Reading
FrameFinder is an open-source multimodal Retrieval-Augmented Generation (RAG) system that combines the dual encoder architecture of OpenCLIP ViT-H-14 and TimeSformer to enable intelligent semantic retrieval and question answering for video content.
Section 01
FrameFinder is an open-source multimodal Retrieval-Augmented Generation (RAG) system that combines the dual encoder architecture of OpenCLIP ViT-H-14 and TimeSformer to enable intelligent semantic retrieval and question answering for video content.
Section 02
Section 03
With the explosive growth of video data, traditional retrieval methods based on text tags or keyframe screenshots can no longer meet the demand. Users want to query video content directly through natural language, just like conversing with a document—such as "Find all clips about machine learning in the video" or "What optimization techniques are covered in this tutorial?" This kind of demand has spurred a strong need for multimodal RAG (Retrieval-Augmented Generation) systems.
FrameFinder is an open-source solution designed to address this pain point. It uses a dual encoder architecture to capture both the spatial visual features and temporal dynamic features of videos, establishing fine-grained semantic indexes for video content.
Section 04
The core innovation of FrameFinder lies in its dual-stream embedding strategy, which handles the spatial and temporal dimensions of videos separately:
Section 05
The system uses OpenCLIP's ViT-H-14 model to extract visual semantic features from each frame. This large-scale vision Transformer can generate high-quality image embeddings, mapping frame content to a high-dimensional semantic space. Whether it's a PPT screenshot, code demonstration, or physical object display, accurate vector representations can be obtained.
Section 06
Spatial features alone cannot capture the dynamic information of videos. FrameFinder introduces the TimeSformer model, which is specifically designed to handle the temporal dimension of videos. TimeSformer extends the self-attention mechanism to the time axis, enabling it to recognize temporal patterns such as action sequences, process demonstrations, and explanation rhythms.
Section 07
The generated bimodal embeddings are indexed into a PostgreSQL database, and efficient similarity search is achieved using the pgvector extension. Compared to dedicated vector databases, this solution is easier to deploy and can leverage PostgreSQL's mature transaction and backup mechanisms.
Section 08
FrameFinder adopts a clear three-layer architecture:
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