fix(retrieval): every semantic search scopes first, then ranks - the note, milestone and system searches join the rule search on one shared shape, _rank_scoped (#4961)

Ordered straight off a *_embeddings table, the planner walks the HNSW index,
takes ~ef_search (40) nearest chunks across every owner and project, and only
then applies the scope: an in-scope record behind 40 nearer ones the caller
cannot see was silently dropped. #4958 fixed the rule search alone; the note,
milestone and system searches kept the fault.

_scoped_chunks builds the in-scope chunks with their distance; _rank_scoped
materializes them as a CTE and ranks exactly. All four searches go through it,
and the row shape is unchanged, so callers and mocks are untouched.

Tests: a structural guard that every semantic_search_* ranks through
_rank_scoped and orders nothing itself (with a replay of the old shape), a
compiled-SQL check of the MATERIALIZED CTE, and integration crowd tests - 80
nearer out-of-scope records - for notes (another user; the reader own other
project), milestones and systems.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

src/scribe/models/embedding.py

@@ -7,9 +7,10 @@ from sqlalchemy.orm import Mapped, mapped_column
 from scribe.models import Base
 
 # bge-small-en-v1.5 produces 384-dim unit-normalized vectors. The column is a
-# native pgvector `vector(384)` (see migration 0067) so similarity search runs
-# as an indexed `ORDER BY embedding <=> :q LIMIT k` in Postgres rather than a
-# full-table Python cosine scan.
+# native pgvector `vector(384)` (see migration 0067) so similarity is ranked in
+# Postgres by `<=>` rather than by a full-table Python cosine scan. The semantic
+# searches rank the CALLER'S in-scope chunks exactly rather than through the
+# HNSW index, which ranks before it filters (#4961; embeddings._rank_scoped).
 EMBEDDING_DIM = 384