🩺 MedQueryDoc AI – Intelligent Medical Document Assistant

Stop Ctrl+F-ing through medical PDFs. Ask in plain English, get answers from Llama-2.

📌 About the Project

This project is an AI-powered medical document question-answering system built using Large Language Models (LLMs) and vector-based information retrieval. It enables users to ask natural language questions and receive accurate, context-aware answers derived from medical PDF documents.

The system works by loading and splitting PDF documents into smaller chunks, generating vector embeddings using a Hugging Face embedding model, and storing them in Pinecone, a scalable vector database. During inference, user queries are matched against relevant document chunks using similarity search, and responses are generated using LLaMA-2 through a retrieval-augmented generation (RAG) pipeline.

This project demonstrates practical implementation of LLMs + LangChain + Vector Databases for real-world AI applications.

🔄 How it works

PDFs → split into chunks → embeddings → Pinecone
                                          ↓
Your question → similarity search → top chunks → Llama-2 → answer…