AI Chatbot with RAG and MCP Integration
Production-ready AI chatbot with Retrieval-Augmented Generation and MCP server integration, achieving 92% query resolution rate and enabling seamless tool interaction for complex workflows.
Overview
Architected and deployed a sophisticated AI chatbot system that combines Large Language Models with Retrieval-Augmented Generation (RAG) and cutting-edge Model Context Protocol (MCP) integration. This system serves as a practical example of how modern AI agents can seamlessly interact with external tools and knowledge bases. The chatbot demonstrates advanced agentic capabilities by dynamically selecting and executing appropriate tools through MCP servers, accessing real-time data, and maintaining conversation context across complex multi-turn interactions. Built with a teacher's attention to clear, educational implementation patterns that other developers can learn from.
Technical Stack
AI/ML
- ▸LangChain
- ▸OpenAI GPT-4
- ▸Anthropic Claude
- ▸ChromaDB
- ▸Sentence Transformers
MCP Integration
- ▸MCP Protocol
- ▸Tool Discovery
- ▸Dynamic Execution
- ▸JSON-RPC
Backend
- ▸Python
- ▸FastAPI
- ▸PostgreSQL
- ▸Redis
- ▸AsyncIO
Infrastructure
- ▸Docker
- ▸Railway
- ▸GitHub Actions
- ▸Monitoring
Key Features
MCP server integration for dynamic tool discovery and execution
Advanced RAG with semantic search and re-ranking
Multi-turn conversation with persistent context
Educational code structure with comprehensive documentation
Real-time tool selection based on query intent
Robust error handling and graceful degradation
Streaming responses for improved user experience
Extensible architecture for adding new capabilities
Code Examples
Technical Challenges
Implementing reliable MCP protocol communication
Balancing response quality with execution speed
Managing complex tool interaction workflows
Creating intuitive educational examples for other developers
Ensuring robust error handling across tool integrations