MCP Agentic Rag

🚀 MCP-agen-tic-RAG

This project implements an MCP (Model Context Protocol) server and client for building enhanced RAG (Retrieval Augmented Generation) applications. The server offers a set of tools to improve the performance of RAG systems, such as entity extraction, query optimization, and relevance checking. The client demonstrates how to connect to the server and utilize its tools.

🚀 Quick Start

To get started with this project, follow these steps:

Installation

1. Clone the repository:

git clone https://github.com/rukshanet/mcp-agentic-rag.git

2. Install the dependencies:

pip install -r requirements.txt

3. Configure environment variables:
   • Create a .env file based on the .env.sample file.
   • Set the OPENAI_MODEL_NAME environment variable to the name of the OpenAI model you want to use.

Usage

1. Start the MCP server:

python server.py

2. Run the MCP client:

python mcp-client.py

✨ Features

Server (server.py)

The server is implemented based on the FastMCP class from the mcp library. It provides the following tools:

• get_time_with_prefix: Returns the current date and time.
• extract_entities_tool: Uses OpenAI to extract entities from a given text query. This tool can be used to identify key entities in user queries, thereby improving the relevance of document retrieval.
• refine_query_tool: Uses OpenAI to optimize a given text query. This tool can be used to enhance the quality of user queries, thus improving the relevance of document retrieval.
• check_relevance: Uses an LLM to check the relevance of text fragments to a given question. This tool can be used to filter out irrelevant content from retrieval results.

Client (mcp-client.py)

The client demonstrates how to connect to the MCP server and use its tools. It establishes a connection to the server through the ClientSession class from the mcp library. The client provides the following examples:

• How to connect to the server
• Listing available tools
• Invoking specific tools and their parameters
• Processing queries using OpenAI and available MCP tools

📦 Installation

The project has the following requirements:

• Python 3.7+
• openai
• mcp
• dotenv

💻 Usage Examples

Server

The server code (server.py) provides a set of useful tools. Here is a simple overview of how it works:

# The server is based on the FastMCP class from the mcp library
# and provides various tools for RAG system enhancement

Client

The client code (mcp-client.py) shows how to interact with the server. Here is a simple example of connecting to the server:

# Connect to the MCP server using the ClientSession class from the mcp library

📄 License

This project is licensed under the MIT license.