sagemaker-gen-ai-jupyterlab-extension 1.0.14

An extension supporting SageMaker's GenAI capabilities in JupyterLab

SageMaker GenAI JupyterLab Extension

A JupyterLab extension that integrates Amazon Q Developer with JupyterLab, providing AI-powered chat assistance for code development and analysis.

Current version: 1.0.11

This extension consists of:

• Python backend (sagemaker_gen_ai_jupyterlab_extension) - Server extension with LSP integration
• TypeScript frontend (@amzn/sagemaker_gen_ai_jupyterlab_extension) - JupyterLab UI components

Architecture Overview

Self-Contained Architecture

The extension follows a self-contained architecture to support Amazon Q Developer in JupyterLab environments on private networks.

SageMaker Distribution Integration:

• LSP Server → Uses aws-lsp-codewhisperer.js from /etc/amazon-q-agentic-chat/artifacts/jupyterlab/servers/
• Client Assets → Serves amazonq-ui.js from /etc/amazon-q-agentic-chat/artifacts/jupyterlab/clients/
• Static Files → Chat UI served from extension's static directory
• WebSocket Communication → Real-time bidirectional communication between frontend and LSP server

Component Architecture

┌─────────────────────────────────────────────────────────────┐
│                    JupyterLab Frontend                      │
├─────────────────────────────────────────────────────────────┤
│  FlareWidget  │  WebSocket Client  │  Context Menu Actions  │
├─────────────────────────────────────────────────────────────┤
│                    Python Backend                           │
├─────────────────────────────────────────────────────────────┤
│  WebSocket Handler  │  LSP Connection  │  Static Handlers   │
├─────────────────────────────────────────────────────────────┤
│  Credential Manager  │  Q Customization  │  File Watchers   │
├─────────────────────────────────────────────────────────────┤
│  Telemetry Collector  │  Request Logger  │  Cancellation    │
├─────────────────────────────────────────────────────────────┤
│                    LSP Server                               │
│              (aws-lsp-codewhisperer.js)                     │
└─────────────────────────────────────────────────────────────┘

Key Features

• Chat Interface → AI-powered conversational assistance
• Context Menu Integration → Right-click actions for code explanation, optimization, and refactoring
• Streaming Responses → Real-time chat responses with cancellation support
• MCP Server Support → Model Context Protocol integration
• Credential Management → Automatic handling of AWS credentials and authentication
• Telemetry Collection → Usage analytics and error reporting
• Q Customization → Support for custom Q profiles and settings

Requirements

• JupyterLab >= 4.0.0
• Python >= 3.8
• Node.js (for development)
• SageMaker Distribution with Amazon Q artifacts (for production)
• AWS Credentials (IAM or SSO)

Install

pip install sagemaker_gen_ai_jupyterlab_extension

Amazon Q Artifact Verification

To verify SageMaker Distribution artifacts are available:

# Check LSP server
ls -la /etc/amazon-q-agentic-chat/artifacts/jupyterlab/servers/aws-lsp-codewhisperer.js

# Check client libraries
ls -la /etc/amazon-q-agentic-chat/artifacts/jupyterlab/clients/amazonq-ui.js

The extension requires these artifacts for local operation in SageMaker environments.

Uninstall

To remove the extension, execute:

pip uninstall sagemaker_gen_ai_jupyterlab_extension

Logging Strategy

The extension implements a structured logging approach optimized for production environments:

Log Levels

• ERROR: Critical failures with full stack traces (exc_info=True)
• WARNING: Non-critical issues that may affect functionality
• INFO: Important business events and system state changes
• DEBUG: Implementation details, file paths, and technical diagnostics

Key Principles

1. Stack Traces on All Errors: All exceptions include full stack traces for debugging
2. Concise Messages: Removed verbose comments and duplicate information
3. Appropriate Levels:
   • File paths and configuration details → DEBUG
   • Successful operations and state changes → INFO
   • System failures and errors → ERROR with stack traces
4. Production Ready: Structured format with timestamps and component names

Configuration

# Default: INFO level with structured format
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

# Debug mode: Set JUPYTER_LOG_LEVEL=DEBUG for detailed diagnostics
export JUPYTER_LOG_LEVEL=DEBUG

Examples

# ERROR: Always includes stack trace
logger.error(f"Failed to initialize LSP server: {e}", exc_info=True)

# INFO: Important events
logger.info("Amazon Q handlers registered")

# DEBUG: Implementation details
logger.debug(f"Client HTML: {CLIENT_HTML_PATH}")

Troubleshoot

Extension Not Working

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

Common Issues

Problem: "You are not subscribed to Amazon Q Developer" message

Solution: Check Q enabled status in ~/.aws/amazon_q/settings.json and ensure proper AWS credentials are configured.

Problem: Extension not loading or WebSocket connection fails

Solution:

1. Verify SageMaker Distribution artifacts exist:

   ls -la /etc/amazon-q-agentic-chat/artifacts/jupyterlab/
   

2. Check JupyterLab logs:

   jupyter lab --log-level=DEBUG
   

3. Ensure proper AWS authentication is configured

Problem: Chat responses not streaming or getting stuck

Solution: Check WebSocket connection and LSP server status in browser developer tools and JupyterLab logs.

Problem: Consistently getting 502 Bad Gateway errors or extension not loading properly

Solution: Reinstall the extension without affecting dependencies:

# Force reinstall specific version without touching dependencies
pip install sagemaker_gen_ai_jupyterlab_extension-1.0.11-py3-none-any.whl --no-deps --force-reinstall

# Or install from PyPI
pip install sagemaker_gen_ai_jupyterlab_extension==1.0.11 --no-deps --force-reinstall

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 SageMakerGenAIJupyterLabExtension directory
# Install package in development mode
pip install -e "."
# 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 sagemaker_gen_ai_jupyterlab_extension

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 sagemaker_gen_ai_jupyterlab_extension
pip uninstall sagemaker_gen_ai_jupyterlab_extension

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 sagemaker_gen_ai_jupyterlab_extension within that folder.

Packaging the extension

See RELEASE

Development Workflow

This extension supports two development environments:

1. Local Development - For rapid iteration and testing
2. SageMaker Unified Studio - For final verification before commits

Prerequisites

• Node.js (check version with which node)
• Python build tools (pip install build)
• Access to SMUS team resources for bearer token generation

Local Development Setup

1. Configure Environment

Update the following values in __init__.py:

# Retrieve `AWS access portal URL` from `IAM Identity Center`
START_URL = "https://d-xxxxx.awsapps.com/start"

# Run `which node`
NODE_PATH = "/Users/xxxxx/.local/share/mise/installs/node/20.9.0/bin/node"

# Copy the absolute path to `SageMakerGenAIJupyterLabExtension` and prepend `file://`
WORKSPACE_FOLDER = "file:///Users/xxxxx/Desktop/workplace/Flare/src/SageMakerGenAIJupyterLabExtension"

# Please reach out to SMUS team for the `generate_bearer_token` notebook
def extract_bearer_token():
  return "<CUSTOM BEARER TOKEN>"

Update the following values in lsp_server_connection.py:

"developerProfiles": False # Change this to False

2. Build and Run

# Build the extension and start JupyterLab
python -m build && jupyter lab

3. Development Tips

• Use the watch mode from the Contributing section for live reloading
• Test changes immediately in your local JupyterLab instance
• Verify functionality before proceeding to SageMaker testing

SageMaker Unified Studio Testing

1. Build Distribution

# Generate distribution package
python -m build

This creates a .tar.gz file in the dist/ folder.

2. Deploy to SageMaker

# Upload the generated .tar.gz to your SMUS workspace
# Then run the following commands in the SageMaker terminal:

pip install sagemaker_gen_ai_jupyterlab_extension-<Version>.tar.gz
restart-sagemaker-ui-jupyter-server

3. Verify Installation

1. Wait for server restart (terminal will disappear)
2. Refresh your browser page
3. Test the side widget chat functionality

Development Best Practices

• Always test locally first for faster iteration
• Verify in SageMaker Unified Studio before committing
• Keep bearer tokens secure and never commit them
• Update version numbers appropriately when building distributions

1. Run python -m build - will generate a .tar.gz file in the dist folder.
2. Upload the .tar.gz in the MD workspace
3. Run pip install sagemaker_gen_ai_jupyterlab_extension-<VERSION>.tar.gz in a terminal
4. Run restart-sagemaker-ui-jupyter-server in a terminal
5. Wait until the server restarts (Terminal disappears)
6. Refresh your page
7. Start chatting using the side widget

Instructions for setting up SMUS remote MCP server alpha

1. paste bin/mcp_dev_setup.sh in your space
2. make sure its executable: chmod +x mcp_dev_setup.sh
3. Set your desired MCP server URL: export MCP_URL="https://your-custom-url.com/mcp"
4. execute the script ./mcp_dev_setup.sh
5. The server will restart. Refresh the page once the restart is complete.