MCP with Gemini Integration

# Project Structure ``` MCP/ ├── .venv/ # Virtual environment (gitignored) ├── .git/ # Git repository ├── .env # Environment variables ├── .env.example # Example environment variables ├── .gitignore # Git ignore file │ ├── gemini-llm-integration/ # Google Gemini LLM integration │ ├── __pycache__/ # Python bytecode cache │ ├── client-simple.py # Enhanced Gemini client with interactive and batch modes │ ├── knowledge_base.json # Comprehensive company knowledge base │ ├── server.py # Enhanced server with semantic search capabilities │ └── README.md # Gemini integration documentation │ ├── plan/ # Project planning and documentation │ └── project_structure.md # This file │ ├── test_gemini.py # Test script for Gemini API ├── requirements.txt # Project dependencies └── README.md # Main project documentation ``` ## Key Components ### Gemini LLM Integration - **Enhanced Client (`client-simple.py`)** - Interactive and batch query modes - Robust error handling and tool integration - Automatic function calling with Gemini - Clean user interface - **Server (`server.py`)** - Semantic search using Gemini 1.5 Flash - Fallback to keyword search - Comprehensive knowledge base covering HR policies, benefits, and procedures - Structured error handling and logging - **Knowledge Base (`knowledge_base.json`)** - Organized Q&A format - Covers policies, benefits, and procedures - Easily extensible structure ### Development - Uses UV for package management - Includes development tools (black, isort, mypy, pytest) - Follows Python best practices - Comprehensive logging and error handling ## Environment Setup 1. Create and activate virtual environment: ```bash uv venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate ``` 2. Install dependencies: ```bash uv pip install -r requirements.txt ``` 3. Copy `.env.example` to `.env` and configure: ```bash cp .env.example .env # Edit .env with your API keys ```