perf(notes): debounce live search + score the query once, not per keystroke - #330
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SMK1705
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Jul 28, 2026
SMK1705
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…e/emission Notes search runs live as you type, and each pass calls SemanticIndex.scores() — which embeds the query, loads every stored vector, recomputes the corpus centroid and mean- centres them all. With the neural embedder on (#329) the embed is a MediaPipe inference, so typing "kitchen sink tap" fired ~16 full semantic passes back to back. That's the jank. Two fixes, both behaviour-preserving: - Debounce the typed query (~220ms) before the search runs, so a keystroke storm settles into ONE pass. Only the query is debounced — filter toggles and a blank query (clearing search / first load) stay instant, so nothing the user expects to be immediate is delayed. - Hoist scores() out of the per-emission map. It was called inside `base.map { ... }`, so a background note write re-ran it and re-embedded the query even though the query hadn't changed. The scores depend only on the query and the embeddings table, not the note list, so it's now computed once per query and the map just re-ranks with the cached scores (rankBySearch -> rankWith, embedding removed). Net: from ~one neural inference + full-vector load PER KEYSTROKE (and again on every note change) to one per settled query. Results are identical — the full unit suite (incl. NotesViewModelTest / NotesScreenTest search cases) stays green.
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