VideoRAG
VideoRAG copied to clipboard
[KDD'2026] "VideoRAG: Chat with Your Videos"
VideoRAG: Chat with Your Videos โข Vimo Desktop
๐ฌ 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.
๐ฅ Watch Vimo in Action
See how Vimo transforms video interaction with intelligent conversations and deep understanding capabilities.
โจ Key Features
For Everyone
- 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
For Power Users
- 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
๐ 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
๐ Table of Contents
- ๐ Quick Start
- โจ Key Features
- ๐ฌ VideoRAG Algorithm
- ๐ ๏ธ Development Setup
- ๐งช Benchmarks & Evaluation
- ๐ Citation
- ๐ค Contributing
- ๐ Acknowledgement
๐ 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:
- Set up the Python backend environment and start the VideoRAG server
- Launch the Electron frontend application
- Start chatting with your videos!
๐ฌ 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:
Experiments and Evaluation
See VideoRAG-algorithm for detailed development setup including:
- Conda environment creation
- Model checkpoints download
- Dependencies installation
- Evaluation scripts
๐งช LongerVideos Benchmark
We created the LongerVideos benchmark to evaluate long-context video understanding:
| Video Type | #Collections | #Videos | #Queries | Avg. Duration |
|---|---|---|---|---|
| Lectures | 12 | 135 | 376 | ~64.3 hours |
| Documentaries | 5 | 12 | 114 | ~28.5 hours |
| Entertainment | 5 | 17 | 112 | ~41.9 hours |
| Total | 22 | 164 | 602 | ~134.6 hours |
For detailed evaluation instructions and reproduction scripts, see VideoRAG-algorithm/reproduce.
๐ 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}
}
๐ค 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!
๐ 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
๐ Transform how you interact with videos. Start your journey with Vimo today!
