feat(fc2b): add SigLIP embedder wrapper

Direct port of ImageRepo's siglip.py. Lazy torch/transformers import so
the web container can import the module (for enqueue logic) without the
torch cost. EMBED_DIM=1152 asserted against the schema's Vector(1152)
columns. Real inference runs in the local integration suite.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

backend/app/services/ml/embedder.py

@@ -0,0 +1,62 @@
+"""SigLIP SO400M image-embedding wrapper (PyTorch CPU).
+
+Direct port of ImageRepo's siglip.py. torch/transformers are imported
+lazily inside load() so this module can be imported in the web container
+(which never runs inference) without paying the torch import cost.
+"""
+
+import os
+from pathlib import Path
+
+import numpy as np
+from PIL import Image, ImageFile
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+MODEL_NAME = os.environ.get(
+    "SIGLIP_MODEL_NAME", "google/siglip-so400m-patch14-384"
+)
+MODEL_VERSION = os.environ.get(
+    "SIGLIP_MODEL_VERSION", "siglip-so400m-patch14-384"
+)
+EMBED_DIM = 1152
+_LOCAL_DIR = Path(os.environ.get("ML_MODEL_DIR", "/models")) / "siglip"
+
+
+class Embedder:
+    def __init__(self, model_dir: Path | None = None):
+        self._model_dir = model_dir or _LOCAL_DIR
+        self._model = None
+        self._processor = None
+        self._torch = None
+
+    def load(self) -> None:
+        if self._model is not None:
+            return
+        import torch
+        from transformers import AutoModel, AutoProcessor
+
+        self._torch = torch
+        self._processor = AutoProcessor.from_pretrained(str(self._model_dir))
+        self._model = AutoModel.from_pretrained(str(self._model_dir))
+        self._model.eval()
+
+    def infer(self, image_path: Path) -> np.ndarray:
+        """Return a 1152-dim float32 embedding (SigLIP MAP-pooled output)."""
+        self.load()
+        img = Image.open(image_path).convert("RGB")
+        with self._torch.no_grad():
+            inputs = self._processor(images=img, return_tensors="pt")
+            out = self._model.get_image_features(**inputs)
+        pooled = out.pooler_output if hasattr(out, "pooler_output") else out
+        return pooled[0].numpy().astype(np.float32)
+
+
+_default_embedder: Embedder | None = None
+
+
+def get_embedder() -> Embedder:
+    global _default_embedder
+    if _default_embedder is None:
+        _default_embedder = Embedder()
+    return _default_embedder