📄 RAG Document Q&A

An advanced AI-powered document chatbot using Retrieval Augmented Generation (RAG) to answer questions from any PDF with high accuracy.

🚀 Live Demo

Click here to try it →

⚙️ How It Works

1. PDF is split into semantic chunks
2. Each chunk is converted to vector embeddings using Sentence Transformers
3. Embeddings stored in a FAISS vector database
4. User question is embedded and matched via similarity search
5. Most relevant chunks retrieved and sent to Groq LLM (LLaMA 3.3) for answer generation

🛠️ Tech Stack

Tool	Purpose
Python	Core language
Streamlit	Frontend UI
Groq API (LLaMA 3.3)	LLM inference
FAISS	Vector database
Sentence Transformers	Text embeddings
PyPDF	PDF parsing

✨ Features

• True RAG pipeline with semantic search
• FAISS vector similarity search
• Context-aware answers from any PDF
• Fast inference via Groq API
• No API key needed

💻 How to Run Locally

pip install -r requirements.txt
streamlit run appdoc.py