bvandeusen
513019786e
Move semantic_search_notes off the full-table Python cosine scan onto a native pgvector column: indexed ORDER BY embedding <=> :q LIMIT k (HNSW, cosine). Migration 0067 enables the extension, converts the JSONB embedding column to vector(384) (stale-dim rows dropped and regenerated by the startup backfill), and builds the HNSW cosine index. Postgres image moves postgres:16-alpine -> pgvector/pgvector:pg17 across prod, quickstart, and CI. Scribe: project 2, milestone 93, task 1031. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Xz4j1H7pjYSjKsEpgcNH5E
102 lines
3.8 KiB
Python
102 lines
3.8 KiB
Python
"""Real-Postgres integration test for pgvector semantic search.
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Runs only in the CI integration lane (real Postgres + `vector` extension +
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schema built by `alembic upgrade head`, which includes migration 0067). This
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exercises what the unit mocks cannot: the native `vector(384)` column, the
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`<=>` cosine-distance operator behind `Vector.cosine_distance`, the HNSW index,
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and the distance->similarity recovery in `semantic_search_notes`.
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The embedder itself is stubbed (get_embedding is patched) so the test does not
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depend on downloading the fastembed model — only the Postgres/pgvector path is
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under test.
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"""
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from unittest.mock import AsyncMock, patch
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import pytest
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import pytest_asyncio
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from sqlalchemy import delete
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from scribe.models import async_session, engine
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from scribe.models.embedding import EMBEDDING_DIM, NoteEmbedding
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from scribe.models.note import Note
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from scribe.models.user import User
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from scribe.services.embeddings import semantic_search_notes
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pytestmark = pytest.mark.integration
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def _vec(*nonzero_first):
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"""A 384-dim vector with the given leading values, zero-padded."""
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v = list(nonzero_first) + [0.0] * (EMBEDDING_DIM - len(nonzero_first))
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return v[:EMBEDDING_DIM]
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@pytest_asyncio.fixture(autouse=True)
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async def _dispose_engine():
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# Per-loop pool: dispose after each test (see test_integration_db_maintenance).
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yield
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await engine.dispose()
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@pytest_asyncio.fixture
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async def seeded():
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"""Insert a user + a near and a far note with hand-crafted embeddings.
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Returns (user_id, near_note_id, far_note_id). Cleaned up after the test.
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"""
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async with async_session() as s:
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user = User(username="pgvec_itest")
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s.add(user)
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await s.flush()
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near = Note(user_id=user.id, title="near", body="near body")
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far = Note(user_id=user.id, title="far", body="far body")
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s.add_all([near, far])
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await s.flush()
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# query vector will be [1,0,0,...]; near ~ identical (sim≈1.0),
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# far is orthogonal (sim≈0.0 -> filtered by the default threshold).
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s.add(NoteEmbedding(note_id=near.id, user_id=user.id, embedding=_vec(1.0)))
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s.add(NoteEmbedding(note_id=far.id, user_id=user.id, embedding=_vec(0.0, 1.0)))
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await s.commit()
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ids = (user.id, near.id, far.id)
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yield ids
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user_id = ids[0]
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async with async_session() as s:
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await s.execute(delete(NoteEmbedding).where(NoteEmbedding.user_id == user_id))
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await s.execute(delete(Note).where(Note.user_id == user_id))
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await s.execute(delete(User).where(User.id == user_id))
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await s.commit()
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@pytest.mark.asyncio
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async def test_semantic_search_ranks_and_thresholds_via_pgvector(seeded):
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user_id, near_id, far_id = seeded
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with patch(
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"scribe.services.embeddings.get_embedding",
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AsyncMock(return_value=_vec(1.0)),
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):
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results = await semantic_search_notes(user_id=user_id, query="anything", limit=10)
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ids = [note.id for _score, note in results]
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# Near note returned and ranked first; far (orthogonal, sim≈0) excluded by
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# the default 0.45 similarity threshold.
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assert near_id in ids
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assert far_id not in ids
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assert ids[0] == near_id
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top_score = results[0][0]
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assert top_score == pytest.approx(1.0, abs=1e-3)
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@pytest.mark.asyncio
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async def test_low_threshold_lets_orthogonal_through(seeded):
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user_id, near_id, far_id = seeded
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with patch(
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"scribe.services.embeddings.get_embedding",
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AsyncMock(return_value=_vec(1.0)),
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):
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results = await semantic_search_notes(
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user_id=user_id, query="anything", limit=10, threshold=-1.0,
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)
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ids = [note.id for _score, note in results]
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# With the floor dropped, both come back and near still ranks above far.
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assert ids.index(near_id) < ids.index(far_id)
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