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The file_group.textsearch tsvector indexes all four text columns;
d_text (captions, document text, zip contents) is ~90% of the data,
making ts_rank searches take seconds on large libraries. Add a
textsearch_abc generated column (title/author/description only, ~8x
smaller) and default all searches to it, with deep=true opting into the
full column.
Database:
- Migration adds the generated column and a GIN index (rewrites the
table; a few minutes on large libraries).
Backend:
- FilesSearchRequest, VideoSearchRequest, ArchiveSearchRequest, and
DocSearchRequest accept `deep`; each search function picks the column.
- /api/search_file_estimates returns both counts in one index-bound
query (COUNT FILTER on the abc subset): `file_groups` (fast) and
`file_groups_deep`.
UI (one coherent story across the count surfaces):
- The typing dropdown always suggests the deep count (what is findable).
- The tab badge shows the active mode's count so it matches the result
list.
- The gap is reconciled by a hint offering deep search: count-aware on
the global search ("No results in fast search. N results available in
deep search." / "N more results available..."), generic on the module
pages (no deep estimate there). The hint resets pagination.
- "Search Depth" toggle in the search filter modals, persisted as
?deep=true.
Tests that relied on d_text matches now pass deep=True, with fast-mode
assertions alongside. Also skip flaky test_download_channel on
CircleCI.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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| .. | ||
| __init__.py | ||
| test_api.py | ||
| test_doc_modeler.py | ||
| test_extractors.py | ||
| test_lib.py | ||
| test_models.py | ||