Source-linked AI summary
VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos
Xubin Ren, Lingrui Xu, Long Xia, Shuaiqiang Wang, Dawei Yin, Chao Huang
TL;DR
Text-focused RAG leaves multi-modal, extremely long-context video knowledge insufficiently addressed. VideoRAG combines graph-based textual grounding with multi-modal context encoding, and its evaluation on LongerVideos reports superior performance against existing alternatives and long-video methods.
Problem
Existing RAG methods primarily target text, while video understanding must handle visual, audio, textual, temporal, and cross-modal information across long contexts.
Method
VideoRAG builds graph-based textual knowledge grounding alongside multi-modal context encoding for indexing and retrieving information from unlimited-length videos.
Results
VideoRAG demonstrates superior performance across multiple dimensions on the LongerVideos benchmark compared with existing RAG alternatives and long-video understanding methods.
Takeaways & Limitations
LongerVideos enables evaluation of reasoning across multiple long-context videos using over 160 videos totaling 134+ hours across three content categories.
Takeaways & Limitations
The framework formulates its target setting as an unconstrained video knowledge base with arbitrary video duration and unrestricted video count.
Abstract
from arXiv · showhide
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.
1 Introduction
VideoRAG addresses the difficulty of applying retrieval-augmented generation to multi-modal, extremely long-context videos. Its dual-channel design combines graph-based textual grounding with multi-modal context encoding, and evaluation uses a diverse long-video benchmark.
- RAG’s success in text applications has not fully extended to multi-modal video understanding, where videos combine visual, temporal, and semantic complexity.
- Long-video models designed for short clips struggle to capture temporal dependencies spanning multiple hours and to reason across multiple videos.
- VideoRAG uses graph-based textual knowledge grounding and multi-modal context encoding to preserve cross-video semantic relationships and fine-grained cross-modal information.
- VideoRAG’s evaluation reports advantages over existing RAG alternatives and large vision models, while ablations identify graph grounding and multi-modal retrieval as important components.
- LongerVideos contains over 160 videos totaling 134+ hours across lecture, documentary, and entertainment categories for evaluating reasoning across multiple long-context videos.
2 Preliminary
RAG organizes external knowledge for retrieval-augmented language-model responses, while VideoRAG extends this paradigm to unconstrained video knowledge bases. The video setting requires multi-modal and temporal modeling beyond standard text-based retrieval.
- RAG uses indexing to organize a knowledge database and retrieval to find query-specific knowledge for language-model response generation.
- Extending RAG to videos requires capturing visual, audio, and textual characteristics together with their temporal dynamics and cross-modal dependencies.
- VideoRAG targets a video knowledge base with arbitrary video durations and an unrestricted number of videos.
3 The VideoRAG Framework
VideoRAG combines graph-based textual grounding with multi-modal context encoding to index and retrieve knowledge from unlimited-length videos. Its framework integrates visual, audio, and semantic information while preserving relationships within and across videos.
- Framework Overview: VideoRAG addresses unlimited-length video understanding through multi-modal knowledge indexing and knowledge-grounded retrieval of relevant clips.The framework is designed to organize visual, audio, and semantic information for response generation with large language models.
- Multi-Modal Knowledge Indexing: Visual clips are converted into textual descriptions by sampling chronologically ordered frames and combining VLM-generated captions with ASR transcripts.Each clip uses up to 10 sampled frames, while ASR extracts dialogue and narration for unified textual representation.
- Graph-based Textual Knowledge Grounding: The graph-based indexing module extracts entities and relationships from text chunks, then incrementally unifies entities and integrates new cross-video connections.LLM-powered semantic synthesis maintains comprehensive and consistent entity descriptions across video contexts.
- Graph-based Textual Knowledge Grounding: Text chunks are embedded for efficient retrieval, while the resulting knowledge graph and chunk embeddings form the core graph indexing components.For each chunk H, a text encoder produces an embedding, and the collection is represented as E^t_H.
- Multi-Modal Context Encoding: Multi-modal context encoding preserves visual nuances lost during vision-to-text grounding by mapping video content and textual queries into a shared retrieval space.The hybrid index combines graph-based textual knowledge with multi-modal context embeddings.
- Multi-Modal Retrieval: The retrieval paradigm combines textual semantic matching with visual content matching to identify informative clips and generate query-specific descriptions.This hybrid process is intended to capture both semantic understanding and visual context during retrieval.
4 Evaluation
VideoRAG is evaluated on LongerVideos against RAG baselines and long-context video understanding models, with additional ablations and qualitative case studies. Across these evaluations, the framework is reported to outperform comparison methods, while graph-based grounding and visual processing are identified as important components.
- Experimental Settings: LongerVideos evaluates VideoRAG across lecture, documentary, and entertainment videos, addressing long-form multi-video understanding.The benchmark supports evaluation across multiple video categories and long-context scenarios.
- Overall Comparison (RQ1): VideoRAG consistently outperforms RAG baselines across evaluation metrics through multi-modal knowledge indexing and retrieval.Its retrieval combines textual semantic matching with visual content embedding-based retrieval for relevant clip selection.
- Overall Comparison (RQ1): VideoRAG achieves superior performance to GraphRAG and LightRAG by fusing visual-textual information and using query-aware multi-modal retrieval.The reported advantages concern knowledge indexing, unified representations, and contextually coherent response generation.
- Overall Comparison (RQ2): VideoRAG consistently outperforms compared long-context video understanding methods across various metrics.The paper attributes this result partly to graph-enhanced indexing and retrieval that extends processing beyond conventional length constraints.
- Ablation Study (RQ3): The -Graph variant degrades across all evaluation metrics, indicating that graph-based retrieval supports inter-video relationships and cross-video knowledge dependencies.Removing the graph-based index-retrieval pipeline limits the model’s ability to establish multi-video relationships.
- Ablation Study (RQ3): The -Vision variant shows substantially decreased win rates, underscoring the role of visual processing in multi-modal context fusion.The ablation removes visual indexing and retrieval, while the full framework integrates visual features with other modalities.
5 Related Work
Related work establishes RAG’s text-centric foundations and highlights the difficulty of extending it to long, multi-video, multimodal understanding. VideoRAG addresses these gaps through integrated knowledge organization and retrieval over visual-textual video information.
- RAG Foundations: RAG combines indexing, retrieval, and generation to incorporate external knowledge into language-model responses.The indexing stage structures source data, while retrieval selects query-relevant information for generation.
- Text-Centric RAG: Chunk-based and graph-based RAG methods improve text segmentation, retrieval, or structured knowledge representation, but remain primarily text-centric.These approaches follow distinct trajectories focused on vector-space embeddings or graph-structured retrieval.
- VideoRAG: VideoRAG combines a comprehensive multi-video knowledge graph with multimodal retrieval matching to support accurate, contextually relevant responses.Its design synthesizes text-based and multimodal RAG techniques for organizing and extracting video knowledge.
- Long Video Understanding: Long-video understanding becomes computationally demanding as video length and quantity increase, requiring a balance between coverage and available resources.Large video language models commonly convert frames into vision tokens, which increases demands for longer inputs.
- VideoRAG: VideoRAG constructs a graph integrating information from multiple videos with visual features for general knowledge extraction and query-specific retrieval.The framework is designed to accommodate input videos of arbitrary lengths and quantities.
6 Conclusion
The paper introduces VideoRAG for extremely long-context video understanding through a dual-channel architecture. Evaluation on LongerVideos reports superior performance against existing RAG alternatives and long-video understanding methods.
- Conclusion: VideoRAG integrates graph-based textual knowledge grounding with multimodal context encoding to process, index, and retrieve information from unlimited-length videos.The architecture preserves cross-video semantic relationships while retaining visual information for large-language-model enhancement.
- Conclusion: Evaluation on LongerVideos demonstrates superior VideoRAG performance across multiple dimensions compared with existing RAG alternatives and long-video understanding methods.The reported capabilities include precise video knowledge structures, multimodal content retrieval, and processing across multiple long-context videos.
A Details of LongerVideos
LongerVideos is a benchmark for evaluating comprehension and knowledge extraction from diverse, long-form video lists. It assesses whether models can synthesize responses across multiple sources using completeness, accuracy, and diversity criteria.
- Benchmark Purpose: LongerVideos evaluates models’ ability to comprehend and extract knowledge from long-form videos using semantically connected multiple sources.The benchmark is designed for knowledge-based question answering across video lists.
- Evaluation Criteria: The benchmark evaluates generated answers for completeness, accuracy, and diversity.These criteria target content understanding, information synthesis, and well-rounded response generation.
- Benchmark Structure: Each benchmark instance provides a diverse video list, open-ended questions, and expected responses based on information extracted from the videos.Video durations range from minutes to hours.
- Dataset Construction: LongerVideos was constructed by curating diverse thematic video lists from YouTube, including online course recordings organized by course chapters.The videos were downloaded in 720P and questions were prepared for each list with assistance from NotebookLM.
- Dataset Construction: The final dataset contains 22 carefully curated video lists.Detailed dataset statistics are reported in Table 5.
B Details of Case Study
The case study examines VideoRAG’s retrieval and synthesis for a question about graders in OpenAI’s 12-video series. The reported response provides operational detail about grading and is compared with LightRAG-related case-study material.
- Case Study Setup: The case study uses OpenAI’s 12 Days of OpenAI series to examine graders in reinforcement fine-tuning.The model retrieves content specifically addressing graders’ role in that context.
- Comparison: Table 6 provides further details on LightRAG’s response to the same question.The table is framed as a continuation of the case-study comparison.
- Response Analysis: VideoRAG’s answer includes grading criteria, examples of partial scores, and the processes through which graders operate in reinforcement learning.The comparison describes the answer as more detailed and associates this detail with improved response quality.
- Response Analysis: The case study presents empowerment and trustworthiness as qualities of VideoRAG’s response.The response explains scoring, input comparison, and the feedback loop while aligning with established machine-learning concepts.
C Instructions for Win-Rate & Quantitative Performance Comparison
The evaluation compares answers using both win-rate and quantitative scoring procedures, with instructions designed to reduce positional bias and apply consistent criteria.
- Win-Rate Comparison: Win-rate comparison evaluates two competing answers alongside the query while alternating answer positions across iterations to mitigate positional bias.The competing responses are designated answer1 and answer2.
- Quantitative Comparison: Quantitative comparison scores an evaluation answer against a standard NaiveRAG baseline answer on a 1-to-5 scale.The baseline is labeled baseline_answer and the evaluated response evaluation_answer.
- Quantitative Comparison: Using a shared standard answer enables comparison of multiple models without adjusting their answer positions.
- Evaluation Criteria: The answer-comparison criteria are comprehensiveness, empowerment, trustworthiness, depth, and density.