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fix(search): close three P2 correctness gaps from code review

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- vector: increment embedding_revision whenever delete_embedding is
  called so the HNSW cache is invalidated after image/folder deletions,
  not just after inserts

- hnsw_index: fix race in build_index — read the revision before
  fetching embeddings and again after the parallel_insert; retry the
  whole build if the revision advanced during construction, ensuring
  the cached index always reflects a consistent snapshot

- commands: replace the fixed 4× over-fetch in semantic_search_images
  with a progressive doubling loop; start at exactly `limit` candidates
  and double the batch (capped at 8192) until the page is filled or the
  vector table is exhausted, preventing both under- and over-fetching
This commit is contained in:
LyAhn
2026-06-07 22:46:05 +01:00
parent 20c43d2056
commit ae3d6e1034
3 changed files with 84 additions and 53 deletions
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src-tauri/src/commands.rs
+34 -21
View File
@@ -532,34 +532,47 @@ pub async fn semantic_search_images(
let conn = db.get().map_err(|e| e.to_string())?;
let limit = params.limit.unwrap_or(64);
// When post-query filters are active the vector search may discard many
// candidates ranked just beyond the bare limit. Over-fetch by 4× so that
// well-rated or folder-scoped matches are not silently omitted.
let has_filters = params.folder_id.is_some()
|| params.media_kind.is_some()
|| params.favorites_only.unwrap_or(false)
|| params.rating_min.map_or(false, |r| r > 0);
let fetch_limit = if has_filters { limit * 4 } else { limit };
let ids = vector::search_image_ids_by_embedding(&conn, &embedding, fetch_limit)
.map_err(|e| e.to_string())?;
let mut images = db::get_images_by_ids(&conn, &ids).map_err(|e| e.to_string())?;
if !has_filters {
// No post-query filtering — a single fetch of exactly `limit` results is optimal.
let ids = vector::search_image_ids_by_embedding(&conn, &embedding, limit)
.map_err(|e| e.to_string())?;
return db::get_images_by_ids(&conn, &ids).map_err(|e| e.to_string());
}
if let Some(folder_id) = params.folder_id {
images.retain(|image| image.folder_id == folder_id);
}
if let Some(media_kind) = params.media_kind.as_deref() {
images.retain(|image| image.media_kind == media_kind);
}
if params.favorites_only.unwrap_or(false) {
images.retain(|image| image.favorite);
}
if let Some(rating_min) = params.rating_min {
images.retain(|image| image.rating >= rating_min);
}
images.truncate(limit);
// Post-query filters are active. Progressively double the fetch batch until we
// collect enough results or exhaust the vector table, so we never over-fetch
// more than necessary while still filling the requested page.
let mut batch = limit;
loop {
let ids = vector::search_image_ids_by_embedding(&conn, &embedding, batch)
.map_err(|e| e.to_string())?;
let exhausted = ids.len() < batch;
let mut images = db::get_images_by_ids(&conn, &ids).map_err(|e| e.to_string())?;
Ok(images)
if let Some(folder_id) = params.folder_id {
images.retain(|image| image.folder_id == folder_id);
}
if let Some(media_kind) = params.media_kind.as_deref() {
images.retain(|image| image.media_kind == media_kind);
}
if params.favorites_only.unwrap_or(false) {
images.retain(|image| image.favorite);
}
if let Some(rating_min) = params.rating_min {
images.retain(|image| image.rating >= rating_min);
}
if images.len() >= limit || exhausted {
images.truncate(limit);
return Ok(images);
}
batch = (batch * 2).min(8192);
}
}
#[tauri::command]