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/routers/search.py

@@ -0,0 +1,74 @@
+"""
+Search API router — unified hybrid search endpoint.
+"""
+from typing import Optional
+from fastapi import APIRouter, Depends
+from pydantic import BaseModel
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select
+
+from app.database import get_db
+from app.models import Photo
+from app.services.search import hybrid_search
+
+router = APIRouter()
+
+
+class SearchRequest(BaseModel):
+    q: Optional[str] = None
+    filters: Optional[dict] = None
+    limit: int = 50
+    offset: int = 0
+
+
+@router.post("")
+async def search_photos(body: SearchRequest, db: AsyncSession = Depends(get_db)):
+    """Unified search endpoint. Every query runs hybrid (FTS + semantic)
+    by default — the user never picks a mode.
+
+    Filters:
+      - tag_ids: list of tag IDs (any kind: user, object, face_cluster)
+      - date_from / date_to: ISO date strings
+    """
+    filters = body.filters or {}
+
+    results = await hybrid_search(
+        db=db,
+        q=body.q,
+        tag_ids=filters.get("tag_ids"),
+        date_from=filters.get("date_from"),
+        date_to=filters.get("date_to"),
+        limit=body.limit,
+        offset=body.offset,
+    )
+
+    if not results:
+        return {"results": [], "total": 0}
+
+    # Hydrate with photo data
+    photo_ids = [r["photo_id"] for r in results]
+    stmt = select(Photo).where(Photo.id.in_(photo_ids))
+    rows = (await db.execute(stmt)).scalars().all()
+    photo_map = {p.id: p for p in rows}
+
+    hydrated = []
+    for r in results:
+        photo = photo_map.get(r["photo_id"])
+        if not photo:
+            continue
+        hydrated.append({
+            "id": photo.id,
+            "filename": photo.filename,
+            "filepath": photo.filepath,
+            "media_type": photo.media_type,
+            "width": photo.width,
+            "height": photo.height,
+            "taken_at": photo.taken_at.isoformat() if photo.taken_at else None,
+            "rating": photo.rating,
+            "color_label": photo.color_label,
+            "thumb_small": photo.thumb_small,
+            "thumb_medium": photo.thumb_medium,
+            "score": r["score"],
+        })
+
+    return {"results": hydrated, "total": len(hydrated)}