feat(US2): image quad extraction — POST /api/v1/image/extract

- app/models/image_models.py: BBox, QuadrupleItem, ImageExtract{Request,Response}
- app/services/image_service.py: download → base64 LLM → bbox clamp → crop upload
- app/routers/image.py: POST /image/extract handler
- tests: 4 service + 3 router tests, 7/7 passing

app/services/image_service.py

@@ -0,0 +1,90 @@
+import base64
+import io
+
+import cv2
+import numpy as np
+
+from app.clients.llm.base import LLMClient
+from app.clients.storage.base import StorageClient
+from app.core.config import get_config
+from app.core.json_utils import extract_json
+from app.core.logging import get_logger
+from app.models.image_models import (
+    BBox,
+    ImageExtractRequest,
+    ImageExtractResponse,
+    QuadrupleItem,
+)
+
+logger = get_logger(__name__)
+
+_DEFAULT_PROMPT = (
+    "请分析这张图片，提取其中的知识四元组，以 JSON 数组格式返回，每条包含字段："
+    "subject（主体实体）、predicate（关系/属性）、object（客体实体）、"
+    "qualifier（修饰信息，可为 null）、bbox（{{x, y, w, h}} 像素坐标）。"
+)
+
+
+async def extract_quads(
+    req: ImageExtractRequest,
+    llm: LLMClient,
+    storage: StorageClient,
+) -> ImageExtractResponse:
+    cfg = get_config()
+    bucket = cfg["storage"]["buckets"]["source_data"]
+    model = req.model or cfg["models"]["default_vision"]
+
+    image_bytes = await storage.download_bytes(bucket, req.file_path)
+
+    # Decode with OpenCV for cropping; encode as base64 for LLM
+    nparr = np.frombuffer(image_bytes, np.uint8)
+    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+    img_h, img_w = img.shape[:2]
+
+    b64 = base64.b64encode(image_bytes).decode()
+    image_data_url = f"data:image/jpeg;base64,{b64}"
+
+    prompt = req.prompt_template or _DEFAULT_PROMPT
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image_url", "image_url": {"url": image_data_url}},
+                {"type": "text", "text": prompt},
+            ],
+        }
+    ]
+
+    raw = await llm.chat_vision(model, messages)
+    logger.info("image_extract", extra={"file": req.file_path, "model": model})
+
+    items_raw = extract_json(raw)
+    items: list[QuadrupleItem] = []
+
+    for idx, item in enumerate(items_raw):
+        b = item["bbox"]
+        # Clamp bbox to image dimensions
+        x = max(0, min(int(b["x"]), img_w - 1))
+        y = max(0, min(int(b["y"]), img_h - 1))
+        w = min(int(b["w"]), img_w - x)
+        h = min(int(b["h"]), img_h - y)
+
+        crop = img[y : y + h, x : x + w]
+        _, crop_buf = cv2.imencode(".jpg", crop)
+        crop_bytes = crop_buf.tobytes()
+
+        crop_path = f"crops/{req.task_id}/{idx}.jpg"
+        await storage.upload_bytes(bucket, crop_path, crop_bytes, "image/jpeg")
+
+        items.append(
+            QuadrupleItem(
+                subject=item["subject"],
+                predicate=item["predicate"],
+                object=item["object"],
+                qualifier=item.get("qualifier"),
+                bbox=BBox(x=x, y=y, w=w, h=h),
+                cropped_image_path=crop_path,
+            )
+        )
+
+    return ImageExtractResponse(items=items)