MCP Chat Client

A real-time chat application with WebSocket support, built with FastAPI, PostgreSQL, and Redis.

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New here? Start with our WELCOME.md or .ai-ide-welcome.md for a quick overview and links to all onboarding resources!

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Features

• Real-time chat using WebSocket connections
• User authentication with JWT tokens
• Message history and persistence
• Rate limiting and connection management
• Health monitoring and telemetry
• Comprehensive test suite

Project Structure

mcp_chat_client/
├── backend/                 # FastAPI backend
│   ├── app/                # Application code
│   │   ├── api/           # API endpoints and routers
│   │   ├── core/          # Core functionality
│   │   ├── db/            # Database models and sessions
│   │   ├── models/        # SQLAlchemy models
│   │   ├── schemas/       # Pydantic schemas
│   │   └── services/      # Business logic services
│   ├── tests/             # Test suite
│   ├── alembic/           # Database migrations
│   └── Dockerfile         # Backend container definition
├── docker/                 # Docker configuration
│   ├── dev/               # Development environment
│   └── test/              # Test environment
├── docker-compose.dev.yml  # Development compose file
├── docker-compose.test.yml # Test compose file
└── .env.example           # Environment variables template

Prerequisites

• Docker and Docker Compose
• Python 3.9+
• Node.js 16+ (for frontend development)

Quick Start

1. Clone the repository:

   git clone https://github.com/yourusername/mcp_chat_client.git
   cd mcp_chat_client

2. Copy the environment template:

   cp .env.example .env

3. Update the .env file with your configuration:

   • Set your database credentials
   • Configure Redis settings
   • Set up JWT secret key
   • Configure API keys for AI services

4. Start the development environment:

   docker compose -f docker-compose.dev.yml up -d

5. Access the application:

   • Backend API: http://localhost:8000
   • API Documentation: http://localhost:8000/docs

Development

Backend Development

1. Install dependencies:

   cd backend
   pip install -r requirements.txt
   pip install -r requirements-test.txt

2. Run database migrations:

   ./run_migration.sh

3. Start the development server:

   uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Testing

1. Start the test environment:

   docker compose -f docker-compose.test.yml up -d

2. Run the test suite:

   cd backend
   pytest

3. Generate coverage report:

   pytest --cov=app tests/

API Documentation

The API documentation is available at /docs when running the application. It includes:

• REST API endpoints
• WebSocket connection details
• Authentication requirements
• Request/response schemas

Environment Variables

See .env.example for all required environment variables. Key variables include:

• Database configuration (PostgreSQL)
• Redis settings
• JWT configuration
• WebSocket settings
• Test environment settings
• Monitoring configuration
• AI provider API keys

To set up your environment:

1. Copy .env.example to .env and fill in the values as needed.
2. Run bash validate_env.sh to check for missing or empty variables.

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Admin API

See docs/admin_endpoints.md for a full guide to all admin-only API endpoints, including usage tips and example requests.

• Troubleshooting & Environment Issues: docs/env_troubleshooting.md
• Known Issues: KNOWN_ISSUES.md

AI-IDE Onboarding API

To serve onboarding, rules, and knowledge graph data via HTTP for AI-IDE tools:

make -f Makefile.ai ai-ide-api-build   # Build the Docker image
make -f Makefile.ai ai-ide-api-up      # Start the API at http://localhost:8080
make -f Makefile.ai ai-ide-api-down    # Stop the API

Example endpoints:

• http://localhost:8080/metadata
• http://localhost:8080/rules
• http://localhost:8080/knowledge-graph