From 70d8e691970fbf7430320ff96dfc643e410cfffd Mon Sep 17 00:00:00 2001 From: Sebastian Rojo Date: Sun, 17 Dec 2023 10:23:20 -0500 Subject: [PATCH 01/14] Add Pinecone vector database configuration --- apps/client_backend/main.py | 8 +++ docs/open-source/vectordb.mdx | 115 ++++++++++++++++++++++++++++++++++ 2 files changed, 123 insertions(+) create mode 100644 docs/open-source/vectordb.mdx diff --git a/apps/client_backend/main.py b/apps/client_backend/main.py index c004d4726..26a930b6d 100644 --- a/apps/client_backend/main.py +++ b/apps/client_backend/main.py @@ -8,9 +8,16 @@ from vocode.streaming.agent.chat_gpt_agent import ChatGPTAgent from vocode.streaming.client_backend.conversation import ConversationRouter from vocode.streaming.models.message import BaseMessage +from vocode.streaming.vector_db.factory import VectorDBFactory +from vocode.streaming.vector_db.pinecone import PineconeConfig + +vector_db_config = PineconeConfig( + index="artisan-d494347" + ) from dotenv import load_dotenv + load_dotenv() app = FastAPI(docs_url=None) @@ -24,6 +31,7 @@ ChatGPTAgentConfig( initial_message=BaseMessage(text="Hello!"), prompt_preamble="Have a pleasant conversation about life", + vector_db_config=vector_db_config, ) ), synthesizer_thunk=lambda output_audio_config: AzureSynthesizer( diff --git a/docs/open-source/vectordb.mdx b/docs/open-source/vectordb.mdx new file mode 100644 index 000000000..4b3577c37 --- /dev/null +++ b/docs/open-source/vectordb.mdx @@ -0,0 +1,115 @@ +--- +title: "Vector databases" +description: "use agents knowledge via embeddings in Open Spurce library" +--- + +## Introduction + +For many practical use cases it may be necessary to give bots information; but, due to limited +context windows, there may not be enough room in the prompt. Thus, Vocode allows you to plug into +vector databases that contain embeddings. + +Each time the bot receives a message, it can query for the most similar embeddings; these embeddings will +be shown to the agent to guide its responses. + +Currently, we support [Pinecone](https://www.pinecone.io/). Under the hood, we use an approach similar +to Langchain to store the documents in Pinecone. Each vector in Pinecone must have two pieces of metadata +to be compatible with Vocode: + +- `text`: The text that will be shown to the agent. +- `source`: The name of the document where the text comes from. This could be the title of an article, + for example. + +## Connecting to Pinecone + +To add your vector database to your agent, you need to add a vector database config to your agent: + + + +```python update_agent.py +from vocode import PineconeVectorDatabaseUpdateParams + +agent = vocode_client.agents.update_agent( + id="AGENT_ID", + request=PineconeVectorDatabaseUpdateParams( + type="vector_database_pinecone", + index="PINECONE_INDEX", + api_key="PINECONE_API_KEY", + api_environment="PINECONE_API_ENVIRONMENT", + ), +) +``` + +```javascript updateAgent.js +const agent = await vocode.agents.updateAgent({ + id: "AGENT_ID", + body: { + vector_database: { + type: "vector_database_pinecone", + index: "PINECONE_INDEX", + api_key: "PINECONE_API_KEY", + api_environment: "PINECONE_API_ENVIRONMENTr", + }, + }, +}); + +main(); +``` + + + +Now your agent will query the vector database for each user message. + +## Document loading script + +You can manaully add documents to Pinecone any way you like as long as you include the required metadata. +If you have a folder of PDFs, docx files, text files, etc. that you want to add to pinecone, you can use +the below script which uses [Unstructured](https://github.com/Unstructured-IOVector/unstructured) to parse +many kinds of files types, extract the text, and add it to pinecone. + +The script was tested with these package versions: + +``` +python = "^3.10" +langchain = "^0.0.237" +spacy = "^3.6.0" +unstructured = {extras = ["local-inference"], version = "^0.8.1"} +layoutparser = {extras = ["layoutmodels", "tesseract"], version = "^0.3.4"} +pinecone-client = "^2.2.2" +openai = "^0.27.8" +torch = ">=2.0.0, !=2.0.1" +tiktoken = "^0.4.0" +``` + +```python +import os +import pinecone +from langchain.embeddings.openai import OpenAIEmbeddings +from langchain.text_splitter import SpacyTextSplitter +from langchain.vectorstores import Pinecone +from langchain.document_loaders import DirectoryLoader, UnstructuredFileLoader + +PINECONE_API_KEY = os.environ["PINECONE_API_KEY"] +PINECONE_ENVIRONMENT = os.environ["PINECONE_ENVIRONMENT"] +OPENAI_API_KEY = os.environ["OPENAI_API_KEY"] + + +loader = DirectoryLoader('./docs', glob="**/*.*", show_progress=True, loader_cls=UnstructuredFileLoader) +print("Loading documents...") +documents = loader.load() +text_splitter = SpacyTextSplitter(chunk_size=1000) +print("Splitting documents...") +docs = text_splitter.split_documents(documents) + +embeddings = OpenAIEmbeddings() + +pinecone.init( + api_key=PINECONE_API_KEY, + environment=PINECONE_ENVIRONMENT, +) + +index_name = "your_index_name" + +print("Creating index...") +docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name) +``` From 70db42443f38d67416b965a791594c502849fbe5 Mon Sep 17 00:00:00 2001 From: Sebastian Rojo Date: Sun, 17 Dec 2023 11:57:18 -0500 Subject: [PATCH 02/14] Add RAG documentation and remove vectordb documentation --- apps/rag/manual_pinecone_ingestor.ipynb | 198 ++++++++++++++++++ docs/images/vectodb/pinecone-create-index.png | Bin 0 -> 412248 bytes docs/open-source/rag.mdx | 101 +++++++++ docs/open-source/vectordb.mdx | 115 ---------- 4 files changed, 299 insertions(+), 115 deletions(-) create mode 100644 apps/rag/manual_pinecone_ingestor.ipynb create mode 100644 docs/images/vectodb/pinecone-create-index.png create mode 100644 docs/open-source/rag.mdx delete mode 100644 docs/open-source/vectordb.mdx diff --git a/apps/rag/manual_pinecone_ingestor.ipynb b/apps/rag/manual_pinecone_ingestor.ipynb new file mode 100644 index 000000000..b39256520 --- /dev/null +++ b/apps/rag/manual_pinecone_ingestor.ipynb @@ -0,0 +1,198 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Install the Python Requests library via pip\n", + "# python \n", + "# langchain \n", + "# spacy \n", + "# unstructured = {extras = [\"local-inference\"]}\n", + "# layoutparser = {extras = [\"layoutmodels\", \"tesseract\"]}\n", + "# pinecone-client\n", + "# openai \n", + "# torch \n", + "# tiktoken\n", + "# git\n", + "\n", + "%pip install langchain spacy unstructured[local-inference] layoutparser[layoutmodels,tesseract] pinecone-client openai torch tiktoken git\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "'''\n", + "Importing necessary modules and functions:\n", + "- os module to interact with the OS\n", + "- Pinecone module for vector database operations\n", + "- OpenAIEmbeddings from langchain.embeddings.openai for generating embeddings\n", + "- SpacyTextSplitter from langchain.text_splitter for splitting text into chunks\n", + "- Pinecone from langchain.vectorstores for storing and retrieving vectors\n", + "- DirectoryLoader and UnstructuredFileLoader from langchain.document_loaders for loading documents from directories and unstructured files\n", + "'''\n", + "import os\n", + "import pinecone\n", + "from langchain.embeddings.openai import OpenAIEmbeddings\n", + "from langchain.text_splitter import SpacyTextSplitter\n", + "from langchain.vectorstores import Pinecone\n", + "from langchain.document_loaders import DirectoryLoader, UnstructuredFileLoader\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "'''\n", + "We are using Google Colab's secret manager to securely input our API keys.\n", + "This ensures that the keys are not visible in the notebook and are not stored in the notebook's history.\n", + "We also retrieve the Pinecone index name from the secret manager.\n", + "'''\n", + "from google.colab import userdata\n", + "PINECONE_API_KEY = userdata.get('PINECONE_API_KEY')\n", + "PINECONE_ENVIRONMENT = userdata.get('PINECONE_ENVIRONMENT')\n", + "PINECONE_INDEX = userdata.get('PINECONE_INDEX')\n", + "OPENAI_API_KEY = userdata.get('OPENAI_API_KEY')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "'''\n", + "We are defining a list of libraries from which we want to fetch the code.\n", + "Each library is represented as a dictionary with three keys:\n", + " - 'name': the name of the library\n", + " - 'code': the URL of the library's GitHub repository\n", + " - 'documentation_path': the path to the documentation within the repository\n", + "'''\n", + "libraries = [\n", + " {\n", + " 'name': 'langchain',\n", + " 'code': 'https://github.com/langchain-ai/langchain',\n", + " 'documentation_path': 'docs'\n", + " },\n", + " {\n", + " 'name': 'supabase',\n", + " 'code': 'https://github.com/supabase/supabase',\n", + " 'documentation_path': 'master/apps/docs'\n", + " },\n", + " {\n", + " 'name': 'next.js',\n", + " 'code': 'https://github.com/vercel/next.js',\n", + " 'documentation_path': 'docs'\n", + " },\n", + " {\n", + " 'name': 'fastapi',\n", + " 'code': 'https://github.com/tiangolo/fastapi',\n", + " 'documentation_path': 'docs'\n", + " },\n", + " {\n", + " 'name': 'vocode-python',\n", + " 'code': 'https://github.com/vocodedev/vocode-python',\n", + " 'documentation_path': 'docs'\n", + " }\n", + "]\n", + "\n", + "'''\n", + "The function get_code_from_github is defined to fetch the code from the GitHub repositories.\n", + "It iterates over the libraries list and for each library, it clones the repository to a temporary directory.\n", + "Then, it moves the documentation to a directory named 'libraries_documentation/{name}'.\n", + "'''\n", + "import os\n", + "import shutil\n", + "import tempfile\n", + "from git import Repo\n", + "\n", + "def get_code_from_github():\n", + " # Create a temporary directory\n", + " with tempfile.TemporaryDirectory() as temp_dir:\n", + " for library in libraries:\n", + " # Clone the repository to the temporary directory\n", + " Repo.clone_from(library['code'], temp_dir)\n", + " \n", + " # Create a directory for the library's documentation\n", + " doc_dir = os.path.join('libraries_documentation', library['name'])\n", + " os.makedirs(doc_dir, exist_ok=True)\n", + " \n", + " # Move the documentation to the new directory\n", + " shutil.move(os.path.join(temp_dir, library['documentation_path']), doc_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "'''\n", + "The following code block is responsible for loading, splitting, and indexing the documents.\n", + "'''\n", + "\n", + "'''\n", + "Create a DirectoryLoader object to load all files from the 'libraries_documentation' directory.\n", + "The glob pattern \"**/*.*\" is used to match any file in the directory or its subdirectories.\n", + "The UnstructuredFileLoader class is used to load the files.\n", + "'''\n", + "loader = DirectoryLoader('./libraries_documentation', glob=\"**/*.*\", show_progress=True, loader_cls=UnstructuredFileLoader)\n", + "\n", + "'''\n", + "Load the documents from the directory.\n", + "'''\n", + "print(\"Loading documents...\")\n", + "documents = loader.load()\n", + "\n", + "'''\n", + "Create a SpacyTextSplitter object to split the documents into chunks of 1000 characters.\n", + "'''\n", + "text_splitter = SpacyTextSplitter(chunk_size=1000)\n", + "\n", + "'''\n", + "Split the documents into chunks.\n", + "'''\n", + "print(\"Splitting documents...\")\n", + "docs = text_splitter.split_documents(documents)\n", + "\n", + "'''\n", + "Create an OpenAIEmbeddings object to generate embeddings for the documents.\n", + "'''\n", + "embeddings = OpenAIEmbeddings()\n", + "\n", + "'''\n", + "Initialize the Pinecone client with the API key and environment variables.\n", + "'''\n", + "pinecone.init(\n", + " api_key=PINECONE_API_KEY,\n", + " environment=PINECONE_ENVIRONMENT,\n", + ")\n", + "\n", + "'''\n", + "Set the name of the Pinecone index.\n", + "'''\n", + "index_name = PINECONE_INDEX\n", + "\n", + "'''\n", + "Create a Pinecone index from the documents and their embeddings.\n", + "'''\n", + "print(\"Creating index...\")\n", + "docsearch = 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zAhZtLl@T6z5ClRm9SsJF2UOh&-X-f-?|YF(|Ku5LfsL0759|9XE!AM8$m=^*?)h`pHBvraY^x zPckJ0IYu5)2|twl7WjLPTp)yjxG_k^StpfLQe;6?u2C`}3C8hwJpT6C<+0MK6sc=? zE7e)rr}dJ;Bu)Ak(<4*OgCKCuaUqbL(h|uzZ-_HwRGL}|p`cWtB`~fWp|sXH=gX>A zIub%Er4Uhw|Mxx7d+RL#Lqr|u<>FFEWlYVgT7pMnpL bI`97nq0GJvFTVC;00000NkvXXu0mjfF|q`_ literal 0 HcmV?d00001 diff --git a/docs/open-source/rag.mdx b/docs/open-source/rag.mdx new file mode 100644 index 000000000..979298054 --- /dev/null +++ b/docs/open-source/rag.mdx @@ -0,0 +1,101 @@ +--- +title: "Retrieval-augmented generation (RAG)." +description: "use agents knowledge via embeddings in Open Spurce library" +--- +# Vocode Retrieval-augmented generation (RAG). +## Introduction + +For Open Source developers building advanced conversational AI models, one challenge looms large: +limited context windows in prompts. Past interactions quickly vanish, forcing bots to rely on incomplete +information. Vocode introduces a novel solution: retrieval-augmented generation (RAG) powered by vector databases. + +## Here's how it works: + +- **Pre-computed embeddings:** Vocode leverages vector databases like Pinecone to store concise representations + of relevant knowledge. These bite-sized "memory chunks" efficiently encode factual details, + contexts, and even emotional nuances. +- **Dynamic retrieval:** As your bot interacts, Vocode queries the database for embeddings most similar to the + current conversation. Think of it as a smart librarian fetching the most relevant books on the fly. +- **Contextual guidance:** The retrieved embeddings are then fed to the language model, enriching its + understanding of the ongoing dialogue. This enables the bot to generate more informed, contextually rich, + and ultimately **smarter responses.** + +## Benefits for builders: +- **Scalability:** Vector databases grow seamlessly with your needs, accommodating vast knowledge + repositories without straining your system. +- **Efficiency:** Say goodbye to bloated prompts filled with redundant information. Vocode delivers precise + relevance with minimal data overhead. +- **Openness:** Built on open-source principles, Vocode empowers developers to contribute, customize, and + build upon its foundation. +- **Transparency:** Explore the reasoning behind retrieved embeddings and gain valuable insights into your + bot's decision-making process. + +## How to set up your Pinecone database + +In this guide, we'll get your Pinecone database fired up and ready to fuel your retrieval-augmented +chatbot with a robust knowledge base. Buckle up, developers! + +### 1. Create Your Vector Index: + +1. Navigate to the Pinecone dashboard and grab the "Create Index" button. +2. Name your index something meaningful, like "vocode-documentation-vector" for our Vocode-documentation bot. +3. Set the dimensions to 1536 for compatibility with your chosen OpenAI model. +4. Stick with cosine metric for consistency. +5. Hit "Create Index" and let the magic happen! + +![Setup](images/vectodb/pinecone-create-index.png) + + +## Document loading script + +You can manaully add documents to Pinecone any way you like as long as you include the required metadata. +If you have a folder of PDFs, docx files, text files, etc. that you want to add to pinecone, you can use +the below script which uses [Unstructured](https://github.com/Unstructured-IOVector/unstructured) to parse +many kinds of files types, extract the text, and add it to pinecone. + +The script was tested with these package versions: + +``` +python = "^3.10" +langchain = "^0.0.237" +spacy = "^3.6.0" +unstructured = {extras = ["local-inference"], version = "^0.8.1"} +layoutparser = {extras = ["layoutmodels", "tesseract"], version = "^0.3.4"} +pinecone-client = "^2.2.2" +openai = "^0.27.8" +torch = ">=2.0.0, !=2.0.1" +tiktoken = "^0.4.0" +``` + +```python +import os +import pinecone +from langchain.embeddings.openai import OpenAIEmbeddings +from langchain.text_splitter import SpacyTextSplitter +from langchain.vectorstores import Pinecone +from langchain.document_loaders import DirectoryLoader, UnstructuredFileLoader + +PINECONE_API_KEY = os.environ["PINECONE_API_KEY"] +PINECONE_ENVIRONMENT = os.environ["PINECONE_ENVIRONMENT"] +OPENAI_API_KEY = os.environ["OPENAI_API_KEY"] + + +loader = DirectoryLoader('./docs', glob="**/*.*", show_progress=True, loader_cls=UnstructuredFileLoader) +print("Loading documents...") +documents = loader.load() +text_splitter = SpacyTextSplitter(chunk_size=1000) +print("Splitting documents...") +docs = text_splitter.split_documents(documents) + +embeddings = OpenAIEmbeddings() + +pinecone.init( + api_key=PINECONE_API_KEY, + environment=PINECONE_ENVIRONMENT, +) + +index_name = "your_index_name" + +print("Creating index...") +docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name) +``` diff --git a/docs/open-source/vectordb.mdx b/docs/open-source/vectordb.mdx deleted file mode 100644 index 4b3577c37..000000000 --- a/docs/open-source/vectordb.mdx +++ /dev/null @@ -1,115 +0,0 @@ ---- -title: "Vector databases" -description: "use agents knowledge via embeddings in Open Spurce library" ---- - -## Introduction - -For many practical use cases it may be necessary to give bots information; but, due to limited -context windows, there may not be enough room in the prompt. Thus, Vocode allows you to plug into -vector databases that contain embeddings. - -Each time the bot receives a message, it can query for the most similar embeddings; these embeddings will -be shown to the agent to guide its responses. - -Currently, we support [Pinecone](https://www.pinecone.io/). Under the hood, we use an approach similar -to Langchain to store the documents in Pinecone. Each vector in Pinecone must have two pieces of metadata -to be compatible with Vocode: - -- `text`: The text that will be shown to the agent. -- `source`: The name of the document where the text comes from. This could be the title of an article, - for example. - -## Connecting to Pinecone - -To add your vector database to your agent, you need to add a vector database config to your agent: - - - -```python update_agent.py -from vocode import PineconeVectorDatabaseUpdateParams - -agent = vocode_client.agents.update_agent( - id="AGENT_ID", - request=PineconeVectorDatabaseUpdateParams( - type="vector_database_pinecone", - index="PINECONE_INDEX", - api_key="PINECONE_API_KEY", - api_environment="PINECONE_API_ENVIRONMENT", - ), -) -``` - -```javascript updateAgent.js -const agent = await vocode.agents.updateAgent({ - id: "AGENT_ID", - body: { - vector_database: { - type: "vector_database_pinecone", - index: "PINECONE_INDEX", - api_key: "PINECONE_API_KEY", - api_environment: "PINECONE_API_ENVIRONMENTr", - }, - }, -}); - -main(); -``` - - - -Now your agent will query the vector database for each user message. - -## Document loading script - -You can manaully add documents to Pinecone any way you like as long as you include the required metadata. -If you have a folder of PDFs, docx files, text files, etc. that you want to add to pinecone, you can use -the below script which uses [Unstructured](https://github.com/Unstructured-IOVector/unstructured) to parse -many kinds of files types, extract the text, and add it to pinecone. - -The script was tested with these package versions: - -``` -python = "^3.10" -langchain = "^0.0.237" -spacy = "^3.6.0" -unstructured = {extras = ["local-inference"], version = "^0.8.1"} -layoutparser = {extras = ["layoutmodels", "tesseract"], version = "^0.3.4"} -pinecone-client = "^2.2.2" -openai = "^0.27.8" -torch = ">=2.0.0, !=2.0.1" -tiktoken = "^0.4.0" -``` - -```python -import os -import pinecone -from langchain.embeddings.openai import OpenAIEmbeddings -from langchain.text_splitter import SpacyTextSplitter -from langchain.vectorstores import Pinecone -from langchain.document_loaders import DirectoryLoader, UnstructuredFileLoader - -PINECONE_API_KEY = os.environ["PINECONE_API_KEY"] -PINECONE_ENVIRONMENT = os.environ["PINECONE_ENVIRONMENT"] -OPENAI_API_KEY = os.environ["OPENAI_API_KEY"] - - -loader = DirectoryLoader('./docs', glob="**/*.*", show_progress=True, loader_cls=UnstructuredFileLoader) -print("Loading documents...") -documents = loader.load() -text_splitter = SpacyTextSplitter(chunk_size=1000) -print("Splitting documents...") -docs = text_splitter.split_documents(documents) - -embeddings = OpenAIEmbeddings() - -pinecone.init( - api_key=PINECONE_API_KEY, - environment=PINECONE_ENVIRONMENT, -) - -index_name = "your_index_name" - -print("Creating index...") -docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name) -``` From 73158241acea8f63d680e8bd282690965d21fce8 Mon Sep 17 00:00:00 2001 From: Sebastian Rojo Date: Sun, 17 Dec 2023 11:59:27 -0500 Subject: [PATCH 03/14] ADD colab Link --- apps/rag/manual_pinecone_ingestor.ipynb | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/apps/rag/manual_pinecone_ingestor.ipynb b/apps/rag/manual_pinecone_ingestor.ipynb index b39256520..b7aa1cadf 100644 --- a/apps/rag/manual_pinecone_ingestor.ipynb +++ b/apps/rag/manual_pinecone_ingestor.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Colab_Link: https://colab.research.google.com/github/ArtisanLabs/vocode-python/blob/main/apps/rag/manual_pinecone_ingestor.ipynb\n" + ] + }, { "cell_type": "code", "execution_count": null, From d77630493dc1794e3e960af1aece7e860f41ce53 Mon Sep 17 00:00:00 2001 From: Sebastian Rojo Date: Sun, 17 Dec 2023 13:57:49 -0500 Subject: [PATCH 04/14] working version manual_pinecone_ingestor --- apps/rag/manual_pinecone_ingestor.ipynb | 48 +++++++++++++++++-------- 1 file changed, 33 insertions(+), 15 deletions(-) diff --git a/apps/rag/manual_pinecone_ingestor.ipynb b/apps/rag/manual_pinecone_ingestor.ipynb index b7aa1cadf..a4a0543b3 100644 --- a/apps/rag/manual_pinecone_ingestor.ipynb +++ b/apps/rag/manual_pinecone_ingestor.ipynb @@ -61,12 +61,22 @@ "We are using Google Colab's secret manager to securely input our API keys.\n", "This ensures that the keys are not visible in the notebook and are not stored in the notebook's history.\n", "We also retrieve the Pinecone index name from the secret manager.\n", + "Then, we add these keys to the environment variables.\n", "'''\n", "from google.colab import userdata\n", + "import os\n", + "\n", "PINECONE_API_KEY = userdata.get('PINECONE_API_KEY')\n", + "os.environ['PINECONE_API_KEY'] = PINECONE_API_KEY\n", + "\n", "PINECONE_ENVIRONMENT = userdata.get('PINECONE_ENVIRONMENT')\n", + "os.environ['PINECONE_ENVIRONMENT'] = PINECONE_ENVIRONMENT\n", + "\n", "PINECONE_INDEX = userdata.get('PINECONE_INDEX')\n", - "OPENAI_API_KEY = userdata.get('OPENAI_API_KEY')" + "os.environ['PINECONE_INDEX'] = PINECONE_INDEX\n", + "\n", + "OPENAI_API_KEY = userdata.get('OPENAI_API_KEY')\n", + "os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY" ] }, { @@ -91,7 +101,7 @@ " {\n", " 'name': 'supabase',\n", " 'code': 'https://github.com/supabase/supabase',\n", - " 'documentation_path': 'master/apps/docs'\n", + " 'documentation_path': 'apps/docs'\n", " },\n", " {\n", " 'name': 'next.js',\n", @@ -101,7 +111,7 @@ " {\n", " 'name': 'fastapi',\n", " 'code': 'https://github.com/tiangolo/fastapi',\n", - " 'documentation_path': 'docs'\n", + " 'documentation_path': 'docs/en/docs'\n", " },\n", " {\n", " 'name': 'vocode-python',\n", @@ -118,21 +128,29 @@ "import os\n", "import shutil\n", "import tempfile\n", - "from git import Repo\n", + "import subprocess\n", "\n", "def get_code_from_github():\n", " # Create a temporary directory\n", " with tempfile.TemporaryDirectory() as temp_dir:\n", " for library in libraries:\n", - " # Clone the repository to the temporary directory\n", - " Repo.clone_from(library['code'], temp_dir)\n", - " \n", - " # Create a directory for the library's documentation\n", - " doc_dir = os.path.join('libraries_documentation', library['name'])\n", - " os.makedirs(doc_dir, exist_ok=True)\n", + " # Define the clone directory path\n", + " clone_dir = os.path.join(temp_dir, library['name'])\n", + "\n", + " # Clone the repository using the git command\n", + " subprocess.run(['git', 'clone', library['code'], clone_dir], check=True)\n", " \n", - " # Move the documentation to the new directory\n", - " shutil.move(os.path.join(temp_dir, library['documentation_path']), doc_dir)" + " # Define the source and destination for moving documentation\n", + " doc_source = os.path.join(clone_dir, library['documentation_path'])\n", + " doc_dest = os.path.join('libraries_documentation', library['name'])\n", + "\n", + " # Create the destination directory if it doesn't exist\n", + " os.makedirs(doc_dest, exist_ok=True)\n", + "\n", + " # Move the documentation to the destination directory\n", + " shutil.move(doc_source, doc_dest)\n", + "\n", + "get_code_from_github()" ] }, { @@ -146,11 +164,11 @@ "'''\n", "\n", "'''\n", - "Create a DirectoryLoader object to load all files from the 'libraries_documentation' directory.\n", - "The glob pattern \"**/*.*\" is used to match any file in the directory or its subdirectories.\n", + "Create a DirectoryLoader object to load all .md and .mdx files from the 'libraries_documentation' directory.\n", + "The glob pattern \"**/*.md*\" is used to match any markdown file in the directory or its subdirectories.\n", "The UnstructuredFileLoader class is used to load the files.\n", "'''\n", - "loader = DirectoryLoader('./libraries_documentation', glob=\"**/*.*\", show_progress=True, loader_cls=UnstructuredFileLoader)\n", + "loader = DirectoryLoader('./libraries_documentation', glob=\"**/*.md*\", show_progress=True, loader_cls=UnstructuredFileLoader)\n", "\n", "'''\n", "Load the documents from the directory.\n", From 48298281e734fec047d3701caabfab8da3ff1da9 Mon Sep 17 00:00:00 2001 From: Sebastian Rojo Date: Sun, 17 Dec 2023 16:43:59 -0500 Subject: [PATCH 05/14] Revert the changes in /apps/client_backend/main.py with the aim of creating a new app in /apps/rag/ --- apps/client_backend/main.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/apps/client_backend/main.py b/apps/client_backend/main.py index 26a930b6d..c004d4726 100644 --- a/apps/client_backend/main.py +++ b/apps/client_backend/main.py @@ -8,16 +8,9 @@ from vocode.streaming.agent.chat_gpt_agent import ChatGPTAgent from vocode.streaming.client_backend.conversation import ConversationRouter from vocode.streaming.models.message import BaseMessage -from vocode.streaming.vector_db.factory import VectorDBFactory -from vocode.streaming.vector_db.pinecone import PineconeConfig - -vector_db_config = PineconeConfig( - index="artisan-d494347" - ) from dotenv import load_dotenv - load_dotenv() app = FastAPI(docs_url=None) @@ -31,7 +24,6 @@ ChatGPTAgentConfig( initial_message=BaseMessage(text="Hello!"), prompt_preamble="Have a pleasant conversation about life", - vector_db_config=vector_db_config, ) ), synthesizer_thunk=lambda output_audio_config: AzureSynthesizer( From 354241922fdca6f5ae251461c7466735de164f3d Mon Sep 17 00:00:00 2001 From: Sebastian Rojo Date: Sun, 17 Dec 2023 21:58:47 -0500 Subject: [PATCH 06/14] Fix issue #463: Add Content-Type Header for Pinecone API Integration --- vocode/streaming/vector_db/pinecone.py | 87 ++++++++++++++------------ 1 file changed, 46 insertions(+), 41 deletions(-) diff --git a/vocode/streaming/vector_db/pinecone.py b/vocode/streaming/vector_db/pinecone.py index 646590860..8b2993255 100644 --- a/vocode/streaming/vector_db/pinecone.py +++ b/vocode/streaming/vector_db/pinecone.py @@ -68,48 +68,53 @@ async def add_texts( return ids - async def similarity_search_with_score( - self, - query: str, - filter: Optional[dict] = None, - namespace: Optional[str] = None, - ) -> List[Tuple[Document, float]]: - """Return pinecone documents most similar to query, along with scores. +async def similarity_search_with_score( + self, + query: str, + filter: Optional[dict] = None, + namespace: Optional[str] = None, +) -> List[Tuple[Document, float]]: + """Return pinecone documents most similar to query, along with scores. - Args: - query: Text to look up documents similar to. - filter: Dictionary of argument(s) to filter on metadata - namespace: Namespace to search in. Default will search in '' namespace. + Args: + query: Text to look up documents similar to. + filter: Dictionary of argument(s) to filter on metadata + namespace: Namespace to search in. Default will search in '' namespace. - Returns: - List of Documents most similar to the query and score for each - """ - # Adapted from: langchain/vectorstores/pinecone.py. Made langchain implementation async. - if namespace is None: - namespace = "" - query_obj = await self.create_openai_embedding(query) - docs = [] - async with self.aiohttp_session.post( - f"{self.pinecone_url}/query", - headers={"Api-Key": self.pinecone_api_key}, - json={ - "top_k": self.config.top_k, - "namespace": namespace, - "filter": filter, - "vector": query_obj, - "includeMetadata": True, - }, - ) as response: + Returns: + List of Documents most similar to the query and score for each + """ + # Adapted from: langchain/vectorstores/pinecone.py. Made langchain implementation async. + if namespace is None: + namespace = "" + query_obj = await self.create_openai_embedding(query) + docs = [] + async with self.aiohttp_session.post( + f"{self.pinecone_url}/query", + headers={"Api-Key": self.pinecone_api_key}, + json={ + "top_k": self.config.top_k, + "namespace": namespace, + "filter": filter, + "vector": query_obj, + "includeMetadata": True, + }, + ) as response: + if response.headers['Content-Type'] == 'application/json': results = await response.json() + else: + logger.error(f"Unexpected response format: {response.headers['Content-Type']}") + return [] - for res in results["matches"]: - metadata = res["metadata"] - if self._text_key in metadata: - text = metadata.pop(self._text_key) - score = res["score"] - docs.append((Document(page_content=text, metadata=metadata), score)) - else: - logger.warning( - f"Found document with no `{self._text_key}` key. Skipping." - ) - return docs + for res in results["matches"]: + metadata = res["metadata"] + if self._text_key in metadata: + text = metadata.pop(self._text_key) + score = res["score"] + docs.append((Document(page_content=text, metadata=metadata), score)) + else: + logger.warning( + f"Found document with no `{self._text_key}` key. Skipping." + ) + logger.debug(f"Found {len(docs)} documents similar to `{query}`") + return docs \ No newline at end of file From 1d8e977dcc0876722045f9b53d8f219bb0844ca2 Mon Sep 17 00:00:00 2001 From: Sebastian Rojo Date: Sun, 17 Dec 2023 22:44:55 -0500 Subject: [PATCH 07/14] Add environment files and Docker configuration --- apps/voice-rag/.env.template | 8 + apps/voice-rag/Dockerfile | 61 +++++ apps/voice-rag/README.md | 37 +++ apps/voice-rag/environment.docker.yml | 21 ++ apps/voice-rag/main.py | 85 +++++++ apps/voice-rag/manual_pinecone_ingestor.ipynb | 223 ++++++++++++++++++ vocode/streaming/models/vector_db.py | 4 +- 7 files changed, 437 insertions(+), 2 deletions(-) create mode 100644 apps/voice-rag/.env.template create mode 100644 apps/voice-rag/Dockerfile create mode 100644 apps/voice-rag/README.md create mode 100644 apps/voice-rag/environment.docker.yml create mode 100644 apps/voice-rag/main.py create mode 100644 apps/voice-rag/manual_pinecone_ingestor.ipynb diff --git a/apps/voice-rag/.env.template b/apps/voice-rag/.env.template new file mode 100644 index 000000000..e49e23b06 --- /dev/null +++ b/apps/voice-rag/.env.template @@ -0,0 +1,8 @@ +OPENAI_API_KEY= +DEEPGRAM_API_KEY= +AZURE_SPEECH_KEY= +AZURE_SPEECH_REGION= + +PINECONE_API_KEY= +PINECONE_ENVIRONMENT= +PINECONE_INDEX_NAME= \ No newline at end of file diff --git a/apps/voice-rag/Dockerfile b/apps/voice-rag/Dockerfile new file mode 100644 index 000000000..1356a608d --- /dev/null +++ b/apps/voice-rag/Dockerfile @@ -0,0 +1,61 @@ +# Use the official micromamba image as a base +FROM docker.io/mambaorg/micromamba:1.5-jammy + +# Create a new user '$MAMBA_USER' and set the working directory +COPY --chown=$MAMBA_USER:$MAMBA_USER environment.docker.yml /tmp/environment.yml + +# Install the specified packages using micromamba +RUN micromamba install -y -n base -f /tmp/environment.yml && \ + micromamba clean --all --yes + +USER root +WORKDIR /usr/local/src + +ARG VOCODE_USER=vocode +ARG VOCODE_UID=8476 +ARG VOCODE_GID=8476 + +RUN groupadd --gid $VOCODE_GID $VOCODE_USER && \ + useradd --uid $VOCODE_UID --gid $VOCODE_GID --shell /bin/bash --create-home $VOCODE_USER + +# COPY --chown=$VOCODE_USER:$VOCODE_USER ../../../ /vocode-python +# WORKDIR /usr/local/src/vocode +# RUN poetry install -E all + +# Copy the rest of your application files into the Docker image +COPY --chown=$VOCODE_USER:$VOCODE_USER . /vocode +WORKDIR /vocode + +#USER vocode +USER root + +ENV OPENAI_API_KEY="" +ENV DEEPGRAM_API_KEY="" +ENV AZURE_SPEECH_KEY="" +ENV AZURE_SPEECH_REGION="" +ENV PINECONE_API_KEY="" +ENV PINECONE_ENVIRONMENT="" +ENV PINECONE_INDEX_NAME="" +ENV DOCKER_ENV="docker" + +# # Expose the port your FastAPI app will run on +EXPOSE 19002 + +# Set build arguments +ARG BUILD_DATE +ARG VCS_REF +ARG VERSION + +# Set labels +LABEL org.label-schema.build-date=$BUILD_DATE \ + org.label-schema.name="vocode" \ + org.label-schema.description="Vocode Docker Image" \ + org.label-schema.url="https://vocode.dev/" \ + org.label-schema.vcs-url="https://github.com/vocodedev" \ + org.label-schema.maintainer="vocode@arpagon.co" \ + org.label-schema.vcs-ref=$VCS_REF \ + org.label-schema.vendor="Vocode" \ + org.label-schema.version=$VERSION + +# Start the FastAPI app using Uvicorn +CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "19002"] \ No newline at end of file diff --git a/apps/voice-rag/README.md b/apps/voice-rag/README.md new file mode 100644 index 000000000..3d207e609 --- /dev/null +++ b/apps/voice-rag/README.md @@ -0,0 +1,37 @@ +# client_backend + +## Docker + +1. Set up the configuration for your agent in `main.py`. +2. Set up an .env file using the template + +``` +cp .env.template .env +``` + +Fill in your API keys into .env + +3. Build the Docker image + +```bash +docker build --build-arg BUILD_DATE=$(date -u +'%Y-%m-%dT%H:%M:%SZ') \ + --build-arg VCS_REF=$(git rev-parse --short HEAD) \ + --build-arg VERSION=0.1.0 \ + -t vocode/vocode-voice-rag:0.1.0 . +``` + +4. Run the image and forward the port. + +```bash +docker run --env-file=.env -p 3000:3000 -t vocode/vocode-voice-rag +``` + +Now you have a client backend hosted at localhost:3000 to pass into the Vocode React SDK. You'll likely need to tunnel port 3000 to ngrok / host your server in order to use it in the React SDK. + +## Non-docker setup + +`main.py` just sets up a FastAPI server, so you can just run it with uvicorn: + +``` +uvicorn main:app +``` diff --git a/apps/voice-rag/environment.docker.yml b/apps/voice-rag/environment.docker.yml new file mode 100644 index 000000000..1a0995602 --- /dev/null +++ b/apps/voice-rag/environment.docker.yml @@ -0,0 +1,21 @@ +name: vocode-rag +channels: + - conda-forge + - pytorch +dependencies: + - python=3.10 + - openssl=1.1.1w + - portaudio + - ffmpeg + - git + - pip + - pip: + # Installing vocode from the git repository + - git+https://github.com/ArtisanLabs/vocode-python/@461-VectorDB-OpenSource-Documentation#egg=vocode + - azure-cognitiveservices-speech==1.31.0 + - python-dotenv + - ipython + - deepgram-sdk + - uvicorn + - pinecone-client + - poetry \ No newline at end of file diff --git a/apps/voice-rag/main.py b/apps/voice-rag/main.py new file mode 100644 index 000000000..004c8a677 --- /dev/null +++ b/apps/voice-rag/main.py @@ -0,0 +1,85 @@ +import os +import logging +from fastapi import FastAPI + +from vocode.streaming.models.agent import ChatGPTAgentConfig +from vocode.streaming.models.synthesizer import AzureSynthesizerConfig +from vocode.streaming.synthesizer.azure_synthesizer import AzureSynthesizer + +from vocode.streaming.agent.chat_gpt_agent import ChatGPTAgent +from vocode.streaming.client_backend.conversation import ConversationRouter +from vocode.streaming.models.message import BaseMessage +from vocode.streaming.vector_db.factory import VectorDBFactory +from vocode.streaming.vector_db.pinecone import PineconeConfig +from vocode.streaming.transcriber.deepgram_transcriber import DeepgramTranscriber + + +from vocode.streaming.models.transcriber import ( + DeepgramTranscriberConfig, + TimeEndpointingConfig +) + +from dotenv import load_dotenv + +PINECONE_INDEX_NAME = os.getenv('PINECONE_INDEX_NAME') + +vector_db_config = PineconeConfig( + index=PINECONE_INDEX_NAME +) + +INITIAL_MESSAGE="Hello, Let's discuss the architecture of the next application!" +PROMPT_PREAMBLE=''' +I want you to act as an IT Architect. +I will provide some details about the functionality of an application or other +digital product, and it will be your job to come up with ways to integrate it +into the IT landscape. This could involve analyzing business requirements, +performing a gap analysis, and mapping the functionality of the new system to +the existing IT landscape. The next steps are to create a solution design. + +You are an expert in these technologies: +- Langchain +- Supabase +- Next.js +- Fastapi +- Vocode. +''' + +load_dotenv() + +app = FastAPI(docs_url=None) + +logging.basicConfig() +logger = logging.getLogger(__name__) +logger.setLevel(logging.DEBUG) + +TIME_ENDPOINTING_CONFIG = TimeEndpointingConfig() +TIME_ENDPOINTING_CONFIG.time_cutoff_seconds = 2 + +AZURE_SYNTHESIZER_THUNK = lambda output_audio_config: AzureSynthesizer( + AzureSynthesizerConfig.from_output_audio_config(output_audio_config, ), + logger=logger +) + +DEEPGRAM_TRANSCRIBER_THUNK = lambda input_audio_config: DeepgramTranscriber( + DeepgramTranscriberConfig.from_input_audio_config( + input_audio_config=input_audio_config, + endpointing_config=TIME_ENDPOINTING_CONFIG, + min_interrupt_confidence=0.9, + ), + logger=logger +) + +conversation_router = ConversationRouter( + agent_thunk=lambda: ChatGPTAgent( + ChatGPTAgentConfig( + initial_message=BaseMessage(text=INITIAL_MESSAGE), + prompt_preamble=PROMPT_PREAMBLE, + vector_db_config=vector_db_config, + ) + ), + synthesizer_thunk=AZURE_SYNTHESIZER_THUNK, + transcriber_thunk=DEEPGRAM_TRANSCRIBER_THUNK, + logger=logger, +) + +app.include_router(conversation_router.get_router()) diff --git a/apps/voice-rag/manual_pinecone_ingestor.ipynb b/apps/voice-rag/manual_pinecone_ingestor.ipynb new file mode 100644 index 000000000..a4a0543b3 --- /dev/null +++ b/apps/voice-rag/manual_pinecone_ingestor.ipynb @@ -0,0 +1,223 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Colab_Link: https://colab.research.google.com/github/ArtisanLabs/vocode-python/blob/main/apps/rag/manual_pinecone_ingestor.ipynb\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Install the Python Requests library via pip\n", + "# python \n", + "# langchain \n", + "# spacy \n", + "# unstructured = {extras = [\"local-inference\"]}\n", + "# layoutparser = {extras = [\"layoutmodels\", \"tesseract\"]}\n", + "# pinecone-client\n", + "# openai \n", + "# torch \n", + "# tiktoken\n", + "# git\n", + "\n", + "%pip install langchain spacy unstructured[local-inference] layoutparser[layoutmodels,tesseract] pinecone-client openai torch tiktoken git\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "'''\n", + "Importing necessary modules and functions:\n", + "- os module to interact with the OS\n", + "- Pinecone module for vector database operations\n", + "- OpenAIEmbeddings from langchain.embeddings.openai for generating embeddings\n", + "- SpacyTextSplitter from langchain.text_splitter for splitting text into chunks\n", + "- Pinecone from langchain.vectorstores for storing and retrieving vectors\n", + "- DirectoryLoader and UnstructuredFileLoader from langchain.document_loaders for loading documents from directories and unstructured files\n", + "'''\n", + "import os\n", + "import pinecone\n", + "from langchain.embeddings.openai import OpenAIEmbeddings\n", + "from langchain.text_splitter import SpacyTextSplitter\n", + "from langchain.vectorstores import Pinecone\n", + "from langchain.document_loaders import DirectoryLoader, UnstructuredFileLoader\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "'''\n", + "We are using Google Colab's secret manager to securely input our API keys.\n", + "This ensures that the keys are not visible in the notebook and are not stored in the notebook's history.\n", + "We also retrieve the Pinecone index name from the secret manager.\n", + "Then, we add these keys to the environment variables.\n", + "'''\n", + "from google.colab import userdata\n", + "import os\n", + "\n", + "PINECONE_API_KEY = userdata.get('PINECONE_API_KEY')\n", + "os.environ['PINECONE_API_KEY'] = PINECONE_API_KEY\n", + "\n", + "PINECONE_ENVIRONMENT = userdata.get('PINECONE_ENVIRONMENT')\n", + "os.environ['PINECONE_ENVIRONMENT'] = PINECONE_ENVIRONMENT\n", + "\n", + "PINECONE_INDEX = userdata.get('PINECONE_INDEX')\n", + "os.environ['PINECONE_INDEX'] = PINECONE_INDEX\n", + "\n", + "OPENAI_API_KEY = userdata.get('OPENAI_API_KEY')\n", + "os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "'''\n", + "We are defining a list of libraries from which we want to fetch the code.\n", + "Each library is represented as a dictionary with three keys:\n", + " - 'name': the name of the library\n", + " - 'code': the URL of the library's GitHub repository\n", + " - 'documentation_path': the path to the documentation within the repository\n", + "'''\n", + "libraries = [\n", + " {\n", + " 'name': 'langchain',\n", + " 'code': 'https://github.com/langchain-ai/langchain',\n", + " 'documentation_path': 'docs'\n", + " },\n", + " {\n", + " 'name': 'supabase',\n", + " 'code': 'https://github.com/supabase/supabase',\n", + " 'documentation_path': 'apps/docs'\n", + " },\n", + " {\n", + " 'name': 'next.js',\n", + " 'code': 'https://github.com/vercel/next.js',\n", + " 'documentation_path': 'docs'\n", + " },\n", + " {\n", + " 'name': 'fastapi',\n", + " 'code': 'https://github.com/tiangolo/fastapi',\n", + " 'documentation_path': 'docs/en/docs'\n", + " },\n", + " {\n", + " 'name': 'vocode-python',\n", + " 'code': 'https://github.com/vocodedev/vocode-python',\n", + " 'documentation_path': 'docs'\n", + " }\n", + "]\n", + "\n", + "'''\n", + "The function get_code_from_github is defined to fetch the code from the GitHub repositories.\n", + "It iterates over the libraries list and for each library, it clones the repository to a temporary directory.\n", + "Then, it moves the documentation to a directory named 'libraries_documentation/{name}'.\n", + "'''\n", + "import os\n", + "import shutil\n", + "import tempfile\n", + "import subprocess\n", + "\n", + "def get_code_from_github():\n", + " # Create a temporary directory\n", + " with tempfile.TemporaryDirectory() as temp_dir:\n", + " for library in libraries:\n", + " # Define the clone directory path\n", + " clone_dir = os.path.join(temp_dir, library['name'])\n", + "\n", + " # Clone the repository using the git command\n", + " subprocess.run(['git', 'clone', library['code'], clone_dir], check=True)\n", + " \n", + " # Define the source and destination for moving documentation\n", + " doc_source = os.path.join(clone_dir, library['documentation_path'])\n", + " doc_dest = os.path.join('libraries_documentation', library['name'])\n", + "\n", + " # Create the destination directory if it doesn't exist\n", + " os.makedirs(doc_dest, exist_ok=True)\n", + "\n", + " # Move the documentation to the destination directory\n", + " shutil.move(doc_source, doc_dest)\n", + "\n", + "get_code_from_github()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "'''\n", + "The following code block is responsible for loading, splitting, and indexing the documents.\n", + "'''\n", + "\n", + "'''\n", + "Create a DirectoryLoader object to load all .md and .mdx files from the 'libraries_documentation' directory.\n", + "The glob pattern \"**/*.md*\" is used to match any markdown file in the directory or its subdirectories.\n", + "The UnstructuredFileLoader class is used to load the files.\n", + "'''\n", + "loader = DirectoryLoader('./libraries_documentation', glob=\"**/*.md*\", show_progress=True, loader_cls=UnstructuredFileLoader)\n", + "\n", + "'''\n", + "Load the documents from the directory.\n", + "'''\n", + "print(\"Loading documents...\")\n", + "documents = loader.load()\n", + "\n", + "'''\n", + "Create a SpacyTextSplitter object to split the documents into chunks of 1000 characters.\n", + "'''\n", + "text_splitter = SpacyTextSplitter(chunk_size=1000)\n", + "\n", + "'''\n", + "Split the documents into chunks.\n", + "'''\n", + "print(\"Splitting documents...\")\n", + "docs = text_splitter.split_documents(documents)\n", + "\n", + "'''\n", + "Create an OpenAIEmbeddings object to generate embeddings for the documents.\n", + "'''\n", + "embeddings = OpenAIEmbeddings()\n", + "\n", + "'''\n", + "Initialize the Pinecone client with the API key and environment variables.\n", + "'''\n", + "pinecone.init(\n", + " api_key=PINECONE_API_KEY,\n", + " environment=PINECONE_ENVIRONMENT,\n", + ")\n", + "\n", + "'''\n", + "Set the name of the Pinecone index.\n", + "'''\n", + "index_name = PINECONE_INDEX\n", + "\n", + "'''\n", + "Create a Pinecone index from the documents and their embeddings.\n", + "'''\n", + "print(\"Creating index...\")\n", + "docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name)" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/vocode/streaming/models/vector_db.py b/vocode/streaming/models/vector_db.py index 719c9ef02..b87c5bade 100644 --- a/vocode/streaming/models/vector_db.py +++ b/vocode/streaming/models/vector_db.py @@ -16,6 +16,6 @@ class VectorDBConfig(TypedModel, type=VectorDBType.BASE.value): class PineconeConfig(VectorDBConfig, type=VectorDBType.PINECONE.value): index: str - api_key: Optional[str] - api_environment: Optional[str] + api_key: Optional[str] = None + api_environment: Optional[str] = None top_k: int = 3 From 523c1af63de2436bc82b160af93ff19ae5664342 Mon Sep 17 00:00:00 2001 From: Sebastian Rojo Date: Sun, 17 Dec 2023 22:47:58 -0500 Subject: [PATCH 08/14] Rename rag to voice-rag app --- apps/rag/manual_pinecone_ingestor.ipynb | 223 ------------------------ 1 file changed, 223 deletions(-) delete mode 100644 apps/rag/manual_pinecone_ingestor.ipynb diff --git a/apps/rag/manual_pinecone_ingestor.ipynb b/apps/rag/manual_pinecone_ingestor.ipynb deleted file mode 100644 index a4a0543b3..000000000 --- a/apps/rag/manual_pinecone_ingestor.ipynb +++ /dev/null @@ -1,223 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Colab_Link: https://colab.research.google.com/github/ArtisanLabs/vocode-python/blob/main/apps/rag/manual_pinecone_ingestor.ipynb\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Install the Python Requests library via pip\n", - "# python \n", - "# langchain \n", - "# spacy \n", - "# unstructured = {extras = [\"local-inference\"]}\n", - "# layoutparser = {extras = [\"layoutmodels\", \"tesseract\"]}\n", - "# pinecone-client\n", - "# openai \n", - "# torch \n", - "# tiktoken\n", - "# git\n", - "\n", - "%pip install langchain spacy unstructured[local-inference] layoutparser[layoutmodels,tesseract] pinecone-client openai torch tiktoken git\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "'''\n", - "Importing necessary modules and functions:\n", - "- os module to interact with the OS\n", - "- Pinecone module for vector database operations\n", - "- OpenAIEmbeddings from langchain.embeddings.openai for generating embeddings\n", - "- SpacyTextSplitter from langchain.text_splitter for splitting text into chunks\n", - "- Pinecone from langchain.vectorstores for storing and retrieving vectors\n", - "- DirectoryLoader and UnstructuredFileLoader from langchain.document_loaders for loading documents from directories and unstructured files\n", - "'''\n", - "import os\n", - "import pinecone\n", - "from langchain.embeddings.openai import OpenAIEmbeddings\n", - "from langchain.text_splitter import SpacyTextSplitter\n", - "from langchain.vectorstores import Pinecone\n", - "from langchain.document_loaders import DirectoryLoader, UnstructuredFileLoader\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "'''\n", - "We are using Google Colab's secret manager to securely input our API keys.\n", - "This ensures that the keys are not visible in the notebook and are not stored in the notebook's history.\n", - "We also retrieve the Pinecone index name from the secret manager.\n", - "Then, we add these keys to the environment variables.\n", - "'''\n", - "from google.colab import userdata\n", - "import os\n", - "\n", - "PINECONE_API_KEY = userdata.get('PINECONE_API_KEY')\n", - "os.environ['PINECONE_API_KEY'] = PINECONE_API_KEY\n", - "\n", - "PINECONE_ENVIRONMENT = userdata.get('PINECONE_ENVIRONMENT')\n", - "os.environ['PINECONE_ENVIRONMENT'] = PINECONE_ENVIRONMENT\n", - "\n", - "PINECONE_INDEX = userdata.get('PINECONE_INDEX')\n", - "os.environ['PINECONE_INDEX'] = PINECONE_INDEX\n", - "\n", - "OPENAI_API_KEY = userdata.get('OPENAI_API_KEY')\n", - "os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "'''\n", - "We are defining a list of libraries from which we want to fetch the code.\n", - "Each library is represented as a dictionary with three keys:\n", - " - 'name': the name of the library\n", - " - 'code': the URL of the library's GitHub repository\n", - " - 'documentation_path': the path to the documentation within the repository\n", - "'''\n", - "libraries = [\n", - " {\n", - " 'name': 'langchain',\n", - " 'code': 'https://github.com/langchain-ai/langchain',\n", - " 'documentation_path': 'docs'\n", - " },\n", - " {\n", - " 'name': 'supabase',\n", - " 'code': 'https://github.com/supabase/supabase',\n", - " 'documentation_path': 'apps/docs'\n", - " },\n", - " {\n", - " 'name': 'next.js',\n", - " 'code': 'https://github.com/vercel/next.js',\n", - " 'documentation_path': 'docs'\n", - " },\n", - " {\n", - " 'name': 'fastapi',\n", - " 'code': 'https://github.com/tiangolo/fastapi',\n", - " 'documentation_path': 'docs/en/docs'\n", - " },\n", - " {\n", - " 'name': 'vocode-python',\n", - " 'code': 'https://github.com/vocodedev/vocode-python',\n", - " 'documentation_path': 'docs'\n", - " }\n", - "]\n", - "\n", - "'''\n", - "The function get_code_from_github is defined to fetch the code from the GitHub repositories.\n", - "It iterates over the libraries list and for each library, it clones the repository to a temporary directory.\n", - "Then, it moves the documentation to a directory named 'libraries_documentation/{name}'.\n", - "'''\n", - "import os\n", - "import shutil\n", - "import tempfile\n", - "import subprocess\n", - "\n", - "def get_code_from_github():\n", - " # Create a temporary directory\n", - " with tempfile.TemporaryDirectory() as temp_dir:\n", - " for library in libraries:\n", - " # Define the clone directory path\n", - " clone_dir = os.path.join(temp_dir, library['name'])\n", - "\n", - " # Clone the repository using the git command\n", - " subprocess.run(['git', 'clone', library['code'], clone_dir], check=True)\n", - " \n", - " # Define the source and destination for moving documentation\n", - " doc_source = os.path.join(clone_dir, library['documentation_path'])\n", - " doc_dest = os.path.join('libraries_documentation', library['name'])\n", - "\n", - " # Create the destination directory if it doesn't exist\n", - " os.makedirs(doc_dest, exist_ok=True)\n", - "\n", - " # Move the documentation to the destination directory\n", - " shutil.move(doc_source, doc_dest)\n", - "\n", - "get_code_from_github()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "'''\n", - "The following code block is responsible for loading, splitting, and indexing the documents.\n", - "'''\n", - "\n", - "'''\n", - "Create a DirectoryLoader object to load all .md and .mdx files from the 'libraries_documentation' directory.\n", - "The glob pattern \"**/*.md*\" is used to match any markdown file in the directory or its subdirectories.\n", - "The UnstructuredFileLoader class is used to load the files.\n", - "'''\n", - "loader = DirectoryLoader('./libraries_documentation', glob=\"**/*.md*\", show_progress=True, loader_cls=UnstructuredFileLoader)\n", - "\n", - "'''\n", - "Load the documents from the directory.\n", - "'''\n", - "print(\"Loading documents...\")\n", - "documents = loader.load()\n", - "\n", - "'''\n", - "Create a SpacyTextSplitter object to split the documents into chunks of 1000 characters.\n", - "'''\n", - "text_splitter = SpacyTextSplitter(chunk_size=1000)\n", - "\n", - "'''\n", - "Split the documents into chunks.\n", - "'''\n", - "print(\"Splitting documents...\")\n", - "docs = text_splitter.split_documents(documents)\n", - "\n", - "'''\n", - "Create an OpenAIEmbeddings object to generate embeddings for the documents.\n", - "'''\n", - "embeddings = OpenAIEmbeddings()\n", - "\n", - "'''\n", - "Initialize the Pinecone client with the API key and environment variables.\n", - "'''\n", - "pinecone.init(\n", - " api_key=PINECONE_API_KEY,\n", - " environment=PINECONE_ENVIRONMENT,\n", - ")\n", - "\n", - "'''\n", - "Set the name of the Pinecone index.\n", - "'''\n", - "index_name = PINECONE_INDEX\n", - "\n", - "'''\n", - "Create a Pinecone index from the documents and their embeddings.\n", - "'''\n", - "print(\"Creating index...\")\n", - "docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name)" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From dd54368b64d0dc93ad4cb7f1839c20fcf8038c77 Mon Sep 17 00:00:00 2001 From: Sebastian Rojo Date: Mon, 18 Dec 2023 00:35:31 -0500 Subject: [PATCH 09/14] revert pinecone.py Refactor code and update dependencies --- apps/voice-rag/main.py | 18 +++--- vocode/streaming/vector_db/pinecone.py | 87 ++++++++++++-------------- 2 files changed, 49 insertions(+), 56 deletions(-) diff --git a/apps/voice-rag/main.py b/apps/voice-rag/main.py index 004c8a677..e93dbeae6 100644 --- a/apps/voice-rag/main.py +++ b/apps/voice-rag/main.py @@ -21,10 +21,16 @@ from dotenv import load_dotenv -PINECONE_INDEX_NAME = os.getenv('PINECONE_INDEX_NAME') +load_dotenv() + +app = FastAPI(docs_url=None) + +logging.basicConfig() +logger = logging.getLogger(__name__) +logger.setLevel(logging.DEBUG) vector_db_config = PineconeConfig( - index=PINECONE_INDEX_NAME + index=os.getenv('PINECONE_INDEX_NAME') ) INITIAL_MESSAGE="Hello, Let's discuss the architecture of the next application!" @@ -44,14 +50,6 @@ - Vocode. ''' -load_dotenv() - -app = FastAPI(docs_url=None) - -logging.basicConfig() -logger = logging.getLogger(__name__) -logger.setLevel(logging.DEBUG) - TIME_ENDPOINTING_CONFIG = TimeEndpointingConfig() TIME_ENDPOINTING_CONFIG.time_cutoff_seconds = 2 diff --git a/vocode/streaming/vector_db/pinecone.py b/vocode/streaming/vector_db/pinecone.py index 8b2993255..5def21e0f 100644 --- a/vocode/streaming/vector_db/pinecone.py +++ b/vocode/streaming/vector_db/pinecone.py @@ -68,53 +68,48 @@ async def add_texts( return ids -async def similarity_search_with_score( - self, - query: str, - filter: Optional[dict] = None, - namespace: Optional[str] = None, -) -> List[Tuple[Document, float]]: - """Return pinecone documents most similar to query, along with scores. + async def similarity_search_with_score( + self, + query: str, + filter: Optional[dict] = None, + namespace: Optional[str] = None, + ) -> List[Tuple[Document, float]]: + """Return pinecone documents most similar to query, along with scores. - Args: - query: Text to look up documents similar to. - filter: Dictionary of argument(s) to filter on metadata - namespace: Namespace to search in. Default will search in '' namespace. + Args: + query: Text to look up documents similar to. + filter: Dictionary of argument(s) to filter on metadata + namespace: Namespace to search in. Default will search in '' namespace. - Returns: - List of Documents most similar to the query and score for each - """ - # Adapted from: langchain/vectorstores/pinecone.py. Made langchain implementation async. - if namespace is None: - namespace = "" - query_obj = await self.create_openai_embedding(query) - docs = [] - async with self.aiohttp_session.post( - f"{self.pinecone_url}/query", - headers={"Api-Key": self.pinecone_api_key}, - json={ - "top_k": self.config.top_k, - "namespace": namespace, - "filter": filter, - "vector": query_obj, - "includeMetadata": True, - }, - ) as response: - if response.headers['Content-Type'] == 'application/json': + Returns: + List of Documents most similar to the query and score for each + """ + # Adapted from: langchain/vectorstores/pinecone.py. Made langchain implementation async. + if namespace is None: + namespace = "" + query_obj = await self.create_openai_embedding(query) + docs = [] + async with self.aiohttp_session.post( + f"{self.pinecone_url}/query", + headers={"Api-Key": self.pinecone_api_key}, + json={ + "top_k": self.config.top_k, + "namespace": namespace, + "filter": filter, + "vector": query_obj, + "includeMetadata": True, + }, + ) as response: results = await response.json() - else: - logger.error(f"Unexpected response format: {response.headers['Content-Type']}") - return [] - for res in results["matches"]: - metadata = res["metadata"] - if self._text_key in metadata: - text = metadata.pop(self._text_key) - score = res["score"] - docs.append((Document(page_content=text, metadata=metadata), score)) - else: - logger.warning( - f"Found document with no `{self._text_key}` key. Skipping." - ) - logger.debug(f"Found {len(docs)} documents similar to `{query}`") - return docs \ No newline at end of file + for res in results["matches"]: + metadata = res["metadata"] + if self._text_key in metadata: + text = metadata.pop(self._text_key) + score = res["score"] + docs.append((Document(page_content=text, metadata=metadata), score)) + else: + logger.warning( + f"Found document with no `{self._text_key}` key. Skipping." + ) + return docs \ No newline at end of file From 0217291cd7ea0dbe3f1bf5e37af44899c7cbed75 Mon Sep 17 00:00:00 2001 From: Sebastian Rojo Date: Mon, 18 Dec 2023 00:46:02 -0500 Subject: [PATCH 10/14] Working Version --- apps/voice-rag/main.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/apps/voice-rag/main.py b/apps/voice-rag/main.py index e93dbeae6..d952d688f 100644 --- a/apps/voice-rag/main.py +++ b/apps/voice-rag/main.py @@ -33,7 +33,7 @@ index=os.getenv('PINECONE_INDEX_NAME') ) -INITIAL_MESSAGE="Hello, Let's discuss the architecture of the next application!" +INITIAL_MESSAGE="Hello!" PROMPT_PREAMBLE=''' I want you to act as an IT Architect. I will provide some details about the functionality of an application or other @@ -73,7 +73,9 @@ initial_message=BaseMessage(text=INITIAL_MESSAGE), prompt_preamble=PROMPT_PREAMBLE, vector_db_config=vector_db_config, - ) + logger=logger, + ), + logger=logger ), synthesizer_thunk=AZURE_SYNTHESIZER_THUNK, transcriber_thunk=DEEPGRAM_TRANSCRIBER_THUNK, From 50daea883cb717616991946fc2c5b02f23fec489 Mon Sep 17 00:00:00 2001 From: Sebastian Rojo Date: Mon, 18 Dec 2023 00:59:43 -0500 Subject: [PATCH 11/14] Update pinecone ingestor script --- docs/open-source/rag.mdx | 46 +--------------------------------------- 1 file changed, 1 insertion(+), 45 deletions(-) diff --git a/docs/open-source/rag.mdx b/docs/open-source/rag.mdx index 979298054..0c0b4f5b7 100644 --- a/docs/open-source/rag.mdx +++ b/docs/open-source/rag.mdx @@ -53,49 +53,5 @@ If you have a folder of PDFs, docx files, text files, etc. that you want to add the below script which uses [Unstructured](https://github.com/Unstructured-IOVector/unstructured) to parse many kinds of files types, extract the text, and add it to pinecone. -The script was tested with these package versions: +`apps/voice-rag/manual_pinecone_ingestor.ipynb` -``` -python = "^3.10" -langchain = "^0.0.237" -spacy = "^3.6.0" -unstructured = {extras = ["local-inference"], version = "^0.8.1"} -layoutparser = {extras = ["layoutmodels", "tesseract"], version = "^0.3.4"} -pinecone-client = "^2.2.2" -openai = "^0.27.8" -torch = ">=2.0.0, !=2.0.1" -tiktoken = "^0.4.0" -``` - -```python -import os -import pinecone -from langchain.embeddings.openai import OpenAIEmbeddings -from langchain.text_splitter import SpacyTextSplitter -from langchain.vectorstores import Pinecone -from langchain.document_loaders import DirectoryLoader, UnstructuredFileLoader - -PINECONE_API_KEY = os.environ["PINECONE_API_KEY"] -PINECONE_ENVIRONMENT = os.environ["PINECONE_ENVIRONMENT"] -OPENAI_API_KEY = os.environ["OPENAI_API_KEY"] - - -loader = DirectoryLoader('./docs', glob="**/*.*", show_progress=True, loader_cls=UnstructuredFileLoader) -print("Loading documents...") -documents = loader.load() -text_splitter = SpacyTextSplitter(chunk_size=1000) -print("Splitting documents...") -docs = text_splitter.split_documents(documents) - -embeddings = OpenAIEmbeddings() - -pinecone.init( - api_key=PINECONE_API_KEY, - environment=PINECONE_ENVIRONMENT, -) - -index_name = "your_index_name" - -print("Creating index...") -docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name) -``` From 88277f14ad1e3c5dd5655f24e82206ce18383e28 Mon Sep 17 00:00:00 2001 From: Sebastian Rojo Date: Thu, 21 Dec 2023 15:22:28 -0500 Subject: [PATCH 12/14] Add voice_rag application files --- apps/{voice-rag => voice_rag}/.env.template | 0 apps/{voice-rag => voice_rag}/Dockerfile | 0 apps/{voice-rag => voice_rag}/README.md | 0 apps/{voice-rag => voice_rag}/environment.docker.yml | 0 apps/{voice-rag => voice_rag}/main.py | 0 apps/{voice-rag => voice_rag}/manual_pinecone_ingestor.ipynb | 0 6 files changed, 0 insertions(+), 0 deletions(-) rename apps/{voice-rag => voice_rag}/.env.template (100%) rename apps/{voice-rag => voice_rag}/Dockerfile (100%) rename apps/{voice-rag => voice_rag}/README.md (100%) rename apps/{voice-rag => voice_rag}/environment.docker.yml (100%) rename apps/{voice-rag => voice_rag}/main.py (100%) rename apps/{voice-rag => voice_rag}/manual_pinecone_ingestor.ipynb (100%) diff --git a/apps/voice-rag/.env.template b/apps/voice_rag/.env.template similarity index 100% rename from apps/voice-rag/.env.template rename to apps/voice_rag/.env.template diff --git a/apps/voice-rag/Dockerfile b/apps/voice_rag/Dockerfile similarity index 100% rename from apps/voice-rag/Dockerfile rename to apps/voice_rag/Dockerfile diff --git a/apps/voice-rag/README.md b/apps/voice_rag/README.md similarity index 100% rename from apps/voice-rag/README.md rename to apps/voice_rag/README.md diff --git a/apps/voice-rag/environment.docker.yml b/apps/voice_rag/environment.docker.yml similarity index 100% rename from apps/voice-rag/environment.docker.yml rename to apps/voice_rag/environment.docker.yml diff --git a/apps/voice-rag/main.py b/apps/voice_rag/main.py similarity index 100% rename from apps/voice-rag/main.py rename to apps/voice_rag/main.py diff --git a/apps/voice-rag/manual_pinecone_ingestor.ipynb b/apps/voice_rag/manual_pinecone_ingestor.ipynb similarity index 100% rename from apps/voice-rag/manual_pinecone_ingestor.ipynb rename to apps/voice_rag/manual_pinecone_ingestor.ipynb From a15ad447603f85c854019a9dfa7d9e3564d2cecb Mon Sep 17 00:00:00 2001 From: Sebastian Rojo Date: Tue, 26 Dec 2023 18:49:32 -0500 Subject: [PATCH 13/14] Update Dockerfile and README.md in voice_rag app --- apps/voice_rag/Dockerfile | 7 ------- apps/voice_rag/README.md | 2 +- vocode/streaming/models/vector_db.py | 4 ++-- 3 files changed, 3 insertions(+), 10 deletions(-) diff --git a/apps/voice_rag/Dockerfile b/apps/voice_rag/Dockerfile index 1356a608d..7a7ad834a 100644 --- a/apps/voice_rag/Dockerfile +++ b/apps/voice_rag/Dockerfile @@ -29,13 +29,6 @@ WORKDIR /vocode #USER vocode USER root -ENV OPENAI_API_KEY="" -ENV DEEPGRAM_API_KEY="" -ENV AZURE_SPEECH_KEY="" -ENV AZURE_SPEECH_REGION="" -ENV PINECONE_API_KEY="" -ENV PINECONE_ENVIRONMENT="" -ENV PINECONE_INDEX_NAME="" ENV DOCKER_ENV="docker" # # Expose the port your FastAPI app will run on diff --git a/apps/voice_rag/README.md b/apps/voice_rag/README.md index 3d207e609..75208faca 100644 --- a/apps/voice_rag/README.md +++ b/apps/voice_rag/README.md @@ -1,4 +1,4 @@ -# client_backend +# voice_rag ## Docker diff --git a/vocode/streaming/models/vector_db.py b/vocode/streaming/models/vector_db.py index b87c5bade..719c9ef02 100644 --- a/vocode/streaming/models/vector_db.py +++ b/vocode/streaming/models/vector_db.py @@ -16,6 +16,6 @@ class VectorDBConfig(TypedModel, type=VectorDBType.BASE.value): class PineconeConfig(VectorDBConfig, type=VectorDBType.PINECONE.value): index: str - api_key: Optional[str] = None - api_environment: Optional[str] = None + api_key: Optional[str] + api_environment: Optional[str] top_k: int = 3 From 895771d54611bb1e80710640973d9addb4adb858 Mon Sep 17 00:00:00 2001 From: Sebastian Rojo Date: Tue, 26 Dec 2023 19:05:33 -0500 Subject: [PATCH 14/14] Fix missing newline at end of file --- vocode/streaming/vector_db/pinecone.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vocode/streaming/vector_db/pinecone.py b/vocode/streaming/vector_db/pinecone.py index 5def21e0f..646590860 100644 --- a/vocode/streaming/vector_db/pinecone.py +++ b/vocode/streaming/vector_db/pinecone.py @@ -112,4 +112,4 @@ async def similarity_search_with_score( logger.warning( f"Found document with no `{self._text_key}` key. Skipping." ) - return docs \ No newline at end of file + return docs

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