iflow-mcp_digital-duck-st_rag_mcp

# RAG MCP The RAG system is particularly well-designed, using sentence transformers to build rich contextual embeddings and performing semantic search to find the most relevant tools for each query. This makes the system much more robust than traditional keyword-based approaches. ## What is [MCP](https://modelcontextprotocol.io/introduction) MCP stands for Model Context protocol, it helps provide contexts to LLM model for tool-use.  ## Architecture Diagram MCP with RAG  The Mermaid diagram illustrates: - **7 Distinct Layers** with color coding for easy understanding - **Complete Data Flow** from user input through RAG processing to tool execution - **Decision Tree Logic** showing how queries are routed through different parsing modes - **RAG Integration** highlighting how semantic search enhances tool selection - **Multi-LLM Support** showing integration with Google, OpenAI, and Anthropic - **MCP Server Integration** with tool discovery and execution - **Data Persistence** including SQLite storage and caching mechanisms ## Key Features  1. **Intelligent Tool Selection** using RAG to find relevant tools dynamically 2. **Flexible Parsing Strategy** with fallback mechanisms (RAG → LLM → Rule-based) 3. **Comprehensive Logging** for debugging and performance analysis 4. **Caching Strategy** for efficient server discovery 5. **Multi-Provider LLM Support** for flexibility and redundancy ## Setup ```bash # create a virtual environment conda create -n mcp conda activate mcp # obtain source code git clone https://github.com/digital-duck/st_rag_mcp.git cd st_rag_mcp pip install -r requirements.txt # open 1st terminal cd src python mcp_server.py # in 2nd terminal conda activate mcp cd src streamlit run mcp_client.py ``` ## Demo Video - [MCP + RAG](https://youtu.be/K2jgrMsgZxA) ## References - https://www.anthropic.com/news/integrations - [RAG-MC…