Overview
VideoRAG is an open-source framework and a companion desktop application (Vimo) that enables natural-language conversations with videos of arbitrary length. The project combines retrieval-augmented generation (RAG) techniques with graph-driven multi-modal knowledge indexing to build concise, queryable representations of long video content. The repository provides implementation, demo, benchmarks, and scripts for reproducing experiments described in the associated arXiv paper (arXiv:2502.01549).
Key Components
- Graph-Driven Knowledge Indexing: distills long videos into structured multi-modal knowledge graphs to support efficient retrieval and reasoning.
- Hierarchical Context Encoding: encodes spatiotemporal patterns across long sequences to preserve long-range dependencies.
- Adaptive Retrieval: dynamic retrieval mechanisms that align textual queries with visual and audio content for precise moment/scene localization.
- Cross-Video Understanding: models semantic relationships across multiple videos to enable comparative queries and multi-video analysis.
Features
- Interactive Desktop App (Vimo): drag-and-drop upload, natural-language Q&A, multi-format support (MP4/MKV/AVI), cross-platform (macOS/Windows/Linux).
- Extreme Long-Context Processing: the project claims the framework can handle videos from short clips up to hundreds of hours, and is optimized to run on a single high-memory GPU (example: RTX 3090, 24GB) for efficient extraction and retrieval.
- Benchmarking: includes the LongerVideos benchmark (reported ~164 videos / 134.6+ hours across lectures, documentaries, entertainment) and evaluation scripts for reproducing results.
- Extensible & Research-Friendly: modular architecture for researchers to plug in different encoders, retrieval modules, and LLM backends; includes checkpoints, environment setup, and reproducibility instructions.
Benchmarks & Performance
The repository reports evaluation results comparing VideoRAG to prior methods on the LongerVideos benchmark, highlighting improvements in long-context video comprehension and retrieval accuracy. The LongerVideos benchmark covers lecture, documentary, and entertainment domains with per-category statistics provided in the repo (e.g., ~135 lecture videos, average durations listed in the README).
Usage & Deployment
- Options: (1) wait for packaged Vimo releases (macOS Apple Silicon prioritized) or (2) run from source by setting up the Python backend (VideoRAG server) and launching the Electron frontend.
- Quick start: create conda environment, install dependencies, download model checkpoints, run extraction/indexing, and start the desktop frontend.
Resources & Community
- Paper: arXiv:2502.01549 (VideoRAG)
- Demo: repository links to a YouTube demo video and blogpost tutorials
- Community: GitHub issues, Discord/WeChat/Feishu links for discussions
Citation
If used in research, the authors request citing the arXiv preprint (arXiv:2502.01549). The repository provides a bibtex entry for convenience.
Notes
Information above is summarized from the project's GitHub README and linked paper. The repository shows active community materials (demo video, blog, Discord) and reports ~1500 stars on GitHub as of the repository metadata.
