feat: upgrade to SigLIP2 ViT-B/16 for semantic search

Replace OpenCLIP ViT-B/32 (512-d, ~78% recall) with SigLIP2 ViT-B/16
(768-d, ~84% recall) as the default embedding model for significantly
better image-text retrieval quality.

- New SigLIP2Embedder class with 384px input and SigLIP normalization
- ONNX export pipeline for SigLIP2 visual + textual encoders
- Migration 0010: resize embeddings.vector from 512 to 768 dimensions
- Config-driven model selection: "siglip2_vitb16" (default) or
  "openclip_vitb32" (legacy) — both models can coexist
- Content classifier follows the configured embedder family
- Existing embeddings cleared on migration; vision backfill regenerates

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

backend/app/config.py

@@ -31,8 +31,9 @@ class PerformanceSettings(BaseModel):
     db_pool_recycle: int = 3600
 
 class EmbedderSettings(BaseModel):
-    """CLIP / SigLIP embedding model settings"""
-    name: str = "openclip_vitb32"
+    """CLIP / SigLIP embedding model settings.
+    Supported: "openclip_vitb32" (512-d), "siglip2_vitb16" (768-d, default)."""
+    name: str = "siglip2_vitb16"
     batch_size: int = 8
 
 class OCRSettings(BaseModel):