[KDD'2026] "VideoRAG: Chat with Your Videos"

VideoRAG: Chat with Your Videos • Vimo Desktop

🎬 Intelligent Video Conversations | Powered by Advanced AI | Extreme Long-Context Processing

Vimo is a revolutionary desktop application that lets you chat with your videos using cutting-edge AI technology. Built on the powerful VideoRAG framework, Vimo can understand and analyze videos of any length - from short clips to hundreds of hours of content - and answer your questions with remarkable accuracy.

See how Vimo transforms video interaction with intelligent conversations and deep understanding capabilities.

👆 Click to watch the Vimo demo video

• Drag & Drop Upload: Simply drag video files into Vimo
• Smart Conversations: Ask questions in natural language
• Multi-Format Support: Works with MP4, MKV, AVI, and more
• Cross-Platform: Available on macOS, Windows, and Linux

• 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

• 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

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

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

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 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

• 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

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

See VideoRAG-algorithm for detailed development setup including:

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

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

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

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}
}

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!

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!