README.md

---
title: NetraEmbed
emoji: 👁️
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 6.0.2
app_file: app.py
pinned: false
license: mit
short_description: Universal Multilingual Multimodal Document Retrieval
---

# NetraEmbed - Universal Multilingual Multimodal Document Retrieval

This Space demonstrates **NetraEmbed** and **ColNetraEmbed**, state-of-the-art multilingual multimodal document retrieval models based on the BiGemma3 and ColGemma3 architectures.

## Features

- **NetraEmbed (BiGemma3)**: Single-vector embedding with Matryoshka representation for fast retrieval
- **ColNetraEmbed (ColGemma3)**: Multi-vector embedding with late interaction for high-quality retrieval with attention heatmaps
- **ZeroGPU Integration**: Efficient dynamic GPU allocation for on-demand model loading
- **PDF Document Support**: Upload PDFs and perform semantic search across pages
- **Side-by-side Comparison**: Compare both models simultaneously

## Citation

If you use NetraEmbed or ColNetraEmbed in your research, please cite:

```bibtex
@misc{kolavi2025m3druniversalmultilingualmultimodal,
  title={M3DR: Towards Universal Multilingual Multimodal Document Retrieval},
  author={Adithya S Kolavi and Vyoman Jain},
  year={2025},
  eprint={2512.03514},
  archivePrefix={arXiv},
  primaryClass={cs.IR},
  url={https://arxiv.org/abs/2512.03514}
}
```

## Links

- 📄 [Paper](https://arxiv.org/abs/2512.03514)
- 💻 [GitHub](https://github.com/adithya-s-k/colpali)
- 🤗 [Models on Hugging Face](https://huggingface.co/Cognitive-Lab)
- 🌐 [CognitiveLab Website](https://www.cognitivelab.in)

## Usage

1. **Load Model**: Select your preferred model (NetraEmbed, ColNetraEmbed, or Both) and click "Load Model"
2. **Upload PDF**: Upload a PDF document to index
3. **Index Document**: Click "Index Document" to process and embed the pages
4. **Query**: Enter your search query and click "Search" to retrieve relevant pages

This Space uses ZeroGPU for dynamic GPU allocation. Models are loaded on-demand when functions are called.

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference