Module and test docstrings state what is pinned without the record number
or step that introduced it. Incident accounts become the failure shape in
the present tense: the pipefail head, the code_shapes import, the export
order, the facet dialects, the cross-kind repeat, the write-once kind.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
The four embedding tables stamped chunker_version but not the model, and
vector(384) is a width, not an identity: a same-width model swap would
write a second geometry beside the first with no error.
- embedding_model on note/rule/milestone/system embeddings (0109; existing
rows stamped with the only model any install has ever run).
- Every write stamps EMBEDDING_MODEL; every backfill's "current" test is
is_current_stamp(), both halves of calibration_stamp().
- migrate_floor refuses while any row its surface searches is off the live
model, before sampling: re-embed, then migrate.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
The shape ledger showed the same test scaffolding defined over and over:
_bind_user x12 (byte-identical), _dispose_engine x10 in three wordings,
_no_supersession x3, _make_mock_session x7 in three subsets, a get-or-create
User helper x2 (+3 inlined), and fifteen hand-rolled MagicMock note factories
each re-explaining the same "an auto-MagicMock attribute is truthy" hazard
(note 2109).
Now: conftest.py carries _bind_user / _dispose_engine / _no_supersession as
opt-in fixtures (pytestmark = usefixtures(...) per module, so unit tests pay
nothing), and tests/helpers.py carries make_mock_session(), ensure_user() and
fake_note(**attrs) — the hazard documented once, real values on every
attribute the product reads. Call sites were rewritten by AST so titles with
dashes and commas survived; the three SimpleNamespace _note stand-ins that
only feed a single function stay local.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
A note's relevance is now its best chunk's similarity, everywhere:
- semantic_search_notes keeps the indexed raw-distance top-k and over-fetches
chunk rows (x4, composing with the x3 supersession over-fetch), then
collapses to first-appearance-per-note — rows arrive distance-ordered, so
first is best. Every ranked consumer (MCP/REST search, Browse, auto-inject,
write-path, gate) inherits through the one function.
- list_notes semantic q swaps its join for a correlated MIN-distance
subquery — the join would have repeated a long note once per matching chunk
and made total count chunks.
- the duplicate report groups its self-join by note pair on MIN(distance):
pair similarity = closest chunk pair, and the < join now also drops
cross-chunk self-pairs that would flag every long note against itself.
- the write gate queries once per chunk of the candidate (capped at 8), so a
note duplicating an existing record in ONE SECTION is caught — the
whole-document query diluted exactly the section that mattered.
Integration test now seeds a two-chunk note and pins the collapse against
real pgvector.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01UaYUaouG9jjhATyuxCKrQs
note_embeddings becomes one row per chunk: PK (note_id, chunk_index), plus
chunk_text (what this vector actually encodes) and chunker_version. Migration
0077 clears the table — embeddings are derived (0067 precedent) and the old
whole-document rows are indistinguishable from single-chunk notes, so the
startup backfill regenerates the corpus at the new shape. The backfill is now
version-aware: a future shape change is a CHUNKER_VERSION bump that re-embeds
exactly the stale notes, not another wipe.
upsert_note_embedding takes (title, body) and chunks internally — one path
for the write path, the recurrence spawn and the backfill. The recurrence
spawn's own embed call is deleted outright: create_note already embeds via
embed_note (#2056), so the spawn was a second copy of the rule. An emptied
record now CLEARS its stale vectors instead of leaving them findable.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01UaYUaouG9jjhATyuxCKrQs
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
bvandeusenandClaude Opus 5.5