• Extreme Long Videos: Process videos up to hundreds of hours
• Multi-Video Analysis: Compare and analyze multiple videos simultaneously
• Advanced Retrieval: Find specific moments and scenes with precision
• Export Capabilities: Save insights and references for later use

For Researchers

• VideoRAG Framework: Access to cutting-edge retrieval-augmented generation
• Benchmark Dataset: LongerVideos benchmark with 134+ hours of content
• Performance Metrics: Detailed evaluation against existing methods
• Extensible Architecture: Build upon our open-source foundation

🌟 Why Vimo?

For Video Enthusiasts & Professionals:

• Effortless Video Analysis: Upload any video and start asking questions immediately
• Natural Conversations: Chat with your videos as if talking to a human expert
• No Length Limits: Process everything from 30-second clips to 100+ hour documentaries
• Deep Understanding: Combines visual content, audio, and context for comprehensive answers

For Researchers & Developers:

• State-of-the-Art Algorithm: Built on VideoRAG, featuring graph-driven knowledge indexing
• Benchmark Performance: Evaluated on 134+ hours across lectures, documentaries, and entertainment
• Open Source: Full access to VideoRAG implementation and research findings
• Scalable Architecture: Efficient processing with single GPU (RTX 3090) capability

📋 Table of Contents

• 🚀 Quick Start
• ✨ Key Features
• 🔬 VideoRAG Algorithm
• 🛠️ Development Setup
• 🧪 Benchmarks & Evaluation
• 📖 Citation
• 🤝 Contributing
• 🙏 Acknowledgement

🚀 Quick Start of Vimo

Option 1: Download Vimo App (Coming Soon)

[!NOTE] We are preparing the Beta release for macOS Apple Silicon first, with Windows and Linux versions coming soon!

Option 2: Run from Source Code

For detailed setup instructions:

• Vimo Desktop App: See Vimo-desktop for complete installation and configuration steps

Quick Overview:

1. Set up the Python backend environment and start the VideoRAG server
2. Launch the Electron frontend application
3. Start chatting with your videos!

🔬 VideoRAG Algorithm

VideoRAG introduces a novel dual-channel architecture that combines:

• Graph-Driven Knowledge Indexing: Multi-modal knowledge graphs for structured video understanding
• Hierarchical Context Encoding: Preserves spatiotemporal visual patterns across long sequences
• Adaptive Retrieval: Dynamic retrieval mechanisms optimized for video content
• Cross-Video Understanding: Semantic relationship modeling across multiple videos

Technical Highlights

• Efficient Processing: Handle hundreds of hours on a single RTX 3090 (24GB)
• Structured Indexing: Distill long videos into concise knowledge representations
• Multi-Modal Retrieval: Align textual queries with visual and audio content
• LongerVideos Benchmark: 160+ videos, 134+ hours across diverse domains

Performance Comparison

Our VideoRAG algorithm significantly outperforms existing methods in long-context video understanding:

We also evaluate VideoRAG's QA performance on the Video-MME long video track to better understand the gains over the backbone models (included here because of the paper's page limit):

Note: The score may show slight fluctuations across runs due to the instability of LLM generation.

Experiments and Evaluation

See VideoRAG-algorithm for detailed development setup including:

• Conda environment creation
• Model checkpoints download
• Dependencies installation
• Evaluation scripts

🧪 LongerVideos Benchmark

We created the LongerVideos benchmark to evaluate long-context video understanding:

For detailed evaluation instructions and reproduction scripts, see VideoRAG-algorithm/reproduce.

📖 Citation

If you find Vimo or VideoRAG helpful in your research, please cite our paper:

@article{VideoRAG,
  title={VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos},
  author={Ren, Xubin and Xu, Lingrui and Xia, Long and Wang, Shuaiqiang and Yin, Dawei and Huang, Chao},
  journal={arXiv preprint arXiv:2502.01549},
  year={2025}
}

🤝 Contributing

We welcome contributions from the community! Whether you're:

• Reporting bugs or suggesting features for Vimo
• Improving VideoRAG algorithms or adding new capabilities
• Enhancing documentation or creating tutorials
• Designing UI/UX improvements for better user experience

Feel free to submit issues and pull requests. Together, we're building the future of intelligent video interaction!

🙏 Acknowledgement

Vimo builds upon the incredible work of the open-source community:

• VideoRAG: The core algorithm powering Vimo's intelligence
• nano-graphrag & LightRAG: Graph-based retrieval foundations
• ImageBind: Multi-modal representation learning
• uitars-desktop: Desktop application architecture inspiration

🌟 Transform how you interact with videos. Start your journey with Vimo today!