MCP-server-using-Gemini

About

This project is a working implementation of the Model Context Protocol (MCP), showcasing its integration with Gemini APIs. The architecture consists of multiple servers, each handling specific functionalities such as chat, search, retrieval-augmented generation (RAG), and more. The project demonstrates how different services can work together seamlessly using the MCP framework.

Features

• Chat Server: Handles conversational interactions.
• Search Server: Provides search capabilities using Gemini APIs.
• RAG Server: Implements retrieval-augmented generation for enhanced responses.
• Thinking Server: Processes complex logic and reasoning tasks.
• Router: Acts as the central hub, routing requests to the appropriate servers.

Prerequisites

• Python 3.12 or higher must be installed on your system.
• Ensure the pip package manager is available.
• A valid GEMINI_API_KEY is required and should be set in the .env file.

How to Run

1. Clone the repository:

   git clone https://github.com/AditHash/gemini-mcp-server-chatbot.git
   cd gemini-mcp-server-chatbot
   

2. Set up the .env file with your Gemini API key:

   GEMINI_API_KEY=your_gemini_api_key
   

3. Run the install.sh script to set up the environment and start the servers:

   ./install.sh
   

   This script will:

   • Check if Python 3.12 is installed and install it if necessary.
   • Create and activate a virtual environment.
   • Install the required dependencies from requirements.txt.
   • Start all the servers:
     • Chat Server: http://0.0.0.0:8001
     • RAG Server: http://0.0.0.0:8004
     • Search Server: http://0.0.0.0:8002
     • Thinking Server: http://0.0.0.0:8003
     • Router: http://0.0.0.0:8000

Project Structure

• chat/: Contains the chat server implementation.
• search/: Contains the search server implementation.
• rag/: Contains the RAG server implementation.
• think/: Contains the thinking server implementation.
• router/: Contains the router server implementation.
• install.sh: Script to set up the environment and start the servers.
• requirements.txt: Lists the Python dependencies for the project.
• .env: Stores environment variables, including the GEMINI_API_KEY.

Future Plans

• Frontend integration
• Develop a docker-compose.yaml template to simplify deployment using Docker.
• Add more advanced features to the MCP framework, such as dynamic scaling and monitoring.
• Enhance the integration with Gemini APIs for improved performance and reliability.

Notes

• Ensure that the GEMINI_API_KEY is valid and has the necessary permissions for the Gemini APIs.
• If you encounter any issues, check the logs for each server to debug the problem.