Search: fast ABC mode by default, opt-in deep search

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>

modules/docs/api.py

@@ -56,6 +56,7 @@ async def search_docs(_: Request, body: schema.DocSearchRequest):
         offset=offset,
         order_by=body.order_by,
         tag_names=body.tag_names,
+        deep=body.deep,
     )
     ret = dict(file_groups=file_groups, totals=dict(file_groups=total))
     return json_response(ret)