VideoRAG framework, a novel paradigm for achieving extreme long-context video comprehension that addresses the scalability issues inherent in traditional Large Video Language Models (LVLMs).

The core innovation lies in its dual-channel architecture, which processes video data by constructing a structured semantic knowledge graph from transcripts and simultaneously creating multimodal vector embeddings for visual and temporal context.

This hybrid approach enables a hierarchical retrieval process that efficiently searches over massive video corpora (demonstrated with over 134 hours of content) before generating a factually grounded answer, significantly outperforming existing LVLM and single-modality Retrieval-Augmented Generation (RAG) baselines.

The source emphasizes that VideoRAG is a necessary architectural shift that decouples knowledge storage from active reasoning, making cross-video and long-range temporal analysis possible through its combination of logical inference and visual grounding.