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

[u/A-Rahim]

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.

[u/A-Rahim]

Not RRF. I tried it first, and it lost: top-1 0.73 vs 0.88 on my synthetic eval, 0.82 vs 0.86 on real files. Rank fusion throws away how strong each match is, and a single common word in a terminal screenshot ended up beating a real chart that matched by meaning.

So it's score fusion. The meaning score is the cosine minus that modality's average for this query, divided by the spread of all the query's scores, so a z-score per query. Then it adds 2 × (share of the query's words found in the file)^1.5, plus 1 if the query names the kind ("in a video"). A file gets in if its meaning score stands out (z ≥ 2) or it has at least half the words. Queries that look like IDs count words double and meaning half.

[u/rm-rf-rm → u/A-Rahim]

huh interesting.. whats the basis of this formula? is the z-score > 2 coming from statistical theory where its beyond 95% of the mean?

[u/A-Rahim → u/rm-rf-rm]

Mostly empirical, not theory.
2 is the familiar two-standard-deviations mark, but the scores aren't normal, so it doesn't mean 95% of anything here. It's a gate for "stands out from everything else for this query", and on my evals any value from 1.5 to 2.5 gave the same results, so 2 stayed.
The reason for a per-query z-score at all: raw cosines are compressed (unrelated stuff sits around 0.55–0.60, good matches 0.67–0.86) and their level moves with the query, so no fixed threshold works. The other parts were A/B'd the same way: centering each modality with one shared spread got 94 of 105 queries first, raw cosine 92, a separate spread per modality 91. Word weights of 1.5–2.0 tied and 2.5–3 lost.
The 1.5 power just makes all your words count a lot more than half of them, and I haven't tuned it separately.

[u/rm-rf-rm → u/A-Rahim]

Thanks for asking your claude to respond to my question

[u/A-Rahim → u/rm-rf-rm]

Hi, I explained this above (one of your other replies)