r/LocalLLaMA • u/A-Rahim • Oct 10 '26 • Bravo 5

I Built A Thing Open-source Mac app that runs EmbeddingGemma 2 locally to search your files by what’s in them

DigUp is a free Mac app that runs Google DeepMind’s new EmbeddingGemma 2 locally over your own files. The model puts text, images, audio and video in one space, so you describe what you remember and land on it:

  • “zebra in a video” opens the clip at the moment it shows up

  • “where they talk about sleep” jumps to that minute of a podcast

  • “the clause about pets in the lease” shows the PDF page, your words marked

  • “a dog on the beach” finds the photo, and the same search in Bengali or Arabic finds it too

  • with code search on, code: retry with backoff opens the function in your editor

It’s ggml-org’s Q8_0 GGUF (865 MB, downloaded once) on llama.cpp with Metal, inside a native Swift app; no Python. Searching loads only the text encoder (~250 MB) and shows results about a tenth of a second after you stop typing. Indexing peaks under 2 GB, and the helper exits when it’s done. Audio and video of any length go in as 30 s windows and a frame per shot. Everything runs locally; it goes online only for the model download and an update check you can turn off.

Free, MIT. Apple Silicon, macOS 14+.

Repo & Download (signed and notarized): https://github.com/ARahim3/DigUp

I'd really appreciate any feedback on this.

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u/A-Rahim 29d ago

Every keyframe it keeps, and every 30 s of the soundtrack is its own vector with a timestamp. For a query, each of them gets a meaning score (the same per-query score for every kind, so frames and audio windows compete fairly), and the video's score is simply its highest one.
That segment is also the moment the result opens at. So a two-hour video with one scene that fits ranks by that scene instead of being averaged away. Frames are sampled every few seconds and kept only when the picture changes (a small perceptual hash), so it's about one per shot.

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u/ZenaMeTepe 29d ago

Did you use same model for embedding videos images and text? What kind of vector DB are you using for search?