Model Context Protocol Server

# Model Context Protocol Server A FastAPI-based server that implements the Model Context Protocol to provide relevant information to AI models when processing user queries. ## Environment Configuration The server can be configured using environment variables. Create a `.env` file in the root directory with the following variables: ```env # Server Configuration HOST=0.0.0.0 # Server host (0.0.0.0 for all interfaces) PORT=8000 # Server port ENVIRONMENT=development # Environment (development/production) DEBUG=true # Enable debug mode API_PREFIX=/api/v1 # API prefix for all endpoints # Ollama Configuration OLLAMA_BASE_URL=http://localhost:11434 # Ollama server URL OLLAMA_MODEL=llama2 # Default model to use # Database Configuration DATABASE_URL=sqlite:///./catalog.db # Database connection URL ``` ## Deployment ### Local Development 1. Create and activate a virtual environment: ```bash python -m venv .venv-py311 source .venv-py311/bin/activate # On Unix/macOS # or .venv-py311\Scripts\activate # On Windows ``` 2. Install dependencies: ```bash pip install -r requirements.txt ``` 3. Run the server: ```bash uvicorn main:app --reload ``` ### Production Deployment 1. Set up environment variables for production: ```env HOST=0.0.0.0 PORT=8000 ENVIRONMENT=production DEBUG=false API_PREFIX=/api/v1 OLLAMA_BASE_URL=http://your-ollama-server:11434 OLLAMA_MODEL=llama2 ``` 2. Run the server: ```bash uvicorn main:app --host 0.0.0.0 --port 8000 ``` ### Docker Deployment 1. Build the Docker image: ```bash docker build -t mcp-server . ``` 2. Run the container: ```bash docker run -p 8000:8000 \ -e HOST=0.0.0.0 \ -e PORT=8000 \ -e ENVIRONMENT=production \ -e DEBUG=false \ -e OLLAMA_BASE_URL=http://your-ollama-server:11434 \ -e OLLAMA_MODEL=llama2 \ mcp-server ``` ## API Documentation When running in development mode (DEBUG=true), API documentation is available at: - Swagger UI: `http://your-server:8000/api/v1/docs` - ReDoc: `http://your-server:8000/api/v1/redoc` - OpenAPI JSON: `http://your-server:8000/api/v1/openapi.json` ## Security Considerations 1. In production: - Set DEBUG=false to disable API documentation - Use HTTPS - Configure proper authentication - Use secure database credentials - Set appropriate CORS policies 2. For Ollama server: - Ensure Ollama server is properly secured - Use internal network for communication if possible - Consider using API keys or other authentication methods ## Monitoring and Logging The server includes built-in logging with different levels based on the environment: - Development: Debug level logging - Production: Info level logging Logs can be configured to output to files or external logging services. ## Features - Intelligent query routing based on query analysis - Support for multiple data sources (Database, GraphQL, REST) - Integration with Ollama models (Mistral, Qwen, Llama2) - Environment-aware configuration (Development/Production) - Comprehensive logging and error handling - Health check endpoints - Mock data support for development ## Prerequisites - Python 3.8+ - Ollama installed and running locally - Required Ollama models: - mistral - qwen - llama2 ## Installation 1. Clone the repository: ```bash git clone <repository-url> cd mcp-server ``` 2. Create and activate a virtual environment: ```bash python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate ``` 3. Install dependencies: ```bash pip install -r requirements.txt ``` 4. Create a `.env` file: ```bash cp .env.example .env ``` 5. Update the `.env` file with your configuration: ```env ENVIRONMENT=development OLLAMA_MODEL=mistral OLLAMA_BASE_URL=http://localhost:11434 ``` ## Running the Server 1. Start Ollama (if not already running): ```bash ollama serve ``` 2. Start the MCP server: ```bash python main.py ``` The server will be available at `http://localhost:8000` ## API Endpoints ### Get Context ```bash curl -X POST http://localhost:8000/context \ -H "Content-Type: application/json" \ -d '{ "query": "Tell me about iPhone 15", "model": "mistral" }' ``` ### List Available Models ```bash curl http://localhost:8000/models ``` ### Health Check ```bash curl http://localhost:8000/health ``` ## Project Structure ``` mcp-server/ ├── context_providers/ # Data source providers │ ├── database.py # Database provider │ ├── graphql.py # GraphQL provider │ ├── rest.py # REST API provider │ └── provider_factory.py ├── model_providers/ # AI model providers │ ├── base.py # Base model provider │ ├── ollama.py # Ollama integration │ └── provider_factory.py ├── main.py # FastAPI application ├── query_analyzer.py # Query analysis logic ├── logger_config.py # Logging configuration ├── requirements.txt # Project dependencies └── README.md # Project documentation ``` ## Development ### Adding New Providers 1. Create a new provider class in the appropriate directory 2. Implement the required interface methods 3. Register the provider in the factory ### Adding New Models 1. Add the model to the `AVAILABLE_MODELS` dictionary in `model_providers/ollama.py` 2. Update the model validation logic if needed ## 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.