Medical-RAG-Assistant

This repository contains my M.Tech (CSE) Second Semester Seminar Project:
a Medical Report Assistant built using Retrieval-Augmented Generation (RAG).

The system allows users to upload medical documents (PDF/TXT) and ask questions that are answered using retrieved document context along with a large language model.

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Overview

Medical documents often contain large amounts of unstructured text. This project demonstrates how RAG (Retrieval-Augmented Generation) can be used to ground LLM responses in domain-specific documents, improving reliability and relevance.

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Features

• Streamlit-based interactive user interface
• Upload and process medical PDF and TXT files
• Semantic document retrieval using vector embeddings
• Context-aware question answering
• Fallback to general medical knowledge when no relevant context is found

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Architecture

• Frontend: Streamlit
• LLM: LLaMA 3 (via Ollama)
• Embeddings: mxbai-embed-large
• Vector Database: ChromaDB
• Framework: LangChain

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Project Structure

Medical-RAG-Assistant/
│
├── medical_app.py
├── rag_engine.py
├── requirements.txt
├── README.md
│
├── medical_docs/          # Uploaded documents (ignored in Git)
└── medical_chroma_db/     # Vector database (auto-generated, ignored in Git)

yaml Copy code

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How to Run

Step 1: Install Ollama

Download and install Ollama from:
https://ollama.com

Pull the required models:

ollama pull llama3.2
ollama pull mxbai-embed-large