jupyter_ai_router

Core message routing layer for Jupyter AI

This extension provides the foundational message routing functionality for Jupyter AI. It automatically detects new chat sessions and routes messages to registered callbacks based on message type (slash commands vs regular messages). Extensions can register callbacks to handle specific chat events without needing to manage chat lifecycle directly.

Usage

Basic MessageRouter Setup

# The router is available in other extensions via settings
router = self.serverapp.web_app.settings.get("jupyter-ai", {}).get("router")

# Register callbacks for different event types
def on_new_chat(room_id: str, ychat: YChat):
    print(f"New chat connected: {room_id}")

def on_slash_command(room_id: str, command: str, message: Message):
    print(f"Slash command '{command}' in {room_id}: {message.body}")

def on_regular_message(room_id: str, message: Message):
    print(f"Regular message in {room_id}: {message.body}")

# Register the callbacks
router.observe_chat_init(on_new_chat)
router.observe_slash_cmd_msg("room-id", "help", on_slash_command)  # Only /help commands
router.observe_chat_msg("room-id", on_regular_message)

Message Flow

1. Router detects new chats - Automatically listens for chat room initialization events
2. Router connects chats - Establishes observers on YChat message streams
3. Router routes messages - Calls appropriate callbacks based on message type (slash vs regular)
4. Extensions respond - Your callbacks receive room_id and message data

Available Methods

• observe_chat_init(callback) - Called when new chat sessions are initialized with (room_id, ychat)
• observe_slash_cmd_msg(room_id, command_pattern, callback) - Called for specific slash commands matching the pattern in a specific room
• observe_chat_msg(room_id, callback) - Called for regular (non-slash) messages in a specific room

Command Pattern Matching

The observe_slash_cmd_msg method supports regex pattern matching:

# Exact match: Only matches "/help"
router.observe_slash_cmd_msg("room-id", "help", callback)

# Regex pattern: Matches "/ai-generate", "/ai-review", etc.
router.observe_slash_cmd_msg("room-id", "ai-.*", callback)

# Regex with groups: Matches "/export-json", "/export-csv", "/export-xml"
router.observe_slash_cmd_msg("room-id", r"export-(json|csv|xml)", callback)

Callback signature: callback(room_id: str, command: str, message: Message)

• room_id: The chat room identifier
• command: The matched command without the leading slash (e.g., "help", "ai-generate")
• message: Message object with the command removed from the body (only arguments remain)

Install

To install the extension, execute:

pip install jupyter_ai_router

Uninstall

To remove the extension, execute:

pip uninstall jupyter_ai_router

Troubleshoot

If you are seeing the frontend extension, but it is not working, check that the server extension is enabled:

jupyter server extension list

If the server extension is installed and enabled, but you are not seeing the frontend extension, check the frontend extension is installed:

jupyter labextension list

Contributing

Development install

Note: You will need NodeJS to build the extension package.

The jlpm command is JupyterLab's pinned version of yarn that is installed with JupyterLab. You may use yarn or npm in lieu of jlpm below.

# Clone the repo to your local environment
# Change directory to the jupyter_ai_router directory
# Install package in development mode
pip install -e ".[test]"
# Link your development version of the extension with JupyterLab
jupyter labextension develop . --overwrite
# Server extension must be manually installed in develop mode
jupyter server extension enable jupyter_ai_router
# Rebuild extension Typescript source after making changes
jlpm build

You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.

# Watch the source directory in one terminal, automatically rebuilding when needed
jlpm watch
# Run JupyterLab in another terminal
jupyter lab

With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).

By default, the jlpm build command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:

jupyter lab build --minimize=False

Development uninstall

# Server extension must be manually disabled in develop mode
jupyter server extension disable jupyter_ai_router
pip uninstall jupyter_ai_router

In development mode, you will also need to remove the symlink created by jupyter labextension develop command. To find its location, you can run jupyter labextension list to figure out where the labextensions folder is located. Then you can remove the symlink named @jupyter-ai/router within that folder.

Testing the extension

Server tests

This extension is using Pytest for Python code testing.

Install test dependencies (needed only once):

pip install -e ".[test]"
# Each time you install the Python package, you need to restore the front-end extension link
jupyter labextension develop . --overwrite

To execute them, run:

pytest -vv -r ap --cov jupyter_ai_router

Frontend tests

This extension is using Jest for JavaScript code testing.

To execute them, execute:

jlpm
jlpm test

Integration tests

This extension uses Playwright for the integration tests (aka user level tests). More precisely, the JupyterLab helper Galata is used to handle testing the extension in JupyterLab.

More information are provided within the ui-tests README.

Packaging the extension

See RELEASE