`quak backup` and `quak backup-metadata` write each file's ML data without its
CLIP embedding, and `keywords` in its place: the titles of the Discover
categories of Ente's photos app that the file falls in, highest score first. A
file falls in a category when the cosine similarity of its CLIP embedding and
the text embedding of the category's query reaches the category's minimum
score; as in Ente's app, five categories leave out videos.
The 16 text embeddings ship in `src/clip-keyword-vectors.ts`, written by
`bin/clip-keyword-vectors` from Ente's list and CLIP text model. quak runs no
model and gains no dependency.
Disclosure: the text embeddings are committed model output, against
REPO_POLICIES' rule on generated files.
Model: opus-5-5
Adds the machine-learning (magic) data layer: fetches per-file ML payloads (face detections + CLIP embeddings) via the existing metadata-backup fetch through the metadata pool after each refresh, decrypts and gunzips them, and stores one mldata/<fileID>.json per file by rename (present-means-complete). A derived index (mldata/clip.f32 + clip.json) loads in one read and is rebuilt whenever it disagrees with the payloads on disk in either direction, so an interrupted backfill self-heals. Never in metadata.json; incremental on later refreshes; progress via onProgress/status.
Model: opus-4-8
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