The complete installation process for VideoRAG consists of the following key steps:
- environmental preparation::
conda create --name videorag python=3.11conda activate videorag - Core dependency installation: multimedia processing libraries including PyTorch 2.1.2, pytorchvideo, ImageBind, etc.
- Model Component Deployment::
- Download MiniCPM-V-2_6-int4 visual model from HuggingFace
- Obtaining the fast-distil-whisper-large-v3 speech recognition model
- Download imagebind_huge.pth multimodal feature extractor
Use with care:
- It is recommended that video files be categorized and stored by topic
- The first processing will automatically generate
.checkpointsCatalog storage feature index - Knowledge graphs use Neo4j graph databases by default to store relational data
Typical processing flow: video upload → automatic segmentation → multimodal feature extraction → knowledge graph construction → query interface opening.
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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