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>

mulita.yml

@@ -22,3 +22,28 @@ performance:
   cache_ttl: 3600
   db_pool_size: 20
   db_pool_recycle: 3600
+
+# AI vision pipeline — embedding, OCR, object detection, face recognition.
+# Runs on the dedicated `vision` Celery queue (PR4+). Set enabled: false
+# to disable all vision processing.
+vision:
+  enabled: true
+  backend: onnx            # "onnx" (CPU) | "rocm" (future GPU)
+  models_dir: /data/models
+  embedder:
+    name: openclip_vitb32
+    batch_size: 8
+  ocr:
+    enabled: true
+    languages: [en]
+    min_confidence: 0.5
+  detector:
+    enabled: true
+    min_confidence: 0.35
+    max_detections: 50
+  faces:
+    enabled: true
+    min_face_size: 40
+    recognition_threshold: 0.4
+    cluster_eps: 0.35
+  worker_concurrency: 2