RAG MCP Application

This project combines a Retrieval-Augmented Generation (RAG) system with the Model Context Protocol (MCP) to create a powerful, modular AI application. It features a dedicated MCP server (rag_server.py) that exposes RAG capabilities and a weather tool, and a client UI (client_ui.py) that uses an orchestrator LLM to interact with these tools.

Project Structure

• rag-mcp-app/
  • data/: Directory for your PDF documents to be indexed by the RAG system.
  • chroma_db/: Directory where the ChromaDB vector store will be persisted.
  • rag_server.py: The MCP server that hosts the RAG and weather tools.
  • client_ui.py: The client application with a Gradio UI that orchestrates LLM calls and tool usage.
  • ingest.py: A script to load and index your PDF documents into the vector database.
  • requirements.txt: Lists all project dependencies.
  • README.md: This file.

Getting Started

Prerequisites

• Python 3.11+: Ensure you have Python installed.
• Ollama: Install Ollama from ollama.ai and ensure it's running.
• Ollama Model (qwen3:1.7b): Pull the qwen3:1.7b model for the client's orchestrator LLM:

  ollama pull qwen3:1.7b
  

• Ollama Embedding Model (nomic-embed-text): If you plan to use Ollama for embeddings (though Gemini is default), pull this model:

  ollama pull nomic-embed-text
  

• Google API Key: Set your GOOGLE_API_KEY as an environment variable (e.g., in a .env file). This is required for Google Gemini embeddings and the Gemini LLM.

Installation

1. Clone the repository (if you haven't already):

   git clone <your-repo-url>
   cd rag-mcp-app
   

   (Note: If you are following along with the development process, you would have already created this directory and copied files into it.)

2. Create and Activate a Virtual Environment:

   python -m venv venv
   # On Windows:
   .\venv\Scripts\activate
   # On macOS/Linux:
   # source venv/bin/activate
   

3. Install Dependencies:

   uv pip install -r requirements.txt
   

Data Preparation

1. Populate the data/ directory: Place your PDF documents into the rag-mcp-app/data/ directory.

2. Run the Ingestion Script: This needs to be run before you start the RAG server for the first time, or whenever you add new documents to the data/ directory.

   python ingest.py
   

Running the Application

You will need to run two processes: the MCP server and the client UI.

1. Start the MCP Server:

Open a new terminal, activate your virtual environment, and run:

python rag_server.py --llm-provider ollama
# Or to use Gemini for RAG LLM:
# python rag_server.py --llm-provider gemini

This will start the MCP server, making the get_weather and get_rag_response tools available.

2. Start the Client UI:

Open another terminal, activate your virtual environment, and run:

python client_ui.py --mcp-server rag_server.py --model qwen3:1.7b

This command connects the client UI to the MCP server and specifies the orchestrator LLM.

The client UI will launch in your browser. You can then interact with the chatbot, asking questions that might trigger the RAG system or the weather tool.

Example Usage

• Ask a question about your documents: "What is the main topic of the documents?"
• Ask about the weather: "What's the weather like in London?"