Combines a Retrieval-Augmented Generation (RAG) system with the Model Context Protocol (MCP) to create a powerful, modular AI application.
This project establishes a modular AI application by seamlessly integrating a Retrieval-Augmented Generation (RAG) system with the Model Context Protocol (MCP). At its core, an MCP server (`rag_server.py`) makes sophisticated RAG capabilities for PDF documents and a practical weather tool accessible. A user-friendly Gradio client UI (`client_ui.py`) then orchestrates interactions using an LLM, dynamically invoking these tools to provide insightful and contextually relevant responses, making it ideal for building intelligent assistants that can query custom knowledge bases and real-time data.