4395afc210
Makes the local search behave more like YouTube's — finding videos by the uploader's own keywords or the query that surfaced them, not only words in the title. A DB-generated, weighted search_vector (migration 0032) replaces the title-only FTS index: - keywords: the creator's snippet.tags (free — already in the snippet we fetch), stored on enrich. - search_terms: distinct live-search queries that surfaced the video (across all users), appended by the search route — folds YouTube's relevance into local search (a video YT returned for a query becomes findable by it even without a title match), the user's own idea. - description (truncated) for broad recall on the existing catalog. Weighted title(A) > keywords+queries(B) > description(C) so ts_rank keeps title hits on top. A plain GIN index on the generated column guarantees index use (no expression/param matching). Verified on localdev: recall 146->213 for one query; 7 'eurovision' hits via the document but not the title; index scan confirmed.
65 lines
3 KiB
Python
65 lines
3 KiB
Python
"""richer full-text search document (title + keywords + search terms + description)
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Revision ID: 0032_search_document
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Revises: 0031_title_fts
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Create Date: 2026-06-30
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Broadens the feed's full-text search from the title alone to a weighted search document, so the
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local search behaves more like YouTube's — finding videos by the uploader's own keywords or by
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the search query that surfaced them, not only by words in the title:
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- `keywords` — the creator's snippet.tags (already fetched with the snippet we request, so
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zero extra quota), stored space-joined.
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- `search_terms` — distinct live-search queries that surfaced the video (across all users),
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appended by the search route. Folds YouTube's relevance judgement into local
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search: a video YT returned for a query becomes findable by it locally.
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These plus a truncated description feed a STORED generated `search_vector` column, weighted
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title (A) > keywords+search_terms (B) > description (C) so ts_rank ranks title hits highest. A
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plain GIN index on the column guarantees the planner uses it (no expression/param matching). The
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title-only expression index from 0031 is dropped (superseded). Existing rows get title +
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description indexed immediately; keywords/search_terms fill in as videos are (re-)enriched and
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searched. Uses the `unaccent_simple` config from 0031 (accent-insensitive).
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"""
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from typing import Sequence, Union
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import sqlalchemy as sa
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from alembic import op
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revision: str = "0032_search_document"
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down_revision: Union[str, None] = "0031_title_fts"
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branch_labels: Union[str, Sequence[str], None] = None
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depends_on: Union[str, Sequence[str], None] = None
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_SEARCH_VECTOR_EXPR = (
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"setweight(to_tsvector('public.unaccent_simple', coalesce(title, '')), 'A') || "
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"setweight(to_tsvector('public.unaccent_simple', coalesce(keywords, '') || ' ' || "
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"coalesce(search_terms, '')), 'B') || "
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"setweight(to_tsvector('public.unaccent_simple', left(coalesce(description, ''), 1000)), 'C')"
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)
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def upgrade() -> None:
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op.add_column("videos", sa.Column("keywords", sa.Text(), nullable=True))
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op.add_column("videos", sa.Column("search_terms", sa.Text(), nullable=True))
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# Replace the title-only index with a generated weighted search_vector + its GIN index.
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op.execute("DROP INDEX IF EXISTS ix_videos_title_fts")
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op.execute(
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f"ALTER TABLE videos ADD COLUMN search_vector tsvector "
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f"GENERATED ALWAYS AS ({_SEARCH_VECTOR_EXPR}) STORED"
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)
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op.execute(
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"CREATE INDEX ix_videos_search_vector ON videos USING gin (search_vector)"
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)
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def downgrade() -> None:
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op.execute("DROP INDEX IF EXISTS ix_videos_search_vector")
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op.drop_column("videos", "search_vector")
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op.drop_column("videos", "search_terms")
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op.drop_column("videos", "keywords")
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# Recreate the title-only FTS index from 0031.
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op.execute(
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"CREATE INDEX ix_videos_title_fts ON videos "
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"USING gin (to_tsvector('public.unaccent_simple', coalesce(title, '')))"
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)
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