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/base.py

@@ -0,0 +1,89 @@
+"""
+Abstract base classes for vision backends.
+
+Each ABC defines the contract a backend must satisfy. The default
+implementation is ONNXBackend (onnx_backend.py). A ROCm backend can be
+added later by subclassing these ABCs and registering via
+settings.vision.backend.
+"""
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+import numpy as np
+
+
+@dataclass
+class DetectionBox:
+    """A single object detection result."""
+    label: str
+    confidence: float
+    bbox: list[float]  # [x1, y1, x2, y2] normalized 0-1
+
+
+@dataclass
+class OCRResult:
+    """A single OCR text region."""
+    text: str
+    confidence: float
+    bbox: list[float]  # [x1, y1, x2, y2] normalized 0-1
+    language: str = ""
+
+
+@dataclass
+class FaceDetection:
+    """A detected face with its recognition embedding."""
+    bbox: list[float]  # [x1, y1, x2, y2] normalized 0-1
+    embedding: np.ndarray  # float32 vector (128-d for SFace)
+    quality: float
+
+
+class Embedder(ABC):
+    """Generates image and text embeddings (e.g. OpenCLIP ViT-B/32)."""
+
+    @abstractmethod
+    def embed_image(self, image: np.ndarray) -> np.ndarray:
+        """Return a normalized float32 embedding vector for an RGB image."""
+        ...
+
+    @abstractmethod
+    def embed_text(self, text: str) -> np.ndarray:
+        """Return a normalized float32 embedding vector for a text query."""
+        ...
+
+    @property
+    @abstractmethod
+    def dim(self) -> int:
+        """Dimensionality of the output embedding."""
+        ...
+
+
+class OCREngine(ABC):
+    """Extracts text from images (e.g. rapidocr-onnxruntime)."""
+
+    @abstractmethod
+    def run(self, image: np.ndarray) -> list[OCRResult]:
+        """Return OCR results for an RGB image."""
+        ...
+
+
+class ObjectDetector(ABC):
+    """Detects objects in images (e.g. YOLOv8n)."""
+
+    @abstractmethod
+    def detect(self, image: np.ndarray) -> list[DetectionBox]:
+        """Return detections for an RGB image."""
+        ...
+
+
+class FaceProcessor(ABC):
+    """Detects faces and extracts recognition embeddings (e.g. YuNet + SFace)."""
+
+    @abstractmethod
+    def process(self, image: np.ndarray) -> list[FaceDetection]:
+        """Return face detections with embeddings for an RGB image."""
+        ...
+
+    @property
+    @abstractmethod
+    def embedding_dim(self) -> int:
+        """Dimensionality of face embedding vectors."""
+        ...