sagemaker-gen-ai-jupyterlab-extension 1.0.14
An extension supporting SageMaker's GenAI capabilities in JupyterLab
Requires Python: >=3.8

# SageMaker GenAI JupyterLab Extension

[![Github Actions Status](/workflows/Build/badge.svg)](/actions/workflows/build.yml)

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

```bash
pip install sagemaker_gen_ai_jupyterlab_extension
```

### Amazon Q Artifact Verification

To verify SageMaker Distribution artifacts are available:

```bash
# 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:

```bash
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

```python
# 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

```python
# 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:

```bash
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:

```bash
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:
   ```bash
   ls -la /etc/amazon-q-agentic-chat/artifacts/jupyterlab/
   ```
2. Check JupyterLab logs:
   ```bash
   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:
```bash

# 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](https://yarnpkg.com/) that is installed with JupyterLab. You may use
`yarn` or `npm` in lieu of `jlpm` below.

```bash
# 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.

```bash
# 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:

```bash
jupyter lab build --minimize=False
```

### Development uninstall

```bash
# 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](RELEASE.md)

## 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

```bash
# 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

```bash
# Generate distribution package
python -m build
```

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

#### 2. Deploy to SageMaker

```bash
# 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"`
3. execute the script `./mcp_dev_setup.sh`
4. The server will restart. Refresh the page once the restart is complete.