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 # Jupyter package
 
+This package provides a custom Jupyter kernel for the ASKEM project that allows extra communication beyond the usual Jupyter message types to allow for the kernel to interact with an LLM (GPT-4) and answer questions and generate code that is runnable in native notebook code cells.
+
+The kernel connects to the Terarium Data Service to allow contextual queries about Terarium assets such as visualizing or modifying datasets.
+
+
+This package contains 4 different products:
+
+* A Python module named `jupyter_llm` (`pyproject.toml`)
+* A Jupyter kernel (`llmkernel`)
+* A Jupyter service (`main.py`)
+* A standalone development interface (`dev_ui`)
 
 ## Install / setup
 
-### Service
+### jupyter_llm Python module
+
+Normal installation:
+```bash
+# Requires poetry
+$ poetry install
+```
+
+Global installation (e.g. Docker):
+```bash
+$ poetry config virtualenvs.create false
+$ poetry install --no-dev
+```
+
+### Jupyter kernel
+
+To install the kernel, simply copy or symlink the `llmkernel` directory in to one of the directories defined in the following document:
+
+https://jupyter-client.readthedocs.io/en/stable/kernels.html#kernel-specs
+
+
+For example:
+```bash
+$ cp -r llmkernel /usr/share/jupyter/kernels/llmkernel
+```
+
+Once the directory exists and the jupyter service is restarted the kernel should be available for selection.
+
+For development, the kernel is automatically installed in the proper location in your development virtual environment when you run `make dev-install` as explained in the Dev setup section.
+
+
+## Dev setup
+
+This package is bundled with a basic development UI for development and testing.
+
+To get started run this command:
+
+```bash
+$ make dev-install
+```
+
+This will copy install all of the prerequisites.
+
+You will need to update the .env file with your OpenAI/GPT API key to use the LLM. To connect to the data service, you will need to update the .env file with the url of a running instance.
+
+Once you have set up the environment and added your keys you can start the dev server by running:
+
+```bash
+$ make dev
+```
+
+This will start the Jupyter service and launch a specialized notebook interface in your browser similar to if you ran `$ jupyter notebook` normally.