| --- |
| 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. |
|
|
