Agentic RAG With

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✨ 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.

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🖥️ Server — server.py

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

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	✅

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🤝 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

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⚙️ Requirements

• Python 3.9 or higher
• openai Python package
• mcp library
• python-dotenv for environment variable management

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🛠️ 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

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🔐 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"

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🚀 How to Use

1. Start the MCP server:

python server.py

2. Run the MCP client:

python mcp-client.py

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📜 License

This project is licensed under the MIT License.

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Thanks for Reading 🙏