🚀 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.

🖥️ Server — `server.py`

Tool Name	Description	Icon
`get_time_with_prefix`	Returns the current date & time	⏰
`extract_entities_tool`	Uses OpenAI to extract entities from a query — enhancing document retrieval relevance	🧠
`refine_query_tool`	Improves the quality of user queries with OpenAI-powered refinement	✨
`check_relevance`	Filters out irrelevant content by checking chunk relevance with an LLM	✅

🤝 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

Create a .env file (use .env.sample as a template)
Set your OpenAI model in .env:

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

🚀 How to Use

Start the MCP server:

python server.py

Run the MCP client:

python mcp-client.py

📜 License

This project is licensed under the MIT License.

Thanks for Reading 🙏