Enables containerized deployment of the MongoDB database component through docker-compose
Supports configuration through environment variables stored in .env files for secure management of connection strings and credentials
Refactors an existing LangChain implementation to work with the MCP server architecture while maintaining conversational capabilities
Provides real-time product data access through direct MongoDB integration, enabling structured queries with price range and category filtering, text search capabilities, and product recommendations based on category and price similarity
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
- Install dependencies:
- Set up environment variables in
.env
- Start MongoDB:
- Run the application:
API Endpoints
POST /api/v1/chat/
- Text-based chat
Benefits of MCP Integration
- Real-time Inventory: Always current stock levels
- Complex Queries: Price ranges, category filters
- Better Performance: Optimized database queries
- Scalability: Direct MongoDB connection
- 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.
This server cannot be installed
A Model Context Protocol server that provides real-time access to MongoDB product data, enabling sophisticated e-commerce queries with price range filters, category searching, and product recommendations through a conversational interface.
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