Semiconductor Component RAG Search

# MCP-Based RAG System for Semiconductor Component Search This project demonstrates **MCP (Model Context Protocol)** integration with ChromaDB and HuggingFace models for Retrieval-Augmented Generation (RAG). ## Project Overview This system shows how **MCP works** and its **purpose**: - **MCP** provides a standardized protocol for context retrieval - **ChromaDB** stores and retrieves semantic embeddings - **RAG Pipeline** combines retrieval with LLM generation - **Backend API** allows document upload and question-answering ## Architecture ``` User Question → API Endpoint → RAG Pipeline ↓ Retrieval from ChromaDB (via embeddings) ↓ LLM generates answer with context ↓ Response to user ``` ### Key Components 1. **MCP Server** (`mcp_server.py`): Demonstrates MCP protocol for structured context retrieval 2. **RAG Pipeline** (`rag_pipeline.py`): Handles embeddings (encoding) and LLM (decoding) 3. **FastAPI Backend** (`main.py`): REST API for document upload and Q&A 4. **ChromaDB**: Vector database for semantic search ## Installation 1. **Install dependencies:** ```bash pip install -r requirements.txt ``` 2. **Set environment variables:** Create a `.env` file or use the provided HF API key in `config.py` 3. **Create example Excel file:** ```bash python create_example_excel.py ``` ## Usage ### 1. Start the API Server ```bash python main.py ``` The API will be available at `http://localhost:8000` ### 2. Upload Excel Document ```bash curl -X POST "http://localhost:8000/upload" \ -H "accept: application/json" \ -F "file=@examples/semiconductor_components.xlsx" ``` Or use the FastAPI docs at `http://localhost:8000/docs` ### 3. Ask Questions ```bash curl -X POST "http://localhost:8000/ask" \ -H "Content-Type: application/json" \ -d '{"question": "What MOSFET components are available?", "n_results": 3}' ``` ## API Endpoints - `GET /` - API information - `GET /health` - Health check - `POST /upload` - Upload Excel document - `POST /ask` - Ask a question - `GET /info` - Get collection information ## How MCP Works **MCP (Model Context Protocol)** serves as a standardized interface for: - **Context Retrieval**: Structured way to query and retrieve relevant information - **Tool Definition**: Clear specification of available operations - **Protocol Communication**: Standardized communication between components In this project: 1. MCP server defines tools for querying ChromaDB 2. RAG pipeline uses MCP principles for context retrieval 3. Backend integrates MCP concepts for document processing ## Models Used - **Encoding (Embeddings)**: `sentence-transformers/all-MiniLM-L6-v2` - **Decoding (LLM)**: Llama model from HuggingFace (or fallback to GPT-2) ## Example Questions - "What MOSFET components are available?" - "Show me voltage regulators from Texas Instruments" - "What components work with 5V?" - "List all temperature sensors" ## Project Structure ``` MCP2/ ├── main.py # FastAPI backend ├── rag_pipeline.py # RAG pipeline with embeddings & LLM ├── mcp_server.py # MCP server for ChromaDB ├── config.py # Configuration ├── create_example_excel.py # Generate example data ├── requirements.txt # Dependencies ├── examples/ # Example Excel files └── chroma_db/ # ChromaDB storage (created automatically) ``` ## Notes - First run will download models from HuggingFace (requires API key) - ChromaDB data persists in `./chroma_db/` directory - Uploaded files are stored in `./uploads/` directory