wrolpi/modules/docs/schema.py
Roland 73fafcd645
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>
2026-07-03 10:32:16 -06:00

44 lines
1.1 KiB
Python

import dataclasses
from typing import Optional
@dataclasses.dataclass
class DocSearchRequest:
search_str: Optional[str] = None
author: Optional[str] = None
subject: Optional[str] = None
language: Optional[str] = None
mimetype: Optional[str] = None
tag_names: Optional[list] = dataclasses.field(default_factory=list)
order_by: Optional[str] = 'published_datetime'
limit: int = 20
offset: int = 0
deep: bool = False # When True, search all text including document contents (d_text)
@dataclasses.dataclass
class DocSearchResponse:
file_groups: list = dataclasses.field(default_factory=list)
totals: dict = dataclasses.field(default_factory=dict)
@dataclasses.dataclass
class DocStatistics:
doc_count: int = 0
epub_count: int = 0
pdf_count: int = 0
other_count: int = 0
total_size: int = 0
author_count: int = 0
subject_count: int = 0
@dataclasses.dataclass
class DocStatisticsResponse:
statistics: dict = dataclasses.field(default_factory=dict)
@dataclasses.dataclass
class DocFileFormatRequest:
name: str = ''
model_tag: str = 'docs'