🚀 Agentic RAG with MCP Server

✨ Overview

Agentic RAG with MCP Server is a powerful project that brings together an MCP (Model Context Protocol) server and client for building Agentic RAG (Retrieval-Augmented Generation) applications.

This setup empowers your RAG system with advanced tools such as:

• 🕵️‍♂️ Entity Extraction

• 🔍 Query Refinement

• ✅ Relevance Checking

The server hosts these intelligent tools, while the client shows how to seamlessly connect and utilize them.

Related MCP server: vector-mcp

🖥️ Server — server.py

Powered by the FastMCP class from the mcp library, the server exposes these handy tools:

🤝 Client — mcp-client.py

The client demonstrates how to connect and interact with the MCP server:

• Establish a connection with ClientSession from the mcp library

• List all available server tools

• Call any tool with custom arguments

• Process queries leveraging OpenAI or Gemini and MCP tools in tandem

⚙️ Requirements

• Python 3.9 or higher

• openai Python package

• mcp library

• python-dotenv for environment variable management

🛠️ Installation Guide

# Step 1: Clone the repository
git clone https://github.com/ashishpatel26/Agentic-RAG-with-MCP-Server.git

# Step 2: Navigate into the project directory
cd Agentic-RAG-with-MCP-Serve

# Step 3: Install dependencies
pip install -r requirements.txt

🔐 Configuration

1. Create a .env file (use .env.sample as a template)

2. Set your OpenAI model in .env:

OPENAI_MODEL_NAME="your-model-name-here"
GEMINI_API_KEY="your-model-name-here"

🚀 How to Use

1. Start the MCP server:

python server.py

2. Run the MCP client:

python mcp-client.py

📜 License

This project is licensed under the MIT License.

Thanks for Reading 🙏