feat(gallery): visual 'more like this' search (Phase 3 backend)

GalleryService.similar() ranks images by pgvector cosine distance to a source
image's precomputed SigLIP embedding — no query-time ML inference. Composes
with the Phase-1/2 scope filters (AND) but replaces the date sort (always
nearest-first, bounded top-N, no cursor). Returns None for a missing source
(→404), [] for a source with no embedding (video / pending ML); excludes self
and NULL-embedding rows. New GET /api/gallery/similar?similar_to=<id>&limit=N.
Image-detail payload gains has_embedding so the UI can hide the surface.

Alembic 0036 adds an HNSW vector_cosine_ops index on siglip_embedding (1152<2000
dims) so the search is sub-50ms ANN instead of a full scan; one-time ~30-60s
build over existing embeddings on deploy. Shared _gallery_images/_image_json
helpers de-dup the scroll/similar builders.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

alembic/versions/0036_siglip_embedding_hnsw_index.py

@@ -0,0 +1,41 @@
+"""image_record.siglip_embedding: HNSW cosine index for "more like this"
+
+Revision ID: 0036
+Revises: 0035
+Create Date: 2026-06-04
+
+Gallery Phase 3 (visual similarity search) ranks images by
+`siglip_embedding.cosine_distance(source_embedding)`. Without an index that's
+a sequential scan computing a 1152-dim distance for every row — fine at small
+scale, but it grows linearly with the library. Add an HNSW index with
+`vector_cosine_ops` so the top-N nearest search is sub-50ms ANN.
+
+1152 dims is under pgvector's 2000-dim HNSW limit, so HNSW (no training,
+better recall than IVFFlat) is the right choice. ONE-TIME COST: building the
+index over the existing embeddings (~57k vectors on the operator's library)
+locks image_record for ~30-60s during this migration on deploy — acceptable
+for a single-operator homelab. NULL embeddings (videos / not-yet-embedded
+rows) are simply not indexed.
+"""
+from typing import Sequence, Union
+
+from alembic import op
+
+revision: str = "0036"
+down_revision: Union[str, None] = "0035"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # Raw SQL: alembic's create_index doesn't express the `USING hnsw (...
+    # vector_cosine_ops)` access-method + opclass cleanly. Must match the
+    # query's cosine_distance operator class to be usable by the planner.
+    op.execute(
+        "CREATE INDEX ix_image_record_siglip_hnsw "
+        "ON image_record USING hnsw (siglip_embedding vector_cosine_ops)"
+    )
+
+
+def downgrade() -> None:
+    op.drop_index("ix_image_record_siglip_hnsw", table_name="image_record")