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

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:

2. Set environment variables: Create a .env file or use the provided HF API key in config.py

3. Create example Excel file:

Usage

1. Start the API Server

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

2. Upload Excel Document

Or use the FastAPI docs at http://localhost:8000/docs

3. Ask Questions

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

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