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/services/vision/onnx_backend.py

@@ -21,8 +21,13 @@ class ONNXBackend:
         self._settings = vision_settings
 
     def create_embedder(self) -> Embedder:
-        from app.services.vision.embed import OpenCLIPEmbedder
-        return OpenCLIPEmbedder(self._settings)
+        model_name = self._settings.embedder.name
+        if model_name.startswith("siglip2"):
+            from app.services.vision.embed import SigLIP2Embedder
+            return SigLIP2Embedder(self._settings)
+        else:
+            from app.services.vision.embed import OpenCLIPEmbedder
+            return OpenCLIPEmbedder(self._settings)
 
     def create_ocr(self) -> OCREngine:
         from app.services.vision.ocr import RapidOCREngine