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:

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:

python create_example_excel.py

Usage

1. Start the API Server

python main.py

The API will be available at http://localhost:8000

2. Upload Excel Document

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

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