Multimodal Search Optimization Scheme
VideoRAG realizes retrieval efficiency through the following technological innovations:
- Dual Channel Architecture Design::
- Text Channel: Transformer-based Semantic Understanding
- Visual channels: cross-modal feature extraction using ImageBind
- Hybrid Indexing Strategy::
- HNSW algorithm for handling high dimensional vectors
- nano-vectordb implements lightweight storage
- xxhash fast fingerprint matching
- Hands-on Configuration Points::
- Make sure to use the imagebind_huge model when loading checkpoints
- The fast-whisper model requires the large-v3 version.
- Balance precision speed by properly adjusting hnswlib's ef_search parameter
- Query Optimization Tips::
- Combined timestamp and visual keyframe filtering
- Semantic Extension Using Knowledge Graphs
- Setting multimodal feature fusion weights
Advanced Solution: You can try to integrate MiniCPM-V visual language model with the existing process to further improve the graphic correlation comprehension.
This answer comes from the articleVideoRAG: A RAG framework for understanding ultra-long videos with support for multimodal retrieval and knowledge graph constructionThe
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