README.md

metadata

title: Document Question Answering
emoji: 📄
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.38.0
python_version: '3.11'
app_file: app.py
pinned: false

Document Question Answering using Groq

A production-ready Retrieval-Augmented Generation (RAG) application built with:

• LangChain
• ChromaDB
• HuggingFace Embeddings
• Groq LLM
• Gradio

Features

• Upload one or more PDF documents
• Semantic document retrieval
• Retrieval-Augmented Generation (RAG)
• Groq Llama 3.3 integration
• Source citations with page numbers
• Adjustable chunk size
• Adjustable chunk overlap
• Adjustable Top-K retrieval

Deployment

1. Create a Hugging Face Space using the Gradio SDK.
2. Upload app.py, requirements.txt and this README.md.
3. Add a repository secret named GROQ_API_KEY.
4. Paste your Groq API key as the secret value.
5. Wait for the build to complete.

Notes

This project is intended as an educational demonstration of a modern Document Question Answering workflow.