VideoRAG's revolutionary contribution lies in its innovative knowledge graph construction technology, which can transform hundreds of hours of continuous video streams into structured and queryable knowledge assets. The system adopts neo4j graph database as the knowledge storage base, and realizes semantic-level structured representation of video content through core technologies such as automated entity recognition, relationship extraction and event association.
The technical architecture consists of three core processing aspects: firstly, key frames and semantic passages are extracted through a hierarchical sampling strategy; subsequently, a transformer model is applied to analyze multimodal features; and finally, a graph neural network is used to construct a semantic association network across videos. The innovative hnswlib vector indexing technique ensures efficient storage and retrieval of massive video features.
Compared with traditional video tagging systems, VideoRAG's knowledge graph not only records discrete keywords, but also captures the conceptual evolution logic and deep knowledge associations of video content. For example, when dealing with educational videos, the system can automatically identify the knowledge structure of the course, help users quickly locate the core concepts and their related examples, and significantly improve the efficiency of knowledge acquisition.
This answer comes from the articleVideoRAG: A RAG framework for understanding ultra-long videos with support for multimodal retrieval and knowledge graph constructionThe
