1. Abstract
The Medical Knowledge RAG Assistant is a Generative AI-powered application that retrieves information from medical documents and answers health-related questions using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). It helps users explore medical research papers, healthcare guidelines, and educational materials by providing context-based answers with source references.
2. Objectives
- Develop an AI assistant for searching medical knowledge.
- Retrieve relevant information from medical documents.
- Generate context-aware answers using LLMs.
- Summarize medical research papers and healthcare guidelines.
- Provide source references for answer verification.
- Improve access to medical educational information.
3. Existing System
Medical information is often searched manually across books, research papers, websites, and PDF documents.
Disadvantages
- Searching large medical documents takes time.
- Medical information is scattered across multiple sources.
- Technical terminology can be difficult to understand.
- Finding relevant research evidence requires manual effort.
4. Proposed System
The proposed system uses RAG to retrieve relevant passages from a medical knowledge base and generate answers grounded in those passages.
Key features:
- Upload medical PDFs and research papers.
- Semantic search across medical documents.
- AI-powered question answering.
- Medical document summarization.
- Source citations and page references.
- Interactive follow-up questions.
- Document management and chat history.
5. Implementation Procedure
- Collect medical research papers and approved educational documents.
- Extract text from PDFs using PyMuPDF.
- Split documents into searchable text chunks.
- Generate embeddings using an embedding model.
- Store embeddings in FAISS or ChromaDB.
- Retrieve relevant passages for each user question.
- Use an LLM to generate answers based on retrieved context.
- Display answers with supporting sources.
- Test retrieval accuracy and answer relevance.
- Deploy the application using Streamlit or React.js.
6. Software Requirements
- Language: Python
- Frontend: Streamlit or React.js
- Backend: Flask or FastAPI
- LLM: OpenAI API or a compatible model
- RAG Framework: LangChain or LlamaIndex
- PDF Processing: PyMuPDF
- Embeddings: Sentence Transformers
- Vector Database: FAISS or ChromaDB
- Environment: VS Code
- OS: Windows or Linux
7. Hardware Requirements
- Processor: Intel Core i3 or equivalent
- RAM: Minimum 8 GB; 16 GB recommended
- Storage: At least 10 GB free space
- Internet: Required for hosted AI APIs
- GPU: Optional for cloud-based models
8. Advantages of the Project
- Enables faster medical document searches.
- Provides context-based answers from available sources.
- Simplifies lengthy medical research papers.
- Supports evidence verification through citations.
- Improves access to medical educational resources.
- Reduces manual information retrieval effort.