VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos

TL;DR

VideoRAG introduces a novel retrieval-augmented generation framework tailored for extremely long videos, combining graph-based knowledge grounding and multi-modal encoding to enhance understanding and processing of multi-hour video content.

Contribution

It is the first framework to enable retrieval-augmented processing of long videos using a dual-channel architecture for semantic and visual integration.

Findings

Outperforms existing methods on the LongerVideos benchmark

Successfully processes over 160 videos totaling 134+ hours

Demonstrates effective multi-modal and cross-video knowledge integration

Abstract

Retrieval-Augmented Generation (RAG) has demonstrated remarkable success in enhancing Large Language Models (LLMs) through external knowledge integration, yet its application has primarily focused on textual content, leaving the rich domain of multi-modal video knowledge predominantly unexplored. This paper introduces VideoRAG, the first retrieval-augmented generation framework specifically designed for processing and understanding extremely long-context videos. Our core innovation lies in its dual-channel architecture that seamlessly integrates (i) graph-based textual knowledge grounding for capturing cross-video semantic relationships, and (ii) multi-modal context encoding for efficiently preserving visual features. This novel design empowers VideoRAG to process unlimited-length videos by constructing precise knowledge graphs that span multiple videos while maintaining semantic dependencies through specialized multi-modal retrieval paradigms. Through comprehensive empirical evaluation on our proposed LongerVideos benchmark-comprising over 160 videos totaling 134+ hours across lecture, documentary, and entertainment categories-VideoRAG demonstrates substantial performance compared to existing RAG alternatives and long video understanding methods. The source code of VideoRAG implementation and the benchmark dataset are openly available at: https://github.com/HKUDS/VideoRAG.