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/models/embeddings.py

@@ -1,17 +1,19 @@
 """
-Embedding model definition (placeholder for AI features)
+Embedding model — stores CLIP/SigLIP image embeddings via pgvector.
+
+Composite PK (photo_id, model) allows re-embedding with newer models
+without clobbering old vectors.
 """
-from sqlalchemy import Column, String, ForeignKey, LargeBinary
-import uuid
+from sqlalchemy import Column, String, ForeignKey, DateTime, func
+from pgvector.sqlalchemy import Vector
 
 from app.database import Base
 
+
 class Embedding(Base):
-    """
-    Placeholder table for future AI embeddings (CLIP, face recognition, etc.)
-    """
     __tablename__ = 'embeddings'
-    
+
     photo_id = Column(String, ForeignKey('photos.id', ondelete='CASCADE'), primary_key=True)
-    model = Column(String)  # e.g., 'clip-vit-b32', 'face-recognition', etc.
-    vector = Column(LargeBinary)  # raw float32 bytes for embedding vector
+    model = Column(String(64), primary_key=True)  # e.g. 'openclip_vitb32'
+    vector = Column(Vector(512))                   # OpenCLIP ViT-B/32 → 512-d
+    created_at = Column(DateTime(timezone=True), server_default=func.now())