metadata
title: VDoc-RAG
emoji: 📄
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
📄 VDoc-RAG (Visually-Rich Document Retrieval-Augmented Generation)
VDoc-RAG is an intelligent document Q&A system that answers questions from PDFs, resumes, reports, and other visually-rich documents using RAG (Retrieval-Augmented Generation) technology.
🔗 Live Demo: https://huggingface.co/spaces/adi9128/vdoc_rag
✨ Key Features
| Feature | Description |
|---|---|
| 📤 Document Upload | Upload PDF files for instant processing |
| 🔍 Smart Q&A | Ask natural language questions about your documents |
| 🧠 RAG Pipeline | Retrieval-Augmented Generation with ChromaDB vector store |
| 📊 OCR + Table/Chart Extraction | Tesseract OCR with visual element detection |
| 💯 Confidence Scoring | Cosine similarity scores for retrieved chunks |
| 📝 Feedback System | Mark answers as correct/incorrect for improvement |
| 🔄 Document Isolation | Each upload clears previous data for privacy |
| 🎨 Modern UI | Clean gradient design with responsive layout |
🚀 Live Deployment
The app is deployed on HuggingFace Spaces using Docker:
- URL: https://huggingface.co/spaces/adi9128/vdoc_rag
- SDK: Docker (Python 3.11)
- Port: 7860
💻 Local Development
Prerequisites
Install these tools and add them to PATH:
Setup
# Clone the repository
git clone https://github.com/YOUR_USERNAME/vdoc-rag.git
cd vdoc-rag
# Create virtual environment
python -m venv venv
venv\Scripts\activate # Windows
# source venv/bin/activate # Linux/Mac
# Install dependencies
pip install -r requirements.txt
# Set environment variables
# Create .env file with:
GEMINI_API_KEY=your_gemini_api_key_here
# Run the server
uvicorn app.main:app --host 127.0.0.1 --port 8000
Open → http://127.0.0.1:8000
🖥️ Web Interface
| Route | Description |
|---|---|
/ |
Main interface - Upload documents & ask questions |
/feedback_dashboard |
View feedback statistics |
/benchmark_dashboard |
Embedding model evaluation metrics |
📁 Project Structure
vdoc-rag/
├── app/
│ ├── main.py # FastAPI server & routes
│ ├── indexer.py # ChromaDB vector store wrapper
│ ├── embeddings.py # Sentence-transformer embeddings
│ ├── ingest.py # PDF processing & OCR
│ ├── reader.py # LLM (Gemini) integration
│ ├── chart_reasoner.py # Chart/graph understanding
│ ├── feedback_manager.py # User feedback collection
│ ├── visual_highlight.py # PDF region highlighting
│ ├── cache_manager.py # Response caching
│ └── templates/ # HTML templates
│ ├── index.html
│ ├── feedback_dashboard.html
│ └── benchmark_dashboard.html
├── Dockerfile # HuggingFace Spaces deployment
├── requirements.txt # Python dependencies
├── TESTING_GUIDE.md # Step-by-step testing guide
└── README.md
🧠 Technology Stack
| Component | Technology |
|---|---|
| Backend | FastAPI + Uvicorn |
| Vector Database | ChromaDB (persistent) |
| Embeddings | Sentence-Transformers (all-MiniLM-L6-v2) |
| LLM | Google Gemini API |
| OCR | Tesseract + Poppler |
| PDF Processing | pdf2image + PyMuPDF |
| Frontend | Jinja2 + TailwindCSS |
| Deployment | Docker on HuggingFace Spaces |
🔒 Privacy & Document Isolation
- Stateless Design: Each page refresh shows a clean interface
- Document Isolation: Uploading a new document clears all previous data
- No Persistent User Tracking: Documents are processed in-memory per session
- Shared Instance Note: HuggingFace free tier uses a single server instance
📈 How It Works
- Upload: PDF is processed with OCR and chunked into text segments
- Embed: Chunks are converted to vectors using Sentence-Transformers
- Index: Vectors are stored in ChromaDB for semantic search
- Query: User questions are embedded and matched against stored chunks
- Generate: Top matching chunks are sent to Gemini LLM for answer generation
- Display: Answer with confidence scores and source attribution
🧪 Testing
See TESTING_GUIDE.md for step-by-step instructions.
👨💻 Author
Made with ❤️ by Aditya
📄 License
This project is open source and available for educational and research purposes.