📄 AskMyPDF — RAG Document Q&A

Upload any PDF and chat with it using AI. Built with FastAPI, LangChain, ChromaDB, and Groq.

🌍 Live Demo

rag-document-qa-mauve.vercel.app

🛠️ Tech Stack

• Frontend: React + Vite, deployed on Vercel
• Backend: FastAPI + LangChain + ChromaDB + Groq (Llama-3.3-70b)
• Embeddings: HuggingFace (all-MiniLM-L6-v2)
• Containerization: Docker + Docker Compose
• Deployment: HuggingFace Spaces (backend) + Vercel (frontend)

✨ Features

• Upload any PDF up to 50MB
• AI indexes and understands the document
• Ask questions in natural language
• Get answers with source page citations
• Conversation memory across questions
• Drag and drop upload interface

🏗️ Architecture

• PDF Upload goes to FastAPI backend
• LangChain processes and chunks the document
• ChromaDB stores vector embeddings
• Groq Llama-3.3-70b generates answers
• Source citations returned with every answer

🚀 Run Locally

Clone the repo:

git clone https://github.com/Sh10bh/RAG_DOCUMENT_QA

Start backend:

cd backend
pip install -r requirements.txt
uvicorn main:app --reload

Start frontend:

cd frontend
npm install
npm run dev

🐳 Run with Docker

docker-compose up --build

📊 Project Structure

RAG_DOCUMENT_QA/
├── backend/
│   ├── main.py          # FastAPI endpoints
│   ├── rag.py           # LangChain RAG pipeline
│   ├── requirements.txt
│   └── Dockerfile
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   │   ├── UploadPanel.jsx
│   │   │   ├── ChatWindow.jsx
│   │   │   └── SourceCitation.jsx
│   └── Dockerfile
├── docker-compose.yml
└── notebook/
    └── rag_notebook.ipynb

👨‍💻 Author

Shubh Gupta — VIT Bhopal, CSE (2023-2027)