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

Related MCP server: BigGo MCP Server

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:

pip install -r requirements.txt

2. Set up environment variables in .env

3. Start MongoDB:

docker-compose up mongodb -d

4. Run the application:

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.