feat: add embeddings pipeline and semantic search endpoint

Wire the full embedding flow:
- Rewrite Embedding model to use pgvector Vector(512) with HNSW index
- Add embed_photo, vision_fanout, backfill_vision Celery tasks on
  dedicated `vision` queue
- Hook vision_fanout into generate_thumbnails completion
- Add POST /api/v1/photos/search with hybrid RRF ranking (semantic-only
  for now; FTS leg added in PR5)
- Stub ocr_photo, detect_objects, extract_faces tasks for later PRs

Migration 0003 drops/recreates the embeddings table (was never populated).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

backend/alembic/versions/0003_pgvector_embeddings.py

@@ -0,0 +1,52 @@
+"""pgvector embeddings
+
+Revision ID: 0003_pgvector_embeddings
+Revises: 0002_extend_tags
+Create Date: 2026-04-10
+
+Rewrite the embeddings table to use pgvector Vector(512) instead of
+LargeBinary. Add composite PK (photo_id, model), created_at, and
+HNSW index on vector column.
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+revision: str = "0003_pgvector_embeddings"
+down_revision: Union[str, None] = "0002_extend_tags"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # Drop the old placeholder table and recreate with pgvector types.
+    # No data to preserve — it was never populated.
+    op.execute("DROP TABLE IF EXISTS embeddings")
+    op.execute("""
+        CREATE TABLE embeddings (
+            photo_id VARCHAR NOT NULL REFERENCES photos(id) ON DELETE CASCADE,
+            model VARCHAR(64) NOT NULL,
+            vector vector(512),
+            created_at TIMESTAMPTZ DEFAULT now(),
+            PRIMARY KEY (photo_id, model)
+        )
+    """)
+    # HNSW index for cosine similarity search.
+    # Defer creation on large backfills — drop and recreate afterward.
+    op.execute("""
+        CREATE INDEX IF NOT EXISTS ix_embeddings_vector_hnsw
+        ON embeddings USING hnsw (vector vector_cosine_ops)
+    """)
+
+
+def downgrade() -> None:
+    op.execute("DROP TABLE IF EXISTS embeddings")
+    op.execute("""
+        CREATE TABLE embeddings (
+            photo_id VARCHAR NOT NULL REFERENCES photos(id) ON DELETE CASCADE,
+            model VARCHAR,
+            vector BYTEA,
+            PRIMARY KEY (photo_id)
+        )
+    """)