E-commerce MCP Server

# E-commerce Chatbot with MCP Server (Model Context Protocol) This project refactors your existing LangChain + FAISS vector database e-commerce chatbot to use MCP (Model Context Protocol) server for real-time product data access. ## Key Changes ### Removed: - FAISS vector database and embeddings - Static JSON file loading - Vector similarity search ### Added: - MCP Server for MongoDB integration - Real-time product queries - Structured database operations - MongoDB text search indexing ## Features - **Real-time Data**: Always up-to-date product information - **Structured Queries**: Price range, category filtering - **Product Recommendations**: Based on category and price similarity - **Text Input**: Supports text queries - **Session Management**: Maintains conversation context - **Coreference Resolution**: Handles pronouns and references ## Setup 1. Install dependencies: ```bash pip install -r requirements.txt ``` 2. Set up environment variables in `.env` 3. Start MongoDB: ```bash docker-compose up mongodb -d ``` 4. Run the application: ```bash uvicorn main:app --reload ``` ## API Endpoints - `POST /api/v1/chat/` - Text-based chat ## Benefits of MCP Integration 1. **Real-time Inventory**: Always current stock levels 2. **Complex Queries**: Price ranges, category filters 3. **Better Performance**: Optimized database queries 4. **Scalability**: Direct MongoDB connection 5. **Flexibility**: Easy to extend with new query types The MCP server provides a clean abstraction layer between your LLM and database, enabling more sophisticated product queries while maintaining the conversational interface your users expect.