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vtayyab6/retrieval-augmented-document-qa

sourceHugging Faceupdated 7mo agoView on Hugging Face
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App README

Retrieval-Augmented Document QA

A Gradio application that lets users upload documents, build a semantic FAISS index, and ask grounded questions answered by Gemini with retrieved context.

Features

FeatureDetails
Document uploadSupports PDF, TXT, and Markdown (max 3 files for fast demos).
RAG pipelineText extraction -> chunking -> embeddings -> FAISS indexing -> retrieval -> Gemini answering.
Embeddingsall-MiniLM-L6-v2 (SentenceTransformers).
LLMgemini-3.1-flash-lite-preview via HTTP API.
Retrieval transparencyShows retrieved chunks, source references, and pipeline trace.
SecurityAPI key is entered in the UI, used per run, and not stored/logged.
Space speed controlsFile count limit, PDF page limit, text-size cap, lazy model loading, and shared embedding cache.

Project Structure

text
retrieval-augmented-document-qa/
├── app.py
├── requirements.txt
├── README.md
└── src/
    ├── __init__.py
    ├── config.py
    ├── document_loader.py
    ├── chunker.py
    ├── embeddings.py
    ├── vector_store.py
    ├── retriever.py
    ├── rag_pipeline.py
    └── gemini_client.py

How It Works

  1. 1.Upload up to 3 files (.pdf, .txt, .md).
  2. 2.Click Process Documents to build the knowledge base.
  3. 3.Enter your Gemini API key and question.
  4. 4.Click Ask Question.
  5. 5.Review:
  6. 6.final answer
  7. 7.retrieved chunks
  8. 8.sources used
  9. 9.execution trace

Local Setup

bash
cd retrieval-augmented-document-qa
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python app.py

Open http://127.0.0.1:7860.

Notes

  • —This app runs fully on CPU and is designed for Hugging Face Spaces demos.
  • —Retrieval controls answer grounding; if context is missing, the app prompts Gemini to say so.