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/rocm_backend.py

@@ -0,0 +1,25 @@
+"""
+ROCm backend — GPU-accelerated inference for Radeon 760M-class hardware.
+
+Stub: raises NotImplementedError on all factory methods. To enable,
+set `vision.backend: rocm` in mulita.yml once ROCm support is implemented.
+"""
+from app.config import VisionSettings
+from app.services.vision.base import Embedder, OCREngine, ObjectDetector, FaceProcessor
+
+
+class ROCmBackend:
+    def __init__(self, vision_settings: VisionSettings):
+        self._settings = vision_settings
+
+    def create_embedder(self) -> Embedder:
+        raise NotImplementedError("ROCm backend not yet implemented — use 'onnx'")
+
+    def create_ocr(self) -> OCREngine:
+        raise NotImplementedError("ROCm backend not yet implemented — use 'onnx'")
+
+    def create_detector(self) -> ObjectDetector:
+        raise NotImplementedError("ROCm backend not yet implemented — use 'onnx'")
+
+    def create_face_processor(self) -> FaceProcessor:
+        raise NotImplementedError("ROCm backend not yet implemented — use 'onnx'")