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/app/main.py

@@ -11,7 +11,7 @@ import os
 
 from app.config import settings
 from app.database import init_db
-from app.routers import photos, folders, heaps, tags, discard, library
+from app.routers import photos, folders, heaps, tags, discard, library, search
 from app.services.scanner import start_initial_scan, bootstrap_default_source_root
 from app.services.cleanup import cleanup_data_integrity
 
@@ -91,6 +91,7 @@ app.include_router(heaps.router, prefix="/api/v1/heaps", tags=["heaps"])
 app.include_router(tags.router, prefix="/api/v1/tags", tags=["tags"])
 app.include_router(discard.router, prefix="/api/v1/discard", tags=["discard"])
 app.include_router(library.router, prefix="/api/v1/library", tags=["library"])
+app.include_router(search.router, prefix="/api/v1/photos/search", tags=["search"])
 
 @app.get("/")
 async def root():