feat: add vision pipeline scaffolding with ONNX backend

Introduce the app/services/vision/ module with ABC interfaces, ONNX
Runtime backend, model registry, and per-task implementations:
- OpenCLIP ViT-B/32 embedder (image + text, 512-d)
- RapidOCR engine (PP-OCRv4 via ONNX, no PaddlePaddle)
- YOLOv8n object detector (raw ONNX, no ultralytics runtime)
- YuNet + SFace face processor (Apache 2.0, opencv_zoo, 128-d)
- DBSCAN face clustering helper

Add VisionSettings to config (mulita.yml + Pydantic), bootstrap_models.py
for first-boot weight downloads, models_data Docker volume, and ROCm
backend stub for future GPU acceleration.

No Celery tasks wired yet — models load but nothing invokes them.

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

backend/app/services/vision/clustering.py

@@ -0,0 +1,42 @@
+"""
+Face embedding clustering using DBSCAN with cosine distance.
+
+Called by the periodic `recluster_faces` Celery task (PR7).
+"""
+import logging
+
+import numpy as np
+from sklearn.cluster import DBSCAN
+
+logger = logging.getLogger(__name__)
+
+
+def cluster_faces(
+    embeddings: np.ndarray,
+    eps: float = 0.35,
+    min_samples: int = 2,
+) -> np.ndarray:
+    """Cluster face embeddings using DBSCAN with cosine metric.
+
+    Args:
+        embeddings: (N, D) float32 array of L2-normalized face embeddings.
+        eps: Maximum cosine distance between two samples to be in the
+             same neighborhood. Lower = tighter clusters.
+        min_samples: Minimum cluster size.
+
+    Returns:
+        (N,) int array of cluster labels. -1 = noise / unclustered.
+    """
+    if len(embeddings) < min_samples:
+        return np.full(len(embeddings), -1, dtype=int)
+
+    db = DBSCAN(eps=eps, min_samples=min_samples, metric="cosine")
+    labels = db.fit_predict(embeddings)
+
+    n_clusters = len(set(labels) - {-1})
+    n_noise = (labels == -1).sum()
+    logger.info(
+        "Face clustering: %d embeddings → %d clusters, %d noise",
+        len(embeddings), n_clusters, n_noise,
+    )
+    return labels