# 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.