ClipFinder: Natural-Language Video Moment Search in One app.py

ClipFinder is presented as a lightweight video-search application built with Pixeltable that lets users upload MP4 files, sample frames at a chosen rate, and search for visual moments using natural-language queries powered by CLIP embeddings. Rather than requiring separate object storage, frame-extraction jobs, GPU embedding services, vector databases, and synchronization workflows, the approach defines a Videos table and a Frames view whose iterator extracts frames and whose embedding index supports text-to-frame similarity search. New uploads are intended to become searchable automatically through the view and index, without re-embedding the existing library. The example includes a FastAPI-based insert endpoint for video uploads and a search endpoint that returns the most relevant frames along with video metadata, and it describes deployment to local or Pixeltable Cloud environments using the same application file. The post distinguishes this frame-level visual search workflow from consumer search products, audio analysis, and poster-only image search, while suggesting one frame per second as a practical default for interviews and B-roll.

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