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
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@E-commerce MCP Servershow me laptops under $1000"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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: MongoDB MCP Server for LLMs
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:
pip install -r requirements.txtSet up environment variables in
.envStart MongoDB:
docker-compose up mongodb -dRun the application:
uvicorn main:app --reloadAPI 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
Resources
Looking for Admin?
Admins can modify the Dockerfile, update the server description, and track usage metrics. If you are the server author, to access the admin panel.