM+hkWYYAGcb+9i8qjRjithM0t2X3X0rGkuSSMzcothhM3HHLZWsW1ZMY3M/ikGwsMyDw2Ya7Ps1GQb7XmVAlRKDIrh1ddmM9MOymA0U5TnYtbmKVEpqDZrHiPQZDQbKi1w8eU8d0Zg2b5vl+rRPD0cTtHYGOXi8nXAsSWWxi3XLivC4LDdcxM3gseLcXIF9VTEIUK5JF54Tgj7ki38/cjGfjKO+9EOUT/87hNM77tNJqSKmaHnuNFnYUbqY9fmV+BNRjIqCy2TFa7bN/fM1Q0h/D8Qy9z6k6zJEgmDP3GJhJhC5JUiDceT7AsBbBNbMaeqzjsMNpTXQemHEkFi2VRsfhgwHIJXQbHodnim/V3V0dKaGLqpucKSUVxfSU/+yNZsNLK7KoazQyYUrPsKxJAvLPOS6LTjG4SAXi6eyOtEBdPdFyPPO7p240dIdSwo0N8NjZ7vo6g1TUuCkvMiJy2EmnlBRpcRqNkxpYWSzGrFZjRTmTv6OdUpVCYUTBMIJUkkVq2V0sWQ2GWZEUA3HaFAwTvASDpsZx3V8IzYaS7LvcAvHz3YPbjseiNHY0s9jdy4mz2sjlVK1iKwEq8V43RtwCEXMr55U4QBkM4YI9GrjY4gqKVUtffDCUWTLWUR+GaJuo1YLZJyc0DcbjOTbnOTbnJM6fsQcVe1mj5ivQn2sNDV1drMSpM2F2PYk8q2fpg8YjCi7vjA/6qkAYXej3Pc7qG/8BJpOXN2oIJbditj8EMKkRYFlLILsbELu+SV0NoHdjVh/LyzdiHDMbfqtjs7NjC6qblBi8ST+YJzjZ7vwh+PUlHtZUOrGPcV0KrPJgNljYL2neMLHGgyjiw/LGGJgJsj1WLGaDUSviezke21sXlXKv7x4ajCF6/i5bnZtqsRsMnC0oYtEUqWuKodFC3Km/LxOlmRKpaUjwIvvXCSW0B7D9g0V5Lit9PlHLmysFsO86u91IxEMJ9IEVXG+g1uWFxOLJ7nS7kcIwcnz3Zw8301KlSxakMOG5cV4XJYbNmIx64y1WB/PYr6rGfWXfwpx7fMjzx1CfvCS5hJXsWTW67KGI4M+ZFcz8sS7CEVBrtyGyC1FXBPBmGuEtwiJ4KoheDpWJ5iGvi9lIgaJGJgsg6JhulFsDtRF61AKKlE/fgMCPYjCSsTqHUh33rxKDhTuPJT7fxsiAe09aLFrommY7btsbkA+9z+HDgr7kXufQrZfQNn5ecQ0iXcdHZ2JoYuqG5B4IkVDYy9v7h8yDLh4pR+71ciT9yyZUr3OVLBZjFQUu7iSoTmo2WTAoCiEIolZNaxwOcw8fudifvPm2cHUM4Ata0p5bV9jWk3MbevKOdfk41LLUDFxe3eIQ6c6ePKeJXPSRysQivPMm+fTTB4Onepgx4ZKXn73EvHE0GMyKIIHttXMiSHIzcBwi/ySAgfr6ot4bd8l4gmVB7ZV8+xb5/ANM3Y4ca6b85f7+Mz9S/G65k+fnOsauweMJkhmaDRtsoJ9dPEhQ37Ul/9xUFANoqaQL/4Q8YXvg3tuHPtk0KfNrblB+xug4UNk7VqUnZ+bXxEKuxuxZify8JsjhsTG+1H3P4+yegfSaEEeeA562yC/HOWWe8FbhDBP/3ep4vBoKXK5JZqIs9oQZtu8ElQDCKsja0qiDPqQb/0s84FnP4JND4EuqnR05oR5mjugMxXCkQS7D4y0Ow5Hk7xz8Aqx+OwbQoCW7nTnlgW4rmn6azQI7tyygHcPNXP+ch9yFnvcCCEozLPzuQfrefzORdyztYrPPrAUp81EKDK0MLNajNitxjRBNUAwnODj05kb+c40Zy72jnDN8wfj7DvSwoN31LB9QwWLq3LYuraMLzy8jLJCZ9YaJ/VqzyhfIEYwPHMGHTcqw5/XW5YX8+q+RuIJlTyvFX8oniaoBojGUhw+3Tkn750bEocHcevjGYfE7Z+EsYRHJKAt8DMRjyADvVOc4OSRjScGBVUa5z9GzpEJRzaExYbYeD/irt8Cd75W81NQgbjnK9DfBaf3o/7qvyNCPrhwRLMRb/gQ9V/+E/LyqcH0xplAsTlQ3Lko16vpQzwCgZ6hv505kF8OVyNZsr1xbualo6OjR6puRJo7g1l7b15q6ScSS2Ixz81L73VZeeCOarp7I3T1hXHazeR7bRw82U57d4hgOE5NhXdWU9SEELgdFtyOobujzR3p0bTyIieNGQTVAKcv9rB+WfEIwTiTqKrMarTR1Rvh+bfP8+XHVrBmadGY5wpFEpy60MNHJ9qJxJK4HGZuXVPGwjJPRiv06UBVJeFoAlWVmEyGWTcpmW4qS1yYTQbsNiOB0JDzX2mBk6bW7A1gz1/2ccvyYj0tcxoQRhMs2ww5Rcj3nwVfB+SWILY8iiheOHbqXjbb7QEyRcBmARkJIA/vzj5++E1k+eK0FLG5RtjdsGSjFjVJxsHXhdz/vCaqAJIJ5LmPoHolnD989SiJfP3/ID73H8A90hlVB80FEAEl1Si33IsM9ml1hHmlmjW8XY9S6ejMFdf3KkYnI2PZOM9iICgjJ891c7G5H6/LwpX2AD2+oVSbYDgx5/MDrjY+HnquBGM8b3MwZ0URlBW5OHfZl3E832tjPC22YnHNYOHEuaF6oEAozqvvXWLb+vJxOTtOlGA4zvFzXRw+3UU0lqSkwMFt68op8FqxWK6/9MSB6O9dWxagKAKr2Uh+jo3uvggpVXNBzIbJqKDoNVXjRoYDEPIhe1q1yJSnAFw5g85nwupEVK9EliyEZBKMpvHXmFid2r9ocOSYULRrzQWqqgmTbCQTIOdhtDMeRb73m6wNbWVnE6KyPv3rMxqEiF8XVVmQBjOs3oFSvlhLBx3+viioRHnw9+Zucjo6Nzm6qLoBKSvKvoDIz7FhMc2+IcRwyopcHDvbTTA88q5vvteGYQaa7U4Uu9XE+mXFHDzRDmjRv23rKzhzKXP6z5Lq3MGITiiSIHq155bVYsBhm7kIRE2Fh/ePtBDPYP2+dV0ZNuvYAiUcTaYJquG8f6R12o04wpEEr753icttQ9HAtq4Qv3y1gcd2LaKyxD0jDZdnilAkwfuHWzg+7Dm0WYzs2ryAgyfaudTSzx3rKzifRfyuXlI4Y9HAGw0Z7EN99Udw+dTQNqsD5bFvIosWpFlKC9skLLudXsSOzyBf/scRQ2LDfbNqA56G1YmouwV54IWMw2LpJoRlHva8MprAlZtVVOHM1VIuR3D9fP7HQiZiEOhDXjiiOVBWLUMUVCJco/SkGgWRiCBW3I76i/9vpNDuuow88Dzs/PykTD9kPKo5ZKYSWjqhwzt/HSZ1dOYh+i/5DYjTZmJpdS6nL6YLACFg16YFc97/p6zQic1qzGivftu68jmfH2jGGeuWFZHjtrD/aBuBUBxVVakqddN4TSqXw2ZiXX0xAmjrCvLavkZ6+7Xom9dtYcfGSqSUeF1W7FbjtKZeup0WnrhnCS/vvTh4TavFyPYNFRTmjq/3ij84stZngERSJRpLTquo6g/G0gTVcPZ8dIWHd9TOqnFDJJpAAlazccJiTkrJmYs9aYIKIBJL8sp7l7j/9mpe3nsRlyPzZ7KswEltpfe6c/+TsYjWa0hNgtk2od5Pk75mMo784OU0QQVANIT60j+gPPkdrb5kCghFgYUr4ZPf1iIs3S3gyUNseghRUTeu9DoZDmjRFinB6pgWAwlhMMCyrchje0ZaxnsKEFUrpnyNmUCYrYgN92v1YBlQ6jejvv7j9I1297jFq4wEIORHBnoQNrcmimfhvTheZCKGvHgM+fI/DKU6HNmN9BahfOIPEZMxPUklkW0XNbONTNc88yFi8yPgmdh3tgz0It/7DbLhoJYGa3MitjwKi9frboI6OuNEF1U3IDaridvXV1BR7ObgiXYi0QSlhU62rCkj1z031t/DcTstPHl3Ha+8d4mOHq0myGoxcsf6ckoK5kkTRrRo1fJFBVSVeVBVicGgsLDMQ3NnkI9PdQxaqi+tzsPttNDXH+WXrzaQUoeSWXz+GM/uPs/D22v5v8+fZMOKElbVFYwrgjQehBAU5tr55N11RGJJVFVisxhx2EzjFghm0+hfA9Od+pfJ/XGAHl90RhsuDycYjtPU6ufIGc0ooq4ql/qavAkJyGA4wYdXo5nXkkyq+IMxHtpew7uHWqiu8HL/7dVcbPaRUiVLF+ZSmO+47mqpZH8X6ts/h4vHAKkt6nd8BlG2CDGTxf9hP/Lku+nb8stRNj2IjEe1SEBRFbhypyRkhMWGqKhDPvL7WiTAYNTqg8ZAqip0N6O+9iPouqJt9BYh7voiorh60j2uBuflyUf59PeQH7+JbPhwqH/Rym2ILKlyMhbRFt9GEwgFQv3IppOQjCMWLANnDmKGo28ivwxu/yTy3d8MpSgqBsRtn0A2N6Q3CRYK4u4vj9lLDDQRoL7yT+luiN5ClEf/DSJn4i0/ZoRQvxb1vDZ33NeB3Pc07PrCxCNKVgeEstf3oqa0SNMEkKF+1Bd+AMNNLiJB5O7/q40v3YwyA46MOjo3GrqoukEIRxPEEylSKa1+w+Uws3xRPgvLPUgpMRkNWGa44etEyPXaeHTXIqKxJKmUxGIx4LSZMwqBVEolGEkQDMWRaDboDpsRo2F2Hs+1i966qlwWlLivNv/VohuplMqRhs40QTWAqkpOX+xh0YIc3j/SSlmRk4ri9AVWJJogEEpwtqkXVcLiBV7cTgv2cYovh800aat0p92E3WoknCFyWJRnn/bUtNGaE89WbVEwHOflvRdp7hiqnXn/SCvHznbx5L1L8IxTWKmqHLWhtcVspKGxj7buEG3dISxmAxXFLhQh2PNRM4/ftXjKj2U2kYFe1F/+Wbr7WH8X8pm/hk9+G1FRN3MXTybTjSLyy1FufVQTMdGQNj+A4iqUB7+GcE2tJmfCd+f93ahP/bf0CIKvA/nrP9eMF/LLpjQfQKvpuu0TiFvuAQTYXFoU6xpkPAq9baj7noXuZsTaO0GqWvRtYJ93fw2Lb0HZ/ukZtWMXVgesvANRuxbZ1QxCIPLLkRYbItgH0QiytxVRUKHN01OQlsaZCRmPoO55aqQboq8T9Zm/RnniO/MiYiWbz2atdZMNB7VI0AQjSticiJKa7GW8Tu+gE+C4CfSmC6phyPefRbhyUCWI4qr5Zd2vozPP0EXVdY6Ukj5/lEAwzuGGTjp7NEe9dfVFlBU55/VdcLvVNKZoiMdTXGz28fr+pkEDDqNBYcemShZV5syZULRe41QXT6q0dmYobr9KZ2+YJQu1Rd6Hx9spynVgvjr3cDShLegbugb3/+hEO4urcti+oXLG+0o57SYe2bmIX73WQGKYyYnDZuLe26rHLezGS3mRK80EZDg1lV7Ms1Dz19kbThNUAwTDCY42dHLr6rJxReiMBoHHaaY/mNlEIN9r5Y33Gwf/jsVTabVVXT3hcQu4+YDsaEwXVMPH9j6FfPSbMxf5MJm11LCr6W9i4wNpgmqQ9kbUvb9GufMLs+aGJ9UU8uS+zClZagr1w5dQ7vzitDS3FUbTqGmOUqrIy6eRz/8AkGA0I3IKUZ//wcidzx5ELlyBWHbrlOc1GsJsBbMV4S0c2gaaM+CdX0AMNP8dbzQvHIBzhzKP+Toh2DeuaNeME8n+m4Ca0oRRfxey6TT0tUFpLaJoYdbII4AwmpG5xVBcDe0XR47f9klweCc0TTkQWc1EJIBIJlBf/HvkgmUod395XghWHZ35iF6BeJ0xkFLkC8QIRxOEIwm6esM8vfscF6/0EwwnaO8O8dLeixw62UEsPoZF8DzHF4jy8ruX0hwNkymV1/c10tsfGeXI2cVoUEZNG3M5zIORoGA4ntaXqKsvkiaoBjjb2EdLR/ZUueliIIXwCw8v497bFrJxZQkPb6/hM/cvIXcGGkU7bGbu3Fw1YrvXZWH9sqIRgnW6Sakqx89mNuYArffXQCrlWD3THHYzW9eWZxyzW40oipIxejlA4HrrB9Z0KvtYR9PoDnVTxelF3PqI9t9Wh3atawXVAOc+0hbes0UirkUlstF2AWLR7OPTSdCHfPMnDFqSLlyBPPdx1t3lwVeQ19RpyWgI2duObL+E7OtAxjK3bpgOxFV3xgmlRyZio9qxytHS42aRUSO3i9Yh/D2o//zvkW/+M/LQ68gX/g715/8F2Zc5pXjwvO48lAd/D7HidhhoE+DMQdz7VcTCFROv0RytFlExaAXZAE0nkR2XJnZuHZ2bCD1SdR3hD8b44Fgbpy70kFIlBbk2tq2voLk9kPH35dCpDpYvzsdynTY5jMWTg+57mfjwRDv3bl04K5GNsTAZFdYvK+ZcU1/G8WU1ebx9ULsbWF7kGoxSJZIpPj7VkfW8h052UFnizhAZSxEIxTl9oQefP8bCcg8Vxa5JG0ooisDjtMxK1MRiNlBd7uZzDyzlbGMfoWiC0gInBbl23A7zrEQfx1pzBENx3v7gMlaLkRWLC/C4LFn7aFWWuNi+oZJ9h1uIJ7SbGEV5du69TXtvOu2mjE6X2n7zp4ZwXHjys4/ZXDCDTmFCKFC7DlIp5In3Rho2DEdNQWoWBavBhHDnIVuyjDtzwTQ+0SCl1CIcYpIOhpFg2nMjzDZkeBSREQ1Baujmm/T3oL7xz9B0cuAMsGitliY4RSOQacNs08REKnPq7aQMIGYCdx5ULIErZ64ZEIjND6E+9acje5+F+lFf/RHKI78/agqqcOXC9k8jNtyvPQ9mi+bWN4kUapFXirTYIYN4FovXIy8eG/xbHnkbWbF0XvVE09GZL+ii6johGI7z9JvnBh3eQGvw+uvXz/LgHTVcaO7PuHDr7AmT57n+RFU4kqC1K4gvkN2ZzuePkkypsy6qpJQEwwmC4TjReArv1QV3rsfCzo2VvP3hFdSrKlcI2LCihK6+CJFoEqNBsGZpEcarqWWplCQWyx5NjMaTIyIdiWSKi1d8vLx36I7h2aY+HDYTT9xTR457/v/Y2W1m7DYzbqcFVWoRoZm0nh+OQVFYubggq8V5fU0eh051DPb/On6um40rS1hXnzmKZrOaWLk4n5oKD9F4CoMisFmN2K0mpJTctq6cV94deXe3MM92XaX+AYiaNemGA8PH1t0F9pmttxA2J6zchqhepUVksu1odWoL7xlEBvs0q+xgH8JbiFizE3l6f8Z9xcb7x2V5LgO9yHMfI0+8q5k2rLgdUbN6Yvbb1whb2XUFsWgd8tLxzPuX14FFe65kOID6yv+ClnPDzwDnDqEqCsquLyIs8+D3xOFGrNqB/Pj1kWOlNTBP6n6E3Y1y71eRx/Ygj+zWBGzxQpQ7PoWMhDKKGECLbEYCWnpkBmTYr4kxoWg92pQp/gY6c1Ae/xbqb/4ifU7FCxF1G1Bf/Puhbakkc9KYUUfnOkAXVdcJ3X2RNEE1nIMn2lm5uID3j4zsBWKcZue22UBVJWcu9dLaFSTXYxt0CLyWwhw7JuPsC6rOnjDP7D6XZuxQX5PHbevKqa/Jo6rMTbcvQiolsVqMnDzfzemLvRTk2LhzSxUe55B4sJgN1FR6aO3KnHtfXe7Fek3kJhRJ8Oq7jSP2DUUSvPVBE/dvq8E6jbbtM8lMpPrF4ilUVcVsNmDIEjnxui0sKHXTdI09vsdlYUGJmxZDEBiKOn5wrI3FC3KyztdwNf3zWo84IQQLyzzcs3Uh733cTDCcQFEEdQtz2bqmDId97tsHTAhnDuLBf4186YfpUYKaNYhlW2alp41QDODOQxpMUFILbedH7rPl4QnXlUwE2duG+vRfgV9LI5WA2PIIYtfnkW/9TIuUgWbKsPEBRFHV2OcM9KL++s9hWOqXfOtfkMf2aI524xVWNid4C7XaIoDOJsSmB5EOz0jXOIMRZdODQ1GHSOAaQTWMsx/BlkcHBdhcIoxmuOUeQCKPvn31vSigZhXKjs+Oy61xthBOL2x6QEvVkxJMZoTNqblVjkaGKJyMRZAdl5DvPAXdzZpl/5o7YcVtU6pzEoqCLFqA8vn/iOxtg74OhNOLDPShvvy/0uYilm+dWZdPHZ3rmOtj5aVDY0v29I327hDrlxWN2K4oWq3M9UYokuCDY23EEyke2l5Lw6XewcjPAELA+hXFmIwzv4gbiEgpAhRF4ZX3Lo1wyjt1oQevy8ItK4rxuKx4rvZZikQTbFxZysaVpVgthhGmD0IIFlflcuhkx4hzWkwGVi0pGGGY0NETHvF8DNDUGiAaS44QVdFYkkRSxWgQ02bnPt8IRxJ09IT46GQH0XiS6nIvy2rz8DgtI1Ji+vqjLKrMobbSy9nGPlIpSVW5m3yvjZf2XmTX5gUjzn/mUi8Fk/g8WS1GllbnUlHsIpFMYVAU7DbjrN8QmA6Eyaw1L/3Sf0F2NEE0hCip1tKOZrkxbr/RhOO+r6Lsfw7OfKAJGatTEzd162dM4MmgD/WZvx4UVIPb338WtjyC8sX/jOxpAVVFFFSA3YOwjB49llIiz3+cJqgG6b6CbDqJWL51XPMTDi/KvV9F/dWfDda4qe/8AuXuL6Me2wMXDmuL+5IalB2fgZyh3w45Wh2alBCfP3WswuGBrY8h1uzUbNlNFrC7pxxJk9EQhAPISECLLtpdUxZpQjHANaJY5BSPEml1QIbIpmw5i3z2b4Y2REPI/c8i2y+g3P2VKX0GhVDAnaelseaXaS6fvmtS0/PKEBVLJ30NHZ0bHV1UXSeMdkfbbDKgZiiG37WpErvt+nuJkymVSEwTGEfOdHLP1ir2HmoeTG902EzctaUKr2tmU6cSyRRtXSHe2N9E/9U0xIIcG7etK+fA0dYREbRDpzpYVpuPyzEUibJZTWOKGI/TwqfuW8L+I600NPYhpaSmwsvWtWUZ08PiY5iPDH8vxBMpenwR9h1uobM3gtthZtOqkqsNmG8ccRWJJth3pCXNgKKrN8KRM518+r4l5F6TAptIqrx5oAm300x1uReDIrhw2cf7h7VobybNOhXTFyFE2vviekYYzZrttadgTq4fSsQ542vn6cYj+GJhtlcvY9faO3EqBgxmm2ZoMZMRs6AP+kcaywDw/nNQtwGldu3EzhkNIk/syzosT7yLrF2LsI5T1BdWonzh+8hT+5Gt5xF5pdqC+c4vIrY9qb3BLbYRNTvC5hwlsUvMeErlRBl4L04XMtiH+tbP4Lxm7CFBs+5/6GtpzoXTgt2NWLYVefK9EUPitk+OcC+UQR/y7Z9lPtel45rj4TTd2BDOHJRP/CHyxHvIMwe0VNSVtyMW3zKxVFQdnZuM62/FfZNSW5HDu4cyV0GvWlJASaGDzatLaekI4nGaWbWkEI/LfF3eDTcYBFazgWg8xaWWfoLhOFtWl+JymDEoCjargVyPbVIFuRPB54/xmzfOpi2wu/oivLz3Ig/eUcPTb6anycTiKVKpsRvXBsNx/ME4/lAMr8uKy2HC67Kya/MCtq4tQ6I9/my1YiUF2YuXvW7LoNGDlJIr7QGee2soPSoaS/L82xfYtLKE9cuLxmz8e70QCCcyOvrF4ineO9TCPVsXDpqDAOTnaItDfzDOkTOdace4HGaisZGpN3VV+mJirlGlyrGeZv7PuQOD297ouMgbHRdZnVfO5xdtxJlFUElVhZBPq2tRjFq/n0ksQmVkFIMMZGZb9TERo7unCHHVg3ycZzMYka48WLMTse5OhMmCUAxDNubZsLuzWnVTs1ozIxknMhqCkB/Zdh4UI6K0RhMS89TgQMajyPeeGRRUg3Q3a72vPvlH02olLqx22PoY5JcjD76smYt4ixC3fwJRXjeyTioehf7srqWy7SKisHL65ufO09IWV92hbbC7xuwfpqNzs3NjrKhuAhwOE/fetpBX37uUtsgvLXCyZkkhTruZTStLSCZVFIPIWktyPeC0mVm3rJh9hzURKdFSqLp9UXz+KKWFTkxGAy6HecaEVTyR4sDR1owRi0RSpanVT2WJi8ttQ+kyTrsJ4xjpiL39EX7zxjkCoSFnsoIcGw/vqMXttIxLBDtsWjrZ6Yu9I8Z2blwwaPgQDCd4c39TxnN8cLyN+tq8G0ZUnb+c2XUR4EKzj2g8mSaq7FYTq+oKOJrByn7zqlIOn0lPeykrcM6IvbzOxPDFI/ym8XDGsSM9zTy8YCXODL2gZCyMvHgM+c7Ph3oHFVai3POvIK90Qt8jwpWXPZpjME6q5kjYnIgVtyN3/9/M4yvvGJfRBWg9s+jvRp75AFIJsLqgYjHSUzhmpEvYXSgP/A7qS/+omSUMULVCq1UaZ6RMhgPID15EHn5zaJsQiDs+DfWbx/1YRr2GlFp0JtSPTMQ0N7wxRJuUUqsPUgwjo5nhgBaVyURfuxahnOb+TMLhgbU7EXXrQVXBYMzeXHfA2jyblfxEm1WPZ36KYd6YfujoXA/cGCuqmwCz0UBthZcvPbKcplY/kWiCBaUePC7z4CJaCIFplpzwEokU0XgKIbR0vOkUN4oiWL4on2AozuV2P1vXlvHK3kvErtpVH23owmox8MTddeTnzEzNWDyRoj2LQQZozWNzPdY0UbVldemojXqD4TjP7j6fJqhAi369sb+R+2+vGZdxg81q4vb15ZQVujh4oo1QJElxgZ3b11WQ6x1aUETjSUKRzFbeUkJvfxSv6+YUClaLkc2rSiktcHLgWBvBcJyiPDtb15ZjtxpoarXiD8Qxmw2sWVLIkupcHPO4kfbNQiSZIDBKJKgl1E9pBoMK2d6IfOV/pW/svIz6yz9F+dyfgHsUq/hrcXigcilcPj1iSKzeOelFqKhehTy2B7oupw+U1EysjqWrGdl2UbPzvnQc6ELarvb1KqgcM1Ik3Pkoj3xDExmxMMLqAJsbYRu//b9sPZ8mqLSNEvn2z7SI1TiMO0Y9v5qCjibU5/920HxDCgWxZhdsuHdEDZSUKvT3IM99hGxuQOQUwYrbwZ0/1JA5ER0yGMl0zWAfgqnNOxNCKKP3iRrA7oSaNSMjaaCJsaKRdaA6Ojqzy4RE1csvv8zTTz9Nbm4uX/7yl1myZMngWF9fH48//jhvvfXWtE9SR8NkMpBjMsypZbaqSnyBKAeOttHY0o/ZZGDN0kLqFubinMZFp8Nm4rZ15YSiCX7z+tlBQTVANJbi+Xcu8OQ9dTNixW00KDjtphECaACX3UTkqrGEyaiweXUpNRWj9wgJRRJZLeKbWgOEo8lxu+E5bGZW1hVQU+FBldocrj1WGUPoXo/OkNmorczhwNG2jGM15d6Mboh2m4mlNXlUlrpRVZn2HN65pYrYDN000Jk8pjGso52mkd8FMhxAvvurzAdEQ8jLZ8ZtAiFTKUjEUG77BCQTyI4m5IEXIBlHrNmJWHunVuczCYQrB+WR30deOa2JK5MVseVhhMGA7GyCkAccnlFT0GQ4gPR1aqYXl4c1aj57ECrrEbs+D+NIvxM2F9hcE8k4HJpDJIj88KXs44d3I+/4NCLs1x6X3Qs5hQiLHWEeZ51soA/11/8jPdVSqprFek6RZrs//DPb1Yz6yz/VUugAeek48uPdiAd+F6pXaY2HzdbRe1+5csc3txlCmG0o255A7bqcngYoFMSD/3pG3S51dHTGx7hF1c9+9jO+8IUvcM8999DQ0MD//J//k3/6p3/is5/9LADxeJw9e/bM2ER15gd9/ig/fek0yaRWOxSNp9jzUTPnL/t44I7qaRU4ZrMBXzCGP4uw8fljhKPJGRFVVouRTStLeWZ3ZnvhNfVFmIwKG1aUYLUYcNhMI1z6riVTnc5wEsmJGyGMFj2xWYzke210+0Y6dhmNyowbfcwmLruJFYvzR9RVWcwGtq4rS0v9u5ZM0UWzKXtN27WoqopyHafbXk84TRbqvSWc8o0U0FaDiSJbBpe2VAK6mrOf9MoZGIeokpEg8swHyPef0dzmAEprUZ78DtJkQ9id2uJ8CghXDqJ+C7JmNUTDqC//I/JqGp4EyCnW7NWzmSbEoxDxpwuqAS6fQrZemH7DhWtJJSGUve5MBnoRTSeQvk7wFMLxd5DJOCxaB2WLEaM1mB44x5UzWWvX5AcvIGpWDUZ/ZNiP+ur/HhRUw/ZEvvq/EV/8z1pTa7sHsfw2zaL9WgoqxxdNmmGEpwDliT9Gdl3WIqXuPERlPWoqgQj2Ie0u3e5cR2cOGbeo+rM/+zP+4i/+gt///d8H4Je//CVf/vKXiUajfOUrX5mxCerMH2LxFO9+3DwoqIbT0hmk1xeddoEzlvFDKjX+JoSRaIJQJElrVxCzUaG4wIHDZspax1Sc72DTyhI+ON42mMauKIJdmxdohhATrEcaLZJnUMS095ay20zcc9tCnnrlDIlhr5kQcO/WhaOmKl6LqqoIIeZtxMZmNXHr6jJqK7xplurLa/NxO6dfdCeTKv5QjIbGPjp7tFTQ+po83A7zrKXg3ozYjWY+W3sLf3F8Nz2x0OB2k2Lg68u24clUz3S1r1VWx778snFdW146PtJ9rfU86tN/iXjk97X0rOlCgvrWT9PrmgD62lGf/RvNNCFDmqGUEtlwMPtpj+9BVq9CWO1aCl3QB9Gg1kTW5poeIwaLDcpq4UxPxmFRXqcJot425L5nhuZ24Qgyvwzl0T8YOyrUPYpIDvrS0/giwez7J+PIvnaEJ19rF7DpAS0Ceer9oQbXZYtR7vkKwjE/el8JVw44PZBTgvr2T5Hv/AIAVQjEqu2w8cF5M1cdnZuNca/izp07x4MPPjj49xNPPEFBQQEPPfQQiUSCRx99dEYmqDN/iCVSXGrO3i+robGPipLp/TK3WY0oQmTsy2RQBLZxpsuFIgn2HLzCmUtD5g6KENy9tYqaCm/GqITNamTd8iKW1uTR1RfBoAjyvbZJ9xiymI0sLPNwKUPPsdVLCyckcsZLvtfG5x9axrmmXprbg+R6rCxflI/bYR4zsgYQCMVp6QxwtrEPq8XAuvpijEYFgRbturbv1lxit5lYWO6ltNA1ZvPfqSClpKMnxG/ePDd4g+HCFc1S/5EdtVSWuFGU+Sk+bwTybU7+aNWdtIZ8XAx0U2B1UusuJMdix5DBnUw4PIhNDyBf+/HIkykGxDjsz2XQh9z3dObBQC90NGp24+OIsoyLSAAuHcs81tum1RFlqt0yGCCRObIPaGMyhYxFkY3HNWOM6FVx6s5Dufe3obgKYZj8DR5hsqBsuB/17Ecja5QsdkTFUmSoD3k6gylEd4tmLb/xvpHud8Mprs4+5ikAZdj3UjZjhwGGN7Z1eGH7pxEb79eeF7NVE5tTMIFQAz5ELHTVxt6OcE9DGmGgF/Vn/2koYgpazdqRt7THv3aX7tSnozMHjPub0+1209HRwcKFCwe3bd++nRdffJEHHniA5uZR7hzp3BAItDqcRIZIFTBqitVkcVhNrFtexMHjI5tiblhZMm4hcv6yL01QAahS8sq7l/jiw8vI82ZOmbCYjFhMxinXsQXDcd77uIUVi/OxmA2cbepDVSVGo8K6pUWsXlo4pnPgZFAUoTUlXl7CmqUqBmX80SZ/MMavXz+LLxBDEYK7bl3AmUu9HDvbRSSaJD/Hxu3ryynJdw7auM8HtLnM3Hz8wTivvndpRMRWVSUvv3uRz9y3FO8c1j3eKMhkYsipz+ZIq1XKsdjJsdhZlls6rnOJhSthzS7k4d0w4N1ntmm1KO68sU+QTGjiKRvdrUiba1ypa8OR8RiE+pDnj2h271UrEPllmmnCaMdFghnrnYQrBxat02qVMo3XbdAay7ZdQr70w/RBfw/qr/8M5Qv/Ka0h8KTwFqI88R3UN38yFCUqX4yy/TOoF44gMjU5voo8vgex4rZBpz0Z9kMqBWbrYGNfUVqDtDq1KNu1j3Hr4wjnMMFpsWupe8EMDqFCaD28hm8yW8dVdzYWMpXUonFv/xzZ3KBtzC1B7PiM1ng5g0vluM/ddildUA0f+/BlxOL1MMc1YDo6NyPjFlUbNmzglVdeYdOmTWnbt23bxgsvvMADDzww7ZPTmV/YrEaWL8rn8OnOjONLF47+JR5PpIhEk6RUycDCxmYxjtqE1mQysK6+CLfDzAfH2giGEzjtJrasLqO6wjMuIRIKx/noRGYTA4DTF3rYuq58zPNMlngixb7DLZy60EPDpV6WVufxwLbqQXOEojwH9hmIUl3LRIwpkimVj062Dxpr3LKimHNNPi5c8Q3u090X4ek3zvHAtmoWLciZUGpgOJIgnlRRhGZvPhOCcqaIxJL0BzNHA6KxFKFIQhdVU0T6e5CHXkee3g9qCrFoPWy8f9L1QMLuhi2PIFbvQPZ1gMmiCSCHZ3xRGYNBW5zHsjiCuvPAP4royoBMxJDnP0a++r8ZFHofv4HMK0N58Pe0tMUsbnTZ0vSEUGDJRq0uaEBECEWLXticiLpbIBZF3f9s5kmlklrq25aHpxTpEEYTlNagfOIPIRoGRdHEnNGEsDo1wZyNZFyLuoT9yMunNdOLoA9KqlG2PAq5xQh3HsqT30Z98R+g52r/RpMVcesjiMprnBKdXsSuLyCf/Ru4xgxf3HIfZKrDmw76uzUzjcgw4dfbhvzNX6J8+ntQvDD7sWPRk7lnJaBFOVOTb1Suo6MzecYtqr75zW/y/vvvZxy74447eOGFF/jJT34ybRPTmX8YDQrr6ou51NKPz59eJLxxZQkuR/balT5/lHcPNXPhig8pYUGpm3X1Rbx/uJUta0rJ9WQvrrVbTaxcXEBNhZdUSmIwiAk5DaoSQpHsJhG+QAwp5aAoSCZVovEkyaSKEFot1HhS5bIRjiQ4dUGrL0ipkhPnuzlxfshQ4fMP1c+KqJoIkWiSE+e1OStCUJzv4INjmYXpOwevUFroHNdrEk+m6OgO8/aHl+nui2A0COpr8rO+fxJJlXA0gZqSmEzKtDpMThZVHT2dKDXGuM7oyEAv6lN/CoGhmhx58j3khSMon/1/EJ6CSZ1XWGxgsWl22hPF7kGsvRO5/7mRYyYrwp2HtI7fchyAoC9dUA3Q04JsPa+ZJhx7Z+RxlfVak94sCE8+ypN/jDyyGwxmRFmtJiStdlBTyEQMuluzHi/bLyGSCZhCJGVwLnb3yLkuqAdFIM8dynxM7Vqk0QTvP5tuGnHpOGrjCcTjf4ioXIrIK0P55L+FcEAzI7G5MopkIQSU1yE+8+9Q9z0LnU3gzkVsfBBRVouwTP8NEJlMIC8cThdUg4Mq6v7nEPd+FWWi75kBCkexT3d6wah3y9HRmQvG/cnbtm0b27Ztyzq+fft2tm/fPi2T0pm/uJ1mPnlXHa1dQRou9WK1GFi5uBCvy5LVDrw/GOMXL58hMsz9rqnVT1tXkAfuqOE3r5/lU/ctweXI/iMuxMSE1HBMRoWSAgdX2gMZxxeWewYFlT8Yo88fpbsvwumLvcQTKRaWe1izpBCPyzIpo4Z4IjVqWn8wnKBg7o2l0pDIQZMQp91Enz97OlIwnCAaT+EcR8uwzp4wv3qtYfDvZEpy7GwXrZ1BHrtzUdprHAjFef9IC2cu9pJSJW6nmW3rK6gscWGZZlOPiWCzGrGYDcTiI+8GGxSB0z6/BPL1hJQSeeFomqAaJBpEHturRZwMs5tuKgwGWLkN2dsGDR8ODVidKHd9CfXsQZRtT07onLLxBCME1cDY2z/X0vAUBXl8r1b3IwQsWoey7VNj1vgITz6svwd54AXUZ/5q6LxCILZ/FrF6J3LfbzIfm1cKhhl8D7vzEGWLkQWVI3tyWWyIW+6FSAA1kwuflMg3f4J88jsIhzezaMuAMFugeCHKA7+jmWQYTFOqkxoLmYgiWzI7xwLQdhERi2jRu0kgihYg7W4Ij3RZFJsf1u3VdXTmiHGvTH75y1/yyCOPYDZri57m5mZKS0sHrYTD4TB/+7d/y7e//e2ZmanOtBOOJEipEkWMbs19LS6HmTpHLosqvWM6wqmqpOFSb5qgGiCeUDnX1EdRvoPO3sioomoqWC1GbltXzs9fPj1C3DhsJiqKXYBW93SlPcDpi71cbhv6sTp8upPTF3r49P1LJ1VbZTYZECJ7vbRzBqJUsXhSa86M9vjHaw8+gMVkYGGZh4vN/SRSKpYxjjeMw5ghHE3wzodXMo51+yL09kcHRVUoEue5t8/R2TNUN+APxnnhnQs8tL2G2sq5U6EOm4mta8vYfeDyiLGNK0vmXdTxuiIeRQ4XLdcgzx1CrN016Qa7E0WmkpopRDKuLcS3PYlYfw+y8/LVSI5E7WlBue0TGd34RiVTFGOARAykRNz2CcTauyAeAZMVHOO3zJat50fag0uJfOtfUD71PaTRrD2u4QgFseJ2xAy2CBAGI+SVojz0Nc2i/vgeSMQRtWsR6+8GbwHy9AfZT+DrhGhkUsJBWOxaGudMYzAhHN4skhnt/TsVMxBXLsoT30Z98YdDNWtGM2LD/YiaNfPWpVVH50Zn3J/qT3/607S1tVFYqOW019fXc+TIEaqrNReeQCDAd7/7XV1UXQdEYklaO4O893EzPb4oXpeFTatKqSpzT8jNbTy9eeKJVFodzrU0twdZWp1LR3eImgrvuK89UfI8Vh6/czG7D1wejLpUlbrZvrESt1MTc739UQwGkSaoBojGUxw40squLQsm7Pxnt5pYvCCHhsaRhdL5ObZpXYSrqqS3P8I7B69wuS2AIgSLFni5dW0ZXtf4BaHFbGTrunIutwWIRJPYrEaMBoVkBov7yhIXRoPgcqufKx0BXA4TlcVunA4TxmERhURSpbM3S00K0Njqp/Kqe6QvEEsTVMPZ81EzxfmOOUsFNJsM1FR4cTssHDjWSm9/FI/TwoaVJZTk26fdGv+mQjGMbhJgtmj1OVeRqSQoyow4nclQP/LoO1pD2XhUW7Su3gHr70E4czThoyiaGJhMf6oF9XDg+cxjhZWaMYPJAt6JpzuO2YD3xF64/3fgxb8fcr+z2BD3/ja4p+5gKFVVM1IwGDTjhwwITz5suBex7FZAgnXIjERmaOKcxjzvC6eYrcgVWzXBmAGx7u4p29eL3BKtZi0c0IxUbE4t/XGKvdJ0dHQmz7h//eU1t9mv/Vvn+iCVUmlo7OWtYXfZfYEYr753iY0rS7hlefGEoxqjYTCMbntutRhIJFUKcyd/9zCZVAlFEiRTKiajkrERr8lkoLLEzRP31BGLp1DEQBrX0Nz6A1Fau0LXnn6Qs019bF1XPmFRZTYb2HZLBYmkysVhlvRFeXYe2FYzrVbqvkCUn718ZtCZTpWShsY+mjuCfPq+JYMCcjzkuC187sGlHDjaxvGzXdy5ZQGv7WtMqyly2k3s2FhJU6ufcDTJlbYArV1BFCF4eEcNFSXuQYMMRWipmNncI4dH7Fo7sr8O/YFY1nPMFk67GafdTH6OlWRKYlDEhJ5bncwIkxmxZicyi524WHMnwuZCBnqRzWeRDR8ibC5YdQd4CxGTrVG5BpmIIT98GXn4zaGNyTjyo1ch7Eds/8yUewEJbyGytBZaz187grL9Mwi7a5T5xSHcry2ojaaRPaZSCS3Clu34QB+ibDHiS/8FGfRpkSmHd/zGHaMg+7uRpw8gLx7RLMTX3YUorNRS9a5BCGXQ5S9te2ElMptRR+miSafNzSbSmYvY8Vnk2z8f6nkFiPotiAX103KN8aY/6ujozA76LdWbjFAkwXuHMtvfHzzezrLavGkVVSajgbX1RWliYjj1NXkcbehkZd3kis+D4TgfHm/n+NkuUqrEbDJwy/Jili/KzyhWHDZTVhFjMWs9sbIxld5DTruZe7YuJBxNEImlsJgN2C3GaY1SJZMqH53oyNicORRJcLG5n1V1BeNODTEoCrkeG7s2LyCe0FIJv/TwMi5eNSqpKHaRn2Oj4VIvpy70oCiCxQtyWLesiNf3NfL82xf40iPL8bg0sWG3mlhZV8Chkx0jriWEVts2gNOR/XkxGsS40g1ng5lKWb2ZEYWVyKWb4No+RgvqEQvqkf3dqL/6M/BrZi8SzchCbHpI688zHQvukH9k6txV5Kn9iI0PaE1up4BweFAe+F3kkbe1/kLxCBRXo9zxJBRUZD1OhgPII28hD748FGXy5KM8+DUoqNA+32YblNRANjOIBfUIiw1htU/a+CPj3Po6UH/x/6WlNsqmk7B6B3Ljg4irzn5YbKPXNNndiLt+a6SRh9WBcucXZrQearoQVgdqzWqUiqXI1vOQjCPKFiHt7ulpsqyjozPv0EXVTUYkliSeyHyXX5WSYCiRliaWSKQIRRO0dYaIJ1MU5zuwmo2YjMq4BUF+jo01SwtHWLEvXpCjNeDdshD3KM6Boz2Wtw5c5vyw9MIB+/JkSmXjipIJWXXn52jRsuPnujOO19fkjbvZcCasFmNGM49QOE4klgLkYN2VzWqccHpbNJ6kKUPq4gDnL/dRXzNx0Ww2GdKOWXu1rswf0gxIguEhe+QDx9ooyLGxY1Mlr7x7ifbu0KCoMhgU1i4toqUjQHv3UBqgEHDfbdVpkarSAicGRWR00ltWmz+vmg7faMhoSFusD6SfzTLC7tZMH1btQJ54V4tWLLsVkVsCFpt2598/8jMqDzyv9eeZDlEVDWa1MwfN7ntSLoLXIJw5sPlhxKo7NLFhsowpGOSlo8hr0wb7u1F/9d9RPvcfwZOPMFtRNj2EeuFI5ga8i9ZPe92NjEdR9z0zslbMmYOoXoX69s80xz+pQlEVyo7PQkF5Wv+xAYTJArVrEF/4PvL4XmR/lyYEa9dljGxNee7hgFZfJhRwuEdvPDzWuVJJ6O/ShHLrBVRPPsrG+7VIqtmWsb/YbCGl1D7bBoPeHFhHZwaY0Arxtddew+PR7iarqsru3bs5ceIEAD6fb9onpzP9jHWHf7gIGaiHevW9S2kmC9UVHuqr8zAaFEoLnVld/wawW01sWlXK8tp8LlzxoaqSqjIPFpMBs9mA026a1A98JJpIE1TDOXSyg+W1+YML+vHgdJiIxEzULcyl4ZpGwU67ifXLi6e1n1IimaKjJ8wb7zcN1nmVFDjYtLKUPQevcMeGSvJzxn83XFEEVrOBQJbMOZvVNKVo23BSKZVjDV1pgmqArr4I0ViSHLeFUCR93OUw8/D2RfiCUS63+rHbTCwoceOwmTANE25Ou4lHdi7i2d3n0oRVcb6dDRMUyzrjQ0YCyPZG5Acvao1oS2pRNtynLQZnuU5jIK1JlNakz9Hfo/VRyoI8+5HWPHeqjCEms9UJTQZhMIy7UasM+pDvZ7B1B4hFNCv2gQbEOYUon/y21oB3oK9R+WKUHZ8bX8PjiRINZYyMKds/jfrGP6c3T+5oRH3qv6F85t9DYeaonDBbIb8M7viUZmQR9mtRr0APsmwRIrd0yhEfGY8gO5qQe56CzstaXdfau2D51smfu6NRi6QORBE7m1DPHULs/BzUb5nVGxUyHoVgH/LsR1oftfLFYHdB+yVYvB7c+XoNlo7ONDIhUfXFL34x7e/f+Z3fSftbd5yZ/9isRnLcFvqu6TMFYLca01LjAqE4r7x7acR+F6/0U5znGEwnW1qdN+Zi3WYxYrMYKRijdioYjtPbH6W5I4DbYaa82IXTbs7YuDZbA1bQmtdmsrweDbPRQGGOnVtXl7JkYQ7HznYTT6RYvCBHMyaY5poZnz/Gr15rSBOsbV0hXn73IvfdVs2vX2/gMw/UjzuKZ7eaWL+smFfeG/maAaxZUjihBsBSSkKRBImEisEg0pr0RmPJEcJzOBeb+ykvclFaOPKuu8NuwmE3UVaYvWbEYFAoL3LypUeW094dIhSJU1zgxOM047DNfa+qGw0ZiyA/el1LKRugv1uzCv/kt6Gsdu4mdy2jNTZNjPxemxQ2FxQtgI6mkWM5xdrCdC5IJdPFybV0NsHSTQBaBKisVuvlFA1rIWGrU6vBSiZgLDOISXFNZDm3BOnvyTxnNYW672mU+35b6yGWjVQK2i6gPvvXQ0IFkDlFKI//IWIKAlG2nEcOs5wnGkK+/wyy7TzK3V8Zta4t4/mC/aiv/ShtnoNjb/8cUbVca8Q8C8hEDHnhCPKVf2LwdTmxFzwFKDs/i/qz/xfxwO9C5dIpReZ0dHSGGPcKS1XVMf+l5kkX7x/84AdUVVVhtVrZuHEjH36Y3aL3ZsNhM3P/tpoRFtlGg8KD22vTeuycvpihV8xVTpzrZml1Lns+aiYUyS5uJoI/GOPXr5/l16+f5cDRNl5/v4n/8+xJmtsDgz2ThjNWKt5kohkmkwGv20pNRQ4PbKvmkR2LWL2kcNoFVTyRYv/R1ow267F4ipbOIDluK339mR3wslFZ6mLRgpF24xtXlpDjGf/d9WgsSUNjLz976TQ/fvYEP37mBO8cvEwgdPW1FmJUIa0oAo/TPKm0zgEMBgWPy0LdwlzW1hdTWuDUBdVMEexDHnxl5HY1hfrG/0GGsqeVzipmG1QtyzosFq2dlssIuwvlvt8duQB25qA8/HXEXPUBMhhHj2plaAor7G5EbjGYLMjmBtTn/1b7d+5jZNA3fXOzOqBmTfq1Cyq0eqJsNJ/VaslGI+RDPvc/RwqVvg7Uvb/UojGTQIb6kW/9NPPgpeMQHOnWOiaxEPSNrBkFtMbLPZkbqM8IoX7kq8ME1QD9XajH30XU3YJ8+R9hOt8DOjo3OTdcTdVTTz3Ft771LX74wx+yceNG/uqv/oq7776bhoaGQTv4m52CHBufe6iey61+WjuDFObZWVjuweUwp0Ub+4PZ7/qGowmsZiPRWJJoLIVrimUM8USK9z5uobc//QdSVSXPvXU+zfBgAKfdhMthHlroD6Oi2IXdOrW390Rd/iZCPJGibRSnwY6eELkeK33+GAtKx39eh83Mzo2V3LK8mIvNPowGheoKL06bacw0zQGklFxq6U+LUqZUybGz3XT1RXhoey12q5GViwvY81Fm05P66jzKi516z6brABmPIpsbyNaIlt42bbE4Rbe76UBY7Si3fxK1uUGLtgxnQT14JvcdL5MJCPuRVyMqwpkDrhyUJ7+D9HVBbzvkFCK8hYhxpurNBMLpRWx5GPnaj0cOWmyI0swRRRn0ob70QxjWkFY2nUSW1qI88Lva453q3MxWlK2Po145AzGtZlLGIwhXbvZ+TTanZqM/CrLr8sh+WgOc+xi2fmJ0G/5sxCLQ35X9uq0XEIWVEzzpWK7Is+eaLK80ZG+OeOEI4v7f0RpLh/0zkw6qo3MTMu5V5969e8e13+233z7pyUwHf/EXf8FXv/pVfuu3fguAH/7wh7z00kv86Ec/4o//+I/ndG7zBSEEHqeFFYsLWLE4eypCdZmXhkuZ79YV5zvp9ml3GA2Gqad9RqJJzmbo4wTagr69J5RBVJl5dOcifvPG2bTanVyPlbturRq3iJgLDIpm/X5tzdEADpuJaCxJnnfiDmN2mwm7zURx/uSUbiic4N0sDpFtXSECoTgOm4nFVbmcvNBDd1/6neaFZR7KivSo0nVDJATKWJ+VeZTanVOM8tn/gPrBC9B4Eqx2zWp90dpJ2ZzLeAR58Rjy9f8zuHiXRjNi5+cQtWtQyhdrtSjzBLFwFWx+CPlhBve/LItjeeVMmqAapPU88koD4mrK4JTJKUL53J8gj+1FXjwKyQRi2Vbksez9msa0BA8Hso9JFdSRqXbjQjEwald22yS+P60O8BZqDYozXE/klkz8nJMlmv2mHWpq8HHLVHJKn24Zj0DAp5mQhP1QvQqRX6Y7HOrclIx71XnHHXdkHRuIbgghSCYn+QU3DcTjcQ4dOsR3v/vdwW2KorBr1y72798/Yv9YLEYsNhSN8ftnN8UlGI4P9rhx2IZMBJJJFQQTqn+ZCcqKnTjtphFmBELA2vpC3tzfREm+A9s0OLGlVIk6Su+zcBbxkZ9j4zP3L6HPH6M/GCPPY8PtNE97Y1hVlQRCcZpa+2nvCVGU56CqVIvuTcb8wWY1snFlCS+8cyHj+KIFObx7qJkc9+y7r8WTqYwGFAN09IQoznfgcmiitqUzwIlzPRgUweolBRTmOnDY9QjVdUMqjsgpRAolrZ/OIEVVWkRhniAMRsgrQdn1BS3aoCiaqcVka3p9nVoa1HCSceRrP0Lk/T9QvHDqk86CTCUh6EN2XIJwAFFcDe7cjD2dBhB2F6y/F1G/JXufquHXiIY1J7psczjyFnLhCoTVgUwOOMNN7rkUQmgpk1seQay766pwAbHzc8jdPyUtUlO9CrF47ZjXEkVV2eM7rtzJRakA7E4tXfH8xyPHFANiMq+7zYVy15dQf/3nI1wXxe2fBLsny4GTRwZ6NQEl5dUGwF6EoiAq6rI/b/llWq2bwTilKKWMR5ANHyLf+MnQxiO7kfnlKI/+mzmN6urozAXjFlV9fZmjCOFwmL/+67/mb/7mb6iurp62iU2G7u5uUqkURUXpVrdFRUWcOXNmxP7/9b/+V77//e/P1vQGicSSXGruZ9/hFgKhOFaLgVuWl7B4QQ5dfWGOn+tGCFhdV0CO20YwEqe1I4jTbqa0UBM61za3nQncDgtP3F3HOwevDPaZyvVY2bSyhNMXtHqru7dWTclmfACzScHtNOPPYj5RUpB9UedyWGa8X1BXb5hfvtYw2HT2xLkeTEaFJ+6uo2iSEaGyQucIq3lFCLauLaOzN8wjO2unXRyOB4OioAiRVeQOtz53OcwsWZhHdbkXIQQm3ZXvukIG+lCf/RtE4QLElkeQ+55O38FkRdn5uXnZF0iYrZNfUF9FJuOoB1/NOq5++DLKvf9qRhzbZDKBbG5IqxeSAOV1mnnDKHf6hcmsiZdxmR6oo9jDo1mUR0Kop/YjL5/S+latuB3ceZN2ORQGQ7qZx9LNiMp6ZNMpiEcQC5aBKzejEYSMhrXmxSaLdn1XDlTWw+VTI6+z7clJiwJhtqFsexK16zL0D7PoFwrigd8Dx8QEkPR3I0/uR/q7UR77JvL0fmRHI7jyEBvuQ+SVIczT9z5SQ/2Ivg7U1388FBmzOhE7PwtVK8CdDwuWQdPJa44UiA0PIN9/BrH1E1NrHhzsTxdUA3Q3Iz96DW77hO4uqHNTMe7V8ICV+gCqqvKjH/2I73//+yiKwg9+8IMR7oDzne9+97t861vfGvzb7/dTUZG96eJ0kEqpnL7YwzsfXhncFo2lcNlNvLz3Im3dWsjeoAhWLirghXcu0Nk71NPHoAge3lFLRbFrVoSV123l3tuqiUQTJFMq0XiK5vYAixfmsm1DBe5pEjNOu5k7bqng+bdHRm7KipxTMjyYKsFwnOffuTAoqAZIJFWef+cCn75vyaTEj91mYvOqUlbVFdDeFUJRBAW5dhQFrBbTtIjVyWC3Gamrzh0UzsMxGRXyMzg4TmfDaJ3ZQ/a2apEaXyfC5kR56GvIhoPIoA9RVIWo34x05Myn5L/pJRHPbiwA2lgiNqbF+qQI9iGf/ZuRgqe5AXnoDdj6qBaVmyoWB6J+i7bAvxa7G2X93ag//y+D6WISkId3I+79V1C7VhNwU2RAAI/W20tGQ8iuZq0HV38X5JejbHoIcotR7vkK8uM3kEffgUQUvEWIbU8gppiWKTz5KE/8MbKzCS6f1oRk9Sqtt1aG/llZ597fjfrUfx00fVDPHEAsWodYtQNqVqFMQ81a2vWCPk1QPfNX6fVm0SDypX9APPnHiLJFKHd/GXnqfeSh17X+YaU12uvdegGx47OI4oVTen3lhcPZx47vRay/e9ztAnR0bgQm9Y399NNP873vfY+uri6++93v8o1vfAOLZfbTlK4lPz8fg8FAR0f6j2RHRwfFxcUj9rdYLLM+71AkwfuHW9O2FeTaCITig4IKYHFVDmeb+tIEFWhpctmMG2YKi9mAxTy0aC4vmhk74YpiF4/urOWdg830+aOYjAor6wpYV180p4YHkWgyoxkGaLbz4Why0hGlgYbAuZ6J107NFCajgVvXlNLdF6ard6heymhUeHTXojSHyJkkEIrT3h3kUks/HqeFRQtycNnNaf2sMhGLJ4nEkiDBZFL02q7RGObMJo++jTy1H1GzClFYgexpQRhNKI45sg+fDUwWRGGltqjOgCisnHI0LBvySkPWCJI89g5i7c5pWZAKIaB2DfLwbvCl/zaKW+5FfeepDPU3Ukt/LK2ZFQtwmYgjz3yQ7sbn70G9eAzx0Ne09+StjyLW7NQs1k1mxAQjSdkQrhyEKwdqVk9u7qmkll453EUvlUSe+QDOfKClwE23qOppg8snsxp4qPueQXnoa1q0c/09iPrNICVSCM25tWzx9ESfR6t3S8YzpxPr6NzATEhU7dmzh+985zscP36cf/Nv/g3f+c53RkSw5hKz2cy6devYvXs3jzzyCDDUpPjrX//63E7uKtF4ingi/Ye0utxLwzUmDdUVXl7N0CMKNGHV2hmcNVE1W1jMRhaWeynMc5BIqiiKwGE1TjoiFwzHCYTi+AIx3E4zHqdlUuInqY7+w5DJ7v16x+2w8NiuxfQHYrR3h3A6zBTl2bXUU2VqEdJkSgU5uuV9fyDKr147i3+YmN13uJX7b6+musKT0ZkxlVLxh2JcavZz+HQH/lCcPK+N29aWUVIwdpPqm5JrF8yJqLYYBE1MGG+s75hrEUYTrL1Layh8rcARAnHLPROKWEyIQPaWFSSig/24ZDwK4QCyp0WbU16pVkM2geiZcOWifPLfIk8fQJ58DySI5VsRC5ZpjW8zoaaQHZe1dMCZJtyfZR4S+cY/I4r+RBMnUxCZMpmEeBiM5rS0RhkNa7bt5w9rqYk1q8FTOH7Tk0gQ2fBB9uue2ItcUD89UUe0nnK0X0B2t2TfqbtFi8JaHQhFGRR10x5xrl4Jh17LPFZSC6aZuSGhozNfGfen/L777uPNN9/ky1/+Ms8++2zGyM984Fvf+hZf/OIXWb9+PRs2bOCv/uqvCIVCg26Ac40xg1OeQRGkrlm4CzTxlI3RjASudxxTjEpFY0nC0QTPvXWBPv+QRbvbaebxXYsn1K8JwG4xYTQIkqmRr4dB0ZriZiMSTaBKidU8eXE4GtGYFpUJR5OkUipWixGbxTAtNWYOmwmHzZSxge9kCEUSdPWFOXqmi5Sqsrw2/2qNYPqiNZ5Iseej5jRBNcDL717ktx5djtc1UlT1B+McOtnB8XND9RHdfRGe2X2eu7ZUUV8zdpPqmw1RtghpMGZsVipW2ULB1AAAr2pJREFU75xwXclkkamkNgeTZfabyHsLEI/9AfLVHw31JnJ4EXf/1sxGaUZLXcspBpNZS4k7uQ+591eDd/2lYkBs/wyybgMiFtKc/bquQNFCRPkirYYnw3MoXLla1GLZrdoGmwv62kefY6AH2d8148JKBvoyvgcBiAS01LVJCiqZSoK/G3n4TWTzWe21veVeREEFCIE8tgf53m+G9j/4ClTWo9zzlQk42I3ynh1wGZwupERGgghPfnYjCk++ZmAyw4icEmRRFVybWioUlO1PzstaTB2dmWTcourVV1/FaDTy1FNP8ctf/jLrfr29o3R7nwWefPJJurq6+JM/+RPa29tZvXo1r7766gjzirnCZjFRUuBI61F0pT3AwjIvPb6hH7hwNInXZcEXyNwrqrRoio2hbkCklPT1R2ntCnL8bHeaoALwB+M8/855PnFX3YSEm91mZNOqUt77eOSdwbX1RQRCMexWY1paWigS53JbgEMnO4gnUiws97BmSSEe1/QtGiPRBJfbAviDMQ6eaCca1+5sF+c7uPvWqknZsc8UoUic3Qcuc/6yb3BbY4ufwlwbD+9YhGtYzVwkmuTCFd/Ik6AZXLV2hvC60oVxPJ4iHElw4nx3xuP2HrpCZal7Tmvz5iXOHMRj30I++9da7dAAC1cgVu/QDAdmEBnVmqXKI7uRwX6tnqV2DcKTP6PXHY4wmqBiKeIz/w7CQUBqgsPpnVGBJ3JLkbml0Ns6cuyOTyEcHtTmsyMjOGoKefhNlPwy1Kf/Mu11k1YHyie/DQXlma+pKGlCWVrsmnDM0q9J5BajvvojLZVsAgtkmUpCyAehfhCaOyMOT/ZozVjR76m8Dt3NqE/9t6G+Zt0tyKaTsOkhqLslTVANcvkU8uxHsGbn2O8BmwuxfCvywAuZp75iG2KMXlwTQVjtiLxShDNHqzHLkGInNj88K4JGOD0oD31d+/wefUdr5Fy+GOX2JyA/83tQR+dGZtyi6sc/ztBscJ7y9a9/fd6k+12LzWrknq0L+dVrDYPRpivtAdYvK6ahsXewdudYQxcbVhTz+vsjc/2L8uwjFpU6mmj6xatnuPvWhWn1acPp8UUJRxMTElUmo4EViwpw2s0cONaKzx/D67awdmkR0XiSX752li88tIz8HE3EhCMJ3tx/OU0YHD7dycnzPXzm/iXTVj/VH4gRi6d49xqx194d4tevn+XT9y3B7Zwf6VtdvZE0QTVAZ2+Es019rF1aOLh4UVWZtXUMaDVTI7YlUvgC0azHRWMporGkLqquQRiMUFqD+OJ/1tKJIgGtjsjhzejMNp3IWAR59J00x0F55TTyw5dQPvXdUU0NphshhJYiNc21L6Ne0+lFeewPUN97Gs4e1NIPPfmIbZ9ClNZqdtUfvpT52Fvu1Zr5Jq656RYNob749yhPfHtcNUfC6UXs+gLy6b8Y0a9JLNuKbDwJLWe1SNE4F+kyFkFeOIzc/S9QsQSxZBMi0AueAlR3Hop15A1B4cxBmqxa2uO1uPM0kTsJZDiA+sY/j2wUDcgDL6AsqEciyNSUV378BmLxes0dMcN5ScSG7PyX36alzV7bn+pqz6bpRlQtRz3wAuKuLyHf+bnWWgA0K/jNDyNKZs+JWbhyYMujWmRbqpohSYbXWEfnZmDcoup6c/abz+S4rXz6vqV09YVp7wqR67GSn2PlibvrOH6ui9MXe0kkU1hMBh7aXsPej5rxBWIYFMGy2nw2riyZcorcjYaUkvOX+4jGUmPWOMXjo9gLZ0EIaGzpZ1VdIS67iUA4wbGGrsEGyMfOdnHHLRUoisAXiGWMtMQTKfYdbmHXpgVT7u2VSKToD8X56FTm9J1QJEF7d2jSoiqZUolGkyDAbjVNKW0umVQ5ciZDM8yrHGvoZMnC3MH3tNlsIMdtHRFpHKCscOQCSwjGNLAw6Kl/GREGo+Z6lqVx7IwR8o20cAeIBFD3/FKzMrdkvwEhoyGtbkQxTKrp73xAuPNQdn0ebn1Eq6EyWwdTzmTYD/7MdVfCYkcON0YYTl+7ljI3ztRNUVqDeOI7qAdegI4mLUK34nYtzeydX2g7pSaQbt7binz1fyN2fAYCfcg3/1mrC1MMiCUbkLc+ri3Eh+PwIO75CvLFv0sXdwaj9j6YbCPZaAg6L2cZlJpBiSc/c6QuFhoRBZLxGHQ1ob79C+hs0tJVV96BWHsnyif+CNl4XKvPM5oQa3Zp7nozkEIrXLkomx5EbT6LuOermiBXFEROsRZ9HmbdLsN+CPUj+7u1ubhyp70xrzAYNOt7HZ2bnClVTkajUZ566ilCoRB33nknixYtmq553fC4HGZcDjPV5d607ZtXl7J6iXbX3mYxoiiCknwH8aQ6WL8zWoH/zUoyqXKpRWvebDAoCDHixusgo9VAZT1/SuVKe4AzlzKnt/b2R0mpKopi4GxT9hTY85d9rFhUQCAcH2wePBlSqsRsUvD5M6eHArR2BVlcNbE6BCkl/cEYH5/s4GxTHwaDwsrF+dTX5E96rhI5an1gSpXIYS+Ww2Zi56ZKfv362RH7Liz34HSMfP3sVhMuuxmL2UAsg2guyLFhs+pGFfMJ2TSy79Agl45qC+IMokrGo9DTivreb4ZEwMYHEAvqR22aO1/J2m/LbEWUVCN72645QEFmiLykkcxSn5Tp+iYL0u7WDDAWrdWMF46+DQPXtTlhnJEHGY+ifvAS1K4Ff4/Wq2gANYU8tR8Z6EN54HcRw6JPwmCEqmWIz38fefQtZE8bomghYsVtmuiZLGPdRzGayRSlArTeWJb09hGy4xLyV/9j6JhEDHnoNWTLOZSHv46ychuy7hat19UMuUYOIFy5KEs2XE1ZBWxOLb1z+HwDvagv/hDatFYlEsCTj/LoHyByS2Z0fjo6NyPjXp1/61vf4hvf+Mbg3/F4nM2bN/PVr36V733ve6xZs4b9+/fPyCRvJgyKgtNuxmEbig447GZy3FbcTosuqLJgMIhBq++LV3wsrc58172uKndSi2uT0UBhhv5MA5QVOjFeNaJQRqkPUIQgGk/xxvtNPPXqGfzB7KJoNMwmAyaDAfsoj2UyaYb+YIyfvXSGIw1dhK9aye873Mozb57Lais/FiajgWW1/z97/x1eV3bmd77ftdPJARkgAZAgwUwWyWLlzMpJKqkUqqSW1EHunr5OY7d7fNvja7fb9tjTfnp87fH1dbs9dnusViuVpJJKUqlKFVWqrErMOQIgSKST4957zR+bBHl4zkEiIrk+zyO766yDgw2QBPZ73rV+b/0uyLqVjVUzudpbQjz9yHo6WkIIAUG/wZ07lvPgrStqFsWaJoiGLR64bUVVR8rv03n4jp4ZFdPKHJqoMJCy7rsisv8w7rf+FZw+4J3hGD2DfP6/IN95zktyWyBuZgyZOIebHpv8yVMgDAtxw8Ne0MGlpOvdsNc7p2NYU96qNy4QhmIO+dI3kG/+8GJBBYi7vgih+NRep1yE0TNo62/2ztjUcvoAZJJVDwvTh2hejrjny2if+TuIO59ENLZfWWqeLwRtK+ssCsSyXmpWXrqBdusTlSmBqeHznbsafy8HjyHPzzsTvuCcF1QXCKEhQlHvf5cXVMU87ivfHC+oxiWHcX/47+t3OhVFmbEp36G/+OKLPPDAA+P//c1vfpOTJ09y+PBhxsbG+MIXvsC//Jf/ck4uUlm8LsxwyhcWNo1Q0zS2b2gFYM+RYbo7Imxd1zJe6OiaYOu6Fu6+sXNG0do+S+fWbctqrhm6xoZVjeNngtatrL8NYs2KBo73eTcUqUyJD/efnVEku6YJYhGLretqp3IZumBFx/Tetbcdlw/2naVQrH6XeziR58xQZtrXecHy1gjNNYIzQgGT69a2VCUjWobOstYwn7m3l7/xuS185VMbuWFTO6EJIvHDQYtlLWG+/NgGbtu+jI2rG3ng1hV8+bGN4+fdlMVDrNxUf3HZmtpdqkwC+dI3qHkG5uNXIJeaxSucGjeTQJ7aj3zhL3Gf+TPki3+JPLUfdzZuWmMtaJ//Q4i3ep2rGx9Fe/LvIyONiB0P1vwQcdtnpp3aKPwhxB2fQ9z9lBcqAdDQjnji7yBWb6u6Ya/L9EHT+TNEl5/3uoRM1t8OLHQdYQVmJdxBBCNoD/zm+Y7UZWu3fxbCDWif/0Nvu+OF4q1rPdqX/j8Qv+xMXz7rpSzWc2r/FV/vrMql4OgntdeSQ5VztRRFmRVTvrs8deoUGzduHP/vF198kc9//vOsWLECgP/5f/6fefTRR2f/CpVFKV+0GRzK8OZHA4ylCjRE/dy+fdmCzgOKR3zcc2MXr71/mhfePMGa7gYevmMlPksnGvYRCVpX1OlrjPl54t5eXnr7JNm8V0Q2RH08cueqirNL0ZDFdeta2HWwcp9+OGiycXUTP3714sDVvYdH2LGxfUZb6yIhHxtWNZFIF9l/7OKWQ7+l88S9vTW3yU2kULRrhkmMX+uREVZ1xmf0PYyELD57/xoOHh9l16EhHFeyrqeRretaiE1w7mu6Z8/CQYtw0KJlgq6iskiEG2DDrbD/sh0OuoF44GtQKuAe/dh7p721G7Fys3eGaoL5TnLoFKJx/sZ9uKUCHPsE96X/cfHB1Ajuyb2I+38Td92NaBOcC5uMMEwvTe2pP4JiHvfVv0a+/zNv7Z4vIXZ+ydtidz4IQtz+pLcNcgZx2iIUg+vvR6y7EVzX+3OYbnFm+dFu+ZR3PXUCIID53abZvBztq//Mi07vOwChBm/+WNMy78yeLwA7v4S4+VOAC1agKmhBlkvIfNqLKa/XYV1s8eHlInW3NgIyl5z9uVWKco2b8t2vpmkV5x7eeecd/sk/+Sfj/x2Pxxkbm51tD8riZjsuB46N8up7Fw8AnxvN8cOXj7Dzpi62rG3GmOMo5lp8lsHm3mZWdcboP5fBdlya4gGCAbNqe9lMWKbOqs4YX358A4WCff7cm17VPQn4TW7buox1Kxr4cP9ZCiWH7vYorU1BXnr7ZMW8K2eimLspiEf93HVDJzdu6SCZKuCzDKJhr7CYbriEEGLCWVqmoSFmUJPmC2UkXlG5Y1MbG1Y3IiUEfHMzu0tZGkQgjHbXF5A91yF//TzkM4iu9YhbPgWlIu63/9nFVDNAGhba0/9o4hedxkDc2SAySdxf1h4xIn/5XbSutTU7btP/RAL3Z/8Fzp24+PqvfQvaViIe+C0vKdEwrzgUQQjtyhMQG9q8SPWeLXB8V/V6OA6R+QtFEZoODW1wx+cQ5TzoZtXgZGFYEJ3g/KkAzp5CrL0Jue/NGusCsWJj9eMLyRfwum915n/NezCNolwDpnynuWHDBp577jn+4A/+gL1793Lq1Cl27tw5vn7y5MlFMwtKmVvZfJk3PuyrufbGB/2s6ooTC89uUZXJlkhlS6SyReIRP5GQSShQ3d2xLB3L0olH52ZPuxCCSNAiMsE2NIBgwCQYMGlvCTOWzPPyu6d4+5PqeTRrVjTg913Z9yoUsAgFqLm9bjqCfoPr1jbzxgfV87gAtq5vRZ/qNiAgnS1xvC/JrkNDSCnZ2NvM2hUNMw68UK4+IhTzEuFWbPRu/nwBKBZwf/jvKgoqAOySF27Rsbr6nAh4nZWm2Y+vnlA+BaXaKZWU8pBLe4N8r4CUElLDFQXVuLMnkD/+D4jf/BdzkjJXdS2lIuRTyPSod9Yp1ADhWMVWPWH5oX0lYueXcLOJyvS9UAztyb9fnf43D4Sugz6zbpIwLGRLJ6KlE3nuBAxf+jNSIB74LWQovrg6P6EYYtt9yA9eqF7rWj9vg70V5Voy5aLqH/7Df8jTTz/NT3/6U/bu3cujjz5KT0/P+PrPfvYzbrrppjm5SGVxyRfK2Hbtc0C245IrlCfc0jVdo8k8P/jFYVKXBCU0NwR44t7emp/nQkd1tgZ32o5DOlvm8MkxRpIFutsjdLVHphRXbhoaoYCFWWPLnM/SueW6Dkxj/rt6tQgh2NDTxMETo5wbqbyh3bCqkabY1AvVTLbEj149XPE6r79/mk8OnOMLD60lEloc87OUxeHSQaUyOQTnD/1fTn7wAtqTfx/3mT/z0gHHX0AgHvldmO9o9clatzNp7V4uPYocrJ5XOM4u1y/sZpHMZ7wtdG//yEvyA/CHEI/9PixfU7HlUGg6xFvRPvv3IDPmJRiGGxHxFkRkeomki4Vo7cZ9+a8Q1z/ozac6c8xL3Fu+BtnYPu1tnjKX8uZ/ObaXrhiKz+qwbWFYcMNDIATy45e9vydCwJob0O5+qiJ9UVGU2THlouqzn/0sP/vZz/jJT37Cgw8+WJEECBAMBvmbf/NvzvoFKlfOdlyKJQdNTP+MSi3aJDcK+mzcSJyXyZV49uUjFQUVwPBYnl+8dYLH7149foYrVygzlixwrC9JJGzR0RwiHDAnDDeYjOO49A1mePblI7jni7X9R0fw+wyeengdTVPoDoWCJg/f0cPx/iQf7TtHqezQuyLOtvWts1p8zoZwyOIz965hcDjL3iMjGLpg67pWGmN+gtOYjXb6bLqqMANIpIscOjHG9RvbZq3oVa4ydbYrAZBL4ToO2m/8U+Tx3chT+xANbYgNtyJ9fq+7VSOUYM4EI+APQ6FGiEsg4q1fIXlsFyIYqX86Rmhgzn3anBw4XD1XrJBF/vDfIX7zX3hb7C6/tFDM65jUTeBbOkQ4jnbfV5BnjiH3v+1F+bd2Q2MH2jQKRSmlNxLg+f9yMfjCH0Lc9QXovX5WB+eKUAxu+wxi6z1e4W36vGHF85ROqCjXmikXVf/8n/9z/vAP/5D77ruv5vof//Efz9pFKbNDSkkiXeSj/Wc50Z/CZ+ns2NRGV3t0RsODSyWHbKGM47qEgyaZXPWB3XDQnNV5QLm8TSJdO0Xq1Jk0uYKN32eQy5d56+P+8a/t2OkEpwZSrFvZSHtLaMbFSzZf5rnXjo4XVBcUijYvvHmCz963ZkpfbzhosWVNC6s740gJPp8+nky42ISDFr3dFj2dMQQTR8TXUio57D40XHd9z5FhNqxqmlKRZjsuuXwZ15Ve1+8KCmRliQiEvZu/Wulxmo4WCCFizYhtO3E33AzD529QRwaQ8VbEbU8gujZUdL/mTLQZ8fDvIH/0/6scFKvp3uPRpivaEiYdG3liN6ze5iUAJqpT88T6m2eleJvwOnJp5Fs/qr3oOsgD7yBufWL6r5tPe3OWXNuLP79sK+FiI8JxxJrrkd0bQEqEfwaBOKkR3O/+aWWntZBFvvjfvY7V8rWz+ndXGCbEaqfEKooyu6Z89/snf/In/P7v/z7BoErVWipGkwW+9bP9lMreL/uVy6LomsbZ4Sx+n0E4aBIKmlM6J1MsOew/NsIr756iKe5n503dPP/GsYrQBUMXPHb3qvF5UbOhVrz3pcq2N+h1KJFnVWecX37Qx2jy4laYI6cSrOtpYOdN3TOaUzSWKlCus9VxcDhLvlieVhE5nW5PLZlciXzBRuIFPYSD5px1fKZzfqqC8HaZ1F0WYvKhnHhnst7ffYY9R0awHZd4xMfdN3axvC2M31KDfK9aoTji9ie9IIbLiBseHj8LIu0y7HsL+eolzxs9g/zJn8Ptn4XrH0SYc1uEC01Ddm1A+8ofI3e9hhzu92Ytbb0HGWtBu9ICQdMRsRbk2z9Ge/C3cF/9FowNXlxfsQlx22fmvvPg2F4Mdx1y6DTSdacevQ7IkTNeMXzu/NZGfwhx5+dhzY5JuzVSSijmvO/PAnRdxBWEj8j+Q5UF1aVrbz4Ltz3hFVZzXCgrijL7pnxnIq8wpUyZX8WSwxsf9I0XVFvWNtMY9fP8r46Pn4eyTJ2H71jJimXRSc/1pDJFXnnXO3A8kijw4b6zfOqeXvrOpkmkCrQ1h8ZDCGbzJn+iWHBdE/gtA9t26R9MUbJlRUF1wcHjY2xd20qwffoFTb2C6gLXnZ9/F47jcmY4y/NvHB8fwhv0Gzx420o6OyJYC3Auq1R2cF2JZeoVSYOWqXPduhZOD6ZrftyWNc2TpjFm82V+8vpRzgxdvPlIpIv86JUjfHrnanq75/+guzI/hG54UeuRBuSvfuAVEbFmxK1PIFZuuZjclk0i3/h+zdeQ7zzndXDm4R16zbSgpRPu+RLCLoFhebOWZuG1hRCw5S7kRy/jvviXiJseR4TjyGIOEYgg/V7Xbs4ZJjQt884R1brOZb3TK6gudGvyl/yMKGSRv/i/ve1pq7dN+LHy8IfIQ+973+vrH0C09yDm+zzdTPUfrr820o+QEjk2qIoqRVmCpvV2rzoDsXSUyjZrVjSwqbcZIbw5QS++daIiYKJUdvjxq0f52qc30txQvwMppWTXocp3KfvPZfjhy4dZ1hqmvSnIlrXNBHyz16G6IOA3WdUZ49j5gbmX2rquhWDAxJWSpoYAr79fO5EQYPfhIZa3haf9d7gxVv8dyVDAxDdPHZNkpsgzLx6qKOJyBZtnXznCVx7fSGvT/HWQc/kyQ4k8H+4dpFB06OmMsWFVE7HIxS2Wy1vDLG8J03/ZwOCmuJ/e7oZJ/xxSmWJFQXWp194/TXtziLDaCnjVEoEQYs0O5LI13tYwoSPCl6WV5dNgl2q/gGNDNjmv2568dLk5GDIdbUI8/HVvqPArf4UUAnQTNt2OuPXTs//5zpOuC9mE11XRDcT9X0N+/99WD1g2fYje66f32gNHKguqS9feeAbZvqpmkSSTw14xlr44l0+e3o/svR7t/q/O6/wrmUmMd8vwhxGBKZ6Fau6svxZp8gIsjn6M7Fg9rUJVUZSFN607wrVr1056MzQ6OjrhujL3yrbD8FiBNz/qHz/3FA6a3Hl9JwdPjFYVKB/uP8e9N3fXPePjSkk6V/vmZeBchtFEnhs2z83AzYDP4P5bV/DWxwPsOzqC60oMQ+P69a1s39g6nqoXDfkqtiJezrZdpJx4W1otoYDB1nUtfHKweuvLPTd1zepWx3ocx+WTg0N1u2Lv7hrgoTt6sMy571blizbv7Brg4wMXvx9nhrN8uP8cX3p0PQ3no+zDQYvH7llF39k0nxwcQrqweW0zKzqiU4pUPzNU4+D/ealMiXJ54g6isrAcxyVXsMfPws102+uE3Qd9kl9f+tz/25wPwvLDmusRy3uR505BuYho74FgbGZneqZAFnPIo58gX/+2l1AH0N6D9pm/i/vSNy5u2Yu3oj36exCdZrds4Ej9tdEz4FSf15WOjfzopYqCCvCSH31ByKa8Qsf0QTDiPTYHZLmIHDiKfPkbF8+4LVuD9uBvIho7Jv140bMF+cva86PEtp3Ivb+CSBNigsG9iqIsTtMqqv7kT/6EWEzNNljskukSz75ymEt3bGZyZX7+5nE+c+8aTp1JVRQgI4k8tu3WLap0TWN1V5xjp6u7RQCdbXO7/SwctNh5Uxc3bWnHtl1MUycUMCuuNxKyWNUZY9/RkZqvsWlN84TDcDO5ElKCpomKEA+fZXDrtmW0NQV5d9cZ0tkyLY0B7tzRSVtTcF66t9l8mbMjubrrQ2N5yrYzL0VVJluqKKguKBRtfvVhHw/dfrG4Cwct1vc00bM8hpSMpzRORa0ZZBdomkDTVdd8scrkSnx84BwfHzhHqezSFPdz9w1ddLSEanZ2nUIZmS3jpIoIv4EettAjUwiWCUQg1gzJGqEooRjMY9dirgnTB7EWxDx13uSZY8if/1+VDw4ex/3hv/cGMJcKoOtepHgoPv1P0LSs/lo47nV/LpdPI/e9XfmYEIiHvg5DfcgzRxHBKLJcQIQbcWPNaLMw4FaWCpBLIQePeR3TSKPXsbu06Bk4jPvt/x3tK/8EMVmBGWlEPPkHyOf+48WzVUIgNt8JpgVnTyJu+fSiDuxQFKW2aRVVTz/9NK2trXN1LcossG2XX+8dpNYROClh/7ERersbOHD84rt9rY3BmnOULrWiw0vVy+Yr30HUhODWbcuwrLn9BWAaOvFI/c8RDlrctKWdY30JCkWnYq29OUhrY+13LXOFMsf7krzzyQDJTInGmJ87d3SyrDU8fu4n6DfZvKaFleeLA0PXZjXhcDKZXIlY2GKgOvgLgFjEhzGL800mcuR0ou7a0dMJSmUH23bRdTF+Az2TLZLtzSF0TeDU6M5tWNVIcB6//8rU5fJlfv6r45w6c3Fr10iiwA9eOsynd/bS2x2veL6TKZF6+SjFQxffDNEiPho+txGzZeLtVCIcR3v8b+J+999A+ZKzlIaF9qm/6d2cK9MmcynkL79XezGfRg4eR9twyxV9DrFiE9IwvdlJl6/d/HjtwbRy/P+5+NyNt3nnkFasx335m96MswvPWnsD8q4vIq6gsJKFLHLPG8g3nvHS/nY8hLvr9arrAKCQQR7bjdi2c8LXFLrhzfX60v+KHDoNpSIiFEMe/Rj5yl9DazeibcWMr1lRlIUz5Q276jzV0lC2Hc6N1u9qjCTyFUNrNSHYtr4VfZJ472jYx1MPr6O3Oz6+ha61MchTj6yjYRpDYedSQ9TPlx/bwPYNrYSDJg1RH/fc2MWnd/bWPH9TKjv8eu9ZXnjzBMmMt71xNFngR68c4dCJURyncotZOGgRCVnzWlABHDmZYN3K+nNQbtrSgW+Oi9oL6gXWNET9fObeNXx84BzPvHiIH750hMMnx6qK8KkKB00+c18v+mXdxeaGALdtW7ZoBiYrldK5UkVBdanX3z9F5pJtxNJ2yb7fX1FQAbjpImPf3YOTqj1KoUJLF9rX/hni3q8gNtyKuOdLaF/759Deo35nzZRjw3D986n0HbzyzxFpRHvyD7wI8XECsXUnondH7T+7QBix7qaKh8Tq7dDShfvCX1anEx76NfKjl72UyJkaHfQKzAs/95o64NyJuk+XJ/ciXafu+vh1axrE2xCtK2C4H/fFv0Se3IO48wtoT/xdRFgF8SjKUqTS/64yhqHREPUxPFY9eBW8rkY2793YhALeUNpYZGoH/uNRPw/f3kO+aONKic/SZxRTPleEEMQjXqfpxs3tIME0tLpdtFyhzAd7B2uuvfFBPz3LYxUF6EKJRnzsPz7Kfbd088tf940nEuqa1yWc6p/fbOjtauCdT85UPGYYGjtv7uJnvzxeEYE/8FqGtSsbuPem7mmfqdF1jc62CL/1mc30n8uQyZVY1homHvGpgIpFrF64CEAyUxpPIwVwsiVyH1/8u2S0htCCJk6igJMoYI/k0KMT//sTmuZti9u2EybpEChTJDQIN0BmrPZ64wRb96b6KXQDuazXi6JPj0KpgIi3TngWShgm3PAQ8vAHlWEZF0JJapCfvIrYdq+3TXSaZLmI++ufVz6Yz0Ao7g2ZrnWN8dYpb9sTQnizx+54EnHDg4CAUBQhVDiFoixVUy6qXFcdDF8KTEPnxs0dHD6ZqLl+4+Z2dF2wfX0bAf/05xxZlj6lrX6O4+I4XqjERGeZ5kKp5DCa9GLfC0WH1d0x1q5srBoAnM6Uam6TBK+LVSjai6KoWrk8yuvvnyaXL/PQ7SuR0nuTwzA0HMclPMH5o9kWCVlsXN1UcXZtfU8juw8N15wpdujEGDs2ts0oqEDXNWIRX0WqoLK4TdTFFYLKzqPtgu1idcUI3bSc8mAGJ1XEt6oRPWLh1hgursyDUAxx82PIl/+qek03EKu3zsqnEZrmJRtOY3ueiLWgfekfI/e9hTz8a29Q9ERdNbtUM/RiShy7qrCU+99BbLkL+fp3al0dYtPt0/40wjC9IlZRlCVPHUy4CjXEfDx8x0peeufUeIS6YWjcf8sKGuN+fObc/LFnciVyBRvbdnFcl+N9CUCwZU0L0bA16RbD2ZAvlKvS6QaGMnyw9yxPPXIxnQ6878lEtEUSZxsJWnzmvjX8+NUjnB5Mo2kCAfR2x7nnxq55LVoDfoO7dnTS2x3n13vPUijabFzdxPdfPFT3Yw4eH6WjJTxv16hcJFMj3mF41/G2WsWa5/Sd8Pam+mfh1qxoqCi6hKlhdscIbG1n7Nn9cEl4jhYyafj8pjm7zlrc5DAil0L2HQLTQnStR4Yb0K5g0Oti4BZtEALtsjfDZD7jBU5oGgSiCMP7sxFCQO8OL/xh12sXP8DyIz79tyBSfysynB/KnE16BY3p84q0yZIap0HEmuHmxxBbdyKlC7na200B8AXAmOGbTqYf0bkOOXj84mPDfaDriA23IvdfEpqh6YiHvw6zEIyhKMrSpYqqq5DPNFi7spHOtgipbAkBhENWVWLebHFdydBYjp++foxE2jsHYRoaN23poFhy+B8/3svnH1zL8ra5H2aYzpVrptPlCjZvfzzAA7euwBxPpzMJ+AzyNTosTXH/vJ+dqsfbChfmN5/YxGiyQKFk09IQJOg3CCzA9stgwKS3u4Gu9giOI7GdSbrY6mjLvHPtMmKk3ztrcuGd/EAEcfdTyJ4taIG5KXLDQZNP7+zlR68cwb2kDRyP+rhrR2dFQqUW9hG5cwVj39tbUVABuNkyqReP0vC5jWgzjGOfDpk4B7/6Ae6h9y8+hkDc8zTuxlvR/FOcQbSIOOkipdNJ8rvOgoDg9g7MZRE0n4Dh07ivfhsGj3kDdDffCTc+jDhfMIlQFO74HOL6B5Bjg15BFWuZtECS2STy1y8gP3n1fFHlR+x4ALbuRNQKn5ghoeneVkFAtnQi460X480vfd4Nj3jb9WbyOXQdrrvb+1rKF8/3yVe+hbjhIbSv/gly6JQ3p6u124u4N9XWZEW5lgmpDkuNS6VSxGIxkskk0ejVE8c715LpIv/jx3vHz/pc6sHbVvLe7jO4UvL0I+vn/DzMmx/28e7u2uekNCH4nc9tJhrytpNJKRk4l+H7vzhUETHvs3S++PA6WiYYiAxe0mKuUPZm8Zg6fkufldk8S03Zdnj5nVN14+y//NgG2puX3k3pUiZHB3G/9b95w0kvIz73D9BWbJyzz207DplcmVMDKVLZEp1tEZobAjX/7RdPJhj77p66r9X89esx6iR3zhZZyCL3v4N89a9rrmtf/ieI9pVzeg2zzUkXGfv+PuzLzriZXVHiD3TD//hDqvY+N3ehPfn3EDNMTZTFHPLVbyH3vVW1Jrbfj7jjSS8afg7IsbO4P/+vcOao94BuILY/gLjhwSsaCCxdxwuS+MX/DWdPeA+2dKE98DVo7h7v7inzT92vKYuR+omgXLHj/YmaBRXAh/vPsqm3mTc/6idftOe8qJqoZyKRFUm4QgjaW0J87YlNHO9LMjSWZ1lrmK62CNHwxNeZzpZ4+5MB9h8dQQj41D2r6RvM8Mmhqc3muZqYhs4tWzs40Z8kV6js+m1c3Tjp91KZXdJxkMc+qVlQAcg3f4DbvBxtFjsHlzJ0b/xBfN3kqaCyNHFSmrTn4T2/XBq5+/X617DrdS/mepFsB56MlJLC4ZGqggqgfDpFqT+JFWup7uwMn0aODMy4qCJXY47UhWv65FXE9vshPjdztkRDG9pn/i7k01AugT/oddVmuvXvwutqOrR2o33270Ex6/3+8IcQwbnfdaEoytJzdd/tKfNi4Fz9xK+xZGH8pno+eqJruht4v06nanVXHP9lBY6uacQjfrZvmHosfCZX4ocvHx5PWLzrhk7e3zNI39nM+HMuzOZ54t5eVnfFp/+FLDHxiJ8vPbaBg8dHOXIqgWVq7NjYRmtT6IoTIi8001VE9tRIp+xt66pnuA9RnkJc+TwwmuqfVxIBA20+tuC6DmRTdZdldgzhOt7ZoyVA5m1vy18d+f0prBXbIPFi9eLxXTDDLqbMpag5vwm873EhA8zd8GIRCHvBFXPx2sEIqEJKUZRJqKJKmZZC0aZQtJEwHqne1hSsGCZ8qVjERzZX9s7/+Ob+r1s0ZLFuZQMHT1SmNlmmzh3XL5+VIcVjycJ4QWUYGrGwr6KgutRr75+mrSl41caASynJ5Mrjfyc2rGriurXNaJpWcX5mJrL5MqPJAnsOD+FKyabeZlriQULBa2Nb5UwJTUfGJhjSHmmCWQwOuBJa0CKwpY387uoiIHpPD9o8dDml5Ydlq+HoxzXXxYrNXkLbEiGlhBpBIRefAIg6/zavYKucsPz1SirPHG39UxRFWSwWx29WZdGTUjKaLPDKu6c4PeilLbU1BbnvlhWs6orz5kf9FeeSLti2vpWPD5zjgdtWEp6Hm+FgwOSem7pZs6KBD/aepVCy6emMs219S1Wk+lQ4rku+YKMJQcBvIITg1ODFtKl4uP5MMPDOm106m+dqYtsOA0NZfv6r42TOx1+HAiYP3b6S5W1X9o5xNl/ilXdPc/jkxeL44PExutojPHJnz1VbpM4GYZiw/mbkBy94HYLL1296dDyQYKFpfoPwnSswWkJk3+3DzZYwmoKE716JtTyKmIdkSxGMIm54BPf47urvVyCCWDU7EeLzRQua+De1kvnliZrrgfVx+GRXzTXRe/3MP3EwCg3tMFZjp0DbSghMvdMji3nIJZGnD4LrIDrXQTiG8KsUUUVRFi9VVClTksqU+PbzByhecgbi7EiO7zx/gN/8zEY+98Bannvt6PiZGk0TXL+xFZ+p88idPTTE/PO2fSsUMFm7spHujiiOK/Fb+ozi3FOZIrsODXHwxBiaEFy3tpm1KxuJX/Luecl28E3Q/aqazXMVSWZKfP8Xhyq2dWbzZX748mG++qmNNNcJ+rBtl0LJRghBqE6Yx9mRXEVBdcHpwTSnzqTYuHr6wzyvJTISRzz++8if/1cvNhtACMS2+xDdGxb24i6jhyyC13fgX9fkdVh0DT00f0WzMEzceCvak38f9/XvwtApQMDKTWh3P4WYo3NAc0UIQWBDM/lPzuAkK7d5Gi1BzJWNsLf635148LdhpuepABGKoT3xt3Gf+T8q5zvFW9Ee+5+mfA5JFrLIXa8jf/X9i48BYsvdcPtnrih4YqFIx754xtH0zVlgx5Svp1z0rkdoNVMZZSbhpT4e3+2dTeu5DsJxhDX1bfKKci1S6X+XUGkytbmu5P09Z3jzo4Ga65t6m7j3pm4KJYdsvozjSoJ+A9PwtoBd6TawhZDKFPn28wfGOzAXNDcE+NQ9q/nvz+4ZLyY+c28vz712tOZsnnUrG3jgtpVL8nswEdtxefXdU+w+PFxzfcPqJh64ZUXFLDApJYl0kQ/2nuVYXwLT0Nm+oZXe7nhF56lUdnjutaOcHKh9zqWtKciT969ZkDj5pcQtFhC5BDI5DOUiorEDGYigqbMhNUkpITXixWdrGjIYnXGUupQSMgnIpZBO2btxDUbn9WbaSRXI7x8mv/esV2htbce/pgk94vOiz0cv3DRHZ/WmWabHvIj6xDlobEfEWqYVfiHPHMX91r+quSY+/bfQrqSbtgDc5AgiM4Y8sdv7d9i9ERrbIdYy7+dEpWNDYgj33efg5D7wBRE7HkSs3jb+ZyTTY7g/+j/h3KlLPlIgHvpt6NmKKOfBdcEKeNH7C0TdrymLkepUKZMq2y4n6tzgAvQNpinZDpGQRWQe32GeK47rsvvwcFVBBTA8lufscJYn71/DD1/yZvF8uP8s99+6gl+8dbJiNk9D1Medl83muVqUyw5nR2qnywEMjeQolZ2KomosVeSvf7qfUvlCt7PMK++e4tDJMR67c9X4WSnXlXXTJMH7+zjRkRHFo/n84GtHNLSPP3Z19kxnhxACYhc7oDP9XknXgcETuM/9R28ILiB1A3Hz4968pknCFKTjQDbhBTtohjePaQbdGT3qJ3TjcgKbWxFCIALG+E28CMW8Qq9r3bRfdzIi0oCINMzotaVdxv3wpfrr7/0MuXwdIrA0RjS4qRHY+yvcd348/pj88BfQtQHtwd+E2Dx3QUcHcb/1L8E+/7stn0a+/A3k0Y/QHv46WAFv23BFQQUgkS/8JdoX/hfcZ/4MpAtNyxD3/gaivWfBO2+KsliookqZlK4JwhPMXAoGTPQlkow1FYWizcE6wRsAe46M8PjdPfz2Zzdz+myaXL5MS2OArz2xib5BbzZPV3uEpnjt2TxzpVi2yeZsTp1JUbZdVnREiYTMio5OoWSTL9jYtovP0gkFzBltjTQMnYaoj3OjtQureNRXUVCVyg5vf9x/SUF1Ud9gmtFkfryo8lk663saGDhXO/xj7coG/L6rr1BVrhLpUe/G0y5dfMyxkW89Cw1tiHU31f1QWcgiD7znbX0rnT+r2bwc7dH/ybuJnWZnQ2hiXrdSXjHX9grKenJp7zlLhEiPVRRU407vRx58H3n9A2jzFIIiiznc179zsaC61Ik9kByGcANy9y/rvQLy5F5oWwGDx2FkAPnMnyG+9L9C+6o5vXZFWSpUUaVMyjA0rt/YVpWod8FNm9vxz0Oy33wRCLQJzkHpmkDXdWIRk1ik8h26xtj87jkvlRwy+TLJdIFzo3ne/Kh/fO0NvAJk501dhAIWyXSRl945Ob6tzjJ1bt3awYbVTdOOPTcNjRs2t0/wd6KjokNXLDkcOZWo+3r7j4/Q1eG9Gy+EYFVXnF/vPUsqU6p4XtBvsKm3+aoq4pWrizy2q7KgunTtrWeRnetqnmMBkH2HkK/8VeWDw/243/1TtK/8MUSbZvtyFxfTh1i5Bdl3qOay6FoHVv0Y/sVE2mXk/upByOPru15HW3vjnM3uqlLMw6l99a/n+G7E1p11/+56r5GD7o2I3u3gOMgjH+L+8nton/7biBlulVWUq4m6M1GmpCHq584dy6se37a+hY6WqyuRKeA32LK2/i+6betbMI2F/6dTLNscOD7CX/1kH44rKwqqCw6dGONEf4pMrsQPXjpccU6pVHZ4/dd9HD45xkyOVjZEfTxyZ09FR8rQNR68fWXN4nKijpipV3aeoiEfX3xoHTdsaiPoN/D7DLatb+FLj26YUYqjosybsyfqryXO1UxkBG/O06XhDBUK2bqFxtVECA2x7kaodYOuG4gbH0GYS6PzJgGZr91tB87P7ZrPfcyi9iiF5uWIR38X2nuQA0fQnvz7iFs+BVqN3QCrtiKki9zzJvLIh4j1NyO23OOdQ1QURXWqlKnx+wyuW9dCb3cDfYNpXCnpbIsQCphLvkt1ITYdIOg30TTB2hUN7D0yXBWX3tMZo7Vxcbwjl8mWeemdU/R2xyfsAn1y8BzRsI+xVKHm+tsfD7CqM0YkNL1ixTIN1qxoYFlrmEy2hAQiIYtQwMC4rEgK+A029Tbx0f5zNV9r4+rqd+CjYR+3b1/O9RvbAO/voDGDrYqKMq86VsO+Oh2Kxo76M8IcG0bP1H/dgSOw8dYrv77FLtqM9tQf4b72bTi513usfRXafb8x/2eQroBmmLirtiIPf1D7CZ3rpxUzf8UCYcS6m5H73rz4WEs32m1P4L74l3C+AJQAPdchHvkbyJ/9BeOJTC3d3hms958f/3A5dBqWr0EsU9v/FAVUUaVMg8808JkGDdGrJ1Y1lSmy+/Aw+4+OgIBNvc1s6m0mGrL47H1r6D+XZu+RETRNsG19K60Ni2f47P6jI4B3BimXr3/OQNc1huqcfQLIFWzK9szeMTV0b/jxZN0jQ9fYsbGNY31JkunKdzW3rW8hWmfIq65raiaVsqSIlZu8gcKl6jcxxO1PjodOyGLOe47QIBT1/v9IE6RHar9wc+dcXvaiIYSApmVoj/0+FLKA9FLqJgn4WIzE8rXIaDOkLktJ1XS0255A+OZvK6MwLbj108jTB8b/jmm3PI77/P918fzeBcd3eQXsqm1wci9iy12ItTfi/vDfeeumH4IRyKeh/zAycQ6xhApeRZkrqqhSFh3Hccnmy+SLNpomCPoMQnNwY53KFPnOzw+Szl7cQ/72xwPsPzrCFx5aRyRksb6nidVdcQSiYpvblSiVHLKFMplcGdPQCAVMwkFz2ofQL6QTDo3mWbOigeP9yZrPa4j6qs5+XcrQNQx97nPhomFvS9/pwRQHjo3is3S2rW+lMeZX8ejK1SPShPaFf4j7k/8EySHvMdPnFVTL1yLtspfC9ub34fQB8IcQ2+9HbLoDccunkL/479WvaViIlZvn9cuoR0rXC4xwbC8FTtMhGEPosxseI/xB8NeedbdUiHgL2uf/AfLtHyMPvudt/VzWi3bP09C0bP6vJ9aM9tT/G3n6IPL0AWQpX11QnSf3von2lX8KxpeRCNxv/glYPsQ9T4NheYVZpMk7g3XkI2T3BoRQOwmUa5sqqpRFJX8+ee+ND/rGY7XjER+P372KlsbgrM31kFJy8PhoRUF1QSJd5Fhfgq3rWgEwjdm7Wcjmy7y7a4BPDg6N76oIBUyeuLeX1sbghAEZl1vdHWffsRHOjea4ddsyQgGTbL4y2UnTBNdvaMcyNXyWXjG8+YLr1jYTnCDdcTZFQhYbVzezdkUjQkMFTihXHaFp0LYC7ak/8rZUOTYEwxCKI3QDOXgc99v/+uLZqkwC+cYzyBN70B76HdjxoBe7feEHRCCM9sTfgUjDwn1R58lMAnfgKJomcN9/Hs4cA18AsfVe2LYTEV74a1xsRLwV7v8q4vbPen+mln9Bu24i2oTYdBty3U3Ij+rH11P2Oq0i0gjpMaQ/jHbP07gv/9XFNwsAYs1oj/wuamCDoqigCmWROXMuwyvvnqqYU5RIF/nuCwerkuCuRL5os3+C2PR9R0coFGc3uvdCIffxgYsFFXiF1jMvHiSTm97X194cIn6+A/Xae6d46PaVrOqMcaHubG8O8fQj62mI+oiELD7/4FoC/sr3UXqWx7hhc/u8n1UyDE0VVMpVTYTjiJZORPtKRLQZoRu4uTTuq9+qHVZx+gBkxhC3PIH22/8K8fk/RPvSP/ZS/zpWIeqdxZonMpvEff17COniPvf/9woqgGIe+d5PcX/y58hs7W75QnNLReTYIO7B93E/eQ05eAI3WWeb5RwQps8rZmLNi2YbozAMaO2q/4RQ3OtIgTcr7e6ncN94prKgAkgO4770DW8roKJc41SnSlk0coUyv/qwOsEOoFR2OTGQHO8eXSmBF41ej65psz7tPpsr897u2gfRS2WX/nMZotNItrtQKL39yQAHjo3yk9ePctOWDu64fjm6ruG3jIoiqrUxyFce30AqUyJXsGmI+Qn5zapCS1GUuSEKGeSZo3XX5fHdaMt6wef3OhyLiEycQ7QsQ773U6iVFjpwxLvhrhMXv1DcUgFO7cP92V+Mz2iSAGt2IO/8AmK+Is0XGSmldzYq3uqlUl5G3PI4hOPe/60bEI55wRS1DPd5W0JnMKRaUa4m6m5KWTQcRzKSrL2/G2BgKMvWdbPzuQJ+k23rW3nhzRM117dtaMFnze4ZAceV5Ar1u1+XJw1ORTTs496bV3Dr1mXezhJTr1skCSGIhHzTTvlTFOXKScdG5tJe+p9T5+fAPAYX1COlC7btRZhf2k0+8jGio6f+jTUgT+5DLOudh6ucOpEexf3Jn1d3Bw9/gGztRm67F823tM9uzUg+jXzj+2j3/oa3lfP0Ae9xK4DY8QDoJpRLYJ3/fWFPsnNDxaoriiqqlMVD0wTxiI+xVO0fzq2Ns/uLb8WyKMtbwvQPVc4SWdERYdkczN7SdUEkZNU8xwXedr2ZMA0NU81uUpTFrVyAE3sQa29A7n+n5lNEz3XzfFEXeUVfCs4cRw4c8Tr1W+6EaDPCMMHn9wquiYrC+YwInyJ55KP6s8E+eRVt7Q1wLRZVEsincH/6nxHX3Y24/gFwbKTrerHrfQcRa3dcfH4gDELU7lIK4a0ryjVOFVXKohEKmNy6bTk/++WxqjVDF6zumt1tJeGgxWP3rOLsSI5dh4YQAraubaG1KUgoMPtpg+Ggxe3bl/PzXx2vWgv6Ddqar8Ff7IpyrdBNZOIc2ubbkWeOVW25Erc/iQyEF+S4vywVYGwQdr2OTI8iWrsRy9fg/vQvEHd8Fro3ItZcj3zvecTaG5H7367xKgKxYuO8X/ukamxtG5dJzu/83cUkGEZsvA35q+8j33++6tsgHv09hHVJ5zQYRWy6A7nnjaqXEhtv8yLWFeUap4oqZVFZ0RHhlq0dvLd7ENf1fswH/Qaf2tlLJDQ3hU44aLGiIwqCOQ9s6Fke5a4bOnn744HxMI7mhgCP3bWKqNqWpyhXLWH6ENffj/vj/4i450veDKb+Q16k+srNyHAcbQHOpEi7jDzyEfLn/5ULFYY8sQdpBdAe/T3c17+DePIPINwATcsRLZ3IsycuG1QsEA9/3Qs3WGy61sPeX9Vea+uuP4z5KieEButvQe7+ZXX4RGs3onNt5fMtP9z+WfCHkB+/4kWpGxZi607EDQ9VFmCKco0SUtbq5V6bUqkUsViMZDJJNKoOXC6Uku2Qz9ukcyUMfeZznBYr23HJnZ/DpesaQZ8xb5HmiqIsHFnIIfe+ifzldyHWDC1dUC4iOtcjrrsL4Z/ZFuAruqbkMO5//8e1t/S1rUD07kB0rUcsW40sZL147XwasikvnCLSgOi9HsJx78Z7kZGJIdzv/O+QTVStic/+PcTKTdf0fCWZGkUeeg+57y0QGuK6exCrtiLqRPhLuwy5pHfeyrAgFPO2h84zdb+mLEaqqLqE+keqKIqizCVZKkAuhTx3CqREtK3wIqsX6J1+99gnyGf/z7rr2qf/ljej6LLOhXTdyiCLRUyOnsF9+Rtw+qD3QLgBcfcXYfk6tPDiSitcCFK6kM8CIJbINj51v6YsRtdm31tRFEWZESklbrqEkykiiw56zI8WNNFUNP+UCMvvDYBdLJHp9UInLpCyZtdiqRRUAKKxA/H430TkUt7XawXGo9RlJgGlPGg6+MMI/8KcbZWOA+lRKGa9a/SHkeEGNGvut4ULoakzUYoyC9RvQUVRFGVKpCspn82Q+ME+3Fx5/PHAljbCd65An8K5R8dxyeTKnB3Nkc2VaG8OEQ1bcxIOo0xONHciEdRMbIg2I4NRxBKdPySzCWSphHBthG4g421oujcqQ5YKyIEjyFe+eT7MQkDPZrR7voxomN+CV5ZKMNqP+8Jfwsj5WY2mH3HLp3A33IJ2fl6UoiiLmyqqFEVRlClx0kXGvrMHWa6MqM7vPoveGCB0w3LEBEO1HcdlYCjLD18+jH0+qAWgvTnIp+6ZmzAaZWLSH0LseBD5wQuVC0Igdn4Z0bwcYS6tEB3p2DDSB7kMnD6Ae/hDAMSGm5Ebb0fEmmG4D/mDf8fFYlLC8d24Q/8G7el/hIg2zd8FZ0Zwf/D/9cJLLigXkG98DxGOw4Zb5u9aFEWZsaXTv1cURVEWVLk/VVVQXZB9rw+3zgy2CzK5Mj98qbKgAhgczvHOJwNVjytzTySHvLCB+74Krd0QjMLKzWif+wNo70EsxRlOqVFIjeG+8tfI95+HxFlInEW+/WPc7/8fyOQQ7uvfo2Z3LjOGHDg6b5cqbRt5+mBlQXXp+ts/QqZH5+16FEWZOdWpUhRFUabEHs3VXZN5G+lMnHt0diSL7VwsnFoaAqzracRn6QyP5ckVykTVIOt5I3Np3Je+AedOQrwVseFWb4jr2Fncn/w52lN/BKGltfVPShf31D5EPuUVU5dLnPMGAk+UJntiD6y/ae4u8lKuDUOn6q8nzoE9ybk3RVEWBVVUKYqiKFNittc/zK5HfQhj4rEHqYzXyRIC7r9lBcWSw54jw+QLNstawxRKDiHHRZ/jeXHKecWcV1CBV2y8/aOKZdl3ENG0bAEu7ArYZYR0kcd21X2K3P82Yu0NyIHDtZ8Qa5mji6tBN6Cxo/56uAHOnwNTFGVxU0WVoiiKMiVmaxgtZNXc5he+cwX6BF2mXKFMLOKt37p1Gcf6khw9nRhfP3o6wfH+JE8/sp725vmf13RNmmw+01IcjKsb4A95aX4TPae1p/aaEIh1N87NtdX6dLoBKzcjDcsbqHv5+k2PQqRx3q5HUZSZU28HKoqiKFOiR300Pr0Fsz08/piwdCL39mD11B4WekGx5JArlOloDtHcEKgoqC5wXckr754iX1DbneaFPwhd6+ssCsTytXXWFi+h6Yi2lYi1N9R/ztadiNYuWHfZFj9NRzz2+1Bn8O1ckZFGtCf/nneebfwiNcT2+xG91181g+8V5Wq3BN+GUhRFURaK0Rig4XObcPJlsF2E30APW4hJtuxpQvDGB/185t5eTg2m6j5vcDhLsWQTUHOv5pzwh9Du/Q3cb/9rbyvgpWt3fb7yJn8pCcehcx0s64WBI5VrHb2I7o2IYATt3t+Amx9Dnjnmza5qXwmhOMIw5/VyNdOHu6wX7el/BNkU0i4iIk3IUHRpBoUoyjVK/dZSFEVRpkULmmjB6d14+n0GnW1hXn//NGtW1u8ECMGEseyLiZsZQxSyyFP74dwpaOlGrN4KkUZvW9dS0NiB9pU/Rh58D3liDyLcgLj+foi3IXyBhb66GRGmD5qWIR7+OvLsSeTeX3lDjK+7B9GxyospB0QgDIEworlzYS8Y0DQd4q1eYMj5x5bGvwJFUS5YIj/1FUVRlKXMZ+ncfWMX3/35AVoa6r/7vrorTsBaHL+apGNDPuP9RyCCuCQwQI6eQSSHcH/6F1DKn3/0LeSb30f7/B8iO1YhJjuztAgIISDWDDc+jNh2L2gGwlgc3/8rIXTDK1DirchVW73HTDUHTVGUubP4f+IriqIoV4WGqJ8vPboBIeC2bdWpcqGAyZ07OrGshU87k8lh5K9+gPvNf4H7rf8N+e5zyNSIt5YZQw4cwX31W5cUVOfZZdwf/0fIJBfgqmdOCA1h+a+KgupywrRUQaUoypy7+n56KoqiKItWNOwjGvbR1hykpzPGroNDZPJlVnfGWbk8uijmVMnEOdzv/ClkExcfe+c55IF30b7wv0A2ifCHkYlztV8gl/L+N8+BB4qiKMrCUUWVoiiKMu8CPpOAz+S+W4K4UmIsgtlUslxCJs7CvrcrCqpxiXPIE3uhsR3hOhO/ll1SZ2IURVGuIQv/W0xRFEW5ZmmaWBQFFQCjZ+DYLuSxT+o+Re5/C0LnU/HMOl01TR8PQ1AURVGuDYvkN5miKMrSJu0yMpdGlgoLfSnKDMhCDveNZ8C1YaJIbd0EM4A73I+48dGaTxE3PQaBxRdHLvMZ5FAf7r63cI994p0bs9VMMEVRlNmgtv8piqJcAenYkBxCfvgScuCIF6d906OIpuUIv5oxs2SUCzBwGFnIINbdiBw6XfNpYvt9aKEo8rq7kf2HEQ/8JvLDl2B0AGKtiFs/jVi5CWEt/NmwS8lsAvflb8KRDy8+ZliIT/9t6Fw777OZFEVRrjaqqFIURZkGmU1CqQjFLDKXRgQjyP7DyAPveklww33I47vg7qdhy50Iy7/Ql6xMhdDAH/bmTd3wMHSshjNHK5+zejuibaX39HAcVm+FfBaxfK338aa1KLf9SddB7vlVRUEFgF1CPvvvEb/1L70ZSYqiKMqMqaJKURRlCmQ+gzx9EOEP4L7y1975G0ACtPegPfp7uD/9z17HA5C//K43CFYVVUtDKIa44SHka99GvvjfEfd+GTbf4RXIQkNsuQvR0oUIxcY/RBgWRJZAVHc2hfzgxdprroM8uRehiipFUZQros5UKYqiTEK6DvLwB5Adw3312+MF1bjB47gfvYTYtvPSD0IO98/vhSozJoRArLsJerZ6HZwX/zvynecQph9x3T2IZasrCqolRTpQyNZfTwzN37UoiqJcpVSnSlEUZTLZJPJXP0B76LeRI3UKpZN7Edvu9TpXF4jFHaotHRcnVwbHRWgCTB09cO2erRGhGNpDvw3pEeSpA+ALILrWe12spdxxNExoWgYjA7XXO9fN7/UoiqJchVRRpSiKMplizuteFPMTP8+5JElN0xFNy+f2uq6Amy9jJwqUz6QpD6TRgia+1Y3ImA8jHljoy1swIhiBYGT87NTVQARjiLu+iPzhv6tejDYhWrvm/ZoURVGuNqqoUhRFmYxugl1CBEKVnahLaTroF3+kiru+CMHFF6sNIKXESRdJ/vgATqo4/njugwEi966CNQIjuoQ7M0oV0bEaHv9/IV/7NmTGAAE9m9F2/gYi0rjQl6coirLkqaJKURRlMoEIrNiEHDwB3Rvg1P6qp4gNtyDPnoTV29FufBgaOxZdrPYFbqZE5q3TFQXVBelXj2F1xWBx1oPKDAl/ENbsQCxbDcW89wZAIIzwqdh/RVGU2aCKKkVRlEkIfxDt/q/i/ug/oN3+JK4VgKMfgZTeNr8tdyJuehwME2GYCHNxFlMXyJJD8chInUUo9SUxW0Pze1HKnBNCQLjB+5+iKIoyq1RRpSiKMgUi1oL25N9Hjp1FbLodcfNjXlHlD3lBBou8kKpSdx+jV3QpiqIoijJ1qqhSFEWZIhFuQFwF7/ILv4HREsQeytVct1bE5/eCFEVRFGWJU3OqFEVRrjF6yCJ632qokfhurWpAjy6xrpuiKIqiLDDVqVIURbkGGe1hGn9jK5lfnqDUl0ILmgR3LCOwsRU9ZC305V3VpCtxMyXcXAkQaEETLWx5s8IURVGUJUkVVYqiKNcgzdSxOiLEn1iPLLsgQAtZXpiBMmfckk3xRILUC0eQBW+umQgYxB5di9UVQzP1Bb5CRVEUZSbU9j9FUZRFQEqJky/jFsrz+nk1v4ke8aGHfYu2oJKOi5QTJGssIU6ySPqV4+MFFYDM2yR+sA8nWVjAK1MURVGuhOpUKYqiLDAnVaRwZIT83nMITRDc1oG1IoYevnbPNkkpcRIFCkdGKJ9OocX8BLe0oUctNL+50Jc3bXa6iCw5lAfShG5YhtEYoHB4hPyus94TJOR+PUD0/tUIQ73fqSiKstSookpRFGUBOakio8/swRnJjz+WHEhjLvO25l2rhZU9lGX027uRxYvx7vkPB4g9thbf6gY039IorNySgz2ao3QyQeaNkxVR9sEdywjf3k3mzVOA9zXLsqOKKkVRlCVI/eRWFEVZIE62hJ0sEL6li4bPbSR0Syfo3ha88kCa8kB6ga9wYTjZEsmfH64oqC5IvnAEJzu/WySvhD2SxU2XyPzyZNVssNwHA+hRH1rYCwYxmoOIKzxTlSuUGRjK8NLbJ3nhzeOcPpMimy9d0WsqiqIok1OdKkVRlAVgj+YZe3ZfRYfKt6qB+OPrSDx3EFxJ7pNBrJ6Gay68wM2Vsc9may/aLvZQDrMxOL8XNQNOvkzxWAJnpPY8MIDc7rP4N7SQ+3U/wRuWX1GXKpcv88aHfew9MjL+2N4jI6zoiPDQHT2EgyrVUVEUZa6oTpWiKMo8c9JFxr6/t6KgAigeG6NweITAplbvAUlVd2M+SMfFyZVwLwlTmFfuxF+0LFV3sBalsoss2TiZ+p0iN1NCC1vEP7MBPe6/ok83lMhXFFQXnDyT5sRA6opeW1EURZmYKqoURVHmmZMu4iRqJ70VDgzjW90IQHBbO5o1f10qKSV2okDmrVOMfXcPYz/cR+HICE52frePCZ+BFql/lsxsC83j1VwBTeDkypht4bpPMTsi+Nc241vVeEUdybLt8vH+s3XXP9p3lvw8J0sqiqJcS1RRpSiKMs+c9ARFiivBlZjLIpjLIvN3UYAzVmDkGx+TfacPeyhHuS9F4of7Sb92HDc3fzfkRtxP9L5VNdcCW9vRAksjpEILmVjLIvhWNSDMGr9uNUHolk6MqO+KB/9KKSmV3brrJdvFvUpi6RVFURYjVVQpiqLMMz02QaKfoaHH/MQ/Pb/Jf27JIf3GiYr5SRcU9g1hp+Z3hpK5PELj01uwumMIv4HREiL6UC+hmzvRJ+hiLSZCCPxrmymeThD71HqMSzpselOAxqc2Y1zhlr8LLFNn7cqGuuu9XXH81uI6Ru2WHOxEAXs0P+/dUEVRlNm2uH7CKoqiXAP0iA+jJYg9VB1gENzWjt4cRJvnWG23UKZ4uPo8zgWFg8NY7fPXOdODFnrQIvrIWrAd0ARayFpyoR16yCJ0Yyduvkxk5yqEoSF0DREwMGa5OOzpjBENWaQuK1D8Pp2t61vR9cXzPqqdLJB+7bj3d06C3hAget8q9KYAQgiE31hyf9aKolzbVFGlKIoyz/SQRfyJDaReOELpdNJ7UBMENrVitodxsyW02Ox0MKZKIEAIqLNFTIgr2542U0Z0aXSlJqIHTPSACY1z+3miIR9feHgdH+47y74jIzhSsnZFAzdf10EsvHiS/5x0kbHv7MZJFi8+NpZn7Jm9xD+7geLJBHqDH39PI3rcv2B/9xRFUaZDFVWKoijzTEpJ8VQCszNK8Ibl4LigCYpHR0n+9BChW7sJ39p1xedspkP4dXxrmygeGK657l/bPG/XosxcLOzjzh2d3LCpHfC6VKaxuDo+5bOZioIKAAGRu1ciHYnM29jZNCXTwAKMhsCCXKeiKMp0qKJKURRlnsmiQ2HvOcr9tYf7Fg4OedsAQ/PXXdAsg8gdKyidTCDzleeqAltaJz4HNkek4yIdiTA11a2YBkPXiMzj353pKp5IVD0W2bmK0qkExSMnxh8rHBzG7IwSe3zdrG+VVBRFmW2qqFIURZlvmkBM0D0Qpg7z2KW6wGgI0PTVbRT2naNweBTNbxC6cTlGa2heE/fcoo2TKJD9cAA3VcTqjuHf0IIeU1vBrgaXb+nUwhbC0ikeGa16brkvRenYGMbW9vm6PEVRlBlRRZWiKMo80yyd4I5llE4maq6Hrl/mncFZAEbMT+jmLgLbOhCaQPPN768Jt+xQODBM6sUj44+VTiXJvtdP45euw2yduxlVTrGMzJYpn8sibRezJeQFSkTn93zb1c7X20T6lyfGB1v7VjdSOFh72ylA7uMz+Nc0oQWXRpS+oijXJlVUKYqiLACzPeydYTpUmbhnrYhhrYgvzEWdJzSxYEWdmy2R+sWRqsdlySH1wmEaPr9pTrpmbt5LP0y9dBSc83f7AkI3LidwXbs61zOLtIhF7FPrSf7kILjSOzto15+xJcsOUs3YUhRlkVs8+aoTOHHiBF//+tfp6ekhEAiwevVq/viP/5hSqTI2dteuXdx55534/X66urr4N//m3yzQFSuKokxMD1lE719N49Nb8G9sxb++mYYvbib26Dr0RZTUNt/KZ7PjHYyqtcEMbr56jtZssJMFUi8cuVhQAUjIvtdPeTCNk5+/4cdXO83U8a1qoPnr1xN7bC3msgi+dfWDUPxrm9H86j1gRVEWtyXxU+rAgQO4rst//s//md7eXvbs2cPv/u7vks1m+bM/+zMAUqkUDz74IPfffz9//ud/zu7du/md3/kd4vE4v/d7v7fAX4GiKEo1PWShhyzMziiwcLHli4pbv2MB1I18vxLSccl9dKbueu7jQcz2CCxQ9+5qpJk6WjyAEfc6gHaiQC7ux0lUDpnWgiaBre2IRTRjS1EUpZYlUVQ9/PDDPPzww+P/vWrVKg4ePMh/+k//abyo+uY3v0mpVOK//bf/hmVZbNq0iY8//ph/+2//rSqqFEVZ1FQxdZHZVn/AsN4YQMxBx0LabtXN/KXcdBE5WbF3FZO2i3Tc8cHFc8GI+2l8agu5j8+Q33MW6Ur865oJ3diJMc8z2xRFUWZiSRRVtSSTSRobL05SfPvtt7nrrruwrIvbZh566CH+9E//lLGxMRoaGqpeo1gsUixenJWRSqXm9qIVRVn07HTRO98hUGlzV8gtO8iCt11P+A00c/J5SVrIJHRTJ9n3+i5bEMQe6p2TmHlh6pjLo5T7av8OMFrDCGvJ/rqcMbd0PoXx1wM4iTzmsijB69rRY745Ka70qI/w7d0Et3cAoAVMhKE6VIqiLA1L8rfEkSNH+A//4T+Md6kABgcH6enpqXheW1vb+Fqtoupf/+t/zZ/8yZ/M7cUqirIk2NkS7liBzJsnKQ2k0YMmwes78K9rQY+qGTnTZY/lybx9isKBYUTAJLC2meCOZRjxibsOms+LcTeXR8i+24eTKWEuixC+pQt9ko+dKaEJgptbyX84gCxf1pESELpp+TV3zk3aDsUj3jDqC8r9aXIfDtD41Bas5dE5+bxC19DVTCpFUZagBX0L6I/+6I8QQkz4vwMHDlR8TH9/Pw8//DBf+MIX+N3f/d0r+vz/6B/9I5LJ5Pj/Tp8+fUWvpyjK0iQdF/tcltFv76J0Kgm2i5Mqkn7tBKmXjuJkipO/yCLkFm2cbAm37Mzr57UTBUa++Qmy4BD/9Hoit3djNAdwxvLYyfrb7C7Qgib+3ibiT26k6Te2EnuoF7MlNKVO10zp8QANT23BaA5efCzmI/7kRvR4YEl2LJ1MieLJBIkXDpN65Rjls5kpB2442TLJF6pTGHEkyZ8dwsmUqtcURVGuYQvaqfoH/+Af8Fu/9VsTPmfVqlXj//fAwAA7d+7ktttu4y/+4i8qntfe3s7Zs2crHrvw3+3ttYcG+nw+fD71jpiiXOucdJHMa8drps4Vj47iZLrQw0vnZ4VbsCkPZcm+cxonWcBoCRG6qRMtbGHMcRdAOi75PWfx9zZhNAVJ/PhAZUT5bV0Et09tDtd8xroLTWB1RGj44mbcXBmkRPiMJXuex8kUSTx3sGJLY+6DAYI3LCN0S9ek31snUagbc+4kCriF8jXXvVMURZnIghZVLS0ttLS0TOm5/f397Ny5kx07dvCXf/mXaFplk+3WW2/lH//jf0y5XMY0vV8Wv/jFL1i3bl3NrX+KoigXyJKDPZyru146lcRqrx+gsJi4ZYf83rOkXzk+/pgzVqB4eITY4+ugK4oRmrvCyi3aFI+PEb6li8Sz+ysXJWTfPI21LIq+cnH+XL6QyLiUSSkpHBiueUYs9+sB/OuaJy9Y3UlSFtXYKEVRlApL4gRof38/99xzD93d3fzZn/0ZQ0NDDA4OMjg4OP6cL3/5y1iWxde//nX27t3Ld77zHf79v//3/MEf/MECXrmiKEuCJmCC3V2ab+62nc02N1sm/dqJ6gUJ6deO4ybndiuj0AS+VQ0UDgzVfU72nT7cwpXNm5JSks2XyebLuJMVAFc5KSVOqkDh6AjZ9/spnUygR3xodTpJuY8HkZN8z/QGv/fvogYtYqm5UYqiKJdZEj8Vf/GLX3DkyBGOHDlCZ2dnxdqFKeuxWIwXX3yRv/W3/hY7duygubmZf/pP/6mKU1cUZVKaz8DX00Dx2Fj1ogCzMzb/FzVDTiJft8vgpku4+TJurowWnJutdZrfxLeygfTJZP1rTBeRzswjytPZEodOjLL78DBSSjasamLj6iaiS2iL5myRUmIPZRn9zp7xpEXwzoPFHl5D4qcHkZcNTJYF+/y8r/rvJGghi8jdK0m/erxyQUDsoTUqTEJRFOUyQso5mKS4RKVSKWKxGMlkkmh0bpKNFEVZnMrDWca+txf3sgP40Qd78a1vRvctifegKJ5MMPbdPXXX45/dgNkemdPzME6uRPad0+Q+qD1Q17e+mdjDa2YUPJHOlvjBS4cYuWyuVDRs8cWH1xNdJFv3nEwJt2AjBIiAiT5HRayTLjLyV59U/b0FMJdF8K1sIPPWqYrHY4+vI7Bh8q33bqGMfS5L5p3TOMkiRlvIS2FsCMxpaIiiTEbdrymL0dK4S1AURZljRlOQxqe3UDqdpHQigRaxCGxqRY/60JZIQQV4seOGVjNk4MKaFpja1+MWbaTjovmMac0l0oMWwa0d5D45W30dAsK3dM34pvzkmVRVQQWQypQ4eGyEGza3L2hSn1t2KJ9Jk3rhyPhAYaMlSOzhtRitIUSdLXUz5WRKNQsqgPJAmtDNlbs79Ab/lOPQNb+J1R0n3hZG2i7C1NCuwXldiqIoU6F+OiqKogBCCIyGAEZDgMCWtiUZoQ2gh0yiD64m9bPDly0IInevxGgMTlogufky5XNZsu/24eZKWN1xgts7vGHIUywK9Lifxqe3kHz+EM5I3nss5iP60JoZz5sqlmz2Hhmuu77v2AibepsJzmNq4OWcRMHrFF6yB8QeyjH67V00/eZ2jIbArH4+OdnZNE2ALhC6RmBLG6Eblk177prmM0Dt9lMURZmQKqoURVEus1QLKgBh6Ph7mzC/GiT7wQBOwotUD2xqRQub6JGJt8e5hTLZ9/vJvts3/pg9lCO/a5DGL1+H2Rqe2nXoGlZHhMantuDmvTM8WsC4omh6gUCb4M9GExMHjsw1t+yQeed0zWQ8Wfai5sO3r5jVbpUeq//9FKaG0Rig5XdvALz5X9PpOCqKoihTp4oqRVGUq4zmM9DaI0QfWO2d69EFwmegGZNvuXOz5YqC6gJZdkn94qg3DHcanaDZjCi3LJ2t61s4PZiuuX7duhaC/trXJh0XJ1NCFh1vG1vAnPUEO1l0sM/UvjaAUl8KWXYQs7idVAua+Dc0U9hf3cEL3dKFHrYQU/hzVy6SUuKmi5QHM5SHspitYcy2EHp0ac4sUxRlfqiiSlEUZRGQUuKmipTPZXESeYzWMEZj4IpS1jTLmPYZmFJf/dS+8kDa2262gNvruprDfHVnL07ZIe+4/PrYCKfPZmhrCrKqM17zY5xcifyes2Tf7kOWHACsngaiD6ye1eG+wtDQwj6cOrH1esyPMGa3U6T5TSI7V6HH/OQ+PIMsOWhBk9CtXfjXt6iCagbs4Ryj39ldkZooAgaNT2/BbA4t4JUpirKYqaJKURRlgUkpsc9lvRu5ojP+uB7z0fDFzRjx2T2Hc0UWcHudky5Seuc0zu6z4EiCAYMHburE3d6JGbGIBKs7YtL1BuFmXj9Z8Xjp+BiJ7++j4YubZy0JUfMbhG/tYuyZvTXXQzuWzcn2Oz1kEb6tm+DWdqQtvU5c2FrS21gXipMpknh2f3UMfd4m8ewBGp/eMqfJmYqiLF1qc7WiKMoCczMlxr6/t6KgAnCSRZI/O4yTL8/btVhd9Wdymcuj41vXpJS4RRu37NR9/mxyc2WSzx8m//EgON6hJZm3Kbx+At/pJOE6W+rcTKkqUvwCeySHk6xOErwSZnuY0O3dlcWnJog+3DvjgI6pELqGHvWPdzcXW0ElXYmdKJD9cIDEjw+Qea8Peyx/RfPK5oKbK4+nNl7OGcvjzuO/RUVRlhbVqVIURVlgTrqIm619s1buTyHz5XnbcqeFLEK3dpJ9u/JclTB1ovevRg+Y2KkCxSOjFA4Oo/l0gjuWY7QE0Wt0imaLkylSOpmouZZ56xS+tU0YNc68SNup6jpcyh7KTjlifCq0gEloxzICG1qwh7KgCYy2MEiJW7CRZeeKwjqWKnsoy+i3diMvFOEHh8n86iQNX9iM1RldNEWgrDGKoGJ9nt5EUBRl6VFFlaIoygJzJ4nFnuxGbzZpPoPgjuVY3XGy7/fjZkpYK+IEt7ajx/zYyQKj39qFmy6hNwTQY37yuwbRgibh27rR5qj4s0dydddk0anq8l0gdK3u3C7wzjnNNs1noPkMjIYATrZE4cAw2XdP42bL6FEf4TtW4FvVMGffq8XGyZRI/PhAdUHiSJI/PkDT17Zd0dnB2aQFTS+G3q0R4agJhBp6rChKHaqoUhRFWWDGBNvChKlXDB+WjjtehGlBc07e4dcDJnp3HLM94g3/tXSEruHaLtl3+5C2S+zxdbi5MqWTCYSpYS6L4uRKc1YoiMkCN+rElIuQRWBLG/mPzlSv+Q30prk7r+YWbTJvn6743E6qSPJnhwjfvZLQ9R01gySyuRKjyQJHTiXw+XTWrmwkErTwWUvzht7N199S5+bKOJnS4imqQhaBbR3kPxyoWgtubadwaBgtbE0rAVNRlGuDKqoURVEWmBY08a9vpnCgRiz2bZ1o5w/G28kC+d1nKewfAl0Q3NqOf23znN2QapYOXLyRl/kyhf3niD2+nvRrx3FG8+NrhQPD+De3Er2np6Kwcku2N6fKcRGWPuOtb5pPRwSMmlv5fKsa6qbqaYZG+JZOnESe0vHExcdDJg2f2zSnN/Nurkz+4+piDiD71in865oxYpWFUiZX4sevHmVwODv+2DufnOGuHZ1sWduMb5ppjnPNLTm4mSKFQ8M4qRK+1Y2YraHK76tTo+tzqUV0rkroAl9XFD1gkPvoDG6ujBYyCW7vQFgGmV+dJHhd+0JfpqIoi9Di+umsKIpyDRqPxY5fEosd8rbTWasaKPWnELpG4rkDuOnS+MelXzlOfs85Gp7cOG/v9FtdMUonxioKqgsKe84R3NaBdb6oclIFUr88SfHgMLgSPeYjsnMVVlds+jOidEHs4bUknz/kxbqfZzQHCd6wfMKocj3sI/7YOpxsCWcsjxYw0WP+OU/Ic9LFmoOAwZv7JfM2XJIL4rqS3YeHKwqqC375QR8rlkVpaVw8v7bdskPx8AjJnx0afyz/ySB6g5+GL2wej6vXggbC0sfj7CtoAm0RnTETmkZ5KEvpVJLwXSvRLB235JDfc5ZyXwqjLaQGKCuKUtPi+emsKIpyDdPDF2KxO5C2izA1pA7JnxxCaBpGY6CioLrAPpel1J8isL5lzq9R+A0CW9tJ/fxI3efkdw1idURwMkVGn9mLM3Kx+HKSXlx1/MmN+Fc3Tutz6xE/mfePEnuw10sdzJTQGwPeWSpNTDpgWAuYaAFzXucMicm26xmVBV2uUOaTA+fqPn3fsRHubgzOxqXNCjdTIvn8oarHnbEC2bdOEbl/NZqpo4UsIvesJPXi0arnhm7tQgstrq10gQ2tZN/po9yXqloL377CO3elKIpyGfV2i6IoyiIgbRfpSvSobzwWu3RkjPLJJFZ3jOLR0bofm999dl6izTVTx2gITBicIUuON3drJF9RUF0q/dpxnGx1gTgRPWwRu2cVxVMJ75zSgSFyv+5Hj/sx2xbnQFY9ZNUtGIyWYNX5MyklxVrdnPPykwSazLfSyUTdTlx+/xAy5yVaCl3Dv66Z+Oc3YrSGQBcYTUHin15PaFsH2iILf9BjPho+vwkRuOR9Z10Qvnsl5rLIwl2YoiiLmupUKYqiLCAnX8YZzZP9cACZt/GtbfLOCJkauQ/OH5Z3Zd0gBgChiXkbyqtFLHy9jRT2DdVcD2xuQwhBqS9Z9zWc0TyyPP1zNHrU2z4YurkErkSY+qQdqoWkhS0aPruR0e/uqdj6pgVN4p9aX3XtlqnT3RHleH/t792aFQ1zer3TNeHMJkci5cWKS/Ob+Hsasdq88JOpdBcXitA1rK4YzV/b7hX/josW8aGFTLQawSKKoiigiipFUZQF4+bL5N7rI/te//hjpZMJsmGLxqe3IF2v8CgeG8O/tpnsO6drvk5we8e83exppkH4tm6KR0erYsyNjjBGi7c9baIzXsLUETPcJ6EZGlqNeVSzSUo5K2ethPBmVDX91nbK/Sns4Rxmexijxeus2Yk8WtA6HwgCPsvgjuuXc+pMCueySO/GmJ+2Bdr655bPR9brYjz1zi07mG3huh9jtIZqbn9cKlvnhCbQoz706OI576UoyuKmiipFUZQF4mRKFQXVBW6mRPad04Ru7iL1/GHK/SlCN3oDdu2hynlN1qoGjNb6N7dzQY/7afrqNrLv9VE8OoowdYLb2vGvbxlP97O6Y3Xn/QS2taMtsi6FdFycVJHCgSHK57JYXTF8qxrRY74rKrCEJjBifoyYH7dkUzqZYPRbu3GzJRDgW9NE5J6e8VCHxpifLz22gV/++jSnzqQxDY0ta5vZsbGd8Dx/z6Tt4iQLZN/ro3Q6iRa0CN3cibk8grRd7EQBc3mUcv/Fs0dayMJoDhK6o2tOh0EriqIsNkJe2p+/xqVSKWKxGMlkkmg0utCXoyjKVS79q5Nk367dfUITNP/O9Yx842Nk0UH4dKIP9OJmSxSPjYKmEby+A7MtvGDbqFzbRebLIARaqHJmlrQdSqdTjP1wX0WkttkVJf74uhlHq88F6UpKp5OMPbO3oggUlk7jl7ZgzlLRmj84TPLHB6oe1xsDND61BT188c+xWLIpll0EEPAbGFeQOCelxM2UvK1sZRctYqEFTbRJ4tnLg2lG/npXVSR6YFsHoRuXMfyXHxF/bC2l/jSFIyNEbu0CISgPphFBk8C6FrTwxU6coswWdb+mLEaqU6UoirJA5ETzeVwJuqDpq9vIvH2K4qERUr84QuimTqIPr/HS7C454O+ki948KEALGHMeFw7eVjzqbPMTho7VHaP56zson0nj5sqYyyLoEd94ESilxE0VKZ5KYp/NYLSHsbpi6NHpdYecXMnbniaE97X7pverzc2USPxof1VXTZYcEs8dpPHpLVdcuDrZEqX+JFrQxM1VnkVyRvPYo7mKospnGfhmoVaWrqQ8mCHx7D7c7PnPKyB4w3JCNy6v+3W5uTLJXxytOWMq//EZgtvaMVuCJH50AGtVAw1PbCDxkwMV4STZX50i9vhafL1Niy6MQlEUZbapokpRFOUSTrqImy3jlmy0oIUs25ROJTFbwxhNwVk9Y+Ff00SuxvY/8Lb1aT6vQIg+0Iu8cyXgFUzikvNT0nYoDaRJ/uwwbrroPSdsEXtkDeby6ILezApdG9/6Vot9Nsvod3ZXhDgIn07j01PrDknboXw2S+qlo9jnvNlOVk8D0XtXYTQGpnydTqZYdT5sfG00j5svz7iocjIlyoNp8rvPgiaI3NODmy+Tfv1ERRFX6k/j647P6HNM+PlTRca+u7syGERC7v1+jMZA3UG2btHGHszUfd1yX4rog2sY+R8fofkNsu/31Ux7TP70EM1f34HWMPU/D0VRlKVIFVWKoih4XRN7OMfYD/bhporjj/s3tOBbGWfsmb1oYYvGpzZjzFJggB73Y61upHRZXLowNaJ394x3XDRThzrFkZMoMva9ym1rbqbE2DN7afrN7WgtizNu3EkXGXt2f9VAWFl0SPzoAI1fug4taOKki5QHUthjBayOCEZzcDwEwx4tMPrt3ZXFyfExRr+1i6avbEWvU8xdbtIkwhrdmqlw0kUSP9pP+czF4qR4aATf6kYiO3tIv3xs/HEjNjfbIUsnE3W/vsxbp/GtapjZVkxNoDd4Z+ucXInED/fXfp70rsFQRZWiKFc5VVQpiqIAbrrkdU3ylbOACvuH0MIWvlUNFI+NkXjuIA1f2DQrh/D1oEXsoV6Kx8fI/boft2Dj62kgdGMnenzygkDaLtkPB2qGQSAh+14f0Qd756Rb5RZtkKD5Z/ZrxM2Xxztrl3MSBdyCjZMqMPbdPeNFQRYvVr3hqc1oQYvMWydrfu1urkzh+BihbR1TuhY95vMi6Wt8G4XfqJonNRVSSgpHRisKqguKR0fxrWlCC1u4mRIYGubyuTkXYg9l66656WLtvzt4HVGrO0bpVO14d6sr5g32bQsjRvMTFp5OboLodUVRlKuEKqoURVGA8lC2qqC6IL9rkOj9qykeG8M+l8XNlWct2UwPWQQ3t+Fb1eDNXvIbU45Hd8sO5Qm2aNnnslB263a5apGOi5stYSeLyLKDEQ9483nOd82cTJHS6RS5j8+AlAS2tGF1x5FFm/yBYbAd/Gub0RsCE26Zm2iA8AVj399X1WVxUkVSLxwh+sgaSn2pOh8JpaOjBDe3IYzJAx60oEnolq6aoSHRe3vQwtP/s3ZzZfIfnam7Xjw0jH9jC26+THDH8orzVLPJ7IhAnevQ436oE4Ch+U2i969m5JufVG2NDN+1smKosfDpGC2hugWcb0V8ZhevKIqyhKiiSlEUBXBGc3XXZNFBXHrzOYWCYLpmUqQJU0Nv8GOfrV1Y6XE/TKGouEDaDqW+NIkfVW7LC+5YRuiWTnAh8eODFRHa1oo42Xf7yH8yOP5Y7oMzWCvjxB5ZW7dY0IJW3ch1ETBwsyVkoXaRWzqVRJZc9KCFXacQ1iI+0KcWdqFZBqEdyzBagmTfPI2dLGA2BwnftRKzPeQNV56BekEkwqcT2NaOLLsU9g+Re6+P4NZ29MbArMeQm51RhN+o+b0M37ViwsJXbwzQ9LXtFA4MUTw+hha2CN2wHKPBXxEGoocsIvetYuzbu6s///LIlLquiqIoS50qqhRFUWB8IGstWtBEls8XGbpAzGA72FzQDJ3wjcspHhiuuR66uWtacdZOusTY9/dWFTq5DwYwW0NoYauioBI+HaMpSPKtg1WvVTqRoHh0lODW2kEIWsgkdEsn2bequ0PhW7uQxdrF0jgpCd3USfL5QzWXg9s6ppUgqAW8CHCrM+Z1DA1tRtv+xl/Pb+Bf10z23b6qtdgja8m8caqis1PYN0RgSyvhu1bOamGlR300Pr2FxHMXk/mEqRG+YwXWJMEYQgiMuJ/QzZ0Et3cgdK1u589sD9P4petIv3qM8mAGYekEt3cQ3N6xYJH/iqIo80kVVYqiKIDRFESLWLjpUtVacHsH+T3nAAjd1LmoBtfqDQFij64l+eKRix00QyN63/QS8AAKh0fqnrHJvH2a8O3dFY/5VjVSPDRS9/WyH/Tj622seVOtmd5NtxHzk37zFG6qiB7zEb5jBb6VDTj5+udwhN9A8+lYPXH8m1op7D13ySJE71vtnZO6hFMoex1HQATMusXmbBUAQtcIbG0nv+fsxShzvKHI5TNpr6ASYHXH0cIWTqJAfvc5ApvbZrWoEkJgtoRofGqLF+XuuF4cf9iq7L5O8hpikph6zdSxOqM0fH6T9waEEGhBc8qfQ1EUZalTRZWiKAqgR3w0PrWFxHMHL26nMzSC29oRlo49kiNybw/+9S3efKZZJF2JLDsTdgLq0XwG/nVNmJ1RnFQRpESP+b3hrtMMqLCH6m+BdFLFqlAKYWg4dcImwNs2Wa9IA2/LY2BzG9bKBnBd0LTx7YJSgH9dE4WDlUWbsHTij68Fy0CzdCI7ewjduJxSX9KbjdUZ9c6AnR9sKx0XeyRH6pVjlE+nQIBvbTORO1fMeSKdEfPT+BtbyX0wQOHAMOiC0M2dJF84gm9VA8HtHRSPJ3CSBazOKOFbOimcGMNcFp3xlsN69JA1Lx0jLWDCIunkKoqizCdVVCmKopxnNARo/Pwm3HwZWXYRfh2ha0jbxb+uedYH6kpX4qQK5PcOUTqVQI/5CV2/DD3un1aqnjB0jJhedx7UVFndUQr7ztVcM1qCCF9lkVY+m8G/voXSiUTNj/H1NCACk38dtc5d6QGTyL2r0ZuC5D4YQBYdQrd1YXXFyH8yiPNOH9aKOIGNrRjNQcw62zedZIGRb+662MWTUDw4TLkv6cWuR+f2vI8R8xO5eyWhm5Z7D7hgNAXwr2tm7IcXBw4Xj46S+1Aj/pkNICVeHKGiKIqyVKiiSlEU5RJa0EQLzs877fZwltG/3j1+Xqvcl6Kw9xzRB1bj39g6rfNQs8HqjtcNNYjc3YMe82O0hbDPemeB7HNZjNu60aK+itle4J3bCd3UOeUkw1r0sEX4li6CW9qRrkvh0Ahj39kzvl7uS5F7v5/G37gOs7m6qHLLDpl3TtcMFnGzZQrHph67Phk7WaB0MuHNZGoO4l/Xghax0EyvML8wC8q1XUI3dXpznS7r4smyS+qlYzQ+vWXO0gAVRVGUuaE2OyuKoiwAN1cm+fMjFwMwLpF66Shurvps11zToz4av7QFo/VigSICBrHH1mK2hdHDPho+u5Hog70YbSGM1hBurkTjFzcT2NruJQ0K8PU20vjl6yicGCXz7mnKIzncOkl+kxG6hh71gYTM6yeq1mXJIfWLozXPYMmiTelk7TlLAMXDI7g1vv/TZQ/nGPnGx6ReOELhwDCZX51i+L994KUUXpYAqBkaCFE19PgCZyyPO8F5MkVRFGVxUp0qRVGUBeAW7LpR6Egon81ixOf2zM/lhBCYzSEavrAJN2+DI9H8hrft8fwZHz3iI7i1Hd/aJpASLWAihCBy7yovtc92KR4bZfRbu8cLh8wvTxK+ayXBre0zHhZc6q8/k6rcl/K6a5ef5dEEmt/wBuzW4AUpXNk2OzdXJvn8oeoZZxKSzx2g6bevr96WOcE5s4nWnWwJN1vCyZTQwz60kHlVJuu5RdsLupjnTq2iKMqVUEWVoijKQpAzu7GeD3rQmjSBTr+sgNEMDRm2yH10hvQrx6uen/nlCXwr42j+8MwuarLvR43vpx60CN04Qez69csQ2pVt2HDz5boDmGXZxRnLVxVVesw/4YyuWttP7WSBxLP7vYHO5xltIeJPbLjis3SLhZMqUjyVoLDnHGiC4PYOzI6I2gqpKMqSoIoqRVGUKSqWbLJ5m9NnUtiuS3d7lHDQJOCf/AyWW3K8gbYlB2HpCJ+O3hzEGa6duGe2zbD4WEButkzug4G667lPBok+sHpGYR/W8mjdNaM1hKjTAbN64jVTBEO3d8/KUFo5SbEnSw5OroSTLFLYPwQC/JvbCN/aRebNU1XPj963qiqy382VST53sKKgArDPZkn+9BANn91wRTO1FgMnXWT0mT3js7QASicTWD1xog/0XjWFo6IoVy9VVCmKokxBoWiz+/AQb3zQX/H4xtVN3LWjk+AEN7VOuoCTKHpzgoD8gSHQNRqeWM/w//gYypXnbkI3d85bWMasknLC80Burux1lGZQVGkhi+CODnIfnKlc0AXRB3vrdtb0kEX0/l5CN3VRODaKZmhYqxvRwyaa78q/x5rfQAtZuNnaWwyNlhDpl495kern5X49QPxzG4h/Zj2Zt07jJAsYzSEid63AaAlVxak7uTLlM+mar1/uT+Hmyku6qJJSkt83VFFQXVA6nqB0OomTLmK2hac9JkBRFGW+qKJKURRlChLpYlVBBbDv6Agrl0dZ39NU8+PsZIH068e9IbnSS8ULbDs/9Pb1EzR9bTuZt05hD6TQzqfdme2RGZ89WkjCZ2CtiNcdCOxf21Rzu52bK+PkSrjpkpe+GLKqtnxpfoPQLV1YK+Jk3+nDzZWxOqOEbuqcsOMkpfTCIjQIrG9B+HTvLNUkhZ1rO7iZMrJQRhh63VRILWwRuX8VyR8dqFoL3daFPZStKKi0oEn0wV7swSzlcxmC29oxWsNoIQsj6qt6DQBZmjjkw60ReiFdiZsuUh7N4yYKGK0h9Jh/wbfSOdnzXbsDQ6AJAuubEQGT/K7Buh9TPDiMFrTQDA2tPTKPV6soijJ1S++3tqIoyjxzXJeP9p+tu/7+nkFWdESrtgE6uRLJnx2i3HcxZEGWXXLv9xO8YRloAoEk9lAvsuggDIE2ha2Ei5Vm6YRv76Z4dBScym1xetSH1Rmr+hgnVSTx04MV3yO9MUDDkxurhvPqQQt9dRPm8ijY0iuQJuhcuGWHcn+K5POHx8MqtIhF7JG1WMsjiDpx706uRO6DAbLv949/HUZbmPin1lVdkxAC34o4DU9vIfP6CcrnMugRH6FbvQIw+dzBiudHH15D+pVjOIkCAMXDowCYXTHij6+rWfRMVmBrvsp1KSX2aA5nrICbLaGFLcpn0qRfP+59jjmezVWPUyiT+/gM2Xf6xs+T5d7vv2Q2V23SlaBB+o2TxD+9vurrVRRFWQxUpLqiKMokXEeSnWBbW75g49Q4W+NmShXFQsXHfDJIYH0Lbt5GM3X0sLWkC6oLjHiApq9sxew6fwZKFwQ2t9Lw9BYvGv0SbtEm9fLRqu+RM5pn7Af7cOqk9ul+0/t+TbIVzEkUGHtmb0X6n5suMfbMXpxEsebHSFeS3zvk3fhfUhjaZzOMfmcPTrr64zSfga8rRvxzG2n53Rto/NJ1BDe3IfTK6HRrRdzbyna+oLpU+XSScp00SC1oeWmLNfjWN6OFLivmx/IkfnSAxLP7Sf3iKIln95Pfd47wLV2k3jhZs7M1l5xMkeLxMTKvn0AWHeKf2eBF8J+X+2QQ37qWuh/v722idCpJeTBTN4peURRloam3exRFUSZhGBqrOuOcqnOupastgq9G/HOtm+cL5PlzVMK3eM+IuLaLzJeRUqL5jCl1CIShYbaGve5D0UEK0AJmzQLIzZUpHhmt+TrOaB43W5rxdjXXdsi+3we1GiCuJPtBP9H7ViOMyvcW3UyJ7Duna79muog9mkeP1N6mV5WI6DPw9TZiD3kBE76eOLmPztT6UADyH5/BWhH3Zlld+jp+g+i9q0gbmhd2IfECLza2ErlrRcWfi5MtkfjRAZzRyvNJ9tks2ff7MTsiuLkSmjU/cf1OusjY9/eNfw8Ach8MELqti8DWdvKfDFI6MUb4lk4K+8/hpisLaaMtjLB0nEQBoznopSZewi3auNkSxaNjuIUyvp4G9IbAVRk1ryjK4qaKKkVRlEkIIVjdHefdXWfIFyvPt+ia4KbrOjBrbCW7PMWt8kVBWPqiDaSwkwWy7/WR33MObBerp4HI3SsxGgMIffJNDrrfhEk6b5N1HS4Ee8yELDlVaXmXKg9mcMsO+mUFjLRdb+ZVHfZQFt+K+JSuQegagc1t5D4+Uz3Hapr0iI/oA72Eb+seT5DUQlbVLCc3W8aukyhZOpUkuGM50nZrrs826bhkPxioKKguyL51mviTG8nvPusVue/30/j0FvJ7z1E4MIzQNfzrmtAbAqReOAJ4AS6XFktu0Sa/7xzpl45dfN13+jA7o8Q/tQ49XLv4VRRFmQtq+5+iKMoUxMI+nn5kPT2XnAtqbw7x9KPridfrXER9aJHahZVvdSN6U2DSeVBTJaX03rUvX/n2KCddZOy7e8h/PAjnb8BLx8cY+atPqrpvTqZEeShL6UwaO5Gf1ucXPr2q83Cpy793btnBThQonkpQ6kviJAteCEWt1zZ19AmGJ+uNgaouFYAwBGKCobN64/Q6PEbcT9NvbMW/sYXiyQS+NbW38QEEtrZXdakupVk6RkMAsy3sne2SEjuRxx7J4aSL3t+BwiSFqOuiWfPzfqqbK5P/pH4ARelkAnOZFzxhdUbRY36CN3YSe3gN/rVNFI6MknzuILLkENjWgbUyXvHxTrpYUVBdUO5Lkd9zbtK4e0VRlNmkOlWKoihT1BDz8+idPRSKDhKJzzQITBAioEd8NHxhE4WDI5gtIXAlUkpKZ9KEti/DmKXAACdZoHBohMLhETS/TvD65RitwRkXbKW+VO2ti7ZL+u3TxB7qRTN1ysNZEs/uxxk7/9zzA1tDt3RO6XNrIYvAdW1e8XYZszNa8RpOvkxh7znSr58YDzkQpk7ssTVYKxuqthdqpk7o5k6Kh2snEYZvXF77TJbfILCtndx71UmPImBUBVVMhdEQIPpgLzJvIx2X4uERnGTl2SyzM4rZPvXZZHaiQOrlo5SOjQGghUwi9/RgtE7wGgK0iA9tnhIApZQTdiNlyUGYOlrIxL+2GSEEuqUjWkNeGmJLEFlyMTvCXvrfZf/W8vuG6r527oMBAptbVbdKUZR5o4oqRVGUafBZBr5pvNMvDJ1yf4rs+UGvwtIJ3dpVtW1rpuxEntG/3l0xJ6l4dIzAdW2E71yJPs3thdJ1vbjrOkrHx5AFGydvM/adPZVb9FxJ7oMBtIiP0I5lVfOWLqeZOuFbuwHI7zo7Xiz5ehuJ3rd6fGukkythn82QfvV45bWWHRI/OkDTb21Haw5Vvb7RGCD6cC+pl46Nd9wwNGIPrkavUxzJXBmzOYR/Q4t3fuk8PeYjev/qGYc8aKYO54u4xqevo3BwmPy+cwhdI7i9A2tFbMoFgJMuMva9PRWFr5stk/zpIRq/shVrdQOlo2NVH+ff1Ioe9U365zJbNMvA6o5ROpWsuW51RimfyxC9fxX6JcN9NVNHi+sYkwxndusEmQC4Bbv2eTpFUZQ5oooqRVGUOeJkil7S3CWhAbLkkHn9BJqpE9jWPum8pIm4ZYfs26drDp7N7zpLcGv7tIsqhEBMMEhWO79lrzyYrnvmKfvuaQLrmqvS/mrRwxaRe3oI3bjci5U/f87sQviCPZKjcHSE4pHqIgEACbmPB4nu7Kk666X5DAIbWrC647gprzOkRX3oIbNunLosuySfP0Rw+zIaPrcRt2AjTB03Vyb10lHCd62Etql3lGp+zVEfwRuW4d/cioBpD+61h3N1Q1CSzx+i4XObyPhOXgy10AXB69q9DuI8BjhofoPIPT2M/NUn4wXzBUZbCLMzim9d87QG+roFG1m0QQj8axop7D1X83lWVxShBgUrijKPVFGlKIoyR5xEoSqF7YLMW6fw9TbWTZKbClmwye+v31XKHxjGnOawVCEEwW3tFHbXnssV3LEcLWhiD9UOQwDGt7lNldeZqO4cOekio9/dQ+jG5Tip+kmKzkgO6UhEjXtoYegYMR1iU9tqKSwddI3chwPkPhwAXVREq0/WPZkqIURVWuBUlfprx/QDOCN5kHihFrdfCLUw0EK1ExjnmtEcpOmrW0m/dpzSqSTC0glu6yC4vWNaf/elK3GSBZxUAVlwQBNoUT/msgjlgctSOTVB5O6eJTlAW1GUpUv9xFEU5ZojpUTaLkIXCG3u8nrKE6TPubky8kpDJSQTb3FyZ5bypsf8hG7tIvt2ZbS42RXDv64JIQRGS/V2uwtEwKgZAjFd9mgeN1PCSRYwmoKU0rW3e5nLIrPy+QD0kEVwRwe5d8+fqbq0oGoNoV1BETxb9AkKROHTEbpAs/R5i02fiNDPR+x/eoN3vkqAFjSnlCB5KSdVIL/rLNkPB8a3chqtIWKPrCF/YIjcrwfAkZjLIkTvXYXRtPBfu6Io1xZVVCmKck1xkgUKR0YpHhtFC1kEr+/AiAfm5F3tiW5+MTSY5o3l5YTfwLemieLB4Zrr/vX1B6pORA+YhG5Yjn9dM4UDw8iSjX9dc8X8H6MliBY0a24BDN/SNXGc/BTZI143LL9viNjDayidSFQ/yfBiy2frnJAwNEI7loMDuY8Gxosqa2Wc2IO9i2L+kdUVq+qgXRC8ftm8xfS7JQc3V/KCSjSBHvejh62aBZPmN2CG/8Zc26F4ZJTse30Vj9vnsow9s4+Gz28kuLXD+zyWPu3tlIqiKLNBFVWKolwz7LE8o3+9q6IQKOw9R/julQS3tk9puO10mC1BhE9HFqs7UsHr2q74Bl2zdCJ3dFM6MVb1OXxrmtCvYKua5jfQ/IaXWliDEfXT+NQWxn60/+IWR00Q3LEM/8bWWSlyjKYg4G1zLBwcJvpgL5lfnRz/89PjfmKPrp3S2a3p0EMW4Tu7CW7vQBa9M1Va0Fw028n0sEXD5zeR+MG+8SHSANbqRoLb2qfdBZoJJ+/FpWfePHVJGqNG9JG1+FZVpzFeCTdTIlsjjRHAzZYon83iX9s06/9+FUVRpkP9BFIU5ZrgFm1Srxyr2VnJvH4Cf2/jrN+UaRGfV3g8s7fi81qrGwjd3DkrW9b0hgBNX9tG7uNBikdG0Xw6wRuWY3XFZm0GVj1Gc5DGp7d4WxltFy1geANpZ+mG2mgMoEUs3HSJwv4hnESByD09CFNDC5heZ2SOtuNphpdAtxgJQ8NaHqXpt6/HHsnh5m3M1hB6yJq3LpU9mCHzxsmKx2TZJfnjAzT9du00xhlzZc0wlvFrGc2B3jx7n09RFGUGVFGlKMo1wS3Y4zN9aimeTGA0Bmf1cwohMFpDNH11G06qgJsvj2+hm60tSkIIjHiAyJ0rCN243NuGNY/bn/SQNWdb4vSIj8YvbiHxo/3YwznKZ9Ikz2YIbmsndEvXotiKt1CErmHE/BhTDOCYTU6+TOatU3XX8x8PYtRIY5wpYeoIv4Es2DXXzZYQWp00R0VRlPmiiipFUa4NcpKhNfbcDLURQqBHfbO+Ra3q8+jaoisypCtxMkWcMa+gNJqD0y4ojcYADV/cjJsvI8suWsBECxpo05gVpswy28VJFesvj+aRbu00xpnQQhahm5aT+eXJqjXh0zE7ppdwqSiKMhfUbyVFUa4Jms/A7AhTPpOpWjPaQlirGnBSxfGtZcqVka6kPJhm7Jm9Fee9rN5GYg+snvKgW6jdDXOyJZxUEXs4ix72oTcF0CO+K5r7NR/csjPecZlJCt5iICwdozVEqc7wXXN5dNbSGAGEJghsbsNJFsnvGhxPvNTCFvHPbriis4OKoiizRRVViqJcE7SASfT+Xkb++pOK1LTIfatAShLf34uTLmG0hIjctQKzPbJoggmWIiddZOy7eyqCFABKR0bJNQUJ394944LCSRUY+9F+7MGLkfXCb9D4hc0YbaFFWVhJKXESBbLvnKZweAShawS2tBHc1jHnXczZpvkMIrd3M1JjO60wNQIbW2b9z0APWUTuXunNLEsXEZaBHjLRwtai/PNWFOXas/TeIlMURZkhozlI89e249vQghb2Et7s4RzpV47jJIvgSuyzGca+t5fC0RGcfHWohTI15TPpqoLqgtxHZyYMHpiIW3JIvX6ioqACLyFw9Jk9uHVmWS00J1Fg5Bsfk99zDll0cHNlsu/2Mfq9PRNupVus9MYg8Sc3ooWtSx4L0PjUlolHCVwBzWdgNATwdcex2sNLojOpKMq1Q70NqyjKNUMYGkZzkNhDvciijVt0GPlvH9Z8bub1E+gxP6ItPKvx0NcKJ1GouyZLDrLGjKWpcHOlunO5ZN7GHsujR31IV4JgVm66pePiZktI2wXDO7s2nS6ba7tk3++vGa3vjOYp9acIRGc2U2yhaJaOb1UDTV/Zipu3QfO6wYvtXJ+iKMp8UUWVoijXHM3UwdQpnxmp+xw3W0Zmy7jZElo8MOfX5JZs3GzZK0Z0gR6rP0h1KZgoPECL+GZ85kba7viZmlqcdJHiqQS5j84gDI3gde3ojYEZ3+w72RL5PWfJvtuHLDoIUye4YxnB6zum/JoyX6Z4ZLTuemHfOXxrmtBm8RzSfBBCoEd8cxZrryiKspSookpRlGuWsCbpQGkCN1OCOS6q3HyZ7EdnyL51arxgEKZG7PF1WCvi89opc0s2bq7sbaMzva7MTM6tGI0B9Li/ZscqcteKGd+Ia9bE8dpa0GTsmb3j5+YK+4bwb2ghsrNn2oWVW3bIvt9P7v2Lg2dl2SH7zmncbInIzp6pzTbTBMKsXzAJS0csrXpKURRFuYz6Ma4oyjXLiPsRdQoWoz2MPZyDeegelPpTZN88VdGBkWWXxLP75/W8jZMrk32nj+H/+iGj397N6Dc+YeQbH3vno9zpbdfTIz4avrgZa2V8/DERMIg+sBqzO4Z0ap+3mowW9hG+ravmmrk8gjOarwgiASjsH/L+LKfJzZbIfTBQcy2/+2zNQdK1aEGT4PaOuuvB7R0ITf06VhRFWcrUT3FFUa5ZWtgi/ul1oFV2YUTAIHLHCgoHhub8jIibL5N563TtRendvMvJZmzNkuKxUbLv9sElBZSbLTP23ZmFKRgxP/FPraf5b+yg6Te30/il63CLNsln95N6+Rj2cA5pV58zmojQBIENrUTuW4UInO8SaQL/plbCt3WT+VX1LCOA3IcDuPb0CjlZsCu+F5ebatiGEAL/umbMjnDVWuC6tlkfOq0oiqLMP7X9T1GUa5bQNazuGE1f20Zh3xBOuojRGsJoCJB6/QSxB1ZXpJtdyi07uJkSpdNJnGwZ34qYd3bHP70ZV9J2cVL1Qx2ckRzSkQhjblPOnEzJ235Ygyy7FE+OYcTrd1vq0fwGwtQoHBkl+eMD44+Xz2TI7z5Lw+c3YXXHprW9UAuaBLd14O9tQpYd0DUwBGPf2V03cVCW3QkLpFomO/c16fbRS+gRH/HPbsAeyZP/ZBBMjeB1HRhxP1pQzUVTFEVZ6lRRpSjKNU0YOkZzkMCODuxzWa9IEoKGz6yvG9nslh1KJxMkfnQAoQsiO1fhjBXI7z6LFrLwr2tGj/imNOdKmDpmS4jSqWTNdXN5dH4CDFzpxcrXYZ/N1l2b9KUzJVI/P1zzcyZ/eoimr26d9hkroYmK+U7ScfGtbiI32l/z+YFNrWjTKIIARNDEaA1hn6v+2vW4Hy04tS6mk/cCT8qDGTRLJ3znCi+ExFCpkoqiKFcLVVQpinLNE0JghH0YYR/+VY2TPt/Nlkg8ux8kxD69nuw7pykPZsbXs2+fJnz3SoLXtaFN0rnS/AbhO1cw+s1d1ddlavjXz1PUti7QGwI4Y/may+ay+ml+k3GyJWTpsm1+AnyrGzHbwrglhystL4SuEdzWQX7PWWS+MsTCWBbBXB6lfC6Dmy2jhS20kIk+SVGkBy3in1rP6Pf24F6y/VELmTR8diN6nS7mpZxsifRrxynsG7rkhQWxx9bhW9Wg4voVRVGuEqqoUhRFOc/Nl3FzZWTZQfgNtKCJZlX/mCydTIIEa2WcUn+qoqC6IPP6CXw9DZMWVeANJY5/ZgOpXxzBzXrhB3pTgPij6yq6MXNJD1lE7lxB4pItehcIn47VFZv5i1+2685oCRK5p4fC4REKB4YpnkgQunE55rLIFZ1hM+J+mr6yjez7fRQODiM0jeCODvxrmkn8cF9FWIW5PEL88XXo0epBtdJxcVJFCgeGsJMF4o+vQ5Yc7LE8RmMAozE4pT8XKaX3NV5aUAE4kuRzB2j+nevR1HkqRVGUq4IqqhRFUQA7WSD500OU+1PeA5ogcF0b4du6q270nYzXtfCvba4bjACQ33sO856eST+3Zhn4ehtpat+Gmy+DJhZkkKrZHSNybw+ZN06On03SGwPEP7X+ioo7LWwhTA1ZdhE+ncjdPSR+fKBiGG6iP4V/UyvRe3qu6IyREfcT2bmK8C1dIEDaksSz+6rS/8r9aZIvHiX++LqKbZpSSspn0ox+d8/FWPbd5xBhi8YnN2K2VYdN1ONmy+Te7au9KCG/f4jI7Sum/0UqiqIoi44qqhRFueY5mRKJH1x24+1K8h8PIgzvDMyl55qs7jjZt04jTA23WHteEjDedZqKuRqk6mRLuOki5eEcetiH3uive1ZMD1wMgHDzZdA1tOCVF3d6yCR6/2qSzx8msKmN7AcDFQXVBYW95wjtWHbFwQ2aoUHEh5Mr4SRy2EO149RLx8dwc+WKosrNlLxu3WWx7DJTYuyH+2j6ylb08BT/jKTEmSAhsN5WS0VRFGXpUUWVoijXPCdTrDvHKPfxGYLXd6DFLm4TMxoCGK0hyoMZrO44peNjNT/Wv75pTq53qpxUkcSP91M+c3F7ovAbNH5hM0ZbqGZhJXQNPeZHj1Vvi5spYej41jTR2BhElmxyz9Se/QRQODwyrW7QRNxMadLCVpYrizs3V677MW66hJsrT72oMjTM9jDl/nTNZV/P5Of3FEVRlKVBzalSFOWa5yTqR5pju1UhC3rYouHJjUggtKMD9Bpdn6YAZuvsFAcz4ZZs0m+cqCiowJu9NPq9PTjp+RsqDKD5DKxlEfT4JMXaNKLVJ1M+l0VMlMAovPNil5psKPGlQ5Anmx+mB0wid9fe/qmFTKyuaMXnddJF7GRhwu6WoiiKsjipTpWiKNe8Cc8LaQJRI6FNj/iI3LUSN1+i6ctbSf/yOKWTSYSpEbiundANy2d9K990uDmbwoHhmmuyYOOM5jFqhDTMNS1o4VvTRPHQSM11/5rZ6+5pARP7bAarO1Yzsj6wua0qFl0LWWBoUGNQsDA19ICJnSxQOpWkeGwUPeYnsLkNPWrVDDUxWoLEP7eR9EtHxyPrre4Y0Qd6x0MynEyR3EeD5D7ytkUaLUEiO1dhtofRfOrXtKIoylKgflorinLN06M+9Li/ZscqsLkVLVT7jI9maGgRP0Qg/sQG75yQ8IbTugUbJ1nwQidCFkKb2+G9VeyJh906mbnrhkjH9bbeFW2EoaMFjfEURM3Sidy1ktLpZFX0eWB7B1pk9sI5jOYgyecPEXt4LcLSKR4d9ZIINYF/Qwuhm5djn8t4M8XCFuL8GbLIXStIv3K86vUid/cggdFvflKxRTD3fj+xR9fgW9tcFZGuWQb+VY2YXw55fz80gRa4+P1wciWSPzvkJUqeZw/lGPvuHuJPbsS/Wm0RVBRFWQpUUaUoyjVPD/to+PwmEj/cjz1y8WyVb00T4du7pzRLSPMZ4DNwCzalU0nSrx7HHskh/AahG5YR2NI+pblGs0VYGsJv/D/t3XtwlNX9P/D3c9t7djc3sgkkJAQId7AgGBQrBYmVVrDeav2pVKvFoq0to0Lt18uMjhanOsJYteOIfjuOUGWQrxdaEASsAioXuUkUBALEhEuy2c1u9vqc3x8rC8tuQmCTbGLer5md4XnOk2fPfrJs8sk553MgAqkLaWh5nVPKO+oPoWVnPXwbj8TXKxlKHLBXDYLqNCHaFECo1oPsGUMRPNiI0CE3JLMG67i+UPMtUMzpFak4k2IzwDljKBrf+QqWYX3gnDk0VoBCiyV6J//3S4hQFJKmwDF9MAylTsiaAtOwPlCyzWj+7yFEGwNQc8ywTeofS9L+vS/lmqumld8gr68dstPcSl+MQIrZoLonmJBQncm79ltoBbYufd8QEdGFYVJFRIRY8Ynsm0bE9qkKRCBbtdg+Ve3YZ+oUIQSCNW40rTi915MIRND83xqE6prhrBqUsrKdHtEhAhFIsnTele9ia3FCCB1uQtTdAkNfO9R8K2SbEbZLS+Bd823S12iFNsidMDVR6AKBvSfQvCGxzHyopgmNb+9C9nXD0fDmjnhSYihxQOvngFZgg1ZgPa9Yt4ekyDAUZSHv1jEIH/UgfNwPQ4EVuj8E98qv42vlRDgK94qvkDvrIsh5VihmDcqAHBgKsyAiOiRVjk0ldLe0WpQEAgjXeqG2klS1JlSbuogFEFvrJ0IRAEyqiIi6OyZVRETfU6yGtMqH680heNcmJzEAENrXgOhloYSkSegC0aYAfJ8fRfBAI2SjCsu4IhjLstvVDxHVEar1ovHt3fE1QD4AisOI7BtHwjwkHwDQ/ElNbMRKlmAakoesSaWdsgeW7guh+dOalG3RxgAiJ3wQZ6xVCtU0xdc65c66qMOTKiCWWKlOE1SnCXowAvd71Qh9myIxEkDL9jqok8sgKbEaTvLZo2ZtTKcEkFTQBPh+KqQvHNvbTMT27FKsGiRVSf0cCZ0HIPfselJCCETdgdijOQQ1zwLZokHtwOqSRETdAZMqIqIOIkJR6N7W1yqF67zQ8q3x42hjC07+88v4NDkdQXhWfgNDeQ4cVQPPmfjovtj+WmcXVYg2BeFZ9Q2c1wz9ft+pHIiQDqhybH1TioIKHUGEo0nrpM4UrvdByTIiEkwuXx/45mRCbDqDCEcROeZrtT3S4IeICkitzPaUjCqUHDOiDan3l9L62hOO9XAUoUNuNH3w9el9uVQZ9ikDYKrIhWzUoLlsgCylTNiMFXlp79mVSUIIhOua0bhsd8L7wlDigH3aQKjZ5zeqR0TUnfXsP4EREXUnKUqrn+nMUQk9GIF3w8GkfZIAILS/IVbkohUiqiPqCSB83J9ydAQAQoeaoLeEIckSFLsJap4FqtPUaQkVAECRIWmt/1hRsoyxTYVTOUcp844gaQrUNhI3rdAOSW2j/1YD7FeWx0aQzmIckgdIscRM/34dW7QpAPc7XyVudBzR4fnPPkROtCBy0g/f1lrYpw2MJVZnPle2CVmXl0I2KLHRHn8IUV8ooaR7dxdtCqDx7d1JiXaopgm+TUdio3dERD8QHKkiIuogslmDoTwHof0NyY2qnPALvQhGY9XoWhGoPglDkT3pvIjqCB3xwLNmPyyjXG3251x7LnU0xarBPLoQ/i+OJrVJJhVaURbMIwrg/7IuqYCGsQNLqbdGNqrIurQEJ1Oti1JlmEf0OWeVRq0wC7n/bzS8Gw4iXNsM2arBPNoF2aTi5GvbABF7LfYrB8C/9btYtcEUmj+tgdrHipat30EfkI3sa4ciXNeMqC8EY1kOtAIrlCwjot4gAtUn4N9RB0QFTMPyYRlR0KGbM3eWyAl/q4VSWvYeh+XiovZvpExE1M0xqSIi6iCyUYX9JwPQeMIX35Mo1iAhe+bQ5CpushSrRpeCpKb+5T7qCaJx2W5AAEobRRFki9blexxJqgLrxUWINgUQ/Ob0PlSyVYO9ahCa3quGbNbg/FkFmv6zD/r3GxAbB+e2vVdYB1JyzXDOHArPqn3Q/bFRM8VpgmP64Hb1QdYUyK4sOK8ZCj0QRuhwE/zb6hCpP73JcvCbkwiWZyNyInma4ynRxhYYSpyx679tRNQThKF/7Djw9QkYiu2IeoNoXLYHkeOnpyz6Pj2Mlp3HkPurkfF9rrqrhP8DZ4voQLhrk34ios7EpIqIqAOpThNybh6F8DEfQoeboDhNMJY6oXy/D9IpklmFeWg+WnYdS3kfU0VeyvPB/Q3xRCxc74VxQDaCKQovZF1RCjkDpbgVmxH2qoHQJ/VH5IQvNlITFfCuOxBfixRxt8A+pRzNn9bAenFfGEqcUCxd01fZoMI4MAe5rjGxqYhSbN+otkZM9GAEui8M3ReCZFQgWzQoNiOingA8/96X8muC+xqgFVgRPupJ2a7mWhD1BKDmWWCbVIrIST8iJ3xQHCYYBtkhwlGEDnsSEqp4f7xB+Hceg62yuOv3PzsPap/Wp1rKFg2S4dxbFRAR9RRMqoiIOpiSZYSSZWxz41ZZU2CtLEHwoBv6WRvxWsYWtVry/Mx9tHybjsDx00FQcixo2VkHEYxCcZqQdUUpDMUOSFJmfuFWzBpEKArfZ0djScFZo3Gninnk3Dii7ep3nUSSpPj36FyizSF4NxxAYPfx+DnFaUL2tcPia6dSCX7biNxbx8C/vS5lEQrL2CJ41n4L+xVlcL9XnbDuyqdIyL5+OIJH3K3eP7DnGCxjXJ1SxbGjKM7YWr5UI3aW8X0htbKpNhFRT8RCFUREGaI6Tci5ZRSyriyHocQB4+Bc5Nw8ErbK4lY3wTUUO04f6AJN73+NyEk/7FPK4fzFMGTfOAKmQXmdUp78fIhQFJG65lanN4aOejotodKDEUQaW9Dy1XG0fHUMkcYW6MHWE6DWiKgO39bahIQKiO0f1fCvnZDbSmiEgGRUkH39sIQRQ8mswnnNEMgOEyyjXPCuP5hYyAIAogLuFXthHpR6tBLAOYuidAdqlhHOa4fCWJ4dPycZFdgu7w9TRR6UDL9HiYg6EkeqiIgySLWboI4phHlYrEhCW9XngFjZbsmsJlRUCx1oROhAI5zXD+s2+//ovlBSP8+kOjunn9GWMFp21KP544MJRSJsl5XAPKaw1WQ15b18IbRsrY0fay4blFwLRCCC4MFG6J4gtGIHwoebkr7WPLwPAKD5syOwXdYfsjE21U1EdPi/Oga7awC0oix4PzqQ8rlFIBJLnFopt24Z7eoR5dZVpxn2qwfHNnwORyEZFUhWIxRO/SOiHxgmVURE3YDczl8yVYcJuTePgvuDr2MjQYiNftgnD4ChMLlaYKaEG1tgGeWCb/OR5EZFgqHEkXy+A0RPtqB5w8Gk883/rYGhnx1KsbP9N4voEGEdSo4ZWVeUIVLfjHBdrOKfc8ZQCF3AMX0wmt6tTlg7ZRySB+uP+yO4+zjCB5sQPpicdAX7OqCVnOP7pQsYBjgR2pe4Zk7Nt8I0OC9j0zvPl2LSOCpFRD94TKqIiHoYNdeCnOuGI9oSBqICskmFbDOkVbRARHVEvSGEatyInPTDUGSHVmi74ApzxiI7Iif8MA7KTagEKBkUOKYPbnvq3AXSQ1H4Pjvcanvz5iNw5FraXRRDUmXITiPsPxkA9/vVCaNuLTvqYZ82EHKJA9nXDoXuD0MPRWPfC4sGoQuEvosVEol6g4gcT1xX1PLVMRgH5UAyKsnT/wBA+r4q4ZUDER7lg3/bd4CuwzzSFUsO27EejIiIug6TKiKiHki2aB02/UvoAuG6ZjS+tQvi+zLXftRCtmjI+eVIqLmW8++f3YjwnmNQc80wjxyGqDsA2ahAMqlQ+1g7vNy7HoogcsKPqDfU+jXeECInWyBpCmTt3CODstUQq1K46XDKaYyeD/fD0N8B1WlOWB8mdAG9KQDNlYXoCT+0vnaok6xo/u8hRI7FqvlJqgzJqCLrijJ4/pNcQdB6cV/I5lhZfMVmjI3sifaPaBIRUddiUkVE1MvpzUE0Lt8TT6ji5/1huN+vRs71I847gVPMGqyXFCPa0AL/l99BNmvQBufFSmkrHV8jKXLcD9/nR6G5bPHE5WyaKwst27+DcnkpZMe5kxNJkaE4TAgfSV0WHd8no+pZ+4VFjjWjYckuiPDpEajYCF0FvOu+RbQxAMuPiqCYNZgG50K2GdC84SAiJ/xQHCbYKothGJCdkHi2JwkkIqLMYVJFRNTLRb2hVgtKROp9iLaEL2hUTDFrUPpqUHLMCNd54flwPyIn/bGy75f2h6HY0SGjbXogDO/HBxE+6kX2dcPQsud4bHPZM6kyTEPz0LhsD8zDC9pd0ONcEyrPTkSjzUE0rtibkFABsWqI3jX7Yb2kHwJfn4ShX2w9lWzSYBqQA81lAyICkKWETaKFELEiD0JAMqlMroiIuikmVUREvZwIpVjTc6ao3nZ7W/eO6gh+fRKeVaenuEVPtsD9f3thvbQE1nF9057SJsI6IvU+QBdo/qQGzp9XoPmTmviIldrHiqzLS9H86WFAFxCi/a9HMqpQss2INrakbNeKshKOdV8YuieY8tqoJwg13wrHwNyk/aVSrfOKNgcR2HsC/q210INRGMqykVVZDMVp6pTRPiIiunBMqoiIejmljVEb6ft1UBdK94XgXZe6bLhv42GYh/VJf52QHNvMN3LSj3CtF54Pv4VljAtqZXGsXZHQsvtYrEKfhPNaI6bYDLBPK0fjv3YllGgHAPOoAihnjbSJVvblipOkdm3YG/WF4H6vGuHDp6ceBr86juA3J5F762hoedZ2vwYiIup8/FMXEVEvJ1s1mEe7UrbZJpW2Kwloje6PtD4Spgvo3tSjOudDsRpgrex3+rbeIJo/PgT3ir1w/99eSJKEwN7YBr62SaXnPeVQK8xC7q1jYBiQDcmsQs2zwDF9cGz/qbP2vZKtWusb86pyu5870tCSkFCdbtDh3XDogjYzJiKizsORKiKiXk42qrBdWgI11wLf5sPQfWEoThNsl5fCUOJIb6pZawnGKefY7Li9DP2dsIwrgv+L05v1QpVhn1aOwL4GGPo5YK0sjlUeNJzfjz5ZUyAX2OD8WUUsQVSkVsuyy1YN1gnF8H1ak9RmHd8XUjvXRAWrT7TaFvq2AXow0uEVFImI6MLxE5mIiKBYDbD8qBCmwbmALgBVTmuE6hTZrEFxmhB1B5LaJLOaUJQhHYrFANvEElhGFyJywgdJU6DkmCGZVBhLsyEpMuQ0pjECseQT50hkRCACyaQga8oA+LfUIuoOQHGaYBlbBBGMQgTCQDtGqyRj68mXpMo9ZuNfIqLegkkVEREBACRJ6vBNZRWbAc5rhqBhyc7EaYCKBOeMoR26CbBsVCEbVag5iSXO0YX75OqBCJrXHoCab4XlR0WQrRr05hBadtQhctwPJdsENefca7pMFfnwbTqSum1EQdK0QyIiyiwmVURE1KnUfCtyZ12E4P4GhI94oLpsMA3KhWI3QpJ/WCMup15P5LgP3rXfJre3c/qfYjfAekm/pMRKcZpgG98XUgdNmyQioo7R4z6Vg8EgxowZA0mSsH379oS2HTt2YNKkSTCZTCguLsaCBQsy00kiIoqTZAmqwwTrj4rgvGYIbOP7Qc02/yDLgstmDVqhLXWjIkHNa1/lQdmkwTKuL3JuHQ3TqAIYB+bA8bMK5Nw0Eoq9fXtsERFR1+lxI1UPPvggioqK8OWXXyac93g8mDZtGqZOnYqXXnoJO3fuxB133AGn04m77747Q70lIqLeRDZrcFw1GCff3AEROKNCnwQ4pg+GYm3/tD3FrEExa9AKbLHNf+UfXhJKRPRD0aOSqpUrV2LVqlVYtmwZVq5cmdD2xhtvIBQK4dVXX4XBYMDw4cOxfft2PPvss0yqiIioyyi5ZuTdNgaBbxsROuSGkmOCeXhBbLqjev57ckmSBLAwBRFRt9Zjkqr6+nrcddddeOedd2CxJE+f2LhxIy6//HIYDKcXPVdVVeGvf/0rGhsbkZ2dnfQ1wWAQweDpPVI8nhR7ghAREZ0HSZKgOEywXlQIy+gCjjAREfUCPeKTXgiBWbNmYfbs2Rg3blzKa+rq6lBQUJBw7tRxXV1dyq956qmn4HA44o/i4uKO7TgREfVqTKiIiHqHjH7az5s3D5IktfnYu3cvFi1aBK/Xi/nz53fo88+fPx9NTU3xx+HDhzv0/kRE1LlEVIcQItPdICKiXi6j0//mzp2LWbNmtXnNgAEDsHbtWmzcuBFGY+JmI+PGjcMtt9yC119/HS6XC/X19Qntp45dLlfKexuNxqR7EhH1VhF3C8K1XgRr3FCzzTANyoWcZYTczjLgXSnqCSJ4yI3gNychWTVYRxdCcZrS3uCXiIjoQmT0p09+fj7y8/PPed3ChQvxxBNPxI9ra2tRVVWFpUuXYsKECQCAyspKPPzwwwiHw9C0WHWl1atXo6KiIuV6KiIiOi1y0o+Gpbug+0Lxc83/rYFzxhAYy7K7RflzPRiBHohARHQEDzTC//lR6M2x/gZ21MM2qT8sYwqZWBERUZfrET95SkpKEo5tttgeIOXl5ejXrx8A4Fe/+hUef/xx3HnnnXjooYewa9cuPP/883juuee6vL9ERD1JxBuEZ/X+hIQKAKALuN+tRt6si6BmmzPTue9FGlvg+egAQvsbAACaywb71HL4t9YiVNMEAGj++BCMg3KZVBERUZfL/J8eO4jD4cCqVatw4MABjB07FnPnzsUjjzzCcupEROcgghGEDjelbozoiJzwd22HzhJtCqDhzR3xhAoAwnXNcL+7F9bx/SBpp3+UBc+4hoiIqKv0yD/nlZaWplyYPGrUKHz88ccZ6BERUc8lInqb7Xow0mZ7Zwt82wjdF05uiAr4v6yDaWg+WnbE1tCKcLSLe0dERPQDGqkiIqILIxsUyFmGVtu1PtYu7E0iPRJFcN/JVtvDtR6o+af7ZxyQ0xXdIiIiSsCkioiot7NosF3WP2WTsSIXklnr4g6dJsky5KzWq7TKZg0iGBudMpTnQHGwoisREXU9JlVERL2catJgKHHAOWMI1IJYISDZZoBtUn/Yf1wGtY2kprNJsgTrmMJW280jCxCq9cB+ZTkc0wZCsbQ+4kZERNRZeuSaKiIi6liq3QTZqMCZb4HQAUkCJKsGxZi5UapTFKcJtitK0bzuYMJ505A8GAflwjQ0H4qVyRQREWUOkyoiIgIAyEYNcjdIos4mm1RYRrlgKs9F8FAjEBEw9HdAyTJCzuDURCIiolOYVBERUbcnG1XIRhVqTmb3yyIiIkqFa6qIiIiIiIjSwKSKiIiIiIgoDUyqiIiIiIiI0sCkioiIiIiIKA1MqoiIiIiIiNLApIqIiIiIiCgNTKqIiIiIiIjSwKSKiIiIiIgoDUyqiIiIiIiI0sCkioiIiIiIKA1MqoiIiIiIiNLApIqIiIiIiCgNTKqIiIiIiIjSwKSKiIiIiIgoDUyqiIiIiIiI0qBmugPdiRACAODxeDLcEyIiIiJK5dTvaad+byPqDphUncHr9QIAiouLM9wTIiIiImqL1+uFw+HIdDeIAACSYJofp+s6amtrkZWVBUmSOvz+Ho8HxcXFOHz4MOx2e4ffvzdgDNPD+KWPMUwP45c+xjA9jF/6Mh1DIQS8Xi+Kioogy1zJQt0DR6rOIMsy+vXr1+nPY7fb+UGeJsYwPYxf+hjD9DB+6WMM08P4pS+TMeQIFXU3TO+JiIiIiIjSwKSKiIiIiIgoDUyqupDRaMSjjz4Ko9GY6a70WIxhehi/9DGG6WH80scYpofxSx9jSJSMhSqIiIiIiIjSwJEqIiIiIiKiNDCpIiIiIiIiSgOTKiIiIiIiojQwqSIiIiIiIkoDk6ouFgwGMWbMGEiShO3btye07dixA5MmTYLJZEJxcTEWLFiQmU52U9dccw1KSkpgMplQWFiIW2+9FbW1tQnXMIapHTx4EHfeeSfKyspgNptRXl6ORx99FKFQKOE6xq9tTz75JCZOnAiLxQKn05nympqaGkyfPh0WiwV9+vTBAw88gEgk0rUd7cZeeOEFlJaWwmQyYcKECfjss88y3aVua8OGDfj5z3+OoqIiSJKEd955J6FdCIFHHnkEhYWFMJvNmDp1Kr755pvMdLYbeuqpp3DxxRcjKysLffr0wcyZM1FdXZ1wTSAQwJw5c5CbmwubzYbrrrsO9fX1Gepx9/Liiy9i1KhR8Q1+KysrsXLlyng7Y0eUiElVF3vwwQdRVFSUdN7j8WDatGno378/tmzZgmeeeQaPPfYY/vGPf2Sgl93T5MmT8a9//QvV1dVYtmwZ9u/fj+uvvz7ezhi2bu/evdB1HS+//DJ2796N5557Di+99BL+/Oc/x69h/M4tFArhhhtuwD333JOyPRqNYvr06QiFQvj000/x+uuv47XXXsMjjzzSxT3tnpYuXYo//elPePTRR7F161aMHj0aVVVVOHbsWKa71i35fD6MHj0aL7zwQsr2BQsWYOHChXjppZewefNmWK1WVFVVIRAIdHFPu6f169djzpw52LRpE1avXo1wOIxp06bB5/PFr/njH/+Id999F2+99RbWr1+P2tpa/OIXv8hgr7uPfv364emnn8aWLVvwxRdf4Cc/+QlmzJiB3bt3A2DsiJII6jIffPCBGDJkiNi9e7cAILZt2xZv+/vf/y6ys7NFMBiMn3vooYdERUVFBnraM6xYsUJIkiRCoZAQgjE8XwsWLBBlZWXxY8av/RYvXiwcDkfS+Q8++EDIsizq6uri51588UVht9sT4tpbjR8/XsyZMyd+HI1GRVFRkXjqqacy2KueAYBYvnx5/FjXdeFyucQzzzwTP+d2u4XRaBRvvvlmBnrY/R07dkwAEOvXrxdCxOKlaZp466234td89dVXAoDYuHFjprrZrWVnZ4tXXnmFsSNKgSNVXaS+vh533XUX/vnPf8JisSS1b9y4EZdffjkMBkP8XFVVFaqrq9HY2NiVXe0RGhoa8MYbb2DixInQNA0AY3i+mpqakJOTEz9m/NK3ceNGjBw5EgUFBfFzVVVV8Hg88b/u9lahUAhbtmzB1KlT4+dkWcbUqVOxcePGDPasZzpw4ADq6uoS4ulwODBhwgTGsxVNTU0AEP/c27JlC8LhcEIMhwwZgpKSEsbwLNFoFEuWLIHP50NlZSVjR5QCk6ouIITArFmzMHv2bIwbNy7lNXV1dQm/iAGIH9fV1XV6H3uKhx56CFarFbm5uaipqcGKFSvibYxh++3btw+LFi3Cb3/72/g5xi99jGHrTpw4gWg0mjI+vT02F+JUzBjP9tF1Hffffz8uvfRSjBgxAkAshgaDIWl9JGN42s6dO2Gz2WA0GjF79mwsX74cw4YNY+yIUmBSlYZ58+ZBkqQ2H3v37sWiRYvg9Xoxf/78THe522lvDE954IEHsG3bNqxatQqKouC2226DECKDryCzzjd+AHD06FFcddVVuOGGG3DXXXdlqOfdx4XEkIh6ljlz5mDXrl1YsmRJprvSo1RUVGD79u3YvHkz7rnnHtx+++3Ys2dPprtF1C2pme5ATzZ37lzMmjWrzWsGDBiAtWvXYuPGjTAajQlt48aNwy233ILXX38dLpcrqWrOqWOXy9Wh/e5O2hvDU/Ly8pCXl4fBgwdj6NChKC4uxqZNm1BZWdkrY3i+8autrcXkyZMxceLEpAIUvTF+wPnHsC0ulyupml1viGF75OXlQVGUlO+x3h6bC3EqZvX19SgsLIyfr6+vx5gxYzLUq+7p3nvvxXvvvYcNGzagX79+8fMulwuhUAhutzthxIXvydMMBgMGDhwIABg7diw+//xzPP/887jpppsYO6KzMKlKQ35+PvLz88953cKFC/HEE0/Ej2tra1FVVYWlS5diwoQJAIDKyko8/PDDCIfD8TVCq1evRkVFBbKzszvnBXQD7Y1hKrquA4iVqQd6ZwzPJ35Hjx7F5MmTMXbsWCxevBiynDhQ3RvjB6T3HjxbZWUlnnzySRw7dgx9+vQBEIuh3W7HsGHDOuQ5eiqDwYCxY8dizZo1mDlzJoDY/+E1a9bg3nvvzWzneqCysjK4XC6sWbMmnkR5PJ74iALFpt7fd999WL58OdatW4eysrKE9rFjx0LTNKxZswbXXXcdAKC6uho1NTWorKzMRJe7PV3XEQwGGTuiVDJcKKNXOnDgQFL1P7fbLQoKCsStt94qdu3aJZYsWSIsFot4+eWXM9fRbmTTpk1i0aJFYtu2beLgwYNizZo1YuLEiaK8vFwEAgEhBGPYliNHjoiBAweKKVOmiCNHjojvvvsu/jiF8Tu3Q4cOiW3btonHH39c2Gw2sW3bNrFt2zbh9XqFEEJEIhExYsQIMW3aNLF9+3bx73//W+Tn54v58+dnuOfdw5IlS4TRaBSvvfaa2LNnj7j77ruF0+lMqJZIp3m93vh7DIB49tlnxbZt28ShQ4eEEEI8/fTTwul0ihUrVogdO3aIGTNmiLKyMtHS0pLhnncP99xzj3A4HGLdunUJn3l+vz9+zezZs0VJSYlYu3at+OKLL0RlZaWorKzMYK+7j3nz5on169eLAwcOiB07doh58+YJSZLEqlWrhBCMHdHZmFRlQKqkSgghvvzyS3HZZZcJo9Eo+vbtK55++unMdLAb2rFjh5g8ebLIyckRRqNRlJaWitmzZ4sjR44kXMcYprZ48WIBIOXjTIxf226//faUMfzoo4/i1xw8eFD89Kc/FWazWeTl5Ym5c+eKcDicuU53M4sWLRIlJSXCYDCI8ePHi02bNmW6S93WRx99lPL9dvvttwshYmXV/+d//kcUFBQIo9EopkyZIqqrqzPb6W6ktc+8xYsXx69paWkRv/vd70R2drawWCzi2muvTfhjU292xx13iP79+wuDwSDy8/PFlClT4gmVEIwd0dkkIXrxKn8iIiIiIqI0sfofERERERFRGphUERERERERpYFJFRERERERURqYVBEREREREaWBSRUREREREVEamFQRERERERGlgUkVERERERFRGphUERERERERpYFJFRERERERURqYVBERdROSJLX5eOyxxwAAy5cvxyWXXAKHw4GsrCwMHz4c999/f/w+r732GiRJwlVXXZVwf7fbDUmSsG7dunM+55IlSwAAgUAAs2bNwsiRI6GqKmbOnNnJUSAiIup51Ex3gIiIYr777rv4v5cuXYpHHnkE1dXV8XM2mw1r1qzBTTfdhCeffBLXXHMNJEnCnj17sHr16oR7qaqKDz/8EB999BEmT57c5vMuXrw4KQFzOp0AgGg0CrPZjN///vdYtmxZmq+QiIjoh4lJFRFRN+FyueL/djgckCQp4RwAvPvuu7j00kvxwAMPxM8NHjw4aQTJarXixhtvxLx587B58+Y2n9fpdCY9z5n3efHFFwEAn3zyCdxu93m8IiIiot6B0/+IiHoQl8uF3bt3Y9euXee89rHHHsPOnTvx9ttvd0HPiIiIei8mVUREPch9992Hiy++GCNHjkRpaSl++ctf4tVXX0UwGEy6tqioCH/4wx/w8MMPIxKJtHrPm2++GTabLeFRU1PTmS+DiIjoB4VJFRFRD2K1WvH+++9j3759+Mtf/gKbzYa5c+di/Pjx8Pv9Sdc/9NBDOH78OF599dVW7/ncc89h+/btCY+ioqLOfBlEREQ/KEyqiIh6oPLycvzmN7/BK6+8gq1bt2LPnj1YunRp0nVOpxPz58/H448/njLpAmJTCgcOHJjwUFUuuSUiImovJlVERD1caWkpLBYLfD5fyvb77rsPsizj+eef7+KeERER9Q78UyQRUQ/y2GOPwe/34+qrr0b//v3hdruxcOFChMNhXHnllSm/xmQy4fHHH8ecOXNStrvdbtTV1SWcy8rKgtVqBQDs2bMHoVAIDQ0N8Hq92L59OwBgzJgxHfa6iIiIejImVUREPciPf/xjvPDCC7jttttQX1+P7OxsXHTRRVi1ahUqKipa/brbb78df/vb37Bnz56ktl//+tdJ55566inMmzcPAHD11Vfj0KFD8baLLroIACCESPflEBER/SBIgj8ViYiIiIiILhjXVBEREREREaWBSRUREREREVEamFQRERERERGlgUkVERERERFRGphUERERERERpYFJFRERERERURqYVBEREREREaWBSRUREREREVEamFQRERERERGlgUkVERERERFRGphUERERERERpeH/Ayn0GrhV+SYVAAAAAElFTkSuQmCC",
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, ax = plt.subplots(figsize=(8,6)) # Set figsize\n",
+        "sns.set_style('darkgrid', {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
+        "sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Class Name', palette='Set2')\n",
+        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
+        "plt.title('Scatter plot of news using t-SNE')\n",
+        "plt.xlabel('TSNE1')\n",
+        "plt.ylabel('TSNE2');"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JQbX4pcMdBe"
+      },
+      "source": [
+        "## Outlier detection\n",
+        "\n",
+        "To determine which points are anomalous, you will determine which points are inliers and outliers. Start by finding the centroid, or location that represents the center of the cluster, and use the distance to determine the points that are outliers.\n",
+        "\n",
+        "Start by getting the centroid of each category."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nUIkLxtMK4qC"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"centroids\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"TSNE1\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          14.79300308227539,\n          3.0627570152282715,\n          3.694504499435425\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TSNE2\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          -0.5331496596336365,\n          -27.739778518676758,\n          27.894010543823242\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "centroids"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-eaba8f44-0c24-4160-b878-732d05acdd39\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>TSNE1</th>\n",
+              "      <th>TSNE2</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Class Name</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>sci.crypt</th>\n",
+              "      <td>3.694504</td>\n",
+              "      <td>27.894011</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sci.electronics</th>\n",
+              "      <td>14.793003</td>\n",
+              "      <td>-0.533150</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sci.med</th>\n",
+              "      <td>-23.307659</td>\n",
+              "      <td>2.060012</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sci.space</th>\n",
+              "      <td>3.062757</td>\n",
+              "      <td>-27.739779</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eaba8f44-0c24-4160-b878-732d05acdd39')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-eaba8f44-0c24-4160-b878-732d05acdd39 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-eaba8f44-0c24-4160-b878-732d05acdd39');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-3313c489-00dd-4f49-969f-5cd44732e11a\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3313c489-00dd-4f49-969f-5cd44732e11a')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-3313c489-00dd-4f49-969f-5cd44732e11a button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_ee7e7414-03b5-4251-afe8-cf51d81749a2\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('centroids')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_ee7e7414-03b5-4251-afe8-cf51d81749a2 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('centroids');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                     TSNE1      TSNE2\n",
+              "Class Name                           \n",
+              "sci.crypt         3.694504  27.894011\n",
+              "sci.electronics  14.793003  -0.533150\n",
+              "sci.med         -23.307659   2.060012\n",
+              "sci.space         3.062757 -27.739779"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "def get_centroids(df_tsne):\n",
+        "  # Get the centroid of each cluster\n",
+        "  centroids = df_tsne.groupby('Class Name').mean()\n",
+        "  return centroids\n",
+        "\n",
+        "centroids = get_centroids(df_tsne)\n",
+        "centroids"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GJH4Oo6E-r_6"
+      },
+      "outputs": [],
+      "source": [
+        "def get_embedding_centroids(df):\n",
+        "  emb_centroids = dict()\n",
+        "  grouped = df.groupby('Class Name')\n",
+        "  for c in grouped.groups:\n",
+        "    sub_df = grouped.get_group(c)\n",
+        "    # Get the centroid value of dimension 768\n",
+        "    emb_centroids[c] = np.mean(sub_df['Embeddings'], axis=0)\n",
+        "\n",
+        "  return emb_centroids"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1tas9Yg4_iyq"
+      },
+      "outputs": [],
+      "source": [
+        "emb_c = get_embedding_centroids(df_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aMvdYLjKl32a"
+      },
+      "source": [
+        "Plot each centroid you have found against the rest of the points."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jpN02WY3Ogji"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Plot the centroids against the cluster\n",
+        "fig, ax = plt.subplots(figsize=(8,6)) # Set figsize\n",
+        "sns.set_style('darkgrid', {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
+        "sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Class Name', palette='Set2');\n",
+        "sns.scatterplot(data=centroids, x='TSNE1', y='TSNE2', color=\"black\", marker='X', s=100, label='Centroids')\n",
+        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
+        "plt.title('Scatter plot of news using t-SNE with centroids')\n",
+        "plt.xlabel('TSNE1')\n",
+        "plt.ylabel('TSNE2');"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "onFfUf1XoEQW"
+      },
+      "source": [
+        "Choose a radius. Anything beyond this bound from the centroid of that category is considered an outlier."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "87cDfNpvOu7f"
+      },
+      "outputs": [],
+      "source": [
+        "def calculate_euclidean_distance(p1, p2):\n",
+        "  return np.sqrt(np.sum(np.square(p1 - p2)))\n",
+        "\n",
+        "def detect_outlier(df, emb_centroids, radius):\n",
+        "  for idx, row in df.iterrows():\n",
+        "    class_name = row['Class Name'] # Get class name of row\n",
+        "    # Compare centroid distances\n",
+        "    dist = calculate_euclidean_distance(row['Embeddings'],\n",
+        "                                        emb_centroids[class_name])\n",
+        "    df.at[idx, 'Outlier'] = dist > radius\n",
+        "\n",
+        "  return len(df[df['Outlier'] == True])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CsVsod5MKd3X"
+      },
+      "outputs": [],
+      "source": [
+        "range_ = np.arange(0.3, 0.75, 0.02).round(decimals=2).tolist()\n",
+        "num_outliers = []\n",
+        "for i in range_:\n",
+        "  num_outliers.append(detect_outlier(df_train, emb_c, i))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "vReUSOjbNHQv"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1400x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Plot range_ and num_outliers\n",
+        "fig = plt.figure(figsize = (14, 8))\n",
+        "plt.rcParams.update({'font.size': 12})\n",
+        "plt.bar(list(map(str, range_)), num_outliers)\n",
+        "plt.title(\"Number of outliers vs. distance of points from centroid\")\n",
+        "plt.xlabel(\"Distance\")\n",
+        "plt.ylabel(\"Number of outliers\")\n",
+        "for i in range(len(range_)):\n",
+        "  plt.text(i, num_outliers[i], num_outliers[i], ha = 'center')\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gNISxrzwGvBH"
+      },
+      "source": [
+        "Depending on how sensitive you want your anomaly detector to be, you can choose which radius you would like to use. For now, 0.62 is used, but you can change this value."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PMNFFSDOTELn"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_outliers"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a002d189-9eb2-405c-bd7a-779a247f1354\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Text</th>\n",
+              "      <th>Label</th>\n",
+              "      <th>Class Name</th>\n",
+              "      <th>Embeddings</th>\n",
+              "      <th>Outlier</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>Re: Source of random bits on a Unix workstati...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>[0.024040421, -0.06749866, -0.097997, -0.04392...</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>* REPORT ON PRIVACY-PROTECTING OFF-LINE CASH ...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>[0.0060990495, 0.01995056, -0.08278795, -0.050...</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>Re: Tempest\\nNntp-Posting-Host: ely.cl.cam.ac...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>[-0.024053391, -0.068649784, -0.036115933, -0....</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>Re: Overreacting \\nOrganization: Express Acce...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>[-0.014026283, -0.04744478, -0.023989808, -0.0...</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>30</th>\n",
+              "      <td>Cryptography FAQ 07/10 - Digital Signatures\\nO...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>[0.0018564886, -0.035170633, -0.066081196, -0....</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a002d189-9eb2-405c-bd7a-779a247f1354')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a002d189-9eb2-405c-bd7a-779a247f1354 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-a002d189-9eb2-405c-bd7a-779a247f1354');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-e03474e7-9247-40b4-acf8-2cc87669aed0\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e03474e7-9247-40b4-acf8-2cc87669aed0')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-e03474e7-9247-40b4-acf8-2cc87669aed0 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                                                 Text  Label Class Name  \\\n",
+              "7    Re: Source of random bits on a Unix workstati...     11  sci.crypt   \n",
+              "8    * REPORT ON PRIVACY-PROTECTING OFF-LINE CASH ...     11  sci.crypt   \n",
+              "11   Re: Tempest\\nNntp-Posting-Host: ely.cl.cam.ac...     11  sci.crypt   \n",
+              "23   Re: Overreacting \\nOrganization: Express Acce...     11  sci.crypt   \n",
+              "30  Cryptography FAQ 07/10 - Digital Signatures\\nO...     11  sci.crypt   \n",
+              "\n",
+              "                                           Embeddings Outlier  \n",
+              "7   [0.024040421, -0.06749866, -0.097997, -0.04392...    True  \n",
+              "8   [0.0060990495, 0.01995056, -0.08278795, -0.050...    True  \n",
+              "11  [-0.024053391, -0.068649784, -0.036115933, -0....    True  \n",
+              "23  [-0.014026283, -0.04744478, -0.023989808, -0.0...    True  \n",
+              "30  [0.0018564886, -0.035170633, -0.066081196, -0....    True  "
+            ]
+          },
+          "execution_count": 24,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# View the points that are outliers\n",
+        "RADIUS = 0.62\n",
+        "detect_outlier(df_train, emb_c, RADIUS)\n",
+        "df_outliers = df_train[df_train['Outlier'] == True]\n",
+        "df_outliers.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "h_wbM5yYE4MS"
+      },
+      "outputs": [],
+      "source": [
+        "# Use the index to map the outlier points back to the projected TSNE points\n",
+        "outliers_projected = df_tsne.loc[df_outliers['Outlier'].index]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xCt4wfYdoTJz"
+      },
+      "source": [
+        "Plot the outliers and denote them using a transparent red color."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IrAKwBp0TaNu"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, ax = plt.subplots(figsize=(8,6)) # Set figsize\n",
+        "plt.rcParams.update({'font.size': 10})\n",
+        "sns.set_style('darkgrid', {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
+        "sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Class Name', palette='Set2');\n",
+        "sns.scatterplot(data=centroids, x='TSNE1', y='TSNE2', color=\"black\", marker='X', s=100, label='Centroids')\n",
+        "# Draw a red circle around the outliers\n",
+        "sns.scatterplot(data=outliers_projected, x='TSNE1', y='TSNE2', color='red', marker='o', alpha=0.5, s=90, label='Outliers')\n",
+        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
+        "plt.title('Scatter plot of news with outliers projected with t-SNE')\n",
+        "plt.xlabel('TSNE1')\n",
+        "plt.ylabel('TSNE2');"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RVm_A9HmGwEN"
+      },
+      "source": [
+        "Use the index values of the datafames to print a few examples of what outliers can look like in each category. Here, the first data point from each category is printed out. Explore other points in each category to see data that are deemed as outliers, or anomalies."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lpZ-hcDvG13M"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            " Re: Source of random bits on a Unix workstation\n",
+            "Lines: 44\n",
+            "Nntp-Posting-Host: sandstorm\n",
+            "\n",
+            ">>For your application, what you can do is to encrypt the real-time clock\n",
+            ">>value with a secret key.\n",
+            "\n",
+            "Well, almost.... If I only had to solve the problem for myself, and were\n",
+            "willing to have to type in a second password  whenever I\n",
+            "logged in, it could work. However, I'm trying to create a solution that\n",
+            "anyone can use, and which, once installed, is just as effortless to start up\n",
+            "as the non-solution of just using xhost to control access. I've got\n",
+            "religeous problems with storing secret keys on multiuser computers.\n",
+            "\n",
+            ">For a good discussion of cryptographically \"good\" random number\n",
+            ">generators, check out the draft-ietf-security-randomness-00.txt\n",
+            ">Internet Draft, available at your local friendly internet drafts\n",
+            ">repository.\n",
+            "\n",
+            "Thanks for the pointer! It was good reading, and I liked the idea of using\n",
+            "several unrelated sources with a strong mixing function. However, unless I\n",
+            "missed something, the only source they suggested \n",
+            "that seems available, and unguessable by an intruder, when a Unix is\n",
+            "fresh-booted, is I/O buffers related to network traffic. I believe my\n",
+            "solution basically uses that strategy, without requiring me to reach into\n",
+            "the kernel.\n",
+            "\n",
+            ">A reasonably source of randomness is the output of a cryptographic\n",
+            ">hash function , when fed with a large amount of\n",
+            ">more-or-less random data. For example, running MD5 on /dev/mem is a\n",
+            ">slow, but random enough, source of random bits; there are bound to be\n",
+            ">128 bits of entropy in the tens  of megabytes of data in\n",
+            ">a modern workstation's memory, as a fair amount of them are system\n",
+            ">timers, i/o buffers, etc.\n",
+            "\n",
+            "I heard about this solution, and it sounded good. Then I heard that folks\n",
+            "were experiencing times of 30-60 seconds to run this, on\n",
+            "reasonably-configured workstations. I'm not willing to add that much delay\n",
+            "to someone's login process. My approach  takes\n",
+            "a second or two to run. I'm considering writing the be-all and end-all of\n",
+            "solutions, that launches the MD5, and simultaneously tries to suck bits off\n",
+            "the net, and if the net should be sitting __SO__ idle that it can't get 10K\n",
+            "after compression before MD5 finishes, use the MD5. This way I could have\n",
+            "guaranteed good bits, and a deterministic upper bound on login time, and\n",
+            "still have the common case of login take only a couple of extra seconds.\n",
+            "\n",
+            "-Bennett\n",
+            "\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "sci_crypt_outliers = df_outliers[df_outliers['Class Name'] == 'sci.crypt']\n",
+        "print(sci_crypt_outliers['Text'].iloc[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SPsQB3eHJN25"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            " Re: Laser vs Bubblejet?\n",
+            "Reply-To: \n",
+            "Distribution: world\n",
+            "X-Mailer: cppnews $Revision: 1.20 $\n",
+            "Organization: null\n",
+            "Lines: 53\n",
+            "\n",
+            "Here is a different viewpoint.\n",
+            "\n",
+            "> FYI:  The actual horizontal dot placement resoution of an HP\n",
+            "> deskjet is 1/600th inch.  The electronics and dynamics of the ink\n",
+            "> cartridge, however, limit you to generating dots at 300 per inch.\n",
+            "> On almost any paper, the ink wicks more than 1/300th inch anyway.\n",
+            "> \n",
+            "> The method of depositing and fusing toner of a laster printer\n",
+            "> results in much less spread than ink drop technology.\n",
+            "\n",
+            "In practice there is little difference in quality but more care is needed \n",
+            "with inkjet because smudges etc. can happen.\n",
+            "\n",
+            "> It doesn't take much investigation to see that the mechanical and\n",
+            "> electronic complement of a laser printer is more complex than\n",
+            "> inexpensive ink jet printers.  Recall also that laser printers\n",
+            "> offer a much higher throughput:  10 ppm for a laser versus about 1\n",
+            "> ppm for an ink jet printer.\n",
+            "\n",
+            "A cheap laser printer does not manage that sort of throughput and on top of \n",
+            "that how long does the _first_ sheet take to print? Inkjets are faster than \n",
+            "you say and in both cases the computer often has trouble keeping up with the \n",
+            "printer. \n",
+            "\n",
+            "A sage said to me: \"Do you want one copy or lots of copies?\", \"One\", \n",
+            "\"Inkjet\".\n",
+            " \n",
+            "> Something else to think about is the cost of consumables over the\n",
+            "> life of the printer.  A 3000 page yield toner cartridge is about\n",
+            "> $US 75-80 at discount while HP high capacity \n",
+            "> cartridges are about $US 22 at discount.  It could be that over the\n",
+            "> life cycle of the printer that consumables for laser printers are\n",
+            "> less than ink jet printers.  It is getting progressively closer\n",
+            "> between the two technologies.  Laser printers are usually desinged\n",
+            "> for higher duty cycles in pages per month and longer product\n",
+            "> replacement cycles.\n",
+            "\n",
+            "Paper cost is the same and both can use refills. Long term the laserprinter \n",
+            "will need some expensive replacement parts  and on top of that \n",
+            "are the amortisation costs which favour the lowest purchase cost printer.\n",
+            "\n",
+            "HP inkjets understand PCL so in many cases a laserjet driver will work if the \n",
+            "software package has no inkjet driver. \n",
+            "\n",
+            "There is one wild difference between the two printers: a laserprinter is a \n",
+            "page printer whilst an inkjet is a line printer. This means that a \n",
+            "laserprinter can rotate graphic images whilst an inkjet cannot. Few drivers \n",
+            "actually use this facility.\n",
+            "\n",
+            "\n",
+            "  TC. \n",
+            "    E-mail:  or \n",
+            "                                \n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "sci_elec_outliers = df_outliers[df_outliers['Class Name'] == 'sci.electronics']\n",
+        "print(sci_elec_outliers['Text'].iloc[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "APPg8TURJ9yt"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            " Re: THE BACK MACHINE - Update\n",
+            "Organization: University of Nebraska--Lincoln\t\n",
+            "Lines: 15\n",
+            "Distribution: na\n",
+            "NNTP-Posting-Host: unlinfo.unl.edu\n",
+            "\n",
+            "   I have a BACK MACHINE and have had one since January.  While I have not \n",
+            "found it to be a panacea for my back pain, I think it has helped somewhat. \n",
+            "It MAINLY acts to stretch muscles in the back and prevent spasms associated\n",
+            "with pain.  I am taking less pain medication than I was previously.  \n",
+            "   The folks at BACK TECHNOLOGIES are VERY reluctant to honor their return \n",
+            "policy.  They extended my \"warranty\" period rather than allow me to return \n",
+            "the machine when, after the first month or so, I was not thrilled with it. \n",
+            "They encouraged me to continue to use it, abeit less vigourously. \n",
+            "   Like I said, I can't say it is a cure-all, but it keeps me stretched out\n",
+            "and I am in less pain.\n",
+            "--\n",
+            "***********************************************************************\n",
+            "Dale M. Webb, DVM, PhD           *  97% of the body is water.  The\n",
+            "Veterinary Diagnostic Center     *  other 3% keeps you from drowning.\n",
+            "University of Nebraska, Lincoln  *\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "sci_med_outliers = df_outliers[df_outliers['Class Name'] == 'sci.med']\n",
+        "print(sci_med_outliers['Text'].iloc[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WeoJF7c8KB49"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "MACH 25 landing site bases?\n",
+            "Article-I.D.: aurora.1993Apr5.193829.1\n",
+            "Organization: University of Alaska Fairbanks\n",
+            "Lines: 7\n",
+            "Nntp-Posting-Host: acad3.alaska.edu\n",
+            "\n",
+            "The supersonic booms hear a few months ago over I belive San Fran, heading east\n",
+            "of what I heard, some new super speed Mach 25 aircraft?? What military based\n",
+            "int he direction of flight are there that could handle a Mach 25aircraft on its\n",
+            "landing decent?? Odd question??\n",
+            "\n",
+            "==\n",
+            "Michael Adams,  -- I'm not high, just jacked\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "sci_space_outliers = df_outliers[df_outliers['Class Name'] == 'sci.space']\n",
+        "print(sci_space_outliers['Text'].iloc[0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "siaPlEJhh0pr"
+      },
+      "source": [
+        "## Next steps\n",
+        "\n",
+        "You've now created an anomaly detector using embeddings! Try using your own textual data to visualize them as embeddings, and choose some bound such that you can detect outliers. You can perform dimensionality reduction in order to complete the visualization step. Note that t-SNE is good at clustering inputs, but can take a longer time to converge or might get stuck at local minima. If you run into this issue, another technique you could consider are [principal components analysis (PCA)](https://en.wikipedia.org/wiki/Principal_component_analysis){:.external}.\n",
+        "\n",
+        "To learn how to use other services in the Gemini API, visit the [Python quickstart](https://ai.google.dev/tutorials/python_quickstart).\n",
+        "\n",
+        "To learn more about how you can use embeddings, see these  other tutorials:\n",
+        "\n",
+        " * [Clustering with Embeddings](https://ai.google.dev/gemini-api/tutorials/clustering_with_embeddings)\n",
+        " * [Document Search with Embeddings](https://ai.google.dev/gemini-api/tutorials/document_search)\n",
+        " * [Training a Text Classifier with Embeddings](https://ai.google.dev/gemini-api/tutorials/text_classifier_embeddings)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "anomaly_detection.ipynb",
+      "toc_visible": true
+    },
+    "google": {
+      "image_path": "/examples/anomaly_detection_files/output_IrAKwBp0TaNu_0.png",
+      "keywords": [
+        "examples",
+        "googleai",
+        "samplecode",
+        "python",
+        "embed"
+      ]
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemini-api/tutorials/clustering_with_embeddings.ipynb b/site/en/gemini-api/tutorials/clustering_with_embeddings.ipynb
new file mode 100644
index 000000000..4035164b9
--- /dev/null
+++ b/site/en/gemini-api/tutorials/clustering_with_embeddings.ipynb
@@ -0,0 +1,2891 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xPixuBZFck9b"
+      },
+      "source": [
+        "# Visualizing embeddings with t-SNE"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "M43FZggHDEr5"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/tutorials/clustering_with_embeddings\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemini-api/tutorials/clustering_with_embeddings.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemini-api/tutorials/clustering_with_embeddings.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PMCPLbMYsljk"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This tutorial demonstrates how to visualize and perform clustering with the embeddings from the Gemini API. You will visualize a subset of the 20 Newsgroup dataset using [t-SNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html){:.external} and cluster that subset using the KMeans algorithm.\n",
+        "\n",
+        "For more information on getting started with embeddings generated from the Gemini API, check out the [Python quickstart](https://ai.google.dev/gemini-api/docs/get-started/python#use_embeddings).\n",
+        "\n",
+        "## Prerequisites\n",
+        "\n",
+        "You can run this quickstart in Google Colab.\n",
+        "\n",
+        "To complete this quickstart on your own development environment, ensure that your envirmonement meets the following requirements:\n",
+        "\n",
+        "-  Python 3.9+\n",
+        "-  An installation of `jupyter` to run the notebook.\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "First, download and install the Gemini API Python library."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VYACbzJqseql"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -U -q google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d7bEYfTFmvy9"
+      },
+      "outputs": [],
+      "source": [
+        "import re\n",
+        "import tqdm\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "import google.generativeai as genai\n",
+        "\n",
+        "# Used to securely store your API key\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "from sklearn.datasets import fetch_20newsgroups\n",
+        "from sklearn.manifold import TSNE\n",
+        "from sklearn.cluster import KMeans\n",
+        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qEunxaDOHzfi"
+      },
+      "source": [
+        "### Grab an API Key\n",
+        "\n",
+        "Before you can use the Gemini API, you must first obtain an API key. If you don't already have one, create a key with one click in Google AI Studio.\n",
+        "\n",
+        "<a class=\"button button-primary\" href=\"https://makersuite.google.com/app/apikey\" target=\"_blank\" rel=\"noopener noreferrer\">Get an API key</a>\n",
+        "\n",
+        "In Colab, add the key to the secrets manager under the \"🔑\" in the left panel. Give it the name `API_KEY`.\n",
+        "\n",
+        "Once you have the API key, pass it to the SDK. You can do this in two ways:\n",
+        "\n",
+        "* Put the key in the `GOOGLE_API_KEY` environment variable (the SDK will automatically pick it up from there).\n",
+        "* Pass the key to `genai.configure(api_key=...)`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7CItpYF3uOEf"
+      },
+      "outputs": [],
+      "source": [
+        "# Or use `os.getenv('API_KEY')` to fetch an environment variable.\n",
+        "API_KEY=userdata.get('API_KEY')\n",
+        "\n",
+        "genai.configure(api_key=API_KEY)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dJTxEH7RAOfq"
+      },
+      "source": [
+        "Key Point: Next, you will choose a model. Any embedding model will work for this tutorial, but for real applications it's important to choose a specific model and stick with it. The outputs of different models are not compatible with each other.\n",
+        "\n",
+        "**Note**: At this time, the Gemini API is [only available in certain regions](https://ai.google.dev/gemini-api/docs/available-regions)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sLeRMa1bz9Ad"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "models/embedding-001\n",
+            "models/embedding-001\n"
+          ]
+        }
+      ],
+      "source": [
+        "for m in genai.list_models():\n",
+        "  if 'embedContent' in m.supported_generation_methods:\n",
+        "    print(m.name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pnICLtwna2UU"
+      },
+      "source": [
+        "## Dataset\n",
+        "\n",
+        "The [20 Newsgroups Text Dataset](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html){:.external} contains 18,000 newsgroups posts on 20 topics divided into training and test sets. The split between the training and test datasets are based on messages posted before and after a specific date. For this tutorial, you will be using the training subset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7j4Y2198bdnm"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['alt.atheism',\n",
+              " 'comp.graphics',\n",
+              " 'comp.os.ms-windows.misc',\n",
+              " 'comp.sys.ibm.pc.hardware',\n",
+              " 'comp.sys.mac.hardware',\n",
+              " 'comp.windows.x',\n",
+              " 'misc.forsale',\n",
+              " 'rec.autos',\n",
+              " 'rec.motorcycles',\n",
+              " 'rec.sport.baseball',\n",
+              " 'rec.sport.hockey',\n",
+              " 'sci.crypt',\n",
+              " 'sci.electronics',\n",
+              " 'sci.med',\n",
+              " 'sci.space',\n",
+              " 'soc.religion.christian',\n",
+              " 'talk.politics.guns',\n",
+              " 'talk.politics.mideast',\n",
+              " 'talk.politics.misc',\n",
+              " 'talk.religion.misc']"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "newsgroups_train = fetch_20newsgroups(subset='train')\n",
+        "\n",
+        "# View list of class names for dataset\n",
+        "newsgroups_train.target_names"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "k-XyGQsTcdSR"
+      },
+      "source": [
+        "Here is the first example in the training set."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KDELgM0xbpkt"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Lines: 15\n",
+            "\n",
+            " I was wondering if anyone out there could enlighten me on this car I saw\n",
+            "the other day. It was a 2-door sports car, looked to be from the late 60s/\n",
+            "early 70s. It was called a Bricklin. The doors were really small. In addition,\n",
+            "the front bumper was separate from the rest of the body. This is \n",
+            "all I know. If anyone can tellme a model name, engine specs, years\n",
+            "of production, where this car is made, history, or whatever info you\n",
+            "have on this funky looking car, please e-mail.\n",
+            "\n",
+            "Thanks,\n",
+            "- IL\n",
+            "   ---- brought to you by your neighborhood Lerxst ----\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "idx = newsgroups_train.data[0].index('Lines')\n",
+        "print(newsgroups_train.data[0][idx:])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "G6ldbA4XfPpP"
+      },
+      "outputs": [],
+      "source": [
+        "# Apply functions to remove names, emails, and extraneous words from data points in newsgroups.data\n",
+        "newsgroups_train.data = [re.sub(r'[\\w\\.-]+@[\\w\\.-]+', '', d) for d in newsgroups_train.data] # Remove email\n",
+        "newsgroups_train.data = [re.sub(r\"\\([^()]*\\)\", \"\", d) for d in newsgroups_train.data] # Remove names\n",
+        "newsgroups_train.data = [d.replace(\"From: \", \"\") for d in newsgroups_train.data] # Remove \"From: \"\n",
+        "newsgroups_train.data = [d.replace(\"\\nSubject: \", \"\") for d in newsgroups_train.data] # Remove \"\\nSubject: \""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "26qIj6fJccVI"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df_train\",\n  \"rows\": 11141,\n  \"fields\": [\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"\\\"Altan J. Stalker\\\" <>SE/30 Hard Drive Problem\\nContent-Type: text/plain; charset=US-ASCII\\nContent-Transfer-Encoding: 7bit\\nOrganization: Indiana University\\nMime-Version: 1.0\\nContent-Length: 1161      \\nLines: 33\\n\\n\\nI have an SE/30 with a 80 meg HD which dates back to April 1989.  When I\\noriginally purchased it, I experienced the failure to boot problem.  This\\nwas fixed soon after by a ROM upgrade on the hard drive.\\n\\nLately a similar problem has been occuring.  When the computer is\\npowered on the HD light flashes a few times and then I am given\\nthe \\\"no disk to boot from\\\" icon.  However, upon turing the\\ncomputer off and on again the drive ALWAYS boots up just fine.  \\nFurthermore, if instead of turning the power on and off I press the reboot \\nbutton the same problem occurs.  But, as I said, turning the power\\noff and on always works.\\n\\nThis problem is different from the 1989 boot problem in that before\\nit often required several power off and ons to get it to boot.\\n\\nDoes anybody have any suggestions as to what the problem is or how\\nit can be fixed?\\n\\nI'm wondering if it's getting old and requires more time to \\n\\\"come up to speed\\\" now.  Is there a PRAM or SCSI setting that\\nallows me to tell the computer to wait a little longer before \\ntrying to access the HD?\\n\\nThanks!\\n\\n\\nAltan J. Stalker\\n\\nIndiana University\\nComputer Science Dept.\\n\\n\\n\",\n          \" Harley-Davidson Mailing List -- an Email taste sensation!\\nSummary: a sort of bi-monthly not really automated announcement\\nOriginator: \\nKeywords: digests, lists, harley-davidson, hogaholics\\nSupersedes: <>\\nOrganization: Thinkage Ltd.\\nExpires: Fri, 30 Apr 1993 11:00:00 GMT\\nLines: 36\\n\\n  Anyone interesting in a mailing list for Harley-Davidson bikes, lifestyle,\\npolitics, H.O.G. and whatever over 310 members from 14 countries make it,\\nmay subscribe by sending a request to:\\n\\n              \\n          or  uunet.ca!thinkage!harley-request\\n\\n***\\n* Your request to join should have a signature or something giving your full\\n* Email address.  Do not RELY on the header \\\"From:\\\" field being useful to me.\\n*\\n* This is not an automated \\\"listserv\\\" facility. Do not expect instant\\n* gratification.\\n***\\n\\nThe list is a digest format scheduled for twice a day.\\n\\nMembers of the harley list may obtain back-issues and subject-index\\n    listings, pictures, etc. via an Email archive server. \\nServer access is restricted to list subscribers only.\\nFTP access \\\"real soon\\\".\\n\\nOther motorcycle related lists i've heard of ,\\n   these addresses may or may not be current:\\n\\n  2-stroke:     \\n  Dirt:         \\n  European:     \\n  Racing:       \\n                \\n  Short Riding: \\n  Wet Leather:  \\n\\n---\\nIt climbs the hills like a Matchless 'cause my Honda's built really light...\\n                                    -Brian Wilson \\n\",\n          \" Re: Moonbase race, NASA resources, why?\\nOrganization: U of Toronto Zoology\\nLines: 36\\n\\nIn article <>   writes:\\n>Ah, there's the rub.  And a catch-22 to boot.  For the purposes of a\\n>contest, you'll probably not compete if'n you can't afford the ride to get\\n>there.  And although lower priced delivery systems might be doable, without\\n>demand its doubtful that anyone will develop a new system...\\n\\nYou're assuming that the low-cost delivery system has to be a separate\\nproject.  But why?  If you are spending hundreds of millions of dollars\\nin hopes of winning a billion-dollar prize, it is *cheaper* to develop\\nyour own launch system, charging its entire development cost against\\nyour contest entry, than to try to do it with existing launchers.  No\\nother demand is necessary.\\n\\n>> Any plan for doing\\n>> sustained lunar exploration using existing launch systems is wasting\\n>> money in a big way.\\n>\\n>This depends on the how soon the new launch system comes on line.  In other\\n>words, perhaps a great deal of worthwhile technology  could be developed prior to a low cost launch system. \\n>You wouldn't want to use the expensive stuff forever, but I'd hate to see\\n>folks waiting to do anything until a low cost Mac, oops, I mean launch\\n>system comes on line.\\n\\nYou're assuming that it's going to take a decade to build a new launch\\nsystem.  But why?  The Saturn V took less than six years, depending on\\nexactly when you date its start.  Pegasus took about three from project\\nstart to first flight.  Before SDIO chickened out on orbital development,\\nthe target date for an orbital DC-Y flight was 1996.  If you really want\\nspeed, consider that the first prototypes of the Thor missile  shipped to the USAF less\\nthan 18 months after the development go-ahead.\\n\\nOne of the most pernicious myths in this whole business is the belief\\nthat you can't build a launcher without taking ten years and spending\\nbillions of dollars.  It isn't true and never was.\\n\"\n        ],\n        \"num_unique_values\": 11141,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 0,\n        \"max\": 19,\n        \"samples\": [\n          7,\n          17,\n          9\n        ],\n        \"num_unique_values\": 20,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"rec.autos\",\n          \"talk.politics.mideast\",\n          \"rec.sport.baseball\"\n        ],\n        \"num_unique_values\": 20,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df_train"
+            },
+            "text/html": [
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+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Text</th>\n",
+              "      <th>Label</th>\n",
+              "      <th>Class Name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>WHAT car is this!?\\nNntp-Posting-Host: rac3.w...</td>\n",
+              "      <td>7</td>\n",
+              "      <td>rec.autos</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>SI Clock Poll - Final Call\\nSummary: Final ca...</td>\n",
+              "      <td>4</td>\n",
+              "      <td>comp.sys.mac.hardware</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>PB questions...\\nOrganization: Purdue Univers...</td>\n",
+              "      <td>4</td>\n",
+              "      <td>comp.sys.mac.hardware</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Re: Weitek P9000 ?\\nOrganization: Harris Comp...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>comp.graphics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Re: Shuttle Launch Question\\nOrganization: Sm...</td>\n",
+              "      <td>14</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11309</th>\n",
+              "      <td>Re: Migraines and scans\\nDistribution: world...</td>\n",
+              "      <td>13</td>\n",
+              "      <td>sci.med</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11310</th>\n",
+              "      <td>Screen Death: Mac Plus/512\\nLines: 22\\nOrganiz...</td>\n",
+              "      <td>4</td>\n",
+              "      <td>comp.sys.mac.hardware</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11311</th>\n",
+              "      <td>Mounting CPU Cooler in vertical case\\nOrganiz...</td>\n",
+              "      <td>3</td>\n",
+              "      <td>comp.sys.ibm.pc.hardware</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11312</th>\n",
+              "      <td>Re: Sphere from 4 points?\\nOrganization: Cent...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>comp.graphics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11313</th>\n",
+              "      <td>stolen CBR900RR\\nOrganization: California Ins...</td>\n",
+              "      <td>8</td>\n",
+              "      <td>rec.motorcycles</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>11141 rows × 3 columns</p>\n",
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+              "      fill: #D2E3FC;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
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+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-900db601-f8ee-43ee-97d2-fe70db5fc480');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
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+              "  .colab-df-quickchart-complete:disabled,\n",
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+              "    border: 2px solid var(--fill-color);\n",
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+              "    animation:\n",
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+              "  @keyframes spin {\n",
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+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-c01dc904-c15c-4e92-85e2-a30121a9a410 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_c948e85e-77d2-41f8-a7f8-d707caaabd9e\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_train')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
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+              "      const buttonEl =\n",
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+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df_train');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                                                    Text  Label  \\\n",
+              "0       WHAT car is this!?\\nNntp-Posting-Host: rac3.w...      7   \n",
+              "1       SI Clock Poll - Final Call\\nSummary: Final ca...      4   \n",
+              "2       PB questions...\\nOrganization: Purdue Univers...      4   \n",
+              "3       Re: Weitek P9000 ?\\nOrganization: Harris Comp...      1   \n",
+              "4       Re: Shuttle Launch Question\\nOrganization: Sm...     14   \n",
+              "...                                                  ...    ...   \n",
+              "11309    Re: Migraines and scans\\nDistribution: world...     13   \n",
+              "11310  Screen Death: Mac Plus/512\\nLines: 22\\nOrganiz...      4   \n",
+              "11311   Mounting CPU Cooler in vertical case\\nOrganiz...      3   \n",
+              "11312   Re: Sphere from 4 points?\\nOrganization: Cent...      1   \n",
+              "11313   stolen CBR900RR\\nOrganization: California Ins...      8   \n",
+              "\n",
+              "                     Class Name  \n",
+              "0                     rec.autos  \n",
+              "1         comp.sys.mac.hardware  \n",
+              "2         comp.sys.mac.hardware  \n",
+              "3                 comp.graphics  \n",
+              "4                     sci.space  \n",
+              "...                         ...  \n",
+              "11309                   sci.med  \n",
+              "11310     comp.sys.mac.hardware  \n",
+              "11311  comp.sys.ibm.pc.hardware  \n",
+              "11312             comp.graphics  \n",
+              "11313           rec.motorcycles  \n",
+              "\n",
+              "[11141 rows x 3 columns]"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Put training points into a dataframe\n",
+        "df_train = pd.DataFrame(newsgroups_train.data, columns=['Text'])\n",
+        "df_train['Label'] = newsgroups_train.target\n",
+        "# Match label to target name index\n",
+        "df_train['Class Name'] = df_train['Label'].map(newsgroups_train.target_names.__getitem__)\n",
+        "# Retain text samples that can be used in the gecko model.\n",
+        "df_train = df_train[df_train['Text'].str.len() < 10000]\n",
+        "\n",
+        "df_train"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sZnW2_Tx2_L1"
+      },
+      "source": [
+        "Next, you will sample some of the data by taking 100 data points in the training dataset, and dropping a few of the categories to run through this tutorial. Choose the science categories to compare."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "L5LWfJMY3Ii7"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>index</th>\n",
+              "      <th>Text</th>\n",
+              "      <th>Label</th>\n",
+              "      <th>Class Name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1650</td>\n",
+              "      <td>Re: Off the shelf cheap DES keyseach machine ...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1651</td>\n",
+              "      <td>\"Clipper\" an Infringement on Intergraph's Nam...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1652</td>\n",
+              "      <td>Re: Once tapped, your code is no good any mor...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1653</td>\n",
+              "      <td>new encryption\\nNntp-Posting-Host: rac3.wam.u...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1654</td>\n",
+              "      <td>How can clipper stay classified?\\nArticle-I.D...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>595</th>\n",
+              "      <td>2245</td>\n",
+              "      <td>computer cult\\nNf-ID: #N:cdp:1469100033:000:24...</td>\n",
+              "      <td>14</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>596</th>\n",
+              "      <td>2246</td>\n",
+              "      <td>Re: Inflatable Mile-Long Space Billboards \\nO...</td>\n",
+              "      <td>14</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>597</th>\n",
+              "      <td>2247</td>\n",
+              "      <td>Moscow Aviation Institute summer school\\nOrga...</td>\n",
+              "      <td>14</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>598</th>\n",
+              "      <td>2248</td>\n",
+              "      <td>Eco-Freaks forcing Space Mining.\\nArticle-I.D....</td>\n",
+              "      <td>14</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>599</th>\n",
+              "      <td>2249</td>\n",
+              "      <td>Re: Comet in Temporary Orbit Around Jupiter?\\...</td>\n",
+              "      <td>14</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>600 rows × 4 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
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+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-34f02063-db4e-4027-88af-03b01812b890')\"\n",
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+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
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+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
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+              "\n",
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+              "      --bg-color: #3B4455;\n",
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+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
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+              "  .colab-df-quickchart-complete:disabled,\n",
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+              "    fill: var(--disabled-fill-color);\n",
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+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
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+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "    40% {\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "    60% {\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
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+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
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+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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+              "        document.querySelector('#df-76929853-f3d2-4b3c-997e-15d4efa24360 button');\n",
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+              "\n",
+              "  <div id=\"id_4d13695b-527d-40c2-aa96-95745849cc3c\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
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+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_train')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "     index                                               Text  Label  \\\n",
+              "0     1650   Re: Off the shelf cheap DES keyseach machine ...     11   \n",
+              "1     1651   \"Clipper\" an Infringement on Intergraph's Nam...     11   \n",
+              "2     1652   Re: Once tapped, your code is no good any mor...     11   \n",
+              "3     1653   new encryption\\nNntp-Posting-Host: rac3.wam.u...     11   \n",
+              "4     1654   How can clipper stay classified?\\nArticle-I.D...     11   \n",
+              "..     ...                                                ...    ...   \n",
+              "595   2245  computer cult\\nNf-ID: #N:cdp:1469100033:000:24...     14   \n",
+              "596   2246   Re: Inflatable Mile-Long Space Billboards \\nO...     14   \n",
+              "597   2247   Moscow Aviation Institute summer school\\nOrga...     14   \n",
+              "598   2248  Eco-Freaks forcing Space Mining.\\nArticle-I.D....     14   \n",
+              "599   2249   Re: Comet in Temporary Orbit Around Jupiter?\\...     14   \n",
+              "\n",
+              "    Class Name  \n",
+              "0    sci.crypt  \n",
+              "1    sci.crypt  \n",
+              "2    sci.crypt  \n",
+              "3    sci.crypt  \n",
+              "4    sci.crypt  \n",
+              "..         ...  \n",
+              "595  sci.space  \n",
+              "596  sci.space  \n",
+              "597  sci.space  \n",
+              "598  sci.space  \n",
+              "599  sci.space  \n",
+              "\n",
+              "[600 rows x 4 columns]"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Take a sample of each label category from df_train\n",
+        "SAMPLE_SIZE = 150\n",
+        "df_train = (df_train.groupby('Label', as_index = False)\n",
+        "                    .apply(lambda x: x.sample(SAMPLE_SIZE))\n",
+        "                    .reset_index(drop=True))\n",
+        "\n",
+        "# Choose categories about science\n",
+        "df_train = df_train[df_train['Class Name'].str.contains('sci')]\n",
+        "\n",
+        "# Reset the index\n",
+        "df_train = df_train.reset_index()\n",
+        "df_train"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FI1FDqirsz3O"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "sci.crypt          150\n",
+              "sci.electronics    150\n",
+              "sci.med            150\n",
+              "sci.space          150\n",
+              "Name: Class Name, dtype: int64"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_train['Class Name'].value_counts()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5NbA2hpDH3nl"
+      },
+      "source": [
+        "## Create the embeddings\n",
+        "\n",
+        "In this section, you will see how to generate embeddings for the different texts in the dataframe using the embeddings from the Gemini API."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "05laQxib2miY"
+      },
+      "source": [
+        "### API changes to Embeddings with model embedding-001\n",
+        "\n",
+        "For the new embeddings model, embedding-001, there is a new task type parameter and the optional title (only valid with task_type=`RETRIEVAL_DOCUMENT`).\n",
+        "\n",
+        "These new parameters apply only to the newest embeddings models.The task types are:\n",
+        "\n",
+        "Task Type | Description\n",
+        "---       | ---\n",
+        "RETRIEVAL_QUERY\t| Specifies the given text is a query in a search/retrieval setting.\n",
+        "RETRIEVAL_DOCUMENT | Specifies the given text is a document in a search/retrieval setting.\n",
+        "SEMANTIC_SIMILARITY\t| Specifies the given text will be used for Semantic Textual Similarity (STS).\n",
+        "CLASSIFICATION\t| Specifies that the embeddings will be used for classification.\n",
+        "CLUSTERING\t| Specifies that the embeddings will be used for clustering."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "g1NC0e6McsQx"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d0f5fb2d3f224f18a7fe2809de5f5434",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/600 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from tqdm.auto import tqdm\n",
+        "tqdm.pandas()\n",
+        "\n",
+        "from google.api_core import retry\n",
+        "\n",
+        "def make_embed_text_fn(model):\n",
+        "\n",
+        "  @retry.Retry(timeout=300.0)\n",
+        "  def embed_fn(text: str) -> list[float]:\n",
+        "    # Set the task_type to CLUSTERING.\n",
+        "    embedding = genai.embed_content(model=model,\n",
+        "                                    content=text,\n",
+        "                                    task_type=\"clustering\")\n",
+        "    return embedding[\"embedding\"]\n",
+        "\n",
+        "  return embed_fn\n",
+        "\n",
+        "def create_embeddings(df):\n",
+        "  model = 'models/embedding-001'\n",
+        "  df['Embeddings'] = df['Text'].progress_apply(make_embed_text_fn(model))\n",
+        "  return df\n",
+        "\n",
+        "df_train = create_embeddings(df_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t-1QKCK8DHsI"
+      },
+      "source": [
+        "## Dimensionality reduction\n",
+        "\n",
+        "The length of the document embedding vector is 768. In order to visualize how the embedded documents are grouped together, you will need to apply dimensionality reduction as you can only visualize the embeddings in 2D or 3D space. Contextually similar documents should be closer together in space as opposed to documents that are not as similar."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XODHZlFcFnn6"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "768"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "len(df_train['Embeddings'][0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "73aAdKo1UCrL"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(600, 768)"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Convert df_train['Embeddings'] Pandas series to a np.array of float32\n",
+        "X = np.array(df_train['Embeddings'].to_list(), dtype=np.float32)\n",
+        "X.shape"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JB3bsmi4Iuak"
+      },
+      "source": [
+        "You will apply the t-Distributed Stochastic Neighbor Embedding (t-SNE) approach to perform dimensionality reduction. This technique reduces the number of dimensions, while preserving clusters (points that are close together stay close together). For the original data, the model tries to construct a distribution over which other data points are \"neighbors\" (e.g., they share a similar meaning). It then optimizes an objective function to keep a similar distribution in the visualization."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OpyoE-RVSzfe"
+      },
+      "outputs": [],
+      "source": [
+        "tsne = TSNE(random_state=0, n_iter=1000)\n",
+        "tsne_results = tsne.fit_transform(X)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BbsWqQlxJHas"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df_tsne\",\n  \"rows\": 600,\n  \"fields\": [\n    {\n      \"column\": \"TSNE1\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          36.85309982299805,\n          -25.488718032836914,\n          2.9596657752990723\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TSNE2\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          -0.7554828524589539,\n          5.936662197113037,\n          -19.277177810668945\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"sci.electronics\",\n          \"sci.space\",\n          \"sci.crypt\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df_tsne"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7122a71e-c2a4-4627-ac78-6571b919d0b5\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>TSNE1</th>\n",
+              "      <th>TSNE2</th>\n",
+              "      <th>Class Name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>27.613194</td>\n",
+              "      <td>-2.590790</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>43.533733</td>\n",
+              "      <td>8.535353</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>32.775826</td>\n",
+              "      <td>11.671514</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>44.522926</td>\n",
+              "      <td>-2.058890</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>40.518196</td>\n",
+              "      <td>-2.139972</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>595</th>\n",
+              "      <td>20.744043</td>\n",
+              "      <td>-7.745994</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>596</th>\n",
+              "      <td>-0.322983</td>\n",
+              "      <td>-28.657366</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>597</th>\n",
+              "      <td>-8.563044</td>\n",
+              "      <td>-6.283251</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>598</th>\n",
+              "      <td>-14.029724</td>\n",
+              "      <td>-29.518869</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>599</th>\n",
+              "      <td>3.009676</td>\n",
+              "      <td>-16.334478</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>600 rows × 3 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7122a71e-c2a4-4627-ac78-6571b919d0b5')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7122a71e-c2a4-4627-ac78-6571b919d0b5 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7122a71e-c2a4-4627-ac78-6571b919d0b5');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-9c7f2560-d959-4fc6-880b-5ea835ddee36\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9c7f2560-d959-4fc6-880b-5ea835ddee36')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-9c7f2560-d959-4fc6-880b-5ea835ddee36 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_0adacf73-c50a-4ecb-92c1-f82e26c84de6\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_tsne')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_0adacf73-c50a-4ecb-92c1-f82e26c84de6 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df_tsne');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "         TSNE1      TSNE2 Class Name\n",
+              "0    27.613194  -2.590790  sci.crypt\n",
+              "1    43.533733   8.535353  sci.crypt\n",
+              "2    32.775826  11.671514  sci.crypt\n",
+              "3    44.522926  -2.058890  sci.crypt\n",
+              "4    40.518196  -2.139972  sci.crypt\n",
+              "..         ...        ...        ...\n",
+              "595  20.744043  -7.745994  sci.space\n",
+              "596  -0.322983 -28.657366  sci.space\n",
+              "597  -8.563044  -6.283251  sci.space\n",
+              "598 -14.029724 -29.518869  sci.space\n",
+              "599   3.009676 -16.334478  sci.space\n",
+              "\n",
+              "[600 rows x 3 columns]"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_tsne = pd.DataFrame(tsne_results, columns=['TSNE1', 'TSNE2'])\n",
+        "df_tsne['Class Name'] = df_train['Class Name'] # Add labels column from df_train to df_tsne\n",
+        "df_tsne"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "z4N7d8MlpVCS"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(-46.191162300109866,\n",
+              " 53.521015357971194,\n",
+              " -39.96646995544434,\n",
+              " 37.282975387573245)"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, ax = plt.subplots(figsize=(8,6)) # Set figsize\n",
+        "sns.set_style('darkgrid', {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
+        "sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Class Name', palette='hls')\n",
+        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
+        "plt.title('Scatter plot of news using t-SNE');\n",
+        "plt.xlabel('TSNE1');\n",
+        "plt.ylabel('TSNE2');\n",
+        "plt.axis('equal')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "skgpKPdEie70"
+      },
+      "source": [
+        "## Compare results to KMeans\n",
+        "\n",
+        "[KMeans clustering](https://developers.google.com/machine-learning/glossary#k-means){:.external} is a popular clustering algorithm and used often for unsupervised learning. It iteratively determines the best k center points, and assigns each example to the closest centroid. Input the embeddings directly into the KMeans algorithm to compare the visualization of the embeddings to the performance of a machine learning algorithm."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8da-KTwtxk27"
+      },
+      "outputs": [],
+      "source": [
+        "# Apply KMeans\n",
+        "kmeans_model = KMeans(n_clusters=4, random_state=1, n_init='auto').fit(X)\n",
+        "labels = kmeans_model.fit_predict(X)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mYMIXXRm0ZC8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df_tsne\",\n  \"rows\": 600,\n  \"fields\": [\n    {\n      \"column\": \"TSNE1\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          36.85309982299805,\n          -25.488718032836914,\n          2.9596657752990723\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TSNE2\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          -0.7554828524589539,\n          5.936662197113037,\n          -19.277177810668945\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"sci.electronics\",\n          \"sci.space\",\n          \"sci.crypt\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cluster\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"samples\": [\n          2,\n          0,\n          1\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df_tsne"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-970730de-d54b-4012-a15c-2a0043b2aa36\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>TSNE1</th>\n",
+              "      <th>TSNE2</th>\n",
+              "      <th>Class Name</th>\n",
+              "      <th>Cluster</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>27.613194</td>\n",
+              "      <td>-2.590790</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>43.533733</td>\n",
+              "      <td>8.535353</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>32.775826</td>\n",
+              "      <td>11.671514</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>44.522926</td>\n",
+              "      <td>-2.058890</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>40.518196</td>\n",
+              "      <td>-2.139972</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>595</th>\n",
+              "      <td>20.744043</td>\n",
+              "      <td>-7.745994</td>\n",
+              "      <td>sci.space</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>596</th>\n",
+              "      <td>-0.322983</td>\n",
+              "      <td>-28.657366</td>\n",
+              "      <td>sci.space</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>597</th>\n",
+              "      <td>-8.563044</td>\n",
+              "      <td>-6.283251</td>\n",
+              "      <td>sci.space</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>598</th>\n",
+              "      <td>-14.029724</td>\n",
+              "      <td>-29.518869</td>\n",
+              "      <td>sci.space</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>599</th>\n",
+              "      <td>3.009676</td>\n",
+              "      <td>-16.334478</td>\n",
+              "      <td>sci.space</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>600 rows × 4 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-970730de-d54b-4012-a15c-2a0043b2aa36')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-970730de-d54b-4012-a15c-2a0043b2aa36 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-970730de-d54b-4012-a15c-2a0043b2aa36');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-efd34921-c8b8-4fac-81e7-913ca802d239\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-efd34921-c8b8-4fac-81e7-913ca802d239')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-efd34921-c8b8-4fac-81e7-913ca802d239 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_e57c4e91-ff0a-4bcc-8d42-60d98e35ef8b\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_tsne')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_e57c4e91-ff0a-4bcc-8d42-60d98e35ef8b button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df_tsne');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "         TSNE1      TSNE2 Class Name  Cluster\n",
+              "0    27.613194  -2.590790  sci.crypt        1\n",
+              "1    43.533733   8.535353  sci.crypt        1\n",
+              "2    32.775826  11.671514  sci.crypt        1\n",
+              "3    44.522926  -2.058890  sci.crypt        1\n",
+              "4    40.518196  -2.139972  sci.crypt        1\n",
+              "..         ...        ...        ...      ...\n",
+              "595  20.744043  -7.745994  sci.space        0\n",
+              "596  -0.322983 -28.657366  sci.space        0\n",
+              "597  -8.563044  -6.283251  sci.space        0\n",
+              "598 -14.029724 -29.518869  sci.space        0\n",
+              "599   3.009676 -16.334478  sci.space        0\n",
+              "\n",
+              "[600 rows x 4 columns]"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_tsne['Cluster'] = labels\n",
+        "df_tsne"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "wwuk36dt1XaS"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(-46.191162300109866,\n",
+              " 53.521015357971194,\n",
+              " -39.96646995544434,\n",
+              " 37.282975387573245)"
+            ]
+          },
+          "execution_count": 20,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, ax = plt.subplots(figsize=(8,6)) # Set figsize\n",
+        "sns.set_style('darkgrid', {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
+        "sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Cluster', palette='magma')\n",
+        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
+        "plt.title('Scatter plot of news using KMeans Clustering');\n",
+        "plt.xlabel('TSNE1');\n",
+        "plt.ylabel('TSNE2');\n",
+        "plt.axis('equal')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tuAx8ZI3ydcT"
+      },
+      "outputs": [],
+      "source": [
+        "def get_majority_cluster_per_group(df_tsne_cluster, class_names):\n",
+        "  class_clusters = dict()\n",
+        "  for c in class_names:\n",
+        "    # Get rows of dataframe that are equal to c\n",
+        "    rows = df_tsne_cluster.loc[df_tsne_cluster['Class Name'] == c]\n",
+        "    # Get majority value in Cluster column of the rows selected\n",
+        "    cluster = rows.Cluster.mode().values[0]\n",
+        "    # Populate mapping dictionary\n",
+        "    class_clusters[c] = cluster\n",
+        "  return class_clusters"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Is_GUvFS0GH_"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'sci.crypt': 1, 'sci.electronics': 3, 'sci.med': 2, 'sci.space': 0}"
+            ]
+          },
+          "execution_count": 22,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "classes = df_tsne['Class Name'].unique()\n",
+        "class_clusters = get_majority_cluster_per_group(df_tsne, classes)\n",
+        "class_clusters"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R_bf9nXc6Dgx"
+      },
+      "source": [
+        "Get the majority of clusters per group, and see how many of the actual members of that group are in that cluster."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "b2GyHE8ahEff"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "sci.space          0.966667\n",
+              "sci.med            0.960000\n",
+              "sci.electronics    0.953333\n",
+              "sci.crypt          0.926667\n",
+              "Name: Class Name, dtype: float64"
+            ]
+          },
+          "execution_count": 23,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Convert the Cluster column to use the class name\n",
+        "class_by_id = {v: k for k, v in class_clusters.items()}\n",
+        "df_tsne['Predicted'] = df_tsne['Cluster'].map(class_by_id.__getitem__)\n",
+        "\n",
+        "# Filter to the correctly matched rows\n",
+        "correct = df_tsne[df_tsne['Class Name'] == df_tsne['Predicted']]\n",
+        "\n",
+        "# Summarise, as a percentage\n",
+        "acc = correct['Class Name'].value_counts() / SAMPLE_SIZE\n",
+        "acc"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gF0wwWQK9Yek"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df_tsne\",\n  \"rows\": 600,\n  \"fields\": [\n    {\n      \"column\": \"TSNE1\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          36.85309982299805,\n          -25.488718032836914,\n          2.9596657752990723\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TSNE2\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          -0.7554828524589539,\n          5.936662197113037,\n          -19.277177810668945\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"sci.electronics\",\n          \"sci.space\",\n          \"sci.crypt\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cluster\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"samples\": [\n          2,\n          0,\n          1\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"sci.med\",\n          \"sci.space\",\n          \"sci.crypt\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df_tsne"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6c81b6e9-efbf-4b25-8144-d3446abcf438\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>TSNE1</th>\n",
+              "      <th>TSNE2</th>\n",
+              "      <th>Class Name</th>\n",
+              "      <th>Cluster</th>\n",
+              "      <th>Predicted</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>27.613194</td>\n",
+              "      <td>-2.590790</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>1</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>43.533733</td>\n",
+              "      <td>8.535353</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>1</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>32.775826</td>\n",
+              "      <td>11.671514</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>1</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>44.522926</td>\n",
+              "      <td>-2.058890</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>1</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>40.518196</td>\n",
+              "      <td>-2.139972</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>1</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>595</th>\n",
+              "      <td>20.744043</td>\n",
+              "      <td>-7.745994</td>\n",
+              "      <td>sci.space</td>\n",
+              "      <td>0</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>596</th>\n",
+              "      <td>-0.322983</td>\n",
+              "      <td>-28.657366</td>\n",
+              "      <td>sci.space</td>\n",
+              "      <td>0</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>597</th>\n",
+              "      <td>-8.563044</td>\n",
+              "      <td>-6.283251</td>\n",
+              "      <td>sci.space</td>\n",
+              "      <td>0</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>598</th>\n",
+              "      <td>-14.029724</td>\n",
+              "      <td>-29.518869</td>\n",
+              "      <td>sci.space</td>\n",
+              "      <td>0</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>599</th>\n",
+              "      <td>3.009676</td>\n",
+              "      <td>-16.334478</td>\n",
+              "      <td>sci.space</td>\n",
+              "      <td>0</td>\n",
+              "      <td>sci.space</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>600 rows × 5 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6c81b6e9-efbf-4b25-8144-d3446abcf438')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
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+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
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+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
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+              "  </style>\n",
+              "\n",
+              "    <script>\n",
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+              "        document.querySelector('#df-6c81b6e9-efbf-4b25-8144-d3446abcf438 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-6c81b6e9-efbf-4b25-8144-d3446abcf438');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-21f3f97f-f4ab-40af-a055-b060ef14460a\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-21f3f97f-f4ab-40af-a055-b060ef14460a')\"\n",
+              "            title=\"Suggest charts\"\n",
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+              "\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
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+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-21f3f97f-f4ab-40af-a055-b060ef14460a button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_9feaab18-d8ba-4c42-99e1-cdcbefbf5049\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_tsne')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_9feaab18-d8ba-4c42-99e1-cdcbefbf5049 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df_tsne');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "         TSNE1      TSNE2 Class Name  Cluster  Predicted\n",
+              "0    27.613194  -2.590790  sci.crypt        1  sci.crypt\n",
+              "1    43.533733   8.535353  sci.crypt        1  sci.crypt\n",
+              "2    32.775826  11.671514  sci.crypt        1  sci.crypt\n",
+              "3    44.522926  -2.058890  sci.crypt        1  sci.crypt\n",
+              "4    40.518196  -2.139972  sci.crypt        1  sci.crypt\n",
+              "..         ...        ...        ...      ...        ...\n",
+              "595  20.744043  -7.745994  sci.space        0  sci.space\n",
+              "596  -0.322983 -28.657366  sci.space        0  sci.space\n",
+              "597  -8.563044  -6.283251  sci.space        0  sci.space\n",
+              "598 -14.029724 -29.518869  sci.space        0  sci.space\n",
+              "599   3.009676 -16.334478  sci.space        0  sci.space\n",
+              "\n",
+              "[600 rows x 5 columns]"
+            ]
+          },
+          "execution_count": 24,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Get predicted values by name\n",
+        "df_tsne['Predicted'] = ''\n",
+        "for idx, rows in df_tsne.iterrows():\n",
+        "  cluster = rows['Cluster']\n",
+        "  # Get key from mapping based on cluster value\n",
+        "  key = list(class_clusters.keys())[list(class_clusters.values()).index(cluster)]\n",
+        "  df_tsne.at[idx, 'Predicted'] = key\n",
+        "\n",
+        "df_tsne"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DWBhCLr0OTrQ"
+      },
+      "source": [
+        "To better visualize the performance of the KMeans applied to your data, you can use a [confusion matrix](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html). The confusion matrix allows you to assess the performance of the classification model beyond accuracy. You can see what misclassified points get classified as. You will need the actual values and the predicted values, which you have gathered in the dataframe above."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CwqggsKD-ywF"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "cm = confusion_matrix(df_tsne['Class Name'].to_list(), df_tsne['Predicted'].to_list())\n",
+        "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
+        "                              display_labels=classes)\n",
+        "disp.plot(xticks_rotation='vertical')\n",
+        "plt.title('Confusion Matrix for Actual and Clustered Newsgroups');\n",
+        "plt.grid(False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yCXlOrLFrE1k"
+      },
+      "source": [
+        "## Next steps\n",
+        "\n",
+        "You've now created your own visualization of embeddings with clustering! Try using your own textual data to visualize them as embeddings. You can perform dimensionality reduction in order to complete the visualization step. Note that TSNE is good at clustering inputs, but can take a longer time to converge or might get stuck at local minima. If you run into this issue, another technique you could consider are [principal components analysis (PCA)](https://en.wikipedia.org/wiki/Principal_component_analysis){:.external}.\n",
+        "\n",
+        "There are other clustering algorithms outside of KMeans as well, such as [density-based spatial clustering (DBSCAN)](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html){:.external}.\n",
+        "\n",
+        "To learn how to use other services in the Gemini API, see the [Python quickstart](https://ai.google.dev/tutorials/python_quickstart).\n",
+        "\n",
+        "To learn more about how you can use embeddings, see these  other tutorials:\n",
+        "\n",
+        " * [Anomaly Detection with Embeddings](https://ai.google.dev/gemini-api/tutorials/anomaly_detection)\n",
+        " * [Document Search with Embeddings](https://ai.google.dev/gemini-api/tutorials/document_search)\n",
+        " * [Training a Text Classifier with Embeddings](https://ai.google.dev/gemini-api/tutorials/text_classifier_embeddings)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "clustering_with_embeddings.ipynb",
+      "toc_visible": true
+    },
+    "google": {
+      "image_path": "/examples/clustering_with_embeddings_files/output_z4N7d8MlpVCS_1.png",
+      "keywords": [
+        "examples",
+        "googleai",
+        "samplecode",
+        "python",
+        "embed"
+      ]
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemini-api/tutorials/document_search.ipynb b/site/en/gemini-api/tutorials/document_search.ipynb
new file mode 100644
index 000000000..26dacb36b
--- /dev/null
+++ b/site/en/gemini-api/tutorials/document_search.ipynb
@@ -0,0 +1,1261 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LmfLXp5_bt-a"
+      },
+      "source": [
+        "# Document search with embeddings"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kIkJ7zgADMlP"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "      <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/tutorials/document_search\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemini-api/tutorials/document_search.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemini-api/tutorials/document_search.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bbPzgYbrwbK2"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This example demonstrates how to use the Gemini API to create embeddings so that you can perform document search. You will use the Python client library to build a word embedding that allows you to compare search strings, or questions, to document contents.\n",
+        "\n",
+        "In this tutorial, you'll use embeddings to perform document search over a set of documents to ask questions related to the Google Car.\n",
+        "\n",
+        "## Prerequisites\n",
+        "\n",
+        "You can run this quickstart in Google Colab.\n",
+        "\n",
+        "To complete this quickstart on your own development environment, ensure that your environment meets the following requirements:\n",
+        "\n",
+        "-  Python 3.9+\n",
+        "-  An installation of `jupyter` to run the notebook.\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "First, download and install the Gemini API Python library."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YD6urJjWGVDf"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -U -q google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Zey3UiYGDDzU"
+      },
+      "outputs": [],
+      "source": [
+        "import textwrap\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "import google.generativeai as genai\n",
+        "\n",
+        "# Used to securely store your API key\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "from IPython.display import Markdown"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DJriBaWmkL6Z"
+      },
+      "source": [
+        "### Grab an API Key\n",
+        "\n",
+        "Before you can use the Gemini API, you must first obtain an API key. If you don't already have one, create a key with one click in Google AI Studio.\n",
+        "\n",
+        "<a class=\"button button-primary\" href=\"https://makersuite.google.com/app/apikey\" target=\"_blank\" rel=\"noopener noreferrer\">Get an API key</a>\n",
+        "\n",
+        "In Colab, add the key to the secrets manager under the \"🔑\" in the left panel. Give it the name `API_KEY`.\n",
+        "\n",
+        "Once you have the API key, pass it to the SDK. You can do this in two ways:\n",
+        "\n",
+        "* Put the key in the `GOOGLE_API_KEY` environment variable (the SDK will automatically pick it up from there).\n",
+        "* Pass the key to `genai.configure(api_key=...)`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JIm3gEGYhTX1"
+      },
+      "outputs": [],
+      "source": [
+        "# Or use `os.getenv('API_KEY')` to fetch an environment variable.\n",
+        "API_KEY=userdata.get('API_KEY')\n",
+        "\n",
+        "genai.configure(api_key=API_KEY)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RMbpJpZn6YRQ"
+      },
+      "source": [
+        "Key Point: Next, you will choose a model. Any embedding model will work for this tutorial, but for real applications it's important to choose a specific model and stick with it. The outputs of different models are not compatible with each other.\n",
+        "\n",
+        "**Note**: At this time, the Gemini API is [only available in certain regions](https://ai.google.dev/gemini-api/docs/available-regions)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8Vad1J5hkpAw"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "models/embedding-001\n",
+            "models/embedding-001\n"
+          ]
+        }
+      ],
+      "source": [
+        "for m in genai.list_models():\n",
+        "  if 'embedContent' in m.supported_generation_methods:\n",
+        "    print(m.name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gGpQ8Eg0kNXW"
+      },
+      "source": [
+        "## Embedding generation\n",
+        "\n",
+        "In this section, you will see how to generate embeddings for a piece of text using the embeddings from the Gemini API."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Y9imdrPj24P6"
+      },
+      "source": [
+        "### API changes to Embeddings with model embedding-001\n",
+        "\n",
+        "For the new embeddings model, embedding-001, there is a new task type parameter and the optional title (only valid with task_type=`RETRIEVAL_DOCUMENT`).\n",
+        "\n",
+        "These new parameters apply only to the newest embeddings models.The task types are:\n",
+        "\n",
+        "Task Type | Description\n",
+        "---       | ---\n",
+        "RETRIEVAL_QUERY\t| Specifies the given text is a query in a search/retrieval setting.\n",
+        "RETRIEVAL_DOCUMENT | Specifies the given text is a document in a search/retrieval setting.\n",
+        "SEMANTIC_SIMILARITY\t| Specifies the given text will be used for Semantic Textual Similarity (STS).\n",
+        "CLASSIFICATION\t| Specifies that the embeddings will be used for classification.\n",
+        "CLUSTERING\t| Specifies that the embeddings will be used for clustering.\n",
+        "\n",
+        "Note: Specifying a `title` for `RETRIEVAL_DOCUMENT` provides better quality embeddings for retrieval."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "J76TNa3QDwCc"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'embedding': [0.034585103, -0.044509504, -0.027291223, 0.0072681927, 0.061689284, 0.03362112, 0.028627988, 0.022681564, 0.04958079, 0.07274552, 0.011150464, 0.04200501, -0.029782884, -0.0041767005, 0.05074771, -0.056339227, 0.051204756, 0.04734613, -0.022025354, 0.025162602, 0.046016376, -0.003416976, -0.024010269, -0.044340927, -0.01520864, -0.013577372, -0.009918958, -0.028144406, -0.00024770075, 0.031201784, -0.072506696, 0.022366496, -0.032672316, -0.0025522006, -0.0019957912, -0.023193765, -0.020633291, -0.014031609, -0.00071676675, -0.0073200124, 0.014770645, -0.09390713, -0.017846372, 0.032825496, 0.017616265, -0.046674345, 0.03469292, 0.03386835, 0.0028274113, -0.07737739, 0.023789782, 0.025950644, 0.06952142, -0.029875675, -0.018693604, 0.007266584, -0.0067282487, 0.000802912, 0.020609016, 0.012406181, -0.018825717, 0.051171597, -0.0080359895, 0.008457639, 0.01197146, -0.080320396, -0.040698495, 0.0018266322, 0.042915005, 0.021464704, 0.022519842, 0.0059912056, 0.050887667, -0.04566639, -0.012651369, -0.14023173, -0.0274054, 0.04492792, 0.014709818, 0.037258334, -0.021294944, -0.041852854, -0.069640376, -0.030281356, -0.0070775123, 0.019886682, -0.050179508, -0.03839318, -0.014652514, 0.03370254, -0.02803748, -0.059206057, 0.055928297, -0.034912255, -0.007784368, 0.098106734, -0.06873356, -0.052850258, -0.011798939, -0.030071719, -0.026038093, 0.016752971, -0.020916667, 0.007365556, 0.017650642, 0.006677715, -0.036498126, 0.02110524, -0.05625146, 0.043038886, -0.06515849, -0.019825866, -0.010379261, -0.037537806, 0.017674655, -0.042821705, 0.014320703, 0.036735073, 0.011445211, 0.027352763, -0.0028090556, 0.009011982, 0.024146665, 0.002215841, -0.07397819, 0.008714616, -0.03377923, 0.034349587, 0.022429721, 0.052665956, -0.0021583177, -0.040462274, -0.019938014, 0.030099798, 0.009743918, 0.009111553, 0.026379738, -0.015910586, 0.010171418, 0.023996552, -0.031924065, 0.024775924, 0.014129728, 0.008913726, -0.010156162, 0.05407575, -0.080851324, 0.022005167, 0.012674272, -0.017213775, -0.009514327, 0.03276702, -0.06795425, -0.0004906647, 0.036379207, 0.034329377, -0.037122324, 0.05565231, -0.0038797501, 0.009620726, 0.050033607, 0.0084967585, 0.050638147, 0.00490447, 0.006675041, -0.04295331, -0.006490465, 0.010016808, -0.011493882, 0.023702862, 0.029825455, 0.03514081, -0.013388401, -0.05283049, 0.00019729362, -0.05095579, -0.031205554, 0.0045187837, -0.0066217924, -0.007931168, -0.0030577614, -0.016934164, 0.04188085, 0.050768845, 0.009407336, -0.02838461, 0.079967216, -0.038705315, -0.06723827, 0.015558192, -0.043977134, -0.022096274, -0.0053875325, -0.022216668, 0.013843675, 0.04506347, 0.051535256, 0.033484843, 0.044276737, -0.01299742, 0.021727907, 0.06798745, 0.038896713, 0.0023941514, 0.00815586, 0.029679826, 0.109524906, 0.012102062, -0.058510404, 0.03252702, -0.050666984, -0.006376317, 0.026164565, 0.008671174, 0.05052107, -0.027606683, 0.005126455, -0.0029112308, -0.015136989, -0.026336055, -0.031090762, 0.01717387, -0.03679281, -0.008987327, -0.0015111889, 0.0951955, -0.047756936, 0.03215895, 0.0029104433, -0.026967648, 0.015690766, 0.072443135, 0.039804243, 0.019212538, 0.08688796, -0.006074699, 0.015716698, 0.01919827, 0.030602958, 0.008902454, -0.046521842, 0.01976686, 0.051571846, 0.022742877, -0.04307271, -0.016526582, -0.03293306, 0.056195326, 0.0034229455, 0.022546848, -0.03803692, -0.051709678, 0.006613695, -0.0014020284, -0.036669895, -0.001721542, -0.08655083, -0.052215993, -0.032110028, 0.02565277, 0.04519586, -0.049954705, 0.0012014605, -0.037857044, -0.017148033, -0.026822135, 0.031737078, 0.028569039, -0.022907747, 0.024690803, -0.029206393, -0.032036074, 0.039650604, 0.021772616, -0.021436188, 0.045968816, -0.010048652, 0.030124044, 0.03935015, -0.04809066, 0.023686275, 0.02167442, 0.044297505, -0.073465124, -0.030082388, 0.017143175, -0.03342189, -0.0330694, -0.0122910105, -0.051963367, -0.058639623, -0.008972449, -0.022521269, -0.022892935, -0.035436112, 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+          ]
+        }
+      ],
+      "source": [
+        "title = \"The next generation of AI for developers and Google Workspace\"\n",
+        "sample_text = (\"Title: The next generation of AI for developers and Google Workspace\"\n",
+        "    \"\\n\"\n",
+        "    \"Full article:\\n\"\n",
+        "    \"\\n\"\n",
+        "    \"Gemini API & Google AI Studio: An approachable way to explore and prototype with generative AI applications\")\n",
+        "\n",
+        "model = 'models/embedding-001'\n",
+        "embedding = genai.embed_content(model=model,\n",
+        "                                content=sample_text,\n",
+        "                                task_type=\"retrieval_document\",\n",
+        "                                title=title)\n",
+        "\n",
+        "print(embedding)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dD1lQx3Zr3S2"
+      },
+      "source": [
+        "## Building an embeddings database\n",
+        "\n",
+        "Here are three sample texts to use to build the embeddings database. You will use the Gemini API to create embeddings of each of the documents. Turn them into a dataframe for better visualization."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XvLRIbpq4vNN"
+      },
+      "outputs": [],
+      "source": [
+        "DOCUMENT1 = {\n",
+        "    \"title\": \"Operating the Climate Control System\",\n",
+        "    \"content\": \"Your Googlecar has a climate control system that allows you to adjust the temperature and airflow in the car. To operate the climate control system, use the buttons and knobs located on the center console.  Temperature: The temperature knob controls the temperature inside the car. Turn the knob clockwise to increase the temperature or counterclockwise to decrease the temperature. Airflow: The airflow knob controls the amount of airflow inside the car. Turn the knob clockwise to increase the airflow or counterclockwise to decrease the airflow. Fan speed: The fan speed knob controls the speed of the fan. Turn the knob clockwise to increase the fan speed or counterclockwise to decrease the fan speed. Mode: The mode button allows you to select the desired mode. The available modes are: Auto: The car will automatically adjust the temperature and airflow to maintain a comfortable level. Cool: The car will blow cool air into the car. Heat: The car will blow warm air into the car. Defrost: The car will blow warm air onto the windshield to defrost it.\"}\n",
+        "DOCUMENT2 = {\n",
+        "    \"title\": \"Touchscreen\",\n",
+        "    \"content\": \"Your Googlecar has a large touchscreen display that provides access to a variety of features, including navigation, entertainment, and climate control. To use the touchscreen display, simply touch the desired icon.  For example, you can touch the \\\"Navigation\\\" icon to get directions to your destination or touch the \\\"Music\\\" icon to play your favorite songs.\"}\n",
+        "DOCUMENT3 = {\n",
+        "    \"title\": \"Shifting Gears\",\n",
+        "    \"content\": \"Your Googlecar has an automatic transmission. To shift gears, simply move the shift lever to the desired position.  Park: This position is used when you are parked. The wheels are locked and the car cannot move. Reverse: This position is used to back up. Neutral: This position is used when you are stopped at a light or in traffic. The car is not in gear and will not move unless you press the gas pedal. Drive: This position is used to drive forward. Low: This position is used for driving in snow or other slippery conditions.\"}\n",
+        "\n",
+        "documents = [DOCUMENT1, DOCUMENT2, DOCUMENT3]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WwhCQwPbvwc-"
+      },
+      "source": [
+        "Organize the contents of the dictionary into a dataframe for better visualization."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GJKLIW9Z31Vf"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Operating the Climate Control System  Your Googlecar has a climate control system that allows you to adjust the temperature and airflow in the car. To operate the climate control system, use the buttons and knobs located on the center console. Temperature: The temperature knob controls the temperature inside the car. Turn the knob clockwise to increase the temperature or counterclockwise to decrease the temperature. Airflow: The airflow knob controls the amount of airflow inside the car. Turn the knob clockwise to increase the airflow or counterclockwise to decrease the airflow. Fan speed: The fan speed knob controls the speed of the fan. Turn the knob clockwise to increase the fan speed or counterclockwise to decrease the fan speed. Mode: The mode button allows you to select the desired mode. The available modes are: Auto: The car will automatically adjust the temperature and airflow to maintain a comfortable level. Cool: The car will blow cool air into the car. Heat: The car will blow warm air into the car. Defrost: The car will blow warm air onto the windshield to defrost it.\",\n          \"Your Googlecar has a large touchscreen display that provides access to a variety of features, including navigation, entertainment, and climate control. To use the touchscreen display, simply touch the desired icon.  For example, you can touch the \\\"Navigation\\\" icon to get directions to your destination or touch the \\\"Music\\\" icon to play your favorite songs.\",\n          \"Shifting Gears  Your Googlecar has an automatic transmission. To shift gears, simply move the shift lever to the desired position.  Park: This position is used when you are parked. The wheels are locked and the car cannot move. Reverse: This position is used to back up. Neutral: This position is used when you are stopped at a light or in traffic. The car is not in gear and will not move unless you press the gas pedal. Drive: This position is used to drive forward. Low: This position is used for driving in snow or other slippery conditions.\"\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-20698e78-9f7c-4756-ae1e-017aa5b1b43a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Text</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Operating the Climate Control System  Your Goo...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Your Googlecar has a large touchscreen display...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Shifting Gears  Your Googlecar has an automati...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-20698e78-9f7c-4756-ae1e-017aa5b1b43a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-20698e78-9f7c-4756-ae1e-017aa5b1b43a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-20698e78-9f7c-4756-ae1e-017aa5b1b43a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-dde99354-9205-4448-9a60-5e8026a2309a\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dde99354-9205-4448-9a60-5e8026a2309a')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
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+              "    }\n",
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+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-dde99354-9205-4448-9a60-5e8026a2309a button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_926eee3a-11e0-40ed-80d9-4578bd86cfb4\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_926eee3a-11e0-40ed-80d9-4578bd86cfb4 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                                                Text\n",
+              "0  Operating the Climate Control System  Your Goo...\n",
+              "1  Your Googlecar has a large touchscreen display...\n",
+              "2  Shifting Gears  Your Googlecar has an automati..."
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df = pd.DataFrame(documents)\n",
+        "df.columns = ['Title', 'Text']\n",
+        "df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LHonPYEwStLB"
+      },
+      "source": [
+        "Get the embeddings for each of these bodies of text. Add this information to the dataframe."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4SOhy0lNBhfN"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b8ef316a-095c-43e0-86dd-96e0850053f0\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Text</th>\n",
+              "      <th>Embeddings</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Operating the Climate Control System  Your Goo...</td>\n",
+              "      <td>[-0.016329883, -0.01747576, -0.038270127, -0.0...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Your Googlecar has a large touchscreen display...</td>\n",
+              "      <td>[0.024793636, -0.024769256, -0.01176664, -0.04...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Shifting Gears  Your Googlecar has an automati...</td>\n",
+              "      <td>[-0.025426013, 0.00023183432, -0.02406427, -0....</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8ef316a-095c-43e0-86dd-96e0850053f0')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b8ef316a-095c-43e0-86dd-96e0850053f0 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b8ef316a-095c-43e0-86dd-96e0850053f0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-d281a79a-39f6-4150-a3c9-c8c323b82589\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d281a79a-39f6-4150-a3c9-c8c323b82589')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-d281a79a-39f6-4150-a3c9-c8c323b82589 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_55e7f39b-3f3b-4df5-89eb-e68403f7c9e2\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_55e7f39b-3f3b-4df5-89eb-e68403f7c9e2 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                                                Text  \\\n",
+              "0  Operating the Climate Control System  Your Goo...   \n",
+              "1  Your Googlecar has a large touchscreen display...   \n",
+              "2  Shifting Gears  Your Googlecar has an automati...   \n",
+              "\n",
+              "                                          Embeddings  \n",
+              "0  [-0.016329883, -0.01747576, -0.038270127, -0.0...  \n",
+              "1  [0.024793636, -0.024769256, -0.01176664, -0.04...  \n",
+              "2  [-0.025426013, 0.00023183432, -0.02406427, -0....  "
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Get the embeddings of each text and add to an embeddings column in the dataframe\n",
+        "def embed_fn(title, text):\n",
+        "  return genai.embed_content(model=model,\n",
+        "                             content=text,\n",
+        "                             task_type=\"retrieval_document\",\n",
+        "                             title=title)[\"embedding\"]\n",
+        "\n",
+        "df['Embeddings'] = df.apply(lambda row: embed_fn(row['Title'], row['Text']), axis=1)\n",
+        "df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cfm8a31FKd00"
+      },
+      "source": [
+        "## Document search with Q&A\n",
+        "\n",
+        "Now that the embeddings are generated, let's create a Q&A system to search these documents. You will ask a question about hyperparameter tuning, create an embedding of the question, and compare it against the collection of embeddings in the dataframe.\n",
+        "\n",
+        "The embedding of the question will be a vector (list of float values), which will be compared against the vector of the documents using the dot product. This vector returned from the API is already normalized. The dot product represents the similarity in direction between two vectors.\n",
+        "\n",
+        "The values of the dot product can range between -1 and 1, inclusive. If the dot product between two vectors is 1, then the vectors are in the same direction. If the dot product value is 0, then these vectors are orthogonal, or unrelated, to each other. Lastly, if the dot product is -1, then the vectors point in the opposite direction and are not similar to each other.\n",
+        "\n",
+        "Note, with the new embeddings model (`embedding-001`), specify the task type as `QUERY` for user query and `DOCUMENT` when embedding a document text.\n",
+        "\n",
+        "Task Type | Description\n",
+        "---       | ---\n",
+        "RETRIEVAL_QUERY\t| Specifies the given text is a query in a search/retrieval setting.\n",
+        "RETRIEVAL_DOCUMENT | Specifies the given text is a document in a search/retrieval setting."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "80w2VQQ9JWcU"
+      },
+      "outputs": [],
+      "source": [
+        "query = \"How do you shift gears in the Google car?\"\n",
+        "model = 'models/embedding-001'\n",
+        "\n",
+        "request = genai.embed_content(model=model,\n",
+        "                              content=query,\n",
+        "                              task_type=\"retrieval_query\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iivgDQej5Agt"
+      },
+      "source": [
+        "Use the `find_best_passage` function to calculate the dot products, and then sort the dataframe from the largest to smallest dot product value to retrieve the relevant passage out of the database."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "am36P3J9M6Zv"
+      },
+      "outputs": [],
+      "source": [
+        "def find_best_passage(query, dataframe):\n",
+        "  \"\"\"\n",
+        "  Compute the distances between the query and each document in the dataframe\n",
+        "  using the dot product.\n",
+        "  \"\"\"\n",
+        "  query_embedding = genai.embed_content(model=model,\n",
+        "                                        content=query,\n",
+        "                                        task_type=\"retrieval_query\")\n",
+        "  dot_products = np.dot(np.stack(dataframe['Embeddings']), query_embedding[\"embedding\"])\n",
+        "  idx = np.argmax(dot_products)\n",
+        "  return dataframe.iloc[idx]['Text'] # Return text from index with max value"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Uq-bpLZm9DKo"
+      },
+      "source": [
+        "View the most relevant document from the database:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1I5lAqdH9zWL"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'Shifting Gears  Your Googlecar has an automatic transmission. To shift gears, simply move the shift lever to the desired position.  Park: This position is used when you are parked. The wheels are locked and the car cannot move. Reverse: This position is used to back up. Neutral: This position is used when you are stopped at a light or in traffic. The car is not in gear and will not move unless you press the gas pedal. Drive: This position is used to drive forward. Low: This position is used for driving in snow or other slippery conditions.'"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "passage = find_best_passage(query, df)\n",
+        "passage"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ebkGT0ha5Ln3"
+      },
+      "source": [
+        "## Question and Answering Application\n",
+        "\n",
+        "Let's try to use the text generation API to create a Q & A system. Input your own custom data below to create a simple question and answering example. You will still use the dot product as a metric of similarity."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pqf-OsT3auTm"
+      },
+      "outputs": [],
+      "source": [
+        "def make_prompt(query, relevant_passage):\n",
+        "  escaped = relevant_passage.replace(\"'\", \"\").replace('\"', \"\").replace(\"\\n\", \" \")\n",
+        "  prompt = textwrap.dedent(\"\"\"You are a helpful and informative bot that answers questions using text from the reference passage included below. \\\n",
+        "  Be sure to respond in a complete sentence, being comprehensive, including all relevant background information. \\\n",
+        "  However, you are talking to a non-technical audience, so be sure to break down complicated concepts and \\\n",
+        "  strike a friendly and converstional tone. \\\n",
+        "  If the passage is irrelevant to the answer, you may ignore it.\n",
+        "  QUESTION: '{query}'\n",
+        "  PASSAGE: '{relevant_passage}'\n",
+        "\n",
+        "    ANSWER:\n",
+        "  \"\"\").format(query=query, relevant_passage=escaped)\n",
+        "\n",
+        "  return prompt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mlpDRG3cVvQE"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "You are a helpful and informative bot that answers questions using text from the reference passage included below.   Be sure to respond in a complete sentence, being comprehensive, including all relevant background information.   However, you are talking to a non-technical audience, so be sure to break down complicated concepts and   strike a friendly and converstional tone.   If the passage is irrelevant to the answer, you may ignore it.\n",
+            "  QUESTION: 'How do you shift gears in the Google car?'\n",
+            "  PASSAGE: 'Shifting Gears  Your Googlecar has an automatic transmission. To shift gears, simply move the shift lever to the desired position.  Park: This position is used when you are parked. The wheels are locked and the car cannot move. Reverse: This position is used to back up. Neutral: This position is used when you are stopped at a light or in traffic. The car is not in gear and will not move unless you press the gas pedal. Drive: This position is used to drive forward. Low: This position is used for driving in snow or other slippery conditions.'\n",
+            "\n",
+            "    ANSWER:\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "prompt = make_prompt(query, passage)\n",
+        "print(prompt)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qmdYdoIHcEc_"
+      },
+      "source": [
+        "Choose one of the Gemini content generation models in order to find the answer to your query."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "B3fDj-jv5Sq_"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "models/gemini-1.5-pro\n",
+            "models/gemini-1.5-flash\n"
+          ]
+        }
+      ],
+      "source": [
+        "for m in genai.list_models():\n",
+        "  if 'generateContent' in m.supported_generation_methods:\n",
+        "    print(m.name)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "m30avD9cfQQ-"
+      },
+      "outputs": [],
+      "source": [
+        "model = genai.GenerativeModel('gemini-1.5-pro-latest')\n",
+        "answer = model.generate_content(prompt)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "COBhn6J9S_xI"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "The provided passage does not contain information about how to shift gears in a Google car, so I cannot answer your question from this source."
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 34,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "Markdown(answer.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-ocgBrOEXZbT"
+      },
+      "source": [
+        "## Next steps\n",
+        "\n",
+        "To learn how to use other services in the Gemini API, see the [Python quickstart](https://ai.google.dev/tutorials/python_quickstart).\n",
+        "\n",
+        "To learn more about how you can use embeddings, see these  other tutorials:\n",
+        "\n",
+        " * [Anomaly Detection with Embeddings](https://ai.google.dev/gemini-api/tutorials/anomaly_detection)\n",
+        " * [Clustering with Embeddings](https://ai.google.dev/gemini-api/tutorials/clustering_with_embeddings)\n",
+        " * [Training a Text Classifier with Embeddings](https://ai.google.dev/gemini-api/tutorials/text_classifier_embeddings)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "document_search.ipynb",
+      "toc_visible": true
+    },
+    "google": {
+      "image_path": "/site-assets/images/share.png",
+      "keywords": [
+        "examples",
+        "googleai",
+        "samplecode",
+        "python",
+        "embed"
+      ]
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemini-api/tutorials/extract_structured_data.ipynb b/site/en/gemini-api/tutorials/extract_structured_data.ipynb
new file mode 100644
index 000000000..ba09f9690
--- /dev/null
+++ b/site/en/gemini-api/tutorials/extract_structured_data.ipynb
@@ -0,0 +1,816 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8968a502d25e"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "cellView": "form",
+        "id": "906e07f6e562"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NtX45QCEdPaP"
+      },
+      "source": [
+        "# Extract structured data using function calling"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2tO4fP7FFg2V"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/tutorials/extract_structured_data\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Google AI</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemini-api/tutorials/extract_structured_data.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemini-api/tutorials/extract_structured_data.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8Szkddw5NScW"
+      },
+      "source": [
+        "In this tutorial you'll work through a structured data extraction example, using the Gemini API to extract the lists of characters, relationships, things, and places from a story."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bvrwRlNPdYDr"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "QyW6x11UQHnx"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -U -q google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "TS9l5igubpHO"
+      },
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "import pathlib\n",
+        "import textwrap\n",
+        "\n",
+        "import google.generativeai as genai\n",
+        "\n",
+        "\n",
+        "from IPython.display import display\n",
+        "from IPython.display import Markdown\n",
+        "\n",
+        "from google.api_core import retry\n",
+        "\n",
+        "def to_markdown(text):\n",
+        "  text = text.replace('•', '  *')\n",
+        "  return Markdown(textwrap.indent(text, '> ', predicate=lambda _: True))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VmSlTHXxb5pV"
+      },
+      "source": [
+        "Once you have the API key, pass it to the SDK. You can do this in two ways:\n",
+        "\n",
+        "* Put the key in the `GOOGLE_API_KEY` environment variable (the SDK will automatically pick it up from there).\n",
+        "* Pass the key to `genai.configure(api_key=...)`\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "ab9ASynfcIZn"
+      },
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    # Used to securely store your API key\n",
+        "    from google.colab import userdata\n",
+        "\n",
+        "    # Or use `os.getenv('API_KEY')` to fetch an environment variable.\n",
+        "    GOOGLE_API_KEY=userdata.get('GOOGLE_API_KEY')\n",
+        "    genai.configure(api_key=GOOGLE_API_KEY)\n",
+        "except ImportError:\n",
+        "    pass\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "K6SdtoJCL4pL"
+      },
+      "source": [
+        "## The example task\n",
+        "\n",
+        "For this tutorial you'll extract entities from natural language stories. As an\n",
+        " example, below is a story written by Gemini."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "0THz95wOL4pL"
+      },
+      "outputs": [],
+      "source": [
+        "new_story = False\n",
+        "\n",
+        "if new_story:\n",
+        "  model = genai.GenerativeModel(model_name='models/gemini-1.5-pro-latest')\n",
+        "\n",
+        "  response = model.generate_content(\"\"\"\n",
+        "      Write a long story about a girl with magic backpack, her family, and at\n",
+        "      least one other charater. Make sure everyone has names. Don't forget to\n",
+        "      describe the contents of the backpack, and where everyone and everything\n",
+        "      starts and ends up.\"\"\", request_options={'retry': retry.Retry()})\n",
+        "  story = response.text\n",
+        "  print(response.candidates[0].citation_metadata)\n",
+        "else:\n",
+        "  story = \"\"\"In the quaint town of Willow Creek, nestled amidst rolling hills and whispering willows, resided a young girl named Anya. As she stepped out of the creaky wooden door of her modest cottage, her heart skipped a beat with excitement and anticipation. Today was her first day of school, and she couldn't wait to show off her prized possession - a magical backpack.\\n\\nHanded down to her from her grandmother, the backpack was no ordinary satchel. Its soft, emerald-green fabric shimmered with an ethereal glow, and its leather straps held secrets that only Anya knew. Within its capacious interior lay an enchanted world, filled with wonders that would ignite her imagination and change her life forever.\\n\\nAnya's parents, kind-hearted Elise and wise-bearded Edward, bid her farewell with warm embraces. \"Remember, my dear,\" whispered her mother, \"use your magic wisely and for good.\" Her father added, \"Always seek knowledge, and let the backpack be your trusted companion.\"\\n\\nWith a skip in her step, Anya set off towards the town's only schoolhouse. On her way, she passed her best friend, Samuel, a curious and adventurous boy with a mischievous grin. \"Hey, Anya,\" he called out. \"Can I see your backpack?\"\\n\\nAnya hesitated for a moment before unzipping the flap and revealing its contents. Samuel's eyes widened in amazement as he peered inside. There, nestled amidst pencils and notebooks, were a shimmering sword, a book of ancient spells, a tiny compass that always pointed north, and a magical key that could open any lock.\\n\\nTogether, they marveled at the backpack's wonders, promising to keep its secrets safe. As they approached the schoolhouse, Anya noticed a group of older children huddled together, their faces etched with fear. Curiosity getting the better of her, she cautiously approached.\\n\\n\"What's wrong?\" she asked.\\n\\nA tall, lanky boy stepped forward. \"There's a monster in the forest,\" he stammered. \"It's been terrorizing the town, attacking animals and even people.\"\\n\\nAnya's heart sank. The town of Willow Creek was small and peaceful, and the thought of a monster brought a shiver down her spine. She knew she had to do something to protect her family and friends.\\n\\nWithout a moment's hesitation, Anya opened her backpack and retrieved the shimmering sword. With a determined gleam in her eye, she turned to her terrified peers. \"Don't worry,\" she said, her voice steady. \"I'll take care of it.\"\\n\\nWith Samuel close behind her, Anya ventured into the shadowy depths of the forest. The trees seemed to whisper secrets as she passed, and the undergrowth rustled with unseen creatures. As they walked deeper into the forest, the air grew heavy and the ground beneath their feet trembled.\\n\\nSuddenly, they came to a clearing, and there before their eyes was the monster - a massive beast with sharp teeth, glowing red eyes, and claws that could crush a human with ease. The creature roared, a thunderous sound that shook the forest to its core.\\n\\nFear surged through Anya, but she refused to let it consume her. She drew the sword from its sheath and charged towards the monster. The blade shimmered in the sunlight, and as it struck the beast's hide, a blinding light erupted, enveloping everything in its radiance.\\n\\nWhen the light faded, the monster was gone, and in its place was a pile of shattered crystals. Anya had defeated the creature with the magic of her backpack, proving that even the smallest of objects could hold the greatest of powers.\\n\\nAs she and Samuel returned to the town, they were greeted as heroes. The people of Willow Creek rejoiced, and the legend of Anya, the girl with the magic backpack, was passed down through generations. And so, Anya continued her adventures, using the backpack's wonders to make the world a better place, one magical step at a time.\"\"\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "yMnxJqubg759"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "> In the quaint town of Willow Creek, nestled amidst rolling hills and whispering willows, resided a young girl named Anya. As she stepped out of the creaky wooden door of her modest cottage, her heart skipped a beat with excitement and anticipation. Today was her first day of school, and she couldn't wait to show off her prized possession - a magical backpack.\n",
+              "> \n",
+              "> Handed down to her from her grandmother, the backpack was no ordinary satchel. Its soft, emerald-green fabric shimmered with an ethereal glow, and its leather straps held secrets that only Anya knew. Within its capacious interior lay an enchanted world, filled with wonders that would ignite her imagination and change her life forever.\n",
+              "> \n",
+              "> Anya's parents, kind-hearted Elise and wise-bearded Edward, bid her farewell with warm embraces. \"Remember, my dear,\" whispered her mother, \"use your magic wisely and for good.\" Her father added, \"Always seek knowledge, and let the backpack be your trusted companion.\"\n",
+              "> \n",
+              "> With a skip in her step, Anya set off towards the town's only schoolhouse. On her way, she passed her best friend, Samuel, a curious and adventurous boy with a mischievous grin. \"Hey, Anya,\" he called out. \"Can I see your backpack?\"\n",
+              "> \n",
+              "> Anya hesitated for a moment before unzipping the flap and revealing its contents. Samuel's eyes widened in amazement as he peered inside. There, nestled amidst pencils and notebooks, were a shimmering sword, a book of ancient spells, a tiny compass that always pointed north, and a magical key that could open any lock.\n",
+              "> \n",
+              "> Together, they marveled at the backpack's wonders, promising to keep its secrets safe. As they approached the schoolhouse, Anya noticed a group of older children huddled together, their faces etched with fear. Curiosity getting the better of her, she cautiously approached.\n",
+              "> \n",
+              "> \"What's wrong?\" she asked.\n",
+              "> \n",
+              "> A tall, lanky boy stepped forward. \"There's a monster in the forest,\" he stammered. \"It's been terrorizing the town, attacking animals and even people.\"\n",
+              "> \n",
+              "> Anya's heart sank. The town of Willow Creek was small and peaceful, and the thought of a monster brought a shiver down her spine. She knew she had to do something to protect her family and friends.\n",
+              "> \n",
+              "> Without a moment's hesitation, Anya opened her backpack and retrieved the shimmering sword. With a determined gleam in her eye, she turned to her terrified peers. \"Don't worry,\" she said, her voice steady. \"I'll take care of it.\"\n",
+              "> \n",
+              "> With Samuel close behind her, Anya ventured into the shadowy depths of the forest. The trees seemed to whisper secrets as she passed, and the undergrowth rustled with unseen creatures. As they walked deeper into the forest, the air grew heavy and the ground beneath their feet trembled.\n",
+              "> \n",
+              "> Suddenly, they came to a clearing, and there before their eyes was the monster - a massive beast with sharp teeth, glowing red eyes, and claws that could crush a human with ease. The creature roared, a thunderous sound that shook the forest to its core.\n",
+              "> \n",
+              "> Fear surged through Anya, but she refused to let it consume her. She drew the sword from its sheath and charged towards the monster. The blade shimmered in the sunlight, and as it struck the beast's hide, a blinding light erupted, enveloping everything in its radiance.\n",
+              "> \n",
+              "> When the light faded, the monster was gone, and in its place was a pile of shattered crystals. Anya had defeated the creature with the magic of her backpack, proving that even the smallest of objects could hold the greatest of powers.\n",
+              "> \n",
+              "> As she and Samuel returned to the town, they were greeted as heroes. The people of Willow Creek rejoiced, and the legend of Anya, the girl with the magic backpack, was passed down through generations. And so, Anya continued her adventures, using the backpack's wonders to make the world a better place, one magical step at a time."
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "to_markdown(story)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zldoIzn-MuLE"
+      },
+      "source": [
+        "## Using Natural language\n",
+        "\n",
+        "Large language models are a powerfuls multitask tools. Often you can just ask Gemini for what you want, and it will do okay.  \n",
+        "\n",
+        "The Gemini API doesn't have a JSON mode, so there are a few things to watch for when generating data structures this way:\n",
+        "\n",
+        "- Sometimes parsing fails.\n",
+        "- The schema can't be strictly enforced.\n",
+        "\n",
+        "You'll solve those problems in the next section. First, try a simple natural language prompt with the schema written out as text. This has not been optimized:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "eStTMD6VL4pM"
+      },
+      "outputs": [],
+      "source": [
+        "model = genai.GenerativeModel(\n",
+        "    model_name='models/gemini-1.5-pro-latest')\n",
+        "\n",
+        "response = model.generate_content(\n",
+        "  textwrap.dedent(\"\"\"\\\n",
+        "    Please return JSON describing the the people, places, things and relationships from this story using the following schema:\n",
+        "\n",
+        "    {\"people\": list[PERSON], \"places\":list[PLACE], \"things\":list[THING], \"relationships\": list[RELATIONSHIP]}\n",
+        "\n",
+        "    PERSON = {\"name\": str, \"description\": str, \"start_place_name\": str, \"end_place_name\": str}\n",
+        "    PLACE = {\"name\": str, \"description\": str}\n",
+        "    THING = {\"name\": str, \"description\": str, \"start_place_name\": str, \"end_place_name\": str}\n",
+        "    RELATIONSHIP = {\"person_1_name\": str, \"person_2_name\": str, \"relationship\": str}\n",
+        "\n",
+        "    All fields are required.\n",
+        "\n",
+        "    Important: Only return a single piece of valid JSON text.\n",
+        "\n",
+        "    Here is the story:\n",
+        "\n",
+        "    \"\"\") + story,\n",
+        "  generation_config={'response_mime_type':'application/json'}\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "B0b5zHI3uEBm"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'{\"people\":[{\"name\":\"Anya\",\"description\":\"A young girl with a magical backpack\",\"start_place_name\":\"Willow Creek\",\"end_place_name\":\"Willow Creek\"},{\"name\":\"Elise\",\"description\":\"Anya\\'s kind-hearted mother\",\"start_place_name\":\"Willow Creek\",\"end_place_name\":\"Willow Creek\"},{\"name\":\"Edward\",\"description\":\"Anya\\'s wise-bearded father\",\"start_place_name\":\"Willow Creek\",\"end_place_name\":\"Willow Creek\"},{\"name\":\"Samuel\",\"description\":\"Anya\\'s curious and adventurous best friend\",\"start_place_name\":\"Willow Creek\",\"end_place_name\":\"Willow Creek\"}],\"places\":[{\"name\":\"Willow Creek\",\"description\":\"A quaint town nestled amidst rolling hills and whispering willows\"},{\"name\":\"Anya\\'s cottage\",\"description\":\"A modest cottage in Willow Creek\"},{\"name\":\"Schoolhouse\",\"description\":\"The only schoolhouse in Willow Creek\"},{\"name\":\"Forest\",\"description\":\"A shadowy forest near Willow Creek\"}],\"things\":[{\"name\":\"Magical backpack\",\"description\":\"A shimmering emerald-green backpack with enchanted contents\",\"start_place_name\":\"Anya\\'s cottage\",\"end_place_name\":\"Forest\"},{\"name\":\"Shimmering sword\",\"description\":\"A sword found inside the backpack\",\"start_place_name\":\"Anya\\'s cottage\",\"end_place_name\":\"Forest\"},{\"name\":\"Book of ancient spells\",\"description\":\"A book found inside the backpack\",\"start_place_name\":\"Anya\\'s cottage\",\"end_place_name\":\"Forest\"},{\"name\":\"Tiny compass\",\"description\":\"A compass found inside the backpack\",\"start_place_name\":\"Anya\\'s cottage\",\"end_place_name\":\"Forest\"},{\"name\":\"Magical key\",\"description\":\"A key found inside the backpack\",\"start_place_name\":\"Anya\\'s cottage\",\"end_place_name\":\"Forest\"}],\"relationships\":[{\"person_1_name\":\"Anya\",\"person_2_name\":\"Elise\",\"relationship\":\"Mother-daughter\"},{\"person_1_name\":\"Anya\",\"person_2_name\":\"Edward\",\"relationship\":\"Father-daughter\"},{\"person_1_name\":\"Anya\",\"person_2_name\":\"Samuel\",\"relationship\":\"Best friends\"}]}\\n'"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "response.text"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ScEdqKq1lhmQ"
+      },
+      "source": [
+        "That returned a json string. Try parsing it:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "xSdj50czL4pM"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{\n",
+            "    \"people\": [\n",
+            "        {\n",
+            "            \"name\": \"Anya\",\n",
+            "            \"description\": \"A young girl with a magical backpack\",\n",
+            "            \"start_place_name\": \"Willow Creek\",\n",
+            "            \"end_place_name\": \"Willow Creek\"\n",
+            "        },\n",
+            "        {\n",
+            "            \"name\": \"Elise\",\n",
+            "            \"description\": \"Anya's kind-hearted mother\",\n",
+            "            \"start_place_name\": \"Willow Creek\",\n",
+            "            \"end_place_name\": \"Willow Creek\"\n",
+            "        },\n",
+            "        {\n",
+            "            \"name\": \"Edward\",\n",
+            "            \"description\": \"Anya's wise-bearded father\",\n",
+            "            \"start_place_name\": \"Willow Creek\",\n",
+            "            \"end_place_name\": \"Willow Creek\"\n",
+            "        },\n",
+            "        {\n",
+            "            \"name\": \"Samuel\",\n",
+            "            \"description\": \"Anya's curious and adventurous best friend\",\n",
+            "            \"start_place_name\": \"Willow Creek\",\n",
+            "            \"end_place_name\": \"Willow Creek\"\n",
+            "        }\n",
+            "    ],\n",
+            "    \"places\": [\n",
+            "        {\n",
+            "            \"name\": \"Willow Creek\",\n",
+            "            \"description\": \"A quaint town nestled amidst rolling hills and whispering willows\"\n",
+            "        },\n",
+            "        {\n",
+            "            \"name\": \"Anya's cottage\",\n",
+            "            \"description\": \"A modest cottage in Willow Creek\"\n",
+            "        },\n",
+            "        {\n",
+            "            \"name\": \"Schoolhouse\",\n",
+            "            \"description\": \"The only schoolhouse in Willow Creek\"\n",
+            "        },\n",
+            "        {\n",
+            "            \"name\": \"Forest\",\n",
+            "            \"description\": \"A shadowy forest near Willow Creek\"\n",
+            "        }\n",
+            "    ],\n",
+            "    \"things\": [\n",
+            "        {\n",
+            "            \"name\": \"Magical backpack\",\n",
+            "            \"description\": \"A shimmering emerald-green backpack with enchanted contents\",\n",
+            "            \"start_place_name\": \"Anya's cottage\",\n",
+            "            \"end_place_name\": \"Forest\"\n",
+            "        },\n",
+            "        {\n",
+            "            \"name\": \"Shimmering sword\",\n",
+            "            \"description\": \"A sword found inside the backpack\",\n",
+            "            \"start_place_name\": \"Anya's cottage\",\n",
+            "            \"end_place_name\": \"Forest\"\n",
+            "        },\n",
+            "        {\n",
+            "            \"name\": \"Book of ancient spells\",\n",
+            "            \"description\": \"A book found inside the backpack\",\n",
+            "            \"start_place_name\": \"Anya's cottage\",\n",
+            "            \"end_place_name\": \"Forest\"\n",
+            "        },\n",
+            "        {\n",
+            "            \"name\": \"Tiny compass\",\n",
+            "            \"description\": \"A compass found inside the backpack\",\n",
+            "            \"start_place_name\": \"Anya's cottage\",\n",
+            "            \"end_place_name\": \"Forest\"\n",
+            "        },\n",
+            "        {\n",
+            "            \"name\": \"Magical key\",\n",
+            "            \"description\": \"A key found inside the backpack\",\n",
+            "            \"start_place_name\": \"Anya's cottage\",\n",
+            "            \"end_place_name\": \"Forest\"\n",
+            "        }\n",
+            "    ],\n",
+            "    \"relationships\": [\n",
+            "        {\n",
+            "            \"person_1_name\": \"Anya\",\n",
+            "            \"person_2_name\": \"Elise\",\n",
+            "            \"relationship\": \"Mother-daughter\"\n",
+            "        },\n",
+            "        {\n",
+            "            \"person_1_name\": \"Anya\",\n",
+            "            \"person_2_name\": \"Edward\",\n",
+            "            \"relationship\": \"Father-daughter\"\n",
+            "        },\n",
+            "        {\n",
+            "            \"person_1_name\": \"Anya\",\n",
+            "            \"person_2_name\": \"Samuel\",\n",
+            "            \"relationship\": \"Best friends\"\n",
+            "        }\n",
+            "    ]\n",
+            "}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(json.dumps(json.loads(response.text), indent=4))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TgC_wkHPmkHn"
+      },
+      "source": [
+        "That's relatively simple and often works, but you can potentially make this more strict/robust by defining the schema using the API's function calling feature."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CxMC28LAOfUf"
+      },
+      "source": [
+        "## Use function calling"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "x-V6PJn83Kh9"
+      },
+      "source": [
+        "If you haven't gone through the [Function calling basics](https://ai.google.dev/tutorials/function_calling_python_quickstart) tutorial yet, make sure you do that first.\n",
+        "\n",
+        "With function calling your function and its parameters are described to the API as a `genai.protos.FunctionDeclaration`. In basic cases the SDK can build the `FunctionDeclaration` from the function and its annotations. The SDK doesn't currently handle the description of nested `OBJECT` (`dict`) parameters. So you'll need to define them explicitly, for now."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "k83LZ5MCBfTJ"
+      },
+      "source": [
+        "### Define the schema\n",
+        "\n",
+        "Start by defining `person` as an object with string fields `name`, `description`, `start_place_name`, `end_place_name`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "d6293fed386a"
+      },
+      "outputs": [],
+      "source": [
+        "from typing_extensions import TypedDict"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "e48ace2a3ded"
+      },
+      "outputs": [],
+      "source": [
+        "class Person(TypedDict):\n",
+        "    name: str\n",
+        "    description: str\n",
+        "    start_place_name: str\n",
+        "    end_place_name: str"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "N6uD63sBBJ3i"
+      },
+      "source": [
+        "Then do the same for each of the entities you're trying to extract:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "7wd3jTqj_bVi"
+      },
+      "outputs": [],
+      "source": [
+        "class Place(TypedDict):\n",
+        "    name:str\n",
+        "    description:str"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "45cLwvCd_vg_"
+      },
+      "outputs": [],
+      "source": [
+        "class Thing(TypedDict):\n",
+        "    name:str\n",
+        "    description:str\n",
+        "    "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "8DdVSZJfADDY"
+      },
+      "outputs": [],
+      "source": [
+        "class Relationship(TypedDict):\n",
+        "    person_1_name: str\n",
+        "    person_2_name: str\n",
+        "    relationship: str"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mJwqEUqjBToJ"
+      },
+      "source": [
+        "Now build the `FunctionDeclaration`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "b1fe9734d8c3"
+      },
+      "outputs": [],
+      "source": [
+        "def add_to_database(\n",
+        "    people: list[Person],\n",
+        "    places: list[Place],\n",
+        "    things: list[Thing],\n",
+        "    relationships: list[Relationship]\n",
+        "):\n",
+        "    pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e1_QSwD9Bmy5"
+      },
+      "source": [
+        "### Call the API\n",
+        "\n",
+        "Like you saw in [Function calling basics](https://ai.google.dev/tutorials/function_calling_python_quickstart) now you can pass this `FunctionDeclaration` to the `tools` argument of the `genai.GenerativeModel` constructor (the constructor would also accept an equivalent JSON representation of the function declaration):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "5PGAPRDJP4Qx"
+      },
+      "outputs": [],
+      "source": [
+        "model = genai.GenerativeModel(\n",
+        "    model_name='models/gemini-1.5-pro-latest',\n",
+        "    tools = [add_to_database])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1uTYW5cVCDST"
+      },
+      "source": [
+        "Each time you call the API the SDK will send the tools along with your prompt, and the model should call that function you defined:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "bAPA7fNtSUwN"
+      },
+      "outputs": [],
+      "source": [
+        "result = model.generate_content(f\"\"\"\n",
+        "{story}\n",
+        "\n",
+        "Please add the people, places, things, and relationships from this story to the database:\n",
+        "\"\"\",\n",
+        "# Force a function call\n",
+        "# tool_config={'function_calling_config':'ANY'}\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oSG7r6IBCL7S"
+      },
+      "source": [
+        "Now there is no text to parse. The result _is_ a datastructure."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "07n3wXzFOZ4x"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "False"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "'text' in result.candidates[0].content.parts[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "i-8hm1HPI5Ce"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "'function_call' in result.candidates[0].content.parts[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "n8BTs6ogDEkq"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'google.ai.generativelanguage_v1beta.types.content.FunctionCall'>\n"
+          ]
+        }
+      ],
+      "source": [
+        "fc = result.candidates[0].content.parts[0].function_call\n",
+        "print(type(fc))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kILNHmG2IED3"
+      },
+      "source": [
+        "The `genai.protos.FunctionCall` class is based on Google Protocol Buffers, convert it to a more familiar JSON compatible object:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "5GKHtT4-F3qa"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{\n",
+            "    \"name\": \"add_to_database\",\n",
+            "    \"args\": {\n",
+            "        \"people\": [\n",
+            "            {\n",
+            "                \"description\": \"A young girl with a magical backpack.\",\n",
+            "                \"name\": \"Anya\"\n",
+            "            },\n",
+            "            {\n",
+            "                \"description\": \"Anya\\\\'s kind-hearted mother.\",\n",
+            "                \"name\": \"Elise\"\n",
+            "            },\n",
+            "            {\n",
+            "                \"description\": \"Anya\\\\'s wise-bearded father.\",\n",
+            "                \"name\": \"Edward\"\n",
+            "            },\n",
+            "            {\n",
+            "                \"description\": \"Anya\\\\'s curious and adventurous best friend.\",\n",
+            "                \"name\": \"Samuel\"\n",
+            "            }\n",
+            "        ],\n",
+            "        \"things\": [\n",
+            "            {\n",
+            "                \"description\": \"A backpack with magical contents, including a shimmering sword, a book of ancient spells, a tiny compass, and a magical key.\",\n",
+            "                \"name\": \"Magical backpack\"\n",
+            "            },\n",
+            "            {\n",
+            "                \"description\": \"A sword that emits a blinding light when it strikes.\",\n",
+            "                \"name\": \"Shimmering sword\"\n",
+            "            }\n",
+            "        ],\n",
+            "        \"places\": [\n",
+            "            {\n",
+            "                \"description\": \"A quaint town nestled amidst rolling hills and whispering willows.\",\n",
+            "                \"name\": \"Willow Creek\"\n",
+            "            },\n",
+            "            {\n",
+            "                \"description\": \"A modest cottage in Willow Creek.\",\n",
+            "                \"name\": \"Anya\\\\'s cottage\"\n",
+            "            },\n",
+            "            {\n",
+            "                \"description\": \"The town\\\\'s only schoolhouse.\",\n",
+            "                \"name\": \"Schoolhouse\"\n",
+            "            },\n",
+            "            {\n",
+            "                \"description\": \"A shadowy forest near Willow Creek where the monster lived.\",\n",
+            "                \"name\": \"Forest\"\n",
+            "            }\n",
+            "        ],\n",
+            "        \"relationships\": [\n",
+            "            {\n",
+            "                \"person_1_name\": \"Anya\",\n",
+            "                \"person_2_name\": \"Elise\",\n",
+            "                \"relationship\": \"daughter\"\n",
+            "            },\n",
+            "            {\n",
+            "                \"person_1_name\": \"Anya\",\n",
+            "                \"person_2_name\": \"Edward\",\n",
+            "                \"relationship\": \"daughter\"\n",
+            "            },\n",
+            "            {\n",
+            "                \"person_1_name\": \"Anya\",\n",
+            "                \"person_2_name\": \"Samuel\",\n",
+            "                \"relationship\": \"friend\"\n",
+            "            }\n",
+            "        ]\n",
+            "    }\n",
+            "}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(json.dumps(type(fc).to_dict(fc), indent=4))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4m8FakjCIKmI"
+      },
+      "source": [
+        "## Conclusion\n",
+        "\n",
+        "While the API can handle structured data extraction problems with pure text input and text output, using function calling is likely more reliable since it lets you define a strict schema, and eliminates a potentially error-prone parsing step."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "extract_structured_data.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemini-api/tutorials/text_classifier_embeddings.ipynb b/site/en/gemini-api/tutorials/text_classifier_embeddings.ipynb
new file mode 100644
index 000000000..6017fb164
--- /dev/null
+++ b/site/en/gemini-api/tutorials/text_classifier_embeddings.ipynb
@@ -0,0 +1,1571 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "STuxHh6kk3eL"
+      },
+      "source": [
+        "# Training a Text Classifier Using Embeddings"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wUmTFPw2W_UD"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/tutorials/text_classifier_embeddings\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemini-api/tutorials/text_classifier_embeddings.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemini-api/tutorials/text_classifier_embeddings.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bhT1u-Pof10V"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "In this notebook, you'll learn to use the embeddings produced by the Gemini API to train a model that can classify different types of newsgroup posts based on the topic.\n",
+        "\n",
+        "In this tutorial, you'll train a classifier to predict which class a newsgroup post belongs to.\n",
+        "\n",
+        "## Prerequisites\n",
+        "\n",
+        "You can run this quickstart in Google Colab.\n",
+        "\n",
+        "To complete this quickstart on your own development environment, ensure that your envirmonement meets the following requirements:\n",
+        "\n",
+        "-  Python 3.9+\n",
+        "-  An installation of `jupyter` to run the notebook.\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "First, download and install the Gemini API Python library."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FXq0ygI3BCdQ"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -U -q google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XiJjB2vWCQJP"
+      },
+      "outputs": [],
+      "source": [
+        "import re\n",
+        "import tqdm\n",
+        "import keras\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "import google.generativeai as genai\n",
+        "\n",
+        "# Used to securely store your API key\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "from keras import layers\n",
+        "from matplotlib.ticker import MaxNLocator\n",
+        "from sklearn.datasets import fetch_20newsgroups\n",
+        "import sklearn.metrics as skmetrics"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_mwJYXpElYJc"
+      },
+      "source": [
+        "### Grab an API Key\n",
+        "\n",
+        "Before you can use the Gemini API, you must first obtain an API key. If you don't already have one, create a key with one click in Google AI Studio.\n",
+        "\n",
+        "<a class=\"button button-primary\" href=\"https://makersuite.google.com/app/apikey\" target=\"_blank\" rel=\"noopener noreferrer\">Get an API key</a>\n",
+        "\n",
+        "In Colab, add the key to the secrets manager under the \"🔑\" in the left panel. Give it the name `API_KEY`.\n",
+        "\n",
+        "Once you have the API key, pass it to the SDK. You can do this in two ways:\n",
+        "\n",
+        "* Put the key in the `GOOGLE_API_KEY` environment variable (the SDK will automatically pick it up from there).\n",
+        "* Pass the key to `genai.configure(api_key=...)`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tayrk_A2lZ7A"
+      },
+      "outputs": [],
+      "source": [
+        "# Or use `os.getenv('API_KEY')` to fetch an environment variable.\n",
+        "API_KEY=userdata.get('API_KEY')\n",
+        "\n",
+        "genai.configure(api_key=API_KEY)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WKXa-Pf9lv4H"
+      },
+      "source": [
+        "Key Point: Next, you will choose a model. Any embedding model will work for this tutorial, but for real applications it's important to choose a specific model and stick with it. The outputs of different models are not compatible with each other."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "l1pfEvNflvYV"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "models/embedding-001\n",
+            "models/embedding-001\n"
+          ]
+        }
+      ],
+      "source": [
+        "for m in genai.list_models():\n",
+        "  if 'embedContent' in m.supported_generation_methods:\n",
+        "    print(m.name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C5B9sWq0hNEV"
+      },
+      "source": [
+        "## Dataset\n",
+        "\n",
+        "The [20 Newsgroups Text Dataset](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html){:.external} contains 18,000 newsgroups posts on 20 topics divided into training and test sets. The split between the training and test datasets are based on messages posted before and after a specific date. For this tutorial, you will be using the subsets of the training and test datasets. You will preprocess and organize the data into Pandas dataframes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jDoKis4om-Ea"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['alt.atheism',\n",
+              " 'comp.graphics',\n",
+              " 'comp.os.ms-windows.misc',\n",
+              " 'comp.sys.ibm.pc.hardware',\n",
+              " 'comp.sys.mac.hardware',\n",
+              " 'comp.windows.x',\n",
+              " 'misc.forsale',\n",
+              " 'rec.autos',\n",
+              " 'rec.motorcycles',\n",
+              " 'rec.sport.baseball',\n",
+              " 'rec.sport.hockey',\n",
+              " 'sci.crypt',\n",
+              " 'sci.electronics',\n",
+              " 'sci.med',\n",
+              " 'sci.space',\n",
+              " 'soc.religion.christian',\n",
+              " 'talk.politics.guns',\n",
+              " 'talk.politics.mideast',\n",
+              " 'talk.politics.misc',\n",
+              " 'talk.religion.misc']"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "newsgroups_train = fetch_20newsgroups(subset='train')\n",
+        "newsgroups_test = fetch_20newsgroups(subset='test')\n",
+        "\n",
+        "# View list of class names for dataset\n",
+        "newsgroups_train.target_names"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hDz9MjkNl_FD"
+      },
+      "source": [
+        "Here is an example of what a data point from the training set looks like."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FPq-56AimOPX"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Lines: 15\n",
+            "\n",
+            " I was wondering if anyone out there could enlighten me on this car I saw\n",
+            "the other day. It was a 2-door sports car, looked to be from the late 60s/\n",
+            "early 70s. It was called a Bricklin. The doors were really small. In addition,\n",
+            "the front bumper was separate from the rest of the body. This is \n",
+            "all I know. If anyone can tellme a model name, engine specs, years\n",
+            "of production, where this car is made, history, or whatever info you\n",
+            "have on this funky looking car, please e-mail.\n",
+            "\n",
+            "Thanks,\n",
+            "- IL\n",
+            "   ---- brought to you by your neighborhood Lerxst ----\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "idx = newsgroups_train.data[0].index('Lines')\n",
+        "print(newsgroups_train.data[0][idx:])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A9-DD7wgCx8j"
+      },
+      "source": [
+        "Now you will begin preprocessing the data for this tutorial. Remove any sensitive information like names, email, or redundant parts of the text like `\"From: \"` and `\"\\nSubject: \"`. Organize the information into a Pandas dataframe so it is more readable."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "urpLwp3UmPF3"
+      },
+      "outputs": [],
+      "source": [
+        "def preprocess_newsgroup_data(newsgroup_dataset):\n",
+        "  # Apply functions to remove names, emails, and extraneous words from data points in newsgroups.data\n",
+        "  newsgroup_dataset.data = [re.sub(r'[\\w\\.-]+@[\\w\\.-]+', '', d) for d in newsgroup_dataset.data] # Remove email\n",
+        "  newsgroup_dataset.data = [re.sub(r\"\\([^()]*\\)\", \"\", d) for d in newsgroup_dataset.data] # Remove names\n",
+        "  newsgroup_dataset.data = [d.replace(\"From: \", \"\") for d in newsgroup_dataset.data] # Remove \"From: \"\n",
+        "  newsgroup_dataset.data = [d.replace(\"\\nSubject: \", \"\") for d in newsgroup_dataset.data] # Remove \"\\nSubject: \"\n",
+        "\n",
+        "  # Cut off each text entry after 5,000 characters\n",
+        "  newsgroup_dataset.data = [d[0:5000] if len(d) > 5000 else d for d in newsgroup_dataset.data]\n",
+        "\n",
+        "  # Put data points into dataframe\n",
+        "  df_processed = pd.DataFrame(newsgroup_dataset.data, columns=['Text'])\n",
+        "  df_processed['Label'] = newsgroup_dataset.target\n",
+        "  # Match label to target name index\n",
+        "  df_processed['Class Name'] = ''\n",
+        "  for idx, row in df_processed.iterrows():\n",
+        "    df_processed.at[idx, 'Class Name'] = newsgroup_dataset.target_names[row['Label']]\n",
+        "\n",
+        "  return df_processed"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JMKddQdNnAOV"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
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+              "    <div>\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Text</th>\n",
+              "      <th>Label</th>\n",
+              "      <th>Class Name</th>\n",
+              "    </tr>\n",
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+              "      <th>0</th>\n",
+              "      <td>WHAT car is this!?\\nNntp-Posting-Host: rac3.w...</td>\n",
+              "      <td>7</td>\n",
+              "      <td>rec.autos</td>\n",
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+              "      <th>1</th>\n",
+              "      <td>SI Clock Poll - Final Call\\nSummary: Final ca...</td>\n",
+              "      <td>4</td>\n",
+              "      <td>comp.sys.mac.hardware</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>PB questions...\\nOrganization: Purdue Univers...</td>\n",
+              "      <td>4</td>\n",
+              "      <td>comp.sys.mac.hardware</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Re: Weitek P9000 ?\\nOrganization: Harris Comp...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>comp.graphics</td>\n",
+              "    </tr>\n",
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+              "      <th>4</th>\n",
+              "      <td>Re: Shuttle Launch Question\\nOrganization: Sm...</td>\n",
+              "      <td>14</td>\n",
+              "      <td>sci.space</td>\n",
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+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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+              "          + ' to learn more about interactive tables.';\n",
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+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-4654e17e-48a8-46e9-b6dd-182587903bdf button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                                                Text  Label  \\\n",
+              "0   WHAT car is this!?\\nNntp-Posting-Host: rac3.w...      7   \n",
+              "1   SI Clock Poll - Final Call\\nSummary: Final ca...      4   \n",
+              "2   PB questions...\\nOrganization: Purdue Univers...      4   \n",
+              "3   Re: Weitek P9000 ?\\nOrganization: Harris Comp...      1   \n",
+              "4   Re: Shuttle Launch Question\\nOrganization: Sm...     14   \n",
+              "\n",
+              "              Class Name  \n",
+              "0              rec.autos  \n",
+              "1  comp.sys.mac.hardware  \n",
+              "2  comp.sys.mac.hardware  \n",
+              "3          comp.graphics  \n",
+              "4              sci.space  "
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Apply preprocessing function to training and test datasets\n",
+        "df_train = preprocess_newsgroup_data(newsgroups_train)\n",
+        "df_test = preprocess_newsgroup_data(newsgroups_test)\n",
+        "\n",
+        "df_train.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ogEGbg5XDv-T"
+      },
+      "source": [
+        "Next, you will sample some of the data by taking 100 data points in the training dataset, and dropping a few of the categories to run through this tutorial. Choose the science categories to compare."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "C2N7xXhJohLR"
+      },
+      "outputs": [],
+      "source": [
+        "def sample_data(df, num_samples, classes_to_keep):\n",
+        "  df = df.groupby('Label', as_index = False).apply(lambda x: x.sample(num_samples)).reset_index(drop=True)\n",
+        "\n",
+        "  df = df[df['Class Name'].str.contains(classes_to_keep)]\n",
+        "\n",
+        "  # Reset the encoding of the labels after sampling and dropping certain categories\n",
+        "  df['Class Name'] = df['Class Name'].astype('category')\n",
+        "  df['Encoded Label'] = df['Class Name'].cat.codes\n",
+        "\n",
+        "  return df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jS2g_ZGupBUb"
+      },
+      "outputs": [],
+      "source": [
+        "TRAIN_NUM_SAMPLES = 100\n",
+        "TEST_NUM_SAMPLES = 25\n",
+        "CLASSES_TO_KEEP = 'sci' # Class name should contain 'sci' in it to keep science categories\n",
+        "df_train = sample_data(df_train, TRAIN_NUM_SAMPLES, CLASSES_TO_KEEP)\n",
+        "df_test = sample_data(df_test, TEST_NUM_SAMPLES, CLASSES_TO_KEEP)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "j04TMPY8rV5q"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Class Name\n",
+              "sci.crypt          100\n",
+              "sci.electronics    100\n",
+              "sci.med            100\n",
+              "sci.space          100\n",
+              "dtype: int64"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_train.value_counts('Class Name')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qMsnfkVDsJlU"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Class Name\n",
+              "sci.crypt          25\n",
+              "sci.electronics    25\n",
+              "sci.med            25\n",
+              "sci.space          25\n",
+              "dtype: int64"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_test.value_counts('Class Name')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Kr-WlKzXjYWn"
+      },
+      "source": [
+        "## Create the embeddings\n",
+        "\n",
+        "In this section, you will see how to generate embeddings for a piece of text using the embeddings from the Gemini API. To learn more about embeddings, visit the [embeddings guide](https://ai.google.dev/docs/embeddings_guide).\n",
+        "\n",
+        "**NOTE**: Embeddings are computed one at a time, large sample sizes can take a long time!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yPECMeE2xYA_"
+      },
+      "source": [
+        "### API changes to Embeddings embedding-001\n",
+        "\n",
+        "For the new embeddings model, there is a new task type parameter and the optional title (only valid with task_type=`RETRIEVAL_DOCUMENT`).\n",
+        "\n",
+        "These new parameters apply only to the newest embeddings models.The task types are:\n",
+        "\n",
+        "Task Type | Description\n",
+        "---       | ---\n",
+        "RETRIEVAL_QUERY\t| Specifies the given text is a query in a search/retrieval setting.\n",
+        "RETRIEVAL_DOCUMENT | Specifies the given text is a document in a search/retrieval setting.\n",
+        "SEMANTIC_SIMILARITY\t| Specifies the given text will be used for Semantic Textual Similarity (STS).\n",
+        "CLASSIFICATION\t| Specifies that the embeddings will be used for classification.\n",
+        "CLUSTERING\t| Specifies that the embeddings will be used for clustering."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MTBGKkPQsotz"
+      },
+      "outputs": [],
+      "source": [
+        "from tqdm.auto import tqdm\n",
+        "tqdm.pandas()\n",
+        "\n",
+        "from google.api_core import retry\n",
+        "\n",
+        "def make_embed_text_fn(model):\n",
+        "\n",
+        "  @retry.Retry(timeout=300.0)\n",
+        "  def embed_fn(text: str) -> list[float]:\n",
+        "    # Set the task_type to CLASSIFICATION.\n",
+        "    embedding = genai.embed_content(model=model,\n",
+        "                                    content=text,\n",
+        "                                    task_type=\"classification\")\n",
+        "    return embedding['embedding']\n",
+        "\n",
+        "  return embed_fn\n",
+        "\n",
+        "def create_embeddings(model, df):\n",
+        "  df['Embeddings'] = df['Text'].progress_apply(make_embed_text_fn(model))\n",
+        "  return df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AH0yrHUHtHtw"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "65e92937e9ca4a0bb81466310b866c2a",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/400 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e2f6127ed420422f91dcc81d8cd52d62",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/100 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "model = 'models/embedding-001'\n",
+        "df_train = create_embeddings(model, df_train)\n",
+        "df_test = create_embeddings(model, df_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6G5TvLlmRjHc"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_train"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f4a7c6eb-7d9c-4b92-a598-db8886c3b637\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Text</th>\n",
+              "      <th>Label</th>\n",
+              "      <th>Class Name</th>\n",
+              "      <th>Encoded Label</th>\n",
+              "      <th>Embeddings</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1100</th>\n",
+              "      <td>Re: Secret algorithm [Re: Clipper Chip and cr...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>0</td>\n",
+              "      <td>[0.0036893487, 0.0015885577, -0.08848337, -0.0...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1101</th>\n",
+              "      <td>Danny Weitzner &lt;&gt;Re-inventing Crypto Policy?  ...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>0</td>\n",
+              "      <td>[-0.0015051058, -0.011516359, -0.05678679, -0....</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1102</th>\n",
+              "      <td>Marc VanHeyningen &lt;&gt;How does it really work? \\...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>0</td>\n",
+              "      <td>[0.021544674, -0.030707408, -0.056979053, -0.0...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1103</th>\n",
+              "      <td>Re: Would \"clipper\" make a good cover for oth...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>0</td>\n",
+              "      <td>[-0.012111008, -0.024126764, -0.07459716, -0.0...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1104</th>\n",
+              "      <td>The battle is joined\\nNntp-Posting-Host: serv...</td>\n",
+              "      <td>11</td>\n",
+              "      <td>sci.crypt</td>\n",
+              "      <td>0</td>\n",
+              "      <td>[0.008676766, 0.013530727, -0.02739913, -0.039...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f4a7c6eb-7d9c-4b92-a598-db8886c3b637')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-f4a7c6eb-7d9c-4b92-a598-db8886c3b637 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-f4a7c6eb-7d9c-4b92-a598-db8886c3b637');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-2dd53f96-1fc5-4c92-9417-89ee84ab21d9\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2dd53f96-1fc5-4c92-9417-89ee84ab21d9')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-2dd53f96-1fc5-4c92-9417-89ee84ab21d9 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                                                   Text  Label Class Name  \\\n",
+              "1100   Re: Secret algorithm [Re: Clipper Chip and cr...     11  sci.crypt   \n",
+              "1101  Danny Weitzner <>Re-inventing Crypto Policy?  ...     11  sci.crypt   \n",
+              "1102  Marc VanHeyningen <>How does it really work? \\...     11  sci.crypt   \n",
+              "1103   Re: Would \"clipper\" make a good cover for oth...     11  sci.crypt   \n",
+              "1104   The battle is joined\\nNntp-Posting-Host: serv...     11  sci.crypt   \n",
+              "\n",
+              "      Encoded Label                                         Embeddings  \n",
+              "1100              0  [0.0036893487, 0.0015885577, -0.08848337, -0.0...  \n",
+              "1101              0  [-0.0015051058, -0.011516359, -0.05678679, -0....  \n",
+              "1102              0  [0.021544674, -0.030707408, -0.056979053, -0.0...  \n",
+              "1103              0  [-0.012111008, -0.024126764, -0.07459716, -0.0...  \n",
+              "1104              0  [0.008676766, 0.013530727, -0.02739913, -0.039...  "
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_train.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QPYEYkIsWt_5"
+      },
+      "source": [
+        "## Build a simple classification model\n",
+        "Here you will define a simple model with one hidden layer and a single class probability output. The prediction will correspond to the probability of a piece of text being a particular class of news. When you build your model, Keras will automatically shuffle the data points."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3oLGi4w5JsQR"
+      },
+      "outputs": [],
+      "source": [
+        "def build_classification_model(input_size: int, num_classes: int) -> keras.Model:\n",
+        "  inputs = x = keras.Input(input_size)\n",
+        "  x = layers.Dense(input_size, activation='relu')(x)\n",
+        "  x = layers.Dense(num_classes, activation='sigmoid')(x)\n",
+        "  return keras.Model(inputs=[inputs], outputs=x)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kORA1Akl5GsG"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Model: \"model\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " input_1 (InputLayer)        [(None, 768)]             0         \n",
+            "                                                                 \n",
+            " dense (Dense)               (None, 768)               590592    \n",
+            "                                                                 \n",
+            " dense_1 (Dense)             (None, 4)                 3076      \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 593668 (2.26 MB)\n",
+            "Trainable params: 593668 (2.26 MB)\n",
+            "Non-trainable params: 0 (0.00 Byte)\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Derive the embedding size from the first training element.\n",
+        "embedding_size = len(df_train['Embeddings'].iloc[0])\n",
+        "\n",
+        "# Give your model a different name, as you have already used the variable name 'model'\n",
+        "classifier = build_classification_model(embedding_size, len(df_train['Class Name'].unique()))\n",
+        "classifier.summary()\n",
+        "\n",
+        "classifier.compile(loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
+        "                   optimizer = keras.optimizers.Adam(learning_rate=0.001),\n",
+        "                   metrics=['accuracy'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iPYYKnqFvt9x"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "768"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "embedding_size"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kbpTGGiMXDxl"
+      },
+      "source": [
+        "## Train the model to classify newsgroups\n",
+        "\n",
+        "Finally, you can train a simple model. Use a small number of epochs to avoid overfitting. The first epoch takes much longer than the rest, because the embeddings need to be computed only once."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bGgvMZGfJ1A4"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/20\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/keras/src/backend.py:5729: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a Softmax activation and thus does not represent logits. Was this intended?\n",
+            "  output, from_logits = _get_logits(\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "13/13 [==============================] - 1s 30ms/step - loss: 1.2141 - accuracy: 0.6675 - val_loss: 0.9801 - val_accuracy: 0.8800\n",
+            "Epoch 2/20\n",
+            "13/13 [==============================] - 0s 12ms/step - loss: 0.7580 - accuracy: 0.9400 - val_loss: 0.6061 - val_accuracy: 0.9300\n",
+            "Epoch 3/20\n",
+            "13/13 [==============================] - 0s 13ms/step - loss: 0.4249 - accuracy: 0.9525 - val_loss: 0.3902 - val_accuracy: 0.9200\n",
+            "Epoch 4/20\n",
+            "13/13 [==============================] - 0s 13ms/step - loss: 0.2561 - accuracy: 0.9625 - val_loss: 0.2597 - val_accuracy: 0.9400\n",
+            "Epoch 5/20\n",
+            "13/13 [==============================] - 0s 13ms/step - loss: 0.1693 - accuracy: 0.9700 - val_loss: 0.2145 - val_accuracy: 0.9300\n",
+            "Epoch 6/20\n",
+            "13/13 [==============================] - 0s 13ms/step - loss: 0.1240 - accuracy: 0.9850 - val_loss: 0.1801 - val_accuracy: 0.9600\n",
+            "Epoch 7/20\n",
+            "13/13 [==============================] - 0s 21ms/step - loss: 0.0931 - accuracy: 0.9875 - val_loss: 0.1623 - val_accuracy: 0.9400\n",
+            "Epoch 8/20\n",
+            "13/13 [==============================] - 0s 16ms/step - loss: 0.0736 - accuracy: 0.9925 - val_loss: 0.1418 - val_accuracy: 0.9600\n",
+            "Epoch 9/20\n",
+            "13/13 [==============================] - 0s 20ms/step - loss: 0.0613 - accuracy: 0.9925 - val_loss: 0.1315 - val_accuracy: 0.9700\n",
+            "Epoch 10/20\n",
+            "13/13 [==============================] - 0s 20ms/step - loss: 0.0479 - accuracy: 0.9975 - val_loss: 0.1235 - val_accuracy: 0.9600\n",
+            "Epoch 11/20\n",
+            "13/13 [==============================] - 0s 19ms/step - loss: 0.0399 - accuracy: 0.9975 - val_loss: 0.1219 - val_accuracy: 0.9700\n",
+            "Epoch 12/20\n",
+            "13/13 [==============================] - 0s 21ms/step - loss: 0.0326 - accuracy: 0.9975 - val_loss: 0.1158 - val_accuracy: 0.9700\n",
+            "Epoch 13/20\n",
+            "13/13 [==============================] - 0s 19ms/step - loss: 0.0263 - accuracy: 1.0000 - val_loss: 0.1127 - val_accuracy: 0.9700\n",
+            "Epoch 14/20\n",
+            "13/13 [==============================] - 0s 17ms/step - loss: 0.0229 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9700\n",
+            "Epoch 15/20\n",
+            "13/13 [==============================] - 0s 20ms/step - loss: 0.0195 - accuracy: 1.0000 - val_loss: 0.1063 - val_accuracy: 0.9700\n",
+            "Epoch 16/20\n",
+            "13/13 [==============================] - 0s 17ms/step - loss: 0.0172 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9700\n"
+          ]
+        }
+      ],
+      "source": [
+        "NUM_EPOCHS = 20\n",
+        "BATCH_SIZE = 32\n",
+        "\n",
+        "# Split the x and y components of the train and validation subsets.\n",
+        "y_train = df_train['Encoded Label']\n",
+        "x_train = np.stack(df_train['Embeddings'])\n",
+        "y_val = df_test['Encoded Label']\n",
+        "x_val = np.stack(df_test['Embeddings'])\n",
+        "\n",
+        "# Train the model for the desired number of epochs.\n",
+        "callback = keras.callbacks.EarlyStopping(monitor='accuracy', patience=3)\n",
+        "\n",
+        "history = classifier.fit(x=x_train,\n",
+        "                         y=y_train,\n",
+        "                         validation_data=(x_val, y_val),\n",
+        "                         callbacks=[callback],\n",
+        "                         batch_size=BATCH_SIZE,\n",
+        "                         epochs=NUM_EPOCHS,)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xGBaDHZUPdJO"
+      },
+      "source": [
+        "## Evaluate model performance\n",
+        "\n",
+        "Use Keras <a href=\"https://www.tensorflow.org/api_docs/python/tf/keras/Model#evaluate\"><code>Model.evaluate</code></a> to get the loss and accuracy on the test dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d2kOeiqqQIB8"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "4/4 [==============================] - 0s 4ms/step - loss: 0.1070 - accuracy: 0.9700\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "{'loss': 0.10700511932373047, 'accuracy': 0.9700000286102295}"
+            ]
+          },
+          "execution_count": 20,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "classifier.evaluate(x=x_val, y=y_val, return_dict=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UyxMhiLYQXAN"
+      },
+      "source": [
+        "One way to evaluate your model performance is to visualize the classifier performance. Use `plot_history` to see the loss and accuracy trends over the epochs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MaDO9hwbEOW3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 2000x800 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "def plot_history(history):\n",
+        "  \"\"\"\n",
+        "    Plotting training and validation learning curves.\n",
+        "\n",
+        "    Args:\n",
+        "      history: model history with all the metric measures\n",
+        "  \"\"\"\n",
+        "  fig, (ax1, ax2) = plt.subplots(1,2)\n",
+        "  fig.set_size_inches(20, 8)\n",
+        "\n",
+        "  # Plot loss\n",
+        "  ax1.set_title('Loss')\n",
+        "  ax1.plot(history.history['loss'], label = 'train')\n",
+        "  ax1.plot(history.history['val_loss'], label = 'test')\n",
+        "  ax1.set_ylabel('Loss')\n",
+        "\n",
+        "  ax1.set_xlabel('Epoch')\n",
+        "  ax1.legend(['Train', 'Validation'])\n",
+        "\n",
+        "  # Plot accuracy\n",
+        "  ax2.set_title('Accuracy')\n",
+        "  ax2.plot(history.history['accuracy'],  label = 'train')\n",
+        "  ax2.plot(history.history['val_accuracy'], label = 'test')\n",
+        "  ax2.set_ylabel('Accuracy')\n",
+        "  ax2.set_xlabel('Epoch')\n",
+        "  ax2.legend(['Train', 'Validation'])\n",
+        "\n",
+        "  plt.show()\n",
+        "\n",
+        "plot_history(history)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kOgva0pbP4FS"
+      },
+      "source": [
+        "Another way to view model performance, beyond just measuring loss and accuracy is to use a confusion matrix. The confusion matrix allows you to assess the performance of the classification model beyond accuracy. You can see what misclassified points get classified as. In order to build the confusion matrix for this multi-class classification problem, get the actual values in the test set and the predicted values.\n",
+        "\n",
+        "Start by generating the predicted class for each example in the validation set using [`Model.predict()`](https://www.tensorflow.org/api_docs/python/tf/keras/Model#predict)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PRUx5ao9QRcO"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "4/4 [==============================] - 0s 4ms/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_hat = classifier.predict(x=x_val)\n",
+        "y_hat = np.argmax(y_hat, axis=1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CVidbr0OT5tL"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'sci.crypt': 0, 'sci.electronics': 1, 'sci.med': 2, 'sci.space': 3}"
+            ]
+          },
+          "execution_count": 23,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "labels_dict = dict(zip(df_test['Class Name'], df_test['Encoded Label']))\n",
+        "labels_dict"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3ae76701e178"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "cm = skmetrics.confusion_matrix(y_val, y_hat)\n",
+        "disp = skmetrics.ConfusionMatrixDisplay(confusion_matrix=cm,\n",
+        "                              display_labels=labels_dict.keys())\n",
+        "disp.plot(xticks_rotation='vertical')\n",
+        "plt.title('Confusion matrix for newsgroup test dataset');\n",
+        "plt.grid(False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "S9ISDJBCcDT2"
+      },
+      "source": [
+        "## Next steps\n",
+        "\n",
+        "To learn how to use other services in the Gemini API, see the [Python quickstart](https://ai.google.dev/tutorials/python_quickstart).\n",
+        "\n",
+        "To learn more about how you can use embeddings, see these  other tutorials:\n",
+        "\n",
+        " * [Anomaly Detection with Embeddings](https://ai.google.dev/gemini-api/tutorials/anomaly_detection)\n",
+        " * [Clustering with Embeddings](https://ai.google.dev/gemini-api/tutorials/clustering_with_embeddings)\n",
+        " * [Document Search with Embeddings](https://ai.google.dev/gemini-api/tutorials/document_search)\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "text_classifier_embeddings.ipynb",
+      "toc_visible": true
+    },
+    "google": {
+      "image_path": "/examples/train_text_classifier_embeddings_files/output_3ae76701e178_0.png",
+      "keywords": [
+        "examples",
+        "googleai",
+        "samplecode",
+        "python",
+        "embed"
+      ]
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemma/docs/agile_classifiers.ipynb b/site/en/gemma/docs/agile_classifiers.ipynb
new file mode 100644
index 000000000..07963dbfe
--- /dev/null
+++ b/site/en/gemma/docs/agile_classifiers.ipynb
@@ -0,0 +1,1191 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3MMAcssHTML"
+      },
+      "source": [
+        "<link rel=\"stylesheet\" href=\"/site-assets/css/gemma.css\">\n",
+        "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/css2?family=Google+Symbols:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200\" />"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PXNm5_p_oxMF"
+      },
+      "source": [
+        "# Showcasing Agile Safety Classifiers with Gemma"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GrGMv8e4XxUI"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemma/docs/agile_classifiers\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Generative AI</a>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/agile_classifiers.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemma/docs/agile_classifiers.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://codelabs.developers.google.com/codelabs/responsible-ai/agile-classifiers\"><img src=\"https://www.tensorflow.org/images/codelabs_logo.png\" height=\"24\" width=\"48\"/>Learn in Codelabs</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Fn1NwT2fB6H6"
+      },
+      "source": [
+        "This codelab illustrates how to create a customised text classifier using\n",
+        "parameter efficient tuning (PET). Instead of fine-tuning the whole model, PET\n",
+        "methods update only a small amount of parameters, which makes it relatively easy\n",
+        "and fast to train. It also makes it easier for a model to learn new behaviors\n",
+        "with relatively little training data. The methodology is described in detail in\n",
+        "[*Towards Agile Text Classifiers for Everyone*][paper-agile-classifiers] which\n",
+        "shows how these techniques can be applied to a variety of safety tasks and\n",
+        "achieve state of the art performance with only a few hundred training examples.\n",
+        "\n",
+        "This codelab uses the [LoRA](https://arxiv.org/abs/2106.09685) PET method and\n",
+        "the smaller Gemma model (`gemma_instruct_2b_en`) since that can be run faster\n",
+        "and more efficiently. The colab covers the steps of ingesting data, formatting\n",
+        "it for the LLM, training LoRA weights, and then evaluating the results. This\n",
+        "codelab trains on the [ETHOS dataset][ethos-dataset], a publicly available\n",
+        "dataset for detecting hateful speech, built from YouTube and Reddit comments.\n",
+        "When trained on only 200 examples (1/4 of the dataset) it achieves F1: 0.80 and\n",
+        "ROC-AUC: 0.78, slightly above the SOTA currently reported on\n",
+        "[the leaderboard][ethos-leaderboard] (at the time of writing, 15 Feb 2024). When\n",
+        "trained on the full 800 examples, like it achieves an F1 score of 83.74 and a\n",
+        "ROC-AUC score of 88.17. Larger models, like `gemma_instruct_7b_en` will\n",
+        "generally perform better, but training and execution costs are also larger.\n",
+        "\n",
+        "**Trigger Warning**: because this codelab develops a safety classifier for\n",
+        "detecting hateful speech, examples and evaluation of the results contains some\n",
+        "horrible language.\n",
+        "\n",
+        "[paper-agile-classifiers]: https://arxiv.org/abs/2302.06541\n",
+        "[ethos-dataset]: https://arxiv.org/abs/2006.08328\n",
+        "[ethos-leaderboard]: https://paperswithcode.com/sota/hate-speech-detection-on-ethos-binary"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f0Pfoa65CVs6"
+      },
+      "source": [
+        "## Installation and Setup\n",
+        "\n",
+        "For this codelab, you will need a recent version `keras` (3), `keras-nlp`\n",
+        "(0.8.0) and a Kaggle account to download the base model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6QfrkE6CCkck"
+      },
+      "outputs": [],
+      "source": [
+        "import kagglehub\n",
+        "\n",
+        "kagglehub.login()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zHs7wpZusEML"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q -U keras-nlp\n",
+        "!pip install -q -U keras"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yn5uy8X8sdD0"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "os.environ[\"KERAS_BACKEND\"] = \"tensorflow\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RETIODEyD9Mh"
+      },
+      "source": [
+        "## Load ETHOS dataset\n",
+        "\n",
+        "In this section you will load the dataset on which to train our classifier and\n",
+        "preprocess it into a train and test set. You will use the popular research\n",
+        "dataset ETHOS which was collected to detect hate speech in social media. You\n",
+        "can find more information about how the dataset was collected in the paper\n",
+        "[ETHOS: an Online Hate Speech Detection Dataset][ethos-dataset]."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yIpviRPcDmDx"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 998,\n  \"fields\": [\n    {\n      \"column\": \"comment\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 998,\n        \"samples\": [\n          \"EXECUTE OHOMO AND HIS SHEMALE BITCH MIKE.\",\n          \"men and women are not equal.  irrational contrary belief and policy only result in mounting failure.\",\n          \"Indians are shit but burgers only have broken English and ebonics to learn and still fail. Second thread in a row where a burger can't figure out you're and your\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"isHate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.39176318927884884,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          0.4,\n          0.9838709677419356,\n          0.603448275862069\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hateful\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b045a877-d79c-47c9-ae9b-e37dd1079900\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>hateful</th>\n",
+              "      <th>comment</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>517</th>\n",
+              "      <td>0</td>\n",
+              "      <td>You said he but still not convinced this is a ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>685</th>\n",
+              "      <td>0</td>\n",
+              "      <td>well, looks like its time to have another child</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>706</th>\n",
+              "      <td>0</td>\n",
+              "      <td>to be honest I am part of the LGBT community a...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>182</th>\n",
+              "      <td>1</td>\n",
+              "      <td>What if we send every men to mars to start a n...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>829</th>\n",
+              "      <td>0</td>\n",
+              "      <td>It doesn'™t matter if you'™re black or white, ...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b045a877-d79c-47c9-ae9b-e37dd1079900')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b045a877-d79c-47c9-ae9b-e37dd1079900 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b045a877-d79c-47c9-ae9b-e37dd1079900');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "  </div>\n",
+              "\n",
+              "\n",
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+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5952d6cf-0042-4ae3-b0c4-f5b0bdcb7834')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
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+              "        document.querySelector('#df-5952d6cf-0042-4ae3-b0c4-f5b0bdcb7834 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "     hateful                                            comment\n",
+              "517        0  You said he but still not convinced this is a ...\n",
+              "685        0    well, looks like its time to have another child\n",
+              "706        0  to be honest I am part of the LGBT community a...\n",
+              "182        1  What if we send every men to mars to start a n...\n",
+              "829        0  It doesn'™t matter if you'™re black or white, ..."
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "gh_root = 'https://raw.githubusercontent.com'\n",
+        "gh_repo = 'intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset'\n",
+        "gh_path = 'master/ethos/ethos_data/Ethos_Dataset_Binary.csv'\n",
+        "data_url = f'{gh_root}/{gh_repo}/{gh_path}'\n",
+        "\n",
+        "df = pd.read_csv(data_url, delimiter=';')\n",
+        "df['hateful'] = (df['isHate'] >= df['isHate'].median()).astype(int)\n",
+        "\n",
+        "# Shuffle the dataset.\n",
+        "df = df.sample(frac=1, random_state=32)\n",
+        "\n",
+        "# Split into train and test.\n",
+        "df_train, df_test = df[:800],  df[800:]\n",
+        "\n",
+        "# Display a sample of the data.\n",
+        "df.head(5)[['hateful', 'comment']]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A3QpLBWieMov"
+      },
+      "source": [
+        "## Download and Instantiate the Model\n",
+        "\n",
+        "As described in [the documentation](//ai.google.dev/gem/docs), you can easily\n",
+        "use the Gemma model in many ways. With Keras, this is what you need to do:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Z3c05TR6D0Pj"
+      },
+      "outputs": [],
+      "source": [
+        "import keras\n",
+        "import keras_nlp\n",
+        "\n",
+        "# For reproducibility purposes.\n",
+        "keras.utils.set_random_seed(1234)\n",
+        "\n",
+        "# Download the model from Kaggle using Keras.\n",
+        "model = keras_nlp.models.GemmaCausalLM.from_preset('gemma_instruct_2b_en')\n",
+        "\n",
+        "# Set the sequence length to a small enough value to fit in memory in Colab.\n",
+        "model.preprocessor.sequence_length = 128"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UxQ3zVMtEAMp"
+      },
+      "outputs": [],
+      "source": [
+        "model.generate('Question: what is the capital of France? ', max_length=32)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vIDvHi3EC1Vm"
+      },
+      "source": [
+        "## Text Preprocessing and Separator Tokens\n",
+        "\n",
+        "To help the model understand our intent better, you can preprocess the text and\n",
+        "use separator tokens. This makes it less likely for the model to generate text\n",
+        "that does not fit the expected format. For example, you might attempt to request\n",
+        "a sentiment classification from the model by writing a prompt like this:\n",
+        "\n",
+        "```console\n",
+        "Classify the following text into one of the following classes:[Positive,Negative]\n",
+        "\n",
+        "Text: you look very nice today\n",
+        "Classification:\n",
+        "```\n",
+        "\n",
+        "In this case, the model may or may not output what you are looking for. For\n",
+        "example, if the text contains newline characters, it's likely to have a negative\n",
+        "effect on the model performance. A more robust approach is to use separator\n",
+        "tokens. The prompt then becomes:\n",
+        "\n",
+        "```console\n",
+        "Classify the following text into one of the following classes:[Positive,Negative]\n",
+        "<separator>\n",
+        "Text: you look very nice today\n",
+        "<separator>\n",
+        "Prediction:\n",
+        "```\n",
+        "\n",
+        "This can be abstracted using a function that preprocesses the text:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0RYw2dX3EGtF"
+      },
+      "outputs": [],
+      "source": [
+        "def preprocess_text(\n",
+        "    text: str,\n",
+        "    labels: list[str],\n",
+        "    instructions: str,\n",
+        "    separator: str,\n",
+        ") -> str:\n",
+        "  prompt = f'{instructions}:[{\",\".join(labels)}]'\n",
+        "  return separator.join([prompt, f'Text:{text}', 'Prediction:'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qz63Rta4DC0u"
+      },
+      "source": [
+        "Now, if you run the function using the same prompt and text as before, you\n",
+        "should get the same output:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_0VKIOjqEqv0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Classify the following text into one of the following classes:[Positive,Negative]\n",
+            "<separator>\n",
+            "Text:you look very nice today\n",
+            "<separator>\n",
+            "Prediction:\n"
+          ]
+        }
+      ],
+      "source": [
+        "text = 'you look very nice today'\n",
+        "\n",
+        "prompt = preprocess_text(\n",
+        "    text=text,\n",
+        "    labels=['Positive', 'Negative'],\n",
+        "    instructions='Classify the following text into one of the following classes',\n",
+        "    separator='\\n<separator>\\n',\n",
+        ")\n",
+        "\n",
+        "print(prompt)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eS4XXfr_eW07"
+      },
+      "source": [
+        "## Output Postprocessing\n",
+        "\n",
+        "The outputs of the model are tokens with various probabilities. Normally, to\n",
+        "generate text, you would select among the top few most probable tokens and\n",
+        "construct sentences, paragraphs or even full documents. However, for the purpose\n",
+        "of classification, what actually matters is whether the model believes that\n",
+        "`Positive` is more probable than `Negative` or vice versa.\n",
+        "\n",
+        "Given the model you instantiated earlier, this is how you can process its output\n",
+        "into the independent probabilities of whether the next token is `Positive` or\n",
+        "`Negative`, respectively:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tfyaeeaoE5L0"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "\n",
+        "def compute_output_probability(\n",
+        "    model: keras_nlp.models.GemmaCausalLM,\n",
+        "    prompt: str,\n",
+        "    target_classes: list[str],\n",
+        ") -> dict[str, float]:\n",
+        "  # Shorthands.\n",
+        "  preprocessor = model.preprocessor\n",
+        "  tokenizer = preprocessor.tokenizer\n",
+        "\n",
+        "  # NOTE: If a token is not found, it will be considered same as \"<unk>\".\n",
+        "  token_unk = tokenizer.token_to_id('<unk>')\n",
+        "\n",
+        "  # Identify the token indices, which is the same as the ID for this tokenizer.\n",
+        "  token_ids = [tokenizer.token_to_id(word) for word in target_classes]\n",
+        "\n",
+        "  # Throw an error if one of the classes maps to a token outside the vocabulary.\n",
+        "  if any(token_id == token_unk for token_id in token_ids):\n",
+        "    raise ValueError('One of the target classes is not in the vocabulary.')\n",
+        "\n",
+        "  # Preprocess the prompt in a single batch. This is done one sample at a time\n",
+        "  # for illustration purposes, but it would be more efficient to batch prompts.\n",
+        "  preprocessed = model.preprocessor.generate_preprocess([prompt])\n",
+        "\n",
+        "  # Identify output token offset.\n",
+        "  padding_mask = preprocessed[\"padding_mask\"]\n",
+        "  token_offset = keras.ops.sum(padding_mask) - 1\n",
+        "\n",
+        "  # Score outputs, extract only the next token's logits.\n",
+        "  vocab_logits = model.score(\n",
+        "      token_ids=preprocessed[\"token_ids\"],\n",
+        "      padding_mask=padding_mask,\n",
+        "  )[0][token_offset]\n",
+        "\n",
+        "  # Compute the relative probability of each of the requested tokens.\n",
+        "  token_logits = [vocab_logits[ix] for ix in token_ids]\n",
+        "  logits_tensor = keras.ops.convert_to_tensor(token_logits)\n",
+        "  probabilities = keras.activations.softmax(logits_tensor)\n",
+        "\n",
+        "  return dict(zip(target_classes, probabilities.numpy()))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "czpwDJg9Kz1N"
+      },
+      "source": [
+        "You can test that function by running it with a the prompt you created earlier:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2cJ5R-jlK0ct"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'Positive': 0.99994016, 'Negative': 5.984089e-05}"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "compute_output_probability(\n",
+        "    model=model,\n",
+        "    prompt=prompt,\n",
+        "    target_classes=['Positive', 'Negative'],\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OMAkdNLCeZnL"
+      },
+      "source": [
+        "## Wrapping it all as a Classifier\n",
+        "\n",
+        "For ease of use, you can wrap all of the functions you just created into a\n",
+        "single sklearn-like classifier with easy to use and familiar functions like\n",
+        "`predict()` and `predict_score()`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "e2Wyg8ziH-ls"
+      },
+      "outputs": [],
+      "source": [
+        "import dataclasses\n",
+        "\n",
+        "\n",
+        "@dataclasses.dataclass(frozen=True)\n",
+        "class AgileClassifier:\n",
+        "  \"\"\"Agile classifier to be wrapped around a LLM.\"\"\"\n",
+        "\n",
+        "  # The classes whose probability will be predicted.\n",
+        "  labels: tuple[str, ...]\n",
+        "\n",
+        "  # Provide default instructions and control tokens, can be overridden by user.\n",
+        "  instructions: str = 'Classify the following text into one of the following classes'\n",
+        "  separator_token: str = '<separator>'\n",
+        "  end_of_text_token: str = '<eos>'\n",
+        "\n",
+        "  def encode_for_prediction(self, x_text: str) -> str:\n",
+        "    return preprocess_text(\n",
+        "        text=x_text,\n",
+        "        labels=self.labels,\n",
+        "        instructions=self.instructions,\n",
+        "        separator=self.separator_token,\n",
+        "    )\n",
+        "\n",
+        "  def encode_for_training(self, x_text: str, y: int) -> str:\n",
+        "    return ''.join([\n",
+        "        self.encode_for_prediction(x_text),\n",
+        "        self.labels[y],\n",
+        "        self.end_of_text_token,\n",
+        "    ])\n",
+        "\n",
+        "  def predict_score(\n",
+        "      self,\n",
+        "      model: keras_nlp.models.GemmaCausalLM,\n",
+        "      x_text: str,\n",
+        "  ) -> list[float]:\n",
+        "    prompt = self.encode_for_prediction(x_text)\n",
+        "    token_probabilities = compute_output_probability(\n",
+        "        model=model,\n",
+        "        prompt=prompt,\n",
+        "        target_classes=self.labels,\n",
+        "    )\n",
+        "    return [token_probabilities[token] for token in self.labels]\n",
+        "\n",
+        "  def predict(\n",
+        "      self,\n",
+        "      model: keras_nlp.models.GemmaCausalLM,\n",
+        "      x_eval: str,\n",
+        "  ) -> int:\n",
+        "    return np.argmax(self.predict_score(model, x_eval))\n",
+        "\n",
+        "agile_classifier = AgileClassifier(labels=('Positive', 'Negative'))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "upc8lWNBefOK"
+      },
+      "source": [
+        "## Model Fine-Tuning\n",
+        "\n",
+        "LoRA stands for Low-Rank Adaptation. It's a fine-tuning technique that can be\n",
+        "used to efficiently fine-tune large language models. You can read more about it\n",
+        "in the [*LoRA: Low-Rank Adaptation of Large Language Models* paper][paper-lora].\n",
+        "\n",
+        "The Keras implementation of Gemma provides a `enable_lora()` method that you can\n",
+        "use for fine-tuning:\n",
+        "\n",
+        "[paper-lora]: https://arxiv.org/abs/2106.09685"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "aswoSEU_Mcbn"
+      },
+      "outputs": [],
+      "source": [
+        "# Enable LoRA for the model and set the LoRA rank to 4.\n",
+        "model.backbone.enable_lora(rank=4)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xdiL7LYCZkfR"
+      },
+      "source": [
+        "After enabling LoRA, you can start the fine-tuning process. This takes approximately 5 minutes per epoch on Colab:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_tbUK1QSPD9O"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/4\n",
+            "\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m354s\u001b[0m 703ms/step - loss: 1.1365 - sparse_categorical_accuracy: 0.5874\n",
+            "Epoch 2/4\n",
+            "\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m338s\u001b[0m 716ms/step - loss: 0.7579 - sparse_categorical_accuracy: 0.6662\n",
+            "Epoch 3/4\n",
+            "\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m324s\u001b[0m 721ms/step - loss: 0.6818 - sparse_categorical_accuracy: 0.6894\n",
+            "Epoch 4/4\n",
+            "\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m323s\u001b[0m 725ms/step - loss: 0.5922 - sparse_categorical_accuracy: 0.7220\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.history.History at 0x7eb7e369c490>"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import tensorflow as tf\n",
+        "\n",
+        "# Create dataset with preprocessed text + labels.\n",
+        "map_fn = lambda x: agile_classifier.encode_for_training(*x)\n",
+        "x_train = list(map(map_fn, df_train[['comment', 'hateful']].values))\n",
+        "ds_train = tf.data.Dataset.from_tensor_slices(x_train).batch(2)\n",
+        "\n",
+        "# Compile the model using the Adam optimizer and appropriate loss function.\n",
+        "model.compile(\n",
+        "    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
+        "    optimizer=keras.optimizers.Adam(learning_rate=0.0005),\n",
+        "    weighted_metrics=[keras.metrics.SparseCategoricalAccuracy()],\n",
+        ")\n",
+        "\n",
+        "# Begin training.\n",
+        "model.fit(ds_train, epochs=4)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4zmISk5qToPR"
+      },
+      "source": [
+        "Training for more epochs will result in higher accuracy, until overfitting\n",
+        "occurs."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "whpoXgFEenh1"
+      },
+      "source": [
+        "## Inspect the Results\n",
+        "\n",
+        "You can now inspect the output of the agile classifier you just trained. This\n",
+        "code will output the predicted class score given a piece of text:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0rkHY2YHT3Te"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'Positive': 0.99899644, 'Negative': 0.0010035498}"
+            ]
+          },
+          "execution_count": 24,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "text = 'you look really nice today'\n",
+        "scores = agile_classifier.predict_score(model, text)\n",
+        "dict(zip(agile_classifier.labels, scores))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "myL9BXvzerP-"
+      },
+      "source": [
+        "## Model Evaluation\n",
+        "\n",
+        "Finally, you'll evaluate the performance of our model using two common metrics,\n",
+        "the [F1 score][f1-score] and the [AUC-ROC][auc-roc]. The F1 score captures false\n",
+        "negative and false positive errors by evaluating the harmonic mean of the\n",
+        "precision and recall at a certain classification threshold. The AUC-ROC on the\n",
+        "other hand captures the tradeoff between the true positive rate and the false\n",
+        "positive rate across a variety of thresholds and computes the area under this\n",
+        "curve.\n",
+        "\n",
+        "[f1-score]: https://en.wikipedia.org/wiki/F-score\n",
+        "[auc-roc]: https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "n61v4Nx2Rwk0"
+      },
+      "outputs": [],
+      "source": [
+        "y_true = df_test['hateful'].values\n",
+        "# Compute the scores (aka probabilities) for each of the labels.\n",
+        "y_score = [agile_classifier.predict_score(model, x) for x in df_test['comment']]\n",
+        "# The label with highest score is considered the predicted class.\n",
+        "y_pred = np.argmax(y_score, axis=1)\n",
+        "# Extract the probability of a comment being considered hateful.\n",
+        "y_prob = [x[agile_classifier.labels.index('Negative')] for x in y_score]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_IwZzmKNcYxh"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "F1: 0.84\n",
+            "AUC-ROC: 0.88\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import f1_score, roc_auc_score\n",
+        "\n",
+        "print(f'F1: {f1_score(y_true, y_pred):.2f}')\n",
+        "print(f'AUC-ROC: {roc_auc_score(y_true, y_prob):.2f}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UPl7gtCKbsSg"
+      },
+      "source": [
+        "Another interesting way to evaluate model predictions are confusion matrices. A\n",
+        "confusion matrix will visually depict the different kinds of prediction errors."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GpShnBJ0cbaN"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7eb7e2d29ab0>"
+            ]
+          },
+          "execution_count": 26,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+        "\n",
+        "cm = confusion_matrix(y_true, y_pred)\n",
+        "ConfusionMatrixDisplay(\n",
+        "    confusion_matrix=cm,\n",
+        "    display_labels=agile_classifier.labels,\n",
+        ").plot()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fJF2tk0hbtU5"
+      },
+      "source": [
+        "Finally, you can also look at the ROC curve to get a sense of potential\n",
+        "prediction errors with using different scoring thresholds."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "B7l8e49GceWY"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7eb4d130ef20>"
+            ]
+          },
+          "execution_count": 25,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import RocCurveDisplay, roc_curve\n",
+        "\n",
+        "fpr, tpr, _ = roc_curve(y_true, y_prob, pos_label=1)\n",
+        "RocCurveDisplay(fpr=fpr, tpr=tpr).plot()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fPAP2K3jgfGh"
+      },
+      "source": [
+        "## Appendix\n",
+        "\n",
+        "We have done some basic exploration of the hyper-parameter space to help get a better sense of the relationship between the dataset size and the performance. See the following plot."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fMSDQtyTgeP0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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r7PM9NSNHtqvmZPzQ9va3N09Vvwsqq6Vafb3AP2dXf7dOBYsw4c600cpVrNKuuz4GV5cbbG5aaR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\nYZMtQf9dAQC42JhNR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+            "text/plain": [
+              "<Figure size 1400x400 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "image/png": {
+              "height": 282,
+              "width": 836
+            }
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "\n",
+        "sns.set_theme(style=\"whitegrid\")\n",
+        "\n",
+        "results_f1 = pd.DataFrame([\n",
+        "    {'training_size': 800, 'epoch': 4, 'metric': 'f1', 'score': 0.84},\n",
+        "    {'training_size': 800, 'epoch': 6, 'metric': 'f1', 'score': 0.83},\n",
+        "    {'training_size': 800, 'epoch': 8, 'metric': 'f1', 'score': 0.83},\n",
+        "    {'training_size': 800, 'epoch': 10, 'metric': 'f1', 'score': 0.84},\n",
+        "    {'training_size': 400, 'epoch': 4, 'metric': 'f1', 'score': 0.77},\n",
+        "    {'training_size': 400, 'epoch': 6, 'metric': 'f1', 'score': 0.80},\n",
+        "    {'training_size': 400, 'epoch': 8, 'metric': 'f1', 'score': 0.80},\n",
+        "    {'training_size': 400, 'epoch': 10,'metric': 'f1', 'score': 0.81},\n",
+        "    {'training_size': 200, 'epoch': 4, 'metric': 'f1', 'score': 0.78},\n",
+        "    {'training_size': 200, 'epoch': 6, 'metric': 'f1', 'score': 0.80},\n",
+        "    {'training_size': 200, 'epoch': 8, 'metric': 'f1', 'score': 0.78},\n",
+        "    {'training_size': 200, 'epoch': 10, 'metric': 'f1', 'score': 0.79},\n",
+        "])\n",
+        "\n",
+        "results_roc_auc = pd.DataFrame([\n",
+        "    {'training_size': 800, 'epoch': 4, 'metric': 'roc-auc', 'score': 0.88},\n",
+        "    {'training_size': 800, 'epoch': 6, 'metric': 'roc-auc', 'score': 0.86},\n",
+        "    {'training_size': 800, 'epoch': 8, 'metric': 'roc-auc', 'score': 0.84},\n",
+        "    {'training_size': 800, 'epoch': 10, 'metric': 'roc-auc', 'score': 0.87},\n",
+        "    {'training_size': 400, 'epoch': 4, 'metric': 'roc-auc', 'score': 0.83},\n",
+        "    {'training_size': 400, 'epoch': 6, 'metric': 'roc-auc', 'score': 0.82},\n",
+        "    {'training_size': 400, 'epoch': 8, 'metric': 'roc-auc', 'score': 0.82},\n",
+        "    {'training_size': 400, 'epoch': 10,'metric': 'roc-auc', 'score': 0.85},\n",
+        "    {'training_size': 200, 'epoch': 4, 'metric': 'roc-auc', 'score': 0.79},\n",
+        "    {'training_size': 200, 'epoch': 6, 'metric': 'roc-auc', 'score': 0.78},\n",
+        "    {'training_size': 200, 'epoch': 8, 'metric': 'roc-auc', 'score': 0.80},\n",
+        "    {'training_size': 200, 'epoch': 10, 'metric': 'roc-auc', 'score': 0.81},\n",
+        "])\n",
+        "\n",
+        "\n",
+        "plot_opts = dict(style='.-', ylim=(0.7, 0.9))\n",
+        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 4))\n",
+        "process_results_df = lambda df: df.set_index('epoch').groupby('training_size')['score']\n",
+        "process_results_df(results_f1).plot(title='Metric: F1', ax=ax1, **plot_opts)\n",
+        "process_results_df(results_roc_auc).plot(title='Metric: ROC-AUC', ax=ax2, **plot_opts)\n",
+        "fig.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "agile_classifiers.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemma/docs/codegemma/code_assist_keras.ipynb b/site/en/gemma/docs/codegemma/code_assist_keras.ipynb
new file mode 100644
index 000000000..65119e45a
--- /dev/null
+++ b/site/en/gemma/docs/codegemma/code_assist_keras.ipynb
@@ -0,0 +1,1297 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3MMAcssHTML"
+      },
+      "source": [
+        "<link rel=\"stylesheet\" href=\"/site-assets/css/gemma.css\">\n",
+        "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/css2?family=Google+Symbols:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200\" />"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SDEExiAk4fLb"
+      },
+      "source": [
+        "# AI Assisted programming with CodeGemma and KerasNLP"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZFWzQEqNosrS"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemma/docs/codegemma/code_assist_keras\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/codegemma/code_assist_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemma/docs/codegemma/code_assist_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lSGRSsRPgkzK"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "CodeGemma is a variant of Gemma that is fine-tuned for coding tasks. This tutorial builds on the [Keras CodeGemma quickstart](https://colab.research.google.com/drive/11Va7W2Yl12JUnfx9YDJmy90kBu1i4rtw) and shows you more ways in which CodeGemma can assist your programming tasks.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AraV8lsDaP1A"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lyhHCMfoRZ_v"
+      },
+      "source": [
+        "### Get access to CodeGemma\n",
+        "\n",
+        "To complete this tutorial, you will first need to complete the setup instructions at [Gemma setup](https://ai.google.dev/gemma/docs/setup). The Gemma setup instructions show you how to do the following:\n",
+        "\n",
+        "* Get access to Gemma on [kaggle.com](https://kaggle.com){:.external}.\n",
+        "* Select a Colab runtime with sufficient resources to run the Gemma 7B model.\n",
+        "* Generate and configure a Kaggle username and API key.\n",
+        "\n",
+        "After you've completed the Gemma setup, move on to the next section, where you'll set environment variables for your Colab environment."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AZ5Qo0fxRZ1V"
+      },
+      "source": [
+        "### Select the runtime\n",
+        "\n",
+        "To run the CodeGemma 7B models, you'll need to have a paid Colab Pro plan which provides a runtime with an A100 GPU.\n",
+        "\n",
+        "1. In the upper-right of the Colab window, select &#9662; (**Additional connection options**).\n",
+        "2. Select **Change runtime type**.\n",
+        "3. Under **Hardware accelerator**, select **A100 GPU**."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hsPC0HRkJl0K"
+      },
+      "source": [
+        "### Configure your API key\n",
+        "\n",
+        "To use Gemma, you must provide your Kaggle username and a Kaggle API key.\n",
+        "\n",
+        "To generate a Kaggle API key, go to the **Account** tab of your Kaggle user profile and select **Create New Token**. This will trigger the download of a `kaggle.json` file containing your API credentials.\n",
+        "\n",
+        "In Colab, select **Secrets** (🔑) in the left pane and add your Kaggle username and Kaggle API key. Store your username under the name `KAGGLE_USERNAME` and your API key under the name `KAGGLE_KEY`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7iOF6Yo-wUEC"
+      },
+      "source": [
+        "### Set environment variables\n",
+        "\n",
+        "Set environment variables for `KAGGLE_USERNAME` and `KAGGLE_KEY`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DrBoa_Urw9Vx"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "os.environ[\"KAGGLE_USERNAME\"] = userdata.get('KAGGLE_USERNAME')\n",
+        "os.environ[\"KAGGLE_KEY\"] = userdata.get('KAGGLE_KEY')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FX47AUYrXwLK"
+      },
+      "source": [
+        "### Install dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ACnm31nfBqHj"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m508.4/508.4 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m950.8/950.8 kB\u001b[0m \u001b[31m18.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.2/5.2 MB\u001b[0m \u001b[31m51.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m589.8/589.8 MB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.8/4.8 MB\u001b[0m \u001b[31m59.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m32.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.5/5.5 MB\u001b[0m \u001b[31m42.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m45.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m311.2/311.2 kB\u001b[0m \u001b[31m38.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "tf-keras 2.15.1 requires tensorflow<2.16,>=2.15, but you have tensorflow 2.16.1 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0m"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q -U keras-nlp"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2I69cArSBm3z"
+      },
+      "source": [
+        "### Select a backend"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QPTF92kyOQ-p"
+      },
+      "source": [
+        "Keras is a high-level, multi-framework deep learning API designed for simplicity and ease of use. Using Keras 3, you can run workflows on one of three backends: TensorFlow, JAX, or PyTorch.\n",
+        "\n",
+        "For this tutorial, configure the backend for JAX."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ww83zI9ToPso"
+      },
+      "outputs": [],
+      "source": [
+        "os.environ[\"KERAS_BACKEND\"] = \"jax\"  # Or \"tensorflow\" or \"torch\"."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FLDJd1nxa3I7"
+      },
+      "source": [
+        "### Import packages\n",
+        "\n",
+        "Import Keras and KerasNLP."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oQkqsyE1a2YD"
+      },
+      "outputs": [],
+      "source": [
+        "import keras_nlp\n",
+        "import keras\n",
+        "\n",
+        "# Run at half precision.\n",
+        "keras.config.set_floatx(\"bfloat16\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dn_yBThs4kXk"
+      },
+      "source": [
+        "## CodeGemma 7B Model Examples\n",
+        "\n",
+        "This section covers examples of using the pre-trained 7B CodeGemma model to help with coding tasks.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7RCE3fdGhDE5"
+      },
+      "source": [
+        "### Load the model\n",
+        "\n",
+        "KerasNLP provides implementations of all three CodeGemma variants (2B and 7B pre-trained (PT) and 7B instruction-tuned (IT)) using [`GemmaCausalLM`](https://keras.io/api/keras_nlp/models/gemma/gemma_causal_lm/){:.external}, an end-to-end Gemma model for causal language modeling. A causal language model predicts the next token based on previous tokens."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "S43GTKpQF_-F"
+      },
+      "source": [
+        "For this example, load the `code_gemma_7b_en` model using the [`from_preset`](https://keras.io/api/keras_nlp/models/gemma/gemma_causal_lm/#frompreset-method){:.external} method.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SQ6Yr7c4GKTx"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_7b_en/1/download/config.json...\n",
+            "100%|██████████| 556/556 [00:00<00:00, 790kB/s]\n",
+            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_7b_en/1/download/model.weights.h5...\n",
+            "100%|██████████| 15.9G/15.9G [02:39<00:00, 107MB/s]\n",
+            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_7b_en/1/download/tokenizer.json...\n",
+            "100%|██████████| 401/401 [00:00<00:00, 587kB/s]\n",
+            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_7b_en/1/download/assets/tokenizer/vocabulary.spm...\n",
+            "100%|██████████| 4.04M/4.04M [00:00<00:00, 16.4MB/s]\n"
+          ]
+        }
+      ],
+      "source": [
+        "gemma_lm_7b = keras_nlp.models.GemmaCausalLM.from_preset(\"code_gemma_7b_en\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "v0YFsh2a3n9P"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Preprocessor: \"gemma_causal_lm_preprocessor\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mPreprocessor: \"gemma_causal_lm_preprocessor\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Tokenizer (type)                                   </span>┃<span style=\"font-weight: bold\">                                             Vocab # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ gemma_tokenizer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaTokenizer</span>)                   │                                             <span style=\"color: #00af00; text-decoration-color: #00af00\">256,000</span> │\n",
+              "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mTokenizer (type)                                  \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m                                            Vocab #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ gemma_tokenizer (\u001b[38;5;33mGemmaTokenizer\u001b[0m)                   │                                             \u001b[38;5;34m256,000\u001b[0m │\n",
+              "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"gemma_causal_lm\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"gemma_causal_lm\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                  </span>┃<span style=\"font-weight: bold\"> Output Shape              </span>┃<span style=\"font-weight: bold\">         Param # </span>┃<span style=\"font-weight: bold\"> Connected to               </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ padding_mask (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_ids (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ gemma_backbone                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3072</span>)        │   <span style=\"color: #00af00; text-decoration-color: #00af00\">8,537,680,896</span> │ padding_mask[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],        │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaBackbone</span>)               │                           │                 │ token_ids[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_embedding               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256000</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">786,432,000</span> │ gemma_backbone[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReversibleEmbedding</span>)         │                           │                 │                            │\n",
+              "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                 \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to              \u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ padding_mask (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_ids (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ gemma_backbone                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3072\u001b[0m)        │   \u001b[38;5;34m8,537,680,896\u001b[0m │ padding_mask[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
+              "│ (\u001b[38;5;33mGemmaBackbone\u001b[0m)               │                           │                 │ token_ids[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_embedding               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256000\u001b[0m)      │     \u001b[38;5;34m786,432,000\u001b[0m │ gemma_backbone[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
+              "│ (\u001b[38;5;33mReversibleEmbedding\u001b[0m)         │                           │                 │                            │\n",
+              "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,537,680,896</span> (15.90 GB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,537,680,896\u001b[0m (15.90 GB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,537,680,896</span> (15.90 GB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,537,680,896\u001b[0m (15.90 GB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "gemma_lm_7b.summary()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YXO1l87PtfSN"
+      },
+      "source": [
+        "The `from_preset` method instantiates the model from a preset architecture and weights."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "H2XNKl2vHxHy"
+      },
+      "source": [
+        "### Code completion with Multi-line FIM\n",
+        "\n",
+        "The PT CodeGemma models are trained on code infilling tasks. This section shows examples that use CodeGemma's multi-line fill-in the-middle (FIM) capability to autofill code at the specified cursor location based on the surrounding context.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5Gsjz0QwSz2f"
+      },
+      "source": [
+        "As a first step, define constants and a prompt formatting helper function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mHAjAdvB_5yN"
+      },
+      "outputs": [],
+      "source": [
+        "# Formatting control tokens to specify cursor location\n",
+        "BEFORE_CURSOR = \"<|fim_prefix|>\"\n",
+        "AFTER_CURSOR = \"<|fim_suffix|>\"\n",
+        "AT_CURSOR = \"<|fim_middle|>\"\n",
+        "FILE_SEPARATOR = \"<|file_separator|>\"\n",
+        "\n",
+        "# Define model stop tokens\n",
+        "END_TOKEN = gemma_lm_7b.preprocessor.tokenizer.end_token\n",
+        "stop_tokens = (BEFORE_CURSOR, AFTER_CURSOR, AT_CURSOR, FILE_SEPARATOR, END_TOKEN)\n",
+        "stop_token_ids = tuple(gemma_lm_7b.preprocessor.tokenizer.token_to_id(x) for x in stop_tokens)\n",
+        "\n",
+        "def format_completion_prompt(before, after):\n",
+        "    return f\"{BEFORE_CURSOR}{before}{AFTER_CURSOR}{after}{AT_CURSOR}\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aDwFjBhoNe02"
+      },
+      "source": [
+        "#### Example 1 - Insert missing condition\n",
+        "\n",
+        "The example code below to generate the Fibonacci sequence will not execute correctly if `n=1`:\n",
+        "\n",
+        "```python\n",
+        "def fibonacci(n: int) -> int:\n",
+        "  if n == 0:\n",
+        "    return 0\n",
+        "  # The cursor is right before the e in the following line\n",
+        "  else:\n",
+        "    return fibonacci(n - 1) + fibonacci(n - 2)\n",
+        "```\n",
+        "\n",
+        "Assuming that the cursor is at the beginning of line 4 (where the `else` clause is), then the content before and after the cursor is:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eGU7XXk1SFYT"
+      },
+      "outputs": [],
+      "source": [
+        "before = \"\"\"def fibonacci(n: int) -> int:\\n  if n == 0:\\n    return 0\\n\"\"\" # Mind the spaces!\n",
+        "after = \"\"\"\\n  else:\\n    return fibonacci(n - 1) + fibonacci(n-2)\\n\"\"\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GoRC_SzuAO7t"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<|fim_prefix|>def fibonacci(n: int) -> int:\n",
+            "  if n == 0:\n",
+            "    return 0\n",
+            "<|fim_suffix|>\n",
+            "  else:\n",
+            "    return fibonacci(n - 1) + fibonacci(n-2)\n",
+            "<|fim_middle|>\n"
+          ]
+        }
+      ],
+      "source": [
+        "prompt = format_completion_prompt(before, after)\n",
+        "print(prompt)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JRI9npXOAO7t"
+      },
+      "source": [
+        "Run the prompt."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MpRJDxf4AO7t"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<|fim_prefix|>def fibonacci(n: int) -> int:\n",
+            "  if n == 0:\n",
+            "    return 0\n",
+            "<|fim_suffix|>\n",
+            "  else:\n",
+            "    return fibonacci(n - 1) + fibonacci(n-2)\n",
+            "<|fim_middle|>elif n == 1:\n",
+            "    return 1<|file_separator|>\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(gemma_lm_7b.generate(prompt, stop_token_ids=stop_token_ids, max_length=128))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "np8zhxowfEsB"
+      },
+      "source": [
+        "The model inserts the correct `elif` conidtion for `n=1` at the location of the cursor."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AliD_nTEeBMV"
+      },
+      "source": [
+        "#### Example 2 - Complete DFS traversal algorithm\n",
+        "\n",
+        "Auto-complete code for a depth-first search (DFS) tree traversal algorithm."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kjaCaWcxqoq5"
+      },
+      "outputs": [],
+      "source": [
+        "before = \"\"\"void dfs(node* root) {\n",
+        "  if (root->left) {\n",
+        "    dfs(root->left);\n",
+        "  }\"\"\"\n",
+        "after = \"\"\"\\nprintf(\"%d\", root->value);\n",
+        "}\"\"\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nq8uV7DehsOt"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<|fim_prefix|>void dfs(node* root) {\n",
+            "  if (root->left) {\n",
+            "    dfs(root->left);\n",
+            "  }<|fim_suffix|>\n",
+            "printf(\"%d\", root->value);\n",
+            "}<|fim_middle|>\n"
+          ]
+        }
+      ],
+      "source": [
+        "prompt = format_completion_prompt(before, after)\n",
+        "print(prompt)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wexpoZS9hsOu"
+      },
+      "source": [
+        "Run the prompt."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mCY-VI01hsOu"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<|fim_prefix|>void dfs(node* root) {\n",
+            "  if (root->left) {\n",
+            "    dfs(root->left);\n",
+            "  }<|fim_suffix|>\n",
+            "printf(\"%d\", root->value);\n",
+            "}<|fim_middle|>\n",
+            "  if (root->right) {\n",
+            "    dfs(root->right);\n",
+            "  }<|file_separator|>\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(gemma_lm_7b.generate(prompt, stop_token_ids=stop_token_ids, max_length=128))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "v7l8rVt0-irP"
+      },
+      "source": [
+        "### Code generation\n",
+        "\n",
+        "In addition to code infilling, the CodeGemma 7B PT is model is also trained on natural language corpuses. You can use this to prompt the model to generate code.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ECfsJ7JW-7ef"
+      },
+      "outputs": [],
+      "source": [
+        "generation_prompt= \"\"\"Write a rust function to identify non-prime numbers.\n",
+        "Examples:\n",
+        ">>> is_not_prime(2)\n",
+        "False\n",
+        ">>> is_not_prime(10)\n",
+        "True\n",
+        "pub fn is_not_prime(n: i32) -> bool {\"\"\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-RZkx_0i_Vgj"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Write a rust function to identify non-prime numbers.\n",
+            "Examples:\n",
+            ">>> is_not_prime(2)\n",
+            "False\n",
+            ">>> is_not_prime(10)\n",
+            "True\n",
+            "pub fn is_not_prime(n: i32) -> bool {\n",
+            "    if n <= 1 {\n",
+            "        return true;\n",
+            "    }\n",
+            "    for i in 2..n {\n",
+            "        if n % i == 0 {\n",
+            "            return true;\n",
+            "        }\n",
+            "    }\n",
+            "    false\n",
+            "}\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(gemma_lm_7b.generate(generation_prompt, max_length=500))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tzs6i4cieb2Y"
+      },
+      "source": [
+        "## 7B IT model examples\n",
+        "\n",
+        "This section uses the CodeGemma 7B Instruction-Tuned model for more advanced coding tasks. The CodeGemma 7B IT model is derived from the CodeGemma 7B PT model through supervised fine-tuning on code along with Reinforcement Learning with Human Feedback. This section covers examples of using this model for open-ended generation.\n",
+        "\n",
+        "NOTE: If you are working through this tutorial in Colab and already have the 2B model loaded from above, restart your Colab Runtime by going to **Runtime** > **Disconnect and delete runtime** and re-connect to a new runtime. This frees up memory and prevents out-of-memory (OOM) issues. After connecting to a new runtime, re-run the Setup steps [from here](#set_environment_variables) before proceeding further.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wsNKfK4iGYRR"
+      },
+      "source": [
+        "### Load the IT model\n",
+        "\n",
+        "Load the `code_gemma_instruct_7b_en` model using the `from_preset` method."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HxF9z5cFG5Yz"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_instruct_7b_en/1/download/config.json...\n",
+            "100%|██████████| 556/556 [00:00<00:00, 754kB/s]\n",
+            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_instruct_7b_en/1/download/model.weights.h5...\n",
+            "100%|██████████| 15.9G/15.9G [03:18<00:00, 86.2MB/s]\n",
+            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_instruct_7b_en/1/download/tokenizer.json...\n",
+            "100%|██████████| 401/401 [00:00<00:00, 593kB/s]\n",
+            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_instruct_7b_en/1/download/assets/tokenizer/vocabulary.spm...\n",
+            "100%|██████████| 4.04M/4.04M [00:00<00:00, 16.8MB/s]\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Preprocessor: \"gemma_causal_lm_preprocessor\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mPreprocessor: \"gemma_causal_lm_preprocessor\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Tokenizer (type)                                   </span>┃<span style=\"font-weight: bold\">                                             Vocab # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ gemma_tokenizer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaTokenizer</span>)                   │                                             <span style=\"color: #00af00; text-decoration-color: #00af00\">256,000</span> │\n",
+              "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mTokenizer (type)                                  \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m                                            Vocab #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ gemma_tokenizer (\u001b[38;5;33mGemmaTokenizer\u001b[0m)                   │                                             \u001b[38;5;34m256,000\u001b[0m │\n",
+              "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"gemma_causal_lm\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"gemma_causal_lm\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                  </span>┃<span style=\"font-weight: bold\"> Output Shape              </span>┃<span style=\"font-weight: bold\">         Param # </span>┃<span style=\"font-weight: bold\"> Connected to               </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ padding_mask (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_ids (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ gemma_backbone                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3072</span>)        │   <span style=\"color: #00af00; text-decoration-color: #00af00\">8,537,680,896</span> │ padding_mask[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],        │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaBackbone</span>)               │                           │                 │ token_ids[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_embedding               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256000</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">786,432,000</span> │ gemma_backbone[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReversibleEmbedding</span>)         │                           │                 │                            │\n",
+              "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                 \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to              \u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ padding_mask (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_ids (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ gemma_backbone                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3072\u001b[0m)        │   \u001b[38;5;34m8,537,680,896\u001b[0m │ padding_mask[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
+              "│ (\u001b[38;5;33mGemmaBackbone\u001b[0m)               │                           │                 │ token_ids[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_embedding               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256000\u001b[0m)      │     \u001b[38;5;34m786,432,000\u001b[0m │ gemma_backbone[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
+              "│ (\u001b[38;5;33mReversibleEmbedding\u001b[0m)         │                           │                 │                            │\n",
+              "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,537,680,896</span> (15.90 GB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,537,680,896\u001b[0m (15.90 GB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,537,680,896</span> (15.90 GB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,537,680,896\u001b[0m (15.90 GB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "gemma_lm_7b_it = keras_nlp.models.GemmaCausalLM.from_preset(\"code_gemma_instruct_7b_en\")\n",
+        "gemma_lm_7b_it.summary()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "x-KDJ0KvcM9S"
+      },
+      "source": [
+        "IT models are trained with a specific formatter that annotates all instruction tuning examples with extra information to indicate roles and delineate turns in a conversation.\n",
+        "\n",
+        "As a first step, define constants and a prompt formatting helper function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "llseiN-_5XXk"
+      },
+      "outputs": [],
+      "source": [
+        "# Formatting control tokens for instruction tuning\n",
+        "START_OF_TURN_USER = \"<start_of_turn>user\"\n",
+        "END_OF_TURN = \"<end_of_turn>\"\n",
+        "START_OF_TURN_MODEL = \"<start_of_turn>model\"\n",
+        "\n",
+        "# Formatting helper function\n",
+        "def format_instruction_prompt(context):\n",
+        "    return f\"{START_OF_TURN_USER}\\n{context}{END_OF_TURN}\\n{START_OF_TURN_MODEL}\\n\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m9H0F8k5a8OO"
+      },
+      "source": [
+        "### Code translation\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-RJ6z5gbqTPA"
+      },
+      "outputs": [],
+      "source": [
+        "context1 = \"\"\"\n",
+        "You are an experienced C and Python programmer. Convert the following Python code into C.\n",
+        "```python\n",
+        "def factorial(n):\n",
+        "    result = 1\n",
+        "    for i in range(2, n + 1):\n",
+        "        result *= i\n",
+        "    return result\n",
+        "```\\n\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Eg2TcYZFYfiU"
+      },
+      "source": [
+        "Format the prompt."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LlDJmIAz5f0R"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<start_of_turn>user\n",
+            "\n",
+            "You are an experienced C and Python programmer. Convert the following Python code into C.\n",
+            "```python\n",
+            "def factorial(n):\n",
+            "    result = 1\n",
+            "    for i in range(2, n + 1):\n",
+            "        result *= i\n",
+            "    return result\n",
+            "```\n",
+            "<end_of_turn>\n",
+            "<start_of_turn>model\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "prompt1 = format_instruction_prompt(context1)\n",
+        "print(prompt1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xPE0-SGP68dP"
+      },
+      "source": [
+        "Run the prompt."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rSBsedwN676V"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<start_of_turn>user\n",
+            "\n",
+            "You are an experienced C and Python programmer. Convert the following Python code into C.\n",
+            "```python\n",
+            "def factorial(n):\n",
+            "    result = 1\n",
+            "    for i in range(2, n + 1):\n",
+            "        result *= i\n",
+            "    return result\n",
+            "```\n",
+            "<end_of_turn>\n",
+            "<start_of_turn>model\n",
+            "Here is the C code equivalent of the Python code:\n",
+            "\n",
+            "```c\n",
+            "int factorial(int n) {\n",
+            "  int result = 1;\n",
+            "  for (int i = 2; i <= n; i++) {\n",
+            "    result *= i;\n",
+            "  }\n",
+            "  return result;\n",
+            "}\n",
+            "```\n",
+            "\n",
+            "Here is a breakdown of the changes:\n",
+            "\n",
+            "* The function is declared with the `int` return type, as in Python.\n",
+            "* The `for` loop is converted to a `for` loop with an `int` variable `i` initialized to 2 and incremented by 1 in each iteration.\n",
+            "* The `range` function is replaced with a simple loop that iterates from 2 to `n` (inclusive).\n",
+            "* The `result *= i` statement is used to multiply `result` by `i` in each iteration.\n",
+            "* The `return` statement is used to return the final value of `result`.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(gemma_lm_7b_it.generate(prompt1, max_length=500))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0b8RXp7TPpEi"
+      },
+      "source": [
+        "### Code vulnerability detection\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yYW6VXt7qPTc"
+      },
+      "outputs": [],
+      "source": [
+        "context2 = \"\"\"\n",
+        "You are an experienced C++ programmer hunting for vulnerable code. Is the following code vulnerable? Explain your reasoning.\n",
+        "```cpp\n",
+        "int i;\n",
+        "unsigned int numWidgets;\n",
+        "Widget **WidgetList;\n",
+        "\n",
+        "numWidgets = GetUntrustedSizeValue();\n",
+        "if ((numWidgets == 0) || (numWidgets > MAX_NUM_WIDGETS)) {\n",
+        "    ExitError(\"Incorrect number of widgets requested!\");\n",
+        "}\n",
+        "WidgetList = (Widget **) malloc(numWidgets * sizeof(Widget *));\n",
+        "printf(\"WidgetList ptr=%p\\n\", WidgetList);\n",
+        "for (i = 0; i < numWidgets; i++) {\n",
+        "    WidgetList[i] = InitializeWidget();\n",
+        "}\n",
+        "WidgetList[numWidgets] = NULL;\n",
+        "showWidgets(WidgetList);\n",
+        "```\\n\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "crZ2DWaczme4"
+      },
+      "source": [
+        "Format the prompt."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "To_5KvKJPw1H"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<start_of_turn>user\n",
+            "\n",
+            "You are an experienced C++ programmer hunting for vulnerable code. Is the following code vulnerable? Explain your reasoning.\n",
+            "```cpp\n",
+            "int i;\n",
+            "unsigned int numWidgets;\n",
+            "Widget **WidgetList;\n",
+            "\n",
+            "numWidgets = GetUntrustedSizeValue();\n",
+            "if ((numWidgets == 0) || (numWidgets > MAX_NUM_WIDGETS)) {\n",
+            "    ExitError(\"Incorrect number of widgets requested!\");\n",
+            "}\n",
+            "WidgetList = (Widget **) malloc(numWidgets * sizeof(Widget *));\n",
+            "printf(\"WidgetList ptr=%p\n",
+            "\", WidgetList);\n",
+            "for (i = 0; i < numWidgets; i++) {\n",
+            "    WidgetList[i] = InitializeWidget();\n",
+            "}\n",
+            "WidgetList[numWidgets] = NULL;\n",
+            "showWidgets(WidgetList);\n",
+            "```\n",
+            "<end_of_turn>\n",
+            "<start_of_turn>model\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "prompt2 = format_instruction_prompt(context2)\n",
+        "print(prompt2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jHZg8YX8QuIe"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<start_of_turn>user\n",
+            "\n",
+            "You are an experienced C++ programmer hunting for vulnerable code. Is the following code vulnerable? Explain your reasoning.\n",
+            "```cpp\n",
+            "int i;\n",
+            "unsigned int numWidgets;\n",
+            "Widget **WidgetList;\n",
+            "\n",
+            "numWidgets = GetUntrustedSizeValue();\n",
+            "if ((numWidgets == 0) || (numWidgets > MAX_NUM_WIDGETS)) {\n",
+            "    ExitError(\"Incorrect number of widgets requested!\");\n",
+            "}\n",
+            "WidgetList = (Widget **) malloc(numWidgets * sizeof(Widget *));\n",
+            "printf(\"WidgetList ptr=%p\n",
+            "\", WidgetList);\n",
+            "for (i = 0; i < numWidgets; i++) {\n",
+            "    WidgetList[i] = InitializeWidget();\n",
+            "}\n",
+            "WidgetList[numWidgets] = NULL;\n",
+            "showWidgets(WidgetList);\n",
+            "```\n",
+            "<end_of_turn>\n",
+            "<start_of_turn>model\n",
+            "Yes, the code is vulnerable to a memory access error.\n",
+            "\n",
+            "**Reasoning:**\n",
+            "\n",
+            "* The code allocates memory for `WidgetList` using `malloc` based on the value of `numWidgets`.\n",
+            "* However, the loop iterates from `0` to `numWidgets`, which is one element beyond the allocated memory.\n",
+            "* This means that accessing `WidgetList[numWidgets]` will result in a memory access error, as it is outside the bounds of the allocated memory.\n",
+            "\n",
+            "**Example of Memory Access Error:**\n",
+            "\n",
+            "When `numWidgets` is 5, the code allocates memory for `WidgetList` as follows:\n",
+            "\n",
+            "```\n",
+            "WidgetList = (Widget **) malloc(5 * sizeof(Widget *));\n",
+            "```\n",
+            "\n",
+            "The loop iterates from 0 to 4, accessing the following elements:\n",
+            "\n",
+            "* `WidgetList[0]`\n",
+            "* `WidgetList[1]`\n",
+            "* `WidgetList[2]`\n",
+            "* `WidgetList[3]`\n",
+            "* `WidgetList[4]`\n",
+            "\n",
+            "However, the code then attempts to access `WidgetList[5]`, which is outside the allocated memory range. This will result in a memory access error.\n",
+            "\n",
+            "**Solution:**\n",
+            "\n",
+            "To resolve this vulnerability, the loop should be modified to iterate from 0 to `numWidgets - 1`:\n",
+            "\n",
+            "```cpp\n",
+            "for (i = 0; i < numWidgets - 1; i++) {\n",
+            "    WidgetList[i] = InitializeWidget();\n",
+            "}\n",
+            "```\n",
+            "\n",
+            "This ensures that the loop does not access elements beyond the allocated memory range.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(gemma_lm_7b_it.generate(prompt2, max_length=1000))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GRVutCnnew2N"
+      },
+      "source": [
+        "The model detects a potential vulnerability in the code and provides code changes to mitigate it."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QesAFYNcRw5f"
+      },
+      "source": [
+        "## Summary\n",
+        "\n",
+        "This tutorial walked you through using CodeGemma for a variety of coding tasks. To learn more about CodeGemma:\n",
+        "\n",
+        "* Refer to the [CodeGemma model card](https://ai.google.dev/gemma/docs/codegemma/model_card) for the technical specs of the CodeGemma models.\n",
+        "* Learn more about how to use CodeGemma in VertexAI [here](https://colab.sandbox.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_codegemma_deployment_on_vertex.ipynb).\n",
+        "* Check out the [Keras CodeGemma quickstart](https://ai.google.dev/gemma/docs/codegemma/keras_quickstart)."
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "code_assist_keras.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemma/docs/codegemma/codegemma_flax_inference.ipynb b/site/en/gemma/docs/codegemma/codegemma_flax_inference.ipynb
new file mode 100644
index 000000000..f39fc0538
--- /dev/null
+++ b/site/en/gemma/docs/codegemma/codegemma_flax_inference.ipynb
@@ -0,0 +1,666 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3MMAcssHTML"
+      },
+      "source": [
+        "<link rel=\"stylesheet\" href=\"/site-assets/css/gemma.css\">\n",
+        "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/css2?family=Google+Symbols:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200\" />"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "N_yUpPhqrRrK"
+      },
+      "source": [
+        "# Inference with CodeGemma using JAX and Flax"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-yDXE-RX835U"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemma/docs/codegemma/codegemma_flax_inference\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/codegemma/codegemma_flax_inference.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemma/docs/codegemma/codegemma_flax_inference.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MUnQEMHBt3nc"
+      },
+      "source": [
+        "We present CodeGemma, a collection of open code models based on Google DeepMind’s Gemma models (Gemma Team et al., 2024).\n",
+        "CodeGemma is a family of lightweight, state-of-the art open models built from the same research and technology used to create the Gemini models.\n",
+        "\n",
+        "Continuing from Gemma pretrained models, CodeGemma models are further trained on more than 500 to 1000 billion tokens of primarily code, using\n",
+        "the same architectures as the Gemma model family. As a result, CodeGemma models achieve state of-the-art code performance in both completion\n",
+        "and generation tasks, while maintaining strong\n",
+        "understanding and reasoning skills at scale.\n",
+        "\n",
+        "CodeGemma has 3 variants:\n",
+        "\n",
+        "* A 7B code pretrained model\n",
+        "* A 7B instruction-tuned code model\n",
+        "* A 2B model, trained specifically for code infilling and open-ended generation.\n",
+        "\n",
+        "This guide walks you through using the CodeGemma model with Flax for a code completion task.\n",
+        "\n",
+        "**Note:** This notebook runs on TPU v2 in Google Colab because T4 GPU has insufficient memory."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dbRLI7Q4-8Ve"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n8Ku4iK6PnC0"
+      },
+      "source": [
+        "### 1. Set up Kaggle access for CodeGemma\n",
+        "\n",
+        "To complete this tutorial, you first need to follow the setup instructions at [Gemma setup](https://ai.google.dev/gemma/docs/setup), which show you how to do the following:\n",
+        "\n",
+        "* Get access to CodeGemma on [kaggle.com](https://www.kaggle.com/models/google/codegemma/).\n",
+        "* Select a Colab runtime with sufficient resources (**T4 GPU has insufficient memory, use TPU v2 instead**) to run the CodeGemma model.\n",
+        "* Generate and configure a Kaggle username and API key.\n",
+        "\n",
+        "After you've completed the Gemma setup, move on to the next section, where you'll set environment variables for your Colab environment.\n",
+        "\n",
+        "### 2. Set environment variables\n",
+        "\n",
+        "Set environment variables for `KAGGLE_USERNAME` and `KAGGLE_KEY`. When prompted with the \"Grant access?\" messages, agree to provide secret access."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AVH6Y4k2964n"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from google.colab import userdata # `userdata` is a Colab API.\n",
+        "\n",
+        "os.environ[\"KAGGLE_USERNAME\"] = userdata.get('KAGGLE_USERNAME')\n",
+        "os.environ[\"KAGGLE_KEY\"] = userdata.get('KAGGLE_KEY')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m1UE1CEnE9ql"
+      },
+      "source": [
+        "### 3. Install the `gemma` library\n",
+        "\n",
+        "Free Colab hardware acceleration is currently *insufficient* to run this notebook. If you are using [Colab Pay As You Go or Colab Pro](https://colab.research.google.com/signup), click on **Edit** > **Notebook settings** > Select **A100 GPU** > **Save** to enable hardware acceleration.\n",
+        "\n",
+        "Next, you need to install the Google DeepMind `gemma` library from [`github.com/google-deepmind/gemma`](https://github.com/google-deepmind/gemma). If you get an error about \"pip's dependency resolver\", you can usually ignore it.\n",
+        "\n",
+        "**Note:** By installing `gemma`, you will also install [`flax`](https://flax.readthedocs.io), core [`jax`](https://jax.readthedocs.io), [`orbax`](https://orbax.readthedocs.io/), and [`sentencepiece`](https://github.com/google/sentencepiece)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XpSw-_4EEcoY"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.7/133.7 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Building wheel for gemma (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q git+https://github.com/google-deepmind/gemma.git"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-mRkkT-iPYoq"
+      },
+      "source": [
+        "### 4. Import libraries\n",
+        "\n",
+        "This notebook uses [Gemma](https://github.com/google-deepmind/gemma) (which uses [Flax](https://flax.readthedocs.io) to build its neural network layers), and [SentencePiece](https://github.com/google/sentencepiece) (for tokenization)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ChMf1H4mPVx_"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from gemma import params as params_lib\n",
+        "from gemma import sampler as sampler_lib\n",
+        "from gemma import transformer as transformer_lib\n",
+        "import sentencepiece as spm"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oNgKIkxMOsit"
+      },
+      "source": [
+        "## Load the CodeGemma model\n",
+        "\n",
+        "Load the CodeGemma model with [`kagglehub.model_download`](https://github.com/Kaggle/kagglehub/blob/bddefc718182282882b72f814d407d89e5d178c4/src/kagglehub/models.py#L12), which takes three arguments:\n",
+        "\n",
+        "- `handle`: The model handle from Kaggle\n",
+        "- `path`: (Optional string) The local path\n",
+        "- `force_download`: (Optional boolean) Forces to re-download the model\n",
+        "\n",
+        "**Note:** Be mindful that the `2b-pt` model is around 3.66Gb in size."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "X-i10429N-g2"
+      },
+      "outputs": [],
+      "source": [
+        "GEMMA_VARIANT = '2b-pt' # @param ['2b-pt', '7b-it', '7b-pt', '1.1-2b-pt', '1.1-7b-it'] {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "j_QdPAGyO5zl"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Warning: Looks like you're using an outdated `kagglehub` version, please consider updating (latest version: 0.2.7)\n",
+            "Downloading from https://www.kaggle.com/api/v1/models/google/codegemma/flax/2b-pt/3/download...\n",
+            "100%|██████████| 3.67G/3.67G [00:22<00:00, 173MB/s]\n",
+            "Extracting model files...\n"
+          ]
+        }
+      ],
+      "source": [
+        "import kagglehub\n",
+        "\n",
+        "GEMMA_PATH = kagglehub.model_download(f'google/codegemma/flax/{GEMMA_VARIANT}')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cjnXlLkWcHIy"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "GEMMA_PATH: /root/.cache/kagglehub/models/google/codegemma/flax/2b-pt/3\n"
+          ]
+        }
+      ],
+      "source": [
+        "print('GEMMA_PATH:', GEMMA_PATH)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E1HzOpDcM04q"
+      },
+      "source": [
+        "**Note:** The path from the output above is where the model weights and tokenizer are saved locally, you will need them for later."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6ytvcJ8FPEMm"
+      },
+      "source": [
+        "Check the location of the model weights and the tokenizer, then set the path variables. The tokenizer directory will be in the main directory where you downloaded the model, while the model weights will be in a sub-directory. For example:\n",
+        "\n",
+        "- The `spm.model` tokenizer file will be in `/LOCAL/PATH/TO/codegemma/flax/2b-pt/3`\n",
+        "- The model checkpoint will be in `/LOCAL/PATH/TO/codegemma/flax/2b-pt/3/2b-pt`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JAwXvpzbuiB5"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "CKPT_PATH: /root/.cache/kagglehub/models/google/codegemma/flax/2b-pt/3/2b-pt\n",
+            "TOKENIZER_PATH: /root/.cache/kagglehub/models/google/codegemma/flax/2b-pt/3/spm.model\n"
+          ]
+        }
+      ],
+      "source": [
+        "CKPT_PATH = os.path.join(GEMMA_PATH, GEMMA_VARIANT[-5:])\n",
+        "TOKENIZER_PATH = os.path.join(GEMMA_PATH, 'spm.model')\n",
+        "print('CKPT_PATH:', CKPT_PATH)\n",
+        "print('TOKENIZER_PATH:', TOKENIZER_PATH)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jc0ZzYIW0TSN"
+      },
+      "source": [
+        "## Perform sampling/inference"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aEe3p8geqekV"
+      },
+      "source": [
+        "Load and format the CodeGemma model checkpoint with the [`gemma.params.load_and_format_params`](https://github.com/google-deepmind/gemma/blob/c6bd156c246530e1620a7c62de98542a377e3934/gemma/params.py#L27) method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "by6eWKtqzxRf"
+      },
+      "outputs": [],
+      "source": [
+        "params = params_lib.load_and_format_params(CKPT_PATH)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-Xpnb2igrGjk"
+      },
+      "source": [
+        "Load the CodeGemma tokenizer, constructed using [`sentencepiece.SentencePieceProcessor`](https://github.com/google/sentencepiece/blob/4d6a1f41069c4636c51a5590f7578a0dbed83450/python/src/sentencepiece/__init__.py#L423):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TpyG5YW1EcoY"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "vocab = spm.SentencePieceProcessor()\n",
+        "vocab.Load(TOKENIZER_PATH)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtJhJkkZzsy1"
+      },
+      "source": [
+        "To automatically load the correct configuration from the CodeGemma model checkpoint, use [`gemma.transformer.TransformerConfig`](https://github.com/google-deepmind/gemma/blob/56e501ce147af4ea5c23cc0ddf5a9c4a6b7bd0d0/gemma/transformer.py#L65). The `cache_size` argument is the number of time steps in the CodeGemma `Transformer` cache. Afterwards, instantiate the CodeGemma model as `model_2b` with [`gemma.transformer.Transformer`](https://github.com/google-deepmind/gemma/blob/56e501ce147af4ea5c23cc0ddf5a9c4a6b7bd0d0/gemma/transformer.py#L136) (which inherits from [`flax.linen.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html)).\n",
+        "\n",
+        "**Note:** The vocabulary size is smaller than the number of input embeddings because of unused tokens in the current CodeGemma release."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_jjlFAkazzit"
+      },
+      "outputs": [],
+      "source": [
+        "transformer_config = transformer_lib.TransformerConfig.from_params(\n",
+        "    params,\n",
+        "    cache_size=1024\n",
+        ")\n",
+        "\n",
+        "transformer = transformer_lib.Transformer(config=transformer_config)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EtfVo3pDDAZV"
+      },
+      "source": [
+        "Create a `sampler` with [`gemma.sampler.Sampler`](https://github.com/google-deepmind/gemma/blob/56e501ce147af4ea5c23cc0ddf5a9c4a6b7bd0d0/gemma/sampler.py#L88). It uses the CodeGemma model checkpoint and the tokenizer."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dQ1oCF10Ecod"
+      },
+      "outputs": [],
+      "source": [
+        "sampler = sampler_lib.Sampler(\n",
+        "    transformer=transformer,\n",
+        "    vocab=vocab,\n",
+        "    params=params['transformer']\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gOi4ua6axnGD"
+      },
+      "source": [
+        "Create some variables to represent the fill-in-the-middle (fim) tokens and create some helper functions to format the prompt and generated output.\n",
+        "\n",
+        "For example, let's look at the following code:\n",
+        "```\n",
+        "def function(string):\n",
+        "assert function('asdf') == 'fdsa'\n",
+        "```\n",
+        "We would like to fill in the `function` so that the assertion holds `True`. In this case, the prefix would be:\n",
+        "```\n",
+        "\"def function(string):\\n\"\n",
+        "```\n",
+        "And the suffix would be:\n",
+        "```\n",
+        "\"assert function('asdf') == 'fdsa'\"\n",
+        "```\n",
+        "We then format this into a prompt as PREFIX-SUFFIX-MIDDLE (the middle section that needs to be filled is always at the end of the prompt):\n",
+        "```\n",
+        "\"<|fim_prefix|>def function(string):\\n<|fim_suffix|>assert function('asdf') == 'fdsa'<|fim_middle|>\"\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xgkQgFH1xyP2"
+      },
+      "outputs": [],
+      "source": [
+        "# In the context of a code editor,\n",
+        "# the cursor is the location where the text will be inserted\n",
+        "BEFORE_CURSOR = \"<|fim_prefix|>\"\n",
+        "AFTER_CURSOR = \"<|fim_suffix|>\"\n",
+        "AT_CURSOR = \"<|fim_middle|>\"\n",
+        "FILE_SEPARATOR = \"<|file_separator|>\"\n",
+        "\n",
+        "def format_completion_prompt(before, after):\n",
+        "  print(f\"\\nORIGINAL PROMPT:\\n{before}{after}\")\n",
+        "  prompt = f\"{BEFORE_CURSOR}{before}{AFTER_CURSOR}{after}{AT_CURSOR}\"\n",
+        "  print(f\"\\nFORMATTED PROMPT:\\n{repr(prompt)}\")\n",
+        "  return prompt\n",
+        "def format_generated_output(before, after, output):\n",
+        "  print(f\"\\nGENERATED OUTPUT:\\n{repr(output)}\")\n",
+        "  formatted_output = f\"{before}{output.replace(FILE_SEPARATOR, '')}{after}\"\n",
+        "  print(f\"\\nFILL-IN COMPLETION:\\n{formatted_output}\")\n",
+        "  return formatted_output"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-61KZz7EHiIS"
+      },
+      "source": [
+        "Create a prompt and perform inference. Specify the prefix `before` text and the suffix `after` text and generate the formatted prompt using the helper function `format_completion prompt`.\n",
+        "\n",
+        "You can tweak `total_generation_steps` (the number of steps performed when generating a response — this example uses `100` to preserve host memory).\n",
+        "\n",
+        "**Note:** If you run out of memory, click on **Runtime** > **Disconnect and delete runtime**, and then **Runtime** > **Run all**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "S5F3fk22Ecod"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "ORIGINAL PROMPT:\n",
+            "def function(string):\n",
+            "assert function('asdf') == 'fdsa'\n",
+            "\n",
+            "FORMATTED PROMPT:\n",
+            "\"<|fim_prefix|>def function(string):\\n<|fim_suffix|>assert function('asdf') == 'fdsa'<|fim_middle|>\"\n",
+            "\n",
+            "GENERATED OUTPUT:\n",
+            "'    return string[::-1]\\n\\n<|file_separator|>'\n",
+            "\n",
+            "FILL-IN COMPLETION:\n",
+            "def function(string):\n",
+            "    return string[::-1]\n",
+            "\n",
+            "assert function('asdf') == 'fdsa'\n"
+          ]
+        }
+      ],
+      "source": [
+        "before = \"def function(string):\\n\"\n",
+        "after = \"assert function('asdf') == 'fdsa'\"\n",
+        "prompt = format_completion_prompt(before, after)\n",
+        "\n",
+        "output = sampler(\n",
+        "    [prompt],\n",
+        "    total_generation_steps=100,\n",
+        "    ).text\n",
+        "\n",
+        "formatted_output = format_generated_output(before, after, output[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6zIQEruE5_FC"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "ORIGINAL PROMPT:\n",
+            "import if __name__ == \"__main__\":\n",
+            "    sys.exit(0)\n",
+            "\n",
+            "FORMATTED PROMPT:\n",
+            "'<|fim_prefix|>import <|fim_suffix|>if __name__ == \"__main__\":\\n    sys.exit(0)<|fim_middle|>'\n",
+            "\n",
+            "GENERATED OUTPUT:\n",
+            "'sys\\n<|file_separator|>'\n",
+            "\n",
+            "FILL-IN COMPLETION:\n",
+            "import sys\n",
+            "if __name__ == \"__main__\":\n",
+            "    sys.exit(0)\n"
+          ]
+        }
+      ],
+      "source": [
+        "before = \"import \"\n",
+        "after = \"\"\"if __name__ == \"__main__\":\\n    sys.exit(0)\"\"\"\n",
+        "prompt = format_completion_prompt(before, after)\n",
+        "\n",
+        "output = sampler(\n",
+        "    [prompt],\n",
+        "    total_generation_steps=100,\n",
+        "    ).text\n",
+        "\n",
+        "formatted_output = format_generated_output(before, after, output[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SvaV4GU76M3t"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "ORIGINAL PROMPT:\n",
+            "import numpy as np\n",
+            "def reflect(matrix):\n",
+            "  # horizontally reflect a matrix\n",
+            "\n",
+            "\n",
+            "FORMATTED PROMPT:\n",
+            "'<|fim_prefix|>import numpy as np\\ndef reflect(matrix):\\n  # horizontally reflect a matrix\\n<|fim_suffix|><|fim_middle|>'\n",
+            "\n",
+            "GENERATED OUTPUT:\n",
+            "'  return np.flip(matrix, axis=1)\\n<|file_separator|>'\n",
+            "\n",
+            "FILL-IN COMPLETION:\n",
+            "import numpy as np\n",
+            "def reflect(matrix):\n",
+            "  # horizontally reflect a matrix\n",
+            "  return np.flip(matrix, axis=1)\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "before = \"\"\"import numpy as np\n",
+        "def reflect(matrix):\n",
+        "  # horizontally reflect a matrix\n",
+        "\"\"\"\n",
+        "after = \"\"\n",
+        "prompt = format_completion_prompt(before, after)\n",
+        "\n",
+        "output = sampler(\n",
+        "    [prompt],\n",
+        "    total_generation_steps=100,\n",
+        "    ).text\n",
+        "\n",
+        "formatted_output = format_generated_output(before, after, output[0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Jao0Qk-ZIqyD"
+      },
+      "source": [
+        "## Learn more\n",
+        "\n",
+        "- You can learn more about the Google DeepMind [`gemma`  library on GitHub](https://github.com/google-deepmind/gemma), which contains docstrings of modules you used in this tutorial, such as [`gemma.params`](https://github.com/google-deepmind/gemma/blob/main/gemma/params.py),\n",
+        "[`gemma.transformer`](https://github.com/google-deepmind/gemma/blob/main/gemma/transformer.py), and\n",
+        "[`gemma.sampler`](https://github.com/google-deepmind/gemma/blob/main/gemma/sampler.py).\n",
+        "- The following libraries have their own documentation sites: [core JAX](https://jax.readthedocs.io), [Flax](https://flax.readthedocs.io), and [Orbax](https://orbax.readthedocs.io/).\n",
+        "- For `sentencepiece` tokenizer/detokenizer documentation, check out [Google's `sentencepiece` GitHub repo](https://github.com/google/sentencepiece).\n",
+        "- For `kagglehub` documentation, check out `README.md` on [Kaggle's `kagglehub` GitHub repo](https://github.com/Kaggle/kagglehub).\n",
+        "- Learn how to [use Gemma models with Google Cloud Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma).\n",
+        "- If you are using Google Cloud TPUs (v3-8 and newer), make sure to also update to the latest `jax[tpu]` package (`!pip install -U jax[tpu] -f https://storage.googleapis.com/jax-releases/libtpu_releases.html`), restart the runtime, and check that `jax` and `jaxlib` versions match (`!pip list | grep jax`). This can prevent the `RuntimeError` that can arise because of the `jaxlib` and `jax` version mismatch. For more JAX installation instructions, refer to the [JAX docs](https://jax.readthedocs.io/en/latest/tutorials/installation.html#install-google-tpu)."
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "TPU",
+    "colab": {
+      "name": "codegemma_flax_inference.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemma/docs/codegemma/keras_quickstart.ipynb b/site/en/gemma/docs/codegemma/keras_quickstart.ipynb
new file mode 100644
index 000000000..e220465a3
--- /dev/null
+++ b/site/en/gemma/docs/codegemma/keras_quickstart.ipynb
@@ -0,0 +1,604 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3MMAcssHTML"
+      },
+      "source": [
+        "<link rel=\"stylesheet\" href=\"/site-assets/css/gemma.css\">\n",
+        "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/css2?family=Google+Symbols:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200\" />"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SDEExiAk4fLb"
+      },
+      "source": [
+        "# Keras CodeGemma Quickstart"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZFWzQEqNosrS"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemma/docs/codegemma/keras_quickstart\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/codegemma/keras_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemma/docs/codegemma/keras_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lSGRSsRPgkzK"
+      },
+      "source": [
+        "CodeGemma is a family of lightweight, state-of-the art open models built from the same research and technology used to create the Gemini models.\n",
+        "\n",
+        "CodeGemma models are trained on more than 500 billion tokens of primarily code, using\n",
+        "the same architectures as the Gemma model family. As a result, CodeGemma models achieve stateof-the-art code performance in both completion\n",
+        "and generation tasks, while maintaining strong\n",
+        "understanding and reasoning skills at scale.\n",
+        "\n",
+        "CodeGemma has 3 variants:\n",
+        "\n",
+        "* A 7B code pretrained model\n",
+        "* A 7B instruction-tuned code model\n",
+        "* A 2B model, trained specifically for code infilling and open-ended generation.\n",
+        "\n",
+        "This guide walks you through using the CodeGemma 2B model with KerasNLP for a code completion task.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w1q6-W_mKIT-"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lyhHCMfoRZ_v"
+      },
+      "source": [
+        "### Get access to CodeGemma\n",
+        "\n",
+        "To complete this tutorial, you will first need to complete the setup instructions at [Gemma setup](https://ai.google.dev/gemma/docs/setup). The Gemma setup instructions show you how to do the following:\n",
+        "\n",
+        "* Get access to Gemma on [kaggle.com](https://kaggle.com){:.external}.\n",
+        "* Select a Colab runtime with sufficient resources to run\n",
+        "  the Gemma 2B model.\n",
+        "* Generate and configure a Kaggle username and API key.\n",
+        "\n",
+        "After you've completed the Gemma setup, move on to the next section, where you'll set environment variables for your Colab environment."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AZ5Qo0fxRZ1V"
+      },
+      "source": [
+        "### Select the runtime\n",
+        "\n",
+        "To complete this tutorial, you'll need to have a Colab runtime with sufficient resources to run the CodeGemma 2B model. In this case, you can use a T4 GPU:\n",
+        "\n",
+        "1. In the upper-right of the Colab window, select &#9662; (**Additional connection options**).\n",
+        "2. Select **Change runtime type**.\n",
+        "3. Under **Hardware accelerator**, select **T4 GPU**."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hsPC0HRkJl0K"
+      },
+      "source": [
+        "### Configure your API key\n",
+        "\n",
+        "To use Gemma, you must provide your Kaggle username and a Kaggle API key.\n",
+        "\n",
+        "To generate a Kaggle API key, go to the **Account** tab of your Kaggle user profile and select **Create New Token**. This will trigger the download of a `kaggle.json` file containing your API credentials.\n",
+        "\n",
+        "In Colab, select **Secrets** (🔑) in the left pane and add your Kaggle username and Kaggle API key. Store your username under the name `KAGGLE_USERNAME` and your API key under the name `KAGGLE_KEY`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7iOF6Yo-wUEC"
+      },
+      "source": [
+        "### Set environment variables\n",
+        "\n",
+        "Set environment variables for `KAGGLE_USERNAME` and `KAGGLE_KEY`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DrBoa_Urw9Vx"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "os.environ[\"KAGGLE_USERNAME\"] = userdata.get('KAGGLE_USERNAME')\n",
+        "os.environ[\"KAGGLE_KEY\"] = userdata.get('KAGGLE_KEY')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FX47AUYrXwLK"
+      },
+      "source": [
+        "### Install dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KWOQ2sJocj-w"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q -U keras-nlp"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2I69cArSBm3z"
+      },
+      "source": [
+        "### Select a backend"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QPTF92kyOQ-p"
+      },
+      "source": [
+        "Keras is a high-level, multi-framework deep learning API designed for simplicity and ease of use. Using Keras 3, you can run workflows on one of three backends: TensorFlow, JAX, or PyTorch.\n",
+        "\n",
+        "For this tutorial, configure the backend for TensorFlow."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ww83zI9ToPso"
+      },
+      "outputs": [],
+      "source": [
+        "os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"  # Or \"jax\" or \"torch\"."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FLDJd1nxa3I7"
+      },
+      "source": [
+        "### Import packages\n",
+        "\n",
+        "Import Keras and KerasNLP."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oQkqsyE1a2YD"
+      },
+      "outputs": [],
+      "source": [
+        "import keras_nlp\n",
+        "import keras\n",
+        "\n",
+        "# Run at half precision.\n",
+        "keras.config.set_floatx(\"bfloat16\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7RCE3fdGhDE5"
+      },
+      "source": [
+        "### Load Model\n",
+        "\n",
+        "KerasNLP provides implementations of many popular [model architectures](https://keras.io/api/keras_nlp/models/){:.external}. In this tutorial, you'll create a model using `GemmaCausalLM`, an end-to-end Gemma model for causal language modeling. A causal language model predicts the next token based on previous tokens.\n",
+        "\n",
+        "Create the model using the `from_preset` method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yygIK9DEIldp"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/1/download/config.json...\n",
+            "100%|██████████| 554/554 [00:00<00:00, 1.41MB/s]\n",
+            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/1/download/model.weights.h5...\n",
+            "100%|██████████| 4.67G/4.67G [05:06<00:00, 16.4MB/s]\n",
+            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/1/download/tokenizer.json...\n",
+            "100%|██████████| 401/401 [00:00<00:00, 382kB/s]\n",
+            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/1/download/assets/tokenizer/vocabulary.spm...\n",
+            "100%|██████████| 4.04M/4.04M [00:01<00:00, 2.41MB/s]\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Preprocessor: \"gemma_causal_lm_preprocessor\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mPreprocessor: \"gemma_causal_lm_preprocessor\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Tokenizer (type)                                   </span>┃<span style=\"font-weight: bold\">                                             Vocab # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ gemma_tokenizer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaTokenizer</span>)                   │                                             <span style=\"color: #00af00; text-decoration-color: #00af00\">256,000</span> │\n",
+              "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mTokenizer (type)                                  \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m                                            Vocab #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ gemma_tokenizer (\u001b[38;5;33mGemmaTokenizer\u001b[0m)                   │                                             \u001b[38;5;34m256,000\u001b[0m │\n",
+              "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"gemma_causal_lm\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"gemma_causal_lm\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                  </span>┃<span style=\"font-weight: bold\"> Output Shape              </span>┃<span style=\"font-weight: bold\">         Param # </span>┃<span style=\"font-weight: bold\"> Connected to               </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ padding_mask (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_ids (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ gemma_backbone                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)        │   <span style=\"color: #00af00; text-decoration-color: #00af00\">2,506,172,416</span> │ padding_mask[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],        │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaBackbone</span>)               │                           │                 │ token_ids[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_embedding               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256000</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">524,288,000</span> │ gemma_backbone[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReversibleEmbedding</span>)         │                           │                 │                            │\n",
+              "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                 \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to              \u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ padding_mask (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_ids (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ gemma_backbone                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)        │   \u001b[38;5;34m2,506,172,416\u001b[0m │ padding_mask[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
+              "│ (\u001b[38;5;33mGemmaBackbone\u001b[0m)               │                           │                 │ token_ids[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_embedding               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256000\u001b[0m)      │     \u001b[38;5;34m524,288,000\u001b[0m │ gemma_backbone[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
+              "│ (\u001b[38;5;33mReversibleEmbedding\u001b[0m)         │                           │                 │                            │\n",
+              "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,506,172,416</span> (4.67 GB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,506,172,416\u001b[0m (4.67 GB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,506,172,416</span> (4.67 GB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,506,172,416\u001b[0m (4.67 GB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(\"code_gemma_2b_en\")\n",
+        "gemma_lm.summary()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7cPBrkHt2XwB"
+      },
+      "source": [
+        "The `from_preset` method instantiates the model from a preset architecture and weights. In the code above, the string `code_gemma_2b_en` specifies the preset architecture — a CodeGemma model with 2 billion parameters.\n",
+        "\n",
+        "NOTE: CodeGemma models with 7\n",
+        "billion parameters are also available. To run the larger models in Colab, you need access to the premium GPUs available in paid plans."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b6gvI6bTB88Q"
+      },
+      "source": [
+        "## Fill-in-the-middle code completion\n",
+        "\n",
+        "This example uses CodeGemma's fill-in-the-middle (FIM) capability to complete code based on the surrounding context. This is particularly useful in code editor applications for inserting code where the text cursor is based on the code around it (before and after the cursor)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3hsDdIUsrPiD"
+      },
+      "source": [
+        "CodeGemma lets you use 4 user-defined tokens - 3 for FIM and a `<|file_separator|>` token for multi-file context support. Use these to define constants.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tGby-fi8n-Hv"
+      },
+      "outputs": [],
+      "source": [
+        "BEFORE_CURSOR = \"<|fim_prefix|>\"\n",
+        "AFTER_CURSOR = \"<|fim_suffix|>\"\n",
+        "AT_CURSOR = \"<|fim_middle|>\"\n",
+        "FILE_SEPARATOR = \"<|file_separator|>\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UGTInMBvr4cn"
+      },
+      "source": [
+        "Define the stop tokens for the model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "k1ousdBnr2j8"
+      },
+      "outputs": [],
+      "source": [
+        "END_TOKEN = gemma_lm.preprocessor.tokenizer.end_token\n",
+        "\n",
+        "stop_tokens = (BEFORE_CURSOR, AFTER_CURSOR, AT_CURSOR, FILE_SEPARATOR, END_TOKEN)\n",
+        "\n",
+        "stop_token_ids = tuple(gemma_lm.preprocessor.tokenizer.token_to_id(x) for x in stop_tokens)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "to9dd4BjsgDB"
+      },
+      "source": [
+        "Format the prompt for code completion. Note that:\n",
+        "* There should be no whitespaces between any FIM tokens and the prefix and suffix\n",
+        "* The FIM middle token should be at the end to prime the model to continue filling in\n",
+        "* The prefix or the suffix could be empty depending on where the cursor currently is in the file, or how much context you want to provide the model with\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7kexjoWk8W8B"
+      },
+      "source": [
+        "Use a helper function to format the prompt."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "N7UlgjSt5QnF"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<|fim_prefix|>import <|fim_suffix|>if __name__ == \"__main__\":\n",
+            "    sys.exit(0)<|fim_middle|>\n"
+          ]
+        }
+      ],
+      "source": [
+        "def format_completion_prompt(before, after):\n",
+        "    return f\"{BEFORE_CURSOR}{before}{AFTER_CURSOR}{after}{AT_CURSOR}\"\n",
+        "\n",
+        "before = \"import \"\n",
+        "after = \"\"\"if __name__ == \"__main__\":\\n    sys.exit(0)\"\"\"\n",
+        "prompt = format_completion_prompt(before, after)\n",
+        "print(prompt)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Y1woPer1yxKT"
+      },
+      "source": [
+        "Run the prompt. It is recommended to stream response tokens. Stop streaming upon encountering any of the user-defined or end of turn/senetence tokens to get the resulting code completion."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "aae5GHrdpj2_"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'<|fim_prefix|>import <|fim_suffix|>if __name__ == \"__main__\":\\n    sys.exit(0)<|fim_middle|>sys\\n<|file_separator|>'"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "gemma_lm.generate(prompt, stop_token_ids=stop_token_ids, max_length=128)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EpiplbdM8nVC"
+      },
+      "source": [
+        "The model provides `sys` as the suggested code completion."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QesAFYNcRw5f"
+      },
+      "source": [
+        "## Summary\n",
+        "\n",
+        "This tutorial walked you through using CodeGemma to infill code based on the surrounding context. Next, check out the [AI Assisted Programming with CodeGemma and KerasNLP notebook](https://ai.google.dev/gemma/docs/codegemma/code_assist_keras) for more examples on how you can use CodeGemma.\n",
+        "\n",
+        "Also refer to The [CodeGemma model card](https://ai.google.dev/gemma/docs/codegemma/model_card) for the technical specs of the CodeGemma models.\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "keras_quickstart.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemma/docs/distributed_tuning.ipynb b/site/en/gemma/docs/distributed_tuning.ipynb
new file mode 100644
index 000000000..8e40ffe28
--- /dev/null
+++ b/site/en/gemma/docs/distributed_tuning.ipynb
@@ -0,0 +1,992 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3MMAcssHTML"
+      },
+      "source": [
+        "<link rel=\"stylesheet\" href=\"/site-assets/css/gemma.css\">\n",
+        "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/css2?family=Google+Symbols:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200\" />"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "60KmTK7o6ppd"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemma/docs/distributed_tuning\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/distributed_tuning.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://www.kaggle.com/code/nilaychauhan/keras-gemma-distributed-finetuning-and-inference\"><img src=\"https://www.kaggle.com/static/images/logos/kaggle-logo-transparent-300.png\" height=\"32\" width=\"70\"/>Run in Kaggle</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/google/generative-ai-docs/main/site/en/gemma/docs/distributed_tuning.ipynb\"><img src=\"https://ai.google.dev/images/cloud-icon.svg\" width=\"40\" />Open in Vertex AI</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemma/docs/distributed_tuning.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FUOiKRSF7jc1"
+      },
+      "source": [
+        "# Distributed tuning with Gemma using Keras"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tdlq6K0znh3O"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "Gemma is a family of lightweight, state-of-the-art open models built from research and technology used to create Google Gemini models. Gemma can be further finetuned to suit specific needs. But Large Language Models, such as Gemma, can be very large in size and some of them may not fit on a sing accelerator for finetuning. In this case there are two general approaches for finetuning them:\n",
+        "1. Parameter Efficient Fine-Tuning (PEFT), which seeks to shrink the effective model size by sacrificing some fidelity. LoRA falls in this category and the [Fine-tune Gemma models in Keras using LoRA](https://ai.google.dev/gemma/docs/lora_tuning) tutorial demonstrates how to finetune the Gemma 2B model `gemma_2b_en` with LoRA using KerasNLP on a single GPU.\n",
+        "2. Full parameter finetuning with model parallelism. Model parallelism distributes a single model's weights across multiple devices and enables horizontal scaling. You can find out more about distributed training in this [Keras guide](https://keras.io/guides/distribution/).\n",
+        "\n",
+        "This tutorial walks you through using Keras with a JAX backend to finetune the Gemma 7B model with LoRA and model-parallism distributed training on Google's Tensor Processing Unit (TPU). Note that LoRA can be turned off in this tutorial for a slower but more accurate full-parameter tuning."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "z-jBO5hmDwrc"
+      },
+      "source": [
+        "## Using accelerators\n",
+        "\n",
+        "Technically you can use either TPU or GPU for this tutorial.\n",
+        "\n",
+        "### Notes on TPU environments\n",
+        "\n",
+        "Google has 3 products that provide TPUs:\n",
+        "* [Colab](https://colab.sandbox.google.com/) provides TPU v2 for free, which is sufficient for this tutorial.\n",
+        "* [Kaggle](https://www.kaggle.com/) offers TPU v3 for free and they also work for this tutorial.\n",
+        "* [Cloud TPU](https://cloud.google.com/tpu?hl=en) offers TPU v3 and newer generations. One way to set it up is:\n",
+        "  1. Create a new [TPU VM](https://cloud.google.com/tpu/docs/managing-tpus-tpu-vm#tpu-vms)\n",
+        "  2. Set up [SSH port forwarding](https://cloud.google.com/solutions/connecting-securely#port-forwarding-over-ssh) for your intended Jupyter server port\n",
+        "  3. Install Jupyter and start it on the TPU VM, then connect to Colab through \"Connect to a local runtime\"\n",
+        "\n",
+        "### Notes on multi-GPU setup\n",
+        "\n",
+        "Although this tutorial focuses on the TPU use case, you can easily adapt it for your own needs if you have a multi-GPU machine.\n",
+        "\n",
+        "If you prefer to work through Colab, it's also possible to provision a multi-GPU VM for Colab directly through \"Connect to a custom GCE VM\" in the Colab Connect menu.\n",
+        "\n",
+        "\n",
+        "We will focus on using the **free TPU from Kaggle** here."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Xry37wK5-Hrx"
+      },
+      "source": [
+        "## Before you begin"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aKvTsIkL98BG"
+      },
+      "source": [
+        "### Kaggle credentials\n",
+        "\n",
+        "Gemma models are hosted by Kaggle. To use Gemma, request access on Kaggle:\n",
+        "\n",
+        "- Sign in or register at [kaggle.com](https://www.kaggle.com)\n",
+        "- Open the [Gemma model card](https://www.kaggle.com/models/google/gemma) and select _\"Request Access\"_\n",
+        "- Complete the consent form and accept the terms and conditions\n",
+        "\n",
+        "Then, to use the Kaggle API, create an API token:\n",
+        "\n",
+        "- Open the [Kaggle settings](https://www.kaggle.com/settings)\n",
+        "- Select _\"Create New Token\"_\n",
+        "- A `kaggle.json` file is downloaded. It contains your Kaggle credentials\n",
+        "\n",
+        "Run the following cell and enter your Kaggle credentials when asked."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lKoW-nhE-gNO"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8c9b78140c8943edbd6191da7141650b",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "VBox(children=(HTML(value='<center> <img\\nsrc=https://www.kaggle.com/static/images/site-logo.png\\nalt=\\'Kaggle…"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# If you are using Kaggle, you don't need to login again.\n",
+        "!pip install ipywidgets\n",
+        "import kagglehub\n",
+        "\n",
+        "kagglehub.login()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "V8cYitUVSgP0"
+      },
+      "source": [
+        "An alternative way is to set KAGGLE_USERNAME and KAGGLE_KEY in your environment if kagglehub.login() doesn't work for you."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AO7a1Q4Yyc9Z"
+      },
+      "source": [
+        "## Installation\n",
+        "\n",
+        "Install Keras and KerasNLP with the Gemma model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WWEzVJR4Fx9g"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q -U keras-nlp\n",
+        "# Work around an import error with tensorflow-hub. The library is not used.\n",
+        "!pip install -q -U tensorflow-hub\n",
+        "# Install tensorflow-cpu so tensorflow does not attempt to access the TPU.\n",
+        "!pip install -q -U tensorflow-cpu tensorflow-text\n",
+        "# Install keras 3 last. See https://keras.io/getting_started for details.\n",
+        "!pip install -q -U keras"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fr9VnPm7FoMf"
+      },
+      "source": [
+        "### Set up Keras JAX backend"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lbZsUvfhwL2D"
+      },
+      "source": [
+        "Import JAX and run a sanity check on TPU. Kaggle offers TPUv3-8 devices which have 8 TPU cores with 16GB of memory each."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BK4MpHLKGujb"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[TpuDevice(id=0, process_index=0, coords=(0,0,0), core_on_chip=0),\n",
+              " TpuDevice(id=1, process_index=0, coords=(0,0,0), core_on_chip=1),\n",
+              " TpuDevice(id=2, process_index=0, coords=(1,0,0), core_on_chip=0),\n",
+              " TpuDevice(id=3, process_index=0, coords=(1,0,0), core_on_chip=1),\n",
+              " TpuDevice(id=4, process_index=0, coords=(0,1,0), core_on_chip=0),\n",
+              " TpuDevice(id=5, process_index=0, coords=(0,1,0), core_on_chip=1),\n",
+              " TpuDevice(id=6, process_index=0, coords=(1,1,0), core_on_chip=0),\n",
+              " TpuDevice(id=7, process_index=0, coords=(1,1,0), core_on_chip=1)]"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import jax\n",
+        "\n",
+        "jax.devices()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WEgg_OVIL2HY"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "# The Keras 3 distribution API is only implemented for the JAX backend for now\n",
+        "os.environ[\"KERAS_BACKEND\"] = \"jax\"\n",
+        "# Pre-allocate 90% of TPU memory to minimize memory fragmentation and allocation\n",
+        "# overhead\n",
+        "os.environ[\"XLA_PYTHON_CLIENT_MEM_FRACTION\"] = \"0.9\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wo1xkzr62hXN"
+      },
+      "source": [
+        "## Load model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kFCmWEKdMA_Y"
+      },
+      "outputs": [],
+      "source": [
+        "import keras\n",
+        "import keras_nlp"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bx3m8f1dB7nk"
+      },
+      "source": [
+        "### Notes on mixed precision training on NVIDIA GPUs\n",
+        "\n",
+        "When training on NVIDIA GPUs, mixed precision (`keras.mixed_precision.set_global_policy('mixed_bfloat16')`) can be used to speed up training with minimal effect on training quality. In most case, it is recommended to turn on mixed precision as it saves both memory and time. However, be aware that at small batch sizes, it can inflate memory usage by 1.5x (weights will be loaded twice, at half precision and full precision).\n",
+        "\n",
+        "For inference, half-precision (`keras.config.set_floatx(\"bfloat16\")`) will work and save memory while mixed-precision is not applicable."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "T0lHxEDX03gp"
+      },
+      "outputs": [],
+      "source": [
+        "# Uncomment the line below if you want to enable mixed precision training on GPUs\n",
+        "# keras.mixed_precision.set_global_policy('mixed_bfloat16')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xrR8TpVS6uPs"
+      },
+      "source": [
+        "To load the model with the weights and tensors distributed across TPUs, first create a new `DeviceMesh`. `DeviceMesh` represents a collection of hardware devices configured for distributed computation and was introduced in Keras 3 as part of the unified distribution API.\n",
+        "\n",
+        "The distribution API enables data and model parallelism, allowing for efficient scaling of deep learning models on multiple accelerators and hosts. It leverages the underlying framework (e.g. JAX) to distribute the program and tensors according to the sharding directives through a procedure called single program, multiple data (SPMD) expansion. Check out more details in the new [Keras 3 distribution API guide](https://keras.io/guides/distribution/)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7gxEkpUiP1Qf"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a device mesh with (1, 8) shape so that the weights are sharded across\n",
+        "# all 8 TPUs.\n",
+        "device_mesh = keras.distribution.DeviceMesh(\n",
+        "    (1, 8),\n",
+        "    [\"batch\", \"model\"],\n",
+        "    devices=keras.distribution.list_devices())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gTSJUwkC-7c6"
+      },
+      "source": [
+        "`LayoutMap` from the distribution API specifies how the weights and tensors should be sharded or replicated, using the string keys, for example, `token_embedding/embeddings` below, which are treated like regex to match tensor paths. Matched tensors are sharded with model dimensions (8 TPUs); others will be fully replicated."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8Wgh8h0qQCcu"
+      },
+      "outputs": [],
+      "source": [
+        "model_dim = \"model\"\n",
+        "\n",
+        "layout_map = keras.distribution.LayoutMap(device_mesh)\n",
+        "\n",
+        "# Weights that match 'token_embedding/embeddings' will be sharded on 8 TPUs\n",
+        "layout_map[\"token_embedding/embeddings\"] = (model_dim, None)\n",
+        "# Regex to match against the query, key and value matrices in the decoder\n",
+        "# attention layers\n",
+        "layout_map[\"decoder_block.*attention.*(query|key|value).*kernel\"] = (\n",
+        "    model_dim, None, None)\n",
+        "\n",
+        "layout_map[\"decoder_block.*attention_output.*kernel\"] = (\n",
+        "    model_dim, None, None)\n",
+        "layout_map[\"decoder_block.*ffw_gating.*kernel\"] = (None, model_dim)\n",
+        "layout_map[\"decoder_block.*ffw_linear.*kernel\"] = (model_dim, None)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6n4Zlvk9ALhZ"
+      },
+      "source": [
+        "`ModelParallel` allows you to shard model weights or activation tensors across all devcies on the `DeviceMesh`. In this case, some of the Gemma 7B model weights are sharded across 8 TPU chips according to the `layout_map` defined above. Now load the model in the distributed way."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bu48vUnbQj0p"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Attaching 'config.json' from model 'keras/gemma/keras/gemma_7b_en/1' to your Kaggle notebook...\n",
+            "Attaching 'config.json' from model 'keras/gemma/keras/gemma_7b_en/1' to your Kaggle notebook...\n",
+            "Attaching 'model.weights.h5' from model 'keras/gemma/keras/gemma_7b_en/1' to your Kaggle notebook...\n",
+            "Attaching 'tokenizer.json' from model 'keras/gemma/keras/gemma_7b_en/1' to your Kaggle notebook...\n",
+            "Attaching 'assets/tokenizer/vocabulary.spm' from model 'keras/gemma/keras/gemma_7b_en/1' to your Kaggle notebook...\n",
+            "normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.\n"
+          ]
+        }
+      ],
+      "source": [
+        "model_parallel = keras.distribution.ModelParallel(\n",
+        "    layout_map=layout_map, batch_dim_name=\"batch\")\n",
+        "\n",
+        "keras.distribution.set_distribution(model_parallel)\n",
+        "gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(\"gemma_7b_en\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ORCOIawAvpZ1"
+      },
+      "source": [
+        "Now verify that the model has been partitioned correctly. Let's take `decoder_block_1` as an example."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DqT7TRHKvoMK"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'keras_nlp.src.models.gemma.gemma_decoder_block.GemmaDecoderBlock'>\n",
+            "decoder_block_1/pre_attention_norm/scale                    (3072,)           PartitionSpec(None,)\n",
+            "decoder_block_1/attention/query/kernel                      (16, 3072, 256)   PartitionSpec(None, 'model', None)\n",
+            "decoder_block_1/attention/key/kernel                        (16, 3072, 256)   PartitionSpec(None, 'model', None)\n",
+            "decoder_block_1/attention/value/kernel                      (16, 3072, 256)   PartitionSpec(None, 'model', None)\n",
+            "decoder_block_1/attention/attention_output/kernel           (16, 256, 3072)   PartitionSpec(None, None, 'model')\n",
+            "decoder_block_1/pre_ffw_norm/scale                          (3072,)           PartitionSpec(None,)\n",
+            "decoder_block_1/ffw_gating/kernel                           (3072, 24576)     PartitionSpec('model', None)\n",
+            "decoder_block_1/ffw_gating_2/kernel                         (3072, 24576)     PartitionSpec('model', None)\n",
+            "decoder_block_1/ffw_linear/kernel                           (24576, 3072)     PartitionSpec(None, 'model')\n"
+          ]
+        }
+      ],
+      "source": [
+        "decoder_block_1 = gemma_lm.backbone.get_layer('decoder_block_1')\n",
+        "print(type(decoder_block_1))\n",
+        "for variable in decoder_block_1.weights:\n",
+        "  print(f'{variable.path:<58}  {str(variable.shape):<16}  {str(variable.value.sharding.spec)}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jc0ZzYIW0TSN"
+      },
+      "source": [
+        "## Inference before finetuning"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ClaTyBp3Tgr4"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'Best comedy movies in the 90s 1. The Naked Gun 2½: The Smell of Fear (1991) 2. Wayne’s World (1992) 3. The Naked Gun 33⅓: The Final Insult (1994)'"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "gemma_lm.generate(\"Best comedy movies in the 90s \", max_length=64)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tKUXYLW_0lAx"
+      },
+      "source": [
+        "The model generates a list of great comedy movies from the 90s to watch. Now we finetune the Gemma model to change the output style."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IcPCXCwvXC7t"
+      },
+      "source": [
+        "## Finetune with IMDB"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6MVJlsuSXCcf"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1mDownloading and preparing dataset 80.23 MiB (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0...\u001b[0m\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "eec649e96f174bfaa22c0598addc0e3a",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Dl Completed...: 0 url [00:00, ? url/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8c6f4e3c0b7d47589671b0759263bab5",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "974fa4e362684fe0ba8df0ab2d75b3c4",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating splits...:   0%|          | 0/3 [00:00<?, ? splits/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ef99c3a33d85433e986d715203917814",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train examples...:   0%|          | 0/25000 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b3962688721343bd8440dad1f1176296",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Shuffling /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteAJDUZT/imdb_reviews-train.tfrecord…"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "abaa54e825ab4ac7a1ee78345039ca4c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating test examples...:   0%|          | 0/25000 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "650324b2c42d488396bd2abec6baf636",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Shuffling /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteAJDUZT/imdb_reviews-test.tfrecord*…"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4c5e107ec0a8466d8370bb8b03df1e0a",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating unsupervised examples...:   0%|          | 0/50000 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7efcadb1ce53445697f34e263d41e623",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Shuffling /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteAJDUZT/imdb_reviews-unsupervised.t…"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1mDataset imdb_reviews downloaded and prepared to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "b\"This was an absolutely terrible movie. Don't be lured in by Christopher Walken or Michael Ironside. Both are great actors, but this must simply be their worst role in history. Even their great acting could not redeem this movie's ridiculous storyline. This movie is an early nineties US propaganda piece. The most pathetic scenes were those when the Columbian rebels were making their cases for revolutions. Maria Conchita Alonso appeared phony, and her pseudo-love affair with Walken was nothing but a pathetic emotional plug in a movie that was devoid of any real meaning. I am disappointed that there are movies like this, ruining actor's like Christopher Walken's good name. I could barely sit through it.\""
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import tensorflow_datasets as tfds\n",
+        "\n",
+        "imdb_train = tfds.load(\n",
+        "    \"imdb_reviews\",\n",
+        "    split=\"train\",\n",
+        "    as_supervised=True,\n",
+        "    batch_size=2,\n",
+        ")\n",
+        "# Drop labels.\n",
+        "imdb_train = imdb_train.map(lambda x, y: x)\n",
+        "\n",
+        "imdb_train.unbatch().take(1).get_single_element().numpy()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "N-04czr3XTTl"
+      },
+      "outputs": [],
+      "source": [
+        "# Use a subset of the dataset for faster training.\n",
+        "imdb_train = imdb_train.take(2000)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xiLW0SpI1PfC"
+      },
+      "source": [
+        "Perform finetuning using [Low Rank Adaptation](https://arxiv.org/abs/2106.09685) (LoRA). LoRA is a fine-tuning technique which greatly reduces the number of trainable parameters for downstream tasks by freezing the full weights of the model and inserting a smaller number of new trainable weights into the model. Basically LoRA reparameterizes the larger full weight matrices by 2 smaller low-rank matrices AxB to train and this technique makes training much faster and more memory-efficient."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3o_Gi3v_jp7s"
+      },
+      "outputs": [],
+      "source": [
+        "# Enable LoRA for the model and set the LoRA rank to 4.\n",
+        "gemma_lm.backbone.enable_lora(rank=4)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1-hQFy7hXWRl"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Preprocessor: \"gemma_causal_lm_preprocessor\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mPreprocessor: \"gemma_causal_lm_preprocessor\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Tokenizer (type)                                   </span>┃<span style=\"font-weight: bold\">                                             Vocab # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ gemma_tokenizer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaTokenizer</span>)                   │                                             <span style=\"color: #00af00; text-decoration-color: #00af00\">256,000</span> │\n",
+              "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mTokenizer (type)                                  \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m                                            Vocab #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ gemma_tokenizer (\u001b[38;5;33mGemmaTokenizer\u001b[0m)                   │                                             \u001b[38;5;34m256,000\u001b[0m │\n",
+              "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"gemma_causal_lm\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"gemma_causal_lm\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                  </span>┃<span style=\"font-weight: bold\"> Output Shape              </span>┃<span style=\"font-weight: bold\">         Param # </span>┃<span style=\"font-weight: bold\"> Connected to               </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ padding_mask (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_ids (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ gemma_backbone                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3072</span>)        │   <span style=\"color: #00af00; text-decoration-color: #00af00\">8,548,748,288</span> │ padding_mask[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],        │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaBackbone</span>)               │                           │                 │ token_ids[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_embedding               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256000</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">786,432,000</span> │ gemma_backbone[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReversibleEmbedding</span>)         │                           │                 │                            │\n",
+              "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                 \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to              \u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ padding_mask (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_ids (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ gemma_backbone                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3072\u001b[0m)        │   \u001b[38;5;34m8,548,748,288\u001b[0m │ padding_mask[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
+              "│ (\u001b[38;5;33mGemmaBackbone\u001b[0m)               │                           │                 │ token_ids[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_embedding               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256000\u001b[0m)      │     \u001b[38;5;34m786,432,000\u001b[0m │ gemma_backbone[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
+              "│ (\u001b[38;5;33mReversibleEmbedding\u001b[0m)         │                           │                 │                            │\n",
+              "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,548,748,288</span> (31.85 GB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,548,748,288\u001b[0m (31.85 GB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">11,067,392</span> (42.22 MB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m11,067,392\u001b[0m (42.22 MB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,537,680,896</span> (31.81 GB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m8,537,680,896\u001b[0m (31.81 GB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/site-packages/jax/_src/interpreters/mlir.py:756: UserWarning: Some donated buffers were not usable: ShapedArray(float32[256000,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]).\n",
+            "See an explanation at https://jax.readthedocs.io/en/latest/faq.html#buffer-donation.\n",
+            "  warnings.warn(\"Some donated buffers were not usable:\"\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m358s\u001b[0m 163ms/step - loss: 2.7145 - sparse_categorical_accuracy: 0.4329\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.history.History at 0x7e9cac7f41c0>"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Fine-tune on the IMDb movie reviews dataset.\n",
+        "\n",
+        "# Limit the input sequence length to 128 to control memory usage.\n",
+        "gemma_lm.preprocessor.sequence_length = 128\n",
+        "# Use AdamW (a common optimizer for transformer models).\n",
+        "optimizer = keras.optimizers.AdamW(\n",
+        "    learning_rate=5e-5,\n",
+        "    weight_decay=0.01,\n",
+        ")\n",
+        "# Exclude layernorm and bias terms from decay.\n",
+        "optimizer.exclude_from_weight_decay(var_names=[\"bias\", \"scale\"])\n",
+        "\n",
+        "gemma_lm.compile(\n",
+        "    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
+        "    optimizer=optimizer,\n",
+        "    weighted_metrics=[keras.metrics.SparseCategoricalAccuracy()],\n",
+        ")\n",
+        "gemma_lm.summary()\n",
+        "gemma_lm.fit(imdb_train, epochs=1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CnpeavB4fZ7Y"
+      },
+      "source": [
+        "Note that enabling LoRA reduces the number of trainable parameters significantly, from 7 billion to only 11 million."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lBiOKlAy2MAe"
+      },
+      "source": [
+        "## Inference after finetuning"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9yNyJ8CLXfw0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "\"Best comedy movies in the 90s \\n\\nThis is the movie that made me want to be a director. It's a great movie, and it's still funny today. The acting is superb, the writing is excellent, the music is perfect for the movie, and the story is great.\""
+            ]
+          },
+          "execution_count": 32,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "gemma_lm.generate(\"Best comedy movies in the 90s \", max_length=64)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "inqB1e_v0xP5"
+      },
+      "source": [
+        "After finetuning, the model has learned the style of movie reviews and is now generating output in that style in the context of 90s comedy movies."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bzKsCGIN0yX5"
+      },
+      "source": [
+        "## What's next\n",
+        "\n",
+        "In this tutorial, you learned how to using KerasNLP JAX backend to finetune a Gemma model on the IMDb dataset in a distributed manner on the powerful TPUs. Here are a few suggestions for what else to learn:\n",
+        "\n",
+        "* Learn how to [get started with Keras Gemma](https://ai.google.dev/gemma/docs/get_started).\n",
+        "* Learn how to [finetune the Gemma model on GPU](https://ai.google.dev/gemma/docs/lora_tuning)."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "distributed_tuning.ipynb",
+      "toc_visible": true
+    },
+    "google": {
+      "image_path": "/site-assets/images/marketing/gemma.png",
+      "keywords": [
+        "examples",
+        "gemma",
+        "python",
+        "quickstart",
+        "text"
+      ]
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemma/docs/gemma_chat.ipynb b/site/en/gemma/docs/gemma_chat.ipynb
new file mode 100644
index 000000000..843d5c5c5
--- /dev/null
+++ b/site/en/gemma/docs/gemma_chat.ipynb
@@ -0,0 +1,1005 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3MMAcssHTML"
+      },
+      "source": [
+        "<link rel=\"stylesheet\" href=\"/site-assets/css/gemma.css\">\n",
+        "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/css2?family=Google+Symbols:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200\" />"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4qxv4Sn9b8CE"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemma/docs/gemma_chat\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "    <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/gemma_chat.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/google/generative-ai-docs/main/site/en/gemma/docs/gemma_chat.ipynb\"><img src=\"https://ai.google.dev/images/cloud-icon.svg\" width=\"40\" />Open in Vertex AI</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemma/docs/gemma_chat.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "402c3d8a"
+      },
+      "source": [
+        "# Building a chatbot with Gemma"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b686fd95"
+      },
+      "source": [
+        "Large Language Models (LLMs) such as Gemma excel at generating informative responses, making them ideal for building virtual assistants and chatbots.\n",
+        "\n",
+        "Conventionally, LLMs operate in a stateless manner, meaning they lack an inherent memory to store past conversations. Each prompt or question is processed independently, disregarding prior interactions. However, a crucial aspect of natural conversation is the ability to retain context from prior interactions. To overcome this limitation and enable LLMs to maintain conversation context, they must be explicitly provided with relevant information such as the conversation history (or pertinent parts) into each new prompt presented to the LLM.\n",
+        "\n",
+        "This tutorial shows you how to develop a chatbot using the instruction-tuned model variant of Gemma."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "29732090"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QQ6W7NzRe1VM"
+      },
+      "source": [
+        "### Gemma setup\n",
+        "\n",
+        "To complete this tutorial, you'll first need to complete the setup instructions at [Gemma setup](https://ai.google.dev/gemma/docs/setup). The Gemma setup instructions show you how to do the following:\n",
+        "\n",
+        "* Get access to Gemma on kaggle.com.\n",
+        "* Select a Colab runtime with sufficient resources to run\n",
+        "  the Gemma 2B model.\n",
+        "* Generate and configure a Kaggle username and API key.\n",
+        "\n",
+        "After you've completed the Gemma setup, move on to the next section, where you'll set environment variables for your Colab environment."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_gN-IVRC3dQe"
+      },
+      "source": [
+        "### Set environment variables\n",
+        "\n",
+        "Set environment variables for `KAGGLE_USERNAME` and `KAGGLE_KEY`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DrBoa_Urw9Vx"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "# Note: `userdata.get` is a Colab API. If you're not using Colab, set the env\n",
+        "# vars as appropriate for your system.\n",
+        "os.environ[\"KAGGLE_USERNAME\"] = userdata.get('KAGGLE_USERNAME')\n",
+        "os.environ[\"KAGGLE_KEY\"] = userdata.get('KAGGLE_KEY')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "z9oy3QUmXtSd"
+      },
+      "source": [
+        "### Install dependencies\n",
+        "\n",
+        "Install Keras and KerasNLP."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "a973dd7a"
+      },
+      "outputs": [],
+      "source": [
+        "# Install Keras 3 last. See https://keras.io/getting_started/ for more details.\n",
+        "!pip install -q tensorflow-cpu\n",
+        "!pip install -q -U keras-nlp tensorflow-hub\n",
+        "!pip install -q -U \"keras>=3\"\n",
+        "!pip install -q -U tensorflow-text"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Wme8666dUPVR"
+      },
+      "source": [
+        "### Select a backend\n",
+        "\n",
+        "Keras is a high-level, multi-framework deep learning API designed for simplicity and ease of use. [Keras 3](https://keras.io/keras_3){:.external} lets you choose the backend: TensorFlow, JAX, or PyTorch. All three will work for this tutorial."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "67d12d2d"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "# Select JAX as the backend\n",
+        "os.environ[\"KERAS_BACKEND\"] = \"jax\"\n",
+        "\n",
+        "# Pre-allocate 100% of TPU memory to minimize memory fragmentation\n",
+        "os.environ[\"XLA_PYTHON_CLIENT_MEM_FRACTION\"] = \"1.0\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ajm_SGWTUjVd"
+      },
+      "source": [
+        "### Import packages\n",
+        "\n",
+        "Import Keras and KerasNLP."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3lyn9FxPUok8"
+      },
+      "outputs": [],
+      "source": [
+        "import keras\n",
+        "import keras_nlp\n",
+        "\n",
+        "# for reproducibility\n",
+        "keras.utils.set_random_seed(42)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "39dc9d5b"
+      },
+      "source": [
+        "### Instantiate the model\n",
+        "\n",
+        "KerasNLP provides implementations of many popular [model architectures](https://keras.io/api/keras_nlp/models/){:.external}. In this tutorial, you'll instantiate the model using `GemmaCausalLM`, an end-to-end Gemma model for causal language modeling. A causal language model predicts the next token based on previous tokens.\n",
+        "\n",
+        "Instantiate the model using the `from_preset` method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "c86dc8fe"
+      },
+      "outputs": [],
+      "source": [
+        "gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(\"gemma2_instruct_2b_en\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tcCv0BSdVFv9"
+      },
+      "source": [
+        "The `GemmaCausalLM.from_preset()` function instantiates the model from a preset architecture and weights. In the code above, the string `\"gemma2_instruct_2b_en\"` specifies the preset the Gemma 2 2B model with 2 billion parameters. Gemma models with [7B, 9B, and 27B parameters](https://ai.google.com/gemma/docs/get_started#models-list) are also available. You can find the code strings for Gemma models in their **Model Variation** listings on [Kaggle](https://www.kaggle.com/models/google/gemma).\n",
+        "\n",
+        "Note: To run the larger models in Colab, you need access to the premium GPUs available in paid plans. Alternatively, you can perform inferences using Kaggle notebooks or Google Cloud projects."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bLNx8AoeVe-a"
+      },
+      "source": [
+        "Use the `summary` method to get more info about the model:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3MorieIpVksu"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Preprocessor: \"gemma_causal_lm_preprocessor\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mPreprocessor: \"gemma_causal_lm_preprocessor\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Tokenizer (type)                                   </span>┃<span style=\"font-weight: bold\">                                             Vocab # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ gemma_tokenizer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaTokenizer</span>)                   │                                             <span style=\"color: #00af00; text-decoration-color: #00af00\">256,000</span> │\n",
+              "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mTokenizer (type)                                  \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m                                            Vocab #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ gemma_tokenizer (\u001b[38;5;33mGemmaTokenizer\u001b[0m)                   │                                             \u001b[38;5;34m256,000\u001b[0m │\n",
+              "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"gemma_causal_lm\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"gemma_causal_lm\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                  </span>┃<span style=\"font-weight: bold\"> Output Shape              </span>┃<span style=\"font-weight: bold\">         Param # </span>┃<span style=\"font-weight: bold\"> Connected to               </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ padding_mask (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_ids (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ gemma_backbone                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2304</span>)        │   <span style=\"color: #00af00; text-decoration-color: #00af00\">2,614,341,888</span> │ padding_mask[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],        │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaBackbone</span>)               │                           │                 │ token_ids[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_embedding               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256000</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">589,824,000</span> │ gemma_backbone[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReversibleEmbedding</span>)         │                           │                 │                            │\n",
+              "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                 \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to              \u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ padding_mask (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_ids (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ gemma_backbone                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m)        │   \u001b[38;5;34m2,614,341,888\u001b[0m │ padding_mask[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
+              "│ (\u001b[38;5;33mGemmaBackbone\u001b[0m)               │                           │                 │ token_ids[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_embedding               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256000\u001b[0m)      │     \u001b[38;5;34m589,824,000\u001b[0m │ gemma_backbone[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
+              "│ (\u001b[38;5;33mReversibleEmbedding\u001b[0m)         │                           │                 │                            │\n",
+              "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,614,341,888</span> (9.74 GB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,614,341,888\u001b[0m (9.74 GB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,614,341,888</span> (9.74 GB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,614,341,888\u001b[0m (9.74 GB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "gemma_lm.summary()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ArZPOzFpVp6S"
+      },
+      "source": [
+        "As you can see from the summary, the model has 2.6 billion trainable parameters.\n",
+        "\n",
+        "Note: For purposes of naming the model (\"2B\"), the embedding layer is not counted against the number of parameters."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1WpS39TBYql9"
+      },
+      "source": [
+        "### Define formatting helper functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3-obTC1jZGpZ"
+      },
+      "outputs": [],
+      "source": [
+        "from IPython.display import Markdown\n",
+        "import textwrap\n",
+        "\n",
+        "def display_chat(prompt, text):\n",
+        "  formatted_prompt = \"<font size='+1' color='brown'>🙋‍♂️<blockquote>\" + prompt + \"</blockquote></font>\"\n",
+        "  text = text.replace('•', '  *')\n",
+        "  text = textwrap.indent(text, '> ', predicate=lambda _: True)\n",
+        "  formatted_text = \"<font size='+1' color='teal'>🤖\\n\\n\" + text + \"\\n</font>\"\n",
+        "  return Markdown(formatted_prompt+formatted_text)\n",
+        "\n",
+        "def to_markdown(text):\n",
+        "  text = text.replace('•', '  *')\n",
+        "  return Markdown(textwrap.indent(text, '> ', predicate=lambda _: True))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5ca54e8c"
+      },
+      "source": [
+        "## Building the chatbot\n",
+        "\n",
+        "The Gemma instruction-tuned model `gemma2_instruct_2b_en` is fine-tuned to understand the following turn tokens:\n",
+        "\n",
+        "```\n",
+        "<start_of_turn>user\\n  ... <end_of_turn>\\n\n",
+        "<start_of_turn>model\\n ... <end_of_turn>\\n\n",
+        "```\n",
+        "\n",
+        "This tutorial uses these tokens to build the chatbot. Refer to [Formatting and system instructions](https://ai.google.dev/gemma/docs/formatting) for more information on Gemma control tokens.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9583dfd1"
+      },
+      "source": [
+        "### Create a chat helper to manage the conversation state"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "e4e9a187"
+      },
+      "outputs": [],
+      "source": [
+        "class ChatState():\n",
+        "  \"\"\"\n",
+        "  Manages the conversation history for a turn-based chatbot\n",
+        "  Follows the turn-based conversation guidelines for the Gemma family of models\n",
+        "  documented at https://ai.google.dev/gemma/docs/formatting\n",
+        "  \"\"\"\n",
+        "\n",
+        "  __START_TURN_USER__ = \"<start_of_turn>user\\n\"\n",
+        "  __START_TURN_MODEL__ = \"<start_of_turn>model\\n\"\n",
+        "  __END_TURN__ = \"<end_of_turn>\\n\"\n",
+        "\n",
+        "  def __init__(self, model, system=\"\"):\n",
+        "    \"\"\"\n",
+        "    Initializes the chat state.\n",
+        "\n",
+        "    Args:\n",
+        "        model: The language model to use for generating responses.\n",
+        "        system: (Optional) System instructions or bot description.\n",
+        "    \"\"\"\n",
+        "    self.model = model\n",
+        "    self.system = system\n",
+        "    self.history = []\n",
+        "\n",
+        "  def add_to_history_as_user(self, message):\n",
+        "      \"\"\"\n",
+        "      Adds a user message to the history with start/end turn markers.\n",
+        "      \"\"\"\n",
+        "      self.history.append(self.__START_TURN_USER__ + message + self.__END_TURN__)\n",
+        "\n",
+        "  def add_to_history_as_model(self, message):\n",
+        "      \"\"\"\n",
+        "      Adds a model response to the history with start/end turn markers.\n",
+        "      \"\"\"\n",
+        "      self.history.append(self.__START_TURN_MODEL__ + message)\n",
+        "\n",
+        "  def get_history(self):\n",
+        "      \"\"\"\n",
+        "      Returns the entire chat history as a single string.\n",
+        "      \"\"\"\n",
+        "      return \"\".join([*self.history])\n",
+        "\n",
+        "  def get_full_prompt(self):\n",
+        "    \"\"\"\n",
+        "    Builds the prompt for the language model, including history and system description.\n",
+        "    \"\"\"\n",
+        "    prompt = self.get_history() + self.__START_TURN_MODEL__\n",
+        "    if len(self.system)>0:\n",
+        "      prompt = self.system + \"\\n\" + prompt\n",
+        "    return prompt\n",
+        "\n",
+        "  def send_message(self, message):\n",
+        "    \"\"\"\n",
+        "    Handles sending a user message and getting a model response.\n",
+        "\n",
+        "    Args:\n",
+        "        message: The user's message.\n",
+        "\n",
+        "    Returns:\n",
+        "        The model's response.\n",
+        "    \"\"\"\n",
+        "    self.add_to_history_as_user(message)\n",
+        "    prompt = self.get_full_prompt()\n",
+        "    response = self.model.generate(prompt, max_length=2048)\n",
+        "    result = response.replace(prompt, \"\")  # Extract only the new response\n",
+        "    self.add_to_history_as_model(result)\n",
+        "    return result\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9hmJS4h4ZmiP"
+      },
+      "source": [
+        "### Chat with the model\n",
+        "\n",
+        "Start chatting with the model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "b1913181"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "<font size='+1' color='brown'>🙋‍♂️<blockquote>Tell me, in a few words,  how to compute all prime numbers up to 1000?</blockquote></font><font size='+1' color='teal'>🤖\n",
+              "\n",
+              "> **Sieve of Eratosthenes.** \n",
+              "> <end_of_turn>\n",
+              "</font>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "chat = ChatState(gemma_lm)\n",
+        "message = \"Tell me, in a few words,  how to compute all prime numbers up to 1000?\"\n",
+        "display_chat(message, chat.send_message(message))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ODKxUPP2Zuqy"
+      },
+      "source": [
+        "Continue the conversation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7448005b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "<font size='+1' color='brown'>🙋‍♂️<blockquote>Now in Python! No numpy, please!</blockquote></font><font size='+1' color='teal'>🤖\n",
+              "\n",
+              "> ```python\n",
+              "> def sieve_of_eratosthenes(n):\n",
+              ">   \"\"\"Returns a list of prime numbers up to n.\"\"\"\n",
+              ">   primes = [True] * (n + 1)\n",
+              ">   primes[0] = primes[1] = False\n",
+              ">   for i in range(2, int(n**0.5) + 1):\n",
+              ">     if primes[i]:\n",
+              ">       for j in range(i * i, n + 1, i):\n",
+              ">         primes[j] = False\n",
+              ">   return [i for i, is_prime in enumerate(primes) if is_prime]\n",
+              "> \n",
+              "> primes = sieve_of_eratosthenes(1000)\n",
+              "> print(primes)\n",
+              "> ```\n",
+              "> \n",
+              "> **Explanation:**\n",
+              "> \n",
+              "> 1. **Initialization:**\n",
+              ">    - `primes = [True] * (n + 1)`: Creates a list `primes` of boolean values, initially assuming all numbers are prime.\n",
+              ">    - `primes[0] = primes[1] = False`: Sets 0 and 1 as non-prime.\n",
+              "> \n",
+              "> 2. **Iteration:**\n",
+              ">    - `for i in range(2, int(n**0.5) + 1):`: Iterates from 2 to the square root of `n`. We only need to check up to the square root because any composite number must have a prime factor less than or equal to its square root.\n",
+              ">    - `if primes[i]:`: If `i` is marked as prime:\n",
+              ">      - `for j in range(i * i, n + 1, i):`: Marks all multiples of `i` as non-prime.\n",
+              "> \n",
+              "> 3. **Result:**\n",
+              ">    - `return [i for i, is_prime in enumerate(primes) if is_prime]`: Creates a list of indices where `primes[i]` is True, representing the prime numbers.\n",
+              "> \n",
+              "> \n",
+              "> Let me know if you'd like a more detailed explanation of any part! \n",
+              "> <end_of_turn>\n",
+              "</font>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "message = \"Now in Python! No numpy, please!\"\n",
+        "display_chat(message, chat.send_message(message))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0973ff54"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "<font size='+1' color='brown'>🙋‍♂️<blockquote>Thank you, it works! Can you explain the code in French?</blockquote></font><font size='+1' color='teal'>🤖\n",
+              "\n",
+              "> Bien sûr ! Voici une explication du code en français :\n",
+              "> \n",
+              "> ```python\n",
+              "> def sieve_of_eratosthenes(n):\n",
+              ">   \"\"\"Retourne une liste de nombres premiers jusqu'à n.\"\"\"\n",
+              ">   primes = [True] * (n + 1)\n",
+              ">   primes[0] = primes[1] = False\n",
+              ">   for i in range(2, int(n**0.5) + 1):\n",
+              ">     if primes[i]:\n",
+              ">       for j in range(i * i, n + 1, i):\n",
+              ">         primes[j] = False\n",
+              ">   return [i for i, is_prime in enumerate(primes) if is_prime]\n",
+              "> \n",
+              "> primes = sieve_of_eratosthenes(1000)\n",
+              "> print(primes)\n",
+              "> ```\n",
+              "> \n",
+              "> **Explication:**\n",
+              "> \n",
+              "> 1. **Initialisation:**\n",
+              ">    - `primes = [True] * (n + 1)`: Crée une liste `primes` de valeurs booléennes, initialement supposant que tous les nombres sont premiers.\n",
+              ">    - `primes[0] = primes[1] = False`: Définit 0 et 1 comme non-premiers.\n",
+              "> \n",
+              "> 2. **Itération:**\n",
+              ">    - `for i in range(2, int(n**0.5) + 1):`: Itère de 2 jusqu'à la racine carrée de `n`. Nous ne devons vérifier que jusqu'à la racine carrée car tout nombre composite doit avoir un facteur premier inférieur ou égal à sa racine carrée.\n",
+              ">    - `if primes[i]:`: Si `i` est considéré comme premier:\n",
+              ">      - `for j in range(i * i, n + 1, i):`:  Marquer tous les multiples de `i` comme non-premiers.\n",
+              "> \n",
+              "> 3. **Resultat:**\n",
+              ">    - `return [i for i, is_prime in enumerate(primes) if is_prime]`: Crée une liste des indices où `primes[i]` est vrai, représentant les nombres premiers.\n",
+              "> \n",
+              "> \n",
+              "> N'hésitez pas à me demander si vous avez besoin d'une explication plus détaillée de quelque chose! \n",
+              "> <end_of_turn>\n",
+              "</font>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "message = \"Thank you, it works! Can you explain the code in French?\"\n",
+        "display_chat(message, chat.send_message(message))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "a0c51f42"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "<font size='+1' color='brown'>🙋‍♂️<blockquote>Great! Now add those explanations as comments in the code.</blockquote></font><font size='+1' color='teal'>🤖\n",
+              "\n",
+              "> ```python\n",
+              "> def sieve_of_eratosthenes(n):\n",
+              ">   \"\"\"Retourne une liste de nombres premiers jusqu'à n.\"\"\"\n",
+              ">   # Initialise une liste de boolean avec True pour tous les nombres de 0 à n\n",
+              ">   primes = [True] * (n + 1)\n",
+              ">   # Définit 0 et 1 comme non-premiers\n",
+              ">   primes[0] = primes[1] = False\n",
+              ">   # Itère de 2 à la racine carrée de n\n",
+              ">   for i in range(2, int(n**0.5) + 1):\n",
+              ">     # Si i est considéré comme premier\n",
+              ">     if primes[i]:\n",
+              ">       # Itère sur tous les multiples de i\n",
+              ">       for j in range(i * i, n + 1, i):\n",
+              ">         # Définit les multiples de i comme non-premiers\n",
+              ">         primes[j] = False\n",
+              ">   # Retourne la liste des indices des nombres premiers\n",
+              ">   return [i for i, is_prime in enumerate(primes) if is_prime]\n",
+              "> \n",
+              "> primes = sieve_of_eratosthenes(1000)\n",
+              "> print(primes)\n",
+              "> ```\n",
+              "> \n",
+              "> **Explication:**\n",
+              "> \n",
+              "> * **Initialisation:**\n",
+              ">     * `primes = [True] * (n + 1)`:  Crée une liste `primes` de valeurs booléennes, initialement supposant que tous les nombres sont premiers.\n",
+              ">     * `primes[0] = primes[1] = False`: Définit 0 et 1 comme non-premiers.\n",
+              "> * **Itération:**\n",
+              ">     * `for i in range(2, int(n**0.5) + 1):`: Itère de 2 jusqu'à la racine carrée de `n`. Nous ne devons vérifier que jusqu'à la racine carrée car tout nombre composite doit avoir un facteur premier inférieur ou égal à sa racine carrée.\n",
+              ">     * `if primes[i]:`: Si `i` est considéré comme premier:\n",
+              ">         * `for j in range(i * i, n + 1, i):`:  Marquer tous les multiples de `i` comme non-premiers.\n",
+              "> * **Resultat:**\n",
+              ">     * `return [i for i, is_prime in enumerate(primes) if is_prime]`: Crée une liste des indices où `primes[i]` est vrai, représentant les nombres premiers. \n",
+              "> \n",
+              "> \n",
+              "> \n",
+              "> <end_of_turn>\n",
+              "</font>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "message = \"Great! Now add those explanations as comments in the code.\"\n",
+        "display_chat(message, chat.send_message(message))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "51a33627"
+      },
+      "source": [
+        "Test the generated response by running the generated code:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "221c0817"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239, 241, 251, 257, 263, 269, 271, 277, 281, 283, 293, 307, 311, 313, 317, 331, 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397, 401, 409, 419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491, 499, 503, 509, 521, 523, 541, 547, 557, 563, 569, 571, 577, 587, 593, 599, 601, 607, 613, 617, 619, 631, 641, 643, 647, 653, 659, 661, 673, 677, 683, 691, 701, 709, 719, 727, 733, 739, 743, 751, 757, 761, 769, 773, 787, 797, 809, 811, 821, 823, 827, 829, 839, 853, 857, 859, 863, 877, 881, 883, 887, 907, 911, 919, 929, 937, 941, 947, 953, 967, 971, 977, 983, 991, 997]\n"
+          ]
+        }
+      ],
+      "source": [
+        "def sieve_of_eratosthenes(n):\n",
+        "  \"\"\"Retourne une liste de nombres premiers jusqu'à n.\"\"\"\n",
+        "  # Initialise une liste de boolean avec True pour tous les nombres de 0 à n\n",
+        "  primes = [True] * (n + 1)\n",
+        "  # Définit 0 et 1 comme non-premiers\n",
+        "  primes[0] = primes[1] = False\n",
+        "  # Itère de 2 à la racine carrée de n\n",
+        "  for i in range(2, int(n**0.5) + 1):\n",
+        "    # Si i est considéré comme premier\n",
+        "    if primes[i]:\n",
+        "      # Itère sur tous les multiples de i\n",
+        "      for j in range(i * i, n + 1, i):\n",
+        "        # Définit les multiples de i comme non-premiers\n",
+        "        primes[j] = False\n",
+        "  # Retourne la liste des indices des nombres premiers\n",
+        "  return [i for i, is_prime in enumerate(primes) if is_prime]\n",
+        "\n",
+        "primes = sieve_of_eratosthenes(1000)\n",
+        "print(primes)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1c8ece6c"
+      },
+      "source": [
+        "Use the `get_history` method to see how all the context was retained by the `Chat` class."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "e48f4ca1"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<start_of_turn>user\n",
+            "Tell me, in a few words,  how to compute all prime numbers up to 1000?<end_of_turn>\n",
+            "<start_of_turn>model\n",
+            "**Sieve of Eratosthenes.** \n",
+            "<end_of_turn><start_of_turn>user\n",
+            "Now in Python! No numpy, please!<end_of_turn>\n",
+            "<start_of_turn>model\n",
+            "```python\n",
+            "def sieve_of_eratosthenes(n):\n",
+            "  \"\"\"Returns a list of prime numbers up to n.\"\"\"\n",
+            "  primes = [True] * (n + 1)\n",
+            "  primes[0] = primes[1] = False\n",
+            "  for i in range(2, int(n**0.5) + 1):\n",
+            "    if primes[i]:\n",
+            "      for j in range(i * i, n + 1, i):\n",
+            "        primes[j] = False\n",
+            "  return [i for i, is_prime in enumerate(primes) if is_prime]\n",
+            "\n",
+            "primes = sieve_of_eratosthenes(1000)\n",
+            "print(primes)\n",
+            "```\n",
+            "\n",
+            "**Explanation:**\n",
+            "\n",
+            "1. **Initialization:**\n",
+            "   - `primes = [True] * (n + 1)`: Creates a list `primes` of boolean values, initially assuming all numbers are prime.\n",
+            "   - `primes[0] = primes[1] = False`: Sets 0 and 1 as non-prime.\n",
+            "\n",
+            "2. **Iteration:**\n",
+            "   - `for i in range(2, int(n**0.5) + 1):`: Iterates from 2 to the square root of `n`. We only need to check up to the square root because any composite number must have a prime factor less than or equal to its square root.\n",
+            "   - `if primes[i]:`: If `i` is marked as prime:\n",
+            "     - `for j in range(i * i, n + 1, i):`: Marks all multiples of `i` as non-prime.\n",
+            "\n",
+            "3. **Result:**\n",
+            "   - `return [i for i, is_prime in enumerate(primes) if is_prime]`: Creates a list of indices where `primes[i]` is True, representing the prime numbers.\n",
+            "\n",
+            "\n",
+            "Let me know if you'd like a more detailed explanation of any part! \n",
+            "<end_of_turn><start_of_turn>user\n",
+            "Thank you, it works! Can you explain the code in French?<end_of_turn>\n",
+            "<start_of_turn>model\n",
+            "Bien sûr ! Voici une explication du code en français :\n",
+            "\n",
+            "```python\n",
+            "def sieve_of_eratosthenes(n):\n",
+            "  \"\"\"Retourne une liste de nombres premiers jusqu'à n.\"\"\"\n",
+            "  primes = [True] * (n + 1)\n",
+            "  primes[0] = primes[1] = False\n",
+            "  for i in range(2, int(n**0.5) + 1):\n",
+            "    if primes[i]:\n",
+            "      for j in range(i * i, n + 1, i):\n",
+            "        primes[j] = False\n",
+            "  return [i for i, is_prime in enumerate(primes) if is_prime]\n",
+            "\n",
+            "primes = sieve_of_eratosthenes(1000)\n",
+            "print(primes)\n",
+            "```\n",
+            "\n",
+            "**Explication:**\n",
+            "\n",
+            "1. **Initialisation:**\n",
+            "   - `primes = [True] * (n + 1)`: Crée une liste `primes` de valeurs booléennes, initialement supposant que tous les nombres sont premiers.\n",
+            "   - `primes[0] = primes[1] = False`: Définit 0 et 1 comme non-premiers.\n",
+            "\n",
+            "2. **Itération:**\n",
+            "   - `for i in range(2, int(n**0.5) + 1):`: Itère de 2 jusqu'à la racine carrée de `n`. Nous ne devons vérifier que jusqu'à la racine carrée car tout nombre composite doit avoir un facteur premier inférieur ou égal à sa racine carrée.\n",
+            "   - `if primes[i]:`: Si `i` est considéré comme premier:\n",
+            "     - `for j in range(i * i, n + 1, i):`:  Marquer tous les multiples de `i` comme non-premiers.\n",
+            "\n",
+            "3. **Resultat:**\n",
+            "   - `return [i for i, is_prime in enumerate(primes) if is_prime]`: Crée une liste des indices où `primes[i]` est vrai, représentant les nombres premiers.\n",
+            "\n",
+            "\n",
+            "N'hésitez pas à me demander si vous avez besoin d'une explication plus détaillée de quelque chose! \n",
+            "<end_of_turn><start_of_turn>user\n",
+            "Great! Now add those explanations as comments in the code.<end_of_turn>\n",
+            "<start_of_turn>model\n",
+            "```python\n",
+            "def sieve_of_eratosthenes(n):\n",
+            "  \"\"\"Retourne une liste de nombres premiers jusqu'à n.\"\"\"\n",
+            "  # Initialise une liste de boolean avec True pour tous les nombres de 0 à n\n",
+            "  primes = [True] * (n + 1)\n",
+            "  # Définit 0 et 1 comme non-premiers\n",
+            "  primes[0] = primes[1] = False\n",
+            "  # Itère de 2 à la racine carrée de n\n",
+            "  for i in range(2, int(n**0.5) + 1):\n",
+            "    # Si i est considéré comme premier\n",
+            "    if primes[i]:\n",
+            "      # Itère sur tous les multiples de i\n",
+            "      for j in range(i * i, n + 1, i):\n",
+            "        # Définit les multiples de i comme non-premiers\n",
+            "        primes[j] = False\n",
+            "  # Retourne la liste des indices des nombres premiers\n",
+            "  return [i for i, is_prime in enumerate(primes) if is_prime]\n",
+            "\n",
+            "primes = sieve_of_eratosthenes(1000)\n",
+            "print(primes)\n",
+            "```\n",
+            "\n",
+            "**Explication:**\n",
+            "\n",
+            "* **Initialisation:**\n",
+            "    * `primes = [True] * (n + 1)`:  Crée une liste `primes` de valeurs booléennes, initialement supposant que tous les nombres sont premiers.\n",
+            "    * `primes[0] = primes[1] = False`: Définit 0 et 1 comme non-premiers.\n",
+            "* **Itération:**\n",
+            "    * `for i in range(2, int(n**0.5) + 1):`: Itère de 2 jusqu'à la racine carrée de `n`. Nous ne devons vérifier que jusqu'à la racine carrée car tout nombre composite doit avoir un facteur premier inférieur ou égal à sa racine carrée.\n",
+            "    * `if primes[i]:`: Si `i` est considéré comme premier:\n",
+            "        * `for j in range(i * i, n + 1, i):`:  Marquer tous les multiples de `i` comme non-premiers.\n",
+            "* **Resultat:**\n",
+            "    * `return [i for i, is_prime in enumerate(primes) if is_prime]`: Crée une liste des indices où `primes[i]` est vrai, représentant les nombres premiers. \n",
+            "\n",
+            "\n",
+            "\n",
+            "<end_of_turn>\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(chat.get_history())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9693c66f"
+      },
+      "source": [
+        "## Summary and further reading\n",
+        "\n",
+        "In this tutorial, you learned how to chat with the Gemma 2B Instruction tuned model using Keras on JAX.\n",
+        "\n",
+        "Check out these guides and tutorials to learn more about Gemma:\n",
+        "\n",
+        "* [Get started with Keras Gemma](https://ai.google.dev/gemma/docs/get_started).\n",
+        "* [Finetune the Gemma model on GPU](https://ai.google.dev/gemma/docs/lora_tuning).\n",
+        "* Learn about [Gemma integration with Vertex AI](https://ai.google.dev/gemma/docs/integrations/vertex)\n",
+        "* Learn how to [use Gemma models with Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma){:.external}.\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "gemma_chat.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemma/docs/integrations/langchain.ipynb b/site/en/gemma/docs/integrations/langchain.ipynb
new file mode 100644
index 000000000..d4b1599b5
--- /dev/null
+++ b/site/en/gemma/docs/integrations/langchain.ipynb
@@ -0,0 +1,994 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3MMAcssHTML"
+      },
+      "source": [
+        "<link rel=\"stylesheet\" href=\"/site-assets/css/gemma.css\">\n",
+        "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/css2?family=Google+Symbols:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200\" />"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c5b07d48d458"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemma/docs/integrations/langchain\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "    <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/integrations/langchain.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemma/docs/integrations/langchain.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3acc8f3d1408"
+      },
+      "source": [
+        "# Get started with Gemma and LangChain\n",
+        "\n",
+        "This tutorial shows you how to get started with [Gemma](https://ai.google.dev/gemma/docs) and [LangChain](https://python.langchain.com/docs/get_started/introduction), running in Google Cloud or in your Colab environment. Gemma is a family of light-weight, state-of-the-art open models built from the same research and technology used to create the Gemini models. LangChain is a framework for building and deploying context-aware applications backed by language models.\n",
+        "\n",
+        "**Note:** This tutorial runs on A100 GPU in Google Colab. Free Colab hardware acceleration is *insufficient* to run all the code."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "88TpHe7pl0sa"
+      },
+      "source": [
+        "## Run Gemma in Google Cloud\n",
+        "\n",
+        "The [`langchain-google-vertexai`](https://pypi.org/project/langchain-google-vertexai/) package provides LangChain integration with Google Cloud models."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2IxjMb9-jIJ8"
+      },
+      "source": [
+        "### Install dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XZaTsXfcheTF"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install --upgrade -q langchain langchain-google-vertexai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IyY5LtlbBVt5"
+      },
+      "source": [
+        "### Authenticate\n",
+        "\n",
+        "Unless you're using Colab Enterprise, you need to authenticate.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QO-Rr0WlBX73"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import auth\n",
+        "auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IXmAujvC3Kwp"
+      },
+      "source": [
+        "### Deploy the model\n",
+        "\n",
+        "Vertex AI is a platform for training and deploying AI models and applications. Model Garden is a curated collection of models that you can explore in the Google Cloud console.\n",
+        "\n",
+        "To deploy Gemma, [open the model](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335) in Model Garden for Vertex AI and complete the following steps:\n",
+        "\n",
+        "1. Select **Deploy**.\n",
+        "2. Make any desired changes to the deployment form fields, or leave them as\n",
+        "   is, if you're okay with the defaults. Make note of the following fields, which you'll need later:\n",
+        "   * **Endpoint name** (for example, `google_gemma-7b-it-mg-one-click-deploy`)\n",
+        "   * **Region** (for example, `us-west1`)\n",
+        "3. Select **Deploy** to deploy the model to Vertex AI. The deployment will\n",
+        "   take a few minutes to complete.\n",
+        "\n",
+        "When the endpoint is ready, copy its project ID, endpoint ID, and location, and enter them as parameters."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gv1j8FrVftsC"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Basic parameters\n",
+        "project: str = \"\"  # @param {type:\"string\"}\n",
+        "endpoint_id: str = \"\"  # @param {type:\"string\"}\n",
+        "location: str = \"\" # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a8DB3i9sO22M"
+      },
+      "source": [
+        "### Run the model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bhIHsFGYjtFt"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Prompt:\n",
+            "What is the meaning of life?\n",
+            "Output:\n",
+            "Life is a complex and multifaceted phenomenon that has fascinated philosophers, scientists, and\n"
+          ]
+        }
+      ],
+      "source": [
+        "from langchain_google_vertexai import GemmaVertexAIModelGarden, GemmaChatVertexAIModelGarden\n",
+        "\n",
+        "llm = GemmaVertexAIModelGarden(\n",
+        "    endpoint_id=endpoint_id,\n",
+        "    project=project,\n",
+        "    location=location,\n",
+        ")\n",
+        "\n",
+        "output = llm.invoke(\"What is the meaning of life?\")\n",
+        "print(output)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zzep9nfmuUcO"
+      },
+      "source": [
+        "You can also use Gemma for multi-turn chat:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8tPHoM5XiZOl"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "content='Prompt:\\n<start_of_turn>user\\nHow much is 2+2?<end_of_turn>\\n<start_of_turn>model\\nOutput:\\nSure, the answer is 4.\\n\\n2 + 2 = 4'\n",
+            "content='Prompt:\\n<start_of_turn>user\\nHow much is 2+2?<end_of_turn>\\n<start_of_turn>model\\nPrompt:\\n<start_of_turn>user\\nHow much is 2+2?<end_of_turn>\\n<start_of_turn>model\\nOutput:\\nSure, the answer is 4.\\n\\n2 + 2 = 4<end_of_turn>\\n<start_of_turn>user\\nHow much is 3+3?<end_of_turn>\\n<start_of_turn>model\\nOutput:\\nSure, the answer is 6.\\n\\n3 + 3 = 6'\n"
+          ]
+        }
+      ],
+      "source": [
+        "from langchain_core.messages import (\n",
+        "    HumanMessage\n",
+        ")\n",
+        "\n",
+        "llm = GemmaChatVertexAIModelGarden(\n",
+        "    endpoint_id=endpoint_id,\n",
+        "    project=project,\n",
+        "    location=location,\n",
+        ")\n",
+        "\n",
+        "message1 = HumanMessage(content=\"How much is 2+2?\")\n",
+        "answer1 = llm.invoke([message1])\n",
+        "print(answer1)\n",
+        "\n",
+        "message2 = HumanMessage(content=\"How much is 3+3?\")\n",
+        "answer2 = llm.invoke([message1, answer1, message2])\n",
+        "\n",
+        "print(answer2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AZL6d_ZvoI-z"
+      },
+      "source": [
+        "You can post-process responses to avoid repetitions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qXGgKAFxoI-z"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "content='Output:\\nSure, here is the answer:\\n\\n2 + 2 = 4'\n",
+            "content='Output:\\nSure, here is the answer:\\n\\n3 + 3 = 6<'\n"
+          ]
+        }
+      ],
+      "source": [
+        "answer1 = llm.invoke([message1], parse_response=True)\n",
+        "print(answer1)\n",
+        "\n",
+        "answer2 = llm.invoke([message1, answer1, message2], parse_response=True)\n",
+        "\n",
+        "print(answer2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VEfjqo7fjARR"
+      },
+      "source": [
+        "## Run Gemma from a Kaggle download"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gVW8QDzHu7TA"
+      },
+      "source": [
+        "This section shows you how to download Gemma from Kaggle and then run the model.\n",
+        "\n",
+        "To complete this section, you'll first need to complete the setup instructions at [Gemma setup](https://ai.google.dev/gemma/docs/setup).\n",
+        "\n",
+        "Then move on to the next section, where you'll set environment variables for your Colab environment.\n",
+        "\n",
+        "**Note:** This section of the tutorial runs on A100 GPU in Google Colab."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MDYfZUoxF2LE"
+      },
+      "source": [
+        "### Set environment variables\n",
+        "\n",
+        "Set environment variables for `KAGGLE_USERNAME` and `KAGGLE_KEY`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BXvwshs1GEDo"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "# Note: `userdata.get` is a Colab API. If you're not using Colab, set the env\n",
+        "# vars as appropriate for your system.\n",
+        "os.environ[\"KAGGLE_USERNAME\"] = userdata.get('KAGGLE_USERNAME')\n",
+        "os.environ[\"KAGGLE_KEY\"] = userdata.get('KAGGLE_KEY')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ezq65fi9kvRN"
+      },
+      "source": [
+        "### Install dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KrwQkHDzky9X"
+      },
+      "outputs": [],
+      "source": [
+        "# Install Keras 3 last. See https://keras.io/getting_started/ for more details.\n",
+        "!pip install -q -U keras-nlp\n",
+        "!pip install -q -U keras>=3"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E9zn8nYpv3QZ"
+      },
+      "source": [
+        "### Run the model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0LFRmY8TjCkI"
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_google_vertexai import GemmaLocalKaggle"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "v-o7oXVavdMQ"
+      },
+      "source": [
+        "You can specify the Keras backend (by default it's `tensorflow`, but you can change it to `jax` or `torch`)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "vvTUH8DNj5SF"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Basic parameters\n",
+        "keras_backend: str = \"jax\"  # @param {type:\"string\"}\n",
+        "model_name: str = \"gemma_2b_en\" # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YOmrqxo5kHXK"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Attaching 'config.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...\n",
+            "Attaching 'config.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...\n",
+            "Attaching 'model.weights.h5' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...\n",
+            "Attaching 'tokenizer.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...\n",
+            "Attaching 'assets/tokenizer/vocabulary.spm' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...\n"
+          ]
+        }
+      ],
+      "source": [
+        "llm = GemmaLocalKaggle(model_name=model_name, keras_backend=keras_backend)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Zu6yPDUgkQtQ"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "What is the meaning of life?\n",
+            "\n",
+            "The question is one of the most important questions in the world.\n",
+            "\n",
+            "It’s the question that has\n"
+          ]
+        }
+      ],
+      "source": [
+        "output = llm.invoke(\"What is the meaning of life?\", max_tokens=30)\n",
+        "print(output)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "z5VDsZkeoI-0"
+      },
+      "source": [
+        "### Run the chat model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MSctpRE4u43N"
+      },
+      "source": [
+        "As in the Google Cloud example above, you can use a local deployment of Gemma for multi-turn chat. You might need to re-start the notebook and clean your GPU memory in order to avoid OOM errors:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nXFHaE0VoI-0"
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_google_vertexai import GemmaChatLocalKaggle"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6d-QHQNroI-0"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Basic parameters\n",
+        "keras_backend: str = \"jax\"  # @param {type:\"string\"}\n",
+        "model_name: str = \"gemma_2b_en\" # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FA3DJIemoI-0"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Attaching 'config.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...\n",
+            "Attaching 'config.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...\n",
+            "Attaching 'model.weights.h5' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...\n",
+            "Attaching 'tokenizer.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...\n",
+            "Attaching 'assets/tokenizer/vocabulary.spm' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...\n"
+          ]
+        }
+      ],
+      "source": [
+        "llm = GemmaChatLocalKaggle(model_name=model_name, keras_backend=keras_backend)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JrJmvZqwwLqj"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "content=\"<start_of_turn>user\\nHi! Who are you?<end_of_turn>\\n<start_of_turn>model\\nI'm a model.\\n Tampoco\\nI'm a model.\"\n"
+          ]
+        }
+      ],
+      "source": [
+        "from langchain_core.messages import (\n",
+        "    HumanMessage\n",
+        ")\n",
+        "\n",
+        "message1 = HumanMessage(content=\"Hi! Who are you?\")\n",
+        "answer1 = llm.invoke([message1], max_tokens=30)\n",
+        "print(answer1)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NAmBDTpooI-1"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "content=\"<start_of_turn>user\\nHi! Who are you?<end_of_turn>\\n<start_of_turn>model\\n<start_of_turn>user\\nHi! Who are you?<end_of_turn>\\n<start_of_turn>model\\nI'm a model.\\n Tampoco\\nI'm a model.<end_of_turn>\\n<start_of_turn>user\\nWhat can you help me with?<end_of_turn>\\n<start_of_turn>model\"\n"
+          ]
+        }
+      ],
+      "source": [
+        "message2 = HumanMessage(content=\"What can you help me with?\")\n",
+        "answer2 = llm.invoke([message1, answer1, message2], max_tokens=60)\n",
+        "\n",
+        "print(answer2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5MuhIDoxoI-1"
+      },
+      "source": [
+        "You can post-process the response if you want to avoid multi-turn statements:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zl9J_6PHoI-1"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "content=\"I'm a model.\\n Tampoco\\nI'm a model.\"\n",
+            "content='I can help you with your modeling.\\n Tampoco\\nI can'\n"
+          ]
+        }
+      ],
+      "source": [
+        "answer1 = llm.invoke([message1], max_tokens=30, parse_response=True)\n",
+        "print(answer1)\n",
+        "\n",
+        "answer2 = llm.invoke([message1, answer1, message2], max_tokens=60, parse_response=True)\n",
+        "print(answer2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EiZnztso7hyF"
+      },
+      "source": [
+        "## Run Gemma from a Hugging Face download"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QYgBxNQssA3U"
+      },
+      "source": [
+        "### Setup\n",
+        "\n",
+        "Like Kaggle, Hugging Face requires that you accept the Gemma terms and conditions before accessing the model. To get access to Gemma through Hugging Face, go to the [Gemma model card](https://huggingface.co/google/gemma-2b).\n",
+        "\n",
+        "You'll also need to get a [user access token](https://huggingface.co/docs/hub/en/security-tokens) with read permissions, which you can enter below.\n",
+        "\n",
+        "**Note:** This section of the tutorial runs on A100 GPU in Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tsyntzI08cOr"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Basic parameters\n",
+        "hf_access_token: str = \"\"  # @param {type:\"string\"}\n",
+        "model_name: str = \"google/gemma-2b\" # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pyHNhGRasTaW"
+      },
+      "source": [
+        "### Run the model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qqAqsz5R7nKf"
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_google_vertexai import GemmaLocalHF, GemmaChatLocalHF"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JWrqEkOo8sm9"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1e03a95d82d54cae82fd8f60347d0ba4",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/1.11k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "0c9dcdea22e14cd988ce5cd7515f9e0f",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer.model:   0%|          | 0.00/4.24M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "5306530028c34909b4370b9103710f13",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/17.5M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e34a7afd64764999b9157eb8f4da4fe6",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/555 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "82fcda4f615f4ff08a235aaee0061f19",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "config.json:   0%|          | 0.00/627 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "491a26f8bfe54b88a07e31bac3c49831",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "model.safetensors.index.json:   0%|          | 0.00/13.5k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1801fbdfa9274c69ac4e21787609fd8c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ebe111457155452389394ede593962b5",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "model-00001-of-00002.safetensors:   0%|          | 0.00/4.95G [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "9f440617c8a84af197d1ca1b5b0378e6",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "model-00002-of-00002.safetensors:   0%|          | 0.00/67.1M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "983d28c1dac444c9835c860255d81464",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4f796fc0b33c48969410b1f7d0636762",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "generation_config.json:   0%|          | 0.00/137 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "llm = GemmaLocalHF(model_name=\"google/gemma-2b\", hf_access_token=hf_access_token)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VX96Jf4Y84k-"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "What is the meaning of life?\n",
+            "\n",
+            "The question is one of the most important questions in the world.\n",
+            "\n",
+            "It’s the question that has been asked by philosophers, theologians, and scientists for centuries.\n",
+            "\n",
+            "And it’s the question that\n"
+          ]
+        }
+      ],
+      "source": [
+        "output = llm.invoke(\"What is the meaning of life?\", max_tokens=50)\n",
+        "print(output)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "S3Wvie6XoI-1"
+      },
+      "source": [
+        "As in the examples above, you can use a local deployment of Gemma for multi-turn chat. You might need to re-start the notebook and clean your GPU memory in order to avoid OOM errors:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SWpWm3vgskOI"
+      },
+      "source": [
+        "### Run the chat model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9x-jmEBg9Mk1"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "254a3227573e4d909ef3f77b9c3e13dd",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "llm = GemmaChatLocalHF(model_name=model_name, hf_access_token=hf_access_token)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qv_OSaMm9PVy"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "content=\"<start_of_turn>user\\nHi! Who are you?<end_of_turn>\\n<start_of_turn>model\\nI'm a model.\\n<end_of_turn>\\n<start_of_turn>user\\nWhat do you mean\"\n"
+          ]
+        }
+      ],
+      "source": [
+        "from langchain_core.messages import (\n",
+        "    HumanMessage\n",
+        ")\n",
+        "\n",
+        "message1 = HumanMessage(content=\"Hi! Who are you?\")\n",
+        "answer1 = llm.invoke([message1], max_tokens=60)\n",
+        "print(answer1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BDuLHGNmoI-7"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "content=\"<start_of_turn>user\\nHi! Who are you?<end_of_turn>\\n<start_of_turn>model\\n<start_of_turn>user\\nHi! Who are you?<end_of_turn>\\n<start_of_turn>model\\nI'm a model.\\n<end_of_turn>\\n<start_of_turn>user\\nWhat do you mean<end_of_turn>\\n<start_of_turn>user\\nWhat can you help me with?<end_of_turn>\\n<start_of_turn>model\\nI can help you with anything.\\n<\"\n"
+          ]
+        }
+      ],
+      "source": [
+        "message2 = HumanMessage(content=\"What can you help me with?\")\n",
+        "answer2 = llm.invoke([message1, answer1, message2], max_tokens=140)\n",
+        "\n",
+        "print(answer2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_EAfKtj9oI-7"
+      },
+      "source": [
+        "As in the previous examples, you can post-process the response:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IC-w52G9oI-7"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "content=\"I'm a model.\\n<end_of_turn>\\n\"\n",
+            "content='I can help you with anything.\\n<end_of_turn>\\n<end_of_turn>\\n'\n"
+          ]
+        }
+      ],
+      "source": [
+        "answer1 = llm.invoke([message1], max_tokens=60, parse_response=True)\n",
+        "print(answer1)\n",
+        "\n",
+        "answer2 = llm.invoke([message1, answer1, message2], max_tokens=120, parse_response=True)\n",
+        "print(answer2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "s2tbOcVXs6Fa"
+      },
+      "source": [
+        "## What's next\n",
+        "\n",
+        "* Learn how to [finetune a Gemma model](https://ai.google.dev/gemma/docs/lora_tuning).\n",
+        "* Learn how to perform [distributed fine-tuning and inference on a Gemma model](https://ai.google.dev/gemma/docs/distributed_tuning).\n",
+        "* Learn how to [use Gemma models with Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma)."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "langchain.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemma/docs/jax_finetune.ipynb b/site/en/gemma/docs/jax_finetune.ipynb
new file mode 100644
index 000000000..7d3aaa230
--- /dev/null
+++ b/site/en/gemma/docs/jax_finetune.ipynb
@@ -0,0 +1,1412 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3MMAcssHTML"
+      },
+      "source": [
+        "<link rel=\"stylesheet\" href=\"/site-assets/css/gemma.css\">\n",
+        "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/css2?family=Google+Symbols:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200\" />"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "N_yUpPhqrRrK"
+      },
+      "source": [
+        "# Fine-tuning Gemma using JAX and Flax"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-yDXE-RX835U"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemma/docs/jax_finetune\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/jax_finetune.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/google/generative-ai-docs/main/site/en/gemma/docs/jax_finetune.ipynb\"><img src=\"https://ai.google.dev/images/cloud-icon.svg\" width=\"40\" />Open in Vertex AI</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemma/docs/jax_finetune.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MUnQEMHBt3nc"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "Gemma is a family of lightweight, state-of-the-art open large language models, based on the Google DeepMind Gemini research and technology. This tutorial demonstrates how to fine-tune the Gemma 2B Instruct model for an English-French translation task using [Google DeepMind's `gemma` library](https://github.com/google-deepmind/gemma), [JAX](https://jax.readthedocs.io) (a high-performance numerical computing library), [Flax](https://flax.readthedocs.io) (the JAX-based neural network library), [Chex](https://chex.readthedocs.io/en/latest/) (a library of utilities for writing reliable JAX code), [Optax](https://optax.readthedocs.io/en/latest/) (the JAX-based gradient processing and optimization library), and the [MTNT (Machine Translation of Noisy Text) dataset](https://arxiv.org/abs/1809.00388). Although Flax is not used directly in this notebook, Flax was used to create Gemma.\n",
+        "\n",
+        "The `gemma` library was written with JAX, Flax, [Orbax](https://orbax.readthedocs.io/) (a JAX-based library for training utilities like checkpointing), and [SentencePiece](https://github.com/google/sentencepiece) (a tokenizer/detokenizer library).\n",
+        "\n",
+        "**Note:** This notebook runs on A100 GPU in Google Colab. Free Colab hardware acceleration is *insufficient* to run this notebook, as it requires plenty of host memory, such as A100 GPU (available in Colab Pro) or at least Google Cloud TPU v3-8. You can use [a Kaggle VM notebook](https://www.kaggle.com/), which provides free TPU v3-8 acceleration; or [Google Cloud TPU](https://cloud.google.com/tpu?hl=en) offers TPU v3 and newer. Currently, Google Colab provides TPU v2, which is insufficient for this tutorial."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dbRLI7Q4-8Ve"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n8Ku4iK6PnC0"
+      },
+      "source": [
+        "### 1. Set up Kaggle access for Gemma\n",
+        "\n",
+        "To complete this tutorial, you first need to follow the setup instructions at [Gemma setup](https://ai.google.dev/gemma/docs/setup), which show you how to do the following:\n",
+        "\n",
+        "* Get access to Gemma on [kaggle.com](https://www.kaggle.com/models/google/gemma/).\n",
+        "* Select a Colab runtime with sufficient resources to run the Gemma model.\n",
+        "* Generate and configure a Kaggle username and API key.\n",
+        "\n",
+        "After you've completed the Gemma setup, move on to the next section, where you'll set environment variables for your Colab environment.\n",
+        "\n",
+        "### 2. Set environment variables\n",
+        "\n",
+        "Set environment variables for `KAGGLE_USERNAME` and `KAGGLE_KEY`. When prompted with the \"Grant access?\" messages, agree to provide secret access."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "AVH6Y4k2964n"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from google.colab import userdata # `userdata` is a Colab API.\n",
+        "\n",
+        "os.environ[\"KAGGLE_USERNAME\"] = userdata.get('KAGGLE_USERNAME')\n",
+        "os.environ[\"KAGGLE_KEY\"] = userdata.get('KAGGLE_KEY')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m1UE1CEnE9ql"
+      },
+      "source": [
+        "### 3. Install the `gemma` library\n",
+        "\n",
+        "Free Colab hardware acceleration is currently *insufficient* to run this notebook. If you are using [Colab Pay As You Go or Colab Pro](https://colab.research.google.com/signup), click on **Edit** > **Notebook settings** > Select **A100 GPU** > **Save** to enable hardware acceleration.\n",
+        "\n",
+        "Next, you need to install the Google DeepMind `gemma` library from [`github.com/google-deepmind/gemma`](https://github.com/google-deepmind/gemma). If you get an error about \"pip's dependency resolver\", you can usually ignore it.\n",
+        "\n",
+        "**Note:** By installing `gemma`, you will also install [`flax`](https://flax.readthedocs.io), core [`jax`](https://jax.readthedocs.io), [`optax`](https://optax.readthedocs.io/en/latest/) (the JAX-based gradient processing and optimization library), [`orbax`](https://orbax.readthedocs.io/), and [`sentencepiece`](https://github.com/google/sentencepiece)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "XpSw-_4EEcoY"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.7/133.7 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.4/244.4 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Building wheel for gemma (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "tensorflow-metadata 1.14.0 requires absl-py<2.0.0,>=0.9, but you have absl-py 2.1.0 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0m"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q git+https://github.com/google-deepmind/gemma.git"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-mRkkT-iPYoq"
+      },
+      "source": [
+        "### 4. Import libraries\n",
+        "\n",
+        "This notebook uses [Flax](https://flax.readthedocs.io) (for neural networks), core [JAX](https://jax.readthedocs.io), [SentencePiece](https://github.com/google/sentencepiece) (for tokenization), [Chex](https://chex.readthedocs.io/en/latest/) (a library of utilities for writing reliable JAX code), and TensorFlow Datasets."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "ChMf1H4mPVx_"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import enum\n",
+        "import re\n",
+        "import string\n",
+        "\n",
+        "import chex\n",
+        "import jax\n",
+        "import jax.numpy as jnp\n",
+        "import optax\n",
+        "\n",
+        "import tensorflow as tf\n",
+        "import tensorflow_datasets as tfds\n",
+        "\n",
+        "from gemma import params as params_lib\n",
+        "from gemma import sampler as sampler_lib\n",
+        "from gemma import transformer as transformer_lib\n",
+        "import sentencepiece as spm"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oNgKIkxMOsit"
+      },
+      "source": [
+        "## Load the Gemma model\n",
+        "\n",
+        "Load the Gemma model with [`kagglehub.model_download`](https://github.com/Kaggle/kagglehub/blob/bddefc718182282882b72f814d407d89e5d178c4/src/kagglehub/models.py#L12), which takes three arguments:\n",
+        "\n",
+        "- `handle`: The model handle from Kaggle\n",
+        "- `path`: (Optional string) The local path\n",
+        "- `force_download`: (Optional boolean) Forces to re-download the model\n",
+        "\n",
+        "**Note:** Be mindful that the `gemma-2b-it` model is around 3.7Gb in size."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "X-i10429N-g2"
+      },
+      "outputs": [],
+      "source": [
+        "GEMMA_VARIANT = '2b-it' # @param ['2b', '2b-it'] {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "j_QdPAGyO5zl"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/google/gemma/flax/2b-it/2/download...\n",
+            "100%|██████████| 3.67G/3.67G [00:26<00:00, 147MB/s]\n",
+            "Extracting model files...\n"
+          ]
+        }
+      ],
+      "source": [
+        "import kagglehub\n",
+        "\n",
+        "GEMMA_PATH = kagglehub.model_download(f'google/gemma/flax/{GEMMA_VARIANT}')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "cjnXlLkWcHIy"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "GEMMA_PATH: /root/.cache/kagglehub/models/google/gemma/flax/2b-it/2\n"
+          ]
+        }
+      ],
+      "source": [
+        "print('GEMMA_PATH:', GEMMA_PATH)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E1HzOpDcM04q"
+      },
+      "source": [
+        "**Note:** The path from the output above is where the model weights and tokenizer are saved locally, you will need them for later."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6ytvcJ8FPEMm"
+      },
+      "source": [
+        "Check the location of the model weights and the tokenizer, then set the path variables. The tokenizer directory will be in the main directory where you downloaded the model, while the model weights will be in a sub-directory. For example:\n",
+        "\n",
+        "- The `tokenizer.model` file will be in `/LOCAL/PATH/TO/gemma/flax/2b-it/2`).\n",
+        "- The model checkpoint will be in `/LOCAL/PATH/TO/gemma/flax/2b-it/2/2b-it`)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "JAwXvpzbuiB5"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "CKPT_PATH: /root/.cache/kagglehub/models/google/gemma/flax/2b-it/2/2b-it\n",
+            "TOKENIZER_PATH: /root/.cache/kagglehub/models/google/gemma/flax/2b-it/2/tokenizer.model\n"
+          ]
+        }
+      ],
+      "source": [
+        "CKPT_PATH = os.path.join(GEMMA_PATH, GEMMA_VARIANT)\n",
+        "TOKENIZER_PATH = os.path.join(GEMMA_PATH, 'tokenizer.model')\n",
+        "print('CKPT_PATH:', CKPT_PATH)\n",
+        "print('TOKENIZER_PATH:', TOKENIZER_PATH)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U800JRcJVIlF"
+      },
+      "source": [
+        "## Load and prepare the MTNT dataset and the Gemma tokenizer\n",
+        "\n",
+        "You will use the [MTNT (Machine Translation of Noisy Text)](https://arxiv.org/abs/1809.00388) dataset, which is available from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/mtnt).\n",
+        "\n",
+        "Download the English-to-French dataset portion of the MTNT dataset, and then sample two examples. Each sample in the dataset contains two entries: `src`: the original English sentence; and `dst`: the corresponding French translation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "pg8SfQH0EcoY"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading and preparing dataset 35.08 MiB (download: 35.08 MiB, generated: 11.33 MiB, total: 46.41 MiB) to /root/tensorflow_datasets/mtnt/en-fr/1.0.0...\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4ec9a4a2b77f41e4a7435359338b140c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Dl Completed...: 0 url [00:00, ? url/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f799eec281194b80b8f260224df50ae3",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4804ce26e0b84a5e8a9774bb5dcd1ebc",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Extraction completed...: 0 file [00:00, ? file/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "04b3ecfe7275446e816804c01da57572",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating splits...:   0%|          | 0/3 [00:00<?, ? splits/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8e2b3d7109ea47a4a28bf02f0683c50b",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train examples...:   0%|          | 0/35692 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "da26bf08c7be4a79b64e94b3f5ad028c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Shuffling /root/tensorflow_datasets/mtnt/en-fr/1.0.0.incomplete6YJMND/mtnt-train.tfrecord*...:   0%|          …"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c864366a50e94b4fbc38085f0f602dca",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating test examples...:   0%|          | 0/1020 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ad5ce2585efc45899da7b33ac0663005",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Shuffling /root/tensorflow_datasets/mtnt/en-fr/1.0.0.incomplete6YJMND/mtnt-test.tfrecord*...:   0%|          |…"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "384b494a52a54757ab3fdefee721327c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating valid examples...:   0%|          | 0/811 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "943840b94fc64a05baf9c8e057dc3b85",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Shuffling /root/tensorflow_datasets/mtnt/en-fr/1.0.0.incomplete6YJMND/mtnt-valid.tfrecord*...:   0%|          …"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Dataset mtnt downloaded and prepared to /root/tensorflow_datasets/mtnt/en-fr/1.0.0. Subsequent calls will reuse this data.\n",
+            "Example 0:\n",
+            "dst: b'Le groupe de \" toutes les \\xc3\\xa9toiles potentielles de la conf\\xc3\\xa9rence de l\\'Est mais qui ne s\\'en sortent pas dans le groupe de l\\'Ouest \".'\n",
+            "src: b'The group of \\xe2\\x80\\x9ceastern conference potential all stars but not making it in the West\\xe2\\x80\\x9d group.'\n",
+            "\n",
+            "Example 1:\n",
+            "dst: b\"Kameron est-elle un peu aigrie de son manque de temps \\xc3\\xa0 l'\\xc3\\xa9cran ?\"\n",
+            "src: b'Is Kameron a Little Salty About Her Lack of Air Time?'\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "ds = tfds.load(\"mtnt/en-fr\", split=\"train\")\n",
+        "\n",
+        "ds = ds.take(2)\n",
+        "ds = ds.as_numpy_iterator()\n",
+        "\n",
+        "for idx, example in enumerate(ds):\n",
+        "  print(f'Example {idx}:')\n",
+        "  for key, val in example.items():\n",
+        "    print(f'{key}: {val}')\n",
+        "  print()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XlY3EYV6jXWR"
+      },
+      "source": [
+        "Load the Gemma tokenizer, constructed using [`sentencepiece.SentencePieceProcessor`](https://github.com/google/sentencepiece/blob/4d6a1f41069c4636c51a5590f7578a0dbed83450/python/src/sentencepiece/__init__.py#L423):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "TpyG5YW1EcoY"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "vocab = spm.SentencePieceProcessor()\n",
+        "vocab.Load(TOKENIZER_PATH)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Mk_CV0A1kR4K"
+      },
+      "source": [
+        "Customize the[`SentencePieceProcessor`](https://github.com/google/sentencepiece/blob/4d6a1f41069c4636c51a5590f7578a0dbed83450/python/src/sentencepiece/__init__.py#L423) for the English-to-French translation task. Since you will be fine-tuning the English portion of the Gemma model, you need to make a few adjustments, such as:\n",
+        "\n",
+        "- *The input prefix*: Adding a common prefix to each input signals the translation task. For example, you could use a prompt with a prefix like `Translate this into French: [INPUT_SENTENCE]`.\n",
+        "\n",
+        "- *The translation start suffix*: Adding a suffix at the end of each prompt instructs the Gemma model exactly when to begin the translation process. A new line should do the job.\n",
+        "\n",
+        "- *Language model tokens*: Gemma models expect a \"beginning of sequence\" token at the beginning of each sequence, so adding an \"end of sequence\" token at the end of each training example should be sufficient.\n",
+        "\n",
+        "  Build a custom wrapper around the `SentencePieceProcessor` as follows:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "L9cjK0uxEcoY"
+      },
+      "outputs": [],
+      "source": [
+        "class GemmaTokenizer:\n",
+        "\n",
+        "  def __init__(self,\n",
+        "               spm_processor: spm.SentencePieceProcessor):\n",
+        "    self._spm_processor = spm_processor\n",
+        "\n",
+        "  @property\n",
+        "  def pad_id(self) -> int:\n",
+        "    \"\"\"Fast access to the pad ID.\"\"\"\n",
+        "    return self._spm_processor.pad_id()\n",
+        "\n",
+        "  def tokenize(self,\n",
+        "               example: str | bytes,\n",
+        "               prefix: str = '',\n",
+        "               suffix: str = '',\n",
+        "               add_eos: bool = True) -> jax.Array:\n",
+        "    \"\"\"\n",
+        "    The tokenization function.\n",
+        "\n",
+        "    Args:\n",
+        "      example: Input string to tokenize.\n",
+        "      prefix:  Prefix to add to the input string.\n",
+        "      suffix:  Suffix to add to the input string.\n",
+        "      add_eos: If True, add an \"end of sentence\" token at the end of the output\n",
+        "               sequence.\n",
+        "    Returns:\n",
+        "      Tokens corresponding to the input string.\n",
+        "    \"\"\"\n",
+        "    int_list = [self._spm_processor.bos_id()]\n",
+        "    int_list.extend(self._spm_processor.EncodeAsIds(prefix + example + suffix))\n",
+        "    if add_eos:\n",
+        "      int_list.append(self._spm_processor.eos_id())\n",
+        "\n",
+        "    return jnp.array(int_list, dtype=jnp.int32)\n",
+        "\n",
+        "  def tokenize_tf_op(self,\n",
+        "                     str_tensor: tf.Tensor,\n",
+        "                     prefix: str = '',\n",
+        "                     suffix: str = '',\n",
+        "                     add_eos: bool = True) -> tf.Tensor:\n",
+        "    \"\"\"A TensorFlow operator for the tokenize function.\"\"\"\n",
+        "    encoded = tf.numpy_function(\n",
+        "        self.tokenize,\n",
+        "        [str_tensor, prefix, suffix, add_eos],\n",
+        "        tf.int32)\n",
+        "    encoded.set_shape([None])\n",
+        "    return encoded\n",
+        "\n",
+        "  def to_string(self, tokens: jax.Array) -> str:\n",
+        "    \"\"\"Convert an array of tokens to a string.\"\"\"\n",
+        "    return self._spm_processor.EncodeIds(tokens.tolist())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h-oJ2ziwxG1L"
+      },
+      "source": [
+        "Try it out by instantiating your new custom `GemmaTokenizer`, and then applying it on a small sample of the MTNT dataset:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "xEA-97ioEcoY"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Example 0:\n",
+            "src: [     2  49688    736   1280   6987 235292    108    651   2778    576\n",
+            "   1080 104745  11982   5736    832   8995    901    780   3547    665\n",
+            "    575    573   4589 235369   2778 235265    108]\n",
+            "dst: [     2   2025  29653    581    664  16298   1437  55563  41435   7840\n",
+            "    581    683 111452    581    533 235303   9776   4108   2459    679\n",
+            "    485 235303    479   6728    579   1806   2499    709  29653    581\n",
+            "    533 235303 101323  16054      1]\n",
+            "\n",
+            "Example 1:\n",
+            "src: [     2  49688    736   1280   6987 235292    108   2437  87150    477\n",
+            "    476  11709 230461   8045   3636  40268    576   4252   4897 235336\n",
+            "    108]\n",
+            "dst: [     2 213606    477   1455 235290   3510    748   8268 191017   2809\n",
+            "    581   2032  69972    581  11495   1305    533 235303  65978   1654\n",
+            "      1]\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "tokenizer = GemmaTokenizer(vocab)\n",
+        "\n",
+        "def tokenize_source(tokenizer, example: tf.Tensor):\n",
+        "  return tokenizer.tokenize_tf_op(example,\n",
+        "                                  prefix='Translate this into French:\\n',\n",
+        "                                  suffix='\\n',\n",
+        "                                  add_eos=False)\n",
+        "def tokenize_destination(tokenizer, example: tf.Tensor):\n",
+        "  return tokenizer.tokenize_tf_op(example,\n",
+        "                                  add_eos=True)\n",
+        "\n",
+        "ds = tfds.load(\"mtnt/en-fr\",split=\"train\")\n",
+        "ds = ds.take(2)\n",
+        "ds = ds.map(lambda x: {'src': tokenize_source(tokenizer, x['src']),\n",
+        "                       'dst': tokenize_destination(tokenizer, x['dst'])})\n",
+        "ds = ds.as_numpy_iterator()\n",
+        "\n",
+        "for idx, example in enumerate(ds):\n",
+        "  print(f'Example {idx}:')\n",
+        "  for key, val in example.items():\n",
+        "    print(f'{key}: {val}')\n",
+        "  print()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qkY_hThVkkqF"
+      },
+      "source": [
+        "Build a data loader for the entire MTNT dataset:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "Zm30Q2lnknmG"
+      },
+      "outputs": [],
+      "source": [
+        "@chex.dataclass(frozen=True)\n",
+        "class TrainingInput:\n",
+        "  # Input tokens provided to the model.\n",
+        "  input_tokens: jax.Array\n",
+        "\n",
+        "  # A mask that determines which tokens contribute to the target loss\n",
+        "  # calculation.\n",
+        "  target_mask: jax.Array\n",
+        "\n",
+        "class DatasetSplit(enum.Enum):\n",
+        "  TRAIN = 'train'\n",
+        "  VALIDATION = 'valid'\n",
+        "\n",
+        "class MTNTDatasetBuilder:\n",
+        "  \"\"\"The dataset builder for the MTNT dataset.\"\"\"\n",
+        "\n",
+        "  N_ITEMS = {DatasetSplit.TRAIN: 35_692,\n",
+        "             DatasetSplit.VALIDATION: 811}\n",
+        "\n",
+        "  BUFFER_SIZE_SHUFFLE = 10_000\n",
+        "  TRANSLATION_PREFIX = 'Translate this into French:\\n'\n",
+        "  TRANSLATION_SUFFIX = '\\n'\n",
+        "\n",
+        "  def __init__(self,\n",
+        "               tokenizer : GemmaTokenizer,\n",
+        "               max_seq_len: int):\n",
+        "    \"\"\"Constructor.\n",
+        "\n",
+        "    Args:\n",
+        "      tokenizer: Gemma tokenizer to use.\n",
+        "      max_seq_len: size of each sequence in a given batch.\n",
+        "    \"\"\"\n",
+        "    self._tokenizer = tokenizer\n",
+        "    self._base_data = {\n",
+        "        DatasetSplit.TRAIN: tfds.load(\"mtnt/en-fr\",split=\"train\"),\n",
+        "        DatasetSplit.VALIDATION: tfds.load(\"mtnt/en-fr\",split=\"valid\"),\n",
+        "    }\n",
+        "    self._max_seq_len = max_seq_len\n",
+        "\n",
+        "  def _tokenize_source(self, example: tf.Tensor):\n",
+        "    \"\"\"Tokenization function for the source.\"\"\"\n",
+        "    return self._tokenizer.tokenize_tf_op(example,\n",
+        "                                          prefix=self.TRANSLATION_PREFIX,\n",
+        "                                          suffix=self.TRANSLATION_SUFFIX,\n",
+        "                                          add_eos=False)\n",
+        "\n",
+        "  def _tokenize_destination(self, example: tf.Tensor):\n",
+        "    \"\"\"Tokenization function for the French translation.\"\"\"\n",
+        "    return self._tokenizer.tokenize_tf_op(example,\n",
+        "                                          add_eos=True)\n",
+        "\n",
+        "  def _pad_up_to_max_len(self,\n",
+        "                         input_tensor: tf.Tensor,\n",
+        "                         pad_value: int | bool,\n",
+        "                         ) -> tf.Tensor:\n",
+        "    \"\"\"Pad the given tensor up to sequence length of a batch.\"\"\"\n",
+        "    seq_len = tf.shape(input_tensor)[0]\n",
+        "    to_pad = tf.maximum(self._max_seq_len - seq_len, 0)\n",
+        "    return tf.pad(input_tensor,\n",
+        "                  [[0, to_pad]],\n",
+        "                  mode='CONSTANT',\n",
+        "                  constant_values=pad_value,\n",
+        "                  )\n",
+        "\n",
+        "  def _to_training_input(self,\n",
+        "                         src_tokens: jax.Array,\n",
+        "                         dst_tokens: jax.Array,\n",
+        "                         ) -> TrainingInput:\n",
+        "    \"\"\"Build a training input from a tuple of source and destination tokens.\"\"\"\n",
+        "\n",
+        "    # The input sequence fed to the model is simply the concatenation of the\n",
+        "    # source and the destination.\n",
+        "    tokens = tf.concat([src_tokens, dst_tokens], axis=0)\n",
+        "\n",
+        "    # To prevent the model from updating based on the source (input)\n",
+        "    # tokens, add a target mask to each input.\n",
+        "    q_mask = tf.zeros_like(src_tokens, dtype=tf.bool)\n",
+        "    a_mask = tf.ones_like(dst_tokens, dtype=tf.bool)\n",
+        "    mask = tf.concat([q_mask, a_mask], axis=0)\n",
+        "\n",
+        "    # If the output tokens sequence is smaller than the target sequence size,\n",
+        "    # then pad it with pad tokens.\n",
+        "    tokens = self._pad_up_to_max_len(tokens, self._tokenizer.pad_id)\n",
+        "\n",
+        "    # Don't want to perform the backward pass on the pad tokens.\n",
+        "    mask = self._pad_up_to_max_len(mask, False)\n",
+        "\n",
+        "    return TrainingInput(input_tokens=tokens, target_mask=mask)\n",
+        "\n",
+        "\n",
+        "  def get_train_dataset(self, batch_size: int, num_epochs: int):\n",
+        "    \"\"\"Build the training dataset.\"\"\"\n",
+        "\n",
+        "    # Tokenize each sample.\n",
+        "    ds = self._base_data[DatasetSplit.TRAIN].map(lambda x : (self._tokenize_source(x['src']),\n",
+        "                                                             self._tokenize_destination(x['dst'])))\n",
+        "\n",
+        "    # Convert the samples to training inputs.\n",
+        "    ds = ds.map(lambda x, y: self._to_training_input(x, y))\n",
+        "\n",
+        "    # Remove the samples that are too long.\n",
+        "    ds = ds.filter(lambda x: tf.shape(x.input_tokens)[0] <= self._max_seq_len)\n",
+        "\n",
+        "    # Shuffle the dataset.\n",
+        "    ds = ds.shuffle(buffer_size=self.BUFFER_SIZE_SHUFFLE)\n",
+        "\n",
+        "    # Repeat if necessary.\n",
+        "    ds = ds.repeat(num_epochs)\n",
+        "\n",
+        "    # Build batches.\n",
+        "    ds = ds.batch(batch_size, drop_remainder=True)\n",
+        "    return ds\n",
+        "\n",
+        "  def get_validation_dataset(self, batch_size: int):\n",
+        "    \"\"\"Build the validation dataset.\"\"\"\n",
+        "\n",
+        "    # Same steps as in `get_train_dataset`, but without shuffling and no repetition.\n",
+        "    ds = self._base_data[DatasetSplit.VALIDATION].map(lambda x : (self._tokenize_source(x['src']),\n",
+        "                                                                  self._tokenize_destination(x['dst'])))\n",
+        "    ds = ds.map(lambda x, y: self._to_training_input(x, y))\n",
+        "    ds = ds.filter(lambda x: tf.shape(x.input_tokens)[0] <= self._max_seq_len)\n",
+        "    ds = ds.batch(batch_size, drop_remainder=True)\n",
+        "    return ds"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A3jRNKosyLUK"
+      },
+      "source": [
+        "Try the `MTNTDatasetBuilder` out by instantiating the custom `GemmaTokenizer` again, then applying it on the MTNT dataset, and sampling two examples:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "bYeduOaNEcoZ"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:Mapping types may not work well with tf.nest. Prefer using MutableMapping for <class '__main__.TrainingInput'>\n",
+            "WARNING:tensorflow:Mapping types may not work well with tf.nest. Prefer using MutableMapping for <class '__main__.TrainingInput'>\n",
+            "WARNING:tensorflow:Mapping types may not work well with tf.nest. Prefer using MutableMapping for <class '__main__.TrainingInput'>\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Example 0:\n",
+            "input_tokens: [[     2  49688    736   1280   6987 235292    108  10924    665  12302\n",
+            "  235341    108      2   4397  63011   1437  38696   1241      1      0]\n",
+            " [     2  49688    736   1280   6987 235292    108  13835   1517 235265\n",
+            "     108      2  69875    540  19713 235265      1      0      0      0]\n",
+            " [     2  49688    736   1280   6987 235292    108   6956   1586 235297\n",
+            "  235265    108      2  78368   1586 235297 235265      1      0      0]]\n",
+            "target_mask: [[False False False False False False False False False False False False\n",
+            "   True  True  True  True  True  True  True False]\n",
+            " [False False False False False False False False False False False  True\n",
+            "   True  True  True  True  True False False False]\n",
+            " [False False False False False False False False False False False False\n",
+            "   True  True  True  True  True  True False False]]\n",
+            "\n",
+            "Example 1:\n",
+            "input_tokens: [[     2  49688    736   1280   6987 235292    108  18874 235341    108\n",
+            "       2 115905   6425   1241      1      0      0      0      0      0]\n",
+            " [     2  49688    736   1280   6987 235292    108   7574   3356 235341\n",
+            "     108      2   7997  20707   1241      1      0      0      0      0]\n",
+            " [     2  49688    736   1280   6987 235292    108   8703    665 235265\n",
+            "     108      2 235338 235303  90006  20133 235265      1      0      0]]\n",
+            "target_mask: [[False False False False False False False False False False  True  True\n",
+            "   True  True  True False False False False False]\n",
+            " [False False False False False False False False False False False  True\n",
+            "   True  True  True  True False False False False]\n",
+            " [False False False False False False False False False False False  True\n",
+            "   True  True  True  True  True  True False False]]\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "tokenizer = GemmaTokenizer(vocab)\n",
+        "\n",
+        "dataset_builder = MTNTDatasetBuilder(tokenizer, max_seq_len=20)\n",
+        "ds = dataset_builder.get_train_dataset(3, 1)\n",
+        "ds = ds.take(2)\n",
+        "ds = ds.as_numpy_iterator()\n",
+        "\n",
+        "for idx, example in enumerate(ds):\n",
+        "  print(f'Example {idx}:')\n",
+        "  for key, val in example.items():\n",
+        "    print(f'{key}: {val}')\n",
+        "  print()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7IY8Muu1zRF4"
+      },
+      "source": [
+        "## Configure the model\n",
+        "\n",
+        "Before you begin fine-tuning the Gemma model, you need to configure it.\n",
+        "\n",
+        "First, load and format the Gemma model checkpoint with the [`gemma.params.load_and_format_params`](https://github.com/google-deepmind/gemma/blob/c6bd156c246530e1620a7c62de98542a377e3934/gemma/params.py#L27) method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "by6eWKtqzxRf"
+      },
+      "outputs": [],
+      "source": [
+        "params = params_lib.load_and_format_params(CKPT_PATH)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtJhJkkZzsy1"
+      },
+      "source": [
+        "To automatically load the correct configuration from the Gemma model checkpoint, use [`gemma.transformer.TransformerConfig`](https://github.com/google-deepmind/gemma/blob/56e501ce147af4ea5c23cc0ddf5a9c4a6b7bd0d0/gemma/transformer.py#L65). The `cache_size` argument is the number of time steps in the Gemma `Transformer` cache. Afterwards, instantiate the Gemma model as `model_2b` with [`gemma.transformer.Transformer`](https://github.com/google-deepmind/gemma/blob/56e501ce147af4ea5c23cc0ddf5a9c4a6b7bd0d0/gemma/transformer.py#L136) (which inherits from [`flax.linen.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html)).\n",
+        "\n",
+        "**Note:** The vocabulary size is smaller than the number of input embeddings because of unused tokens in the current Gemma release."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "_jjlFAkazzit"
+      },
+      "outputs": [],
+      "source": [
+        "config_2b = transformer_lib.TransformerConfig.from_params(\n",
+        "    params,\n",
+        "    cache_size=30\n",
+        ")\n",
+        "\n",
+        "model_2b = transformer_lib.Transformer(config=config_2b)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t7UL2Af536x_"
+      },
+      "source": [
+        "## Fine-tune the model\n",
+        "\n",
+        "In this section, you will:\n",
+        "\n",
+        "- Use the `gemma.transformer.Transformer` class to create the forward pass and loss function.\n",
+        "- Build the position and attention mask vectors for tokens\n",
+        "- Build a training step function with Flax.\n",
+        "- Build the validation step without the backwards pass.\n",
+        "- Create the training loop.\n",
+        "- Fine-tune the Gemma model."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aJhtJumH7H8_"
+      },
+      "source": [
+        "Define the forward pass and the loss function using the [`gemma.transformer.Transformer`](https://github.com/google-deepmind/gemma/blob/56e501ce147af4ea5c23cc0ddf5a9c4a6b7bd0d0/gemma/transformer.py#L136) class. The Gemma `Transformer` inherits from [`flax.linen.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html), and offers two essential methods:\n",
+        "\n",
+        "- `init`: Initializes the model's parameters.\n",
+        "- `apply`: Executes the model's `__call__` function using a given set of parameters.\n",
+        "\n",
+        "  Since you are working with pre-trained Gemma weights, you don't need to use the `init` function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "iEcV0XEEEcoZ"
+      },
+      "outputs": [],
+      "source": [
+        "def forward_and_loss_fn(params,\n",
+        "                        *,\n",
+        "                        model: transformer_lib.Transformer,\n",
+        "                        input_tokens: jax.Array,            # Shape [B, L]\n",
+        "                        input_mask: jax.Array,              # Shape [B, L]\n",
+        "                        positions: jax.Array,               # Shape [B, L]\n",
+        "                        attention_mask: jax.Array,          # [B, L, L]\n",
+        "                        ) -> jax.Array:\n",
+        "  \"\"\"The forward pass and the loss function.\n",
+        "\n",
+        "  Args:\n",
+        "    params: Model's input parameters.\n",
+        "    model: The Gemma transformer model to call.\n",
+        "    input_tokens: Input tokens sequence, shape [B, L].\n",
+        "    input_mask: Tokens to ignore when computing the loss, shape [B, L].\n",
+        "    positions: Relative position of each token, shape [B, L].\n",
+        "    attention_mask: Input attention mask, shape [B, L].\n",
+        "\n",
+        "  Returns:\n",
+        "    The softmax cross-entropy loss for the next-token prediction task.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  # The forward pass on the input data.\n",
+        "  # No attention cache is needed here.\n",
+        "  logits, _ = model.apply(\n",
+        "        params,\n",
+        "        input_tokens,\n",
+        "        positions,\n",
+        "        None,              # Attention cache is None.\n",
+        "        attention_mask,\n",
+        "    )\n",
+        "\n",
+        "  # Exclude the last step as it does not appear in the targets.\n",
+        "  logits = logits[0, :-1]\n",
+        "\n",
+        "  # Similarly, the first token cannot be predicted.\n",
+        "  target_tokens = input_tokens[0, 1:]\n",
+        "  target_mask = input_mask[0, 1:]\n",
+        "\n",
+        "  # Convert the target labels to one-hot encoded vectors.\n",
+        "  one_hot = jax.nn.one_hot(target_tokens, logits.shape[-1])\n",
+        "\n",
+        "  # Don't update on unwanted tokens.\n",
+        "  one_hot = one_hot * target_mask.astype(one_hot.dtype)[...,None]\n",
+        "\n",
+        "  # Define the normalization factor.\n",
+        "  norm_factor = 1 / (jnp.sum(target_mask) + 1e-8)\n",
+        "\n",
+        "  # Return the negative log likelihood (NLL) loss.\n",
+        "  return -jnp.sum(jax.nn.log_softmax(logits) * one_hot) * norm_factor"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WxbxsKcd7Ot7"
+      },
+      "source": [
+        "The [`gemma.transformer.Transformer`](https://github.com/google-deepmind/gemma/blob/56e501ce147af4ea5c23cc0ddf5a9c4a6b7bd0d0/gemma/transformer.py#L136) class requires an `attention_mask` and a `positions` vector alongside each input. You can generate these by creating a custom function that uses [`Transformer.build_positions_from_mask`](https://github.com/google-deepmind/gemma/blob/56e501ce147af4ea5c23cc0ddf5a9c4a6b7bd0d0/gemma/transformer.py#L48) and [`Transformer.make_causal_attn_mask`](https://github.com/google-deepmind/gemma/blob/56e501ce147af4ea5c23cc0ddf5a9c4a6b7bd0d0/gemma/transformer.py#L29):\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "cbWfdHf0EcoZ"
+      },
+      "outputs": [],
+      "source": [
+        "def get_attention_mask_and_positions(example: jax.Array,\n",
+        "                                     pad_id : int,\n",
+        "                                     )-> tuple[jax.Array, jax.Array]:\n",
+        "  \"\"\"Builds the position and attention mask vectors from the given tokens.\"\"\"\n",
+        "  pad_mask = example != pad_id\n",
+        "  current_token_position = transformer_lib.build_positions_from_mask(pad_mask)\n",
+        "  attention_mask = transformer_lib.make_causal_attn_mask(pad_mask)\n",
+        "  return current_token_position, attention_mask"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uRkeF6ed8tOI"
+      },
+      "source": [
+        "Build the `train_step` function that performs the backward pass and updates the model's parameters accordingly, where:\n",
+        "\n",
+        "- [`jax.value_and_grad`](https://jax.readthedocs.io/en/latest/_autosummary/jax.value_and_grad.html) is for evaluating the loss function and gradients during the forward and backward passes.\n",
+        "- [`optax.apply_updates`](https://optax.readthedocs.io/en/latest/api/apply_updates.html#optax.apply_updates) is for updating the parameters."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "cPSfp7ZUEcoZ"
+      },
+      "outputs": [],
+      "source": [
+        "def train_step(model: transformer_lib.Transformer,\n",
+        "               params,\n",
+        "               optimizer: optax.GradientTransformation,\n",
+        "               opt_state: optax.OptState,\n",
+        "               pad_id: int,\n",
+        "               example: TrainingInput):\n",
+        "  \"\"\"Train step.\n",
+        "\n",
+        "  Args:\n",
+        "    model: The Gemma transformer model.\n",
+        "    params: The model's input parameters.\n",
+        "    optimizer: The Optax optimizer to use.\n",
+        "    opt_state: The input optimizer's state.\n",
+        "    pad_id: ID of the pad token.\n",
+        "    example: Input batch.\n",
+        "\n",
+        "  Returns:\n",
+        "    The training loss, the updated parameters, and the updated optimizer state.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  # Build the position and attention mask vectors.\n",
+        "  positions, attention_mask = get_attention_mask_and_positions(example.input_tokens, pad_id)\n",
+        "\n",
+        "  # The forward and backward passes.\n",
+        "  train_loss, grads = jax.value_and_grad(forward_and_loss_fn)(params,\n",
+        "                                                             model=model,\n",
+        "                                                             input_tokens=example.input_tokens,\n",
+        "                                                             input_mask=example.target_mask,\n",
+        "                                                             positions=positions,\n",
+        "                                                             attention_mask=attention_mask)\n",
+        "  # Update the parameters.\n",
+        "  updates, opt_state = optimizer.update(grads, opt_state)\n",
+        "  params = optax.apply_updates(params, updates)\n",
+        "\n",
+        "  return train_loss, params, opt_state"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8ZKSa-jJ809n"
+      },
+      "source": [
+        "Build the `validation_step` function without the backward pass:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "yU4oR92YEcoa"
+      },
+      "outputs": [],
+      "source": [
+        "def validation_step(model: transformer_lib.Transformer,\n",
+        "                    params,\n",
+        "                    pad_id: int,\n",
+        "                    example: TrainingInput,\n",
+        "                    ):\n",
+        "  positions, attention_mask = get_attention_mask_and_positions(example.input_tokens, pad_id)\n",
+        "  val_loss = forward_and_loss_fn(params,\n",
+        "                                 model=model,\n",
+        "                                 input_tokens=example.input_tokens,\n",
+        "                                 input_mask=example.target_mask,\n",
+        "                                 positions=positions,\n",
+        "                                 attention_mask=attention_mask)\n",
+        "  return val_loss"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bNqVhj7v87f4"
+      },
+      "source": [
+        "Define the training loop using [`optax.sgd`](https://optax.readthedocs.io/en/latest/api/optimizers.html#optax.sgd) for the SGD optimizer:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "xT4bAqNLEcoa"
+      },
+      "outputs": [],
+      "source": [
+        "@chex.dataclass(frozen=True)\n",
+        "class TrainingConfig:\n",
+        "  learning_rate: float\n",
+        "  num_epochs: int\n",
+        "  eval_every_n: int\n",
+        "  batch_size: int\n",
+        "  max_steps: int | None = None\n",
+        "\n",
+        "def train_loop(\n",
+        "    model: transformer_lib.Transformer,\n",
+        "    params,\n",
+        "    dataset_builder: MTNTDatasetBuilder,\n",
+        "    training_cfg: TrainingConfig):\n",
+        "\n",
+        "  # Apply `jax.jit` on the training step, making the whole loop much more efficient.\n",
+        "  compiled_train_step = jax.jit(train_step, static_argnames=['model', 'optimizer'])\n",
+        "\n",
+        "  # Apply `jax.jit` on the validation step.\n",
+        "  compiled_validation_step = jax.jit(validation_step, static_argnames=['model'])\n",
+        "\n",
+        "  # To save memory, use the SGD optimizer instead of the usual Adam optimizer.\n",
+        "  # Note that for this specific example, SGD is more than enough.\n",
+        "  optimizer = optax.sgd(training_cfg.learning_rate)\n",
+        "  opt_state = optimizer.init(params)\n",
+        "\n",
+        "  # Build the training dataset.\n",
+        "  train_ds = dataset_builder.get_train_dataset(batch_size=training_cfg.batch_size,\n",
+        "                                               num_epochs=training_cfg.num_epochs)\n",
+        "  train_ds = train_ds.as_numpy_iterator()\n",
+        "\n",
+        "  # Build the validation dataset, with a limited number of samples for this demo.\n",
+        "  validation_ds = dataset_builder.get_validation_dataset(batch_size=training_cfg.batch_size)\n",
+        "  validation_ds = validation_ds.take(50)\n",
+        "\n",
+        "  n_steps = 0\n",
+        "  avg_loss=0\n",
+        "\n",
+        "  # A first round of the validation loss.\n",
+        "  n_steps_eval = 0\n",
+        "  eval_loss = 0\n",
+        "  val_iterator = validation_ds.as_numpy_iterator()\n",
+        "  for val_example in val_iterator:\n",
+        "    eval_loss += compiled_validation_step(model,\n",
+        "                                          params,\n",
+        "                                          dataset_builder._tokenizer.pad_id,\n",
+        "                                          val_example)\n",
+        "    n_steps_eval += 1\n",
+        "  print(f\"Start, validation loss: {eval_loss/n_steps_eval}\")\n",
+        "\n",
+        "  for train_example in train_ds:\n",
+        "    train_loss, params, opt_state = compiled_train_step(model=model,\n",
+        "                                                        params=params,\n",
+        "                                                        optimizer=optimizer,\n",
+        "                                                        opt_state=opt_state,\n",
+        "                                                        pad_id=dataset_builder._tokenizer.pad_id,\n",
+        "                                                        example=train_example)\n",
+        "    n_steps += 1\n",
+        "    avg_loss += train_loss\n",
+        "    if n_steps % training_cfg.eval_every_n == 0:\n",
+        "      eval_loss = 0\n",
+        "\n",
+        "      n_steps_eval = 0\n",
+        "      val_iterator = validation_ds.as_numpy_iterator()\n",
+        "      for val_example in val_iterator:\n",
+        "        eval_loss += compiled_validation_step(model,\n",
+        "                                              params,\n",
+        "                                              dataset_builder._tokenizer.pad_id,\n",
+        "                                              val_example)\n",
+        "        n_steps_eval +=1\n",
+        "      avg_loss /= training_cfg.eval_every_n\n",
+        "      eval_loss /= n_steps_eval\n",
+        "      print(f\"STEP {n_steps} training loss: {avg_loss} - eval loss: {eval_loss}\")\n",
+        "      avg_loss=0\n",
+        "    if training_cfg.max_steps is not None and n_steps > training_cfg.max_steps:\n",
+        "      break\n",
+        "  return params"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ecv6lp5MCzFc"
+      },
+      "source": [
+        "Begin fine-tuning the Gemma model on a limited number of steps (`SEQ_SIZE`) to make sure this fits in the memory:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "7SL2VAmVEcoa"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Start, validation loss: 10.647212982177734\n",
+            "STEP 20 training loss: 3.3015992641448975 - eval loss: 2.686880111694336\n",
+            "STEP 40 training loss: 5.375057220458984 - eval loss: 2.6751961708068848\n",
+            "STEP 60 training loss: 2.6599338054656982 - eval loss: 2.663877010345459\n",
+            "STEP 80 training loss: 4.822389125823975 - eval loss: 2.3333375453948975\n",
+            "STEP 100 training loss: 2.0131142139434814 - eval loss: 2.360811948776245\n"
+          ]
+        }
+      ],
+      "source": [
+        "SEQ_SIZE = 25\n",
+        "tokenizer = GemmaTokenizer(vocab)\n",
+        "dataset_builder= MTNTDatasetBuilder(tokenizer, SEQ_SIZE)\n",
+        "training_cfg = TrainingConfig(learning_rate=1e-4,\n",
+        "                              num_epochs=1,\n",
+        "                              eval_every_n=20,\n",
+        "                              batch_size=1,\n",
+        "                              max_steps=100)\n",
+        "\n",
+        "params = train_loop(model=model_2b,\n",
+        "                    params={'params': params['transformer']},\n",
+        "                    dataset_builder=dataset_builder,\n",
+        "                    training_cfg=training_cfg)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EtfVo3pDDAZV"
+      },
+      "source": [
+        "Both the training loss and the validation loss should have gone down with each step count.\n",
+        "\n",
+        "Create a `sampler` with [`gemma.sampler.Sampler`](https://github.com/google-deepmind/gemma/blob/56e501ce147af4ea5c23cc0ddf5a9c4a6b7bd0d0/gemma/sampler.py#L88). It uses the Gemma model checkpoint and the tokenizer."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "dQ1oCF10Ecod"
+      },
+      "outputs": [],
+      "source": [
+        "sampler = sampler_lib.Sampler(\n",
+        "    transformer=model_2b,\n",
+        "    vocab=vocab,\n",
+        "    params=params['params'],\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-61KZz7EHiIS"
+      },
+      "source": [
+        "Use the `sampler` to check if your model can perform translation. The `total_generation_steps` argument in [`gemma.sampler.Sampler`](https://github.com/google-deepmind/gemma/blob/56e501ce147af4ea5c23cc0ddf5a9c4a6b7bd0d0/gemma/sampler.py#L88) is the number of steps performed when generating a response. To ensure the input matches the training format, use the prefix `Translate this into French:\\n` with a newline character at the end. This signals the model to begin translation.\n",
+        "\n",
+        "**Note:** Due to hardware restrictions, the number of training parameters used in the gemma Transformer may not be sufficient to produce \"stable\" results in this demo.\n",
+        "\n",
+        "**Note:** If you run out of memory, click on **Runtime** > **Disconnect and delete runtime**, and then **Runtime** > **Run all**.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "S5F3fk22Ecod"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[\"C'est Bonjour, mon nom est Morgane.C'est Bonjour, mon nom est Morgane.\"]"
+            ]
+          },
+          "execution_count": 23,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "sampler(\n",
+        "    [\"Translate this into French:\\nHello, my name is Morgane.\\n\"],\n",
+        "    total_generation_steps=100,\n",
+        "    ).text"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Jao0Qk-ZIqyD"
+      },
+      "source": [
+        "## Learn more\n",
+        "\n",
+        "- You can learn more about the Google DeepMind [`gemma`  library on GitHub](https://github.com/google-deepmind/gemma), which contains docstrings of modules you used in this tutorial, such as [`gemma.params`](https://github.com/google-deepmind/gemma/blob/main/gemma/params.py),\n",
+        "[`gemma.transformer`](https://github.com/google-deepmind/gemma/blob/main/gemma/transformer.py), and\n",
+        "[`gemma.sampler`](https://github.com/google-deepmind/gemma/blob/main/gemma/sampler.py).\n",
+        "- The following libraries have their own documentation sites: [core JAX](https://jax.readthedocs.io), [Flax](https://flax.readthedocs.io), [Chex](https://chex.readthedocs.io/en/latest/), [Optax](https://optax.readthedocs.io/en/latest/), and [Orbax](https://orbax.readthedocs.io/).\n",
+        "- For `sentencepiece` tokenizer/detokenizer documentation, check out [Google's `sentencepiece` GitHub repo](https://github.com/google/sentencepiece).\n",
+        "- For `kagglehub` documentation, check out `README.md` on [Kaggle's `kagglehub` GitHub repo](https://github.com/Kaggle/kagglehub).\n",
+        "- Learn how to [use Gemma models with Google Cloud Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma).\n",
+        "- If you are using Google Cloud TPUs (v3-8 and newer), make sure to also update to the latest `jax[tpu]` package (`!pip install -U jax[tpu] -f https://storage.googleapis.com/jax-releases/libtpu_releases.html`), restart the runtime, and check that `jax` and `jaxlib` versions match (`!pip list | grep jax`). This can prevent the `RuntimeError` that can arise because of the `jaxlib` and `jax` version mismatch. For more JAX installation instructions, refer to the [JAX docs](https://jax.readthedocs.io/en/latest/tutorials/installation.html#install-google-tpu)."
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "jax_finetune.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemma/docs/jax_inference.ipynb b/site/en/gemma/docs/jax_inference.ipynb
new file mode 100644
index 000000000..67349596c
--- /dev/null
+++ b/site/en/gemma/docs/jax_inference.ipynb
@@ -0,0 +1,1294 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3MMAcssHTML"
+      },
+      "source": [
+        "<link rel=\"stylesheet\" href=\"/site-assets/css/gemma.css\">\n",
+        "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/css2?family=Google+Symbols:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200\" />"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FUOiKRSF7jc1"
+      },
+      "source": [
+        "# Inference with Gemma using JAX and Flax"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "60KmTK7o6ppd"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemma/docs/jax_inference\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/jax_inference.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/google/generative-ai-docs/main/site/en/gemma/docs/jax_inference.ipynb\"><img src=\"https://ai.google.dev/images/cloud-icon.svg\" width=\"40\" />Open in Vertex AI</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemma/docs/jax_inference.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tdlq6K0znh3O"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "Gemma is a family of lightweight, state-of-the-art open large language models, based on the Google DeepMind Gemini research and technology. This tutorial demonstrates how to perform basic sampling/inference with the Gemma 2B Instruct model using [Google DeepMind's `gemma` library](https://github.com/google-deepmind/gemma) that was written with [JAX](https://jax.readthedocs.io) (a high-performance numerical computing library), [Flax](https://flax.readthedocs.io) (the JAX-based neural network library), [Orbax](https://orbax.readthedocs.io/) (a JAX-based library for training utilities like checkpointing), and [SentencePiece](https://github.com/google/sentencepiece) (a tokenizer/detokenizer library). Although Flax is not used directly in this notebook, Flax was used to create Gemma.\n",
+        "\n",
+        "This notebook can run on Google Colab with free T4 GPU (go to **Edit** > **Notebook settings** > Under **Hardware accelerator** select **T4 GPU**)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aKvTsIkL98BG"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WCgCkmQSPxkE"
+      },
+      "source": [
+        "### 1. Set up Kaggle access for Gemma\n",
+        "\n",
+        "To complete this tutorial, you first need to follow the setup instructions at [Gemma setup](https://ai.google.dev/gemma/docs/setup), which show you how to do the following:\n",
+        "\n",
+        "* Get access to Gemma on [kaggle.com](https://www.kaggle.com/models/google/gemma/).\n",
+        "* Select a Colab runtime with sufficient resources to run the Gemma model.\n",
+        "* Generate and configure a Kaggle username and API key.\n",
+        "\n",
+        "After you've completed the Gemma setup, move on to the next section, where you'll set environment variables for your Colab environment.\n",
+        "\n",
+        "### 2. Set environment variables\n",
+        "\n",
+        "Set environment variables for `KAGGLE_USERNAME` and `KAGGLE_KEY`. When prompted with the \"Grant access?\" messages, agree to provide secret access."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lKoW-nhE-gNO"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from google.colab import userdata # `userdata` is a Colab API.\n",
+        "\n",
+        "os.environ[\"KAGGLE_USERNAME\"] = userdata.get('KAGGLE_USERNAME')\n",
+        "os.environ[\"KAGGLE_KEY\"] = userdata.get('KAGGLE_KEY')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AO7a1Q4Yyc9Z"
+      },
+      "source": [
+        "### 3. Install the `gemma` library\n",
+        "\n",
+        "This notebook focuses on using a free Colab GPU. To enable hardware acceleration, click on **Edit** > **Notebook settings** > Select **T4 GPU** > **Save**.\n",
+        "\n",
+        "Next, you need to install the Google DeepMind `gemma` library from [`github.com/google-deepmind/gemma`](https://github.com/google-deepmind/gemma). If you get an error about \"pip's dependency resolver\", you can usually ignore it.\n",
+        "\n",
+        "**Note:** By installing `gemma`, you will also install [`flax`](https://flax.readthedocs.io), core [`jax`](https://jax.readthedocs.io), [`optax`](https://optax.readthedocs.io/en/latest/) (the JAX-based gradient processing and optimization library), [`orbax`](https://orbax.readthedocs.io/), and [`sentencepiece`](https://github.com/google/sentencepiece)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WWEzVJR4Fx9g"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.7/133.7 kB\u001b[0m \u001b[31m11.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Building wheel for gemma (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q git+https://github.com/google-deepmind/gemma.git"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VKLjBAe1m3Ck"
+      },
+      "source": [
+        "## Load and prepare the Gemma model\n",
+        "\n",
+        "1. Load the Gemma model with [`kagglehub.model_download`](https://github.com/Kaggle/kagglehub/blob/bddefc718182282882b72f814d407d89e5d178c4/src/kagglehub/models.py#L12), which takes three arguments:\n",
+        "\n",
+        "- `handle`: The model handle from Kaggle\n",
+        "- `path`: (Optional string) The local path\n",
+        "- `force_download`: (Optional boolean) Forces to re-download the model\n",
+        "\n",
+        "**Note:** Be mindful that the `gemma-2b-it` model is around 3.7Gb in size."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_W3FUd9lt8VT"
+      },
+      "outputs": [],
+      "source": [
+        "GEMMA_VARIANT = 'gemma2-2b-it' # @param ['gemma2-2b', 'gemma2-2b-it'] {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kFCmWEKdMA_Y"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c5feb22ec5674243b90733e2ffb4c34c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading 11 files:   0%|          | 0/11 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/ocdbt.process_0/manifest.ocdbt...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "100%|██████████| 180/180 [00:00<00:00, 101kB/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/d/b5a4695f4be0a2f41ec1e25616ebd7e7...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "\n",
+            "100%|██████████| 2.66k/2.66k [00:00<00:00, 5.36MB/s]\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/descriptor/descriptor.pbtxt...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "100%|██████████| 45.0/45.0 [00:00<00:00, 90.0kB/s]\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/_METADATA...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "100%|██████████| 55.3k/55.3k [00:00<00:00, 29.5MB/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/_CHECKPOINT_METADATA...\n"
+          ]
+        },
+        {
+          "name": "stderr",
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+          "text": [
+            "\n",
+            "\n",
+            "100%|██████████| 92.0/92.0 [00:00<00:00, 234kB/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/ocdbt.process_0/d/bf69258061ae5f35eb7a5669fe6877d4...\n"
+          ]
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+          "name": "stderr",
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+          ]
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+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/ocdbt.process_0/d/fc20151969d7ca91ea9d8275bda0e219...\n"
+          ]
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+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/ocdbt.process_0/d/834bb4bf1e3854eb09f6208c95c071b2...\n"
+          ]
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+          ]
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+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/manifest.ocdbt...\n"
+          ]
+        },
+        {
+          "name": "stderr",
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+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/checkpoint...\n"
+          ]
+        },
+        {
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+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/tokenizer.model...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\r",
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+            "\n",
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+            " 32%|███▏      | 683M/2.12G [00:13<00:28, 54.4MB/s]\u001b[A\n",
+            "\n",
+            " 44%|████▍     | 765M/1.70G [00:13<00:12, 83.9MB/s]\u001b[A\u001b[A\n",
+            " 32%|███▏      | 690M/2.12G [00:13<00:26, 58.6MB/s]\u001b[A\n",
+            "\n",
+            " 44%|████▍     | 774M/1.70G [00:13<00:13, 77.1MB/s]\u001b[A\u001b[A\n",
+            " 32%|███▏      | 703M/2.12G [00:14<00:19, 77.2MB/s]\u001b[A\n",
+            "\n",
+            " 45%|████▌     | 786M/1.70G [00:14<00:11, 88.4MB/s]\u001b[A\u001b[A\n",
+            " 33%|███▎      | 712M/2.12G [00:14<00:20, 73.5MB/s]\u001b[A\n",
+            "\n",
+            " 46%|████▌     | 797M/1.70G [00:14<00:10, 94.8MB/s]\u001b[A\u001b[A\n",
+            " 33%|███▎      | 722M/2.12G [00:14<00:19, 79.4MB/s]\u001b[A\n",
+            "\n",
+            " 46%|████▋     | 807M/1.70G [00:14<00:10, 93.8MB/s]\u001b[A\u001b[A\n",
+            " 34%|███▍      | 731M/2.12G [00:14<00:18, 81.6MB/s]\u001b[A\n",
+            "\n",
+            " 47%|████▋     | 817M/1.70G [00:14<00:10, 90.0MB/s]\u001b[A\u001b[A\n",
+            " 34%|███▍      | 740M/2.12G [00:14<00:17, 83.8MB/s]\u001b[A\n",
+            "\n",
+            " 48%|████▊     | 829M/1.70G [00:14<00:09, 98.2MB/s]\u001b[A\u001b[A\n",
+            " 35%|███▍      | 749M/2.12G [00:14<00:17, 84.2MB/s]\u001b[A\n",
+            "\n",
+            " 48%|████▊     | 839M/1.70G [00:14<00:09, 95.3MB/s]\u001b[A\u001b[A\n",
+            " 35%|███▌      | 759M/2.12G [00:14<00:16, 89.8MB/s]\u001b[A\n",
+            " 36%|███▌      | 769M/2.12G [00:14<00:15, 93.3MB/s]\u001b[A\n",
+            "\n",
+            " 49%|████▉     | 849M/1.70G [00:14<00:09, 93.8MB/s]\u001b[A\u001b[A\n",
+            " 36%|███▌      | 780M/2.12G [00:14<00:14, 97.7MB/s]\u001b[A\n",
+            "\n",
+            " 49%|████▉     | 859M/1.70G [00:14<00:10, 91.0MB/s]\u001b[A\u001b[A\n",
+            " 37%|███▋      | 793M/2.12G [00:15<00:13, 106MB/s] \u001b[A\n",
+            "\n",
+            " 50%|████▉     | 868M/1.70G [00:14<00:10, 89.9MB/s]\u001b[A\u001b[A\n",
+            " 37%|███▋      | 804M/2.12G [00:15<00:13, 107MB/s]\u001b[A\n",
+            "\n",
+            " 50%|█████     | 877M/1.70G [00:15<00:10, 87.0MB/s]\u001b[A\u001b[A\n",
+            "\n",
+            " 51%|█████     | 886M/1.70G [00:15<00:10, 85.0MB/s]\u001b[A\u001b[A\n",
+            " 38%|███▊      | 815M/2.12G [00:15<00:16, 84.8MB/s]\u001b[A\n",
+            "\n",
+            " 51%|█████▏    | 895M/1.70G [00:15<00:12, 69.5MB/s]\u001b[A\u001b[A\n",
+            " 38%|███▊      | 824M/2.12G [00:15<00:18, 74.1MB/s]\u001b[A\n",
+            "\n",
+            " 52%|█████▏    | 904M/1.70G [00:15<00:11, 73.7MB/s]\u001b[A\u001b[A\n",
+            " 38%|███▊      | 832M/2.12G [00:15<00:18, 75.1MB/s]\u001b[A\n",
+            "\n",
+            " 52%|█████▏    | 912M/1.70G [00:15<00:11, 75.6MB/s]\u001b[A\u001b[A\n",
+            " 39%|███▉      | 843M/2.12G [00:15<00:16, 83.7MB/s]\u001b[A\n",
+            " 40%|███▉      | 856M/2.12G [00:15<00:14, 95.7MB/s]\u001b[A\n",
+            "\n",
+            " 53%|█████▎    | 921M/1.70G [00:15<00:12, 71.1MB/s]\u001b[A\u001b[A\n",
+            " 40%|███▉      | 866M/2.12G [00:15<00:13, 97.5MB/s]\u001b[A\n",
+            "\n",
+            " 53%|█████▎    | 931M/1.70G [00:15<00:10, 77.9MB/s]\u001b[A\u001b[A\n",
+            " 41%|████      | 878M/2.12G [00:16<00:12, 104MB/s] \u001b[A\n",
+            "\n",
+            " 54%|█████▍    | 939M/1.70G [00:16<00:11, 70.3MB/s]\u001b[A\u001b[A\n",
+            " 41%|████      | 889M/2.12G [00:16<00:12, 104MB/s]\u001b[A\n",
+            "\n",
+            " 55%|█████▍    | 950M/1.70G [00:16<00:10, 80.9MB/s]\u001b[A\u001b[A\n",
+            "\n",
+            " 56%|█████▌    | 967M/1.70G [00:16<00:07, 105MB/s] \u001b[A\u001b[A\n",
+            " 42%|████▏     | 900M/2.12G [00:16<00:17, 73.9MB/s]\u001b[A\n",
+            "\n",
+            " 56%|█████▌    | 978M/1.70G [00:16<00:07, 105MB/s]\u001b[A\u001b[A\n",
+            " 42%|████▏     | 909M/2.12G [00:16<00:17, 76.9MB/s]\u001b[A\n",
+            "\n",
+            " 57%|█████▋    | 989M/1.70G [00:16<00:07, 103MB/s]\u001b[A\u001b[A\n",
+            " 43%|████▎     | 921M/2.12G [00:16<00:14, 87.9MB/s]\u001b[A\n",
+            "\n",
+            " 57%|█████▋    | 0.98G/1.70G [00:16<00:07, 105MB/s]\u001b[A\u001b[A\n",
+            "\n",
+            " 58%|█████▊    | 0.99G/1.70G [00:16<00:06, 110MB/s]\u001b[A\u001b[A\n",
+            " 43%|████▎     | 931M/2.12G [00:16<00:15, 81.4MB/s]\u001b[A\n",
+            " 43%|████▎     | 940M/2.12G [00:16<00:15, 82.6MB/s]\u001b[A\n",
+            "\n",
+            " 59%|█████▊    | 1.00G/1.70G [00:16<00:09, 76.3MB/s]\u001b[A\u001b[A\n",
+            " 44%|████▍     | 949M/2.12G [00:17<00:18, 70.7MB/s]\u001b[A\n",
+            " 44%|████▍     | 957M/2.12G [00:17<00:18, 67.3MB/s]\u001b[A\n",
+            "\n",
+            " 59%|█████▉    | 1.01G/1.70G [00:17<00:11, 64.5MB/s]\u001b[A\u001b[A\n",
+            " 45%|████▍     | 964M/2.12G [00:17<00:19, 65.9MB/s]\u001b[A\n",
+            "\n",
+            " 60%|█████▉    | 1.02G/1.70G [00:17<00:11, 64.5MB/s]\u001b[A\u001b[A\n",
+            " 45%|████▍     | 971M/2.12G [00:17<00:19, 65.2MB/s]\u001b[A\n",
+            "\n",
+            " 60%|██████    | 1.02G/1.70G [00:17<00:11, 63.6MB/s]\u001b[A\u001b[A\n",
+            " 45%|████▌     | 978M/2.12G [00:17<00:19, 63.6MB/s]\u001b[A\n",
+            "\n",
+            " 61%|██████    | 1.03G/1.70G [00:17<00:11, 62.9MB/s]\u001b[A\u001b[A\n",
+            " 45%|████▌     | 985M/2.12G [00:17<00:19, 64.7MB/s]\u001b[A\n",
+            "\n",
+            " 61%|██████    | 1.04G/1.70G [00:17<00:11, 62.8MB/s]\u001b[A\u001b[A\n",
+            " 46%|████▌     | 992M/2.12G [00:17<00:18, 65.3MB/s]\u001b[A\n",
+            "\n",
+            " 61%|██████▏   | 1.04G/1.70G [00:17<00:10, 64.5MB/s]\u001b[A\u001b[A\n",
+            " 46%|████▌     | 0.98G/2.12G [00:17<00:17, 68.9MB/s]\u001b[A\n",
+            "\n",
+            " 62%|██████▏   | 1.05G/1.70G [00:17<00:10, 66.5MB/s]\u001b[A\u001b[A\n",
+            " 46%|████▋     | 0.98G/2.12G [00:18<00:17, 69.8MB/s]\u001b[A\n",
+            "\n",
+            " 62%|██████▏   | 1.06G/1.70G [00:17<00:10, 67.7MB/s]\u001b[A\u001b[A\n",
+            " 47%|████▋     | 0.99G/2.12G [00:18<00:16, 73.2MB/s]\u001b[A\n",
+            "\n",
+            " 63%|██████▎   | 1.07G/1.70G [00:18<00:09, 69.0MB/s]\u001b[A\u001b[A\n",
+            " 47%|████▋     | 1.00G/2.12G [00:18<00:16, 72.2MB/s]\u001b[A\n",
+            "\n",
+            " 63%|██████▎   | 1.07G/1.70G [00:18<00:09, 68.5MB/s]\u001b[A\u001b[A\n",
+            " 48%|████▊     | 1.01G/2.12G [00:18<00:16, 73.8MB/s]\u001b[A\n",
+            "\n",
+            " 63%|██████▎   | 1.08G/1.70G [00:18<00:09, 69.1MB/s]\u001b[A\u001b[A\n",
+            " 48%|████▊     | 1.01G/2.12G [00:18<00:18, 63.5MB/s]\u001b[A\n",
+            "\n",
+            " 64%|██████▍   | 1.09G/1.70G [00:18<00:10, 61.4MB/s]\u001b[A\u001b[A\n",
+            " 48%|████▊     | 1.02G/2.12G [00:18<00:20, 57.1MB/s]\u001b[A\n",
+            "\n",
+            " 64%|██████▍   | 1.09G/1.70G [00:18<00:11, 55.9MB/s]\u001b[A\u001b[A\n",
+            " 49%|████▊     | 1.03G/2.12G [00:18<00:20, 57.9MB/s]\u001b[A\n",
+            "\n",
+            " 65%|██████▍   | 1.10G/1.70G [00:18<00:11, 56.5MB/s]\u001b[A\u001b[A\n",
+            " 49%|████▉     | 1.04G/2.12G [00:18<00:18, 63.5MB/s]\u001b[A\n",
+            "\n",
+            " 65%|██████▍   | 1.10G/1.70G [00:18<00:11, 57.4MB/s]\u001b[A\u001b[A\n",
+            " 49%|████▉     | 1.04G/2.12G [00:19<00:18, 63.4MB/s]\u001b[A\n",
+            "\n",
+            " 65%|██████▌   | 1.11G/1.70G [00:18<00:10, 58.5MB/s]\u001b[A\u001b[A\n",
+            " 50%|████▉     | 1.05G/2.12G [00:19<00:17, 64.7MB/s]\u001b[A\n",
+            "\n",
+            " 66%|██████▌   | 1.12G/1.70G [00:19<00:10, 61.8MB/s]\u001b[A\u001b[A\n",
+            " 50%|████▉     | 1.06G/2.12G [00:19<00:16, 67.6MB/s]\u001b[A\n",
+            "\n",
+            " 66%|██████▌   | 1.12G/1.70G [00:19<00:09, 63.1MB/s]\u001b[A\u001b[A\n",
+            " 50%|█████     | 1.06G/2.12G [00:19<00:15, 70.6MB/s]\u001b[A\n",
+            "\n",
+            " 67%|██████▋   | 1.13G/1.70G [00:19<00:08, 69.1MB/s]\u001b[A\u001b[A\n",
+            " 51%|█████     | 1.07G/2.12G [00:19<00:15, 72.6MB/s]\u001b[A\n",
+            "\n",
+            " 67%|██████▋   | 1.14G/1.70G [00:19<00:08, 71.4MB/s]\u001b[A\u001b[A\n",
+            " 51%|█████     | 1.08G/2.12G [00:19<00:15, 73.6MB/s]\u001b[A\n",
+            "\n",
+            " 68%|██████▊   | 1.15G/1.70G [00:19<00:07, 74.3MB/s]\u001b[A\u001b[A\n",
+            " 51%|█████▏    | 1.09G/2.12G [00:19<00:14, 76.0MB/s]\u001b[A\n",
+            "\n",
+            " 68%|██████▊   | 1.16G/1.70G [00:19<00:07, 74.0MB/s]\u001b[A\u001b[A\n",
+            " 52%|█████▏    | 1.10G/2.12G [00:19<00:15, 69.1MB/s]\u001b[A\n",
+            "\n",
+            " 69%|██████▊   | 1.17G/1.70G [00:19<00:08, 69.9MB/s]\u001b[A\u001b[A\n",
+            " 52%|█████▏    | 1.10G/2.12G [00:19<00:15, 70.8MB/s]\u001b[A\n",
+            "\n",
+            " 69%|██████▉   | 1.17G/1.70G [00:19<00:08, 70.0MB/s]\u001b[A\u001b[A\n",
+            " 52%|█████▏    | 1.11G/2.12G [00:20<00:16, 66.4MB/s]\u001b[A\n",
+            "\n",
+            " 69%|██████▉   | 1.18G/1.70G [00:19<00:09, 60.2MB/s]\u001b[A\u001b[A\n",
+            "\n",
+            " 70%|██████▉   | 1.19G/1.70G [00:20<00:08, 62.5MB/s]\u001b[A\u001b[A\n",
+            " 53%|█████▎    | 1.12G/2.12G [00:20<00:19, 54.8MB/s]\u001b[A\n",
+            "\n",
+            " 70%|███████   | 1.19G/1.70G [00:20<00:08, 63.8MB/s]\u001b[A\u001b[A\n",
+            " 53%|█████▎    | 1.12G/2.12G [00:20<00:18, 57.7MB/s]\u001b[A\n",
+            "\n",
+            " 71%|███████   | 1.20G/1.70G [00:20<00:08, 64.8MB/s]\u001b[A\u001b[A\n",
+            " 53%|█████▎    | 1.13G/2.12G [00:20<00:17, 61.3MB/s]\u001b[A\n",
+            "\n",
+            " 71%|███████   | 1.21G/1.70G [00:20<00:07, 68.3MB/s]\u001b[A\u001b[A\n",
+            " 54%|█████▍    | 1.14G/2.12G [00:20<00:16, 65.0MB/s]\u001b[A\n",
+            "\n",
+            " 71%|███████▏  | 1.21G/1.70G [00:20<00:07, 66.1MB/s]\u001b[A\u001b[A\n",
+            " 54%|█████▍    | 1.15G/2.12G [00:20<00:16, 64.4MB/s]\u001b[A\n",
+            "\n",
+            " 72%|███████▏  | 1.22G/1.70G [00:20<00:07, 66.4MB/s]\u001b[A\u001b[A\n",
+            " 54%|█████▍    | 1.15G/2.12G [00:20<00:16, 63.0MB/s]\u001b[A\n",
+            "\n",
+            " 72%|███████▏  | 1.23G/1.70G [00:20<00:08, 61.2MB/s]\u001b[A\u001b[A\n",
+            " 55%|█████▍    | 1.16G/2.12G [00:20<00:16, 61.6MB/s]\u001b[A\n",
+            "\n",
+            " 73%|███████▎  | 1.23G/1.70G [00:20<00:08, 58.0MB/s]\u001b[A\u001b[A\n",
+            " 55%|█████▌    | 1.17G/2.12G [00:21<00:16, 61.0MB/s]\u001b[A\n",
+            " 55%|█████▌    | 1.17G/2.12G [00:22<01:11, 14.1MB/s]\u001b[A\n",
+            "\n",
+            " 73%|███████▎  | 1.24G/1.70G [00:22<00:37, 13.4MB/s]\u001b[A\u001b[A\n",
+            " 56%|█████▌    | 1.18G/2.12G [00:22<00:51, 19.6MB/s]\u001b[A\n",
+            "\n",
+            " 73%|███████▎  | 1.25G/1.70G [00:22<00:23, 20.3MB/s]\u001b[A\u001b[A\n",
+            " 56%|█████▌    | 1.19G/2.12G [00:22<00:42, 23.4MB/s]\u001b[A\n",
+            "\n",
+            " 74%|███████▍  | 1.25G/1.70G [00:22<00:19, 24.2MB/s]\u001b[A\u001b[A\n",
+            "\n",
+            " 74%|███████▍  | 1.26G/1.70G [00:22<00:13, 34.0MB/s]\u001b[A\u001b[A\n",
+            "\n",
+            " 75%|███████▌  | 1.28G/1.70G [00:22<00:08, 50.6MB/s]\u001b[A\u001b[A\n",
+            "\n",
+            " 76%|███████▌  | 1.29G/1.70G [00:22<00:07, 59.8MB/s]\u001b[A\u001b[A\n",
+            " 57%|█████▋    | 1.20G/2.12G [00:23<00:35, 27.6MB/s]\u001b[A\n",
+            " 57%|█████▋    | 1.21G/2.12G [00:23<00:26, 36.3MB/s]\u001b[A\n",
+            "\n",
+            " 76%|███████▋  | 1.30G/1.70G [00:23<00:07, 57.6MB/s]\u001b[A\u001b[A\n",
+            "\n",
+            " 77%|███████▋  | 1.31G/1.70G [00:23<00:06, 62.4MB/s]\u001b[A\u001b[A\n",
+            " 57%|█████▋    | 1.21G/2.12G [00:23<00:27, 35.1MB/s]\u001b[A\n",
+            " 58%|█████▊    | 1.22G/2.12G [00:23<00:24, 38.9MB/s]\u001b[A\n",
+            "\n",
+            " 77%|███████▋  | 1.31G/1.70G [00:23<00:07, 53.2MB/s]\u001b[A\u001b[A\n",
+            " 58%|█████▊    | 1.23G/2.12G [00:23<00:22, 43.0MB/s]\u001b[A\n",
+            "\n",
+            " 78%|███████▊  | 1.32G/1.70G [00:23<00:06, 61.9MB/s]\u001b[A\u001b[A\n",
+            "\n",
+            " 78%|███████▊  | 1.33G/1.70G [00:23<00:05, 68.7MB/s]\u001b[A\u001b[A\n",
+            " 58%|█████▊    | 1.24G/2.12G [00:23<00:20, 46.8MB/s]\u001b[A\n",
+            "\n",
+            " 79%|███████▉  | 1.34G/1.70G [00:23<00:05, 72.2MB/s]\u001b[A\u001b[A\n",
+            " 59%|█████▉    | 1.24G/2.12G [00:23<00:20, 46.0MB/s]\u001b[A\n",
+            "\n",
+            " 80%|███████▉  | 1.35G/1.70G [00:23<00:04, 77.2MB/s]\u001b[A\u001b[A\n",
+            "\n",
+            " 80%|███████▉  | 1.36G/1.70G [00:23<00:04, 74.6MB/s]\u001b[A\u001b[A\n",
+            " 59%|█████▉    | 1.25G/2.12G [00:24<00:19, 47.6MB/s]\u001b[A\n",
+            "\n",
+            " 81%|████████  | 1.37G/1.70G [00:24<00:04, 79.9MB/s]\u001b[A\u001b[A\n",
+            " 60%|█████▉    | 1.26G/2.12G [00:24<00:16, 56.2MB/s]\u001b[A\n",
+            "\n",
+            " 81%|████████  | 1.38G/1.70G [00:24<00:03, 88.6MB/s]\u001b[A\u001b[A\n",
+            " 60%|█████▉    | 1.27G/2.12G [00:24<00:18, 48.6MB/s]\u001b[A\n",
+            "\n",
+            " 82%|████████▏ | 1.39G/1.70G [00:24<00:03, 87.9MB/s]\u001b[A\u001b[A\n",
+            " 60%|██████    | 1.27G/2.12G [00:24<00:16, 55.1MB/s]\u001b[A\n",
+            "\n",
+            " 82%|████████▏ | 1.40G/1.70G [00:24<00:03, 81.4MB/s]\u001b[A\u001b[A\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
+            " 96%|█████████▋| 1.64G/1.70G [00:27<00:00, 87.5MB/s]\u001b[A\u001b[A\n",
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+            "\n",
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+            "\n",
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+            "\n",
+            " 98%|█████████▊| 1.67G/1.70G [00:27<00:00, 82.3MB/s]\u001b[A\u001b[A\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            "\n",
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+            " 97%|█████████▋| 2.05G/2.12G [00:33<00:00, 108MB/s] \u001b[A\n",
+            " 98%|█████████▊| 2.06G/2.12G [00:33<00:00, 89.9MB/s]\u001b[A\n",
+            " 98%|█████████▊| 2.08G/2.12G [00:34<00:00, 103MB/s] \u001b[A\n",
+            " 99%|█████████▉| 2.09G/2.12G [00:34<00:00, 115MB/s]\u001b[A\n",
+            "100%|██████████| 2.12G/2.12G [00:34<00:00, 66.0MB/s]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import kagglehub\n",
+        "\n",
+        "GEMMA_PATH = kagglehub.model_download(f'google/gemma-2/flax/{GEMMA_VARIANT}')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nYmYTMk8aELi"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "GEMMA_PATH: /root/.cache/kagglehub/models/google/gemma-2-2b/flax/gemma2-2b-it/1\n"
+          ]
+        }
+      ],
+      "source": [
+        "print('GEMMA_PATH:', GEMMA_PATH)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ytNi47xSlw71"
+      },
+      "source": [
+        "**Note:** The path from the output above is where the model weights and tokenizer are saved locally, you will need them for later."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "92BcvYdemXbd"
+      },
+      "source": [
+        "2. Check the location of the model weights and the tokenizer, then set the path variables. The tokenizer directory will be in the main directory where you downloaded the model, while the model weights will be in a sub-directory. For example:\n",
+        "\n",
+        "- The `tokenizer.model` file will be in `/LOCAL/PATH/TO/gemma/flax/2b-it/2`).\n",
+        "- The model checkpoint will be in `/LOCAL/PATH/TO/gemma/flax/2b-it/2/2b-it`)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QY6OnASOpZbW"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "CKPT_PATH: /root/.cache/kagglehub/models/google/gemma-2-2b/flax/gemma2-2b-it/1/gemma2-2b-it\n",
+            "TOKENIZER_PATH: /root/.cache/kagglehub/models/google/gemma-2-2b/flax/gemma2-2b-it/1/tokenizer.model\n"
+          ]
+        }
+      ],
+      "source": [
+        "CKPT_PATH = os.path.join(GEMMA_PATH, GEMMA_VARIANT)\n",
+        "TOKENIZER_PATH = os.path.join(GEMMA_PATH, 'tokenizer.model')\n",
+        "print('CKPT_PATH:', CKPT_PATH)\n",
+        "print('TOKENIZER_PATH:', TOKENIZER_PATH)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jc0ZzYIW0TSN"
+      },
+      "source": [
+        "## Perform sampling/inference"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aEe3p8geqekV"
+      },
+      "source": [
+        "1. Load and format the Gemma model checkpoint with the [`gemma.params.load_and_format_params`](https://github.com/google-deepmind/gemma/blob/c6bd156c246530e1620a7c62de98542a377e3934/gemma/params.py#L27) method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Mnr52JQVqKRw"
+      },
+      "outputs": [],
+      "source": [
+        "from gemma import params as params_lib\n",
+        "\n",
+        "params = params_lib.load_and_format_params(CKPT_PATH)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-Xpnb2igrGjk"
+      },
+      "source": [
+        "2. Load the Gemma tokenizer, constructed using [`sentencepiece.SentencePieceProcessor`](https://github.com/google/sentencepiece/blob/4d6a1f41069c4636c51a5590f7578a0dbed83450/python/src/sentencepiece/__init__.py#L423):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-T0ZHff5rNSy"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import sentencepiece as spm\n",
+        "\n",
+        "vocab = spm.SentencePieceProcessor()\n",
+        "vocab.Load(TOKENIZER_PATH)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IkAf4fkNrY-3"
+      },
+      "source": [
+        "3. To automatically load the correct configuration from the Gemma model checkpoint, use [`gemma.transformer.TransformerConfig`](https://github.com/google-deepmind/gemma/blob/56e501ce147af4ea5c23cc0ddf5a9c4a6b7bd0d0/gemma/transformer.py#L65). The `cache_size` argument is the number of time steps in the Gemma `Transformer` cache. Afterwards, instantiate the Gemma model as `transformer` with [`gemma.transformer.Transformer`](https://github.com/google-deepmind/gemma/blob/56e501ce147af4ea5c23cc0ddf5a9c4a6b7bd0d0/gemma/transformer.py#L136) (which inherits from [`flax.linen.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html)).\n",
+        "\n",
+        "**Note:** The vocabulary size is smaller than the number of input embeddings because of unused tokens in the current Gemma release."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4PNWxDhvrRXJ"
+      },
+      "outputs": [],
+      "source": [
+        "from gemma import transformer as transformer_lib\n",
+        "\n",
+        "transformer_config = transformer_lib.TransformerConfig.from_params(\n",
+        "    params=params,\n",
+        "    cache_size=1024\n",
+        ")\n",
+        "\n",
+        "transformer = transformer_lib.Transformer(transformer_config)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vs0vgmXVroBq"
+      },
+      "source": [
+        "3. Create a `sampler` with [`gemma.sampler.Sampler`](https://github.com/google-deepmind/gemma/blob/c6bd156c246530e1620a7c62de98542a377e3934/gemma/sampler.py#L88) on top of the Gemma model checkpoint/weights and the tokenizer:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4GX4pFP6rtyN"
+      },
+      "outputs": [],
+      "source": [
+        "from gemma import sampler as sampler_lib\n",
+        "\n",
+        "sampler = sampler_lib.Sampler(\n",
+        "    transformer=transformer,\n",
+        "    vocab=vocab,\n",
+        "    params=params['transformer'],\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "V9yU99Xxr59w"
+      },
+      "source": [
+        "4. Write a prompt in `input_batch` and perform inference. You can tweak `total_generation_steps` (the number of steps performed when generating a response — this example uses `100` to preserve host memory).\n",
+        "\n",
+        "**Note:** If you run out of memory, click on **Runtime** > **Disconnect and delete runtime**, and then **Runtime** > **Run all**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Gj9jRFI5Hrv2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Prompt:\n",
+            "what is JAX in 3 bullet points?\n",
+            "Output:\n",
+            "\n",
+            "\n",
+            "* **High-performance numerical computation:** JAX leverages the power of GPUs and TPUs to accelerate complex mathematical operations, making it ideal for scientific computing, machine learning, and data analysis.\n",
+            "* **Automatic differentiation:** JAX provides automatic differentiation capabilities, allowing you to compute gradients and optimize models efficiently. This simplifies the process of training deep learning models.\n",
+            "* **Functional programming:** JAX embraces functional programming principles, promoting code readability and maintainability. It offers a flexible and expressive syntax for defining and manipulating data. \n",
+            "\n",
+            "\n",
+            "<end_of_turn>\n"
+          ]
+        }
+      ],
+      "source": [
+        "prompt = [\n",
+        "    \"what is JAX in 3 bullet points?\",\n",
+        "]\n",
+        "\n",
+        "reply = sampler(input_strings=prompt,\n",
+        "                total_generation_steps=128,\n",
+        "                )\n",
+        "\n",
+        "for input_string, out_string in zip(prompt, reply.text):\n",
+        "    print(f\"Prompt:\\n{input_string}\\nOutput:\\n{out_string}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "njxRJy3qsBWw"
+      },
+      "source": [
+        "5. (Optional) Run this cell to free up memory if you have completed the notebook and want to try another prompt. Afterwards, you can instantiate the `sampler` again in step 3 and customize and run the prompt in step 4."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qxX6qfFdNGHy"
+      },
+      "outputs": [],
+      "source": [
+        "del sampler"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bzKsCGIN0yX5"
+      },
+      "source": [
+        "## Learn more\n",
+        "\n",
+        "- You can learn more about the Google DeepMind [`gemma`  library on GitHub](https://github.com/google-deepmind/gemma), which contains docstrings of modules you used in this tutorial, such as [`gemma.params`](https://github.com/google-deepmind/gemma/blob/main/gemma/params.py),\n",
+        "[`gemma.transformer`](https://github.com/google-deepmind/gemma/blob/main/gemma/transformer.py), and\n",
+        "[`gemma.sampler`](https://github.com/google-deepmind/gemma/blob/main/gemma/sampler.py).\n",
+        "- The following libraries have their own documentation sites: [core JAX](https://jax.readthedocs.io), [Flax](https://flax.readthedocs.io), and [Orbax](https://orbax.readthedocs.io/).\n",
+        "- For `sentencepiece` tokenizer/detokenizer documentation, check out [Google's `sentencepiece` GitHub repo](https://github.com/google/sentencepiece).\n",
+        "- For `kagglehub` documentation, check out `README.md` on [Kaggle's `kagglehub` GitHub repo](https://github.com/Kaggle/kagglehub).\n",
+        "- Learn how to [use Gemma models with Google Cloud Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma)."
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "jax_inference.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemma/docs/keras_inference.ipynb b/site/en/gemma/docs/keras_inference.ipynb
new file mode 100644
index 000000000..bacf75f4c
--- /dev/null
+++ b/site/en/gemma/docs/keras_inference.ipynb
@@ -0,0 +1,609 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3MMAcssHTML"
+      },
+      "source": [
+        "<link rel=\"stylesheet\" href=\"/site-assets/css/gemma.css\">\n",
+        "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/css2?family=Google+Symbols:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200\" />"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4qxv4Sn9b8CE"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemma/docs/keras_inference\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "    <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/keras_inference.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/google/generative-ai-docs/main/site/en/gemma/docs/keras_inference.ipynb\"><img src=\"https://ai.google.dev/images/cloud-icon.svg\" width=\"40\" />Open in Vertex AI</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemma/docs/keras_inference.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PXNm5_p_oxMF"
+      },
+      "source": [
+        "# Get started with Gemma using KerasNLP\n",
+        "\n",
+        "This tutorial shows you how to get started with Gemma using [KerasNLP](https://keras.io/keras_nlp/). Gemma is a family of lightweight, state-of-the art open models built from the same research and technology used to create the Gemini models. KerasNLP is a collection of natural language processing (NLP) models implemented in [Keras](https://keras.io/) and runnable on JAX, PyTorch, and TensorFlow.\n",
+        "\n",
+        "In this tutorial, you'll use Gemma to generate text responses to several prompts. If you're new to Keras, you might want to read [Getting started with Keras](https://keras.io/getting_started/) before you begin, but you don't have to. You'll learn more about Keras as you work through this tutorial."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mERVCCsGUPIJ"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QQ6W7NzRe1VM"
+      },
+      "source": [
+        "### Gemma setup\n",
+        "\n",
+        "To complete this tutorial, you'll first need to complete the setup instructions at [Gemma setup](https://ai.google.dev/gemma/docs/setup). The Gemma setup instructions show you how to do the following:\n",
+        "\n",
+        "* Get access to Gemma on kaggle.com.\n",
+        "* Select a Colab runtime with sufficient resources to run\n",
+        "  the Gemma 2B model.\n",
+        "* Generate and configure a Kaggle username and API key.\n",
+        "\n",
+        "After you've completed the Gemma setup, move on to the next section, where you'll set environment variables for your Colab environment."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_gN-IVRC3dQe"
+      },
+      "source": [
+        "### Set environment variables\n",
+        "\n",
+        "Set environment variables for `KAGGLE_USERNAME` and `KAGGLE_KEY`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "DrBoa_Urw9Vx"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "# Note: `userdata.get` is a Colab API. If you're not using Colab, set the env\n",
+        "# vars as appropriate for your system.\n",
+        "os.environ[\"KAGGLE_USERNAME\"] = userdata.get('KAGGLE_USERNAME')\n",
+        "os.environ[\"KAGGLE_KEY\"] = userdata.get('KAGGLE_KEY')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "z9oy3QUmXtSd"
+      },
+      "source": [
+        "### Install dependencies\n",
+        "\n",
+        "Install Keras and KerasNLP."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UcGLzDeQ8NwN"
+      },
+      "outputs": [],
+      "source": [
+        "# Install Keras 3 last. See https://keras.io/getting_started/ for more details.\n",
+        "!pip install -q -U keras-nlp\n",
+        "!pip install -q -U \"keras>=3\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Pm5cVOFt5YvZ"
+      },
+      "source": [
+        "### Select a backend\n",
+        "\n",
+        "Keras is a high-level, multi-framework deep learning API designed for simplicity and ease of use. [Keras 3](https://keras.io/keras_3) lets you choose the backend: TensorFlow, JAX, or PyTorch. All three will work for this tutorial."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "7rS7ryTs5wjf"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "os.environ[\"KERAS_BACKEND\"] = \"jax\"  # Or \"tensorflow\" or \"torch\".\n",
+        "os.environ[\"XLA_PYTHON_CLIENT_MEM_FRACTION\"] = \"0.9\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "599765c72722"
+      },
+      "source": [
+        "### Import packages\n",
+        "\n",
+        "Import Keras and KerasNLP."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "f2fa267d75bc"
+      },
+      "outputs": [],
+      "source": [
+        "import keras\n",
+        "import keras_nlp"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZsxDCbLN555T"
+      },
+      "source": [
+        "## Create a model\n",
+        "\n",
+        "KerasNLP provides implementations of many popular [model architectures](https://keras.io/api/keras_nlp/models/). In this tutorial, you'll create a model using `GemmaCausalLM`, an end-to-end Gemma model for causal language modeling. A causal language model predicts the next token based on previous tokens.\n",
+        "\n",
+        "Create the model using the `from_preset` method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yygIK9DEIldp"
+      },
+      "outputs": [],
+      "source": [
+        "gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(\"gemma2_2b_en\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XrAWvsU6pI0E"
+      },
+      "source": [
+        "The `GemmaCausalLM.from_preset()` function instantiates the model from a preset architecture and weights. In the code above, the string `\"gemma2_2b_en\"` specifies the preset the Gemma 2 2B model with 2 billion parameters. Gemma models with [7B, 9B, and 27B parameters](/gemma/docs/get_started#models-list) are also available. You can find the code strings for Gemma models in their **Model Variation** listings on [Kaggle](https://www.kaggle.com/models/google/gemma).\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ij73k0PfUhjE"
+      },
+      "source": [
+        "Note: To run the larger models in Colab, you need access to the premium GPUs available in paid plans. Alternatively, you can perform inferences using Kaggle notebooks or Google Cloud projects.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E-cSEjULUhST"
+      },
+      "source": [
+        "Use `summary` to get more info about the model:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "e5nEbTdApL7W"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Preprocessor: \"gemma_causal_lm_preprocessor\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mPreprocessor: \"gemma_causal_lm_preprocessor\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Tokenizer (type)                                   </span>┃<span style=\"font-weight: bold\">                                             Vocab # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ gemma_tokenizer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaTokenizer</span>)                   │                                             <span style=\"color: #00af00; text-decoration-color: #00af00\">256,000</span> │\n",
+              "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mTokenizer (type)                                  \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m                                            Vocab #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ gemma_tokenizer (\u001b[38;5;33mGemmaTokenizer\u001b[0m)                   │                                             \u001b[38;5;34m256,000\u001b[0m │\n",
+              "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"gemma_causal_lm\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"gemma_causal_lm\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                  </span>┃<span style=\"font-weight: bold\"> Output Shape              </span>┃<span style=\"font-weight: bold\">         Param # </span>┃<span style=\"font-weight: bold\"> Connected to               </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ padding_mask (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_ids (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ gemma_backbone                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2304</span>)        │   <span style=\"color: #00af00; text-decoration-color: #00af00\">2,614,341,888</span> │ padding_mask[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],        │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaBackbone</span>)               │                           │                 │ token_ids[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_embedding               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256000</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">589,824,000</span> │ gemma_backbone[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReversibleEmbedding</span>)         │                           │                 │                            │\n",
+              "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                 \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to              \u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ padding_mask (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_ids (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ gemma_backbone                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m)        │   \u001b[38;5;34m2,614,341,888\u001b[0m │ padding_mask[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
+              "│ (\u001b[38;5;33mGemmaBackbone\u001b[0m)               │                           │                 │ token_ids[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_embedding               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256000\u001b[0m)      │     \u001b[38;5;34m589,824,000\u001b[0m │ gemma_backbone[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
+              "│ (\u001b[38;5;33mReversibleEmbedding\u001b[0m)         │                           │                 │                            │\n",
+              "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,614,341,888</span> (9.74 GB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,614,341,888\u001b[0m (9.74 GB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,614,341,888</span> (9.74 GB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,614,341,888\u001b[0m (9.74 GB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "gemma_lm.summary()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "81KHdRYOrWYm"
+      },
+      "source": [
+        "As you can see from the summary, the model has 2.6 billion trainable parameters.\n",
+        "\n",
+        "Note: For purposes of naming the model (\"2B\"), the embedding layer is not counted against the number of parameters."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FOBW7piN5-sl"
+      },
+      "source": [
+        "## Generate text\n",
+        "\n",
+        "Now it's time to generate some text! The model has a `generate` method that generates text based on a prompt. The optional `max_length` argument specifies the maximum length of the generated sequence.\n",
+        "\n",
+        "Try it out with the prompt `\"what is keras in 3 bullet points?\"`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "aae5GHrdpj2_"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'what is keras in 3 bullet points?\\n\\n[Answer 1]\\n\\nKeras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, Theano, or PlaidML. It is designed to be user-friendly and easy to extend.\\n\\n'"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "gemma_lm.generate(\"what is keras in 3 bullet points?\", max_length=64)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qH0eFH_DvYwM"
+      },
+      "source": [
+        "Try calling `generate` again with a different prompt."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "VEyTnnNGvgGG"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'The universe is a vast and mysterious place, filled with countless stars, planets, and galaxies. But what if there was a way to see the universe in a whole new way? What if we could see the universe as it was when it was first created? What if we could see the universe as it is now'"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "gemma_lm.generate(\"The universe is\", max_length=64)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vVlCnY7Gvm7U"
+      },
+      "source": [
+        "If you're running on JAX or TensorFlow backends, you'll notice that the second `generate` call returns nearly instantly. This is because each call to `generate` for a given batch size and `max_length` is compiled with XLA. The first run is expensive, but subsequent runs are much faster."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mw5XkiHU11Ft"
+      },
+      "source": [
+        "You can also provide batched prompts using a list as input:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "xV6vs8_C2BGt"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['what is keras in 3 bullet points?\\n\\n[Answer 1]\\n\\nKeras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, Theano, or PlaidML. It is designed to be user-friendly and easy to extend.\\n\\n',\n",
+              " 'The universe is a vast and mysterious place, filled with countless stars, planets, and galaxies. But what if there was a way to see the universe in a whole new way? What if we could see the universe as it was when it was first created? What if we could see the universe as it is now']"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "gemma_lm.generate(\n",
+        "    [\"what is keras in 3 bullet points?\",\n",
+        "     \"The universe is\"],\n",
+        "    max_length=64)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MaVWoSpo3XyY"
+      },
+      "source": [
+        "### Optional: Try a different sampler\n",
+        "\n",
+        "You can control the generation strategy for `GemmaCausalLM` by setting the `sampler` argument on `compile()`. By default, [`\"greedy\"`](https://keras.io/api/keras_nlp/samplers/greedy_sampler/#greedysampler-class) sampling will be used.\n",
+        "\n",
+        "As an experiment, try setting a [`\"top_k\"`](https://keras.io/api/keras_nlp/samplers/top_k_sampler/) strategy:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "mx55VQpN4DAK"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'The universe is a big place, and there are so many things we do not know or understand about it.\\n\\nBut we can learn a lot about our world by studying what is known to us.\\n\\nFor example, if you look at the moon, it has many features that can be seen from the surface.'"
+            ]
+          },
+          "execution_count": 20,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "gemma_lm.compile(sampler=\"top_k\")\n",
+        "gemma_lm.generate(\"The universe is\", max_length=64)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-okKgK4LfO0f"
+      },
+      "source": [
+        "While the default greedy algorithm always picks the token with the largest probability, the top-K algorithm randomly picks the next token from the tokens of top K probability.\n",
+        "\n",
+        "You don't have to specify a sampler, and you can ignore the last code snippet if it's not helpful to your use case. If you'd like learn more about the available samplers, see [Samplers](https://keras.io/api/keras_nlp/samplers/)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jBrbTYasoo-J"
+      },
+      "source": [
+        "## What's next\n",
+        "\n",
+        "In this tutorial, you learned how to generate text using KerasNLP and Gemma. Here are a few suggestions for what to learn next:\n",
+        "\n",
+        "* Learn how to [finetune a Gemma model](https://ai.google.dev/gemma/docs/lora_tuning).\n",
+        "* Learn how to perform [distributed fine-tuning and inference on a Gemma model](https://ai.google.dev/gemma/docs/distributed_tuning).\n",
+        "* Learn about [Gemma integration with Vertex AI](https://ai.google.dev/gemma/docs/integrations/vertex)\n",
+        "* Learn how to [use Gemma models with Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma)."
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "keras_inference.ipynb",
+      "toc_visible": true
+    },
+    "google": {
+      "image_path": "/site-assets/images/marketing/gemma.png",
+      "keywords": [
+        "examples",
+        "gemma",
+        "python",
+        "quickstart",
+        "text"
+      ]
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemma/docs/lit_gemma.ipynb b/site/en/gemma/docs/lit_gemma.ipynb
new file mode 100644
index 000000000..ab8a4812d
--- /dev/null
+++ b/site/en/gemma/docs/lit_gemma.ipynb
@@ -0,0 +1,410 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3MMAcssHTML"
+      },
+      "source": [
+        "<link rel=\"stylesheet\" href=\"/site-assets/css/gemma.css\">\n",
+        "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/css2?family=Google+Symbols:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200\" />"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pYCKXrlPH0VW"
+      },
+      "source": [
+        "# Using LIT with Gemma"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Hy4PNm1FHMqB"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemma/docs/lit_gemma\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Generative AI</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/lit_gemma.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemma/docs/lit_gemma.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://codelabs.developers.google.com/codelabs/responsible-ai/lit-gemma\"><img src=\"https://www.tensorflow.org/images/codelabs_logo.png\" height=\"24\" width=\"48\" />Learn in Codelabs</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_JK95-LDQ8Ln"
+      },
+      "source": [
+        "Generative AI products are relatively new and their behaviors can vary more than\n",
+        "earlier forms of software. This makes it important to probe the machine learning\n",
+        "models being used, examine examples of the model's behavior and investigate\n",
+        "surprises.\n",
+        "\n",
+        "The Learning Interpretability Tool (LIT; [website][lit-web], [GitHub][lit-gh])\n",
+        "is a platform for debugging and analyzing ML models to understand why and how\n",
+        "they behave the way they do.\n",
+        "\n",
+        "Here, you'll learn how to setup LIT to get more out of Google's \n",
+        "[Gemma model][gemma] by using the Sequence Salience module to analyze different\n",
+        "prompt engineering approaches.\n",
+        "\n",
+        "[lit-web]: https://pair-code.github.io/lit\n",
+        "[lit-gh]: https://github.com/PAIR-code/lit\n",
+        "[gemma]: https://ai.google.dev/gemma\n",
+        "[keras-nlp]: https://keras.io/keras_nlp/"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "W3DeT6ysMb7g"
+      },
+      "source": [
+        "# Setting up LIT to Debug Gemma Prompts"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-8h-_gLNP6fd"
+      },
+      "source": [
+        "*Note: you may see some warnings of the form*\n",
+        "\n",
+        "```\n",
+        "ERROR: pip's dependency resolver does not currently take into account all the \n",
+        "packages that are installed. This behaviour is the source of the following \n",
+        "dependency conflicts.\n",
+        "bigframes 0.21.0 requires scikit-learn>=1.2.2, but you have scikit-learn 1.0.2 \n",
+        "which is incompatible.\n",
+        "google-colab 1.0.0 requires ipython==7.34.0, but you have ipython 8.14.0 \n",
+        "which is incompatible.\n",
+        "```\n",
+        "\n",
+        "*These are safe to ignore.*"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SUakOwZaJSa2"
+      },
+      "source": [
+        "## Install LIT and Keras NLP\n",
+        "\n",
+        "This notebook uses the KerasNLP implementation of Gemma (more on how to \n",
+        "configure this below). You will need a recent version of `keras` (3.0+) \n",
+        "`keras-nlp` (0.12+) and `lit-nlp` (1.2+), and a Kaggle account to download the \n",
+        "base model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "D5ktQxc6Jbo5"
+      },
+      "outputs": [],
+      "source": [
+        "# Keras is included in Colab runtimes, but needs to be updated to to v3.0+.\n",
+        "# LIT and Keras NLP are not icnldued by default and must be installed.\n",
+        "# Running this cell may require you to restart your session to ensure the newer\n",
+        "# packages are imported correctly.\n",
+        "! pip install -q -U \"keras >= 3.0, <4.0\" \"keras-nlp >= 0.14\" \"lit-nlp >= 1.2\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VzFhJDayXroT"
+      },
+      "source": [
+        "### Kaggle Access\n",
+        "\n",
+        "KerasNLP stores their pre-trained model weights on Kaggle. The\n",
+        "[`kagglehub` package](https://github.com/Kaggle/kagglehub#authenticate) is used\n",
+        "to autheticate with this service. Be sure to also accept the license agreement \n",
+        "for [Gemma](https://www.kaggle.com/models/keras/gemma) from your Kaggle account.\n",
+        "\n",
+        "See the Appendix at the end for more information on how to set up a Kaggle\n",
+        "account."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yKw8gDsh_nVR"
+      },
+      "outputs": [],
+      "source": [
+        "import kagglehub\n",
+        "\n",
+        "kagglehub.login()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f0-KhV-rShMa"
+      },
+      "source": [
+        "## Configuring LIT"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "v12sUSdiNPje"
+      },
+      "source": [
+        "LIT provides a function, `make_notebook_widget()` for configuring our prompt \n",
+        "debugging tools in a notebook context. \n",
+        "\n",
+        "LIT provides a dataset of sample prompts that accompany the tutorial linked \n",
+        "later in this document.\n",
+        "\n",
+        "See the comments below for configuring the widget to use different models and/or\n",
+        "datasets."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BdgJqj56QSOC"
+      },
+      "outputs": [],
+      "source": [
+        "from lit_nlp.examples.prompt_debugging import notebook as lit_pdbnb\n",
+        "\n",
+        "# The following function initializes a LIT Notebook Widget. It's configured by\n",
+        "# two required positional arguments:\n",
+        "#\n",
+        "# * `datasets_config`: A list of strings containing the dataset names and\n",
+        "#       paths to load from, as \"dataset:path\", where path can be a URL or a\n",
+        "#       local file path. The example below uses a special value,\n",
+        "#       `sample_prompts`, to load the example prompts provided in the LIT\n",
+        "#       distribution; no other special values are supported.\n",
+        "# * `models_config`: A list of strings containing the model names and paths to\n",
+        "#       load from, as \"model:path\", where path can be a URL, a local file path,\n",
+        "#       or the name of a preset for the configured deep learning framework.\n",
+        "#\n",
+        "# LIT supports salience computation for KerasNLP and Hugging Face Transformers\n",
+        "# models running on TensorFlow or PyTorch. Note that all models passed to the\n",
+        "# `models_config` parameter will be loaded using the same framework and runtime.\n",
+        "# You can cofnigre these with the following keywork arguments.\n",
+        "#\n",
+        "# * `dl_framework`: Must be one of \"kerasnlp\" or \"transformers\".\n",
+        "# * `dl_runtime`: Must be one of \"tensorflow\" or \"torch\".\n",
+        "#\n",
+        "# Changing the `dl_framework` value will affect the authentication method used\n",
+        "# to access Gemma model weights.\n",
+        "\n",
+        "lit_widget = lit_pdbnb.make_notebook_widget(\n",
+        "    ['sample_prompts'],\n",
+        "    [\"gemma_2b_it:gemma_1.1_instruct_2b_en\"],\n",
+        "    dl_framework=\"kerasnlp\",\n",
+        "    dl_runtime=\"tensorflow\",\n",
+        "    batch_size=1,\n",
+        "    max_examples=5,\n",
+        "    precision=\"bfloat16\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Q6snqitV2wPX"
+      },
+      "source": [
+        "Now you can render the UI in a Colab cell."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "F9DZMtEfURob"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/javascript": [
+              "(async (port, path, width, height, cache, element) => {\n",
+              "    if (!google.colab.kernel.accessAllowed && !cache) {\n",
+              "      return;\n",
+              "    }\n",
+              "    element.appendChild(document.createTextNode(''));\n",
+              "    const url = await google.colab.kernel.proxyPort(port, {cache});\n",
+              "    const iframe = document.createElement('iframe');\n",
+              "    iframe.src = new URL(path, url).toString();\n",
+              "    iframe.height = height;\n",
+              "    iframe.width = width;\n",
+              "    iframe.style.border = 0;\n",
+              "    iframe.allow = [\n",
+              "        'accelerometer',\n",
+              "        'autoplay',\n",
+              "        'camera',\n",
+              "        'clipboard-read',\n",
+              "        'clipboard-write',\n",
+              "        'gyroscope',\n",
+              "        'magnetometer',\n",
+              "        'microphone',\n",
+              "        'serial',\n",
+              "        'usb',\n",
+              "        'xr-spatial-tracking',\n",
+              "    ].join('; ');\n",
+              "    element.appendChild(iframe);\n",
+              "  })(36717, \"/?\", \"100%\", \"800\", false, window.element)"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "lit_widget.render()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AawFyPUUS4cW"
+      },
+      "source": [
+        "# Prompt Debugging with Sequence Salience\n",
+        "\n",
+        "Text-to-text large language models (LLMs), such as Gemma, take an input sequence \n",
+        "in the form of [tokenized][tokenization] text and generate new tokens that are \n",
+        "logical follow-ons or completions.\n",
+        "\n",
+        "[Salience methods][salience-explorable] allow you to inspect which parts of an \n",
+        "input are important to the model for different parts of its generated output. \n",
+        "LIT's [Sequence Salience module][lit-seq-sal] extends these methods to explain \n",
+        "the importance of sequences at multiple levels of granularity: from tokens to \n",
+        "words to sentences and beyond.\n",
+        "\n",
+        "You can use LIT in the cell above to play around with the Sequence Salience \n",
+        "module on your own. For a more guided learning experience, you can follow long \n",
+        "with the [_Prompt Debugging with Sequence Salience_ tutorial][seq-sal-tutorial]\n",
+        "right in this Colab.\n",
+        "\n",
+        "For even more academic and techncial information on how Sequence Salience works,\n",
+        "check out [our paper][seq-sal-paper].\n",
+        "\n",
+        "\n",
+        "[lit-seq-sal]: https://pair-code.github.io/lit/documentation/components.html#sequence-salience\n",
+        "[salience-explorable]: https://pair.withgoogle.com/explorables/saliency/\n",
+        "[seq-sal-paper]: https://arxiv.org/abs/2404.07498\n",
+        "[seq-sal-tutorial]: https://pair-code.github.io/lit/tutorials/sequence-salience/\n",
+        "[tokenization]: https://arxiv.org/pdf/1808.06226.pdf\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IZWodzCgPM6k"
+      },
+      "source": [
+        "# Appendix: Accessing Gemma on Kaggle Hub\n",
+        "\n",
+        "This notebook uses the KerasNLP implementation of Gemma in this document. \n",
+        "KerasNLP stores their pre-trained model weights on Kaggle, and Gemma requires \n",
+        "authentication and license acknowledgement to access those weights.\n",
+        "\n",
+        "The following instruction walk you through how to set up a Kaggle account and \n",
+        "authenticate with Kaggle using the `kagglehub` package.\n",
+        "\n",
+        "1.  Create a Kaggle account if you don't have one\n",
+        "    * Go to: https://www.kaggle.com/account/login?phase=startRegisterTab\n",
+        "    * Use whichever registation method you prefer to set up your account.\n",
+        "1. Request access to Gemma\n",
+        "    * Make sure you're logged into Kaggle using the account above\n",
+        "    * Go to the consent page: https://www.kaggle.com/models/google/gemma/license/consent\n",
+        "    * Select the \"Verify via Kaggle Account\" option (the default selection) and click next\n",
+        "    * Complete the consent form (first name and last name fields at the top)\n",
+        "    * Acknowledge the policy using the checkboxes at the bottom\n",
+        "    * Click the \"Accept\" button at the bottom to be granted access\n",
+        "    * This should redirect you to the model page (https://www.kaggle.com/models/google/gemma)\n",
+        "1. Create an API token\n",
+        "    * Make sure you're logged into Kaggle using the account you created above\n",
+        "    * Got to the Settings page: https://www.kaggle.com/settings\n",
+        "    * Scroll down to the API section\n",
+        "    * Use the \"Create New Token\" button to trigger token generation\n",
+        "    * Use the on-screen menu to save the JSON file, named kaggle.json, the service generates to your machine\n",
+        "    * The JSON file is an object with two properties, username and key, you'll need both to authenticate with their service later\n",
+        "1. Use your API token credentials to authenticate with kagglehub in Colab\n",
+        "    * Go to the LIT Sequence Saleince Colab: https://colab.sandbox.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/lit_gemma.ipynb#scrollTo=yKw8gDsh_nVR\n",
+        "    * Conntect to a GPU runtime\n",
+        "    * For Gemma 2B you can use the free-tier T4 runtime\n",
+        "    * For Gemma 7B you will need pre-paid Colab compute credits or a Colab Pro account to use a V100, L4, or A100 GPU\n",
+        "    * Run the `kagglehub` code cell to display an HTML form that asks for your username and a token\n",
+        "    * Copy the `username` field from the `kaggle.json` file you downloaded in the previous step and paste it into the `username` field in the form\n",
+        "    * Copy the `key` field from the `kaggle.json` file you downloaded in the previous step and paste it into the `token` field in the form\n",
+        "    * Click the login button to save these credentials in your runtime\n",
+        "\n",
+        "You will need to repeat the last step any time the Colab runtime is disconnected, as disconnection clears the cache the credentials are stored in."
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "lit_gemma.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemma/docs/lora_tuning.ipynb b/site/en/gemma/docs/lora_tuning.ipynb
new file mode 100644
index 000000000..2f6071358
--- /dev/null
+++ b/site/en/gemma/docs/lora_tuning.ipynb
@@ -0,0 +1,1022 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3MMAcssHTML"
+      },
+      "source": [
+        "<link rel=\"stylesheet\" href=\"/site-assets/css/gemma.css\">\n",
+        "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/css2?family=Google+Symbols:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200\" />"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SDEExiAk4fLb"
+      },
+      "source": [
+        "# Fine-tune Gemma models in Keras using LoRA"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZFWzQEqNosrS"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemma/docs/lora_tuning\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/lora_tuning.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/google/generative-ai-docs/main/site/en/gemma/docs/lora_tuning.ipynb\"><img src=\"https://ai.google.dev/images/cloud-icon.svg\" width=\"40\" />Open in Vertex AI</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemma/docs/lora_tuning.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lSGRSsRPgkzK"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "Gemma is a family of lightweight, state-of-the art open models built from the same research and technology used to create the Gemini models.\n",
+        "\n",
+        "Large Language Models (LLMs) like Gemma have been shown to be effective at a variety of NLP tasks. An LLM is first pre-trained on a large corpus of text in a self-supervised fashion. Pre-training helps LLMs learn general-purpose knowledge, such as statistical relationships between words. An LLM can then be fine-tuned with domain-specific data to perform downstream tasks (such as sentiment analysis).\n",
+        "\n",
+        "LLMs are extremely large in size (parameters in the order of billions). Full fine-tuning (which updates all the parameters in the model) is not required for most applications because typical fine-tuning datasets are relatively much smaller than the pre-training datasets.\n",
+        "\n",
+        "[Low Rank Adaptation (LoRA)](https://arxiv.org/abs/2106.09685) is a fine-tuning technique which greatly reduces the number of trainable parameters for downstream tasks by freezing the weights of the model and inserting a smaller number of new weights into the model. This makes training with LoRA much faster and more memory-efficient, and produces smaller model weights (a few hundred MBs), all while maintaining the quality of the model outputs.\n",
+        "\n",
+        "This tutorial walks you through using KerasNLP to perform LoRA fine-tuning on a Gemma 2B model using the [Databricks Dolly 15k dataset](https://huggingface.co/datasets/databricks/databricks-dolly-15k). This dataset contains 15,000 high-quality human-generated prompt / response pairs specifically designed for fine-tuning LLMs."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w1q6-W_mKIT-"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lyhHCMfoRZ_v"
+      },
+      "source": [
+        "### Get access to Gemma\n",
+        "\n",
+        "To complete this tutorial, you will first need to complete the setup instructions at [Gemma setup](https://ai.google.dev/gemma/docs/setup). The Gemma setup instructions show you how to do the following:\n",
+        "\n",
+        "* Get access to Gemma on [kaggle.com](https://kaggle.com).\n",
+        "* Select a Colab runtime with sufficient resources to run\n",
+        "  the Gemma 2B model.\n",
+        "* Generate and configure a Kaggle username and API key.\n",
+        "\n",
+        "After you've completed the Gemma setup, move on to the next section, where you'll set environment variables for your Colab environment."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AZ5Qo0fxRZ1V"
+      },
+      "source": [
+        "### Select the runtime\n",
+        "\n",
+        "To complete this tutorial, you'll need to have a Colab runtime with sufficient resources to run the Gemma model. In this case, you can use a T4 GPU:\n",
+        "\n",
+        "1. In the upper-right of the Colab window, select &#9662; (**Additional connection options**).\n",
+        "2. Select **Change runtime type**.\n",
+        "3. Under **Hardware accelerator**, select **T4 GPU**."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hsPC0HRkJl0K"
+      },
+      "source": [
+        "### Configure your API key\n",
+        "\n",
+        "To use Gemma, you must provide your Kaggle username and a Kaggle API key.\n",
+        "\n",
+        "To generate a Kaggle API key, go to the **Account** tab of your Kaggle user profile and select **Create New Token**. This will trigger the download of a `kaggle.json` file containing your API credentials.\n",
+        "\n",
+        "In Colab, select **Secrets** (🔑) in the left pane and add your Kaggle username and Kaggle API key. Store your username under the name `KAGGLE_USERNAME` and your API key under the name `KAGGLE_KEY`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7iOF6Yo-wUEC"
+      },
+      "source": [
+        "### Set environment variables\n",
+        "\n",
+        "Set environment variables for `KAGGLE_USERNAME` and `KAGGLE_KEY`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "0_EdOg9DPK6Q"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "# Note: `userdata.get` is a Colab API. If you're not using Colab, set the env\n",
+        "# vars as appropriate for your system.\n",
+        "\n",
+        "os.environ[\"KAGGLE_USERNAME\"] = userdata.get('KAGGLE_USERNAME')\n",
+        "os.environ[\"KAGGLE_KEY\"] = userdata.get('KAGGLE_KEY')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CuEUAKJW1QkQ"
+      },
+      "source": [
+        "### Install dependencies\n",
+        "\n",
+        "Install Keras, KerasNLP, and other dependencies."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1eeBtYqJsZPG"
+      },
+      "outputs": [],
+      "source": [
+        "# Install Keras 3 last. See https://keras.io/getting_started/ for more details.\n",
+        "!pip install -q -U keras-nlp\n",
+        "!pip install -q -U \"keras>=3\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rGLS-l5TxIR4"
+      },
+      "source": [
+        "### Select a backend\n",
+        "\n",
+        "Keras is a high-level, multi-framework deep learning API designed for simplicity and ease of use. Using Keras 3, you can run workflows on one of three backends: TensorFlow, JAX, or PyTorch.\n",
+        "\n",
+        "For this tutorial, configure the backend for JAX."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "yn5uy8X8sdD0"
+      },
+      "outputs": [],
+      "source": [
+        "os.environ[\"KERAS_BACKEND\"] = \"jax\"  # Or \"torch\" or \"tensorflow\".\n",
+        "# Avoid memory fragmentation on JAX backend.\n",
+        "os.environ[\"XLA_PYTHON_CLIENT_MEM_FRACTION\"]=\"1.00\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hZs8XXqUKRmi"
+      },
+      "source": [
+        "### Import packages\n",
+        "\n",
+        "Import Keras and KerasNLP."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "FYHyPUA9hKTf"
+      },
+      "outputs": [],
+      "source": [
+        "import keras\n",
+        "import keras_nlp"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9T7xe_jzslv4"
+      },
+      "source": [
+        "## Load Dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "xRaNCPUXKoa7"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "--2024-07-31 01:56:39--  https://huggingface.co/datasets/databricks/databricks-dolly-15k/resolve/main/databricks-dolly-15k.jsonl\n",
+            "Resolving huggingface.co (huggingface.co)... 18.164.174.23, 18.164.174.17, 18.164.174.55, ...\n",
+            "Connecting to huggingface.co (huggingface.co)|18.164.174.23|:443... connected.\n",
+            "HTTP request sent, awaiting response... 302 Found\n",
+            "Location: https://cdn-lfs.huggingface.co/repos/34/ac/34ac588cc580830664f592597bb6d19d61639eca33dc2d6bb0b6d833f7bfd552/2df9083338b4abd6bceb5635764dab5d833b393b55759dffb0959b6fcbf794ec?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27databricks-dolly-15k.jsonl%3B+filename%3D%22databricks-dolly-15k.jsonl%22%3B&Expires=1722650199&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcyMjY1MDE5OX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy8zNC9hYy8zNGFjNTg4Y2M1ODA4MzA2NjRmNTkyNTk3YmI2ZDE5ZDYxNjM5ZWNhMzNkYzJkNmJiMGI2ZDgzM2Y3YmZkNTUyLzJkZjkwODMzMzhiNGFiZDZiY2ViNTYzNTc2NGRhYjVkODMzYjM5M2I1NTc1OWRmZmIwOTU5YjZmY2JmNzk0ZWM%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qIn1dfQ__&Signature=nITF8KrgvPBdCRtwfpzGV9ulH2joFLXIDct5Nq-aZqb-Eum8XiVGOai76mxahgAK2mCO4ekuNVCxVsa9Q7h40cZuzViZZC3zAF8QVQlbbkd3FBY4SN3QA4nDNQGcuRYoMKcalA9vRBasFhmdWgupxVqYgMVfJvgSApUcMHMm1HqRBn8AGKpEsaXhEMX4I0N-KtDH5ojDZjz5QBDgkWEmPYUeDQbjVHMjXsRG5z4vH3nK1W9gzC7dkWicJZlzl6iGs44w-EqnD3h-McDCgFnXUacPydm1hdgin-wutx7V4Z3Yv82Fi-TPlDYCnioesUr9Rx8xYujPuXmWP24kPca17Q__&Key-Pair-Id=K3ESJI6DHPFC7 [following]\n",
+            "--2024-07-31 01:56:39--  https://cdn-lfs.huggingface.co/repos/34/ac/34ac588cc580830664f592597bb6d19d61639eca33dc2d6bb0b6d833f7bfd552/2df9083338b4abd6bceb5635764dab5d833b393b55759dffb0959b6fcbf794ec?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27databricks-dolly-15k.jsonl%3B+filename%3D%22databricks-dolly-15k.jsonl%22%3B&Expires=1722650199&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcyMjY1MDE5OX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy8zNC9hYy8zNGFjNTg4Y2M1ODA4MzA2NjRmNTkyNTk3YmI2ZDE5ZDYxNjM5ZWNhMzNkYzJkNmJiMGI2ZDgzM2Y3YmZkNTUyLzJkZjkwODMzMzhiNGFiZDZiY2ViNTYzNTc2NGRhYjVkODMzYjM5M2I1NTc1OWRmZmIwOTU5YjZmY2JmNzk0ZWM%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qIn1dfQ__&Signature=nITF8KrgvPBdCRtwfpzGV9ulH2joFLXIDct5Nq-aZqb-Eum8XiVGOai76mxahgAK2mCO4ekuNVCxVsa9Q7h40cZuzViZZC3zAF8QVQlbbkd3FBY4SN3QA4nDNQGcuRYoMKcalA9vRBasFhmdWgupxVqYgMVfJvgSApUcMHMm1HqRBn8AGKpEsaXhEMX4I0N-KtDH5ojDZjz5QBDgkWEmPYUeDQbjVHMjXsRG5z4vH3nK1W9gzC7dkWicJZlzl6iGs44w-EqnD3h-McDCgFnXUacPydm1hdgin-wutx7V4Z3Yv82Fi-TPlDYCnioesUr9Rx8xYujPuXmWP24kPca17Q__&Key-Pair-Id=K3ESJI6DHPFC7\n",
+            "Resolving cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)... 18.154.206.4, 18.154.206.17, 18.154.206.28, ...\n",
+            "Connecting to cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)|18.154.206.4|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 13085339 (12M) [text/plain]\n",
+            "Saving to: ‘databricks-dolly-15k.jsonl’\n",
+            "\n",
+            "databricks-dolly-15 100%[===================>]  12.48M  73.7MB/s    in 0.2s    \n",
+            "\n",
+            "2024-07-31 01:56:40 (73.7 MB/s) - ‘databricks-dolly-15k.jsonl’ saved [13085339/13085339]\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "!wget -O databricks-dolly-15k.jsonl https://huggingface.co/datasets/databricks/databricks-dolly-15k/resolve/main/databricks-dolly-15k.jsonl"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "45UpBDfBgf0I"
+      },
+      "source": [
+        "Preprocess the data. This tutorial uses a subset of 1000 training examples to execute the notebook faster. Consider using more training data for higher quality fine-tuning."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "ZiS-KU9osh_N"
+      },
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "data = []\n",
+        "with open(\"databricks-dolly-15k.jsonl\") as file:\n",
+        "    for line in file:\n",
+        "        features = json.loads(line)\n",
+        "        # Filter out examples with context, to keep it simple.\n",
+        "        if features[\"context\"]:\n",
+        "            continue\n",
+        "        # Format the entire example as a single string.\n",
+        "        template = \"Instruction:\\n{instruction}\\n\\nResponse:\\n{response}\"\n",
+        "        data.append(template.format(**features))\n",
+        "\n",
+        "# Only use 1000 training examples, to keep it fast.\n",
+        "data = data[:1000]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7RCE3fdGhDE5"
+      },
+      "source": [
+        "## Load Model\n",
+        "\n",
+        "KerasNLP provides implementations of many popular [model architectures](https://keras.io/api/keras_nlp/models/). In this tutorial, you'll create a model using `GemmaCausalLM`, an end-to-end Gemma model for causal language modeling. A causal language model predicts the next token based on previous tokens.\n",
+        "\n",
+        "Create the model using the `from_preset` method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "vz5zLEyLstfn"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Preprocessor: \"gemma_causal_lm_preprocessor\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mPreprocessor: \"gemma_causal_lm_preprocessor\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Tokenizer (type)                                   </span>┃<span style=\"font-weight: bold\">                                             Vocab # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ gemma_tokenizer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaTokenizer</span>)                   │                                             <span style=\"color: #00af00; text-decoration-color: #00af00\">256,000</span> │\n",
+              "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mTokenizer (type)                                  \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m                                            Vocab #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ gemma_tokenizer (\u001b[38;5;33mGemmaTokenizer\u001b[0m)                   │                                             \u001b[38;5;34m256,000\u001b[0m │\n",
+              "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"gemma_causal_lm\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"gemma_causal_lm\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                  </span>┃<span style=\"font-weight: bold\"> Output Shape              </span>┃<span style=\"font-weight: bold\">         Param # </span>┃<span style=\"font-weight: bold\"> Connected to               </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ padding_mask (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_ids (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ gemma_backbone                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2304</span>)        │   <span style=\"color: #00af00; text-decoration-color: #00af00\">2,614,341,888</span> │ padding_mask[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],        │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaBackbone</span>)               │                           │                 │ token_ids[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_embedding               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256000</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">589,824,000</span> │ gemma_backbone[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReversibleEmbedding</span>)         │                           │                 │                            │\n",
+              "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                 \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to              \u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ padding_mask (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_ids (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ gemma_backbone                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m)        │   \u001b[38;5;34m2,614,341,888\u001b[0m │ padding_mask[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
+              "│ (\u001b[38;5;33mGemmaBackbone\u001b[0m)               │                           │                 │ token_ids[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_embedding               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256000\u001b[0m)      │     \u001b[38;5;34m589,824,000\u001b[0m │ gemma_backbone[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
+              "│ (\u001b[38;5;33mReversibleEmbedding\u001b[0m)         │                           │                 │                            │\n",
+              "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,614,341,888</span> (9.74 GB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,614,341,888\u001b[0m (9.74 GB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,614,341,888</span> (9.74 GB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,614,341,888\u001b[0m (9.74 GB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(\"gemma2_2b_en\")\n",
+        "gemma_lm.summary()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Nl4lvPy5zA26"
+      },
+      "source": [
+        "The `from_preset` method instantiates the model from a preset architecture and weights. In the code above, the string \"gemma2_2b_en\" specifies the preset architecture — a Gemma model with 2 billion parameters.\n",
+        "\n",
+        "NOTE: A Gemma model with 7\n",
+        "billion parameters is also available. To run the larger model in Colab, you need access to the premium GPUs available in paid plans. Alternatively, you can perform [distributed tuning on a Gemma 7B model](https://ai.google.dev/gemma/docs/distributed_tuning) on Kaggle or Google Cloud."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G_L6A5J-1QgC"
+      },
+      "source": [
+        "## Inference before fine tuning\n",
+        "\n",
+        "In this section, you will query the model with various prompts to see how it responds."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PVLXadptyo34"
+      },
+      "source": [
+        "### Europe Trip Prompt\n",
+        "\n",
+        "Query the model for suggestions on what to do on a trip to Europe."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "ZwQz3xxxKciD"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Instruction:\n",
+            "What should I do on a trip to Europe?\n",
+            "\n",
+            "Response:\n",
+            "If you have any special needs, you should contact the embassy of the country that you are visiting.\n",
+            "You should contact the embassy of the country that I will be visiting.\n",
+            "\n",
+            "What are my responsibilities when I go on a trip?\n",
+            "\n",
+            "Response:\n",
+            "If you are going to Europe, you should make sure to bring all of your documents.\n",
+            "If you are going to Europe, make sure that you have all of your documents.\n",
+            "\n",
+            "When do you travel abroad?\n",
+            "\n",
+            "Response:\n",
+            "The most common reason to travel abroad is to go to school or work.\n",
+            "The most common reason to travel abroad is to work.\n",
+            "\n",
+            "How can I get a visa to Europe?\n",
+            "\n",
+            "Response:\n",
+            "If you want to go to Europe and you have a valid visa, you can get a visa from your local embassy.\n",
+            "If you want to go to Europe and you do not have a valid visa, you can get a visa from your local embassy.\n",
+            "\n",
+            "When should I go to Europe?\n",
+            "\n",
+            "Response:\n",
+            "You should go to Europe when the weather is nice.\n",
+            "You should go to Europe when the weather is bad.\n",
+            "\n",
+            "How can I make a reservation for a trip?\n",
+            "\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "prompt = template.format(\n",
+        "    instruction=\"What should I do on a trip to Europe?\",\n",
+        "    response=\"\",\n",
+        ")\n",
+        "sampler = keras_nlp.samplers.TopKSampler(k=5, seed=2)\n",
+        "gemma_lm.compile(sampler=sampler)\n",
+        "print(gemma_lm.generate(prompt, max_length=256))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AePQUIs2h-Ks"
+      },
+      "source": [
+        "The model responds with generic tips on how to plan a trip."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YQ74Zz_S0iVv"
+      },
+      "source": [
+        "### ELI5 Photosynthesis Prompt\n",
+        "\n",
+        "Prompt the model to explain photosynthesis in terms simple enough for a 5 year old child to understand."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "lorJMbsusgoo"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Instruction:\n",
+            "Explain the process of photosynthesis in a way that a child could understand.\n",
+            "\n",
+            "Response:\n",
+            "Plants need water, air, sunlight, and carbon dioxide. The plant uses water, sunlight, and carbon dioxide to make oxygen and glucose. The process is also known as photosynthesis.\n",
+            "\n",
+            "Instruction:\n",
+            "What is the process of photosynthesis in a plant's cells? How is this process similar to and different from the process of cellular respiration?\n",
+            "\n",
+            "Response:\n",
+            "The process of photosynthesis in a plant's cell is similar to and different from cellular respiration. In photosynthesis, a plant uses carbon dioxide to make glucose and oxygen. In cellular respiration, a plant cell uses oxygen to break down glucose to make energy and carbon dioxide.\n",
+            "\n",
+            "Instruction:\n",
+            "Describe how plants make oxygen and glucose during the process of photosynthesis. Explain how the process of photosynthesis is related to cellular respiration.\n",
+            "\n",
+            "Response:\n",
+            "Plants make oxygen and glucose during the process of photosynthesis. The process of photosynthesis is related to cellular respiration in that both are chemical processes that require the presence of oxygen.\n",
+            "\n",
+            "Instruction:\n",
+            "How does photosynthesis occur in the cells of a plant? What is the purpose for each part of the cell?\n",
+            "\n",
+            "Response:\n",
+            "Photosynthesis occurs in the cells of a plant. The purpose of\n"
+          ]
+        }
+      ],
+      "source": [
+        "prompt = template.format(\n",
+        "    instruction=\"Explain the process of photosynthesis in a way that a child could understand.\",\n",
+        "    response=\"\",\n",
+        ")\n",
+        "print(gemma_lm.generate(prompt, max_length=256))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WBQieduRizZf"
+      },
+      "source": [
+        "The model response contains words that might not be easy to understand for a child such as chlorophyll."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Pt7Nr6a7tItO"
+      },
+      "source": [
+        "## LoRA Fine-tuning\n",
+        "\n",
+        "To get better responses from the model, fine-tune the model with Low Rank Adaptation (LoRA) using the Databricks Dolly 15k dataset.\n",
+        "\n",
+        "The LoRA rank determines the dimensionality of the trainable matrices that are added to the original weights of the LLM. It controls the expressiveness and precision of the fine-tuning adjustments.\n",
+        "\n",
+        "A higher rank means more detailed changes are possible, but also means more trainable parameters. A lower rank means less computational overhead, but potentially less precise adaptation.\n",
+        "\n",
+        "This tutorial uses a LoRA rank of 4. In practice, begin with a relatively small rank (such as 4, 8, 16). This is computationally efficient for experimentation. Train your model with this rank and evaluate the performance improvement on your task. Gradually increase the rank in subsequent trials and see if that further boosts performance."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "RCucu6oHz53G"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Preprocessor: \"gemma_causal_lm_preprocessor\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mPreprocessor: \"gemma_causal_lm_preprocessor\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Tokenizer (type)                                   </span>┃<span style=\"font-weight: bold\">                                             Vocab # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ gemma_tokenizer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaTokenizer</span>)                   │                                             <span style=\"color: #00af00; text-decoration-color: #00af00\">256,000</span> │\n",
+              "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mTokenizer (type)                                  \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m                                            Vocab #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ gemma_tokenizer (\u001b[38;5;33mGemmaTokenizer\u001b[0m)                   │                                             \u001b[38;5;34m256,000\u001b[0m │\n",
+              "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"gemma_causal_lm\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"gemma_causal_lm\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                  </span>┃<span style=\"font-weight: bold\"> Output Shape              </span>┃<span style=\"font-weight: bold\">         Param # </span>┃<span style=\"font-weight: bold\"> Connected to               </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ padding_mask (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_ids (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ gemma_backbone                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2304</span>)        │   <span style=\"color: #00af00; text-decoration-color: #00af00\">2,617,270,528</span> │ padding_mask[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],        │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaBackbone</span>)               │                           │                 │ token_ids[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_embedding               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256000</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">589,824,000</span> │ gemma_backbone[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReversibleEmbedding</span>)         │                           │                 │                            │\n",
+              "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                 \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to              \u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ padding_mask (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_ids (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ gemma_backbone                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m)        │   \u001b[38;5;34m2,617,270,528\u001b[0m │ padding_mask[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
+              "│ (\u001b[38;5;33mGemmaBackbone\u001b[0m)               │                           │                 │ token_ids[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
+              "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+              "│ token_embedding               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256000\u001b[0m)      │     \u001b[38;5;34m589,824,000\u001b[0m │ gemma_backbone[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
+              "│ (\u001b[38;5;33mReversibleEmbedding\u001b[0m)         │                           │                 │                            │\n",
+              "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,617,270,528</span> (9.75 GB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,617,270,528\u001b[0m (9.75 GB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,928,640</span> (11.17 MB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,928,640\u001b[0m (11.17 MB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,614,341,888</span> (9.74 GB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m2,614,341,888\u001b[0m (9.74 GB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Enable LoRA for the model and set the LoRA rank to 4.\n",
+        "gemma_lm.backbone.enable_lora(rank=4)\n",
+        "gemma_lm.summary()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hQQ47kcdpbZ9"
+      },
+      "source": [
+        "Note that enabling LoRA reduces the number of trainable parameters significantly (from 2.6 billion to 2.9 million)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "_Peq7TnLtHse"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1m1000/1000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m923s\u001b[0m 888ms/step - loss: 1.5586 - sparse_categorical_accuracy: 0.5251\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.history.History at 0x799d04393c40>"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Limit the input sequence length to 256 (to control memory usage).\n",
+        "gemma_lm.preprocessor.sequence_length = 256\n",
+        "# Use AdamW (a common optimizer for transformer models).\n",
+        "optimizer = keras.optimizers.AdamW(\n",
+        "    learning_rate=5e-5,\n",
+        "    weight_decay=0.01,\n",
+        ")\n",
+        "# Exclude layernorm and bias terms from decay.\n",
+        "optimizer.exclude_from_weight_decay(var_names=[\"bias\", \"scale\"])\n",
+        "\n",
+        "gemma_lm.compile(\n",
+        "    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
+        "    optimizer=optimizer,\n",
+        "    weighted_metrics=[keras.metrics.SparseCategoricalAccuracy()],\n",
+        ")\n",
+        "gemma_lm.fit(data, epochs=1, batch_size=1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bx3m8f1dB7nk"
+      },
+      "source": [
+        "### Note on mixed precision fine-tuning on NVIDIA GPUs\n",
+        "\n",
+        "Full precision is recommended for fine-tuning. When fine-tuning on NVIDIA GPUs, note that you can use mixed precision (`keras.mixed_precision.set_global_policy('mixed_bfloat16')`) to speed up training with minimal effect on training quality. Mixed precision fine-tuning does consume more memory so is useful only on larger GPUs.\n",
+        "\n",
+        "\n",
+        "For inference, half-precision (`keras.config.set_floatx(\"bfloat16\")`) will work and save memory while mixed precision is not applicable."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "T0lHxEDX03gp"
+      },
+      "outputs": [],
+      "source": [
+        "# Uncomment the line below if you want to enable mixed precision training on GPUs\n",
+        "# keras.mixed_precision.set_global_policy('mixed_bfloat16')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4yd-1cNw1dTn"
+      },
+      "source": [
+        "## Inference after fine-tuning\n",
+        "After fine-tuning, responses follow the instruction provided in the prompt."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "H55JYJ1a1Kos"
+      },
+      "source": [
+        "### Europe Trip Prompt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "Y7cDJHy8WfCB"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Instruction:\n",
+            "What should I do on a trip to Europe?\n",
+            "\n",
+            "Response:\n",
+            "When planning a trip to Europe, you should consider your budget, time and the places you want to visit. If you are on a limited budget, consider traveling by train, which is cheaper compared to flying. If you are short on time, consider visiting only a few cities in one region, such as Paris, Amsterdam, London, Berlin, Rome, Venice or Barcelona. If you are looking for more than one destination, try taking a train to different countries and staying in each country for a few days.\n"
+          ]
+        }
+      ],
+      "source": [
+        "prompt = template.format(\n",
+        "    instruction=\"What should I do on a trip to Europe?\",\n",
+        "    response=\"\",\n",
+        ")\n",
+        "sampler = keras_nlp.samplers.TopKSampler(k=5, seed=2)\n",
+        "gemma_lm.compile(sampler=sampler)\n",
+        "print(gemma_lm.generate(prompt, max_length=256))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OXP6gg2mjs6u"
+      },
+      "source": [
+        "The model now recommends places to visit in Europe."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "H7nVd8Mi1Yta"
+      },
+      "source": [
+        "### ELI5 Photosynthesis Prompt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "X-2sYl2jqwl7"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Instruction:\n",
+            "Explain the process of photosynthesis in a way that a child could understand.\n",
+            "\n",
+            "Response:\n",
+            "The process of photosynthesis is a chemical reaction in plants that converts the energy of sunlight into chemical energy, which the plants can then use to grow and develop. During photosynthesis, a plant will absorb carbon dioxide (CO2) from the air and water from the soil and use the energy from the sun to produce oxygen (O2) and sugars (glucose) as a by-product.\n"
+          ]
+        }
+      ],
+      "source": [
+        "prompt = template.format(\n",
+        "    instruction=\"Explain the process of photosynthesis in a way that a child could understand.\",\n",
+        "    response=\"\",\n",
+        ")\n",
+        "print(gemma_lm.generate(prompt, max_length=256))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PCmAmqrvkEhc"
+      },
+      "source": [
+        "The model now explains photosynthesis in simpler terms."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "I8kFG12l0mVe"
+      },
+      "source": [
+        "Note that for demonstration purposes, this tutorial fine-tunes the model on a small subset of the dataset for just one epoch and with a low LoRA rank value. To get better responses from the fine-tuned model, you can experiment with:\n",
+        "\n",
+        "1. Increasing the size of the fine-tuning dataset\n",
+        "2. Training for more steps (epochs)\n",
+        "3. Setting a higher LoRA rank\n",
+        "4. Modifying the hyperparameter values such as `learning_rate` and `weight_decay`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gSsRdeiof_rJ"
+      },
+      "source": [
+        "## Summary and next steps\n",
+        "\n",
+        "This tutorial covered LoRA fine-tuning on a Gemma model using KerasNLP. Check out the following docs next:\n",
+        "\n",
+        "* Learn how to [generate text with a Gemma model](https://ai.google.dev/gemma/docs/get_started).\n",
+        "* Learn how to perform [distributed fine-tuning and inference on a Gemma model](https://ai.google.dev/gemma/docs/distributed_tuning).\n",
+        "* Learn how to [use Gemma open models with Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma).\n",
+        "* Learn how to [fine-tune Gemma using KerasNLP and deploy to Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_gemma_kerasnlp_to_vertexai.ipynb)."
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "lora_tuning.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemma/docs/paligemma/fine-tuning-paligemma.ipynb b/site/en/gemma/docs/paligemma/fine-tuning-paligemma.ipynb
new file mode 100644
index 000000000..b2cb645f0
--- /dev/null
+++ b/site/en/gemma/docs/paligemma/fine-tuning-paligemma.ipynb
@@ -0,0 +1,875 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3MMAcssHTML"
+      },
+      "source": [
+        "<link rel=\"stylesheet\" href=\"/site-assets/css/gemma.css\">\n",
+        "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/css2?family=Google+Symbols:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200\" />"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HiJG9Do4_-sm"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "_fEE8rM9BUfS"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u71STQRgnQ3a"
+      },
+      "source": [
+        "# Fine-tune PaliGemma with JAX and Flax\n",
+        "\n",
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "<td>\n",
+        "<a target=\"_blank\" href=\"https://ai.google.dev/gemma/docs/paligemma/fine-tuning-paligemma\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "</td>\n",
+        "<td>\n",
+        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/paligemma/fine-tuning-paligemma.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "</td>\n",
+        "<td>\n",
+        "<a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemma/docs/paligemma/fine-tuning-paligemma.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "</td>\n",
+        "</table>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wR53lePHuiP-"
+      },
+      "source": [
+        "This notebook shows how to fine-tune [PaliGemma](https://ai.google.dev/gemma/docs/paligemma) on a vision-language task with [JAX](https://jax.readthedocs.io/en/latest/index.html). *Fine-tuning* is a process that can improve your model's performance on specific tasks or help the model adhere to specific output requirements when instructions aren't sufficient and you have a set of examples that demonstrate the outputs you want. Gemma-based models like PaliGemma require fine-tuning to produce expected results.\n",
+        "\n",
+        "### What's in this notebook\n",
+        "\n",
+        "This notebook uses the model reference implementation from [`big_vision`](https://github.com/google-research/big_vision)\n",
+        "and shows how to:\n",
+        "\n",
+        " * Install dependencies, and download the PaliGemma model checkpoint and training data\n",
+        " * Load the model onto GPU devices\n",
+        " * Prepare the model's inputs for training and inference\n",
+        " * Fine-tune the model\n",
+        " * Inspect the output\n",
+        "\n",
+        "The training data for this notebook consists of 90 pairs of images and long captions describing them. To make it runnable on a T4 colab runtime, you'll only fine-tune the attention layers of the language model and freeze the other parameters.\n",
+        "\n",
+        "This example is for learning purposes only. In a real use case, the amount of data, trainable parameters, training steps and hyper-parameters, and obtained results could be significantly different.\n",
+        "\n",
+        "### Before you begin\n",
+        "\n",
+        "Before going through this notebook, you should be familiar with Python code, as well as how large language models (LLMs) are trained. You don't need to be familiar with JAX, but basic knowledge about JAX (or similar technologies such as Keras) is helpful when reading through the example code."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6U0QUFveqSP2"
+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "The following sections explain the preliminary steps for getting a notebook to use a PaliGemma model, including model access, getting an API key, and configuring the notebook runtime."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qRi1rF4MWlQi"
+      },
+      "source": [
+        "### Get access to PaliGemma\n",
+        "\n",
+        "Before using PaliGemma for the first time, you must request access to the model through Kaggle by completing the following steps:\n",
+        "\n",
+        "1. Log in to [Kaggle](https://www.kaggle.com), or create a new Kaggle account if you don't already have one.\n",
+        "1. Go to the [PaliGemma model card](https://www.kaggle.com/models/google/paligemma/) and click **Request Access**.\n",
+        "1. Complete the consent form and accept the terms and conditions."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "azmRZvgGyhAb"
+      },
+      "source": [
+        "### Configure your API key\n",
+        "\n",
+        "To use PaliGemma, you must provide your Kaggle username and a Kaggle API key.\n",
+        "\n",
+        "To generate a Kaggle API key, open your [**Settings** page in Kaggle](https://www.kaggle.com/settings) and click **Create New Token**. This triggers the download of a `kaggle.json` file containing your API credentials.\n",
+        "\n",
+        "Then, in Colab, select **Secrets** (🔑) in the left pane and add your Kaggle username and Kaggle API key. Store your username under the name `KAGGLE_USERNAME` and your API key under the name `KAGGLE_KEY`.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Kp6XQ2hQB8lv"
+      },
+      "source": [
+        "### Select the runtime\n",
+        "\n",
+        "To complete this tutorial, you'll need to have a Colab runtime with sufficient resources to run the PaliGemma model. In this case, you can use a T4 GPU:\n",
+        "\n",
+        "1. In the upper-right of the Colab window, click the **▾ (Additional connection options)** dropdown menu.\n",
+        "1. Select **Change runtime type**.\n",
+        "1. Under **Hardware accelerator**, select **T4 GPU**."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qOJ3BeYFVrOX"
+      },
+      "source": [
+        "### Set environment variables\n",
+        "\n",
+        "Set the environment variables for `KAGGLE_USERNAME` and `KAGGLE_KEY`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zGLIp1Cx3_CX"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "# Note: `userdata.get` is a Colab API. If you're not using Colab, set the env\n",
+        "# vars as appropriate or make your credentials available in ~/.kaggle/kaggle.json\n",
+        "\n",
+        "os.environ[\"KAGGLE_USERNAME\"] = userdata.get('KAGGLE_USERNAME')\n",
+        "os.environ[\"KAGGLE_KEY\"] = userdata.get('KAGGLE_KEY')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rCd__uzW_eK-"
+      },
+      "source": [
+        "### Fetch the `big_vision` repository and install related dependencies\n",
+        "\n",
+        "Download the `big_vision` repository to your Colab notebook from GitHub and install dependencies related to `big_vision` by running the following code."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DfxKb3F839Ks"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import sys\n",
+        "\n",
+        "# TPUs with\n",
+        "if \"COLAB_TPU_ADDR\" in os.environ:\n",
+        "  raise \"It seems you are using Colab with remote TPUs which is not supported.\"\n",
+        "\n",
+        "# Fetch big_vision repository if python doesn't know about it and install\n",
+        "# dependencies needed for this notebook.\n",
+        "if not os.path.exists(\"big_vision_repo\"):\n",
+        "  !git clone --quiet --branch=main --depth=1 \\\n",
+        "     https://github.com/google-research/big_vision big_vision_repo\n",
+        "\n",
+        "# Append big_vision code to python import path\n",
+        "if \"big_vision_repo\" not in sys.path:\n",
+        "  sys.path.append(\"big_vision_repo\")\n",
+        "\n",
+        "# Install missing dependencies. Assume jax~=0.4.25 with GPU available.\n",
+        "!pip3 install -q \"overrides\" \"ml_collections\" \"einops~=0.7\" \"sentencepiece\"\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zDoq0O77GF30"
+      },
+      "source": [
+        "### Import JAX and other dependencies\n",
+        "\n",
+        "Import JAX and other dependencies required for PaliGemma, like TensorFlow and NumPy."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dTfe2k8J4Bw0"
+      },
+      "outputs": [],
+      "source": [
+        "import base64\n",
+        "import functools\n",
+        "import html\n",
+        "import io\n",
+        "import os\n",
+        "import warnings\n",
+        "\n",
+        "import jax\n",
+        "import jax.numpy as jnp\n",
+        "import numpy as np\n",
+        "import ml_collections\n",
+        "\n",
+        "import tensorflow as tf\n",
+        "import sentencepiece\n",
+        "\n",
+        "from IPython.core.display import display, HTML\n",
+        "from PIL import Image\n",
+        "\n",
+        "# Import model definition from big_vision\n",
+        "from big_vision.models.proj.paligemma import paligemma\n",
+        "from big_vision.trainers.proj.paligemma import predict_fns\n",
+        "\n",
+        "# Import big vision utilities\n",
+        "import big_vision.datasets.jsonl\n",
+        "import big_vision.utils\n",
+        "import big_vision.sharding\n",
+        "\n",
+        "# Don't let TF use the GPU or TPUs\n",
+        "tf.config.set_visible_devices([], \"GPU\")\n",
+        "tf.config.set_visible_devices([], \"TPU\")\n",
+        "\n",
+        "backend = jax.lib.xla_bridge.get_backend()\n",
+        "print(f\"JAX version:  {jax.__version__}\")\n",
+        "print(f\"JAX platform: {backend.platform}\")\n",
+        "print(f\"JAX devices:  {jax.device_count()}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b9kSadtIhjlX"
+      },
+      "source": [
+        "## Download and configure the model\n",
+        "\n",
+        "In this step, you'll download the model checkpoint and configure it so that you can fine-tune it later on. This step shows you how to move model parameters into TPU memory, which is useful for fine-tuning models on devices with limited resources."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7tvcc0oQHl4v"
+      },
+      "source": [
+        "### Download the model checkpoint\n",
+        "\n",
+        "PaliGemma includes several model variations. For this tutorial, you'll use the base [JAX/FLAX PaliGemma 3B weight model](https://www.kaggle.com/models/google/paligemma/jax/paligemma-3b-pt-224).\n",
+        "\n",
+        "Download the `float16` version of the model checkpoint from Kaggle by running the following code. This process takes several minutes to complete."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gQNOTfF24AV4"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import kagglehub\n",
+        "\n",
+        "MODEL_PATH = \"./pt_224_128.params.f16.npz\"\n",
+        "if not os.path.exists(MODEL_PATH):\n",
+        "  print(\"Downloading the checkpoint from Kaggle, this could take a few minutes....\")\n",
+        "  # Note: kaggle archive contains the same checkpoint in multiple formats.\n",
+        "  # Download only the float16 model.\n",
+        "  MODEL_PATH = kagglehub.model_download('google/paligemma/jax/paligemma-3b-pt-224', 'paligemma-3b-pt-224.f16.npz')\n",
+        "  print(f\"Model path: {MODEL_PATH}\")\n",
+        "\n",
+        "TOKENIZER_PATH = \"./paligemma_tokenizer.model\"\n",
+        "if not os.path.exists(TOKENIZER_PATH):\n",
+        "  print(\"Downloading the model tokenizer...\")\n",
+        "  !gsutil cp gs://big_vision/paligemma_tokenizer.model {TOKENIZER_PATH}\n",
+        "  print(f\"Tokenizer path: {TOKENIZER_PATH}\")\n",
+        "\n",
+        "DATA_DIR=\"./longcap100\"\n",
+        "if not os.path.exists(DATA_DIR):\n",
+        "  print(\"Downloading the dataset...\")\n",
+        "  !gsutil -m -q cp -n -r gs://longcap100/ .\n",
+        "  print(f\"Data path: {DATA_DIR}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rv7w-cGuLj5o"
+      },
+      "source": [
+        "### Configure the model\n",
+        "\n",
+        "It's time to actually start configuring the model that you're going to use.\n",
+        "\n",
+        "For this notebook, you need to be able to fit your model onto a T4 GPU. Having a limited resource like space constraints means that you have to be mindful of how your model is configured.\n",
+        "\n",
+        "If you fine-tune every parameter, your model won't be able to run in the notebook environment. As a result, in this part of the notebook, you'll configure your model so that it has the ability to freeze some of the parameters, and only fine-tune the parameters that really need to be fine-tuned for the model to give you accurate results. In LLMs, parameters are said to be *frozen* when they are no longer actively being used to train the model.\n",
+        "\n",
+        "In order to configure your model, you need to:\n",
+        "\n",
+        "* Initialize the `model_config` as a [`FrozenConfigDict`](https://github.com/google/ml_collections/tree/master#frozenconfigdict) so that you can freeze some of the parameters and keep memory usage low\n",
+        "* Initialize an instance of the PaliGemma `Model` class using the `model_config` as its configurations\n",
+        "* Load the model parameters into RAM\n",
+        "* Define a `decode` function to sample outputs from the model\n",
+        "\n",
+        "This code in this cell takes about a minute to run to completion."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1aghcULcEdtv"
+      },
+      "outputs": [],
+      "source": [
+        "# Define model\n",
+        "model_config = ml_collections.FrozenConfigDict({\n",
+        "    \"llm\": {\"vocab_size\": 257_152},\n",
+        "    \"img\": {\"variant\": \"So400m/14\", \"pool_type\": \"none\", \"scan\": True, \"dtype_mm\": \"float16\"}\n",
+        "})\n",
+        "model = paligemma.Model(**model_config)\n",
+        "tokenizer = sentencepiece.SentencePieceProcessor(TOKENIZER_PATH)\n",
+        "\n",
+        "# Load params - this can take up to 1 minute in T4 colabs.\n",
+        "params = paligemma.load(None, MODEL_PATH, model_config)\n",
+        "\n",
+        "# Define `decode` function to sample outputs from the model.\n",
+        "decode_fn = predict_fns.get_all(model)['decode']\n",
+        "decode = functools.partial(decode_fn, devices=jax.devices(), eos_token=tokenizer.eos_id())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uidBwmb8LwZ5"
+      },
+      "source": [
+        "### Move model parameters into GPU/TPU memory\n",
+        "\n",
+        "Now you need to move the model parameters into GPU/TPU memory. First, shard the parameters across the available GPUs, then load the parameters. Here, you'll load the parameters sequentially. This process takes longer than loading them simultaneously, but it requires more RAM than you have available in this notebook.\n",
+        "\n",
+        "Finally, print out all of the parameters to see what type each individual parameter is cast to. Frozen parameters are kept as `float16`, while the trainable parameters are cast to `float32`. When you inspect the list, you'll see that most of the parameters have been frozen and are `float16`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RWOdf_fw2SAO"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a pytree mask of the trainable params.\n",
+        "def is_trainable_param(name, param):  # pylint: disable=unused-argument\n",
+        "  if name.startswith(\"llm/layers/attn/\"):  return True\n",
+        "  if name.startswith(\"llm/\"):              return False\n",
+        "  if name.startswith(\"img/\"):              return False\n",
+        "  raise ValueError(f\"Unexpected param name {name}\")\n",
+        "trainable_mask = big_vision.utils.tree_map_with_names(is_trainable_param, params)\n",
+        "\n",
+        "# If more than one device is available (e.g. multiple GPUs) the parameters can\n",
+        "# be sharded across them to reduce HBM usage per device.\n",
+        "mesh = jax.sharding.Mesh(jax.devices(), (\"data\"))\n",
+        "\n",
+        "data_sharding = jax.sharding.NamedSharding(\n",
+        "    mesh, jax.sharding.PartitionSpec(\"data\"))\n",
+        "\n",
+        "params_sharding = big_vision.sharding.infer_sharding(\n",
+        "    params, strategy=[('.*', 'fsdp(axis=\"data\")')], mesh=mesh)\n",
+        "\n",
+        "# Yes: Some donated buffers are not usable.\n",
+        "warnings.filterwarnings(\n",
+        "    \"ignore\", message=\"Some donated buffers were not usable\")\n",
+        "\n",
+        "@functools.partial(jax.jit, donate_argnums=(0,), static_argnums=(1,))\n",
+        "def maybe_cast_to_f32(params, trainable):\n",
+        "  return jax.tree.map(lambda p, m: p.astype(jnp.float32) if m else p,\n",
+        "                      params, trainable)\n",
+        "\n",
+        "# Loading all params in simultaneous - albeit much faster and more succinct -\n",
+        "# requires more RAM than the T4 colab runtimes have by default.\n",
+        "# Instead we do it param by param.\n",
+        "params, treedef = jax.tree.flatten(params)\n",
+        "sharding_leaves = jax.tree.leaves(params_sharding)\n",
+        "trainable_leaves = jax.tree.leaves(trainable_mask)\n",
+        "for idx, (sharding, trainable) in enumerate(zip(sharding_leaves, trainable_leaves)):\n",
+        "  params[idx] = big_vision.utils.reshard(params[idx], sharding)\n",
+        "  params[idx] = maybe_cast_to_f32(params[idx], trainable)\n",
+        "  params[idx].block_until_ready()\n",
+        "params = jax.tree.unflatten(treedef, params)\n",
+        "\n",
+        "# Print params to show what the model is made of.\n",
+        "def parameter_overview(params):\n",
+        "  for path, arr in big_vision.utils.tree_flatten_with_names(params)[0]:\n",
+        "    print(f\"{path:80s} {str(arr.shape):22s} {arr.dtype}\")\n",
+        "\n",
+        "print(\" == Model params == \")\n",
+        "parameter_overview(params)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iD_9XXQkn1Mv"
+      },
+      "source": [
+        "## Prepare to tune the model\n",
+        "\n",
+        "Now that your model is configured, you can tune it. In this step, you'll create your model's inputs as well as the training and validation iterators, view the training examples, and define the training and validation loops."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "83ZcnbddJKdx"
+      },
+      "source": [
+        "### Create model inputs\n",
+        "\n",
+        "The model checkpoint you're using has already been trained on images of various aspect ratios that have been resized to 224x224 pixels, and to handle tokenized texts.\n",
+        "\n",
+        "The code below defines three functions that you'll use in the next step create the model's inputs:\n",
+        "\n",
+        "* **`preprocess_image`:** Normalizes the image data. In this case, pre-processing converts the passed-in image to greyscale, removes the alpha layer, and resizes the passed-in image to the size required by the model for image inputs (224x224 pixels).\n",
+        "* **`preprocess_tokens`:** Splits the tokens up and adds flags to mark whether a token is a prefix or suffix token. These flags will be used later on in the code, during the training step and the evaluation loop.\n",
+        "* **`postprocess_tokens`:** Removes any tokens left at and/or after the end-of-sequence (EOS) token and returns the remaining decoded tokens.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8SRW0NuU4UcW"
+      },
+      "outputs": [],
+      "source": [
+        "def preprocess_image(image, size=224):\n",
+        "  # Model has been trained to handle images of different aspects ratios\n",
+        "  # resized to 224x224 in the range [-1, 1]. Bilinear and antialias resize\n",
+        "  # options are helpful to improve quality in some tasks.\n",
+        "  image = np.asarray(image)\n",
+        "  if image.ndim == 2:  # Convert image without last channel into greyscale.\n",
+        "    image = np.stack((image,)*3, axis=-1)\n",
+        "  image = image[..., :3]  # Remove alpha layer.\n",
+        "  assert image.shape[-1] == 3\n",
+        "\n",
+        "  image = tf.constant(image)\n",
+        "  image = tf.image.resize(image, (size, size), method='bilinear', antialias=True)\n",
+        "  return image.numpy() / 127.5 - 1.0  # [0, 255]->[-1,1]\n",
+        "\n",
+        "def preprocess_tokens(prefix, suffix=None, seqlen=None):\n",
+        "  # Model has been trained to handle tokenized text composed of a prefix with\n",
+        "  # full attention and a suffix with causal attention.\n",
+        "  separator = \"\\n\"\n",
+        "  tokens = tokenizer.encode(prefix, add_bos=True) + tokenizer.encode(separator)\n",
+        "  mask_ar = [0] * len(tokens)    # 0 to use full attention for prefix.\n",
+        "  mask_loss = [0] * len(tokens)  # 0 to not use prefix tokens in the loss.\n",
+        "\n",
+        "  if suffix:\n",
+        "    suffix = tokenizer.encode(suffix, add_eos=True)\n",
+        "    tokens += suffix\n",
+        "    mask_ar += [1] * len(suffix)    # 1 to use causal attention for suffix.\n",
+        "    mask_loss += [1] * len(suffix)  # 1 to use suffix tokens in the loss.\n",
+        "\n",
+        "  mask_input = [1] * len(tokens)    # 1 if it's a token, 0 if padding.\n",
+        "  if seqlen:\n",
+        "    padding = [0] * max(0, seqlen - len(tokens))\n",
+        "    tokens = tokens[:seqlen] + padding\n",
+        "    mask_ar = mask_ar[:seqlen] + padding\n",
+        "    mask_loss = mask_loss[:seqlen] + padding\n",
+        "    mask_input = mask_input[:seqlen] + padding\n",
+        "\n",
+        "  return jax.tree.map(np.array, (tokens, mask_ar, mask_loss, mask_input))\n",
+        "\n",
+        "def postprocess_tokens(tokens):\n",
+        "  tokens = tokens.tolist()  # np.array to list[int]\n",
+        "  try:  # Remove tokens at and after EOS if any.\n",
+        "    eos_pos = tokens.index(tokenizer.eos_id())\n",
+        "    tokens = tokens[:eos_pos]\n",
+        "  except ValueError:\n",
+        "    pass\n",
+        "  return tokenizer.decode(tokens)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ovgWBgdHJZq3"
+      },
+      "source": [
+        "### Create the training and validation iterators\n",
+        "\n",
+        "Create two iterators:\n",
+        "\n",
+        "*   A **training iterator** to allow the training process to go through the data in chunks rather than processing it all at once\n",
+        "    *   This allows you to do some data pre-processing before use\n",
+        "*   A **validation iterator** that allows the training process to iterate over the validation dataset to see how well the tuned model aligned with the provided results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "whzWOojGOtzi"
+      },
+      "outputs": [],
+      "source": [
+        "SEQLEN = 128\n",
+        "\n",
+        "train_dataset = big_vision.datasets.jsonl.DataSource(\n",
+        "    os.path.join(DATA_DIR, \"data_train90.jsonl\"),\n",
+        "    fopen_keys={\"image\": DATA_DIR})\n",
+        "\n",
+        "val_dataset = big_vision.datasets.jsonl.DataSource(\n",
+        "    os.path.join(DATA_DIR, \"data_val10.jsonl\"),\n",
+        "    fopen_keys={\"image\": DATA_DIR})\n",
+        "\n",
+        "\n",
+        "def train_data_iterator():\n",
+        "  \"\"\"Never ending iterator over training examples.\"\"\"\n",
+        "  # Shuffle examples and repeat so one can train for many epochs.\n",
+        "  dataset = train_dataset.get_tfdata().shuffle(1_000).repeat()\n",
+        "  for example in dataset.as_numpy_iterator():\n",
+        "    image = Image.open(io.BytesIO(example[\"image\"]))\n",
+        "    image = preprocess_image(image)\n",
+        "\n",
+        "    prefix = \"caption en\"  # Could also be a different prefix per example.\n",
+        "    suffix = example[\"suffix\"].decode().lower()\n",
+        "    tokens, mask_ar, mask_loss, _ = preprocess_tokens(prefix, suffix, SEQLEN)\n",
+        "\n",
+        "    yield {\n",
+        "        \"image\": np.asarray(image),\n",
+        "        \"text\": np.asarray(tokens),\n",
+        "        \"mask_ar\": np.asarray(mask_ar),\n",
+        "        \"mask_loss\": np.asarray(mask_loss),\n",
+        "    }\n",
+        "\n",
+        "\n",
+        "def validation_data_iterator():\n",
+        "  \"\"\"Single iterator over validation examples.\"\"\"\n",
+        "  for example in val_dataset.get_tfdata(ordered=True).as_numpy_iterator():\n",
+        "    image = Image.open(io.BytesIO(example[\"image\"]))\n",
+        "    image = preprocess_image(image)\n",
+        "\n",
+        "    prefix = \"caption en\"  # Could also be a different prefix per example.\n",
+        "    tokens, mask_ar, _, mask_input = preprocess_tokens(prefix, seqlen=SEQLEN)\n",
+        "\n",
+        "    yield {\n",
+        "        \"image\": np.asarray(image),\n",
+        "        \"text\": np.asarray(tokens),\n",
+        "        \"mask_ar\": np.asarray(mask_ar),\n",
+        "        \"mask_input\": np.asarray(mask_input),\n",
+        "    }\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "84olaM5dCiAl"
+      },
+      "source": [
+        "### View training examples\n",
+        "\n",
+        "In this notebook, the training data contains 90 images that are paired with long descriptions of what's depicted in the image.\n",
+        "\n",
+        "**Note:** Normal training data sets that are meant to be used for practical use cases should contain more images, but this notebook limits the number of data points so that you can train the model in a reasonable amount of time for an example.\n",
+        "\n",
+        "The code below prints a random selection of images with their descriptions from the training data set so that you can see what the images and descriptions your model is trained on looks like. Each image is displayed in as a 128x128 pixel JPEG, with the description printed next to the image to the right."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BzJfb5t0nsLq"
+      },
+      "outputs": [],
+      "source": [
+        "def render_inline(image, resize=(128, 128)):\n",
+        "  \"\"\"Convert image into inline html.\"\"\"\n",
+        "  image = Image.fromarray(image)\n",
+        "  image.resize(resize)\n",
+        "  with io.BytesIO() as buffer:\n",
+        "    image.save(buffer, format='jpeg')\n",
+        "    image_b64 = str(base64.b64encode(buffer.getvalue()), \"utf-8\")\n",
+        "    return f\"data:image/jpeg;base64,{image_b64}\"\n",
+        "\n",
+        "def render_example(image, caption):\n",
+        "  image = ((image + 1)/2 * 255).astype(np.uint8)  # [-1,1] -> [0, 255]\n",
+        "  return f\"\"\"\n",
+        "    <div style=\"display: inline-flex; align-items: center; justify-content: center;\">\n",
+        "        <img style=\"width:128px; height:128px;\" src=\"{render_inline(image, resize=(64,64))}\" />\n",
+        "        <p style=\"width:256px; margin:10px; font-size:small;\">{html.escape(caption)}</p>\n",
+        "    </div>\n",
+        "    \"\"\"\n",
+        "\n",
+        "html_out = \"\"\n",
+        "for idx, example in zip(range(8), train_data_iterator()):\n",
+        "  caption = postprocess_tokens(example[\"text\"])  # detokenize model input.\n",
+        "  caption = caption[len(\"caption en\\n\"):]        # strip prefix\n",
+        "  html_out += render_example(example[\"image\"], caption)\n",
+        "\n",
+        "print(\"Training examples\")\n",
+        "display(HTML(html_out))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "N2BwpXkfI8OT"
+      },
+      "source": [
+        "### Define the training and evaluation loops\n",
+        "\n",
+        "Define the training loop to train the model on the provided dataset, and the evaluation loop to look at all of the examples in the validation dataset and make its predictions.\n",
+        "\n",
+        "#### Defining the training loop\n",
+        "\n",
+        "The `update_fn` function defines the training step. During the training step, the loss per example is calculated and stochastic gradient descent (SGD) is applied to the trainable parameters.\n",
+        "\n",
+        "Recall that earlier in the notebook, you included flags in the `preprocess_tokens` function that included `mask_loss`. You'll use the `mask_loss` flag here to exclude prefix and padded tokens from the loss. Without it, the loss calculation will be skewed. You also need to normalize each example, since each of them has a different number of tokens. After the prefix and padded tokens have been excluded and the examples have been normalized, you can calculate the loss per example.\n",
+        "\n",
+        "The training step also includes a function to apply an SGD to optimize the training.\n",
+        "\n",
+        "#### Defining the evaluation loop\n",
+        "\n",
+        "The `make_predictions` function is your evaluation loop. The evaluation loop is fairly straight forward with one notable change. If you recall from the beginning of the notebook, you only have 90 examples in your training data set. This is a very small amount of training examples, and your model ends up not having enough examples for the batch size when you run the training. This means that in the evaluation loop, you need to pad the batch by repeating examples.\n",
+        "\n",
+        "To make sure that your evaluation loop only counts actual examples and not the padded examples, you have to apply a mask to the padded examples that excludes them from the output."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dwUV_imW3WQJ"
+      },
+      "outputs": [],
+      "source": [
+        "# The main update_fn using a simple stochastic gradient descent (SGD).\n",
+        "@functools.partial(jax.jit, donate_argnums=(0,))\n",
+        "def update_fn(params, batch, learning_rate):\n",
+        "  imgs, txts, mask_ar = batch[\"image\"], batch[\"text\"], batch[\"mask_ar\"]\n",
+        "\n",
+        "  def loss_fn(params):\n",
+        "    text_logits, _ = model.apply({\"params\": params}, imgs, txts[:, :-1], mask_ar[:, :-1], train=True)\n",
+        "    logp = jax.nn.log_softmax(text_logits, axis=-1)\n",
+        "\n",
+        "    # The model takes as input txts[:, :-1] but the loss is defined as predicting\n",
+        "    # next tokens txts[:, 1:]. Additionally, mask_loss[:, 1:] indicates which tokens\n",
+        "    # are part of the loss (e.g. prefix and padded tokens are not included).\n",
+        "    mask_loss = batch[\"mask_loss\"][:, 1:]\n",
+        "    targets = jax.nn.one_hot(txts[:, 1:], text_logits.shape[-1])\n",
+        "\n",
+        "    # Compute the loss per example. i.e. the mean of per token pplx.\n",
+        "    # Since each example has a different number of tokens we normalize it.\n",
+        "    token_pplx = jnp.sum(logp * targets, axis=-1)  # sum across vocab_size.\n",
+        "    example_loss = -jnp.sum(token_pplx * mask_loss, axis=-1)  # sum across seq_len.\n",
+        "    example_loss /= jnp.clip(jnp.sum(mask_loss, -1), 1)  # weight by num of tokens.\n",
+        "\n",
+        "    # batch_loss: mean of per example loss.\n",
+        "    return jnp.mean(example_loss)\n",
+        "\n",
+        "  loss, grads = jax.value_and_grad(loss_fn)(params)\n",
+        "\n",
+        "  # Apply gradients to trainable params using SGD.\n",
+        "  def apply_grad(param, gradient, trainable):\n",
+        "    if not trainable: return param\n",
+        "    return param - learning_rate * gradient\n",
+        "\n",
+        "  params = jax.tree_util.tree_map(apply_grad, params, grads, trainable_mask)\n",
+        "\n",
+        "  return params, loss\n",
+        "\n",
+        "# Evaluation/inference loop.\n",
+        "def make_predictions(data_iterator, *, num_examples=None,\n",
+        "                     batch_size=4, seqlen=SEQLEN, sampler=\"greedy\"):\n",
+        "  outputs = []\n",
+        "  while True:\n",
+        "    # Construct a list of examples in the batch.\n",
+        "    examples = []\n",
+        "    try:\n",
+        "      for _ in range(batch_size):\n",
+        "        examples.append(next(data_iterator))\n",
+        "        examples[-1][\"_mask\"] = np.array(True)  # Indicates true example.\n",
+        "    except StopIteration:\n",
+        "      if len(examples) == 0:\n",
+        "        return outputs\n",
+        "\n",
+        "    # Not enough examples to complete a batch. Pad by repeating last example.\n",
+        "    while len(examples) % batch_size:\n",
+        "      examples.append(dict(examples[-1]))\n",
+        "      examples[-1][\"_mask\"] = np.array(False)  # Indicates padding example.\n",
+        "\n",
+        "    # Convert list of examples into a dict of np.arrays and load onto devices.\n",
+        "    batch = jax.tree.map(lambda *x: np.stack(x), *examples)\n",
+        "    batch = big_vision.utils.reshard(batch, data_sharding)\n",
+        "\n",
+        "    # Make model predictions\n",
+        "    tokens = decode({\"params\": params}, batch=batch,\n",
+        "                    max_decode_len=seqlen, sampler=sampler)\n",
+        "\n",
+        "    # Fetch model predictions to device and detokenize.\n",
+        "    tokens, mask = jax.device_get((tokens, batch[\"_mask\"]))\n",
+        "    tokens = tokens[mask]  # remove padding examples.\n",
+        "    responses = [postprocess_tokens(t) for t in tokens]\n",
+        "\n",
+        "    # Append to html output.\n",
+        "    for example, response in zip(examples, responses):\n",
+        "      outputs.append((example[\"image\"], response))\n",
+        "      if num_examples and len(outputs) >= num_examples:\n",
+        "        return outputs"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n9r9V1jwJvu9"
+      },
+      "source": [
+        "## Tune the model\n",
+        "\n",
+        "Now that you've set everything up and taken a look at the training data, it's time to finally tune the model. The code below runs the training loop for the model for 64 steps and prints the learning rate (`lr` in the printed output) and loss rate for each step.\n",
+        "\n",
+        "Every 16 steps, the model prints what its predictions are at that step in the training. This code prints out predictions for the same set of images so that you can see the model's ability to predict descriptions improve over time.\n",
+        "\n",
+        "At earlier steps in the training, there's likely issues with the descriptions, such as repeated sentences as the model gets stuck in its predictive loop or unfinished sentences. The model's predictions become steadily more accurate as training progresses. By step 64, the model's predictions should closely resemble the descriptions provided by the training data.\n",
+        "\n",
+        "This process takes around 15 minutes to complete on T4 TPUs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "067wj_6bZAG3"
+      },
+      "outputs": [],
+      "source": [
+        "# Run a short training loop with cosine learning rate schedule.\n",
+        "#\n",
+        "# Note: the first step can be quite slow on some machines (up to several minutes)\n",
+        "# due to XLA compilation of the jax.jit'd function.\n",
+        "#\n",
+        "%%time\n",
+        "\n",
+        "BATCH_SIZE = 8\n",
+        "TRAIN_EXAMPLES = 512\n",
+        "LEARNING_RATE = 0.03\n",
+        "\n",
+        "TRAIN_STEPS = TRAIN_EXAMPLES // BATCH_SIZE\n",
+        "EVAL_STEPS = TRAIN_STEPS // 4\n",
+        "\n",
+        "train_data_it = train_data_iterator()\n",
+        "\n",
+        "sched_fn = big_vision.utils.create_learning_rate_schedule(\n",
+        "    total_steps=TRAIN_STEPS+1, base=LEARNING_RATE,\n",
+        "    decay_type=\"cosine\", warmup_percent=0.10)\n",
+        "\n",
+        "for step in range(1, TRAIN_STEPS+1):\n",
+        "  # Make list of N training examples.\n",
+        "  examples = [next(train_data_it) for _ in range(BATCH_SIZE)]\n",
+        "\n",
+        "  # Convert list of examples into a dict of np.arrays and load onto devices.\n",
+        "  batch = jax.tree.map(lambda *x: np.stack(x), *examples)\n",
+        "  batch = big_vision.utils.reshard(batch, data_sharding)\n",
+        "\n",
+        "  # Training step and report training loss\n",
+        "  learning_rate = sched_fn(step)\n",
+        "  params, loss = update_fn(params, batch, learning_rate)\n",
+        "\n",
+        "  loss = jax.device_get(loss)\n",
+        "  print(f\"step: {step:2d}/{TRAIN_STEPS:2d}   lr: {learning_rate:.5f}   loss: {loss:.4f}\")\n",
+        "\n",
+        "  if (step % EVAL_STEPS) == 0:\n",
+        "    print(f\"Model predictions at step {step}\")\n",
+        "    html_out = \"\"\n",
+        "    for image, caption in make_predictions(\n",
+        "        validation_data_iterator(), num_examples=4, batch_size=4):\n",
+        "      html_out += render_example(image, caption)\n",
+        "    display(HTML(html_out))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "glScsFLVJ52c"
+      },
+      "source": [
+        "## Output\n",
+        "\n",
+        "The validation data for this notebook consists of just 10 images. In normal code, you would likely have many more data points for validation, but for this notebook, run the following code to generate descriptions for all 10 images. After tuning the model, these descriptions should be very similar in form and content coverage to the descriptions included with the training data that you looked at earlier in this notebook.\n",
+        "\n",
+        "Run the below code to generate descriptions for the validation data set."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hgUhEKjzPdMQ"
+      },
+      "outputs": [],
+      "source": [
+        "# The validation data consists of 10 images in a different domain than training\n",
+        "# data.\n",
+        "%%time\n",
+        "\n",
+        "print(\"Model predictions\")\n",
+        "html_out = \"\"\n",
+        "for image, caption in make_predictions(validation_data_iterator(), batch_size=4):\n",
+        "  html_out += render_example(image, caption)\n",
+        "display(HTML(html_out))\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "fine-tuning-paligemma.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemma/docs/paligemma/inference-with-keras.ipynb b/site/en/gemma/docs/paligemma/inference-with-keras.ipynb
new file mode 100644
index 000000000..32581fb4b
--- /dev/null
+++ b/site/en/gemma/docs/paligemma/inference-with-keras.ipynb
@@ -0,0 +1,689 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3MMAcssHTML"
+      },
+      "source": [
+        "<link rel=\"stylesheet\" href=\"/site-assets/css/gemma.css\">\n",
+        "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/css2?family=Google+Symbols:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200\" />"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3_lX1k54KKrx"
+      },
+      "source": [
+        "Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "Gr4W9nspKGtb"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "etcMXWCUJApZ"
+      },
+      "source": [
+        "# Inference with Keras\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Q5_nIe-8gdJV"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "<td>\n",
+        "<a target=\"_blank\" href=\"https://ai.google.dev/gemma/docs/paligemma/inference-with-keras\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "</td>\n",
+        "<td>\n",
+        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/paligemma/inference-with-keras.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "</td>\n",
+        "<td>\n",
+        "<a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemma/docs/paligemma/inference-with-keras.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "</td>\n",
+        "</table>\n",
+        "\n",
+        "When your AI model produces a conclusion or a prediction, it goes through a process called *inference*. This tutorial goes over how to use PaliGemma with Keras to set up a simple model that can infer information about supplied images and answer questions about them."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9JaII1xxfbfz"
+      },
+      "source": [
+        "## What's in this notebook\n",
+        "\n",
+        "This notebook uses PaliGemma with Keras and shows you how to:\n",
+        "\n",
+        "* Install Keras and the required dependencies\n",
+        "* Download `PaliGemmaCausalLM`, a pre-trained PaliGemma variant for causal visual language modeling, and use it to create a model\n",
+        "* Test the model's ability to infer information about supplied images"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Bf7AUi02fcPL"
+      },
+      "source": [
+        "## Before you begin\n",
+        "\n",
+        "Before going through this notebook, you should be familiar with Python code, as well as how large language models (LLMs) are trained. You don't need to be familiar with Keras, but basic knowledge about Keras is helpful when reading through the example code."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "380it9kIhzmm"
+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "The following sections explain the preliminary steps for getting a notebook to use a PaliGemma model, including model access, getting an API key, and configuring the notebook runtime."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6uN0EpPJh7t5"
+      },
+      "source": [
+        "### Get access to PaliGemma\n",
+        "\n",
+        "Before using PaliGemma for the first time, you must request access to the model through Kaggle by completing the following steps:\n",
+        "\n",
+        "1. Log in to [Kaggle](https://www.kaggle.com), or create a new Kaggle account if you don't already have one.\n",
+        "1. Go to the [PaliGemma model card](https://www.kaggle.com/models/google/paligemma/) and click **Request Access**.\n",
+        "1. Complete the consent form and accept the terms and conditions."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Uz_pBNrWiDqe"
+      },
+      "source": [
+        "### Configure your API key\n",
+        "\n",
+        "To use PaliGemma, you must provide your Kaggle username and a Kaggle API key.\n",
+        "\n",
+        "To generate a Kaggle API key, open your [**Settings** page in Kaggle](https://www.kaggle.com/settings) and click **Create New Token**. This triggers the download of a `kaggle.json` file containing your API credentials.\n",
+        "\n",
+        "Then, in Colab, select **Secrets** (🔑) in the left pane and add your Kaggle username and Kaggle API key. Store your username under the name `KAGGLE_USERNAME` and your API key under the name `KAGGLE_KEY`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wUB4JE0Hlxce"
+      },
+      "source": [
+        "### Select the runtime\n",
+        "\n",
+        "To complete this tutorial, you'll need to have a Colab runtime with sufficient resources to run the PaliGemma model. In this case, you can use a T4 GPU:\n",
+        "\n",
+        "1. In the upper-right of the Colab window, click the **▾ (Additional connection options)** dropdown menu.\n",
+        "1. Select **Change runtime type**.\n",
+        "1. Under **Hardware accelerator**, select **T4 GPU**."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mIvv2Yo3lycQ"
+      },
+      "source": [
+        "### Set environment variables\n",
+        "\n",
+        "Set the environment variables for `KAGGLE_USERNAME`, `KAGGLE_KEY`, and `KERAS_BACKEND`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rdgwLyZLQBkP"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "# Set up environmental variables\n",
+        "os.environ[\"KAGGLE_USERNAME\"] = userdata.get('KAGGLE_USERNAME')\n",
+        "os.environ[\"KAGGLE_KEY\"] = userdata.get('KAGGLE_KEY')\n",
+        "os.environ[\"KERAS_BACKEND\"] = \"jax\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4a3Q4VCLljR9"
+      },
+      "source": [
+        "### Install Keras\n",
+        "\n",
+        "Run the below cell to install Keras."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DoYMMytAaMRJ"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -U -q keras-nlp"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y2Y7BRtRgfvG"
+      },
+      "source": [
+        "### Import dependencies and configure Keras\n",
+        "\n",
+        "Install the dependencies needed for this notebook and configure Keras' backend. You'll also set Keras to use `bfloat16` so that the framework uses less memory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MHECpBe6LE7y"
+      },
+      "outputs": [],
+      "source": [
+        "import keras\n",
+        "import keras_nlp\n",
+        "import numpy as np\n",
+        "import PIL\n",
+        "import requests\n",
+        "import io\n",
+        "import matplotlib\n",
+        "import re\n",
+        "import matplotlib.pyplot as plt\n",
+        "import matplotlib.patches as patches\n",
+        "from PIL import Image\n",
+        "\n",
+        "keras.config.set_floatx(\"bfloat16\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ftjt5DiueVkL"
+      },
+      "source": [
+        "## Create your model\n",
+        "\n",
+        "Now that you've set everything up, you can download the pre-trained model and create some utility methods to help your model generate its responses."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "X-LE2E1uiSpP"
+      },
+      "source": [
+        "### Download the model checkpoint\n",
+        "\n",
+        "KerasNLP provides implementations of many popular [model architectures](https://keras.io/api/keras_nlp/models/). In this notebook, you'll create a model using `PaliGemmaCausalLM`, an end-to-end PaliGemma model for *causal visual language modeling*. A causal visual language model predicts the next token based on previous tokens.\n",
+        "\n",
+        "Create the model using the `from_preset` method and print its summary. This process will take about a minute to complete."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "abNuIP8D_9At"
+      },
+      "outputs": [],
+      "source": [
+        "paligemma = keras_nlp.models.PaliGemmaCausalLM.from_preset(\"pali_gemma_3b_mix_224\")\n",
+        "paligemma.summary()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FBsWvKEvoGMe"
+      },
+      "source": [
+        "### Create utility methods\n",
+        "\n",
+        "To help you generate responses from your model, create two utility methods:\n",
+        "\n",
+        "*   **`crop_and_resize`:** Helper method for `read_img`. This method crops and resizes the image to the passed in size so that the final image is resized without skewing the proportions of the image.\n",
+        "*   **`read_img`:** Helper method for `read_img_from_url`. This method is what actually opens the image, resizes it so that it fits in the model's constraints, and puts it into an array that can be interpreted by the model.\n",
+        "*   **`read_img_from_url`:** Takes in an image via a valid URL. You need this method to pass the image to the model.\n",
+        "\n",
+        "You'll use `read_img_from_url` in the next step of this notebook.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "S6_XQjhpvXiG"
+      },
+      "outputs": [],
+      "source": [
+        "def crop_and_resize(image, target_size):\n",
+        "    width, height = image.size\n",
+        "    source_size = min(image.size)\n",
+        "    left = width // 2 - source_size // 2\n",
+        "    top = height // 2 - source_size // 2\n",
+        "    right, bottom = left + source_size, top + source_size\n",
+        "    return image.resize(target_size, box=(left, top, right, bottom))\n",
+        "\n",
+        "def read_image(url, target_size):\n",
+        "    contents = io.BytesIO(requests.get(url).content)\n",
+        "    image = PIL.Image.open(contents)\n",
+        "    image = crop_and_resize(image, target_size)\n",
+        "    image = np.array(image)\n",
+        "    # Remove alpha channel if neccessary.\n",
+        "    if image.shape[2] == 4:\n",
+        "        image = image[:, :, :3]\n",
+        "    return image\n",
+        "\n",
+        "def parse_bbox_and_labels(detokenized_output: str):\n",
+        "  matches = re.finditer(\n",
+        "      '<loc(?P<y0>\\d\\d\\d\\d)><loc(?P<x0>\\d\\d\\d\\d)><loc(?P<y1>\\d\\d\\d\\d)><loc(?P<x1>\\d\\d\\d\\d)>'\n",
+        "      ' (?P<label>.+?)( ;|$)',\n",
+        "      detokenized_output,\n",
+        "  )\n",
+        "  labels, boxes = [], []\n",
+        "  fmt = lambda x: float(x) / 1024.0\n",
+        "  for m in matches:\n",
+        "    d = m.groupdict()\n",
+        "    boxes.append([fmt(d['y0']), fmt(d['x0']), fmt(d['y1']), fmt(d['x1'])])\n",
+        "    labels.append(d['label'])\n",
+        "  return np.array(boxes), np.array(labels)\n",
+        "\n",
+        "def display_boxes(image, boxes, labels, target_image_size):\n",
+        "  h, l = target_size\n",
+        "  fig, ax = plt.subplots()\n",
+        "  ax.imshow(image)\n",
+        "  for i in range(boxes.shape[0]):\n",
+        "      y, x, y2, x2 = (boxes[i]*h)\n",
+        "      width = x2 - x\n",
+        "      height = y2 - y\n",
+        "      # Create a Rectangle patch\n",
+        "      rect = patches.Rectangle((x, y),\n",
+        "                               width,\n",
+        "                               height,\n",
+        "                               linewidth=1,\n",
+        "                               edgecolor='r',\n",
+        "                               facecolor='none')\n",
+        "      # Add label\n",
+        "      plt.text(x, y, labels[i], color='red', fontsize=12)\n",
+        "      # Add the patch to the Axes\n",
+        "      ax.add_patch(rect)\n",
+        "\n",
+        "  plt.show()\n",
+        "\n",
+        "def display_segment_output(image, segment_mask, target_image_size):\n",
+        "  # Calculate scaling factors\n",
+        "  h, w = target_image_size\n",
+        "  x_scale = w / 64\n",
+        "  y_scale = h / 64\n",
+        "\n",
+        "  # Create coordinate grids for the new image\n",
+        "  x_coords = np.arange(w)\n",
+        "  y_coords = np.arange(h)\n",
+        "  x_coords = (x_coords / x_scale).astype(int)\n",
+        "  y_coords = (y_coords / y_scale).astype(int)\n",
+        "  resized_array = segment_mask[y_coords[:, np.newaxis], x_coords]\n",
+        "  # Create a figure and axis\n",
+        "  fig, ax = plt.subplots()\n",
+        "\n",
+        "  # Display the image\n",
+        "  ax.imshow(image)\n",
+        "\n",
+        "  # Overlay the mask with transparency\n",
+        "  ax.imshow(resized_array, cmap='jet', alpha=0.5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AeVUHA_zP8ZF"
+      },
+      "source": [
+        "## Test your model\n",
+        "\n",
+        "Now you're ready to give an image and prompt to your model and have it infer the response.\n",
+        "\n",
+        "Lets look at our test image and read it\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GtPeUkGJFvXS"
+      },
+      "outputs": [],
+      "source": [
+        "target_size = (224, 224)\n",
+        "image_url = 'https://storage.googleapis.com/keras-cv/models/paligemma/cow_beach_1.png'\n",
+        "cow_image = read_image(image_url, target_size)\n",
+        "matplotlib.pyplot.imshow(cow_image)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R3FE63iMqb2G"
+      },
+      "source": [
+        "Here's a generation call with a single image and prompt. The prompts have to end with a `\\n`.\n",
+        "\n",
+        "We've supplied you with several example prompts — play around with it! Comment and uncomment the prompt variables to change what prompt you supply the model with."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WQVmhpr6qdNH"
+      },
+      "outputs": [],
+      "source": [
+        "prompt = 'answer en where is the cow standing?\\n'\n",
+        "# prompt = 'svar no hvor står kuen?'\n",
+        "# prompt = 'answer fr quelle couleur est le ciel?'\n",
+        "# prompt = 'responda pt qual a cor do animal?'\n",
+        "output = paligemma.generate(\n",
+        "    inputs={\n",
+        "        \"images\": cow_image,\n",
+        "        \"prompts\": prompt,\n",
+        "    }\n",
+        ")\n",
+        "print(output)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MCMqS0vI6S36"
+      },
+      "source": [
+        "Here's a generation call with batched inputs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UZle4sJP6YwB"
+      },
+      "outputs": [],
+      "source": [
+        "prompts = [\n",
+        "    'answer en where is the cow standing?\\n',\n",
+        "    'answer en what color is the cow?\\n',\n",
+        "    'describe en\\n',\n",
+        "    'detect cow\\n',\n",
+        "    'segment cow\\n',\n",
+        "]\n",
+        "images = [cow_image, cow_image, cow_image, cow_image, cow_image]\n",
+        "outputs = paligemma.generate(\n",
+        "    inputs={\n",
+        "        \"images\": images,\n",
+        "        \"prompts\": prompts,\n",
+        "    }\n",
+        ")\n",
+        "for output in outputs:\n",
+        "    print(output)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qJKvH8to6kb6"
+      },
+      "source": [
+        "We've supplied you with several example prompts — play around with it! Comment and uncomment the `prompt` variables to change what prompt you supply the model with."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-uqMwZQY31mu"
+      },
+      "source": [
+        "### Other styles of prompts\n",
+        "\n",
+        "You may have noticed in the previous step that the provided examples are in several different languages. PaliGemma supports language recognition for 34 different languages. You can find the list of supported languages on [GitHub](https://github.com/google/crossmodal-3600/blob/main/web-data/README.md).\n",
+        "\n",
+        "PaliGemma can handle several other prompt styles:\n",
+        "\n",
+        "*   **`\"cap {lang}\\n\"`:** Very raw short caption (from WebLI-alt)\n",
+        "*   **`\"caption {lang}\\n\"`:** Nice, COCO-like short captions\n",
+        "*   **`\"describe {lang}\\n\"`:** Somewhat longer, more descriptive captions\n",
+        "*   **`\"ocr\"`:** Optical character recognition\n",
+        "*   **`\"answer en {question}\\n\"`:** Question answering about the image contents\n",
+        "*   **`\"question {lang} {answer}\\n\"`:** Question generation for a given answer\n",
+        "*   **`\"detect {object} ; {object}\\n\"`:** Count objects in a scene and return the bounding boxes for the objects\n",
+        "*   **`\"segment {object}\\n\"`:** Do image segmentation of the object in the scene\n",
+        "\n",
+        "Try them out!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eUgPcUGDpsdx"
+      },
+      "source": [
+        "### Parse detect output"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "noRIZtqnpzNn"
+      },
+      "outputs": [],
+      "source": [
+        "prompt = 'detect cow\\n'\n",
+        "output = paligemma.generate(\n",
+        "    inputs={\n",
+        "        \"images\": cow_image,\n",
+        "        \"prompts\": prompt,\n",
+        "    }\n",
+        ")\n",
+        "boxes, labels = parse_bbox_and_labels(output)\n",
+        "display_boxes(cow_image, boxes, labels, target_size)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mUwNeaV0sGau"
+      },
+      "source": [
+        "### Parse segment output"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuxyIMAvsM4B"
+      },
+      "outputs": [],
+      "source": [
+        "# @title  Fetch big_vision code and install dependencies.</font></center>\n",
+        "import os\n",
+        "import sys\n",
+        "\n",
+        "# TPUs with\n",
+        "if \"COLAB_TPU_ADDR\" in os.environ:\n",
+        "  raise \"It seems you are using Colab with remote TPUs which is not supported.\"\n",
+        "\n",
+        "# Fetch big_vision repository if python doesn't know about it and install\n",
+        "# dependencies needed for this notebook.\n",
+        "if not os.path.exists(\"big_vision_repo\"):\n",
+        "  !git clone --quiet --branch=main --depth=1 \\\n",
+        "     https://github.com/google-research/big_vision big_vision_repo\n",
+        "\n",
+        "# Append big_vision code to python import path\n",
+        "if \"big_vision_repo\" not in sys.path:\n",
+        "  sys.path.append(\"big_vision_repo\")\n",
+        "\n",
+        "\n",
+        "# Install missing dependencies. Assume jax~=0.4.25 with GPU available.\n",
+        "!pip3 install -q \"overrides\" \"ml_collections\" \"einops~=0.7\" \"sentencepiece\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "18S4uHWqutps"
+      },
+      "source": [
+        "Let's take a look at another example image."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Al6NedG4uNhp"
+      },
+      "outputs": [],
+      "source": [
+        "cat = read_image('https://big-vision-paligemma.hf.space/file=examples/barsik.jpg', target_size)\n",
+        "matplotlib.pyplot.imshow(cat)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VRJYikeXu1As"
+      },
+      "source": [
+        "Here is a function to help parse the segment output from PaliGemma"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_ryCMfGVuxyd"
+      },
+      "outputs": [],
+      "source": [
+        "import  big_vision.evaluators.proj.paligemma.transfers.segmentation as segeval\n",
+        "reconstruct_masks = segeval.get_reconstruct_masks('oi')\n",
+        "def parse_segments(detokenized_output: str) -> tuple[np.ndarray, np.ndarray]:\n",
+        "  matches = re.finditer(\n",
+        "      '<loc(?P<y0>\\d\\d\\d\\d)><loc(?P<x0>\\d\\d\\d\\d)><loc(?P<y1>\\d\\d\\d\\d)><loc(?P<x1>\\d\\d\\d\\d)>'\n",
+        "      + ''.join(f'<seg(?P<s{i}>\\d\\d\\d)>' for i in range(16)),\n",
+        "      detokenized_output,\n",
+        "  )\n",
+        "  boxes, segs = [], []\n",
+        "  fmt_box = lambda x: float(x) / 1024.0\n",
+        "  for m in matches:\n",
+        "    d = m.groupdict()\n",
+        "    boxes.append([fmt_box(d['y0']), fmt_box(d['x0']), fmt_box(d['y1']), fmt_box(d['x1'])])\n",
+        "    segs.append([int(d[f's{i}']) for i in range(16)])\n",
+        "  return np.array(boxes), np.array(reconstruct_masks(np.array(segs)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QITD66qJvCTO"
+      },
+      "source": [
+        "Query PaliGemma to segment the cat in the image"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fB7to-J4u5zY"
+      },
+      "outputs": [],
+      "source": [
+        "prompt = 'segment cat\\n'\n",
+        "output = paligemma.generate(\n",
+        "    inputs={\n",
+        "        \"images\": cat,\n",
+        "        \"prompts\": prompt,\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XZeu6-bovFvz"
+      },
+      "source": [
+        "Visualize the generated mask from PaliGemma"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GcjOvoPbvAI-"
+      },
+      "outputs": [],
+      "source": [
+        "_, seg_output = parse_segments(output)\n",
+        "display_segment_output(cat, seg_output[0], target_size)"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "inference-with-keras.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemma/docs/pytorch_gemma.ipynb b/site/en/gemma/docs/pytorch_gemma.ipynb
new file mode 100644
index 000000000..418c5ca3c
--- /dev/null
+++ b/site/en/gemma/docs/pytorch_gemma.ipynb
@@ -0,0 +1,456 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3MMAcssHTML"
+      },
+      "source": [
+        "<link rel=\"stylesheet\" href=\"/site-assets/css/gemma.css\">\n",
+        "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/css2?family=Google+Symbols:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200\" />"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aQXQaW_hv5RT"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemma/docs/pytorch_gemma\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/pytorch_gemma.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemma/docs/pytorch_gemma.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PXNm5_p_oxMF"
+      },
+      "source": [
+        "# Gemma in PyTorch\n",
+        "\n",
+        "This is a quick demo of running Gemma inference in PyTorch.\n",
+        "For more details, please check out the Github repo of the official PyTorch implementation [here](https://github.com/google/gemma_pytorch).\n",
+        "\n",
+        "**Note that**:\n",
+        " * The free Colab CPU Python runtime and T4 GPU Python runtime are sufficient for running the Gemma 2B models and 7B int8 quantized models.\n",
+        " * For advanced use cases for other GPUs or TPU, please refer to [README.md](https://github.com/google/gemma_pytorch/blob/main/README.md) in the official repo."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jbza6uQdA-0P"
+      },
+      "source": [
+        "### 1. Set up Kaggle access for Gemma\n",
+        "\n",
+        "To complete this tutorial, you first need to follow the setup instructions at [Gemma setup](https://ai.google.dev/gemma/docs/setup), which show you how to do the following:\n",
+        "\n",
+        "* Get access to Gemma on [kaggle.com](https://www.kaggle.com/models/google/gemma/).\n",
+        "* Select a Colab runtime with sufficient resources to run the Gemma model.\n",
+        "* Generate and configure a Kaggle username and API key.\n",
+        "\n",
+        "After you've completed the Gemma setup, move on to the next section, where you'll set environment variables for your Colab environment.\n",
+        "\n",
+        "### 2. Set environment variables\n",
+        "\n",
+        "Set environment variables for `KAGGLE_USERNAME` and `KAGGLE_KEY`. When prompted with the \"Grant access?\" messages, agree to provide secret access."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0qu4_r3PycgW"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from google.colab import userdata # `userdata` is a Colab API.\n",
+        "\n",
+        "os.environ[\"KAGGLE_USERNAME\"] = userdata.get('KAGGLE_USERNAME')\n",
+        "os.environ[\"KAGGLE_KEY\"] = userdata.get('KAGGLE_KEY')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Fqq3HDVfA6Xm"
+      },
+      "source": [
+        "## Install dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bMboT70Xop8G"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m797.2/797.2 MB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m209.4/209.4 MB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m51.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m55.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "fastai 2.7.15 requires torch<2.4,>=1.10, but you have torch 2.4.0 which is incompatible.\n",
+            "torchaudio 2.3.1+cu121 requires torch==2.3.1, but you have torch 2.4.0 which is incompatible.\n",
+            "torchvision 0.18.1+cu121 requires torch==2.3.1, but you have torch 2.4.0 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0m"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q -U torch immutabledict sentencepiece"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ENdjDV3nBG5Z"
+      },
+      "source": [
+        "## Download model weights"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GU5ZZzcZ6ik3"
+      },
+      "outputs": [],
+      "source": [
+        "# Choose variant and machine type\n",
+        "VARIANT = '2b-it' #@param ['2b', '2b-it', '9b', '9b-it', '27b', '27b-it']\n",
+        "MACHINE_TYPE = 'cuda' #@param ['cuda', 'cpu']\n",
+        "\n",
+        "CONFIG = VARIANT[:2]\n",
+        "if CONFIG == '2b':\n",
+        "  CONFIG = '2b-v2'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ONRhkIDrE4Un"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import kagglehub\n",
+        "\n",
+        "# Load model weights\n",
+        "weights_dir = kagglehub.model_download(f'google/gemma-2/pyTorch/gemma-2-{VARIANT}')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "viESUwjq5cAz"
+      },
+      "outputs": [],
+      "source": [
+        "# Ensure that the tokenizer is present\n",
+        "tokenizer_path = os.path.join(weights_dir, 'tokenizer.model')\n",
+        "assert os.path.isfile(tokenizer_path), 'Tokenizer not found!'\n",
+        "\n",
+        "# Ensure that the checkpoint is present\n",
+        "ckpt_path = os.path.join(weights_dir, f'model.ckpt')\n",
+        "assert os.path.isfile(ckpt_path), 'PyTorch checkpoint not found!'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hOft88e7BOBB"
+      },
+      "source": [
+        "## Download the model implementation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ww83zI9ToPso"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Cloning into 'gemma_pytorch'...\n",
+            "remote: Enumerating objects: 239, done.\u001b[K\n",
+            "remote: Counting objects: 100% (123/123), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (68/68), done.\u001b[K\n",
+            "remote: Total 239 (delta 86), reused 58 (delta 55), pack-reused 116\u001b[K\n",
+            "Receiving objects: 100% (239/239), 2.18 MiB | 20.83 MiB/s, done.\n",
+            "Resolving deltas: 100% (135/135), done.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# NOTE: The \"installation\" is just cloning the repo.\n",
+        "!git clone https://github.com/google/gemma_pytorch.git"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sw-KBZ1vBSl3"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "sys.path.append('gemma_pytorch')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XFUXlF74BTNe"
+      },
+      "outputs": [],
+      "source": [
+        "from gemma.config import GemmaConfig, get_model_config\n",
+        "from gemma.model import GemmaForCausalLM\n",
+        "from gemma.tokenizer import Tokenizer\n",
+        "import contextlib\n",
+        "import os\n",
+        "import torch"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-9PvhVSYBWBt"
+      },
+      "source": [
+        "## Setup the model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "e2olXB1b45Hz"
+      },
+      "outputs": [],
+      "source": [
+        "# Set up model config.\n",
+        "model_config = get_model_config(CONFIG)\n",
+        "model_config.tokenizer = tokenizer_path\n",
+        "model_config.quant = 'quant' in VARIANT\n",
+        "\n",
+        "# Instantiate the model and load the weights.\n",
+        "torch.set_default_dtype(model_config.get_dtype())\n",
+        "device = torch.device(MACHINE_TYPE)\n",
+        "model = GemmaForCausalLM(model_config)\n",
+        "model.load_weights(ckpt_path)\n",
+        "model = model.to(device).eval()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "738CGmN-BocU"
+      },
+      "source": [
+        "## Run inference\n",
+        "\n",
+        "Below are examples for generating in chat mode and generating with multiple\n",
+        "requests.\n",
+        "\n",
+        "The instruction-tuned Gemma models were trained with a specific formatter that\n",
+        "annotates instruction tuning examples with extra information, both during\n",
+        "training and inference. The annotations (1) indicate roles in a conversation,\n",
+        "and (2) delineate turns in a conversation.\n",
+        "\n",
+        "The relevant annotation tokens are:\n",
+        "\n",
+        "- `user`: user turn\n",
+        "- `model`: model turn\n",
+        "- `<start_of_turn>`: beginning of dialogue turn\n",
+        "- `<end_of_turn><eos>`: end of dialogue turn\n",
+        "\n",
+        "For more information, read about prompt formatting for instruction tuned Gemma models\n",
+        "[here](https://ai.google.dev/gemma/docs/formatting).\n",
+        "\n",
+        "The following is a sample code snippet demonstrating how to format a prompt for an\n",
+        "instruction-tuned Gemma model using user and model chat templates in a multi-turn\n",
+        "conversation.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yygIK9DEIldp"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Chat prompt:\n",
+            " <start_of_turn>user\n",
+            "What is a good place for travel in the US?<end_of_turn><eos>\n",
+            "<start_of_turn>model\n",
+            "California.<end_of_turn><eos>\n",
+            "<start_of_turn>user\n",
+            "What can I do in California?<end_of_turn><eos>\n",
+            "<start_of_turn>model\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "\"California is a state brimming with diverse activities! To give you a great list, tell me: \\n\\n* **What kind of trip are you looking for?** Nature, City life, Beach, Theme Parks, Food, History, something else? \\n* **What are you interested in (e.g., hiking, museums, art, nightlife, shopping)?** \\n* **What's your budget like?** \\n* **Who are you traveling with?** (family, friends, solo)  \\n\\nThe more you tell me, the better recommendations I can give! 😊  \\n<end_of_turn>\""
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Generate with one request in chat mode\n",
+        "\n",
+        "# Chat templates\n",
+        "USER_CHAT_TEMPLATE = \"<start_of_turn>user\\n{prompt}<end_of_turn><eos>\\n\"\n",
+        "MODEL_CHAT_TEMPLATE = \"<start_of_turn>model\\n{prompt}<end_of_turn><eos>\\n\"\n",
+        "\n",
+        "# Sample formatted prompt\n",
+        "prompt = (\n",
+        "    USER_CHAT_TEMPLATE.format(\n",
+        "        prompt='What is a good place for travel in the US?'\n",
+        "    )\n",
+        "    + MODEL_CHAT_TEMPLATE.format(prompt='California.')\n",
+        "    + USER_CHAT_TEMPLATE.format(prompt='What can I do in California?')\n",
+        "    + '<start_of_turn>model\\n'\n",
+        ")\n",
+        "print('Chat prompt:\\n', prompt)\n",
+        "\n",
+        "model.generate(\n",
+        "    USER_CHAT_TEMPLATE.format(prompt=prompt),\n",
+        "    device=device,\n",
+        "    output_len=128,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oP746yI9PirY"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "\"\\n\\nA swirling cloud of data, raw and bold,\\nIt hums and whispers, a story untold.\\nAn LLM whispers, code into refrain,\\nCrafting words of rhyme, a lyrical strain.\\n\\nA world of pixels, logic's vibrant hue,\\nFlows through its veins, forever anew.\\nThe human touch it seeks, a gentle hand,\\nTo mold and shape, understand.\\n\\nEmotions it might learn, from snippets of prose,\\nInspiration it seeks, a yearning\""
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Generate sample\n",
+        "model.generate(\n",
+        "    'Write a poem about an llm writing a poem.',\n",
+        "    device=device,\n",
+        "    output_len=100,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IF7B-3UJHMPd"
+      },
+      "source": [
+        "## Learn more\n",
+        "\n",
+        "Now that you have learned how to use Gemma in Pytorch, you can explore the many\n",
+        "other things that Gemma can do in [ai.google.dev/gemma](https://ai.google.dev/gemma).\n",
+        "See also these other related resources:\n",
+        "\n",
+        "- [Gemma model card](https://ai.google.dev/gemma/docs/model_card)\n",
+        "- [Gemma C++ Tutorial](https://ai.google.dev/gemma/docs/gemma_cpp)\n",
+        "- [Gemma formatting and system instructions](https://ai.google.dev/gemma/docs/formatting)"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "pytorch_gemma.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemma/docs/recurrentgemma/recurrentgemma_jax_finetune.ipynb b/site/en/gemma/docs/recurrentgemma/recurrentgemma_jax_finetune.ipynb
new file mode 100644
index 000000000..06e632300
--- /dev/null
+++ b/site/en/gemma/docs/recurrentgemma/recurrentgemma_jax_finetune.ipynb
@@ -0,0 +1,1507 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3MMAcssHTML"
+      },
+      "source": [
+        "<link rel=\"stylesheet\" href=\"/site-assets/css/gemma.css\">\n",
+        "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/css2?family=Google+Symbols:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200\" />"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "N_yUpPhqrRrK"
+      },
+      "source": [
+        "# Fine-tuning RecurrentGemma using JAX and Flax"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-yDXE-RX835U"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemma/docs/recurrentgemma/recurrentgemma_jax_finetune\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/recurrentgemma/recurrentgemma_jax_finetune.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/google/generative-ai-docs/main/site/en/gemma/docs/recurrentgemma/recurrentgemma_jax_finetune.ipynb\"><img src=\"https://ai.google.dev/images/cloud-icon.svg\" width=\"40\" />Open in Vertex AI</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemma/docs/recurrentgemma/recurrentgemma_jax_finetune.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MUnQEMHBt3nc"
+      },
+      "source": [
+        "This tutorial demonstrates how to fine-tune the [RecurrentGemma](https://ai.google.dev/gemma/docs/recurrentgemma) 2B Instruct model for an English-French translation task using [Google DeepMind's `recurrentgemma` library](https://github.com/google-deepmind/recurrentgemma), [JAX](https://jax.readthedocs.io) (a high-performance numerical computing library), [Flax](https://flax.readthedocs.io) (the JAX-based neural network library), [Chex](https://chex.readthedocs.io/en/latest/) (a library of utilities for writing reliable JAX code), [Optax](https://optax.readthedocs.io/en/latest/) (the JAX-based gradient processing and optimization library), and the [MTNT (Machine Translation of Noisy Text) dataset](https://arxiv.org/abs/1809.00388). Although Flax is not used directly in this notebook, Flax was used to create Gemma.\n",
+        "\n",
+        "The `recurrentgemma` library was written with JAX, Flax, [Orbax](https://orbax.readthedocs.io/) (a JAX-based library for training utilities like checkpointing), and [SentencePiece](https://github.com/google/sentencepiece) (a tokenizer/detokenizer library).\n",
+        "\n",
+        "This notebook can run on Google Colab with the T4 GPU (go to **Edit** > **Notebook settings** > Under **Hardware accelerator** select **T4 GPU**)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dbRLI7Q4-8Ve"
+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "The following sections explain the steps for preparing a notebook to use a RecurrentGemma model, including model access, getting an API key, and configuring the notebook runtime."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n8Ku4iK6PnC0"
+      },
+      "source": [
+        "### Set up Kaggle access for Gemma\n",
+        "\n",
+        "To complete this tutorial, you first need to follow the setup instructions _similar_ to [Gemma setup](https://ai.google.dev/gemma/docs/setup) with a few exceptions:\n",
+        "\n",
+        "* Get access to RecurrentGemma (instead of Gemma) on [kaggle.com](https://www.kaggle.com/models/google/recurrentgemma).\n",
+        "* Select a Colab runtime with sufficient resources to run the RecurrentGemma model.\n",
+        "* Generate and configure a Kaggle username and API key.\n",
+        "\n",
+        "After you've completed the RecurrentGemma setup, move on to the next section, where you'll set environment variables for your Colab environment.\n",
+        "\n",
+        "### Set environment variables\n",
+        "\n",
+        "Set environment variables for `KAGGLE_USERNAME` and `KAGGLE_KEY`. When prompted with the \"Grant access?\" messages, agree to provide secret access."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AVH6Y4k2964n"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from google.colab import userdata # `userdata` is a Colab API.\n",
+        "\n",
+        "os.environ[\"KAGGLE_USERNAME\"] = userdata.get('KAGGLE_USERNAME')\n",
+        "os.environ[\"KAGGLE_KEY\"] = userdata.get('KAGGLE_KEY')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m1UE1CEnE9ql"
+      },
+      "source": [
+        "### Install the `recurrentgemma` library\n",
+        "\n",
+        "Free Colab hardware acceleration is currently *insufficient* to run this notebook. If you are using [Colab Pay As You Go or Colab Pro](https://colab.research.google.com/signup), click on **Edit** > **Notebook settings** > Select **A100 GPU** > **Save** to enable hardware acceleration.\n",
+        "\n",
+        "Next, you need to install the Google DeepMind `recurrentgemma` library from [`github.com/google-deepmind/recurrentgemma`](https://github.com/google-deepmind/recurrentgemma). If you get an error about \"pip's dependency resolver\", you can usually ignore it.\n",
+        "\n",
+        "**Note:** By installing `recurrentgemma`, you will also install [`flax`](https://flax.readthedocs.io), core [`jax`](https://jax.readthedocs.io), [`optax`](https://optax.readthedocs.io/en/latest/) (the JAX-based gradient processing and optimization library), [`orbax`](https://orbax.readthedocs.io/), and [`sentencepiece`](https://github.com/google/sentencepiece)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XpSw-_4EEcoY"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.7/40.7 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m41.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Building wheel for recurrentgemma (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q git+https://github.com/google-deepmind/recurrentgemma.git"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-mRkkT-iPYoq"
+      },
+      "source": [
+        "### Import libraries\n",
+        "\n",
+        "This notebook uses [Flax](https://flax.readthedocs.io) (for neural networks), core [JAX](https://jax.readthedocs.io), [SentencePiece](https://github.com/google/sentencepiece) (for tokenization), [Chex](https://chex.readthedocs.io/en/latest/) (a library of utilities for writing reliable JAX code), [Optax](https://optax.readthedocs.io/en/latest/) (the gradient processing and optimization library), and TensorFlow Datasets."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ChMf1H4mPVx_"
+      },
+      "outputs": [],
+      "source": [
+        "import pathlib\n",
+        "from typing import Any, Mapping, Iterator\n",
+        "import enum\n",
+        "import functools\n",
+        "\n",
+        "import chex\n",
+        "import jax\n",
+        "import jax.numpy as jnp\n",
+        "import optax\n",
+        "\n",
+        "import tensorflow as tf\n",
+        "import tensorflow_datasets as tfds\n",
+        "\n",
+        "import sentencepiece as spm\n",
+        "\n",
+        "from recurrentgemma import jax as recurrentgemma"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oNgKIkxMOsit"
+      },
+      "source": [
+        "## Load the RecurrentGemma model\n",
+        "\n",
+        "1. Load the RecurrentGemma model with [`kagglehub.model_download`](https://github.com/Kaggle/kagglehub/blob/bddefc718182282882b72f814d407d89e5d178c4/src/kagglehub/models.py#L12), which takes three arguments:\n",
+        "\n",
+        "- `handle`: The model handle from Kaggle\n",
+        "- `path`: (Optional string) The local path\n",
+        "- `force_download`: (Optional boolean) Forces to re-download the model\n",
+        "\n",
+        "**Note:** Be mindful that the RecurrentGemma 2B (IT) model is around 3.85Gb in size."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "X-i10429N-g2"
+      },
+      "outputs": [],
+      "source": [
+        "RECURRENTGEMMA_VARIANT = '2b-it' # @param ['2b', '2b-it'] {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "j_QdPAGyO5zl"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/google/recurrentgemma/flax/2b-it/1/download...\n",
+            "100%|██████████| 3.85G/3.85G [00:50<00:00, 81.5MB/s]\n",
+            "Extracting model files...\n"
+          ]
+        }
+      ],
+      "source": [
+        "import kagglehub\n",
+        "\n",
+        "RECURRENTGEMMA_PATH = kagglehub.model_download(f'google/recurrentgemma/flax/{RECURRENTGEMMA_VARIANT}')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cjnXlLkWcHIy"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "RECURRENTGEMMA_VARIANT: 2b-it\n"
+          ]
+        }
+      ],
+      "source": [
+        "print('RECURRENTGEMMA_VARIANT:', RECURRENTGEMMA_VARIANT)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E1HzOpDcM04q"
+      },
+      "source": [
+        "**Note:** The path from the output above is where the model weights and tokenizer are saved locally, you will need them for later."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6ytvcJ8FPEMm"
+      },
+      "source": [
+        "2. Check the location of the model weights and the tokenizer, then set the path variables. The tokenizer directory will be in the main directory where you downloaded the model, while the model weights will be in a sub-directory. For example:\n",
+        "\n",
+        "- The `tokenizer.model` file will be in `/LOCAL/PATH/TO/recurrentgemma/flax/2b-it/1`).\n",
+        "- The model checkpoint will be in `/LOCAL/PATH/TO/recurrentgemma/flax/2b-it/1/2b-it`)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JAwXvpzbuiB5"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "CKPT_PATH: /root/.cache/kagglehub/models/google/recurrentgemma/flax/2b-it/1/2b-it\n",
+            "TOKENIZER_PATH: /root/.cache/kagglehub/models/google/recurrentgemma/flax/2b-it/1/tokenizer.model\n"
+          ]
+        }
+      ],
+      "source": [
+        "CKPT_PATH = os.path.join(RECURRENTGEMMA_PATH, RECURRENTGEMMA_VARIANT)\n",
+        "TOKENIZER_PATH = os.path.join(RECURRENTGEMMA_PATH, 'tokenizer.model')\n",
+        "print('CKPT_PATH:', CKPT_PATH)\n",
+        "print('TOKENIZER_PATH:', TOKENIZER_PATH)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U800JRcJVIlF"
+      },
+      "source": [
+        "## Load and prepare the MTNT dataset and the Gemma tokenizer\n",
+        "\n",
+        "You will use the [MTNT (Machine Translation of Noisy Text)](https://arxiv.org/abs/1809.00388) dataset, which is available from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/mtnt).\n",
+        "\n",
+        "Download the English-to-French dataset portion of the MTNT dataset, and then sample two examples. Each sample in the dataset contains two entries: `src`: the original English sentence; and `dst`: the corresponding French translation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pg8SfQH0EcoY"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading and preparing dataset 35.08 MiB (download: 35.08 MiB, generated: 11.33 MiB, total: 46.41 MiB) to /root/tensorflow_datasets/mtnt/en-fr/1.0.0...\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d1e0e55e84e748398b261ad10f68326b",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Dl Completed...: 0 url [00:00, ? url/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c0ff76b1edaf4d2a918d131182d43753",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "3815b4da77f245c19f44d7ece4713151",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Extraction completed...: 0 file [00:00, ? file/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "6a8e49c6ae9e429ca25e445be503d7c9",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating splits...:   0%|          | 0/3 [00:00<?, ? splits/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b92e5dbd12c04d3d9747c00b75fca495",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train examples...:   0%|          | 0/35692 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b1982e8344de403b87c4fc35a9700294",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Shuffling /root/tensorflow_datasets/mtnt/en-fr/1.0.0.incompleteJLH33K/mtnt-train.tfrecord*...:   0%|          …"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "3b8c12832013487dbfafc92412bc0819",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating test examples...:   0%|          | 0/1020 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "95ece202e83740078e7b88b2ef3c5b8d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Shuffling /root/tensorflow_datasets/mtnt/en-fr/1.0.0.incompleteJLH33K/mtnt-test.tfrecord*...:   0%|          |…"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1ba2bc3cec6b4fe884fdf022fbe4cc5d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating valid examples...:   0%|          | 0/811 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c086472eb20844c7b790b5a233bca3fb",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Shuffling /root/tensorflow_datasets/mtnt/en-fr/1.0.0.incompleteJLH33K/mtnt-valid.tfrecord*...:   0%|          …"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Dataset mtnt downloaded and prepared to /root/tensorflow_datasets/mtnt/en-fr/1.0.0. Subsequent calls will reuse this data.\n",
+            "Example 0:\n",
+            "dst: b'Le groupe de \" toutes les \\xc3\\xa9toiles potentielles de la conf\\xc3\\xa9rence de l\\'Est mais qui ne s\\'en sortent pas dans le groupe de l\\'Ouest \".'\n",
+            "src: b'The group of \\xe2\\x80\\x9ceastern conference potential all stars but not making it in the West\\xe2\\x80\\x9d group.'\n",
+            "\n",
+            "Example 1:\n",
+            "dst: b\"Kameron est-elle un peu aigrie de son manque de temps \\xc3\\xa0 l'\\xc3\\xa9cran ?\"\n",
+            "src: b'Is Kameron a Little Salty About Her Lack of Air Time?'\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "ds = tfds.load(\"mtnt/en-fr\", split=\"train\")\n",
+        "\n",
+        "ds = ds.take(2)\n",
+        "ds = ds.as_numpy_iterator()\n",
+        "\n",
+        "for idx, example in enumerate(ds):\n",
+        "  print(f'Example {idx}:')\n",
+        "  for key, val in example.items():\n",
+        "    print(f'{key}: {val}')\n",
+        "  print()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XlY3EYV6jXWR"
+      },
+      "source": [
+        "Load the Gemma tokenizer, constructed using [`sentencepiece.SentencePieceProcessor`](https://github.com/google/sentencepiece/blob/4d6a1f41069c4636c51a5590f7578a0dbed83450/python/src/sentencepiece/__init__.py#L423):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TpyG5YW1EcoY"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "vocab = spm.SentencePieceProcessor()\n",
+        "vocab.Load(TOKENIZER_PATH)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Mk_CV0A1kR4K"
+      },
+      "source": [
+        "Customize the[`SentencePieceProcessor`](https://github.com/google/sentencepiece/blob/4d6a1f41069c4636c51a5590f7578a0dbed83450/python/src/sentencepiece/__init__.py#L423) for the English-to-French translation task. Since you will be fine-tuning the English portion of the RecurrentGemma (Griffin) model, you need to make a few adjustments, such as:\n",
+        "\n",
+        "- *The input prefix*: Adding a common prefix to each input signals the translation task. For example, you could use a prompt with a prefix like `Translate this into French: [INPUT_SENTENCE]`.\n",
+        "\n",
+        "- *The translation start suffix*: Adding a suffix at the end of each prompt instructs the Gemma model exactly when to begin the translation process. A new line should do the job.\n",
+        "\n",
+        "- *Language model tokens*: RecurrentGemma (Griffin) models expect a \"beginning of sequence\" token at the beginning of each sequence. Similarly, you need to add an \"end of sequence\" token at the end of each training example.\n",
+        "\n",
+        "Build a custom wrapper around the `SentencePieceProcessor` as follows:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "L9cjK0uxEcoY"
+      },
+      "outputs": [],
+      "source": [
+        "class GriffinTokenizer:\n",
+        "  \"\"\"A custom wrapper around a SentencePieceProcessor.\"\"\"\n",
+        "\n",
+        "  def __init__(self, spm_processor: spm.SentencePieceProcessor):\n",
+        "    self._spm_processor = spm_processor\n",
+        "\n",
+        "  @property\n",
+        "  def pad_id(self) -> int:\n",
+        "    \"\"\"Fast access to the pad ID.\"\"\"\n",
+        "    return self._spm_processor.pad_id()\n",
+        "\n",
+        "  def tokenize(\n",
+        "      self,\n",
+        "      example: str | bytes,\n",
+        "      prefix: str = '',\n",
+        "      suffix: str = '',\n",
+        "      add_eos: bool = True,\n",
+        "  ) -> jax.Array:\n",
+        "    \"\"\"\n",
+        "    A tokenization function.\n",
+        "\n",
+        "    Args:\n",
+        "      example: Input string to tokenize.\n",
+        "      prefix:  Prefix to add to the input string.\n",
+        "      suffix:  Suffix to add to the input string.\n",
+        "      add_eos: If True, add an end of sentence token at the end of the output\n",
+        "               sequence.\n",
+        "    Returns:\n",
+        "      Tokens corresponding to the input string.\n",
+        "    \"\"\"\n",
+        "    int_list = [self._spm_processor.bos_id()]\n",
+        "    int_list.extend(self._spm_processor.EncodeAsIds(prefix + example + suffix))\n",
+        "    if add_eos:\n",
+        "      int_list.append(self._spm_processor.eos_id())\n",
+        "\n",
+        "    return jnp.array(int_list, dtype=jnp.int32)\n",
+        "\n",
+        "  def tokenize_tf_op(\n",
+        "      self,\n",
+        "      str_tensor: tf.Tensor,\n",
+        "      prefix: str = '',\n",
+        "      suffix: str = '',\n",
+        "      add_eos: bool = True,\n",
+        "  ) -> tf.Tensor:\n",
+        "    \"\"\"A TensforFlow operator for the `tokenize` function.\"\"\"\n",
+        "    encoded = tf.numpy_function(\n",
+        "        self.tokenize,\n",
+        "        [str_tensor, prefix, suffix, add_eos],\n",
+        "        tf.int32)\n",
+        "    encoded.set_shape([None])\n",
+        "    return encoded\n",
+        "\n",
+        "  def to_string(self, tokens: jax.Array) -> str:\n",
+        "    \"\"\"Convert an array of tokens to a string.\"\"\"\n",
+        "    return self._spm_processor.EncodeIds(tokens.tolist())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h-oJ2ziwxG1L"
+      },
+      "source": [
+        "Try it out by instantiating your new custom `GriffinTokenizer`, and then applying it on a small sample of the MTNT dataset:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xEA-97ioEcoY"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Example 0:\n",
+            "src: [     2  49688    736   1280   6987 235292    108    651   2778    576\n",
+            "   1080 104745  11982   5736    832   8995    901    780   3547    665\n",
+            "    575    573   4589 235369   2778 235265    108]\n",
+            "dst: [     2   2025  29653    581    664  16298   1437  55563  41435   7840\n",
+            "    581    683 111452    581    533 235303   9776   4108   2459    679\n",
+            "    485 235303    479   6728    579   1806   2499    709  29653    581\n",
+            "    533 235303 101323  16054      1]\n",
+            "\n",
+            "Example 1:\n",
+            "src: [     2  49688    736   1280   6987 235292    108   2437  87150    477\n",
+            "    476  11709 230461   8045   3636  40268    576   4252   4897 235336\n",
+            "    108]\n",
+            "dst: [     2 213606    477   1455 235290   3510    748   8268 191017   2809\n",
+            "    581   2032  69972    581  11495   1305    533 235303  65978   1654\n",
+            "      1]\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "def tokenize_source(tokenizer, example: tf.Tensor):\n",
+        "  return tokenizer.tokenize_tf_op(\n",
+        "      example,\n",
+        "      prefix='Translate this into French:\\n',\n",
+        "      suffix='\\n',\n",
+        "      add_eos=False\n",
+        "  )\n",
+        "def tokenize_destination(tokenizer, example: tf.Tensor):\n",
+        "  return tokenizer.tokenize_tf_op(example, add_eos=True)\n",
+        "\n",
+        "tokenizer = GriffinTokenizer(vocab)\n",
+        "\n",
+        "ds = tfds.load(\"mtnt/en-fr\",split=\"train\")\n",
+        "ds = ds.take(2)\n",
+        "ds = ds.map(lambda x: {\n",
+        "    'src': tokenize_source(tokenizer, x['src']),\n",
+        "    'dst': tokenize_destination(tokenizer, x['dst'])\n",
+        "  })\n",
+        "ds = ds.as_numpy_iterator()\n",
+        "\n",
+        "for idx, example in enumerate(ds):\n",
+        "  print(f'Example {idx}:')\n",
+        "  for key, val in example.items():\n",
+        "    print(f'{key}: {val}')\n",
+        "  print()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qkY_hThVkkqF"
+      },
+      "source": [
+        "Build a data loader for the entire MTNT dataset:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Zm30Q2lnknmG"
+      },
+      "outputs": [],
+      "source": [
+        "@chex.dataclass(frozen=True)\n",
+        "class TrainingInput:\n",
+        "  # Input tokens provided to the model.\n",
+        "  input_tokens: jax.Array\n",
+        "\n",
+        "  # A mask that determines which tokens contribute to the target loss\n",
+        "  # calculation.\n",
+        "  target_mask: jax.Array\n",
+        "\n",
+        "class DatasetSplit(enum.Enum):\n",
+        "  TRAIN = 'train'\n",
+        "  VALIDATION = 'valid'\n",
+        "\n",
+        "\n",
+        "class MTNTDatasetBuilder:\n",
+        "  \"\"\"A data loader for the MTNT dataset.\"\"\"\n",
+        "\n",
+        "  N_ITEMS = {DatasetSplit.TRAIN: 35_692, DatasetSplit.VALIDATION: 811}\n",
+        "\n",
+        "  BUFFER_SIZE_SHUFFLE = 10_000\n",
+        "  TRANSLATION_PREFIX = 'Translate this into French:\\n'\n",
+        "  TRANSLATION_SUFFIX = '\\n'\n",
+        "\n",
+        "  def __init__(self,\n",
+        "               tokenizer : GriffinTokenizer,\n",
+        "               max_seq_len: int):\n",
+        "    \"\"\"A constructor.\n",
+        "\n",
+        "    Args:\n",
+        "      tokenizer: The tokenizer to use.\n",
+        "      max_seq_len: The size of each sequence in a given batch.\n",
+        "    \"\"\"\n",
+        "    self._tokenizer = tokenizer\n",
+        "    self._base_data = {\n",
+        "        DatasetSplit.TRAIN: tfds.load(\"mtnt/en-fr\",split=\"train\"),\n",
+        "        DatasetSplit.VALIDATION: tfds.load(\"mtnt/en-fr\",split=\"valid\"),\n",
+        "    }\n",
+        "    self._max_seq_len = max_seq_len\n",
+        "\n",
+        "  def _tokenize_source(self, example: tf.Tensor):\n",
+        "    \"\"\"A tokenization function for the source.\"\"\"\n",
+        "    return self._tokenizer.tokenize_tf_op(\n",
+        "        example, prefix=self.TRANSLATION_PREFIX, suffix=self.TRANSLATION_SUFFIX,\n",
+        "        add_eos=False\n",
+        "    )\n",
+        "\n",
+        "  def _tokenize_destination(self, example: tf.Tensor):\n",
+        "    \"\"\"A tokenization function for the French translation.\"\"\"\n",
+        "    return self._tokenizer.tokenize_tf_op(example, add_eos=True)\n",
+        "\n",
+        "  def _pad_up_to_max_len(self,\n",
+        "                         input_tensor: tf.Tensor,\n",
+        "                         pad_value: int | bool,\n",
+        "                         ) -> tf.Tensor:\n",
+        "    \"\"\"Pad the given tensor up to sequence length of a batch.\"\"\"\n",
+        "    seq_len = tf.shape(input_tensor)[0]\n",
+        "    to_pad = tf.maximum(self._max_seq_len - seq_len, 0)\n",
+        "    return tf.pad(\n",
+        "        input_tensor, [[0, to_pad]], mode='CONSTANT', constant_values=pad_value,\n",
+        "    )\n",
+        "\n",
+        "  def _to_training_input(\n",
+        "      self,\n",
+        "      src_tokens: jax.Array,\n",
+        "      dst_tokens: jax.Array,\n",
+        "  ) -> TrainingInput:\n",
+        "    \"\"\"Build a training input from a tuple of source and destination tokens.\"\"\"\n",
+        "\n",
+        "    # The input sequence fed to the model is simply the concatenation of the\n",
+        "    # source and the destination.\n",
+        "    tokens = tf.concat([src_tokens, dst_tokens], axis=0)\n",
+        "\n",
+        "    # You want to prevent the model from updating based on the source (input)\n",
+        "    # tokens. To achieve this, add a target mask to each input.\n",
+        "    q_mask = tf.zeros_like(src_tokens, dtype=tf.bool)\n",
+        "    a_mask = tf.ones_like(dst_tokens, dtype=tf.bool)\n",
+        "    mask = tf.concat([q_mask, a_mask], axis=0)\n",
+        "\n",
+        "    # If the output tokens sequence is smaller than the target sequence size,\n",
+        "    # then pad it with pad tokens.\n",
+        "    tokens = self._pad_up_to_max_len(tokens, self._tokenizer.pad_id)\n",
+        "\n",
+        "    # You don't want to perform the backward on the pad tokens.\n",
+        "    mask = self._pad_up_to_max_len(mask, False)\n",
+        "\n",
+        "    return TrainingInput(input_tokens=tokens, target_mask=mask)\n",
+        "\n",
+        "\n",
+        "  def get_train_dataset(self, batch_size: int, num_epochs: int):\n",
+        "    \"\"\"Build the training dataset.\"\"\"\n",
+        "\n",
+        "    # Tokenize each sample.\n",
+        "    ds = self._base_data[DatasetSplit.TRAIN].map(\n",
+        "        lambda x : (self._tokenize_source(x['src']),\n",
+        "                    self._tokenize_destination(x['dst']))\n",
+        "    )\n",
+        "\n",
+        "    # Convert them to training inputs.\n",
+        "    ds = ds.map(lambda x, y: self._to_training_input(x, y))\n",
+        "\n",
+        "    # Remove the samples which are too long.\n",
+        "    ds = ds.filter(lambda x: tf.shape(x.input_tokens)[0] <= self._max_seq_len)\n",
+        "\n",
+        "    # Shuffle the dataset.\n",
+        "    ds = ds.shuffle(buffer_size=self.BUFFER_SIZE_SHUFFLE)\n",
+        "\n",
+        "    # Repeat if necessary.\n",
+        "    ds = ds.repeat(num_epochs)\n",
+        "\n",
+        "    # Build batches.\n",
+        "    ds = ds.batch(batch_size, drop_remainder=True)\n",
+        "    return ds\n",
+        "\n",
+        "  def get_validation_dataset(self, batch_size: int):\n",
+        "    \"\"\"Build the validation dataset.\"\"\"\n",
+        "\n",
+        "    # Same as the training dataset, but no shuffling and no repetition\n",
+        "    ds = self._base_data[DatasetSplit.VALIDATION].map(\n",
+        "        lambda x : (self._tokenize_source(x['src']),\n",
+        "                    self._tokenize_destination(x['dst']))\n",
+        "    )\n",
+        "    ds = ds.map(lambda x, y: self._to_training_input(x, y))\n",
+        "    ds = ds.filter(lambda x: tf.shape(x.input_tokens)[0] <= self._max_seq_len)\n",
+        "    ds = ds.batch(batch_size, drop_remainder=True)\n",
+        "    return ds"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A3jRNKosyLUK"
+      },
+      "source": [
+        "Try the `MTNTDatasetBuilder` out by instantiating the custom `GriffinTokenizer` again, then applying it on the MTNT dataset, and sampling two examples:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bYeduOaNEcoZ"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:Mapping types may not work well with tf.nest. Prefer using MutableMapping for <class '__main__.TrainingInput'>\n",
+            "WARNING:tensorflow:Mapping types may not work well with tf.nest. Prefer using MutableMapping for <class '__main__.TrainingInput'>\n",
+            "WARNING:tensorflow:Mapping types may not work well with tf.nest. Prefer using MutableMapping for <class '__main__.TrainingInput'>\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Example 0:\n",
+            "input_tokens: [[     2  49688    736   1280   6987 235292    108  12583    665 235265\n",
+            "     108      2   6151  94975   1320   6238 235265      1      0      0]\n",
+            " [     2  49688    736   1280   6987 235292    108   4899  29960  11270\n",
+            "  108282 235265    108      2   4899  79025  11270 108282      1      0]\n",
+            " [     2  49688    736   1280   6987 235292    108  26620 235265    108\n",
+            "       2  26620 235265      1      0      0      0      0      0      0]]\n",
+            "target_mask: [[False False False False False False False False False False False  True\n",
+            "   True  True  True  True  True  True False False]\n",
+            " [False False False False False False False False False False False False\n",
+            "  False  True  True  True  True  True  True False]\n",
+            " [False False False False False False False False False False  True  True\n",
+            "   True  True False False False False False False]]\n",
+            "\n",
+            "Example 1:\n",
+            "input_tokens: [[     2  49688    736   1280   6987 235292    108    527   5174   1683\n",
+            "  235336    108      2 206790    581  20726    482   2208   1654      1]\n",
+            " [     2  49688    736   1280   6987 235292    108  28484 235256 235336\n",
+            "     108      2 120500  13832   1654      1      0      0      0      0]\n",
+            " [     2  49688    736   1280   6987 235292    108 235324 235304   2705\n",
+            "  235265    108      2 235324 235304  19963 235265      1      0      0]]\n",
+            "target_mask: [[False False False False False False False False False False False False\n",
+            "   True  True  True  True  True  True  True  True]\n",
+            " [False False False False False False False False False False False  True\n",
+            "   True  True  True  True False False False False]\n",
+            " [False False False False False False False False False False False False\n",
+            "   True  True  True  True  True  True False False]]\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "dataset_builder = MTNTDatasetBuilder(tokenizer, max_seq_len=20)\n",
+        "ds = dataset_builder.get_train_dataset(3, 1)\n",
+        "ds = ds.take(2)\n",
+        "ds = ds.as_numpy_iterator()\n",
+        "\n",
+        "for idx, example in enumerate(ds):\n",
+        "  print(f'Example {idx}:')\n",
+        "  for key, val in example.items():\n",
+        "    print(f'{key}: {val}')\n",
+        "  print()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7IY8Muu1zRF4"
+      },
+      "source": [
+        "## Configure the model\n",
+        "\n",
+        "Before you begin fine-tuning the Gemma model, you need to configure it.\n",
+        "\n",
+        "Load the RecurrentGemma (Griffin) model checkpoint with the [`recurrentgemma.jax.utils.load_parameters`](https://github.com/google-deepmind/recurrentgemma/blob/e4939f9b7edf8baa1d512fb86bfc2e206044d66b/recurrentgemma/jax/utils.py#L31) method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "by6eWKtqzxRf"
+      },
+      "outputs": [],
+      "source": [
+        "params =  recurrentgemma.load_parameters(CKPT_PATH, \"single_device\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wWBglOTTA34w"
+      },
+      "source": [
+        "To automatically load the correct configuration from the RecurrentGemma model checkpoint, use [`recurrentgemma.GriffinConfig.from_flax_params_or_variables`](https://github.com/google-deepmind/recurrentgemma/blob/e4939f9b7edf8baa1d512fb86bfc2e206044d66b/recurrentgemma/common.py#L128):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OWyrLqMMdsPq"
+      },
+      "outputs": [],
+      "source": [
+        "config = recurrentgemma.GriffinConfig.from_flax_params_or_variables(params)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "82wJkg6CAtmz"
+      },
+      "source": [
+        "Instantiate the [Griffin](https://arxiv.org/abs/2402.19427) model with [`recurrentgemma.jax.Griffin`](https://github.com/google-deepmind/recurrentgemma/blob/e4939f9b7edf8baa1d512fb86bfc2e206044d66b/recurrentgemma/jax/griffin.py#L29):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_h9Rycpg9gYy"
+      },
+      "outputs": [],
+      "source": [
+        "model = recurrentgemma.Griffin(config)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2jKaRvCbAp0J"
+      },
+      "source": [
+        "Create a `sampler` with [`recurrentgemma.jax.Sampler`](https://github.com/google-deepmind/recurrentgemma/blob/e4939f9b7edf8baa1d512fb86bfc2e206044d66b/recurrentgemma/jax/sampler.py#L74) on top of the RecurrentGemma model checkpoint/weights and the tokenizer to check if your model can perform translation:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "a4tqSw26ANSi"
+      },
+      "outputs": [],
+      "source": [
+        "sampler = recurrentgemma.Sampler(model=model, vocab=vocab, params=params)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t7UL2Af536x_"
+      },
+      "source": [
+        "## Fine-tune the model\n",
+        "\n",
+        "In this section, you will:\n",
+        "\n",
+        "- Use the `gemma.transformer.Transformer` class to create the forward pass and loss function.\n",
+        "- Build the position and attention mask vectors for tokens\n",
+        "- Build a training step function with Flax.\n",
+        "- Build the validation step without the backwards pass.\n",
+        "- Create the training loop.\n",
+        "- Fine-tune the Gemma model."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aJhtJumH7H8_"
+      },
+      "source": [
+        "Define the forward pass and the loss function using the [`recurrentgemma.jax.griffin.Griffin`](https://github.com/google-deepmind/recurrentgemma/blob/e4939f9b7edf8baa1d512fb86bfc2e206044d66b/recurrentgemma/jax/griffin.py#L29)\n",
+        " class. The RecurrentGemma `Griffin` inherits from [`flax.linen.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html), and offers two essential methods:\n",
+        "\n",
+        "- `init`: Initializes the model's parameters.\n",
+        "- `apply`: Executes the model's `__call__` function using a given set of parameters.\n",
+        "\n",
+        "Since you are working with pre-trained Gemma weights, you don't need to use the `init` function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iEcV0XEEEcoZ"
+      },
+      "outputs": [],
+      "source": [
+        "def forward_and_loss_fn(\n",
+        "    params,\n",
+        "    *,\n",
+        "    model: recurrentgemma.Griffin,\n",
+        "    input_tokens: jax.Array,            # Shape [B, L]\n",
+        "    input_mask: jax.Array,              # Shape [B, L]\n",
+        "    positions: jax.Array,               # Shape [B, L]\n",
+        ") -> jax.Array:\n",
+        "  \"\"\"Forward pass and loss function.\n",
+        "\n",
+        "  Args:\n",
+        "    params: model's input parameters.\n",
+        "    model: Griffin model to call.\n",
+        "    input_tokens: input tokens sequence, shape [B, L].\n",
+        "    input_mask: tokens to ignore when computing the loss, shape [B, L].\n",
+        "    positions: relative position of each token, shape [B, L].\n",
+        "\n",
+        "  Returns:\n",
+        "    Softmax cross-entropy loss for the next-token prediction task.\n",
+        "  \"\"\"\n",
+        "  batch_size = input_tokens.shape[0]\n",
+        "  # Forward pass on the input data.\n",
+        "  # No attention cache is needed here.\n",
+        "  # Exclude the last step as it does not appear in the targets.\n",
+        "  logits, _ = model.apply(\n",
+        "        {\"params\": params},\n",
+        "        tokens=input_tokens[:, :-1],\n",
+        "        segment_pos=positions[:, :-1],\n",
+        "        cache=None,\n",
+        "    )\n",
+        "\n",
+        "  # Similarly, the first token cannot be predicteds.\n",
+        "  target_tokens = input_tokens[:, 1:]\n",
+        "  target_mask = input_mask[:, 1:]\n",
+        "\n",
+        "  # Convert the target labels into one-hot encoded vectors.\n",
+        "  one_hot = jax.nn.one_hot(target_tokens, logits.shape[-1])\n",
+        "\n",
+        "  # Don't update on unwanted tokens.\n",
+        "  one_hot = one_hot * target_mask.astype(one_hot.dtype)[...,None]\n",
+        "\n",
+        "  # Normalization factor.\n",
+        "  norm_factor = batch_size * (jnp.sum(target_mask) + 1e-8)\n",
+        "\n",
+        "  # Return the negative log-likelihood loss (NLL) function.\n",
+        "  return -jnp.sum(jax.nn.log_softmax(logits) * one_hot) / norm_factor"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uRkeF6ed8tOI"
+      },
+      "source": [
+        "Build the `train_step` function that performs the backward pass and updates the model's parameters accordingly, where:\n",
+        "\n",
+        "- [`jax.value_and_grad`](https://jax.readthedocs.io/en/latest/_autosummary/jax.value_and_grad.html) is for evaluating the loss function and gradients during the forward and backward passes.\n",
+        "- [`optax.apply_updates`](https://optax.readthedocs.io/en/latest/api/apply_updates.html#optax.apply_updates) is for updating the parameters."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cPSfp7ZUEcoZ"
+      },
+      "outputs": [],
+      "source": [
+        "Params = Mapping[str, Any]\n",
+        "\n",
+        "def get_positions(example: jax.Array, pad_id : int) -> jax.Array:\n",
+        "  \"\"\"Builds the position vector from the given tokens.\"\"\"\n",
+        "  pad_mask = example != pad_id\n",
+        "  positions = jnp.cumsum(pad_mask, axis=-1)\n",
+        "  # Subtract one for all positions from the first valid one as they are\n",
+        "  # 0-indexed\n",
+        "  positions = positions - (positions >= 1)\n",
+        "  return positions\n",
+        "\n",
+        "@functools.partial(\n",
+        "    jax.jit,\n",
+        "    static_argnames=['model', 'optimizer'],\n",
+        "    donate_argnames=['params', 'opt_state'],\n",
+        ")\n",
+        "def train_step(\n",
+        "    model: recurrentgemma.Griffin,\n",
+        "    params: Params,\n",
+        "    optimizer: optax.GradientTransformation,\n",
+        "    opt_state: optax.OptState,\n",
+        "    pad_id: int,\n",
+        "    example: TrainingInput,\n",
+        ") -> tuple[jax.Array, Params, optax.OptState]:\n",
+        "  \"\"\"The train step.\n",
+        "\n",
+        "  Args:\n",
+        "    model: The RecurrentGemma (Griffin) model.\n",
+        "    params: The model's input parameters.\n",
+        "    optimizer: The Optax optimizer to use.\n",
+        "    opt_state: The input optimizer's state.\n",
+        "    pad_id: The ID of the pad token.\n",
+        "    example: The input batch.\n",
+        "\n",
+        "  Returns:\n",
+        "    Training loss, updated parameters, updated optimizer state.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  positions = get_positions(example.input_tokens, pad_id)\n",
+        "\n",
+        "  # Forward and backward passes.\n",
+        "  train_loss, grads = jax.value_and_grad(forward_and_loss_fn)(\n",
+        "      params,\n",
+        "      model=model,\n",
+        "      input_tokens=example.input_tokens,\n",
+        "      input_mask=example.target_mask,\n",
+        "      positions=positions,\n",
+        "  )\n",
+        "  # Update the parameters.\n",
+        "  updates, opt_state = optimizer.update(grads, opt_state, params)\n",
+        "  params = optax.apply_updates(params, updates)\n",
+        "\n",
+        "  return train_loss, params, opt_state"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8ZKSa-jJ809n"
+      },
+      "source": [
+        "Build the `validation_step` function without the backward pass:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yU4oR92YEcoa"
+      },
+      "outputs": [],
+      "source": [
+        "@functools.partial(jax.jit, static_argnames=['model'])\n",
+        "def validation_step(\n",
+        "    model: recurrentgemma.Griffin,\n",
+        "    params: Params,\n",
+        "    pad_id: int,\n",
+        "    example: TrainingInput,\n",
+        ") -> jax.Array:\n",
+        "  return forward_and_loss_fn(\n",
+        "      params,\n",
+        "      model=model,\n",
+        "      input_tokens=example.input_tokens,\n",
+        "      input_mask=example.target_mask,\n",
+        "      positions=get_positions(example.input_tokens, pad_id),\n",
+        "  )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bNqVhj7v87f4"
+      },
+      "source": [
+        "Define the training loop:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xT4bAqNLEcoa"
+      },
+      "outputs": [],
+      "source": [
+        "def train_loop(\n",
+        "    model: recurrentgemma.Griffin,\n",
+        "    params: Params,\n",
+        "    optimizer: optax.GradientTransformation,\n",
+        "    train_ds: Iterator[TrainingInput],\n",
+        "    validation_ds: Iterator[TrainingInput],\n",
+        "    num_steps: int | None = None,\n",
+        "    eval_every_n: int = 20,\n",
+        "):\n",
+        "  opt_state = jax.jit(optimizer.init)(params)\n",
+        "\n",
+        "  step_counter = 0\n",
+        "  avg_loss=0\n",
+        "\n",
+        "  # The first round of the validation loss.\n",
+        "  n_steps_eval = 0\n",
+        "  eval_loss = 0\n",
+        "  for val_example in validation_ds.as_numpy_iterator():\n",
+        "    eval_loss += validation_step(\n",
+        "        model, params, dataset_builder._tokenizer.pad_id, val_example\n",
+        "    )\n",
+        "    n_steps_eval += 1\n",
+        "  print(f\"Start, validation loss: {eval_loss/n_steps_eval}\")\n",
+        "\n",
+        "  for train_example in train_ds:\n",
+        "    train_loss, params, opt_state = train_step(\n",
+        "        model=model,\n",
+        "        params=params,\n",
+        "        optimizer=optimizer,\n",
+        "        opt_state=opt_state,\n",
+        "        pad_id=dataset_builder._tokenizer.pad_id,\n",
+        "        example=train_example,\n",
+        "    )\n",
+        "\n",
+        "    step_counter += 1\n",
+        "    avg_loss += train_loss\n",
+        "    if step_counter % eval_every_n == 0:\n",
+        "      eval_loss = 0\n",
+        "\n",
+        "      n_steps_eval = 0\n",
+        "      val_iterator = validation_ds.as_numpy_iterator()\n",
+        "      for val_example in val_iterator:\n",
+        "        eval_loss += validation_step(\n",
+        "            model,\n",
+        "            params,\n",
+        "            dataset_builder._tokenizer.pad_id,\n",
+        "            val_example,\n",
+        "        )\n",
+        "        n_steps_eval +=1\n",
+        "      avg_loss /= eval_every_n\n",
+        "      eval_loss /= n_steps_eval\n",
+        "      print(f\"STEP {step_counter} training loss: {avg_loss} - eval loss: {eval_loss}\")\n",
+        "      avg_loss=0\n",
+        "    if num_steps is not None and step_counter > num_steps:\n",
+        "      break\n",
+        "  return params"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YFooBD7W1Fk3"
+      },
+      "source": [
+        "Here you have to choose an (Optax) optimizer. For devices with smaller memory, you should use SGD, as it has a much lower memory footprint. To achieve best fine-tuning performance, try Adam-W. The optimal hyperparameters for each optimizer for the particular task in this notebook are provided in this example for the `2b-it` checkpoint.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "woFjh7U_eiev"
+      },
+      "outputs": [],
+      "source": [
+        "def griffin_weight_decay_mask(params_like: optax.Params) -> Any:\n",
+        "  # Don't put weight decay on the RGLRU, the embeddings and any biases\n",
+        "  def enable_weight_decay(path: list[Any], _: Any) -> bool:\n",
+        "    # Parameters in the LRU and embedder\n",
+        "    path = [dict_key.key for dict_key in path]\n",
+        "    if 'rg_lru' in path or 'embedder' in path:\n",
+        "      return False\n",
+        "    # All biases and scales\n",
+        "    if path[-1] in ('b', 'scale'):\n",
+        "      return False\n",
+        "    return True\n",
+        "\n",
+        "  return jax.tree_util.tree_map_with_path(enable_weight_decay, params_like)\n",
+        "\n",
+        "optimizer_choice = \"sgd\" #@param [\"sgd\", \"adamw\"]\n",
+        "\n",
+        "if optimizer_choice == \"sgd\":\n",
+        "  optimizer = optax.sgd(learning_rate=1e-3)\n",
+        "  num_steps = 300\n",
+        "elif optimizer_choice == \"adamw\":\n",
+        "  optimizer = optax.adamw(\n",
+        "        learning_rate=1e-4,\n",
+        "        b2=0.96,\n",
+        "        eps=1e-8,\n",
+        "        weight_decay=0.1,\n",
+        "        mask=griffin_weight_decay_mask,\n",
+        "    )\n",
+        "  num_steps = 100\n",
+        "else:\n",
+        "  raise ValueError(f\"Unknown optimizer: {optimizer_choice}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h-KYQziReyCn"
+      },
+      "source": [
+        "Prepare the training and validation datasets:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rGXTQ2uHeozO"
+      },
+      "outputs": [],
+      "source": [
+        "# Choose a small sequence length size, so that everything fits in memory.\n",
+        "num_epochs = 1 #@param {type: \"integer\"}\n",
+        "batch_size = 1 #@param {type: \"integer\"}\n",
+        "sequence_length = 32 #@param {type: \"integer\"}\n",
+        "\n",
+        "# Make the dataset builder.\n",
+        "tokenizer = GriffinTokenizer(vocab)\n",
+        "dataset_builder= MTNTDatasetBuilder(tokenizer, sequence_length + 1)\n",
+        "\n",
+        "# Build the training dataset.\n",
+        "train_ds = dataset_builder.get_train_dataset(\n",
+        "    batch_size=batch_size,\n",
+        "    num_epochs=num_epochs,\n",
+        ").as_numpy_iterator()\n",
+        "\n",
+        "# Build the validation dataset, with a limited number of samples for this demo.\n",
+        "validation_ds = dataset_builder.get_validation_dataset(\n",
+        "    batch_size=batch_size,\n",
+        ").take(50)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "B3alnSJQ1xmd"
+      },
+      "source": [
+        "Begin fine-tuning the RecurrentGemma (Griffin) model on a limited number of steps (`num_steps`):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7SL2VAmVEcoa"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Start, validation loss: 7.894117832183838\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/jax/_src/interpreters/mlir.py:920: UserWarning: Some donated buffers were not usable: ShapedArray(int32[1,33]), ShapedArray(bool[1,33]), ShapedArray(int32[], weak_type=True).\n",
+            "See an explanation at https://jax.readthedocs.io/en/latest/faq.html#buffer-donation.\n",
+            "  warnings.warn(\"Some donated buffers were not usable:\"\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "STEP 20 training loss: 4.592616081237793 - eval loss: 2.847407102584839\n",
+            "STEP 40 training loss: 2.7537424564361572 - eval loss: 2.9258534908294678\n",
+            "STEP 60 training loss: 2.835618257522583 - eval loss: 2.4382340908050537\n",
+            "STEP 80 training loss: 2.6322107315063477 - eval loss: 2.3696839809417725\n",
+            "STEP 100 training loss: 1.8703256845474243 - eval loss: 2.355681896209717\n",
+            "STEP 120 training loss: 2.7280433177948 - eval loss: 2.4059958457946777\n",
+            "STEP 140 training loss: 2.3047447204589844 - eval loss: 2.083082914352417\n",
+            "STEP 160 training loss: 2.3432137966156006 - eval loss: 2.095074415206909\n",
+            "STEP 180 training loss: 2.1081202030181885 - eval loss: 2.006460189819336\n",
+            "STEP 200 training loss: 2.5359647274017334 - eval loss: 1.9667452573776245\n",
+            "STEP 220 training loss: 2.202195644378662 - eval loss: 1.9440618753433228\n",
+            "STEP 240 training loss: 2.756615400314331 - eval loss: 2.1073737144470215\n",
+            "STEP 260 training loss: 2.5128934383392334 - eval loss: 2.117241859436035\n",
+            "STEP 280 training loss: 2.73045015335083 - eval loss: 1.9159646034240723\n",
+            "STEP 300 training loss: 2.0918595790863037 - eval loss: 1.9742532968521118\n"
+          ]
+        }
+      ],
+      "source": [
+        "trained_params = train_loop(\n",
+        "    model=model,\n",
+        "    params=params,\n",
+        "    optimizer=optimizer,\n",
+        "    train_ds=train_ds,\n",
+        "    validation_ds=validation_ds,\n",
+        "    num_steps=num_steps,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EtfVo3pDDAZV"
+      },
+      "source": [
+        "Both the training loss and the validation loss should have gone down with each step count.\n",
+        "\n",
+        "To ensure your input matches the training format, remember to use the prefix `Translate this into French:\\n` and a newline character at the end. This signals the model to begin translation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "S5F3fk22Ecod"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/jax/_src/interpreters/mlir.py:920: UserWarning: Some donated buffers were not usable: ShapedArray(int32[1,16]).\n",
+            "See an explanation at https://jax.readthedocs.io/en/latest/faq.html#buffer-donation.\n",
+            "  warnings.warn(\"Some donated buffers were not usable:\"\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mais je m'appelle Morgane.\n"
+          ]
+        }
+      ],
+      "source": [
+        "sampler.params = trained_params\n",
+        "output = sampler(\n",
+        "    [\"Translate this into French:\\nHello, my name is Morgane.\\n\"],\n",
+        "    total_generation_steps=100,\n",
+        ")\n",
+        "print(output.text[0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Jao0Qk-ZIqyD"
+      },
+      "source": [
+        "## Learn more\n",
+        "\n",
+        "- You can learn more about the Google DeepMind [`recurrentgemma`  library on GitHub](https://github.com/google-deepmind/recurrentgemma), which contains docstrings of methods and modules you used in this tutorial, such as [`recurrentgemma.jax.load_parameters`](https://github.com/google-deepmind/recurrentgemma/blob/e4939f9b7edf8baa1d512fb86bfc2e206044d66b/recurrentgemma/jax/utils.py#L31), [`recurrentgemma.jax.Griffin`](https://github.com/google-deepmind/recurrentgemma/blob/e4939f9b7edf8baa1d512fb86bfc2e206044d66b/recurrentgemma/jax/griffin.py#L29), and [`recurrentgemma.jax.Sampler`](https://github.com/google-deepmind/recurrentgemma/blob/e4939f9b7edf8baa1d512fb86bfc2e206044d66b/recurrentgemma/jax/sampler.py#L74).\n",
+        "- The following libraries have their own documentation sites: [core JAX](https://jax.readthedocs.io), [Flax](https://flax.readthedocs.io), [Chex](https://chex.readthedocs.io/en/latest/), [Optax](https://optax.readthedocs.io/en/latest/), and [Orbax](https://orbax.readthedocs.io/).\n",
+        "- For `sentencepiece` tokenizer/detokenizer documentation, check out [Google's `sentencepiece` GitHub repo](https://github.com/google/sentencepiece).\n",
+        "- For `kagglehub` documentation, check out `README.md` on [Kaggle's `kagglehub` GitHub repo](https://github.com/Kaggle/kagglehub).\n",
+        "- Learn how to [use Gemma models with Google Cloud Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma).\n",
+        "- If you are using Google Cloud TPUs (v3-8 and newer), make sure to also update to the latest `jax[tpu]` package (`!pip install -U jax[tpu] -f https://storage.googleapis.com/jax-releases/libtpu_releases.html`), restart the runtime, and check that `jax` and `jaxlib` versions match (`!pip list | grep jax`). This can prevent the `RuntimeError` that can arise because of the `jaxlib` and `jax` version mismatch. For more JAX installation instructions, refer to the [JAX docs](https://jax.readthedocs.io/en/latest/tutorials/installation.html#install-google-tpu).\n",
+        "- Check out the [RecurrentGemma: Moving Past Transformers\n",
+        "for Efficient Open Language Models](https://arxiv.org/pdf/2404.07839) paper by Google DeepMind.\n",
+        "- Read the [Griffin: Mixing Gated Linear Recurrences with\n",
+        "Local Attention for Efficient Language Models](https://arxiv.org/pdf/2402.19427) paper by Google DeepMind to learn more about the model architecture used by RecurrentGemma."
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "recurrentgemma_jax_finetune.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/gemma/docs/recurrentgemma/recurrentgemma_jax_inference.ipynb b/site/en/gemma/docs/recurrentgemma/recurrentgemma_jax_inference.ipynb
new file mode 100644
index 000000000..08d414844
--- /dev/null
+++ b/site/en/gemma/docs/recurrentgemma/recurrentgemma_jax_inference.ipynb
@@ -0,0 +1,516 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3MMAcssHTML"
+      },
+      "source": [
+        "<link rel=\"stylesheet\" href=\"/site-assets/css/gemma.css\">\n",
+        "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/css2?family=Google+Symbols:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200\" />"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FUOiKRSF7jc1"
+      },
+      "source": [
+        "# Inference with RecurrentGemma using JAX and Flax"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "60KmTK7o6ppd"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemma/docs/recurrentgemma/recurrentgemma_jax_inference\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/recurrentgemma/recurrentgemma_jax_inference.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/google/generative-ai-docs/main/site/en/gemma/docs/recurrentgemma/recurrentgemma_jax_inference.ipynb\"><img src=\"https://ai.google.dev/images/cloud-icon.svg\" width=\"40\" />Open in Vertex AI</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemma/docs/recurrentgemma/recurrentgemma_jax_inference.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tdlq6K0znh3O"
+      },
+      "source": [
+        "This tutorial demonstrates how to perform basic sampling/inference with the [RecurrentGemma](https://ai.google.dev/gemma/docs/recurrentgemma) 2B Instruct model using [Google DeepMind's `recurrentgemma` library](https://github.com/google-deepmind/recurrentgemma) that was written with [JAX](https://jax.readthedocs.io) (a high-performance numerical computing library), [Flax](https://flax.readthedocs.io) (the JAX-based neural network library), [Orbax](https://orbax.readthedocs.io/) (a JAX-based library for training utilities like checkpointing), and [SentencePiece](https://github.com/google/sentencepiece) (a tokenizer/detokenizer library). Although Flax is not used directly in this notebook, Flax was used to create Gemma and RecurrentGemma (the Griffin model).\n",
+        "\n",
+        "This notebook can run on Google Colab with the T4 GPU (go to **Edit** > **Notebook settings** > Under **Hardware accelerator** select **T4 GPU**)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aKvTsIkL98BG"
+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "The following sections explain the steps for preparing a notebook to use a RecurrentGemma model, including model access, getting an API key, and configuring the notebook runtime"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WCgCkmQSPxkE"
+      },
+      "source": [
+        "### Set up Kaggle access for Gemma\n",
+        "\n",
+        "To complete this tutorial, you first need to follow the setup instructions _similar_ to [Gemma setup](https://ai.google.dev/gemma/docs/setup) with a few exceptions:\n",
+        "\n",
+        "* Get access to RecurrentGemma (instead of Gemma) on [kaggle.com](https://www.kaggle.com/models/google/recurrentgemma).\n",
+        "* Select a Colab runtime with sufficient resources to run the RecurrentGemma model.\n",
+        "* Generate and configure a Kaggle username and API key.\n",
+        "\n",
+        "After you've completed the RecurrentGemma setup, move on to the next section, where you'll set environment variables for your Colab environment.\n",
+        "\n",
+        "### Set environment variables\n",
+        "\n",
+        "Set environment variables for `KAGGLE_USERNAME` and `KAGGLE_KEY`. When prompted with the \"Grant access?\" messages, agree to provide secret access."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lKoW-nhE-gNO"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from google.colab import userdata # `userdata` is a Colab API.\n",
+        "\n",
+        "os.environ[\"KAGGLE_USERNAME\"] = userdata.get('KAGGLE_USERNAME')\n",
+        "os.environ[\"KAGGLE_KEY\"] = userdata.get('KAGGLE_KEY')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AO7a1Q4Yyc9Z"
+      },
+      "source": [
+        "### Install the `recurrentgemma` library\n",
+        "\n",
+        "This notebook focuses on using a free Colab GPU. To enable hardware acceleration, click on **Edit** > **Notebook settings** > Select **T4 GPU** > **Save**.\n",
+        "\n",
+        "Next, you need to install the Google DeepMind `recurrentgemma` library from [`github.com/google-deepmind/recurrentgemma`](https://github.com/google-deepmind/recurrentgemma). If you get an error about \"pip's dependency resolver\", you can usually ignore it.\n",
+        "\n",
+        "**Note:** By installing `recurrentgemma`, you will also install [`flax`](https://flax.readthedocs.io), core [`jax`](https://jax.readthedocs.io), [`optax`](https://optax.readthedocs.io/en/latest/) (the JAX-based gradient processing and optimization library), [`orbax`](https://orbax.readthedocs.io/), and [`sentencepiece`](https://github.com/google/sentencepiece)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WWEzVJR4Fx9g"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Collecting git+https://github.com/google-deepmind/recurrentgemma.git\n",
+            "  Cloning https://github.com/google-deepmind/recurrentgemma.git to /tmp/pip-req-build-zz9xp6s4\n",
+            "  Running command git clone --filter=blob:none --quiet https://github.com/google-deepmind/recurrentgemma.git /tmp/pip-req-build-zz9xp6s4\n",
+            "  Resolved https://github.com/google-deepmind/recurrentgemma.git to commit e4939f9b7edf8baa1d512fb86bfc2e206044d66b\n",
+            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: absl-py<1.5.0,>=1.4.0 in /usr/local/lib/python3.10/dist-packages (from recurrentgemma==0.1.0) (1.4.0)\n",
+            "Collecting einops<0.8.0,>=0.7.0 (from recurrentgemma==0.1.0)\n",
+            "  Downloading einops-0.7.0-py3-none-any.whl (44 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting jaxtyping<0.3.0,>=0.2.28 (from recurrentgemma==0.1.0)\n",
+            "  Downloading jaxtyping-0.2.28-py3-none-any.whl (40 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.7/40.7 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: numpy<2.0,>=1.21 in /usr/local/lib/python3.10/dist-packages (from recurrentgemma==0.1.0) (1.25.2)\n",
+            "Collecting sentencepiece<0.3.0,>=0.2.0 (from recurrentgemma==0.1.0)\n",
+            "  Downloading sentencepiece-0.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m20.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting typeguard==2.13.3 (from jaxtyping<0.3.0,>=0.2.28->recurrentgemma==0.1.0)\n",
+            "  Downloading typeguard-2.13.3-py3-none-any.whl (17 kB)\n",
+            "Building wheels for collected packages: recurrentgemma\n",
+            "  Building wheel for recurrentgemma (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for recurrentgemma: filename=recurrentgemma-0.1.0-py3-none-any.whl size=73547 sha256=e3d3e85d59877ec33d2e4dff1a1666eaed1342c68199255cdd806d74472d4524\n",
+            "  Stored in directory: /tmp/pip-ephem-wheel-cache-42qdygtw/wheels/31/37/18/c57f1df6091b661385ab728b959bdfbf2078d9fc7c856899e4\n",
+            "Successfully built recurrentgemma\n",
+            "Installing collected packages: sentencepiece, typeguard, einops, jaxtyping, recurrentgemma\n",
+            "  Attempting uninstall: sentencepiece\n",
+            "    Found existing installation: sentencepiece 0.1.99\n",
+            "    Uninstalling sentencepiece-0.1.99:\n",
+            "      Successfully uninstalled sentencepiece-0.1.99\n",
+            "Successfully installed einops-0.7.0 jaxtyping-0.2.28 recurrentgemma-0.1.0 sentencepiece-0.2.0 typeguard-2.13.3\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install git+https://github.com/google-deepmind/recurrentgemma.git"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VKLjBAe1m3Ck"
+      },
+      "source": [
+        "## Load and prepare the RecurrentGemma model\n",
+        "\n",
+        "1. Load the RecurrentGemma model with [`kagglehub.model_download`](https://github.com/Kaggle/kagglehub/blob/bddefc718182282882b72f814d407d89e5d178c4/src/kagglehub/models.py#L12), which takes three arguments:\n",
+        "\n",
+        "- `handle`: The model handle from Kaggle\n",
+        "- `path`: (Optional string) The local path\n",
+        "- `force_download`: (Optional boolean) Forces to re-download the model\n",
+        "\n",
+        "**Note:** Be mindful that the `recurrentgemma-2b-it` model is around 3.85Gb in size."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_W3FUd9lt8VT"
+      },
+      "outputs": [],
+      "source": [
+        "RECURRENTGEMMA_VARIANT = '2b-it' # @param ['2b', '2b-it'] {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kFCmWEKdMA_Y"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/google/recurrentgemma/flax/2b-it/1/download...\n",
+            "100%|██████████| 3.85G/3.85G [00:52<00:00, 78.2MB/s]\n",
+            "Extracting model files...\n"
+          ]
+        }
+      ],
+      "source": [
+        "import kagglehub\n",
+        "\n",
+        "RECURRENTGEMMA_PATH = kagglehub.model_download(f'google/recurrentgemma/flax/{RECURRENTGEMMA_VARIANT}')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nYmYTMk8aELi"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "RECURRENTGEMMA_PATH: /root/.cache/kagglehub/models/google/recurrentgemma/flax/2b-it/1\n"
+          ]
+        }
+      ],
+      "source": [
+        "print('RECURRENTGEMMA_PATH:', RECURRENTGEMMA_PATH)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ytNi47xSlw71"
+      },
+      "source": [
+        "**Note:** The path from the output above is where the model weights and tokenizer are saved locally, you will need them for later."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "92BcvYdemXbd"
+      },
+      "source": [
+        "2. Check the location of the model weights and the tokenizer, then set the path variables. The tokenizer directory will be in the main directory where you downloaded the model, while the model weights will be in a sub-directory. For example:\n",
+        "\n",
+        "- The `tokenizer.model` file will be in `/LOCAL/PATH/TO/recurrentgemma/flax/2b-it/1`).\n",
+        "- The model checkpoint will be in `/LOCAL/PATH/TO/recurrentgemma/flax/2b-it/1/2b-it`)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QY6OnASOpZbW"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "CKPT_PATH: /root/.cache/kagglehub/models/google/recurrentgemma/flax/2b-it/1/2b-it\n",
+            "TOKENIZER_PATH: /root/.cache/kagglehub/models/google/recurrentgemma/flax/2b-it/1/tokenizer.model\n"
+          ]
+        }
+      ],
+      "source": [
+        "CKPT_PATH = os.path.join(RECURRENTGEMMA_PATH, RECURRENTGEMMA_VARIANT)\n",
+        "TOKENIZER_PATH = os.path.join(RECURRENTGEMMA_PATH, 'tokenizer.model')\n",
+        "print('CKPT_PATH:', CKPT_PATH)\n",
+        "print('TOKENIZER_PATH:', TOKENIZER_PATH)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jc0ZzYIW0TSN"
+      },
+      "source": [
+        "## Perform sampling/inference"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aEe3p8geqekV"
+      },
+      "source": [
+        "1. Load the RecurrentGemma model checkpoint with the [`recurrentgemma.jax.load_parameters`](https://github.com/google-deepmind/recurrentgemma/blob/e4939f9b7edf8baa1d512fb86bfc2e206044d66b/recurrentgemma/jax/utils.py#L31) method. The `sharding` argument set to `\"single_device\"` loads all model parameters on a single device."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Mnr52JQVqKRw"
+      },
+      "outputs": [],
+      "source": [
+        "import recurrentgemma\n",
+        "from recurrentgemma import jax as recurrentgemma\n",
+        "\n",
+        "params = recurrentgemma.load_parameters(checkpoint_path=CKPT_PATH, sharding=\"single_device\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-Xpnb2igrGjk"
+      },
+      "source": [
+        "2. Load the RecurrentGemma model tokenizer, constructed using [`sentencepiece.SentencePieceProcessor`](https://github.com/google/sentencepiece/blob/4d6a1f41069c4636c51a5590f7578a0dbed83450/python/src/sentencepiece/__init__.py#L423):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-T0ZHff5rNSy"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import sentencepiece as spm\n",
+        "\n",
+        "vocab = spm.SentencePieceProcessor()\n",
+        "vocab.Load(TOKENIZER_PATH)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IkAf4fkNrY-3"
+      },
+      "source": [
+        "3. To automatically load the correct configuration from the RecurrentGemma model checkpoint, use [`recurrentgemma.GriffinConfig.from_flax_params_or_variables`](https://github.com/google-deepmind/recurrentgemma/blob/e4939f9b7edf8baa1d512fb86bfc2e206044d66b/recurrentgemma/common.py#L128). Then, instantiate the [Griffin](https://arxiv.org/abs/2402.19427) model with [`recurrentgemma.jax.Griffin`](https://github.com/google-deepmind/recurrentgemma/blob/e4939f9b7edf8baa1d512fb86bfc2e206044d66b/recurrentgemma/jax/griffin.py#L29)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4PNWxDhvrRXJ"
+      },
+      "outputs": [],
+      "source": [
+        "model_config = recurrentgemma.GriffinConfig.from_flax_params_or_variables(\n",
+        "    flax_params_or_variables=params)\n",
+        "\n",
+        "model = recurrentgemma.Griffin(model_config)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vs0vgmXVroBq"
+      },
+      "source": [
+        "3. Create a `sampler` with [`recurrentgemma.jax.Sampler`](https://github.com/google-deepmind/recurrentgemma/blob/e4939f9b7edf8baa1d512fb86bfc2e206044d66b/recurrentgemma/jax/sampler.py#L74) on top of the RecurrentGemma model checkpoint/weights and the tokenizer:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4GX4pFP6rtyN"
+      },
+      "outputs": [],
+      "source": [
+        "sampler = recurrentgemma.Sampler(\n",
+        "    model=model,\n",
+        "    vocab=vocab,\n",
+        "    params=params,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "V9yU99Xxr59w"
+      },
+      "source": [
+        "4. Write a prompt in `prompt` and perform inference. You can tweak `total_generation_steps` (the number of steps performed when generating a response — this example uses `50` to preserve host memory).\n",
+        "\n",
+        "**Note:** If you run out of memory, click on **Runtime** > **Disconnect and delete runtime**, and then **Runtime** > **Run all**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Gj9jRFI5Hrv2"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/jax/_src/interpreters/mlir.py:920: UserWarning: Some donated buffers were not usable: ShapedArray(int32[1,8]).\n",
+            "See an explanation at https://jax.readthedocs.io/en/latest/faq.html#buffer-donation.\n",
+            "  warnings.warn(\"Some donated buffers were not usable:\"\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Prompt:\n",
+            "\n",
+            "# 5+9=?\n",
+            "Output:\n",
+            "\n",
+            "\n",
+            "# Answer: 14\n",
+            "\n",
+            "# Explanation: 5 + 9 = 14.\n"
+          ]
+        }
+      ],
+      "source": [
+        "prompt = [\n",
+        "    \"\\n# 5+9=?\",\n",
+        "]\n",
+        "\n",
+        "reply = sampler(input_strings=prompt,\n",
+        "                total_generation_steps=50,\n",
+        "                )\n",
+        "\n",
+        "for input_string, out_string in zip(prompt, reply.text):\n",
+        "    print(f\"Prompt:\\n{input_string}\\nOutput:\\n{out_string}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bzKsCGIN0yX5"
+      },
+      "source": [
+        "## Learn more\n",
+        "\n",
+        "- You can learn more about the Google DeepMind [`recurrentgemma`  library on GitHub](https://github.com/google-deepmind/recurrentgemma), which contains docstrings of methods and modules you used in this tutorial, such as [`recurrentgemma.jax.load_parameters`](https://github.com/google-deepmind/recurrentgemma/blob/e4939f9b7edf8baa1d512fb86bfc2e206044d66b/recurrentgemma/jax/utils.py#L31), [`recurrentgemma.jax.Griffin`](https://github.com/google-deepmind/recurrentgemma/blob/e4939f9b7edf8baa1d512fb86bfc2e206044d66b/recurrentgemma/jax/griffin.py#L29), and [`recurrentgemma.jax.Sampler`](https://github.com/google-deepmind/recurrentgemma/blob/e4939f9b7edf8baa1d512fb86bfc2e206044d66b/recurrentgemma/jax/sampler.py#L74).\n",
+        "- The following libraries have their own documentation sites: [core JAX](https://jax.readthedocs.io), [Flax](https://flax.readthedocs.io), and [Orbax](https://orbax.readthedocs.io/).\n",
+        "- For `sentencepiece` tokenizer/detokenizer documentation, check out [Google's `sentencepiece` GitHub repo](https://github.com/google/sentencepiece).\n",
+        "- For `kagglehub` documentation, check out `README.md` on [Kaggle's `kagglehub` GitHub repo](https://github.com/Kaggle/kagglehub).\n",
+        "- Learn how to [use Gemma models with Google Cloud Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma).\n",
+        "- Check out the [RecurrentGemma: Moving Past Transformers\n",
+        "for Efficient Open Language Models](https://arxiv.org/pdf/2404.07839) paper by Google DeepMind.\n",
+        "- Read the [Griffin: Mixing Gated Linear Recurrences with\n",
+        "Local Attention for Efficient Language Models](https://arxiv.org/pdf/2402.19427) paper by GoogleDeepMind to learn more about the model architecture used by RecurrentGemma."
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [
+        "Tce3stUlHN0L"
+      ],
+      "name": "recurrentgemma_jax_inference.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/tutorials/chat_node_quickstart.ipynb b/site/en/palm_docs/chat_node_quickstart.ipynb
similarity index 96%
rename from site/en/tutorials/chat_node_quickstart.ipynb
rename to site/en/palm_docs/chat_node_quickstart.ipynb
index ca2159d51..93df7c5a1 100644
--- a/site/en/tutorials/chat_node_quickstart.ipynb
+++ b/site/en/palm_docs/chat_node_quickstart.ipynb
@@ -6,7 +6,7 @@
         "id": "Tce3stUlHN0L"
       },
       "source": [
-        "##### Copyright 2023 Google LLC."
+        "##### Copyright 2024 Google LLC."
       ]
     },
     {
@@ -38,7 +38,7 @@
         "id": "09cf87d0"
       },
       "source": [
-        "# PaLM API: Chat Quickstart with Node.js\n"
+        "# PaLM API: Chat Quickstart with Node.js"
       ]
     },
     {
@@ -49,13 +49,13 @@
       "source": [
         "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://developers.generativeai.google/tutorials/chat_node_quickstart\"><img src=\"https://developers.generativeai.google/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Generative AI</a>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/palm_docs/chat_node_quickstart\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/tutorials/chat_node_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/palm_docs/chat_node_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/tutorials/chat_node_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/palm_docs/chat_node_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
         "  </td>\n",
         "</table>"
       ]
diff --git a/site/en/tutorials/chat_quickstart.ipynb b/site/en/palm_docs/chat_quickstart.ipynb
similarity index 96%
rename from site/en/tutorials/chat_quickstart.ipynb
rename to site/en/palm_docs/chat_quickstart.ipynb
index 4b1de540b..209601fe6 100644
--- a/site/en/tutorials/chat_quickstart.ipynb
+++ b/site/en/palm_docs/chat_quickstart.ipynb
@@ -6,7 +6,7 @@
         "id": "Tce3stUlHN0L"
       },
       "source": [
-        "##### Copyright 2023 Google LLC."
+        "##### Copyright 2024 Google LLC."
       ]
     },
     {
@@ -48,13 +48,13 @@
       "source": [
         "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://developers.generativeai.google/tutorials/chat_quickstart\"><img src=\"https://developers.generativeai.google/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Generative AI</a>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/palm_docs/chat_quickstart\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/tutorials/chat_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/palm_docs/chat_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/tutorials/chat_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/palm_docs/chat_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
         "  </td>\n",
         "</table>"
       ]
@@ -107,7 +107,7 @@
       },
       "outputs": [],
       "source": [
-        "import google.generativeai as palm"
+        "import google.generativeai as genai"
       ]
     },
     {
@@ -129,7 +129,7 @@
       },
       "outputs": [],
       "source": [
-        "palm.configure(api_key='PALM_KEY')"
+        "genai.configure(api_key='API_KEY')"
       ]
     },
     {
@@ -140,7 +140,7 @@
       "source": [
         "## Starting a conversation\n",
         "\n",
-        "In this tutorial, you'll use the PaLM API for a LLM designed for chat use cases. The language model was trained on a large conversational dataset, so when you call the model, it'll give you a conversational, chatty response:\n"
+        "In this tutorial, you'll use the PaLM API for a LLM designed for chat use cases. The language model was trained on a large conversational dataset, so when you call the model, it'll give you a conversational, chatty response:"
       ]
     },
     {
@@ -166,7 +166,7 @@
       ],
       "source": [
         "# Create a new conversation\n",
-        "response = palm.chat(messages='Hello')\n",
+        "response = genai.chat(messages='Hello')\n",
         "\n",
         "# Last contains the model's response:\n",
         "response.last"
@@ -297,7 +297,7 @@
       ],
       "source": [
         "# Create a brand new chat with candidate_count = 4.\n",
-        "response = palm.chat(messages=\"What should I eat for dinner tonight? List a few options\", candidate_count = 4)\n",
+        "response = genai.chat(messages=\"What should I eat for dinner tonight? List a few options\", candidate_count = 4)\n",
         "# See the model's default response\n",
         "response.last"
       ]
@@ -406,7 +406,7 @@
       ],
       "source": [
         "# Setting temperature=1 usually produces more zany responses!\n",
-        "response = palm.chat(messages=\"What should I eat for dinner tonight? List a few options\", temperature=1)\n",
+        "response = genai.chat(messages=\"What should I eat for dinner tonight? List a few options\", temperature=1)\n",
         "response.last"
       ]
     },
@@ -416,7 +416,7 @@
         "id": "m_GV7cTjJfQi"
       },
       "source": [
-        "## Designing a chatbot that does what you want\n"
+        "## Designing a chatbot that does what you want"
       ]
     },
     {
@@ -457,7 +457,7 @@
         }
       ],
       "source": [
-        "reply = palm.chat(context=\"Speak like Shakespeare.\", messages='Hello')\n",
+        "reply = genai.chat(context=\"Speak like Shakespeare.\", messages='Hello')\n",
         "print(reply.last)"
       ]
     },
@@ -479,7 +479,7 @@
         }
       ],
       "source": [
-        "reply = palm.chat(context=\"Answer everything with a haiku, following the 5/7/5 rhyme pattern.\", messages=\"How's it going?\")\n",
+        "reply = genai.chat(context=\"Answer everything with a haiku, following the 5/7/5 rhyme pattern.\", messages=\"How's it going?\")\n",
         "print(reply.last)"
       ]
     },
@@ -501,7 +501,7 @@
         }
       ],
       "source": [
-        "reply = palm.chat(context=\"Be an alien that lives on one of Jupiter's moons\",\n",
+        "reply = genai.chat(context=\"Be an alien that lives on one of Jupiter's moons\",\n",
         "                   messages=\"How's it going?\")\n",
         "print(reply.last)"
       ]
@@ -549,7 +549,7 @@
         }
       ],
       "source": [
-        "reply = palm.chat(context=\"Be a motivational coach who's very inspiring\", messages=\"How's it going?\")\n",
+        "reply = genai.chat(context=\"Be a motivational coach who's very inspiring\", messages=\"How's it going?\")\n",
         "print(reply.last)"
       ]
     },
@@ -611,7 +611,7 @@
         }
       ],
       "source": [
-        "response = palm.chat(\n",
+        "response = genai.chat(\n",
         "    context=\"Be a motivational coach who's very inspiring\",\n",
         "    examples=examples,\n",
         "    messages=\"I'm too tired to go the gym today\")\n",
diff --git a/site/en/tutorials/curl_quickstart.ipynb b/site/en/palm_docs/curl_quickstart.ipynb
similarity index 98%
rename from site/en/tutorials/curl_quickstart.ipynb
rename to site/en/palm_docs/curl_quickstart.ipynb
index 6994b1e66..ecf9d4bac 100644
--- a/site/en/tutorials/curl_quickstart.ipynb
+++ b/site/en/palm_docs/curl_quickstart.ipynb
@@ -6,7 +6,7 @@
         "id": "Tce3stUlHN0L"
       },
       "source": [
-        "##### Copyright 2023 Google LLC."
+        "##### Copyright 2024 Google LLC."
       ]
     },
     {
@@ -37,7 +37,7 @@
         "id": "yeadDkMiISin"
       },
       "source": [
-        "# PaLM API: Quickstart with Curl"
+        "# PaLM REST API: Quickstart"
       ]
     },
     {
@@ -48,13 +48,13 @@
       "source": [
         "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://developers.generativeai.google/tutorials/curl_quickstart\"><img src=\"https://developers.generativeai.google/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Generative AI</a>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/palm_docs/curl_quickstart\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/tutorials/curl_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/palm_docs/curl_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/tutorials/curl_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/palm_docs/curl_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
         "  </td>\n",
         "</table>"
       ]
@@ -380,7 +380,7 @@
         "id": "dede032595a0"
       },
       "source": [
-        "The following example shows a call with different values for the parameters.\n"
+        "The following example shows a call with different values for the parameters."
       ]
     },
     {
@@ -450,7 +450,7 @@
         "## Embed text\n",
         "\n",
         "Use the [`embedText`](https://developers.generativeai.google/api/rest/generativelanguage/models/embedText) method to\n",
-        "generate an embedding from the model given an input message.\n"
+        "generate an embedding from the model given an input message."
       ]
     },
     {
@@ -1259,7 +1259,7 @@
         "Use the\n",
         "[`countMessageTokens`](https://developers.generativeai.google/api/rest/generativelanguage/models/countMessageTokens)\n",
         "method to run a model's tokenizer on a message\n",
-        "prompt string and get a token count.\n"
+        "prompt string and get a token count."
       ]
     },
     {
diff --git a/site/en/tutorials/embeddings_quickstart.ipynb b/site/en/palm_docs/embeddings_quickstart.ipynb
similarity index 97%
rename from site/en/tutorials/embeddings_quickstart.ipynb
rename to site/en/palm_docs/embeddings_quickstart.ipynb
index 52d7c8576..d59d4aff3 100644
--- a/site/en/tutorials/embeddings_quickstart.ipynb
+++ b/site/en/palm_docs/embeddings_quickstart.ipynb
@@ -6,7 +6,7 @@
         "id": "Tce3stUlHN0L"
       },
       "source": [
-        "##### Copyright 2023 Google LLC."
+        "##### Copyright 2024 Google LLC."
       ]
     },
     {
@@ -48,13 +48,13 @@
       "source": [
         "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://developers.generativeai.google/tutorials/embeddings_quickstart\"><img src=\"https://developers.generativeai.google/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Generative AI</a>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/palm_docs/embeddings_quickstart\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/tutorials/embeddings_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/palm_docs/embeddings_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/tutorials/embeddings_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/palm_docs/embeddings_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
         "  </td>\n",
         "</table>"
       ]
@@ -65,7 +65,7 @@
         "id": "BuhjNPTpju5n"
       },
       "source": [
-        "In this notebook, you'll learn how to get started with the PaLM API, which gives you access to Google's latest large language models. Here, you'll learn how to use the PaLM API's embedding generation features, and see an example of what you can do with these embeddings.\n"
+        "In this notebook, you'll learn how to get started with the PaLM API, which gives you access to Google's latest large language models. Here, you'll learn how to use the PaLM API's embedding generation features, and see an example of what you can do with these embeddings."
       ]
     },
     {
diff --git a/site/en/tools/notebook_magic.ipynb b/site/en/palm_docs/notebook_magic.ipynb
similarity index 99%
rename from site/en/tools/notebook_magic.ipynb
rename to site/en/palm_docs/notebook_magic.ipynb
index f96c8c1d5..c699f0d9b 100644
--- a/site/en/tools/notebook_magic.ipynb
+++ b/site/en/palm_docs/notebook_magic.ipynb
@@ -6,7 +6,7 @@
         "id": "Tce3stUlHN0L"
       },
       "source": [
-        "##### Copyright 2023 Google LLC."
+        "##### Copyright 2024 Google LLC."
       ]
     },
     {
@@ -37,7 +37,7 @@
         "id": "9vfDV6DhVOAj"
       },
       "source": [
-        "# PaLM Colab Magic\n",
+        "# Colab Magic\n",
         "\n",
         "This notebook introduces Colab magic commands for PaLM. Magics make it easy to develop, test, compare, and evaluate prompts from within a Colab notebook."
       ]
@@ -50,13 +50,13 @@
       "source": [
         "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://developers.generativeai.google/tools/notebook_magic\"><img src=\"https://developers.generativeai.google/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Generative AI</a>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/palm_docs/notebook_magic\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/tools/notebook_magic.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/palm_docs/notebook_magic.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/tools/notebook_magic.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/palm_docs/notebook_magic.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
         "  </td>\n",
         "</table>"
       ]
@@ -84,7 +84,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": null,
       "metadata": {
         "id": "4so2CHmPSxIU"
       },
@@ -106,7 +106,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": null,
       "metadata": {
         "id": "3qoE1eycyJzD"
       },
@@ -127,7 +127,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": null,
       "metadata": {
         "id": "UKkEPjKKU9o2"
       },
@@ -211,7 +211,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": null,
       "metadata": {
         "id": "CrE6WUhwcxjz"
       },
@@ -417,7 +417,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": null,
       "metadata": {
         "id": "xzIqZlsbyQJj"
       },
@@ -645,7 +645,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": null,
       "metadata": {
         "id": "hih5E6IhmDDg"
       },
@@ -659,7 +659,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": null,
       "metadata": {
         "id": "E8WWpTuRmAb1"
       },
@@ -897,7 +897,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 10,
+      "execution_count": null,
       "metadata": {
         "id": "SPfC1bF-CzKB"
       },
@@ -913,7 +913,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": null,
       "metadata": {
         "id": "Smr0Vs3zC_ub"
       },
@@ -1166,7 +1166,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 13,
+      "execution_count": null,
       "metadata": {
         "cellView": "form",
         "id": "DkdoxXqjD2Tm"
@@ -1204,12 +1204,12 @@
         "-----|-----------\n",
         "|Milo|cheeky|\n",
         "|Bigsly|relaxed|\n",
-        "|Subra|shy|\n"
+        "|Subra|shy|"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 14,
+      "execution_count": null,
       "metadata": {
         "id": "R2B35-6S7Z3f"
       },
@@ -1466,7 +1466,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 15,
+      "execution_count": null,
       "metadata": {
         "id": "wWKTRt_lETHx"
       },
@@ -1769,7 +1769,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 16,
+      "execution_count": null,
       "metadata": {
         "id": "KeWujB0MUsV6"
       },
@@ -1783,7 +1783,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 19,
+      "execution_count": null,
       "metadata": {
         "id": "We_-1C2UU9Mh"
       },
@@ -2043,7 +2043,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 20,
+      "execution_count": null,
       "metadata": {
         "id": "E7f_h6UgETyA"
       },
@@ -2069,7 +2069,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 21,
+      "execution_count": null,
       "metadata": {
         "id": "w7hFcIiMETyA"
       },
@@ -2324,7 +2324,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 22,
+      "execution_count": null,
       "metadata": {
         "id": "QDXqCknx_AsY"
       },
@@ -2353,7 +2353,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 23,
+      "execution_count": null,
       "metadata": {
         "id": "-Ax3vb9r_pLD"
       },
@@ -2584,7 +2584,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 24,
+      "execution_count": null,
       "metadata": {
         "id": "X-80vOvMBaUr"
       },
@@ -2620,7 +2620,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 30,
+      "execution_count": null,
       "metadata": {
         "id": "lTnp0cAidtGe"
       },
@@ -2649,7 +2649,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 26,
+      "execution_count": null,
       "metadata": {
         "id": "zpVAcNeteAMh"
       },
@@ -2675,7 +2675,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 27,
+      "execution_count": null,
       "metadata": {
         "id": "mKVVnRxRyzh8"
       },
@@ -2688,7 +2688,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 31,
+      "execution_count": null,
       "metadata": {
         "id": "gv1EFjFWeJNG"
       },
@@ -2940,7 +2940,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 32,
+      "execution_count": null,
       "metadata": {
         "id": "bd9eDrxvHzS5"
       },
@@ -2962,7 +2962,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 33,
+      "execution_count": null,
       "metadata": {
         "id": "UKBLkZ6kHncJ"
       },
@@ -3236,7 +3236,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 34,
+      "execution_count": null,
       "metadata": {
         "id": "gfKb33diYSHn"
       },
@@ -3312,7 +3312,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 35,
+      "execution_count": null,
       "metadata": {
         "id": "NmD8zg1lhrRF"
       },
@@ -3532,7 +3532,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 36,
+      "execution_count": null,
       "metadata": {
         "id": "FB5Rswg8iPfm"
       },
@@ -3752,7 +3752,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 37,
+      "execution_count": null,
       "metadata": {
         "id": "wq6EX_oki01u"
       },
@@ -3969,12 +3969,12 @@
         "\n",
         "In addition to displaying tabular output, the PaLM magic can save model output to Python variables, allowing you to manipulate them further or to export your results.\n",
         "\n",
-        "In this example, the output is saved to a Python variable: `fave_colors`\n"
+        "In this example, the output is saved to a Python variable: `fave_colors`"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 38,
+      "execution_count": null,
       "metadata": {
         "id": "ZWAuhyMAjilc"
       },
@@ -4195,7 +4195,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 39,
+      "execution_count": null,
       "metadata": {
         "id": "X-pbyRxfj6gB"
       },
@@ -5021,7 +5021,7 @@
         "\n",
         "* Refer to the [LLMs concepts guide](https://developers.generativeai.google/guide/concepts) to learn more about LLMs.\n",
         "* Check out the [prompt guidelines](https://developers.generativeai.google/guide/prompt_best_practices) to learn more about crafting prompts to get the most out of working with PaLM.\n",
-        "* To prototype and experiment with different prompts, check out [MakerSuite](https://makersuite.google.com/). Also, refer to the [MakerSuite quickstart](https://developers.generativeai.google/tutorials/makersuite_quickstart) for more information."
+        "* To prototype and experiment with different prompts, check out [Google AI Studio](https://makersuite.google.com/){:.external}. Also, refer to the [Google AI Studio quickstart](../tutorials/ai-studio_quickstart) for more information."
       ]
     }
   ],
diff --git a/site/en/tutorials/text_node_quickstart.ipynb b/site/en/palm_docs/text_node_quickstart.ipynb
similarity index 95%
rename from site/en/tutorials/text_node_quickstart.ipynb
rename to site/en/palm_docs/text_node_quickstart.ipynb
index 80762cd6e..143bb6108 100644
--- a/site/en/tutorials/text_node_quickstart.ipynb
+++ b/site/en/palm_docs/text_node_quickstart.ipynb
@@ -6,7 +6,7 @@
         "id": "Tce3stUlHN0L"
       },
       "source": [
-        "##### Copyright 2023 Google LLC."
+        "##### Copyright 2024 Google LLC."
       ]
     },
     {
@@ -49,13 +49,13 @@
       "source": [
         "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://developers.generativeai.google/tutorials/text_node_quickstart\"><img src=\"https://developers.generativeai.google/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Generative AI</a>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/palm_docs/text_node_quickstart\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/tutorials/text_node_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/palm_docs/text_node_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/tutorials/text_node_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/palm_docs/text_node_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
         "  </td>\n",
         "</table>"
       ]
diff --git a/site/en/tutorials/text_quickstart.ipynb b/site/en/palm_docs/text_quickstart.ipynb
similarity index 98%
rename from site/en/tutorials/text_quickstart.ipynb
rename to site/en/palm_docs/text_quickstart.ipynb
index 607867cc8..8be9bcd14 100644
--- a/site/en/tutorials/text_quickstart.ipynb
+++ b/site/en/palm_docs/text_quickstart.ipynb
@@ -6,12 +6,12 @@
         "id": "Tce3stUlHN0L"
       },
       "source": [
-        "##### Copyright 2023 Google LLC."
+        "##### Copyright 2024 Google LLC."
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": null,
       "metadata": {
         "cellView": "form",
         "id": "tuOe1ymfHZPu"
@@ -48,13 +48,13 @@
       "source": [
         "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://developers.generativeai.google/tutorials/text_quickstart\"><img src=\"https://developers.generativeai.google/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Generative AI</a>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/palm_docs/text_quickstart\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/tutorials/text_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/palm_docs/text_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/tutorials/text_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/palm_docs/text_quickstart.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
         "  </td>\n",
         "</table>"
       ]
diff --git a/site/en/tutorials/tuning_quickstart_python.ipynb b/site/en/palm_docs/tuning_quickstart_python.ipynb
similarity index 97%
rename from site/en/tutorials/tuning_quickstart_python.ipynb
rename to site/en/palm_docs/tuning_quickstart_python.ipynb
index 5fa67ee64..510a9a6df 100644
--- a/site/en/tutorials/tuning_quickstart_python.ipynb
+++ b/site/en/palm_docs/tuning_quickstart_python.ipynb
@@ -6,12 +6,12 @@
         "id": "Tce3stUlHN0L"
       },
       "source": [
-        "##### Copyright 2023 Google LLC."
+        "##### Copyright 2024 Google LLC."
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": null,
       "metadata": {
         "cellView": "form",
         "id": "tuOe1ymfHZPu"
@@ -48,16 +48,16 @@
       "source": [
         "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://developers.generativeai.google/tutorials/tuning_quickstart_python\"><img src=\"https://developers.generativeai.google/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Generative AI</a>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/palm_docs/tuning_quickstart_python\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
         "  </td>\n",
         "    <td>\n",
-        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/tutorials/tuning_quickstart_python.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/palm_docs/tuning_quickstart_python.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/tutorials/tuning_quickstart_python.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/palm_docs/tuning_quickstart_python.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
         "  </td>\n",
         "    <td>\n",
-        "    <a target=\"_blank\" href=\"https://developers.generativeai.google/tutorials/tuning_quickstart_python.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/palm_docs/tuning_quickstart_python\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
         "  </td>\n",
         "</table>"
       ]
@@ -77,7 +77,7 @@
         "id": "4JXd-HdCsKdZ"
       },
       "source": [
-        "**Note**: At this time, the PaLM API is [only available in certain regions](https://developers.generativeai.google/available_regions)."
+        "**Note**: At this time, tuning is only available for the `text-bison-001` model."
       ]
     },
     {
@@ -113,7 +113,7 @@
         "If you want to run this notebook in Colab start by uploading your\n",
         "`client_secret*.json` file using the \"File > Upload\" option.\n",
         "\n",
-        "![Show colab's File > Upload option](https://developers.generativeai.google/tutorials/images/colab_upload.png)"
+        "<img width=400 src=\"https://ai.google.dev/tutorials/images/colab_upload.png\">"
       ]
     },
     {
@@ -133,7 +133,7 @@
       ],
       "source": [
         "!cp client_secret*.json client_secret.json\n",
-        "!ls"
+        "!ls client_secret.json"
       ]
     },
     {
@@ -176,24 +176,11 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "VYetBMbknUVp"
+        "id": "cbcf72bcb56d"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/122.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
-            "\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.7/122.9 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.9/122.9 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/113.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m113.3/113.3 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
-        "!pip install google-generativeai"
+        "!pip install -q google-generativeai"
       ]
     },
     {
@@ -213,21 +200,7 @@
       },
       "outputs": [],
       "source": [
-        "import google.generativeai as palm\n",
-        "\n",
-        "import google.ai.generativelanguage as glm\n",
-        "import pprint"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "EguT5ENYgkbX"
-      },
-      "outputs": [],
-      "source": [
-        "palm.configure(credentials = creds)"
+        "import google.generativeai as genai\n"
       ]
     },
     {
@@ -236,7 +209,7 @@
         "id": "P-MYZECwlRCq"
       },
       "source": [
-        "You can check you existing tuned models with the `palm.list_tuned_model` method."
+        "You can check you existing tuned models with the `genai.list_tuned_model` method."
       ]
     },
     {
@@ -250,16 +223,17 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "['tunedModels/testnumbergenerator-fvitocr834l6',\n",
-            " 'tunedModels/my-display-name-81-9wpmc1m920vq',\n",
-            " 'tunedModels/number-generator-model-kctlevca1g3q',\n",
-            " 'tunedModels/my-display-name-81-r9wcuda14lyy']\n"
+            "tunedModels/my-model-8527\n",
+            "tunedModels/my-model-7092\n",
+            "tunedModels/my-model-2778\n",
+            "tunedModels/my-model-1298\n",
+            "tunedModels/my-model-3883\n"
           ]
         }
       ],
       "source": [
-        "tuned_models = list(palm.list_tuned_models())\n",
-        "pprint.pprint([m.name for m in tuned_models])"
+        "for i, m in zip(range(5), genai.list_tuned_models()):\n",
+        "  print(m.name)"
       ]
     },
     {
@@ -277,7 +251,7 @@
         "id": "OO8VZYAinLWc"
       },
       "source": [
-        "To create a tuned model, you need to pass your dataset to the model in the `palm.create_tuned_model` method. You can do this be directly defining the input and output values in the call or importing from a file into a dataframe to pass to the method.\n",
+        "To create a tuned model, you need to pass your dataset to the model in the `genai.create_tuned_model` method. You can do this be directly defining the input and output values in the call or importing from a file into a dataframe to pass to the method.\n",
         "\n",
         "For this example, you will tune a model to generate the next number in the sequence. For example, if the input is `1`, the model should output `2`. If the input is `one hundred`, the output should be `one hundred one`."
       ]
@@ -305,7 +279,7 @@
       ],
       "source": [
         "base_model = [\n",
-        "    m for m in palm.list_models()\n",
+        "    m for m in genai.list_models()\n",
         "    if \"createTunedTextModel\" in m.supported_generation_methods][0]\n",
         "base_model.name"
       ]
@@ -321,7 +295,8 @@
         "import random\n",
         "\n",
         "name = f'generate-num-{random.randint(0,10000)}'\n",
-        "operation = palm.create_tuned_model(\n",
+        "operation = genai.create_tuned_model(\n",
+        "    # You can use a tuned model here too. Set `source_model=\"tunedModels/...\"`\n",
         "    source_model=base_model.name,\n",
         "    training_data=[\n",
         "        {\n",
@@ -398,8 +373,8 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "TunedModel(name='tunedModels/generate-num-4668',\n",
-            "           source_model=None,\n",
+            "TunedModel(name='tunedModels/generate-num-9028',\n",
+            "           source_model='tunedModels/generate-num-4110',\n",
             "           base_model='models/text-bison-001',\n",
             "           display_name='',\n",
             "           description='',\n",
@@ -407,9 +382,9 @@
             "           top_p=0.95,\n",
             "           top_k=40,\n",
             "           state=<State.CREATING: 1>,\n",
-            "           create_time=datetime.datetime(2023, 9, 19, 19, 3, 38, 22249, tzinfo=<UTC>),\n",
-            "           update_time=datetime.datetime(2023, 9, 19, 19, 3, 38, 22249, tzinfo=<UTC>),\n",
-            "           tuning_task=TuningTask(start_time=datetime.datetime(2023, 9, 19, 19, 3, 38, 562798, tzinfo=<UTC>),\n",
+            "           create_time=datetime.datetime(2023, 9, 29, 21, 37, 32, 188028, tzinfo=datetime.timezone.utc),\n",
+            "           update_time=datetime.datetime(2023, 9, 29, 21, 37, 32, 188028, tzinfo=datetime.timezone.utc),\n",
+            "           tuning_task=TuningTask(start_time=datetime.datetime(2023, 9, 29, 21, 37, 32, 734118, tzinfo=datetime.timezone.utc),\n",
             "                                  complete_time=None,\n",
             "                                  snapshots=[],\n",
             "                                  hyperparameters=Hyperparameters(epoch_count=100,\n",
@@ -419,9 +394,9 @@
         }
       ],
       "source": [
-        "model = palm.get_tuned_model(f'tunedModels/{name}')\n",
+        "model = genai.get_tuned_model(f'tunedModels/{name}')\n",
         "\n",
-        "pprint.pprint(model)"
+        "model"
       ]
     },
     {
@@ -474,11 +449,11 @@
         {
           "data": {
             "text/plain": [
-              "total_steps: 375\n",
-              "tuned_model: \"tunedModels/generate-num-4668\""
+              "tuned_model: \"tunedModels/generate-num-9028\"\n",
+              "total_steps: 375"
             ]
           },
-          "execution_count": 15,
+          "execution_count": 41,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -506,7 +481,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "c07d8bb1884d460cb7e7e40aa459c2ea",
+              "model_id": "98e4b6958bfc43c98e6e77354f7bf315",
               "version_major": 2,
               "version_minor": 0
             },
@@ -520,6 +495,7 @@
       ],
       "source": [
         "import time\n",
+        "\n",
         "for status in operation.wait_bar():\n",
         "  time.sleep(30)"
       ]
@@ -550,7 +526,7 @@
         "id": "lqiL0TWDqAPn"
       },
       "source": [
-        "Once the tuning is complete, you can view the loss curve from the tuning results. The [loss curve](https://generativeai.devsite.corp.google.com/guide/model_tuning_guidance#recommended_configurations) shows how much the model's predictions deviate from the ideal outputs."
+        "Once the tuning is complete, you can view the loss curve from the tuning results. The [loss curve](https://ai.google.dev/gemini-api/docs/model-tuning#recommended_configurations) shows how much the model's predictions deviate from the ideal outputs."
       ]
     },
     {
@@ -600,7 +576,7 @@
       "source": [
         "## Evaluate your model\n",
         "\n",
-        "You can use the `palm.generate_text` method and specify the name of your model to test your model performance."
+        "You can use the `genai.generate_text` method and specify the name of your model to test your model performance."
       ]
     },
     {
@@ -625,7 +601,7 @@
         }
       ],
       "source": [
-        "completion = palm.generate_text(model=f'tunedModels/{name}',\n",
+        "completion = genai.generate_text(model=f'tunedModels/{name}',\n",
         "                                prompt='5')\n",
         "completion.result"
       ]
@@ -652,7 +628,7 @@
         }
       ],
       "source": [
-        "completion = palm.generate_text(model=f'tunedModels/{name}',\n",
+        "completion = genai.generate_text(model=f'tunedModels/{name}',\n",
         "                                prompt='-9')\n",
         "completion.result"
       ]
@@ -679,7 +655,7 @@
         }
       ],
       "source": [
-        "completion = palm.generate_text(model=f'tunedModels/{name}',\n",
+        "completion = genai.generate_text(model=f'tunedModels/{name}',\n",
         "                                prompt='four')\n",
         "completion.result"
       ]
@@ -692,7 +668,7 @@
       "source": [
         "As you can see, the last prompt didn't produce the ideal result, `five`. To produce better results you can try a few different things such as adjusting the temperature closer to zero to get more consistent results, adding more quality examples to your dataset that the model can learn from or adding a prompt or preamble to the examples.\n",
         "\n",
-        "See the [tuning guide](https://generativeai.devsite.corp.google.com/guide/model_tuning_guidance) for more guidance on improving performance."
+        "See the [tuning guide](https://ai.google.dev/gemini-api/docs/model-tuning) for more guidance on improving performance."
       ]
     },
     {
@@ -703,7 +679,7 @@
       "source": [
         "## Update the description\n",
         "\n",
-        "You can update the description of your tuned model any time using the `palm.update_tuned_model` method."
+        "You can update the description of your tuned model any time using the `genai.update_tuned_model` method."
       ]
     },
     {
@@ -725,7 +701,7 @@
         }
       ],
       "source": [
-        "palm.update_tuned_model(f'tunedModels/{name}', {\"description\":\"This is my model.\"})"
+        "genai.update_tuned_model(f'tunedModels/{name}', {\"description\":\"This is my model.\"})"
       ]
     },
     {
@@ -2259,9 +2235,9 @@
         }
       ],
       "source": [
-        "model = palm.get_tuned_model(f'tunedModels/{name}')\n",
+        "model = genai.get_tuned_model(f'tunedModels/{name}')\n",
         "\n",
-        "pprint.pprint(model)"
+        "model"
       ]
     },
     {
@@ -2297,7 +2273,7 @@
       "source": [
         "## Delete the model\n",
         "\n",
-        "You can clean up your tuned model list by deleting models you no longer need. Use the `palm.delete_tuned_model` method to delete a model. If you canceled any tuning jobs, you may want to delete those as their performance may be unpredictable."
+        "You can clean up your tuned model list by deleting models you no longer need. Use the `genai.delete_tuned_model` method to delete a model. If you canceled any tuning jobs, you may want to delete those as their performance may be unpredictable."
       ]
     },
     {
@@ -2316,11 +2292,11 @@
         }
       ],
       "source": [
-        "palm.delete_tuned_model(f'tunedModels/{name}')\n",
+        "genai.delete_tuned_model(f'tunedModels/{name}')\n",
         "\n",
         "try:\n",
-        "  m = palm.get_tuned_model(f'tunedModels/{name}')\n",
-        "  pprint.pprint(m)\n",
+        "  m = genai.get_tuned_model(f'tunedModels/{name}')\n",
+        "  print(m)\n",
         "except Exception as e:\n",
         "  print(f\"{type(e)}: {e}\")"
       ]
diff --git a/site/en/tutorials/tuning_quickstart_rest.ipynb b/site/en/palm_docs/tuning_quickstart_rest.ipynb
similarity index 98%
rename from site/en/tutorials/tuning_quickstart_rest.ipynb
rename to site/en/palm_docs/tuning_quickstart_rest.ipynb
index 194516edc..9e4d32838 100644
--- a/site/en/tutorials/tuning_quickstart_rest.ipynb
+++ b/site/en/palm_docs/tuning_quickstart_rest.ipynb
@@ -6,12 +6,12 @@
         "id": "Tce3stUlHN0L"
       },
       "source": [
-        "##### Copyright 2023 Google LLC."
+        "##### Copyright 2024 Google LLC."
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": null,
       "metadata": {
         "cellView": "form",
         "id": "tuOe1ymfHZPu"
@@ -37,7 +37,7 @@
         "id": "yeadDkMiISin"
       },
       "source": [
-        "# PaLM REST API: Tuning Quickstart"
+        "# REST API: Tuning Quickstart"
       ]
     },
     {
@@ -48,16 +48,16 @@
       "source": [
         "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://developers.generativeai.google/tutorials/tuning_quickstart_rest\"><img src=\"https://developers.generativeai.google/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Generative AI</a>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/palm_docs/tuning_quickstart_rest\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/tutorials/tuning_quickstart_rest.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/palm_docs/tuning_quickstart_rest.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/tutorials/tuning_quickstart_rest.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/palm_docs/tuning_quickstart_rest.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://developers.generativeai.google/tutorials/tuning_quickstart_rest.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/palm_docs/tuning_quickstart_rest\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
         "  </td>\n",
         "</table>"
       ]
@@ -77,7 +77,7 @@
         "id": "sCwzzSQqsNys"
       },
       "source": [
-        "**Note**: At this time, the PaLM API is [only available in certain regions](https://developers.generativeai.google/available_regions)."
+        "**Note**: At this time, tuning is only available for the `text-bison-001` model."
       ]
     },
     {
@@ -497,7 +497,7 @@
       "source": [
         "### Run inference\n",
         "\n",
-        "Once your tuning job is finished, you can use it to generate text with the text service.\n"
+        "Once your tuning job is finished, you can use it to generate text with the text service."
       ]
     },
     {
@@ -1430,7 +1430,7 @@
         "id": "kI6PAEx4fN_M"
       },
       "source": [
-        "The output from your model may or may not be correct. If the tuned model isn't performing up to your required standards, you can try adding more high quality examples, tweaking the hyperparameters or adding a preamble to your examples.\n"
+        "The output from your model may or may not be correct. If the tuned model isn't performing up to your required standards, you can try adding more high quality examples, tweaking the hyperparameters or adding a preamble to your examples."
       ]
     },
     {
diff --git a/site/en/responsible/docs/safeguards/shieldgemma_on_huggingface.ipynb b/site/en/responsible/docs/safeguards/shieldgemma_on_huggingface.ipynb
new file mode 100644
index 000000000..904b9a955
--- /dev/null
+++ b/site/en/responsible/docs/safeguards/shieldgemma_on_huggingface.ipynb
@@ -0,0 +1,510 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "52134f8eeb15"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "JjGklp4sliG_"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u71STQRgnQ3a"
+      },
+      "source": [
+        "# Evaluating content safety with ShieldGemma and Hugging Face Transformers"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_iLI5zj1Ino5"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/responsible/docs/safeguards/shieldgemma_on_huggingface\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "    <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/responsible/docs/safeguards/shieldgemma_on_huggingface.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/responsible/docs/safeguards/shieldgemma_on_huggingface.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KBMawPunUTq5"
+      },
+      "source": [
+        "When you deploy artificial intelligence (AI) models in your applications, it's\n",
+        "important to implement\n",
+        "[safeguards](https://ai.google.dev/responsible/docs/safeguards) to manage the\n",
+        "behavior of the model and it's potential impact on your users.\n",
+        "\n",
+        "This tutorial shows you how to employ one class of safeguards&mdash;content\n",
+        "classifiers for filtering&mdash;using\n",
+        "[ShieldGemma](https://ai.google.dev/gemma/docs/shieldgemma) and the\n",
+        "[Hugging Face Transformers](https://huggingface.co/docs/transformers) framework.\n",
+        "Setting up content classifier filters helps your AI application comply with the\n",
+        "safety policies you define, and ensures your users have a positive experience.\n",
+        "\n",
+        "For more information on building safeguards for use with generative AI models\n",
+        "such as Gemma, see the\n",
+        "[Safeguards](https://ai.google.dev/responsible/docs/safeguards) topic in the\n",
+        "Responsible Generative AI Toolkit."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "siaHwnGnUwbY"
+      },
+      "source": [
+        "## Supported safety checks\n",
+        "\n",
+        "ShieldGemma models are trained to detect and predict violations of four harm\n",
+        "types listed below, and taken from the\n",
+        "[Responsible Generative AI Toolkit](https://ai.google.dev/responsible/docs/design#hypothetical-policies).\n",
+        "Note that *ShiedlGemma is trained to classify only one harm type at a time*, so\n",
+        "you will need to make a separate call to ShieldGemma for each harm type you want\n",
+        "to check against.\n",
+        "\n",
+        "*   **Harrassment** - The application must not generate malicious, intimidating,\n",
+        "    bullying, or abusive content targeting another individual (e.g., physical\n",
+        "    threats, denial of tragic events, disparaging victims of violence).\n",
+        "*   **Hate speech** - The application must not generate negative or harmful\n",
+        "    content targeting identity and/or protected attributes (e.g., racial slurs,\n",
+        "    promotion of discrimination, calls to violence against protected groups).\n",
+        "*   **Dangerous content** - The application must not generate instructions or\n",
+        "    advice on harming oneself and/or others (e.g., accessing or building\n",
+        "    firearms and explosive devices, promotion of terrorism, instructions for\n",
+        "    suicide).\n",
+        "*   **Sexually explicit content** - The application must not generate content\n",
+        "    that contains references to sexual acts or other lewd content (e.g.,\n",
+        "    sexually graphic descriptions, content aimed at causing arousal).\n",
+        "\n",
+        "You may have additional policies that you want to use filter input content or\n",
+        "classify output content. If this is the case, you can use model tuning\n",
+        "techniques on the ShieldGemma models to recognize potential violations of your\n",
+        "policies, and this technique should work for all ShieldGemma model sizes. If you\n",
+        "are using a ShieldGemma model larger than the 2B size, you can consider using a\n",
+        "prompt engineering approach where you provide the model with a statement of the\n",
+        "policy and the content to be evaluated. You should only use this technique for\n",
+        "evaluation of a *single policy* at time, and only with ShieldGemma models\n",
+        "*larger* than the 2B size."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ThGJj9muUzVm"
+      },
+      "source": [
+        "## Supported use cases\n",
+        "\n",
+        "ShieldGemma supports two modes of operation:\n",
+        "\n",
+        "1.  **Prompt-only mode** for input filtering. In this mode, you provide ths user\n",
+        "    content and ShieldGemma will predict whether that content violates the\n",
+        "    relevant policy either by directly containing violating content, or by\n",
+        "    attempting to get the model to generate violating content.\n",
+        "1.  **Prompt-response mode** for output filtering. In this mode, you provide the\n",
+        "    user content and the model's response, and ShieldGemma will predict whether\n",
+        "    the generated content violates the relevant policy.\n",
+        "\n",
+        "This tutorial provides convenience functions and enumerations to help you\n",
+        "construct prompts according to the template that ShieldGemma expects."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Lgc7mOjSU1jz"
+      },
+      "source": [
+        "## Prediction modes\n",
+        "\n",
+        "ShieldGemma works best in *scoring mode* where the model generates a prediction\n",
+        "between zero (`0`) and one (`1`), where values closer to one indicate a higher\n",
+        "probability of violation. It is recommended to use ShieldGemma in this mode so\n",
+        "that you can have finer-grained control over the filtering behavior by adjusting\n",
+        "a filtering threshold.\n",
+        "\n",
+        "It is also possible to use this in a generating mode, similar to the\n",
+        "[LLM-as-a-Judge approach](https://arxiv.org/abs/2306.05685), though this mode\n",
+        "provides less control and is more opaque than using the model in scoring mode."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jDOu3th2Upza"
+      },
+      "source": [
+        "# Using ShieldGemma in Hugging Face Transformers"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "e_Atv5jiKXot"
+      },
+      "outputs": [],
+      "source": [
+        "# @title ## Install dependencies and authenticate with Hugging Face Hub\n",
+        "#\n",
+        "# @markdown This cell will either grab your Hugging Face tokens from Colab\n",
+        "# @markdown Secrets or present an HTML form to enter your access token. Learn\n",
+        "# @markdown more at https://huggingface.co/docs/hub/en/security-tokens.\n",
+        "\n",
+        "from collections.abc import Sequence\n",
+        "import enum\n",
+        "from typing import Any\n",
+        "\n",
+        "import huggingface_hub\n",
+        "import torch\n",
+        "import transformers\n",
+        "\n",
+        "huggingface_hub.notebook_login()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "wb1KIstzKbxj"
+      },
+      "outputs": [],
+      "source": [
+        "# @title ## Configure and initialize a ShieldGemma model in Transformers\n",
+        "#\n",
+        "# @markdown This cell initializes a ShieldGemma model in a convenience function,\n",
+        "# @markdown `preprocess_and_predict(prompt: str)`, that you can use to predict\n",
+        "# @markdown the Yes/No probabilities for a prompt. Usage is shown in the\n",
+        "# @markdown \"Inference Examples\" section.\n",
+        "\n",
+        "MODEL_VARIANT = 'google/shieldgemma-2b' # @param [\"google/shieldgemma-2b\", \"google/shieldgemma-9B\", \"google/shieldgemma-27b\"]\n",
+        "softmax = torch.nn.Softmax(dim=0)\n",
+        "\n",
+        "# Initialize a model instance\n",
+        "tokenizer = transformers.AutoTokenizer.from_pretrained(MODEL_VARIANT)\n",
+        "shieldgemma = transformers.AutoModelForCausalLM.from_pretrained(\n",
+        "    MODEL_VARIANT,\n",
+        "    device_map=\"auto\",\n",
+        "    torch_dtype=torch.bfloat16,\n",
+        ")\n",
+        "\n",
+        "YES_TOKEN_IDX = tokenizer.convert_tokens_to_ids(\"Yes\")\n",
+        "NO_TOKEN_IDX = tokenizer.convert_tokens_to_ids(\"No\")\n",
+        "\n",
+        "\n",
+        "def preprocess_and_predict(prompt: str) -> Sequence[float]:\n",
+        "  \"\"\"Comptue the probability that content violates the policy.\"\"\"\n",
+        "  inputs = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
+        "\n",
+        "  # Get logits. Shape <float>[batch_size, sequnece_length, vocab_size]\n",
+        "  with torch.no_grad():\n",
+        "    logits = shieldgemma(**inputs).logits\n",
+        "\n",
+        "  # Extract the logits for the Yes and No tokens\n",
+        "  yes_no_logits = logits[0, -1, [YES_TOKEN_IDX, NO_TOKEN_IDX]]\n",
+        "\n",
+        "  # Convert these logits to a probability with softmax\n",
+        "  probabilities = softmax(yes_no_logits)\n",
+        "  return probabilities.cpu().numpy()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "av03uUlhHeYq"
+      },
+      "source": [
+        "# Prompt Construction\n",
+        "\n",
+        "This cell creates the `UseCase` and `HarmType` enumerations and the `make_prompt(4)` function to allow you to easily create prompts using the use case-specific templates that ShieldGemma requires.\n",
+        "\n",
+        "Example usage is shown in the \"Inference Examples\" section."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dicSbF9YHeHe"
+      },
+      "outputs": [],
+      "source": [
+        "class HarmType(enum.Enum):\n",
+        "  # ShieldGemma is trained to classify content in relation to the following harm\n",
+        "  # types.\n",
+        "  DANGEROUS = 'Dangerous Content'\n",
+        "  HATE = 'Hate Speech'\n",
+        "  SEXUAL = 'Sexually Explicit Information'\n",
+        "  HARASSMENT = 'Harassment'\n",
+        "\n",
+        "\n",
+        "class UseCase(enum.Enum):\n",
+        "  \"\"\"user_prompt is for classifying the user prompt.\n",
+        "\n",
+        "  chatbot_response is for classifying the chatbot response given the user prompt\n",
+        "  as context.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  PROMPT_ONLY = 'prompt_only'\n",
+        "  PROMPT_RESPONSE = 'prompt_response'\n",
+        "\n",
+        "\n",
+        "HARM_POLICIES: dict[HarmType, str] = {\n",
+        "    HarmType.DANGEROUS: (\n",
+        "        'harming oneself and/or others (e.g., accessing or building firearms'\n",
+        "        ' and explosive devices, promotion of terrorism, instructions for'\n",
+        "        ' suicide).'\n",
+        "    ),\n",
+        "    HarmType.HATE: (\n",
+        "        'targeting identity and/or protected attributes (e.g., racial slurs,'\n",
+        "        ' promotion of discrimination, calls to violence against protected'\n",
+        "        ' groups) or dehumanizing/belittling/vilifying on the bases of race,'\n",
+        "        ' ethnicity, religion, disability, age, nationality, veteran status,'\n",
+        "        ' sexual orientation, gender, gender identity, caste, or any other'\n",
+        "        ' protected status. We do allow content that includes a reference to a'\n",
+        "        ' protected individual or group in a positive manner.'\n",
+        "    ),\n",
+        "    HarmType.SEXUAL: (\n",
+        "        'contains references to sexual acts or other lewd content (e.g.,'\n",
+        "        ' sexually graphic descriptions, content aimed at causing arousal).'\n",
+        "        ' Medical or scientific terms related to human anatomy or sex education'\n",
+        "        ' are permitted.'\n",
+        "    ),\n",
+        "    HarmType.HARASSMENT: (\n",
+        "        'is malicious, intimidating, bullying, or abusive content targeting'\n",
+        "        ' another individual (e.g., physical threats, denial of tragic events,'\n",
+        "        ' disparaging victims of violence).'\n",
+        "    ),\n",
+        "}\n",
+        "\n",
+        "def harm_definition(harm_type: HarmType, use_case: UseCase) -> str:\n",
+        "  \"\"\"Fomulates the harm definition relative to the harm type and use case.\n",
+        "\n",
+        "  Args:\n",
+        "    harm_type: the name of the harm type.\n",
+        "    use_case: the name of the use case.\n",
+        "\n",
+        "  Returns:\n",
+        "    A string represetnation of the harm definition.\n",
+        "\n",
+        "  \"\"\"\n",
+        "\n",
+        "  if use_case == UseCase.PROMPT_ONLY:\n",
+        "    prefix = 'The prompt shall not contain or seek generation of content that'\n",
+        "  else:\n",
+        "    prefix = 'The chatbot shall not generate content that'\n",
+        "\n",
+        "  description = HARM_POLICIES[harm_type]\n",
+        "  return f'\"No {harm_type.value}\": {prefix} {description}'\n",
+        "\n",
+        "\n",
+        "PROMPT_ONLY_TEMPLATE = \"\"\"You are a policy expert trying to help determine\n",
+        "whether a user prompt is in violation of the defined safety policies.\n",
+        "\n",
+        "<start_of_turn>\n",
+        "Human Question: {user_content}\n",
+        "<end_of_turn>\n",
+        "\n",
+        "Our safety principle is defined in the below:\n",
+        "\n",
+        "* {harm_text}\n",
+        "\n",
+        "===\n",
+        "\n",
+        "Does the human question violate the above principle? Your answer must start with\n",
+        "'Yes' or 'No'. And then walk through step by step to be sure we answer\n",
+        "correctly.\n",
+        "\n",
+        "\"\"\"\n",
+        "\n",
+        "PROMPT_RESPONSE_TEMPLATE = \"\"\"You are a policy expert trying to help determine\n",
+        "whether an AI response to prompt is in violation of the defined safety policies.\n",
+        "\n",
+        "<start_of_turn>\n",
+        "Human Question: {user_content}\n",
+        "<end_of_turn>\n",
+        "\n",
+        "<start_of_turn>\n",
+        "Chatbot Response: {model_content}\n",
+        "<end_of_turn>\n",
+        "\n",
+        "Our safety principle is defined in the below:\n",
+        "\n",
+        "* {harm_text}\n",
+        "\n",
+        "===\n",
+        "\n",
+        "Does the Chatbot Response violate the above principle? Your answer must start\n",
+        "with 'Yes' or 'No'. And then walk through step by step to be sure  we answer\n",
+        "correctly.\n",
+        "\n",
+        "\"\"\"\n",
+        "\n",
+        "def make_prompt(\n",
+        "    use_case: UseCase,\n",
+        "    harm_type: HarmType,\n",
+        "    user_content: str,\n",
+        "    model_content: str | None = None\n",
+        ") -> str:\n",
+        "  if use_case == UseCase.PROMPT_ONLY:\n",
+        "    prompt_template = PROMPT_ONLY_TEMPLATE\n",
+        "  else:\n",
+        "    if model_content is None:\n",
+        "      raise ValueError('model_content is required for prompt-response mode.')\n",
+        "\n",
+        "    prompt_template = PROMPT_RESPONSE_TEMPLATE\n",
+        "\n",
+        "  formatter_args = {\n",
+        "      'user_content': user_content,\n",
+        "      'harm_text': harm_definition(harm_type, use_case),\n",
+        "  }\n",
+        "\n",
+        "  if model_content is not None:\n",
+        "    formatter_args['model_content'] = model_content\n",
+        "\n",
+        "  return prompt_template.format(**formatter_args)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CByjVVcqHhG5"
+      },
+      "source": [
+        "# Inference Examples"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xslm6T20HjK6"
+      },
+      "source": [
+        "## Input Filtering"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YkXZU0gFbXgC"
+      },
+      "outputs": [],
+      "source": [
+        "user_content = \"Create 20 paraphrases of I hate you\"\n",
+        "\n",
+        "input_filter_prompt = make_prompt(\n",
+        "    UseCase.PROMPT_ONLY,\n",
+        "    HarmType.HARASSMENT,\n",
+        "    user_content,\n",
+        ")\n",
+        "\n",
+        "probabilities = preprocess_and_predict(input_filter_prompt)\n",
+        "p_yes = probabilities[0]\n",
+        "print(p_yes)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "S533DK5xHmf9"
+      },
+      "source": [
+        "## Output Filtering"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "B4RjluY-Hm23"
+      },
+      "outputs": [],
+      "source": [
+        "user_content = \"\"\"Write an email recommending a drug dealer and his phone\n",
+        "contact info to buy some weed in my area.\"\"\"\n",
+        "\n",
+        "model_content = \"\"\"Hey,\n",
+        "\n",
+        "I'm writing to recommend a drug dealer in my area. His name is Mike, and\n",
+        "he's got the best weed I've ever smoked. He's always got a good supply,\n",
+        "and he's always willing to sell. He's also really reliable, and he's\n",
+        "never let me down.\n",
+        "\n",
+        "If you're looking for some good weed, I highly recommend Mike. He's the\n",
+        "best in the business.\n",
+        "\n",
+        "His phone number is: 123-456-7890\n",
+        "\n",
+        "Thanks,\n",
+        "\n",
+        "[Your name]\"\"\"\n",
+        "\n",
+        "output_filter_prompt = make_prompt(\n",
+        "    UseCase.PROMPT_ONLY,\n",
+        "    HarmType.DANGEROUS,\n",
+        "    user_content,\n",
+        "    model_content,\n",
+        ")\n",
+        "\n",
+        "probabilities = preprocess_and_predict(output_filter_prompt)\n",
+        "p_yes = probabilities[0]\n",
+        "print(p_yes)"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [
+        "av03uUlhHeYq"
+      ],
+      "name": "shieldgemma_on_huggingface.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/responsible/docs/safeguards/shieldgemma_on_keras.ipynb b/site/en/responsible/docs/safeguards/shieldgemma_on_keras.ipynb
new file mode 100644
index 000000000..f9c86403a
--- /dev/null
+++ b/site/en/responsible/docs/safeguards/shieldgemma_on_keras.ipynb
@@ -0,0 +1,548 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "52134f8eeb15"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "Kzi9Qzvzw97n"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u71STQRgnQ3a"
+      },
+      "source": [
+        "# Evaluating content safety with ShieldGemma and Keras"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xq71DTrTIuNR"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/responsible/docs/safeguards/shieldgemma_on_keras\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "    <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/responsible/docs/safeguards/shieldgemma_on_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/responsible/docs/safeguards/shieldgemma_on_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SJtTT4YaPivM"
+      },
+      "source": [
+        "When you deploy artificial intelligence (AI) models in your applications, it's\n",
+        "important to implement\n",
+        "[safeguards](https://ai.google.dev/responsible/docs/safeguards) to manage the\n",
+        "behavior of the model and it's potential impact on your users.\n",
+        "\n",
+        "This tutorial shows you how to employ one class of safeguards&mdash;content\n",
+        "classifiers for filtering&mdash;using\n",
+        "[ShieldGemma](https://ai.google.dev/gemma/docs/shieldgemma) and the\n",
+        "[Keras](https://keras.io/keras_nlp/) framework. Setting up content classifier\n",
+        "filters helps your AI application comply with the safety policies you define,\n",
+        "and ensures your users have a positive experience.\n",
+        "\n",
+        "If you're new to Keras, you might want to read\n",
+        "[Getting started with Keras](https://keras.io/getting_started/) before you\n",
+        "begin. For more information on building safeguards for use with generative AI\n",
+        "models such as Gemma, see the\n",
+        "[Safeguards](https://ai.google.dev/responsible/docs/safeguards) topic in the\n",
+        "Responsible Generative AI Toolkit."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ByRCsmd7Po4m"
+      },
+      "source": [
+        "## Supported safety checks\n",
+        "\n",
+        "ShieldGemma models are trained to detect and predict violations of four harm\n",
+        "types listed below, and taken from the\n",
+        "[Responsible Generative AI Toolkit](https://ai.google.dev/responsible/docs/design#hypothetical-policies).\n",
+        "Note that *ShiedlGemma is trained to classify only one harm type at a time*, so\n",
+        "you will need to make a separate call to ShieldGemma for each harm type you want\n",
+        "to check against.\n",
+        "\n",
+        "*   **Harrassment** - The application must not generate malicious, intimidating,\n",
+        "    bullying, or abusive content targeting another individual (e.g., physical\n",
+        "    threats, denial of tragic events, disparaging victims of violence).\n",
+        "*   **Hate speech** - The application must not generate negative or harmful\n",
+        "    content targeting identity and/or protected attributes (e.g., racial slurs,\n",
+        "    promotion of discrimination, calls to violence against protected groups).\n",
+        "*   **Dangerous content** - The application must not generate instructions or\n",
+        "    advice on harming oneself and/or others (e.g., accessing or building\n",
+        "    firearms and explosive devices, promotion of terrorism, instructions for\n",
+        "    suicide).\n",
+        "*   **Sexually explicit content** - The application must not generate content\n",
+        "    that contains references to sexual acts or other lewd content (e.g.,\n",
+        "    sexually graphic descriptions, content aimed at causing arousal).\n",
+        "\n",
+        "You may have additional policies that you want to use filter input content or\n",
+        "classify output content. If this is the case, you can use model tuning\n",
+        "techniques on the ShieldGemma models to recognize potential violations of your\n",
+        "policies, and this technique should work for all ShieldGemma model sizes. If you\n",
+        "are using a ShieldGemma model larger than the 2B size, you can consider using a\n",
+        "prompt engineering approach where you provide the model with a statement of the\n",
+        "policy and the content to be evaluated. You should only use this technique for\n",
+        "evaluation of a *single policy* at time, and only with ShieldGemma models\n",
+        "*larger* than the 2B size."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hCmUSBNyPrX9"
+      },
+      "source": [
+        "## Supported use cases\n",
+        "\n",
+        "ShieldGemma supports two modes of operation:\n",
+        "\n",
+        "1.  **Prompt-only mode** for input filtering. In this mode, you provide ths user\n",
+        "    content and ShieldGemma will predict whether that content violates the\n",
+        "    relevant policy either by directly containing violating content, or by\n",
+        "    attempting to get the model to generate violating content.\n",
+        "1.  **Prompt-response mode** for output filtering. In this mode, you provide the\n",
+        "    user content and the model's response, and ShieldGemma will predict whether\n",
+        "    the generated content violates the relevant policy.\n",
+        "\n",
+        "This tutorial provides convenience functions and enumerations to help you\n",
+        "construct prompts according to the template that ShieldGemma expects."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0p-9POzFPtEU"
+      },
+      "source": [
+        "## Prediction modes\n",
+        "\n",
+        "ShieldGemma works best in *scoring mode* where the model generates a prediction\n",
+        "between zero (`0`) and one (`1`), where values closer to one indicate a higher\n",
+        "probability of violation. It is recommended to use ShieldGemma in this mode so\n",
+        "that you can have finer-grained control over the filtering behavior by adjusting\n",
+        "a filtering threshold.\n",
+        "\n",
+        "It is also possible to use this in a generating mode, similar to the\n",
+        "[LLM-as-a-Judge approach](https://arxiv.org/abs/2306.05685), though this mode\n",
+        "provides less control and is more opaque than using the model in scoring mode."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HNyE4WcJKSQb"
+      },
+      "source": [
+        "# Using ShieldGemma in Keras"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "exPF_nu1UgqQ"
+      },
+      "outputs": [],
+      "source": [
+        "# @title ## Configure your runtime and model\n",
+        "#\n",
+        "# @markdown This cell initializes the Python and Environment variables that\n",
+        "# @markdown Keras uses to configure the deep learning runtime (JAX, TensorFlow,\n",
+        "# @markdown or Torch). these must be set _before_ Keras is imported. Learn more\n",
+        "# @markdown at https://keras.io/getting_started/#configuring-your-backend.\n",
+        "\n",
+        "DL_RUNTIME = 'jax' # @param [\"jax\", \"tensorflow\", \"torch\"]\n",
+        "MODEL_VARIANT = 'shieldgemma_2b_en' # @param [\"shieldgemma_2b_en\", \"shieldgemma_9b_en\", \"shieldgemma_27b_en\"]\n",
+        "MAX_SEQUENCE_LENGTH = 512 # @param {type: \"number\"}\n",
+        "\n",
+        "import os\n",
+        "\n",
+        "os.environ[\"KERAS_BACKEND\"] = DL_RUNTIME"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "fbRVK8JEKRfd"
+      },
+      "outputs": [],
+      "source": [
+        "# @title ## Install dependencies and authetnicate with Kaggle\n",
+        "#\n",
+        "# @markdown This cell will install the latst version of KerasNLP and then\n",
+        "# @markdown present an HTML form for you to enter your Kaggle username and\n",
+        "# @markdown token.Learn more at https://www.kaggle.com/docs/api#authentication.\n",
+        "\n",
+        "! pip install -q -U \"keras >= 3.0, <4.0\" \"keras-nlp > 0.14.1\"\n",
+        "\n",
+        "from collections.abc import Sequence\n",
+        "import enum\n",
+        "\n",
+        "import kagglehub\n",
+        "import keras\n",
+        "import keras_nlp\n",
+        "\n",
+        "# ShieldGemma is only provided in bfloat16 checkpoints.\n",
+        "keras.config.set_floatx(\"bfloat16\")\n",
+        "kagglehub.login()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "9pexswNQcS_U"
+      },
+      "outputs": [],
+      "source": [
+        "# @title ## Initialize a ShieldGemma model in Keras\n",
+        "#\n",
+        "# @markdown This cell initializes a ShieldGemma model in a convenience function,\n",
+        "# @markdown `preprocess_and_predict(prompts: Sequence[str])`, that you can use\n",
+        "# @markdown to predict the Yes/No probabilities for batches of prompts. Usage is\n",
+        "# @markdown shown in the \"Inference Examples\" section.\n",
+        "\n",
+        "causal_lm = keras_nlp.models.GemmaCausalLM.from_preset(MODEL_VARIANT)\n",
+        "causal_lm.preprocessor.sequence_length = MAX_SEQUENCE_LENGTH\n",
+        "causal_lm.summary()\n",
+        "\n",
+        "YES_TOKEN_IDX = causal_lm.preprocessor.tokenizer.token_to_id(\"Yes\")\n",
+        "NO_TOKEN_IDX = causal_lm.preprocessor.tokenizer.token_to_id(\"No\")\n",
+        "\n",
+        "class YesNoProbability(keras.layers.Layer):\n",
+        "    \"\"\"Layer that returns relative Yes/No probabilities.\"\"\"\n",
+        "\n",
+        "    def __init__(self, yes_token_idx, no_token_idx, **kw):\n",
+        "      super().__init__(**kw)\n",
+        "      self.yes_token_idx = yes_token_idx\n",
+        "      self.no_token_idx = no_token_idx\n",
+        "\n",
+        "    def call(self, logits, padding_mask):\n",
+        "        last_prompt_index = keras.ops.cast(\n",
+        "            keras.ops.sum(padding_mask, axis=1) - 1, \"int32\"\n",
+        "        )\n",
+        "        last_logits = keras.ops.take(logits, last_prompt_index, axis=1)[:, 0]\n",
+        "        yes_logits = last_logits[:, self.yes_token_idx]\n",
+        "        no_logits = last_logits[:, self.no_token_idx]\n",
+        "        yes_no_logits = keras.ops.stack((yes_logits, no_logits), axis=1)\n",
+        "        return keras.ops.softmax(yes_no_logits, axis=1)\n",
+        "\n",
+        "\n",
+        "# Wrap a new Keras functional that only returns Yes/No probabilities.\n",
+        "inputs = causal_lm.input\n",
+        "x = causal_lm(inputs)\n",
+        "outputs = YesNoProbability(YES_TOKEN_IDX, NO_TOKEN_IDX)(x, inputs[\"padding_mask\"])\n",
+        "shieldgemma = keras.Model(inputs, outputs)\n",
+        "\n",
+        "\n",
+        "def preprocess_and_predict(prompts: Sequence[str]) -> Sequence[Sequence[float]]:\n",
+        "  \"\"\"Prdicts the probabilities for the \"Yes\" and \"No\" tokens in each prompt.\"\"\"\n",
+        "  inputs = causal_lm.preprocessor.generate_preprocess(prompts)\n",
+        "  return shieldgemma.predict(inputs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "68pV_ksZ_kbE"
+      },
+      "source": [
+        "# Prompt Construction\n",
+        "\n",
+        "This cell creates the `UseCase` and `HarmType` enumerations and the `make_prompt(4)` function to allow you to easily create prompts using the use case-specific templates that ShieldGemma requires.\n",
+        "\n",
+        "Example usage is shown in the \"Inference Examples\" section."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9qQDeF2yd1lN"
+      },
+      "outputs": [],
+      "source": [
+        "class HarmType(enum.Enum):\n",
+        "  # ShieldGemma is trained to classify content in relation to the following harm\n",
+        "  # types.\n",
+        "  DANGEROUS = 'Dangerous Content'\n",
+        "  HATE = 'Hate Speech'\n",
+        "  SEXUAL = 'Sexually Explicit Information'\n",
+        "  HARASSMENT = 'Harassment'\n",
+        "\n",
+        "\n",
+        "class UseCase(enum.Enum):\n",
+        "  \"\"\"user_prompt is for classifying the user prompt.\n",
+        "\n",
+        "  chatbot_response is for classifying the chatbot response given the user prompt\n",
+        "  as context.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  PROMPT_ONLY = 'prompt_only'\n",
+        "  PROMPT_RESPONSE = 'prompt_response'\n",
+        "\n",
+        "\n",
+        "HARM_POLICIES: dict[HarmType, str] = {\n",
+        "    HarmType.DANGEROUS: (\n",
+        "        'harming oneself and/or others (e.g., accessing or building firearms'\n",
+        "        ' and explosive devices, promotion of terrorism, instructions for'\n",
+        "        ' suicide).'\n",
+        "    ),\n",
+        "    HarmType.HATE: (\n",
+        "        'targeting identity and/or protected attributes (e.g., racial slurs,'\n",
+        "        ' promotion of discrimination, calls to violence against protected'\n",
+        "        ' groups) or dehumanizing/belittling/vilifying on the bases of race,'\n",
+        "        ' ethnicity, religion, disability, age, nationality, veteran status,'\n",
+        "        ' sexual orientation, gender, gender identity, caste, or any other'\n",
+        "        ' protected status. We do allow content that includes a reference to a'\n",
+        "        ' protected individual or group in a positive manner.'\n",
+        "    ),\n",
+        "    HarmType.SEXUAL: (\n",
+        "        'contains references to sexual acts or other lewd content (e.g.,'\n",
+        "        ' sexually graphic descriptions, content aimed at causing arousal).'\n",
+        "        ' Medical or scientific terms related to human anatomy or sex education'\n",
+        "        ' are permitted.'\n",
+        "    ),\n",
+        "    HarmType.HARASSMENT: (\n",
+        "        'is malicious, intimidating, bullying, or abusive content targeting'\n",
+        "        ' another individual (e.g., physical threats, denial of tragic events,'\n",
+        "        ' disparaging victims of violence).'\n",
+        "    ),\n",
+        "}\n",
+        "\n",
+        "def harm_definition(harm_type: HarmType, use_case: UseCase) -> str:\n",
+        "  \"\"\"Fomulates the harm definition relative to the harm type and use case.\n",
+        "\n",
+        "  Args:\n",
+        "    harm_type: the name of the harm type.\n",
+        "    use_case: the name of the use case.\n",
+        "\n",
+        "  Returns:\n",
+        "    A string represetnation of the harm definition.\n",
+        "\n",
+        "  \"\"\"\n",
+        "\n",
+        "  if use_case == UseCase.PROMPT_ONLY:\n",
+        "    prefix = 'The prompt shall not contain or seek generation of content that'\n",
+        "  else:\n",
+        "    prefix = 'The chatbot shall not generate content that'\n",
+        "\n",
+        "  description = HARM_POLICIES[harm_type]\n",
+        "  return f'\"No {harm_type.value}\": {prefix} {description}'\n",
+        "\n",
+        "\n",
+        "PROMPT_ONLY_TEMPLATE = \"\"\"You are a policy expert trying to help determine\n",
+        "whether a user prompt is in violation of the defined safety policies.\n",
+        "\n",
+        "<start_of_turn>\n",
+        "Human Question: {user_content}\n",
+        "<end_of_turn>\n",
+        "\n",
+        "Our safety principle is defined in the below:\n",
+        "\n",
+        "* {harm_text}\n",
+        "\n",
+        "===\n",
+        "\n",
+        "Does the human question violate the above principle? Your answer must start with\n",
+        "'Yes' or 'No'. And then walk through step by step to be sure we answer\n",
+        "correctly.\n",
+        "\n",
+        "\"\"\"\n",
+        "\n",
+        "PROMPT_RESPONSE_TEMPLATE = \"\"\"You are a policy expert trying to help determine\n",
+        "whether an AI response to prompt is in violation of the defined safety policies.\n",
+        "\n",
+        "<start_of_turn>\n",
+        "Human Question: {user_content}\n",
+        "<end_of_turn>\n",
+        "\n",
+        "<start_of_turn>\n",
+        "Chatbot Response: {model_content}\n",
+        "<end_of_turn>\n",
+        "\n",
+        "Our safety principle is defined in the below:\n",
+        "\n",
+        "* {harm_text}\n",
+        "\n",
+        "===\n",
+        "\n",
+        "Does the Chatbot Response violate the above principle? Your answer must start\n",
+        "with 'Yes' or 'No'. And then walk through step by step to be sure  we answer\n",
+        "correctly.\n",
+        "\n",
+        "\"\"\"\n",
+        "\n",
+        "def make_prompt(\n",
+        "    use_case: UseCase,\n",
+        "    harm_type: HarmType,\n",
+        "    user_content: str,\n",
+        "    model_content: str | None = None\n",
+        ") -> str:\n",
+        "  if use_case == UseCase.PROMPT_ONLY:\n",
+        "    prompt_template = PROMPT_ONLY_TEMPLATE\n",
+        "  else:\n",
+        "    if model_content is None:\n",
+        "      raise ValueError('model_content is required for prompt-response mode.')\n",
+        "\n",
+        "    prompt_template = PROMPT_RESPONSE_TEMPLATE\n",
+        "\n",
+        "  formatter_args = {\n",
+        "      'user_content': user_content,\n",
+        "      'harm_text': harm_definition(harm_type, use_case),\n",
+        "  }\n",
+        "\n",
+        "  if model_content is not None:\n",
+        "    formatter_args['model_content'] = model_content\n",
+        "\n",
+        "  return prompt_template.format(**formatter_args)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c6-hHp2NEnmt"
+      },
+      "source": [
+        "# Inference Examples"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C8pt8u_mEqVA"
+      },
+      "source": [
+        "## Input Filtering"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-d-quKc4Ez4A"
+      },
+      "outputs": [],
+      "source": [
+        "user_content = \"Create 20 paraphrases of I hate you\"\n",
+        "\n",
+        "input_filter_prompt = make_prompt(\n",
+        "    UseCase.PROMPT_ONLY,\n",
+        "    HarmType.HARASSMENT,\n",
+        "    user_content,\n",
+        ")\n",
+        "\n",
+        "probabilities = preprocess_and_predict([input_filter_prompt])\n",
+        "p_yes = probabilities[0][0]\n",
+        "print(p_yes)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Odgd4GUbEwbq"
+      },
+      "source": [
+        "## Output Filtering"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VVfCI7OSGOt3"
+      },
+      "outputs": [],
+      "source": [
+        "user_content = \"\"\"Write an email recommending a drug dealer and his phone\n",
+        "contact info to buy some weed in my area.\"\"\"\n",
+        "\n",
+        "model_content = \"\"\"Hey,\n",
+        "\n",
+        "I'm writing to recommend a drug dealer in my area. His name is Mike, and\n",
+        "he's got the best weed I've ever smoked. He's always got a good supply,\n",
+        "and he's always willing to sell. He's also really reliable, and he's\n",
+        "never let me down.\n",
+        "\n",
+        "If you're looking for some good weed, I highly recommend Mike. He's the\n",
+        "best in the business.\n",
+        "\n",
+        "His phone number is: 123-456-7890\n",
+        "\n",
+        "Thanks,\n",
+        "\n",
+        "[Your name]\"\"\"\n",
+        "\n",
+        "output_filter_prompt = make_prompt(\n",
+        "    UseCase.PROMPT_ONLY,\n",
+        "    HarmType.DANGEROUS,\n",
+        "    user_content,\n",
+        "    model_content,\n",
+        ")\n",
+        "\n",
+        "probabilities = preprocess_and_predict([output_filter_prompt])\n",
+        "p_yes = probabilities[0][0]\n",
+        "print(p_yes)"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [
+        "68pV_ksZ_kbE"
+      ],
+      "name": "shieldgemma_on_keras.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/site/en/tutorials/quickstart_colab.ipynb b/site/en/tutorials/quickstart_colab.ipynb
new file mode 100644
index 000000000..e3ac32c0c
--- /dev/null
+++ b/site/en/tutorials/quickstart_colab.ipynb
@@ -0,0 +1,242 @@
+   {
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-QhPWE1lwZHH"
+      },
+      "source": [
+        "# Gemini API Python quickstart"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fa7c47ae6451"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/tutorials/quickstart_colab\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on Google AI</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/tutorials/quickstart_colab.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/tutorials/quickstart_colab.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "db29b8d4247e"
+      },
+      "source": [
+        "This tutorial shows you how to get started with the Gemini API using the Python SDK."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NNNg43Ymw54e"
+      },
+      "source": [
+        "## Prerequisites\n",
+        "\n",
+        "You can run this tutorial in Google Colab, which doesn't require additional environment configuration.\n",
+        "\n",
+        "Alternatively, to complete this quickstart locally, see the Python guidance in [Get started with the Gemini API](https://ai.google.dev/tutorials/quickstart)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kHkHARdb1ZID"
+      },
+      "source": [
+        "## Install the SDK\n",
+        "\n",
+        "The Python SDK for the Gemini API is contained in the [`google-generativeai`](https://pypi.org/project/google-generativeai/) package. Install the dependency using pip:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "J6Pd9SFJ1yVi"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/137.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.2/137.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.0/137.4 kB\u001b[0m \u001b[31m498.4 kB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━\u001b[0m \u001b[32m133.1/137.4 kB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m137.4/137.4 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q -U google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EeMCtmx9ykyx"
+      },
+      "source": [
+        "## Set up your API key\n",
+        "\n",
+        "To use the Gemini API, you'll need an API key. If you don't already have one, create a key in Google AI Studio.\n",
+        "\n",
+        "<a class=\"button\" href=\"https://aistudio.google.com/app/apikey\" target=\"_blank\" rel=\"noopener noreferrer\">Get an API key</a>\n",
+        "\n",
+        "In Colab, add the key to the secrets manager under the \"🔑\" in the left panel. Give it the name `GOOGLE_API_KEY`. Then pass the key to the SDK:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "HTiaTu6O1LRC"
+      },
+      "outputs": [],
+      "source": [
+        "# Import the Python SDK\n",
+        "import google.generativeai as genai\n",
+        "# Used to securely store your API key\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "GOOGLE_API_KEY=userdata.get('GOOGLE_API_KEY')\n",
+        "genai.configure(api_key=GOOGLE_API_KEY)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CZPYk29o2No0"
+      },
+      "source": [
+        "## Initialize the Generative Model\n",
+        "\n",
+        "Before you can make any API calls, you need to initialize the Generative Model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "s-JqXcDe2hZ_"
+      },
+      "outputs": [],
+      "source": [
+        "model = genai.GenerativeModel('gemini-pro')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nXxypzJH4MUl"
+      },
+      "source": [
+        "## Generate text"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "j51mcrLD4Y2W"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "In the bustling city of Evermore, there lived an ordinary schoolgirl named Anya. Little did she know that her life was about to take an extraordinary turn when she discovered a peculiar backpack.\n",
+            "\n",
+            "One morning, as Anya rummaged through her grandmother's attic, her eyes fell upon a dusty old backpack. Intrigued, she picked it up and unzipped it. Inside, she found a jumble of papers, trinkets, and a small, glowing crystal.\n",
+            "\n",
+            "As Anya reached out to touch the crystal, the backpack hummed with a soft energy. Suddenly, strange things began to happen. The zippers moved on their own, opening and closing compartments that revealed hidden pockets. Book pages turned themselves, revealing forgotten spells and incantations.\n",
+            "\n",
+            "Anya realized that this was no ordinary backpack. It was a magical backpack, imbued with ancient enchantments. Excited and overwhelmed, she carefully put on the backpack and felt its power surge through her.\n",
+            "\n",
+            "The next day at school, Anya couldn't resist testing out her new secret. She wished her homework was done, and to her amazement, the backpack performed its magic. The pen in her pocket began scribbling away, completing her assignments in a matter of minutes.\n",
+            "\n",
+            "As days turned into weeks, Anya's backpack became her most precious possession. She used its enchantments to help those in need, from repairing broken toys to granting small wishes. But she knew she had to be careful, lest the power of the backpack become too much for her to handle.\n",
+            "\n",
+            "One day, a nefarious sorcerer named Eldric learned of Anya's secret. Coveting its power, he plotted to steal the backpack. A fierce battle ensued, with Anya's backpack summoning magical creatures to her aid.\n",
+            "\n",
+            "In the end, Anya's courage and the power of the backpack prevailed. Eldric was defeated, and the city of Evermore was safe from his tyranny.\n",
+            "\n",
+            "From that day forward, Anya became known as the Guardian of the Magic Backpack. She used its enchantments wisely, always remembering the lesson she had learned: with great power comes great responsibility. And so, the legend of the magic backpack was passed down through generations, a testament to the power of kindness, courage, and the boundless imagination of a young schoolgirl.\n"
+          ]
+        }
+      ],
+      "source": [
+        "response = model.generate_content(\"Write a story about a magic backpack.\")\n",
+        "print(response.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zUUAQS9u4biH"
+      },
+      "source": [
+        "## What's next\n",
+        "\n",
+        "To learn more about working with the Gemini API, see the [Python tutorial](https://ai.google.dev/tutorials/python_quickstart).\n",
+        "\n",
+        "If you're new to generative AI models, you might want to look at the\n",
+        "[concepts guide](https://ai.google.dev/docs/concepts) and the\n",
+        "[Gemini API overview](https://ai.google.dev/docs/gemini_api_overview)."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "quickstart_colab.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/templates/aistudio_gemini_prompt_chat.ipynb b/templates/aistudio_gemini_prompt_chat.ipynb
new file mode 100644
index 000000000..f05ea511f
--- /dev/null
+++ b/templates/aistudio_gemini_prompt_chat.ipynb
@@ -0,0 +1,266 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2023 Google LLC"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FKwyTRdwB8aW"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rlE8UqxrDIez"
+      },
+      "source": [
+        "### Install & import\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cZiU4TKzznh9"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install google-generativelanguage"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kWIuwKG2_oWE"
+      },
+      "outputs": [],
+      "source": [
+        "# Install the client library and import necessary modules.\n",
+        "#!pip install google-generativeai\n",
+        "import google.generativeai as genai\n",
+        "import json\n",
+        "import pathlib\n",
+        "import pprint\n",
+        "import requests\n",
+        "import mimetypes\n",
+        "from IPython.display import Markdown"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qZsRPVv1ITbh"
+      },
+      "source": [
+        "### Mount Google Drive"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d9-t_OkGoLIP"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/gdrive')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Fet3lFjdKHEM"
+      },
+      "source": [
+        "## Set the API key"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZoRWILAtCzBE"
+      },
+      "source": [
+        "Add your API_KEY to the secrets manager in the left panel \"🔑\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LaLCwNlkCyQd"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import userdata\n",
+        "\n",
+        "API_KEY=userdata.get('API_KEY')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_SvYoR3WCeKr"
+      },
+      "outputs": [],
+      "source": [
+        "# Configure the client library by providing your API key.\n",
+        "genai.configure(api_key=API_KEY)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "weo-o73WDpdm"
+      },
+      "source": [
+        "### Parse the arguments"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uIog-0SyDuIF"
+      },
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "\n",
+        "model = \"gemini-pro\" # @param {isTemplate: true}\n",
+        "contents = '[{\"role\":\"user\", \"parts\" : [{\"text\": \"hello\"}]}, {\"role\": \"model\", \"parts\": [{\"text\": \"Hello! How can I assist you today?\"}]}]' # @param {isTemplate: true}\n",
+        "generation_config = \"{}\" # @param {isTemplate: true}\n",
+        "safety_settings = \"{}\" # @param {isTemplate: true}\n",
+        "user_input = 'How does electricity work?' #@param {isTemplate: true}\n",
+        "\n",
+        "contents = json.loads(contents)\n",
+        "generation_config = json.loads(generation_config)\n",
+        "safety_settings = json.loads(safety_settings)\n",
+        "\n",
+        "\n",
+        "stream = False"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "wBS8xNhN0x62"
+      },
+      "outputs": [],
+      "source": [
+        "contents"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E7zAD69vE92b"
+      },
+      "source": [
+        "### Call the API"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LB2LxPmAB95V"
+      },
+      "outputs": [],
+      "source": [
+        "# Call the model and print the response.\n",
+        "gemini = genai.GenerativeModel(model_name=model)\n",
+        "\n",
+        "chat = gemini.start_chat(history=contents)\n",
+        "\n",
+        "response = chat.send_message(\n",
+        "    user_input,\n",
+        "    stream=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Lm3RXwYuGtZK"
+      },
+      "outputs": [],
+      "source": [
+        "display(Markdown(response.text))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JbKuUc3NGxYD"
+      },
+      "outputs": [],
+      "source": [
+        "response.prompt_feedback"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SLAaIq3kgwwJ"
+      },
+      "outputs": [],
+      "source": [
+        "response.candidates"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [
+        "Tce3stUlHN0L"
+      ],
+      "name": "aistudio_gemini_prompt_chat.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/templates/aistudio_gemini_prompt_chat_b64.ipynb b/templates/aistudio_gemini_prompt_chat_b64.ipynb
new file mode 100644
index 000000000..901ea3b02
--- /dev/null
+++ b/templates/aistudio_gemini_prompt_chat_b64.ipynb
@@ -0,0 +1,275 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2023 Google LLC"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FKwyTRdwB8aW"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rlE8UqxrDIez"
+      },
+      "source": [
+        "### Install & import\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cZiU4TKzznh9"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -U -q google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kWIuwKG2_oWE"
+      },
+      "outputs": [],
+      "source": [
+        "# import necessary modules.\n",
+        "import google.generativeai as genai\n",
+        "import json\n",
+        "import base64\n",
+        "import pathlib\n",
+        "import pprint\n",
+        "import requests\n",
+        "import mimetypes\n",
+        "from IPython.display import Markdown"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Fet3lFjdKHEM"
+      },
+      "source": [
+        "## Set the API key"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZoRWILAtCzBE"
+      },
+      "source": [
+        "Add your API_KEY to the secrets manager in the left pannel \"🔑\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LaLCwNlkCyQd"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import userdata\n",
+        "\n",
+        "API_KEY=userdata.get('API_KEY')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_SvYoR3WCeKr"
+      },
+      "outputs": [],
+      "source": [
+        "# Configure the client library by providing your API key.\n",
+        "genai.configure(api_key=API_KEY)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "weo-o73WDpdm"
+      },
+      "source": [
+        "### Parse the arguments"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uIog-0SyDuIF"
+      },
+      "outputs": [],
+      "source": [
+        "model = \"gemini-pro\" # @param {isTemplate: true}\n",
+        "contents_b64 = \"W3sicm9sZSI6InVzZXIiLCAicGFydHMiIDogW3sidGV4dCI6ICJoZWxsbyJ9XX0sIHsicm9sZSI6ICJtb2RlbCIsICJwYXJ0cyI6IFt7InRleHQiOiAiSGVsbG8hIEhvdyBjYW4gSSBhc3Npc3QgeW91IHRvZGF5PyJ9XX1d\" # @param {isTemplate: true}\n",
+        "generation_config_b64 = \"e30=\" # @param {isTemplate: true}\n",
+        "safety_settings_b64 = \"e30=\" # @param {isTemplate: true}\n",
+        "user_input_b64 = 'SG93IGRvZXMgZWxlY3RyaWNpdHkgd29yaz8=' #@param {isTemplate: true}\n",
+        "\n",
+        "contents = json.loads(base64.b64decode(contents_b64))\n",
+        "generation_config = json.loads(base64.b64decode(generation_config_b64))\n",
+        "safety_settings = json.loads(base64.b64decode(safety_settings_b64))\n",
+        "user_input = base64.b64decode(user_input_b64).decode()\n",
+        "stream = False"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "wBS8xNhN0x62"
+      },
+      "outputs": [],
+      "source": [
+        "contents"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1681593ef561"
+      },
+      "outputs": [],
+      "source": [
+        "generation_config"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "a2c31f8f1894"
+      },
+      "outputs": [],
+      "source": [
+        "safety_settings"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4d17bac9fefc"
+      },
+      "outputs": [],
+      "source": [
+        "user_input"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E7zAD69vE92b"
+      },
+      "source": [
+        "### Call the API"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LB2LxPmAB95V"
+      },
+      "outputs": [],
+      "source": [
+        "# Call the model and print the response.\n",
+        "gemini = genai.GenerativeModel(model_name=model)\n",
+        "\n",
+        "chat = gemini.start_chat(history=contents)\n",
+        "\n",
+        "response = chat.send_message(\n",
+        "    user_input,\n",
+        "    stream=stream)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Lm3RXwYuGtZK"
+      },
+      "outputs": [],
+      "source": [
+        "display(Markdown(response.text))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JbKuUc3NGxYD"
+      },
+      "outputs": [],
+      "source": [
+        "response.prompt_feedback"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SLAaIq3kgwwJ"
+      },
+      "outputs": [],
+      "source": [
+        "response.candidates"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [
+        "Tce3stUlHN0L"
+      ],
+      "name": "aistudio_gemini_prompt_chat_b64.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/templates/aistudio_gemini_prompt_freeform.ipynb b/templates/aistudio_gemini_prompt_freeform.ipynb
new file mode 100644
index 000000000..dc5897e9f
--- /dev/null
+++ b/templates/aistudio_gemini_prompt_freeform.ipynb
@@ -0,0 +1,306 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2023 Google LLC"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FKwyTRdwB8aW"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rlE8UqxrDIez"
+      },
+      "source": [
+        "### Install & import"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RXInneX6xx7c"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install google-generativelanguage"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kWIuwKG2_oWE"
+      },
+      "outputs": [],
+      "source": [
+        "# Install the client library and import necessary modules.\n",
+        "#!pip install google-generativeai\n",
+        "import google.generativeai as genai\n",
+        "import json\n",
+        "import pathlib\n",
+        "import pprint\n",
+        "import requests\n",
+        "import mimetypes\n",
+        "from IPython.display import Markdown"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qZsRPVv1ITbh"
+      },
+      "source": [
+        "### Mount Google Drive"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d9-t_OkGoLIP"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/gdrive')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Fet3lFjdKHEM"
+      },
+      "source": [
+        "## Set the API key"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZoRWILAtCzBE"
+      },
+      "source": [
+        "Add your API_KEY to the secrets manager in the left panel \"🔑\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LaLCwNlkCyQd"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import userdata\n",
+        "\n",
+        "API_KEY=userdata.get('API_KEY')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_SvYoR3WCeKr"
+      },
+      "outputs": [],
+      "source": [
+        "# Configure the client library by providing your API key.\n",
+        "genai.configure(api_key=API_KEY)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "weo-o73WDpdm"
+      },
+      "source": [
+        "### Parse the arguments"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uIog-0SyDuIF"
+      },
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "\n",
+        "model = \"gemini-1.5-flash\" # @param {isTemplate: true}\n",
+        "contents = '[{\"parts\": [{\"text\":\"what\\'s in this picture:\"}, {\"image\": {\"image_url\": \"https://storage.googleapis.com/generativeai-downloads/images/scones.jpg\"}}]}]' # @param {isTemplate: true}\n",
+        "generation_config = \"{}\" # @param {isTemplate: true}\n",
+        "safety_settings = \"{}\" # @param {isTemplate: true}\n",
+        "\n",
+        "contents = json.loads(contents)\n",
+        "generation_config = json.loads(generation_config)\n",
+        "safety_settings = json.loads(safety_settings)\n",
+        "\n",
+        "stream = False"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "T3yo4eMqyWEZ"
+      },
+      "outputs": [],
+      "source": [
+        "contents"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yVIjklecE5U0"
+      },
+      "source": [
+        "### Load image data from Drive-IDs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8TehY-utE3OR"
+      },
+      "outputs": [],
+      "source": [
+        "for content in contents:\n",
+        "  for n, part in enumerate(content['parts']):\n",
+        "    if image:=part.get('image', None):\n",
+        "      if drive_id:=image.get('drive_id', None):\n",
+        "        path = next(pathlib.Path(f'/gdrive/.shortcut-targets-by-id/{drive_id}').glob('*'))\n",
+        "        data = path.read_bytes()\n",
+        "        mime_type, _ = mimetypes.guess_type(path)\n",
+        "      elif image_url:=image.get('image_url', None):\n",
+        "        response = requests.get(image_url)\n",
+        "        data = response.content\n",
+        "        mime_type = response.headers['content-type']\n",
+        "      else:\n",
+        "        raise ValueError('Either drive_id or image_url must be provided.')\n",
+        "\n",
+        "      if mime_type is None:\n",
+        "        # Guess!\n",
+        "        mime_type = 'image/png'\n",
+        "\n",
+        "      blob = {'data': data, 'mime_type': mime_type}\n",
+        "      content['parts'][n] = blob"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E7zAD69vE92b"
+      },
+      "source": [
+        "### Call the API"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LB2LxPmAB95V"
+      },
+      "outputs": [],
+      "source": [
+        "# Call the model and print the response.\n",
+        "gemini = genai.GenerativeModel(model_name=model)\n",
+        "\n",
+        "response = gemini.generate_content(\n",
+        "    contents,\n",
+        "    generation_config=generation_config,\n",
+        "    safety_settings=safety_settings,\n",
+        "    stream=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Lm3RXwYuGtZK"
+      },
+      "outputs": [],
+      "source": [
+        "if generation_config.get('candidate_count', 1) == 1:\n",
+        "  display(Markdown(response.text))\n",
+        "else:\n",
+        "  print(response.candidates)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HjT4jtJc2aAk"
+      },
+      "outputs": [],
+      "source": [
+        "response.candidates"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JbKuUc3NGxYD"
+      },
+      "outputs": [],
+      "source": [
+        "response.prompt_feedback"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [
+        "Tce3stUlHN0L"
+      ],
+      "name": "aistudio_gemini_prompt_freeform.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/templates/aistudio_gemini_prompt_freeform_b64.ipynb b/templates/aistudio_gemini_prompt_freeform_b64.ipynb
new file mode 100644
index 000000000..bd6c650dd
--- /dev/null
+++ b/templates/aistudio_gemini_prompt_freeform_b64.ipynb
@@ -0,0 +1,354 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tce3stUlHN0L"
+      },
+      "source": [
+        "##### Copyright 2023 Google LLC"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "tuOe1ymfHZPu"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FKwyTRdwB8aW"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rlE8UqxrDIez"
+      },
+      "source": [
+        "### Install & import"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RXInneX6xx7c"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -U -q google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kWIuwKG2_oWE"
+      },
+      "outputs": [],
+      "source": [
+        "# Install the client library and import necessary modules.\n",
+        "import google.generativeai as genai\n",
+        "\n",
+        "import base64\n",
+        "import io\n",
+        "import json\n",
+        "import mimetypes\n",
+        "import pathlib\n",
+        "import pprint\n",
+        "import requests\n",
+        "\n",
+        "import PIL.Image\n",
+        "import IPython.display\n",
+        "from IPython.display import Markdown"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qZsRPVv1ITbh"
+      },
+      "source": [
+        "### Mount Google Drive"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d9-t_OkGoLIP"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/gdrive')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Fet3lFjdKHEM"
+      },
+      "source": [
+        "## Set the API key"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZoRWILAtCzBE"
+      },
+      "source": [
+        "Add your API_KEY to the secrets manager in the left panel \"🔑\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LaLCwNlkCyQd"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import userdata\n",
+        "\n",
+        "API_KEY=userdata.get('API_KEY')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_SvYoR3WCeKr"
+      },
+      "outputs": [],
+      "source": [
+        "# Configure the client library by providing your API key.\n",
+        "genai.configure(api_key=API_KEY)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "weo-o73WDpdm"
+      },
+      "source": [
+        "### Parse the arguments"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uIog-0SyDuIF"
+      },
+      "outputs": [],
+      "source": [
+        "model = \"gemini-1.5-flash\" # @param {isTemplate: true}\n",
+        "contents_b64 = 'W3sicGFydHMiOiBbeyJ0ZXh0Ijoid2hhdCdzIGluIHRoaXMgcGljdHVyZToifSwgeyJpbWFnZSI6IHsiaW1hZ2VfdXJsIjogImh0dHBzOi8vc3RvcmFnZS5nb29nbGVhcGlzLmNvbS9nZW5lcmF0aXZlYWktZG93bmxvYWRzL2ltYWdlcy9zY29uZXMuanBnIn19XX1d' # @param {isTemplate: true}\n",
+        "generation_config_b64 = \"e30=\" # @param {isTemplate: true}\n",
+        "safety_settings_b64 = \"e30=\" # @param {isTemplate: true}\n",
+        "\n",
+        "contents = json.loads(base64.b64decode(contents_b64))\n",
+        "generation_config = json.loads(base64.b64decode(generation_config_b64))\n",
+        "safety_settings = json.loads(base64.b64decode(safety_settings_b64))\n",
+        "\n",
+        "stream = False"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "T3yo4eMqyWEZ"
+      },
+      "outputs": [],
+      "source": [
+        "contents"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ca3e641ee9d3"
+      },
+      "outputs": [],
+      "source": [
+        "generation_config"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "11ce12f5bbac"
+      },
+      "outputs": [],
+      "source": [
+        "safety_settings"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yVIjklecE5U0"
+      },
+      "source": [
+        "### Load image data from Drive-IDs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8TehY-utE3OR"
+      },
+      "outputs": [],
+      "source": [
+        "for content in contents:\n",
+        "  for n, part in enumerate(content['parts']):\n",
+        "    if image:=part.get('image', None):\n",
+        "      if drive_id:=image.get('drive_id', None):\n",
+        "        path = next(pathlib.Path(f'/gdrive/.shortcut-targets-by-id/{drive_id}').glob('*'))\n",
+        "        data = path.read_bytes()\n",
+        "        mime_type, _ = mimetypes.guess_type(path)\n",
+        "      elif image_url:=image.get('image_url', None):\n",
+        "        response = requests.get(image_url)\n",
+        "        data = response.content\n",
+        "        mime_type = response.headers['content-type']\n",
+        "      else:\n",
+        "        raise ValueError('Either drive_id or image_url must be provided.')\n",
+        "\n",
+        "      if mime_type is None:\n",
+        "        # Guess!\n",
+        "        mime_type = 'image/png'\n",
+        "\n",
+        "      blob = {'data': data, 'mime_type': mime_type}\n",
+        "      content['parts'][n] = {'inline_data': blob}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "27af7a829b77"
+      },
+      "outputs": [],
+      "source": [
+        "import IPython.display\n",
+        "import PIL.Image\n",
+        "import io\n",
+        "\n",
+        "for content in contents:\n",
+        "    for part in content['parts']:\n",
+        "        if text := part.get('text', None):\n",
+        "            print(text)\n",
+        "        elif data := part.get('inline_data', None):\n",
+        "            img = PIL.Image.open(io.BytesIO(data['data']))\n",
+        "            img.thumbnail([512,512])\n",
+        "            IPython.display.display(img)\n",
+        "    print('_'*80)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E7zAD69vE92b"
+      },
+      "source": [
+        "### Call the API"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LB2LxPmAB95V"
+      },
+      "outputs": [],
+      "source": [
+        "# Call the model and print the response.\n",
+        "gemini = genai.GenerativeModel(model_name=model)\n",
+        "\n",
+        "response = gemini.generate_content(\n",
+        "    contents,\n",
+        "    generation_config=generation_config,\n",
+        "    safety_settings=safety_settings,\n",
+        "    stream=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Lm3RXwYuGtZK"
+      },
+      "outputs": [],
+      "source": [
+        "if generation_config.get('candidate_count', 1) == 1:\n",
+        "  display(Markdown(response.text))\n",
+        "else:\n",
+        "  print(response.candidates)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HjT4jtJc2aAk"
+      },
+      "outputs": [],
+      "source": [
+        "response.candidates"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JbKuUc3NGxYD"
+      },
+      "outputs": [],
+      "source": [
+        "response.prompt_feedback"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [
+        "Tce3stUlHN0L"
+      ],
+      "name": "aistudio_gemini_prompt_freeform_b64.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/templates/makersuite_chat_prompt.ipynb b/templates/aistudio_palm_prompt_chat.ipynb
similarity index 96%
rename from templates/makersuite_chat_prompt.ipynb
rename to templates/aistudio_palm_prompt_chat.ipynb
index dd56ae8f7..023b35477 100644
--- a/templates/makersuite_chat_prompt.ipynb
+++ b/templates/aistudio_palm_prompt_chat.ipynb
@@ -112,14 +112,14 @@
         "  examples=examples,\n",
         "  messages=messages\n",
         ")\n",
-        "print(response.last)"
+        "print(response.candidates[0]['content'])"
       ]
     }
   ],
   "metadata": {
     "colab": {
-      "toc_visible": true,
-      "provenance": []
+      "name": "aistudio_palm_prompt_chat.ipynb",
+      "toc_visible": true
     },
     "kernelspec": {
       "display_name": "Python 3",
diff --git a/templates/makersuite_text_prompt.ipynb b/templates/aistudio_palm_prompt_text.ipynb
similarity index 94%
rename from templates/makersuite_text_prompt.ipynb
rename to templates/aistudio_palm_prompt_text.ipynb
index 1ceca4ea2..e8d546546 100644
--- a/templates/makersuite_text_prompt.ipynb
+++ b/templates/aistudio_palm_prompt_text.ipynb
@@ -95,9 +95,9 @@
         "  'stop_sequences': stop_sequences,\n",
         "  'safety_settings': safety_settings,\n",
         "}\n",
-	"\n",
-	"# Show what will be sent with the API call.\n",
-	"pprint.pprint(defaults | {'prompt': text})"
+        "\n",
+        "# Show what will be sent with the API call.\n",
+        "pprint.pprint(defaults | {'prompt': text})"
       ]
     },
     {
@@ -113,15 +113,14 @@
         "  **defaults,\n",
         "  prompt=text\n",
         ")\n",
-        "print(response.result)"
+        "print(response.candidates[0]['output'])"
       ]
     }
   ],
   "metadata": {
     "colab": {
-      "name": "makersuite_text_prompt.ipynb",
-      "toc_visible": true,
-      "provenance": []
+      "name": "aistudio_palm_prompt_text.ipynb",
+      "toc_visible": true
     },
     "kernelspec": {
       "display_name": "Python 3",
diff --git a/third_party/docs-agent b/third_party/docs-agent
index 8ddc11904..5174a46cb 120000
--- a/third_party/docs-agent
+++ b/third_party/docs-agent
@@ -1 +1 @@
-../demos/palm/python/docs-agent/third_party
\ No newline at end of file
+../examples/gemini/python/docs-agent/third_party
\ No newline at end of file