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/requirements.txt

@@ -34,6 +34,13 @@ pyexiftool==0.5.6
 # File watching
 watchfiles==0.21.0
 
+# Vision pipeline (ONNX Runtime CPU inference)
+onnxruntime==1.17.1
+open-clip-torch==2.24.0     # tokenizer + export helper; inference via ONNX
+rapidocr-onnxruntime==1.3.22
+scikit-learn==1.4.0          # DBSCAN for face clustering
+numpy>=1.26.0,<2.0
+
 # Utilities
 pyyaml==6.0.1
 pydantic==2.5.3