MCP-GroundX

# MCP RAG Chatbot A powerful **Model Context Protocol (MCP)** based **Retrieval-Augmented Generation (RAG)** chatbot that intelligently searches through your document knowledge base to provide accurate, context-aware responses. Built with GroundX for document storage and retrieval, and Google's Gemini AI for natural language generation. ## Features - **🔍 Intelligent Document Search**: Leverages GroundX's powerful semantic search capabilities - **🤖 AI-Powered Responses**: Uses Google Gemini 2.0 Flash for generating contextual answers - **📚 Document Ingestion**: Easy document upload and processing (PDF, TXT, DOCX, etc.) - **🌐 Web Interface**: Beautiful, responsive chat interface with real-time messaging - **🔌 MCP Integration**: Standard Model Context Protocol implementation for tool interoperability - **🚀 RESTful API**: Complete HTTP API for programmatic access - **📊 Health Monitoring**: Built-in health checks and comprehensive logging - **⚡ Real-time Processing**: Fast document search with context-aware responses ## Architecture ``` ┌─────────────────┐ ┌──────────────┐ ┌─────────────────┐ │ Web Client │◄───┤ Flask Server ├───►│ MCP Server │ └─────────────────┘ └──────────────┘ └─────────────────┘ │ │ ▼ ▼ ┌──────────────┐ ┌─────────────────┐ │ Gemini AI │ │ GroundX │ │ (Text Gen.) │ │ (Doc Storage) │ └──────────────┘ └─────────────────┘ ``` ## Quick Start ### Prerequisites - Python 3.8+ - GroundX API account - Google AI Studio API key ### Installation 1. **Clone the repository:** ```bash git clone https://github.com/yourusername/mcp-rag-chatbot.git cd mcp-rag-chatbot ``` 2. **Install dependencies:** ```bash pip install -r requirements.txt ``` 3. **Set up environment variables:** ```bash cp .env.example .env # Edit .env with your API keys ``` 4. **Configure your `.env` file:** ```env GROUNDX_API_KEY=your_groundx_api_key_here GEMINI_API_KEY=your_gemini_api_key_here ``` ### Running the Server ```bash python newserver.py ``` The server will start with: - **Web Interface**: http://localhost:8080 - **MCP Server**: Running on stdio - **API Endpoints**: Available at base URL ## Usage ### Web Interface 1. Open http://localhost:8080 in your browser 2. Start asking questions about your documents 3. The system will automatically search for relevant context and provide informed answers ### MCP Integration The server implements standard MCP tools that can be used by any MCP-compatible client: - `search_doc_for_rag_context(query: str)` - Search documents for relevant context - `ingest_documents(local_file_path: str, file_type: str)` - Add documents to knowledge base ## Configuration ### Environment Variables | Variable | Description | Default | |----------|-------------|---------| | `GROUNDX_API_KEY` | GroundX API key (required) | | `GEMINI_API_KEY` | Google AI Studio API key (required) | ## API Reference ### Endpoints #### `GET /` Serves the main chat web interface. #### `POST /chat` Main RAG endpoint for conversational queries. **Request:** ```json { "query": "Your question here" } ``` **Response:** ```json { "answer": "AI-generated response", "context_used": true, "query": "Your original question" } ``` ### Dependencies - **fastmcp**: MCP server implementation - **groundx**: Document storage and retrieval - **google-generativeai**: Gemini AI integration - **flask**: Web server framework - **flask-cors**: CORS support - **python-dotenv**: Environment variable management ## How It Works 1. **Document Ingestion**: Upload documents to GroundX buckets for semantic indexing 2. **Query Processing**: User questions are processed and relevant context is retrieved 3. **Context Augmentation**: Retrieved context is combined with the user query 4. **AI Generation**: Gemini AI generates informed responses based on the context 5. **Response Delivery**: Contextual answers are delivered via web interface or API ## Troubleshooting ### Common Issues **"GROUNDX_API_KEY environment variable is not set"** - Ensure your `.env` file contains the correct API key - Verify the `.env` file is in the same directory as `newserver.py` **"No relevant documents found"** - Make sure documents have been ingested into your GroundX bucket - Wait a few minutes after ingestion for documents to be indexed - Try different query phrasings **Connection errors** - Check your internet connection - Verify API keys are valid and have sufficient quotas ### Logs The server provides comprehensive logging. Check the console output for detailed information about: - Document search operations - AI response generation - API connectivity status - Error details