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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.

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.

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

Architecture

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

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

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