3 Commits

Include renames

Author	SHA1	Message	Date
bvandeusenandClaude Fable 5	041d8defbc	feat(embeddings): best-chunk-per-note on every retrieval surface (#280 step 4) ... 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	2026-08-08 23:51:01 -04:00
bvandeusenandClaude Fable 5	0e70a3896b	feat(embeddings): per-chunk rows — schema, write path, version-aware backfill (#280 steps 2+3) ... 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	2026-08-08 23:47:34 -04:00
bvandeusenandClaude Opus 4.8	513019786e	feat(search): pgvector substrate — vector(384) + HNSW for semantic search ... 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	2026-06-22 20:10:15 -04:00