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

src/scribe/services/dedup.py

@@ -69,6 +69,12 @@ _SEMANTIC_THRESHOLD = 0.90
 # structural signals cannot see.
 _SNIPPET_SEMANTIC_THRESHOLD = 0.96
 
+# The gate queries per CHUNK of the candidate (#280) — this caps how many
+# searches one save may cost. Eight chunks ≈ five thousand words of candidate;
+# a duplicate hiding past that is the duplicate report's job to find, not a
+# reason to stall the write path.
+_GATE_MAX_CHUNKS = 8
+
 
 @dataclass
 class DuplicateMatch:
@@ -258,39 +264,43 @@ async def find_duplicate_note(
 
     # --- Signal 3: semantic similarity (only with a substantial body) ---
     if body and len(body.strip()) >= _MIN_BODY_FOR_SEMANTIC:
-        # Built by the SAME function the corpus was embedded with. This one is
-        # the copy that mattered most and was easiest to miss: it is a QUERY
-        # document, compared against embedded ones. Shaped differently from the
-        # corpus it searches, the gate degrades silently — it still returns
-        # neighbours, just less apt ones, and no signal says the query and the
-        # index stopped agreeing (found by the guard in test_embedding_text).
-        query = embeddings_svc.embedding_text(title, body)
-        # Scope the semantic check the same way as the title check: a record in
-        # project P compares only to P; a project-less (orphan) record compares
-        # only to other orphans (orphan_only), NOT across every project — without
-        # this, semantic_search_notes applies no project filter when project_id
-        # is None and would match an orphan note against any project's notes.
+        # Query with the SAME chunker the corpus was embedded with (#280). This
+        # was the copy that mattered most and was easiest to miss: these are
+        # QUERY documents, compared against embedded ones — shaped differently
+        # from the corpus, the gate degrades silently. Chunking also makes the
+        # gate see what the whole-document query diluted: a long candidate that
+        # duplicates an existing record IN ONE SECTION now matches on that
+        # section. Capped so one pathological paste can't turn a save into
+        # dozens of searches — a duplicate past the cap is the duplicate
+        # report's job, not the gate's.
+        for query in embeddings_svc.chunk_document(title, body)[:_GATE_MAX_CHUNKS]:
+            # Scope the semantic check the same way as the title check: a record
+            # in project P compares only to P; a project-less (orphan) record
+            # compares only to other orphans (orphan_only), NOT across every
+            # project — without this, semantic_search_notes applies no project
+            # filter when project_id is None and would match an orphan note
+            # against any project's notes.
             hits = await embeddings_svc.semantic_search_notes(
                 user_id, query, project_id=project_id, is_task=is_task,
                 orphan_only=(project_id is None),
                 limit=3,
                 threshold=(_SNIPPET_SEMANTIC_THRESHOLD
                            if note_type == SNIPPET_NOTE_TYPE else _SEMANTIC_THRESHOLD),
-            # Owner-only, deliberately: this gate BLOCKS a create and tells the
-            # caller to update the match instead. Matching someone else's record
-            # would refuse their write and point them at something they may not
-            # be able to edit.
+                # Owner-only, deliberately: this gate BLOCKS a create and tells
+                # the caller to update the match instead. Matching someone
+                # else's record would refuse their write and point them at
+                # something they may not be able to edit.
                 scope="own",
-            # NOT demoted by supersession (#278). A superseded record is still a
-            # duplicate of what you are about to write — the claim is that it is
-            # no longer CURRENT, not that it is gone. Demoting it here would let
-            # the same note be recorded a second time, and the second copy would
-            # be the one nothing warns about.
+                # NOT demoted by supersession (#278). A superseded record is
+                # still a duplicate of what you are about to write — the claim
+                # is that it is no longer CURRENT, not that it is gone. Demoting
+                # it here would let the same note be recorded a second time, and
+                # the second copy would be the one nothing warns about.
                 demote_superseded=False,
             )
             for score, note in hits:
-            # semantic_search_notes doesn't filter note_type — enforce it here so
-            # a note doesn't shadow a task of the same wording, etc.
+                # semantic_search_notes doesn't filter note_type — enforce it
+                # here so a note doesn't shadow a task of the same wording, etc.
                 if note.note_type == note_type:
                     return DuplicateMatch(note.id, note.title, round(score, 3), "semantic")
 
@@ -502,15 +512,22 @@ async def find_duplicate_records(
     left_note = aliased(Note, name="left_note")
     right_note = aliased(Note, name="right_note")
     distance = left.embedding.cosine_distance(right.embedding)
+    # Chunk grain (#280): a note-pair's similarity is its closest CHUNK pair —
+    # two records duplicate each other where their most similar sections do,
+    # which is the honest definition when one section of a long note restates
+    # another record. GROUP BY collapses the chunk cross-product to one row
+    # per note pair.
+    best = func.min(distance)
 
     pairs: list[tuple[int, int, float]] = []
     try:
         async with async_session() as session:
             stmt = (
-                select(left.note_id, right.note_id, distance.label("distance"))
+                select(left.note_id, right.note_id, best.label("distance"))
                 .select_from(left)
                 # `<` not `!=`: each unordered pair exactly once, and it drops
-                # the self-pair (distance 0) that would otherwise dominate.
+                # the self-pairs (including cross-chunk self-pairs, which would
+                # otherwise flag every multi-chunk note against itself).
                 .join(right, left.note_id < right.note_id)
                 .join(left_note, left_note.id == left.note_id)
                 .join(right_note, right_note.id == right.note_id)
@@ -523,9 +540,10 @@ async def find_duplicate_records(
                     # report is bounded by what merge can actually act on.
                     left_note.user_id == user_id,
                     right_note.user_id == user_id,
-                    distance <= max_distance,
                 )
-                .order_by(distance.asc())
+                .group_by(left.note_id, right.note_id)
+                .having(best <= max_distance)
+                .order_by(best.asc())
                 .limit(max(1, limit))
             )
             rows = list((await session.execute(stmt)).all())

src/scribe/services/embeddings.py

@@ -124,6 +124,14 @@ _SUPERSESSION_PENALTY = 0.05
 # whose neighbours sit ~0.01-0.02 apart.
 _SUPERSESSION_OVERFETCH = 3
 
+# Chunk rows fetched per requested result (#280). The HNSW top-k runs at CHUNK
+# grain — several chunks of one strong note can occupy consecutive ranks, and
+# each collapses into a single result. Four ranks of headroom per result keeps
+# the top-k indexed while making it effectively impossible for collapsing to
+# starve the result list: that would need every requested note to be shadowed
+# by four chunks of notes ranked above it.
+_CHUNK_OVERFETCH = 4
+
 
 async def _apply_supersession_penalty(
     scored: list[tuple[float, "Note"]], limit: int
@@ -524,7 +532,9 @@ async def semantic_search_notes(
             # penalty far smaller than the window's score spread, that case
             # needs the true answer to be more than _SUPERSESSION_OVERFETCH
             # ranks down, which no observed query comes close to.
-            fetch = limit * _SUPERSESSION_OVERFETCH if demote_superseded else limit
+            fetch = limit * _CHUNK_OVERFETCH * (
+                _SUPERSESSION_OVERFETCH if demote_superseded else 1
+            )
             stmt = (
                 stmt.where(distance <= max_distance)
                 .order_by(distance.asc())
@@ -535,8 +545,19 @@ async def semantic_search_notes(
         logger.warning("Failed to query note embeddings", exc_info=True)
         return []
 
-    # Recover similarity (1 - distance) and preserve the highest-first contract.
-    scored = [(1.0 - float(dist), note) for note, dist in rows]
+    # Collapse chunk rows to BEST-CHUNK-PER-NOTE (#280): rows arrive ordered by
+    # distance, so the first appearance of a note is its best chunk and later
+    # appearances are the same note matched less well. A note's relevance IS
+    # its best section's relevance — a query about one topic of a long record
+    # must find that record as strongly as if the topic were the whole record.
+    # Recover similarity (1 - distance); order stays highest-first.
+    scored: list[tuple[float, Note]] = []
+    seen: set[int] = set()
+    for note, dist in rows:
+        if int(note.id) in seen:
+            continue
+        seen.add(int(note.id))
+        scored.append((1.0 - float(dist), note))
     if not demote_superseded:
         return scored[:limit]
     return await _apply_supersession_penalty(scored, limit)

src/scribe/services/notes.py

@@ -236,15 +236,25 @@ async def list_notes(
             if query_vec is not None:
                 from scribe.models.embedding import NoteEmbedding
                 from scribe.services.embeddings import INTERACTIVE_SEARCH_THRESHOLD
-                distance = NoteEmbedding.embedding.cosine_distance(query_vec)
-                sem_filter = distance <= (1.0 - INTERACTIVE_SEARCH_THRESHOLD)
-                query = query.join(
-                    NoteEmbedding, NoteEmbedding.note_id == Note.id
-                ).where(sem_filter)
-                count_query = count_query.join(
-                    NoteEmbedding, NoteEmbedding.note_id == Note.id
-                ).where(sem_filter)
-                semantic_order = distance.asc()
+                # Best-chunk-per-note as a correlated MIN, not a join (#280):
+                # a note stores one embedding row PER CHUNK, so the plain join
+                # this used to be would repeat a long note once per matching
+                # chunk — duplicated list rows and a total that counts chunks.
+                # This query is filter-heavy and paginated, never HNSW-bound,
+                # so the scalar subquery costs what the join did.
+                best_distance = (
+                    select(
+                        func.min(
+                            NoteEmbedding.embedding.cosine_distance(query_vec)
+                        )
+                    )
+                    .where(NoteEmbedding.note_id == Note.id)
+                    .scalar_subquery()
+                )
+                sem_filter = best_distance <= (1.0 - INTERACTIVE_SEARCH_THRESHOLD)
+                query = query.where(sem_filter)
+                count_query = count_query.where(sem_filter)
+                semantic_order = best_distance.asc()
             else:
                 terms = _strip_type_nouns(q)
                 for term in terms:

tests/test_chunking.py

@@ -136,6 +136,36 @@ def test_a_monster_single_paragraph_is_hard_split_not_dropped():
     assert total_words == 2000
 
 
+# --- the read path: best chunk wins (#280 step 4) ----------------------------
+
+
+async def test_search_collapses_chunk_rows_to_best_chunk_per_note():
+    """Rows arrive at CHUNK grain ordered by distance; a note appearing via
+    several chunks must come back ONCE, scored by its best chunk — otherwise a
+    long record fills the top-k with copies of itself."""
+    from unittest.mock import AsyncMock, MagicMock, patch
+
+    from scribe.services import embeddings as emb
+
+    note_a, note_b = MagicMock(id=1), MagicMock(id=2)
+    rows = [(note_a, 0.10), (note_b, 0.20), (note_a, 0.25), (note_a, 0.30)]
+    result = MagicMock()
+    result.all.return_value = rows
+    session, ctx = _session_ctx()
+    session.execute = AsyncMock(return_value=result)
+
+    with (
+        patch.object(emb, "async_session", return_value=ctx),
+        patch.object(emb, "get_embedding", AsyncMock(return_value=[0.0] * 384)),
+    ):
+        out = await emb.semantic_search_notes(
+            1, "a query", limit=8, demote_superseded=False
+        )
+
+    assert [note.id for _s, note in out] == [1, 2]
+    assert out[0][0] == 1.0 - 0.10  # the BEST chunk's score, not a later one
+
+
 # --- the write path: one row per chunk (#280 step 3) -------------------------
 
 

tests/test_integration_pgvector_search.py

@@ -68,7 +68,11 @@ async def seeded():
         await s.flush()
         # query vector will be [1,0,0,...]; near ~ identical (sim≈1.0),
         # far is orthogonal (sim≈0.0 -> filtered by the default threshold).
+        # near gets a SECOND, weaker chunk (sim≈0.6) — the collapse to
+        # best-chunk-per-note (#280) is under test: near must come back once,
+        # at its best chunk's score, not twice.
         s.add(_emb(near.id, user.id, 0, _vec(1.0)))
+        s.add(_emb(near.id, user.id, 1, _vec(0.6, 0.8)))
         s.add(_emb(far.id, user.id, 0, _vec(0.0, 1.0)))
         await s.commit()
         ids = (user.id, near.id, far.id)
@@ -96,6 +100,9 @@ async def test_semantic_search_ranks_and_thresholds_via_pgvector(seeded):
     assert near_id in ids
     assert far_id not in ids
     assert ids[0] == near_id
+    # Chunk collapse (#280): near has TWO chunk rows above the floor (sim≈1.0
+    # and ≈0.6) and must appear exactly once, at its best chunk's score.
+    assert ids.count(near_id) == 1
     top_score = results[0][0]
     assert top_score == pytest.approx(1.0, abs=1e-3)
 

tests/test_services_dedup.py

@@ -68,6 +68,33 @@ async def test_semantic_match_when_body_substantial():
     assert dup.similarity == 0.93
 
 
+@pytest.mark.asyncio
+async def test_gate_catches_a_duplicate_hiding_in_a_later_chunk():
+    """The capability #280 adds to the gate: a long candidate that duplicates
+    an existing record in ONE SECTION is caught, where the whole-document
+    query this replaces diluted exactly the section that mattered. The gate
+    queries once per chunk and any chunk's hit blocks."""
+    para = ("This section restates an existing decision in enough words to be "
+            "a real paragraph of content for the chunker to keep. ") * 4
+    body = "\n\n".join(f"## Topic {i}\n\n{para} (t{i})" for i in range(8))
+
+    from scribe.services.embeddings import chunk_document
+    n_chunks = len(chunk_document("Title", body))
+    assert n_chunks > 1, "test body must actually chunk"
+
+    hit = _fake_note(id=30, title="The existing decision", note_type="note")
+    # Every chunk misses except the LAST one the gate will ask about.
+    sem = AsyncMock(side_effect=[[] for _ in range(n_chunks - 1)] + [[(0.94, hit)]])
+    with patch("scribe.services.dedup.async_session",
+               return_value=_session_returning(None)), \
+         patch("scribe.services.dedup.embeddings_svc.semantic_search_notes", sem):
+        dup = await find_duplicate_note(
+            7, "Title", body=body, project_id=2, is_task=False, note_type="note",
+        )
+    assert dup is not None and dup.id == 30
+    assert sem.await_count == n_chunks
+
+
 @pytest.mark.asyncio
 async def test_semantic_match_of_other_note_type_is_ignored():
     other = _fake_note(id=21, title="X", note_type="process")