25109d756d
Retrieval generalised beyond questions
`_text_for_question`, `embed_question` and `hybrid_question_ids` all hardcoded
the questions table, so there was nothing to call for an article or a card. That
layer is now corpus-agnostic:
- `Embeddable` mixin gives articles and flashcards the same embedding,
embedding_model and embedded_at columns questions have, plus a weighted
full-text vector (migration u3a4b5c6d7e8).
- `embed_record(row, kind)` is one code path for all three — they share an
embedding space, so they must share the model and provenance rules too.
- `hybrid_ids(db, query, kind)` ranks any corpus; `hybrid_question_ids` stays as
a thin alias for existing callers.
- Article and flashcard search moved off `ILIKE '%term%'`, which could not find
a jaundice article from "yellow newborn".
- The retry task and full regeneration now sweep every corpus, and the health
report breaks down current/stale/missing per kind.
- Articles embed on create and on edit, with failures left to the retry task.
Quoted phrases replace the keyword-only mode
`websearch_to_tsquery` already gives "absence seizure" exact-phrase semantics,
and the semantic ranker sits out a quoted query. That covers the one case a
keyword-only toggle was for — exact lookup — per query rather than as a sticky
setting whose every position returns a subset of the default.
Full-page question editor (/questions/new, /questions/:id)
Editing happened in a cramped modal. There is now a page with room for the stem,
per-option explanations, a searchable category picker with primary plus extras,
difficulty, and images. It shows the question's id with a copy button, and
Duplicate creates a variant without retyping the stem. `GET /questions/detail/{id}`
backs it, pathed under /detail/ so it cannot shadow the static routes.
Question bank filter bar restyled — the toggle and count read as one control
instead of two grey pills crowding the result count.
Tests: 101 backend green.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01PpfzbZ1QTLMeVYxM2kyq8m
49 lines
2 KiB
Python
49 lines
2 KiB
Python
"""Give articles and flashcards the same retrieval columns as questions.
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Both were searched with `ILIKE '%term%'`, so "yellow newborn" could not find a
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jaundice article. They now carry a full-text vector and an embedding, with the
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same provenance columns that make a model change visible.
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Revision ID: u3a4b5c6d7e8
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Revises: t2f3a4b5c697
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"""
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from alembic import op
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revision = "u3a4b5c6d7e8"
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down_revision = "t2f3a4b5c697"
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branch_labels = None
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depends_on = None
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TARGETS = {
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# table: expression whose text is indexed, weighted title/name first
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"articles": """
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setweight(to_tsvector('english', coalesce(title, '')), 'A') ||
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setweight(to_tsvector('english', coalesce(summary, '')), 'B') ||
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setweight(to_tsvector('english', coalesce(content, '')), 'C')
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""",
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"flashcards": """
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setweight(to_tsvector('english', coalesce(front, '')), 'A') ||
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setweight(to_tsvector('english', coalesce(back, '')), 'B')
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""",
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}
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def upgrade():
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for table, expression in TARGETS.items():
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op.execute(f"ALTER TABLE {table} ADD COLUMN IF NOT EXISTS embedding vector(1024)")
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op.execute(f"ALTER TABLE {table} ADD COLUMN IF NOT EXISTS embedding_model VARCHAR(120)")
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op.execute(f"ALTER TABLE {table} ADD COLUMN IF NOT EXISTS embedded_at TIMESTAMP")
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op.execute(f"""
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ALTER TABLE {table} ADD COLUMN IF NOT EXISTS search_vector tsvector
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GENERATED ALWAYS AS ({expression}) STORED
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""")
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op.execute(f"CREATE INDEX IF NOT EXISTS ix_{table}_search_vector ON {table} USING GIN (search_vector)")
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op.execute(f"CREATE INDEX IF NOT EXISTS ix_{table}_embedding_model ON {table}(embedding_model)")
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def downgrade():
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for table in TARGETS:
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op.execute(f"DROP INDEX IF EXISTS ix_{table}_embedding_model")
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op.execute(f"DROP INDEX IF EXISTS ix_{table}_search_vector")
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for column in ("search_vector", "embedded_at", "embedding_model", "embedding"):
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op.execute(f"ALTER TABLE {table} DROP COLUMN IF EXISTS {column}")
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