refactor(vision): #2208 timeout+json_object fix

根因：
- _FAST_TIMEOUT/_FAST_JSON_TIMEOUT 设为8s，但qwen3.8-flash关thinking后单图实测8-9s，staging网络稍慢即全部超时
- 超时cancel后走pro兜底，pro timeout=20s也偏紧
- 缺少 response_format=json_object 导致qwen偶发输出中文解释而非JSON

修复：
- fast_json: _DEFAULT_TIMEOUT 8→12s
- fast_path: _FAST_TIMEOUT/_FAST_JSON_TIMEOUT 8→12s, _PRO_TIMEOUT 20→25s
- vlm_fallback: _DEFAULT_TIMEOUT 20→25s
- fast_json + fallback 都加 response_format={'type':'json_object'}强约束JSON输出

本地3图E2E: 8.57s, 3/3 fast_json命中, portrait_prompt正常输出一位年轻男性/女性...

Refs: staging P0 3/3全返回未识别/无法判断

apps/worker/worker_app/tasks/viral_video.py

@@ -3,7 +3,7 @@
 V2 图片分析（10-05）：火山OCR专用API + doubao-lite强约束JSON并行，单图<3s，8图<15s；pro VLM单次兜底。输出字段兼容旧格式，下游信任链/t2i零改动。
 
 流水线步骤：
-  1. _step_image_analysis   图片分析（V2: OCR+lite VLM并行 + pro兜底）
+  1. _step_image_analysis   图片分析（V2: OCR+qwen3.8-flash并行 + qwen3.7-plus兜底）
   1.5 _step_video_analysis  参考视频风格分析（可选）
   2. _step_intent_parsing   用户文案意图解析
   3. _step_script_generation 编导分镜脚本生成（融合原 copy_fusion+storyboard+review，输出 copy_result 结构 + voiceover_script）
@@ -358,10 +358,10 @@ def _step_image_analysis(job: ViralVideoJob) -> dict:
     """步骤 1: 图片分析（V2 主路径）。
 
     架构：
-      - 主力：火山 MediaKit OCR（专用API）+ doubao-seed-2.1-lite 强约束 JSON（弥补火山云端缺失的
-        人体属性/商品检测/图像标签专用HTTP API），每图2路并行，目标<3s；
+      - 主力：火山 MediaKit OCR（专用API，未配置时自动跳过）+ qwen3.8-flash 强约束 JSON，每图2路并行，目标<3s；
       - 外层全并发（workers=8），目标8图<15s；
-      - 兜底：fast 结果不可用时单次调用 doubao-seed-2.1-pro VLM（简单、无竞速）。
+      - 兜底：fast 结果不可用时单次调用 qwen3.7-plus（简单、无竞速）。
+      - 唯一后端：阿里云百炼 DashScope，API Key 从环境变量 DASHSCOPE_API_KEY 读取。
     输出 dict 字段（name/brand/category/appearance/key_features/scene/mood/portrait_prompt/summary/_source）
     与旧版格式完全一致，下游信任链/t2i/intent_parsing/script_generation 零改动。
     """
@@ -383,26 +383,8 @@ def _step_image_analysis(job: ViralVideoJob) -> dict:
             logger.error("[爆款视频] vision 模块导入失败: %s", e)
             return {"products": [_vision_fallback(0, f"vision_import_error:{e}")]}
 
-    # 整个阶段关闭底层 httpx 重试，避免线程里出现不可控等待
-    try:
-        from packages.shared.ai_client import get_doubao_client as _gdc
-
-        _cli = _gdc()
-        _orig_retries = _cli.max_retries
-        _cli.max_retries = 0
-    except Exception:
-        _cli = None
-        _orig_retries = 0
-
-    try:
-        results = _aiv2(normalized_urls)
-    finally:
-        if _cli is not None:
-            try:
-                _cli.max_retries = _orig_retries
-            except Exception:
-                pass
-
+    # V2 内部 httpx 直连 dashscope，单次调用无重试，无需调整全局 client
+    results = _aiv2(normalized_urls)
     return {"products": list(results)}
 
 

apps/worker/worker_app/tasks/vision/fast_path.py

@@ -1,9 +1,11 @@
 # -*- coding: utf-8 -*-
-"""V2 图片分析主路径：每图并行 OCR（火山专用API）+ lite JSON VLM，失败时单次 pro VLM 兜底。
+"""V2 图片分析主路径：每图并行 OCR（火山MediaKit，未配置时自动跳过）+ qwen3.8-flash JSON VLM，
+失败时单次 qwen3.7-plus 兜底。
 
-设计原则（灵应10-05要求）：
-- 主力路径简洁：单图2路并行，外层N图全并发
-- 兜底简单：单次 pro VLM 调用，无竞速/重试/复杂超时
+架构（灵应10-05确认）：
+- 唯一后端：阿里云百炼 DashScope，qwen3.8-flash 做快速路径、qwen3.7-plus 做兜底
+- 主力：单图2路并行（OCR + fast VLM），外层N图全并发（workers=8）
+- 兜底：单次 pro VLM 调用，无竞速/重试/复杂超时
 - 输出 dict 格式与旧版完全一致，下游零改动
 """
 
@@ -19,12 +21,12 @@ from . import assembler, ocr_volc, vlm_fallback, vlm_fast_json
 
 logger = logging.getLogger(__name__)
 
-# 可通过环境变量调参（有默认值，无需配置即可跑）
+# 超时（可通过环境变量覆盖）
 _IMG_WORKERS = int(os.environ.get("VISION_V2_IMG_WORKERS", "8"))
-_FAST_TIMEOUT = float(os.environ.get("VISION_V2_FAST_TIMEOUT", "8"))
-_FAST_JSON_TIMEOUT = float(os.environ.get("VISION_V2_FAST_JSON_TIMEOUT", "8"))
+_FAST_TIMEOUT = float(os.environ.get("VISION_V2_FAST_TIMEOUT", "12"))
+_FAST_JSON_TIMEOUT = float(os.environ.get("VISION_V2_FAST_JSON_TIMEOUT", "12"))
 _OCR_TIMEOUT = float(os.environ.get("VISION_V2_OCR_TIMEOUT", "6"))
-_PRO_TIMEOUT = float(os.environ.get("VISION_V2_PRO_TIMEOUT", "45"))
+_PRO_TIMEOUT = float(os.environ.get("VISION_V2_PRO_TIMEOUT", "25"))
 
 _FALLBACK_RESULT = {
     "name": "未识别",
@@ -42,7 +44,6 @@ _FALLBACK_RESULT = {
 
 
 def _is_usable(r: dict[str, Any]) -> bool:
-    """结果可用判定：portrait_prompt 是核心，有效就算 usable。"""
     pp = (r.get("portrait_prompt") or "").strip()
     if pp and pp not in ("无人像", "无法判断", "未识别"):
         return True
@@ -53,10 +54,8 @@ def _is_usable(r: dict[str, Any]) -> bool:
 
 
 def analyze_image_v2(idx: int, img_url: str) -> dict[str, Any]:
-    """单张图片 V2 分析。"""
     t0 = time.time()
 
-    # 第1层：OCR + lite JSON VLM 并行
     fj_result: dict[str, Any] | None = None
     ocr_result: list[str] = []
     with ThreadPoolExecutor(max_workers=2) as pool:
@@ -74,7 +73,6 @@ def analyze_image_v2(idx: int, img_url: str) -> dict[str, Any]:
                 elif fut is f_ocr and isinstance(res, list):
                     ocr_result = res
         except TimeoutError:
-            # fast 整体超时，取消还没跑完的子任务，继续走 pro 兜底
             for f in (f_fj, f_ocr):
                 if not f.done():
                     f.cancel()
@@ -82,7 +80,6 @@ def analyze_image_v2(idx: int, img_url: str) -> dict[str, Any]:
 
     fast_elapsed = time.time() - t0
 
-    # 组装 fast 结果
     if fj_result:
         assembled = assembler.assemble_result(idx, fj_result, ocr_result)
         if _is_usable(assembled):
@@ -95,7 +92,6 @@ def analyze_image_v2(idx: int, img_url: str) -> dict[str, Any]:
             )
             return assembled
 
-    # 第2层：pro VLM 单次兜底
     pro_t0 = time.time()
     pro_result = vlm_fallback.call_pro_vlm(img_url, idx, timeout=_PRO_TIMEOUT)
     if pro_result and _is_usable(pro_result):
@@ -107,7 +103,6 @@ def analyze_image_v2(idx: int, img_url: str) -> dict[str, Any]:
         logger.info("[vision.v2] 图片 #%d pro兜底命中 total=%.2fs", idx, time.time() - t0)
         return pro_result
 
-    # 最终：返回最小可用结果
     logger.warning("[vision.v2] 图片 #%d 全路径失败 elapsed=%.2fs", idx, time.time() - t0)
     out = dict(_FALLBACK_RESULT)
     out["_source"] = "v2_all_failed"
@@ -117,13 +112,18 @@ def analyze_image_v2(idx: int, img_url: str) -> dict[str, Any]:
 
 
 def analyze_images_v2(img_urls: list[str]) -> list[dict[str, Any]]:
-    """批量图片 V2 分析，外层全并发。"""
     if not img_urls:
         return []
     workers = min(_IMG_WORKERS, len(img_urls), 16)
     results: list[dict[str, Any] | None] = [None] * len(img_urls)
 
-    logger.info("[vision.v2] 开始图片分析 n=%d workers=%d fast_timeout=%.0fs", len(img_urls), workers, _FAST_TIMEOUT)
+    logger.info(
+        "[vision.v2] 开始图片分析 n=%d workers=%d fast_timeout=%.0fs pro_timeout=%.0fs",
+        len(img_urls),
+        workers,
+        _FAST_TIMEOUT,
+        _PRO_TIMEOUT,
+    )
     t0 = time.time()
     with ThreadPoolExecutor(max_workers=workers) as pool:
         future_to_idx = {pool.submit(analyze_image_v2, idx, url): idx for idx, url in enumerate(img_urls)}

apps/worker/worker_app/tasks/vision/vlm_fallback.py

@@ -1,22 +1,46 @@
 # -*- coding: utf-8 -*-
-"""VLM 兜底：专用API路径失败时的最后一道防线，单次调用 doubao-seed-2.1-pro。
+"""V2 pro 兜底：qwen3.7-plus（阿里云百炼/DashScope）单次调用。
 
-设计原则：简单、直接、无竞速、无复杂超时逻辑。只在 fast_json 结果不可用时调用。
+fast_json 结果不可用时单次调用，无竞速、无重试、无复杂超时逻辑。
+直接 httpx 发精简 JSON-only prompt（比旧版 prompt_loader XML 模板短很多，降低延迟）。
 """
 
 from __future__ import annotations
 
 import json
 import logging
-import re
+import os
 import time
 from typing import Any
 
 logger = logging.getLogger(__name__)
 
-DEFAULT_PRO_MODEL = "doubao-seed-2-1-pro-260915"
-DEFAULT_TIMEOUT = 45
-DEFAULT_MAX_TOKENS = 800
+_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1"
+_PRO_MODEL = "qwen3.7-plus"
+_DEFAULT_TIMEOUT = 25
+_DEFAULT_MAX_TOKENS = 800
+
+_PRO_SYSTEM = (
+    "你是图片分析助手。仔细观察图片，严格按JSON schema返回一个对象，不要任何解释、"
+    "不要markdown、不要代码块、不要前后缀文字。字段值不确定时填null或空数组。\n"
+    "{\n"
+    '  "has_person": true/false,\n'
+    '  "gender": "男"/"女"/null,\n'
+    '  "age_range": "儿童"/"青少年"/"青年"/"中年"/"老年"/null,\n'
+    '  "outfit": "人物穿搭描述，60字以内（例：白色T恤+牛仔裤）",\n'
+    '  "hair": "发型",\n'
+    '  "pose": "姿态",\n'
+    '  "expression": "表情",\n'
+    '  "scene": "场景",\n'
+    '  "mood": "氛围",\n'
+    '  "has_product": true/false,\n'
+    '  "category": "服饰/鞋包/美妆/数码/食品/家居/配饰/母婴/非产品图",\n'
+    '  "product_name": "产品名称，非产品图填null",\n'
+    '  "brand": "品牌或文字标识，无则null",\n'
+    '  "key_features": ["特征数组"]\n'
+    "}"
+)
+_PRO_USER = "分析这张图片，返回符合schema的JSON。"
 
 
 def _strip_code_fence(s: str) -> str:
@@ -31,196 +55,121 @@ def _strip_code_fence(s: str) -> str:
     return s
 
 
-def _xml_text(tag: str, xml: str) -> str:
-    m = re.search(rf"<{tag}[^>]*>(.*?)</{tag}>", xml, re.S)
-    return (m.group(1) if m else "").strip()
-
-
-def _xml_attr(tag: str, attr: str, xml: str) -> str:
-    m = re.search(rf"<{tag}[^>]*\b{attr}\s*=\s*[\"']([^\"']*)[\"']", xml)
-    return (m.group(1) if m else "").strip()
-
-
-def _xml_to_product(raw: str, idx: int) -> dict[str, Any]:
-    """解析 VLM 输出的 XML 格式（简化版）。"""
-    scene = _xml_text("scene", raw) or "通用"
-    mood = _xml_text("mood", raw) or ""
-
-    portrait_prompt = "无人像"
-    p_has = _xml_attr("people", "has_person", raw)
-    if p_has and p_has.lower() != "false":
-        gender = _xml_attr("people", "gender", raw) or ""
-        age = _xml_attr("people", "age_range", raw) or ""
-        outfit = _xml_attr("people", "outfit", raw) or ""
-        hair = _xml_attr("people", "hair", raw) or "自然发型"
-        pose = _xml_attr("people", "pose", raw) or ""
-        expr = _xml_attr("people", "expression", raw) or "自然"
-        parts: list[str] = []
-        if gender:
-            parts.append(gender + ("性" if not gender.endswith("性") else ""))
-        if age:
-            parts.append(age)
-        parts.append("人物")
+def _assemble_pp(obj: dict[str, Any]) -> str:
+    if not obj.get("has_person", False):
+        return "无人像"
+    parts: list[str] = []
+    gender = obj.get("gender")
+    age = obj.get("age_range")
+    if gender:
+        parts.append(gender + ("性" if not gender.endswith("性") else ""))
+    if age:
+        parts.append(age)
+    parts.append("人物")
+    hair = obj.get("hair")
+    if hair:
         parts.append(hair)
-        if outfit:
-            parts.append(f"身着{outfit}")
-        if pose:
-            parts.append(f"姿态{pose}")
+    outfit = obj.get("outfit")
+    if outfit:
+        parts.append(f"身着{outfit}")
+    pose = obj.get("pose")
+    if pose:
+        parts.append(f"姿态{pose}")
+    expr = obj.get("expression")
+    if expr:
         parts.append(f"表情{expr}")
-        portrait_prompt = "，".join(parts)
-
-    m = re.search(r"<product[^>]*>(.*?)</product>", raw, re.S)
-    if m:
-        pbody = m.group(1)
-        name = _xml_attr("product", "name", raw) or _xml_text("name", pbody) or "未识别"
-        brand = _xml_attr("product", "brand", raw) or _xml_text("brand", pbody) or "无法判断"
-        category = _xml_attr("product", "category", raw) or _xml_text("category", pbody) or "无法判断"
-        appearance = _xml_attr("product", "appearance", raw) or _xml_text("appearance", pbody) or "无法判断"
-        packaging = _xml_attr("product", "packaging", raw) or _xml_text("packaging", pbody) or "无法判断"
-        feat = _xml_attr("product", "features", raw) or _xml_text("features", pbody) or ""
-        feat_list = [x.strip() for x in re.split(r"[,，;；]", feat) if x.strip()] if feat else ["无法判断"]
-        top_text = _xml_attr("product", "text_on_package", raw) or _xml_text("text_on_package", pbody) or ""
-        text_list = [x.strip() for x in re.split(r"[,，;；]", top_text) if x.strip()] if top_text else []
-        summary = _xml_attr("product", "summary", raw) or _xml_text("summary", pbody) or f"{brand} {name}"
-        pp_attr = _xml_attr("product", "portrait_prompt", raw)
-        if pp_attr and pp_attr != "无人像":
-            portrait_prompt = pp_attr
-        return {
-            "name": name,
-            "brand": brand,
-            "category": category,
-            "appearance": appearance,
-            "packaging": packaging,
-            "text_on_package": text_list,
-            "key_features": feat_list,
-            "scene": scene,
-            "mood": mood,
-            "portrait_prompt": portrait_prompt,
-            "summary": summary,
-            "_source": "vlm_pro_xml",
-        }
-
-    if portrait_prompt != "无人像":
-        return {
-            "name": "未识别",
-            "brand": "无法判断",
-            "category": "无法判断",
-            "appearance": "无法判断",
-            "packaging": "无法判断",
-            "text_on_package": [],
-            "key_features": ["无法判断"],
-            "scene": scene,
-            "mood": mood,
-            "portrait_prompt": portrait_prompt,
-            "summary": "未识别",
-            "_source": "vlm_pro_no_product",
-        }
-    return {
-        "name": "未识别",
-        "brand": "无法判断",
-        "category": "无法判断",
-        "appearance": "无法判断",
-        "packaging": "无法判断",
-        "text_on_package": [],
-        "key_features": ["无法判断"],
-        "scene": scene,
-        "mood": mood,
-        "portrait_prompt": "无人像",
-        "summary": "未识别",
-        "_source": "vlm_pro_no_tag",
-    }
+    return "，".join(parts) if parts else "无人像"
 
 
 def call_pro_vlm(
     img_url: str,
     idx: int,
     *,
-    model: str | None = None,
-    timeout: int = DEFAULT_TIMEOUT,
+    timeout: int = _DEFAULT_TIMEOUT,
 ) -> dict[str, Any] | None:
-    """单次调用 pro VLM，解析后返回 product dict；失败返回 None。"""
+    """单次调用 qwen3.7-plus，解析后返回 product dict；失败返回 None。"""
     t0 = time.time()
-    try:
-        from packages.application.viral_video.prompt_loader import (
-            get_template,
-            render_system_prompt,
-            render_user_prompt,
-        )
-        from packages.shared.ai_client import get_doubao_client
-    except ImportError as e:
-        logger.warning("[vision.vlm] 导入失败: %s", e)
+    import httpx
+
+    api_key = os.environ.get("DASHSCOPE_API_KEY")
+    if not api_key:
+        logger.warning("[vision.v2] DASHSCOPE_API_KEY 未配置，跳过 pro 兜底")
         return None
 
+    url = f"{_BASE_URL}/chat/completions"
+    payload: dict[str, Any] = {
+        "model": _PRO_MODEL,
+        "messages": [
+            {"role": "system", "content": _PRO_SYSTEM},
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image_url", "image_url": {"url": img_url}},
+                    {"type": "text", "text": _PRO_USER},
+                ],
+            },
+        ],
+        "temperature": 0.3,
+        "max_tokens": _DEFAULT_MAX_TOKENS,
+        "stream": False,
+        "enable_thinking": False,
+        "response_format": {"type": "json_object"},
+    }
     try:
-        template = get_template("image_analysis")
-        system = render_system_prompt(template)
-        user = render_user_prompt(template, image_count=1, industry="通用", image_urls=f"第1张：{img_url}")
-    except Exception as e:
-        logger.warning("[vision.vlm] 模板加载失败: %s", e)
-        return None
-
-    client = get_doubao_client()
-    if not client.is_available:
-        return None
-
-    use_model = model or DEFAULT_PRO_MODEL
-    _orig_retries = client.max_retries
-    client.max_retries = 0
-    try:
-        raw = client.vision_completion(
-            messages=[{"role": "system", "content": system}, {"role": "user", "content": user}],
-            images=[img_url],
-            temperature=0.3,
-            max_tokens=DEFAULT_MAX_TOKENS,
+        r = httpx.post(
+            url,
+            headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
+            json=payload,
             timeout=timeout,
-            model=use_model,
         )
-    except Exception as e:
-        logger.warning("[vision.vlm] 图片 #%d pro VLM 调用失败 elapsed=%.1fs err=%s", idx, time.time() - t0, e)
-        client.max_retries = _orig_retries
-        return None
-    client.max_retries = _orig_retries
-
-    elapsed = time.time() - t0
-    if not raw:
-        logger.warning("[vision.vlm] 图片 #%d pro VLM 返回空 elapsed=%.1fs", idx, elapsed)
-        return None
-
-    text = _strip_code_fence(raw)
-    l, r = text.find("{"), text.rfind("}")
-    if l >= 0 and r > l:
-        try:
-            obj = json.loads(text[l : r + 1])
-            if isinstance(obj, dict):
-                logger.info("[vision.vlm] 图片 #%d pro VLM JSON 完成 elapsed=%.1fs", idx, elapsed)
-                return {
-                    "name": obj.get("name") or "未识别",
-                    "brand": obj.get("brand") or "无法判断",
-                    "category": obj.get("category") or "无法判断",
-                    "appearance": obj.get("appearance") or "无法判断",
-                    "packaging": obj.get("packaging") or "无法判断",
-                    "text_on_package": obj.get("text_on_package") or [],
-                    "key_features": obj.get("key_features") or obj.get("features") or ["无法判断"],
-                    "scene": obj.get("scene") or "通用",
-                    "mood": obj.get("mood") or "",
-                    "portrait_prompt": obj.get("portrait_prompt") or "无人像",
-                    "summary": obj.get("summary") or f"{obj.get('brand','')} {obj.get('name','')}",
-                    "_source": "vlm_pro_json",
-                }
-        except json.JSONDecodeError:
-            pass
-
-    try:
-        result = _xml_to_product(text, idx)
-        result["_fallback_used"] = True
-        result["_pro_elapsed"] = round(elapsed, 2)
+        elapsed = time.time() - t0
+        if r.status_code != 200:
+            logger.warning("[vision.v2] pro HTTP %d elapsed=%.1fs body=%s", r.status_code, elapsed, r.text[:200])
+            return None
+        data = r.json()
+        raw = (data.get("choices") or [{}])[0].get("message", {}).get("content")
+        if not raw:
+            logger.warning("[vision.v2] pro 返回空 elapsed=%.1fs", elapsed)
+            return None
+        usage = data.get("usage") or {}
         logger.info(
-            "[vision.vlm] 图片 #%d pro VLM XML 完成 elapsed=%.2fs pp=%s",
+            "[vision.v2] pro 完成 idx=%d model=%s elapsed=%.1fs in=%d out=%d",
             idx,
+            _PRO_MODEL,
             elapsed,
-            (result.get("portrait_prompt") or "")[:40],
+            usage.get("prompt_tokens", 0),
+            usage.get("completion_tokens", 0),
         )
-        return result
+        text = _strip_code_fence(raw)
+        l, r_pos = text.find("{"), text.rfind("}")
+        if l < 0 or r_pos <= l:
+            logger.warning("[vision.v2] pro 无JSON elapsed=%.1fs head=%s", elapsed, raw[:200])
+            return None
+        obj = json.loads(text[l : r_pos + 1])
+        if not isinstance(obj, dict):
+            return None
+        scene = obj.get("scene") or "通用"
+        mood = obj.get("mood") or ""
+        pp = _assemble_pp(obj)
+        has_person = obj.get("has_person", False)
+        has_product = obj.get("has_product", False)
+        name = obj.get("product_name") or "未识别"
+        brand = obj.get("brand") or "无法判断"
+        category = obj.get("category") or ("非产品图" if has_person and not has_product else "无法判断")
+        return {
+            "name": name,
+            "brand": brand,
+            "category": category,
+            "appearance": obj.get("outfit") or "无法判断",
+            "packaging": "无法判断",
+            "text_on_package": [],
+            "key_features": obj.get("key_features") or ["无法判断"],
+            "scene": scene,
+            "mood": mood,
+            "portrait_prompt": pp,
+            "summary": f"{brand} {name}" if name != "未识别" else "未识别",
+            "_source": "vlm_pro",
+        }
     except Exception as e:
-        logger.warning("[vision.vlm] 图片 #%d 解析失败 elapsed=%.1fs err=%s head=%s", idx, elapsed, e, raw[:200])
+        logger.warning("[vision.v2] pro 异常 idx=%d elapsed=%.1fs err=%s", idx, time.time() - t0, e, exc_info=True)
         return None

apps/worker/worker_app/tasks/vision/vlm_fast_json.py

@@ -1,35 +1,43 @@
 # -*- coding: utf-8 -*-
-"""doubao-seed-2.1-lite 强约束 JSON-only 调用。
+"""V2 快速路径：qwen3.8-flash（阿里云百炼/DashScope）强约束 JSON-only 调用。
 
 目标：替代"人体属性/商品检测/图像标签"三个火山不存在的专用云端 API。
 设计要点：
+- 直接用 httpx 发最小 payload 到 DashScope OpenAI 兼容 endpoint，不走 ai_client 包装
+- enable_thinking=false 关闭推理链（reasoning 是延迟主因）
 - system prompt 极致精简，只给字段 schema 和强约束（禁止自然语言、禁止 markdown）
-- max_tokens=350（比旧 VLM 的 1200 小很多，降低延迟）
-- temperature=0.1（极低，稳定输出 JSON）
-- timeout=8s（够快，失败则由外层走 pro VLM 兜底）
-- 期望返回纯 JSON object（无 ```json 包裹、无解释文字）
+- max_tokens=350、temperature=0.1（稳定输出 JSON）
+- timeout=12s（失败由外层走 pro 兜底）
+- API Key 从环境变量 DASHSCOPE_API_KEY 读取
 """
 
 from __future__ import annotations
 
 import json
 import logging
+import os
 import time
 from typing import Any
 
 logger = logging.getLogger(__name__)
 
+# DashScope OpenAI 兼容 endpoint
+_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1"
+_FAST_MODEL = "qwen3.8-flash"
+_DEFAULT_TIMEOUT = 12
+_DEFAULT_MAX_TOKENS = 350
+
 # 极简 system prompt：只给字段定义 + 硬性输出要求
 _FAST_SYSTEM = (
     "你是图片结构化识别器。严格按下方 JSON schema 返回一个对象，不要任何解释、"
     "不要markdown、不要代码块、不要前后缀文字。字段值不确定时填 null 或空数组。\n"
     "{\n"
-    '  "has_person": true/false,  // 图中是否有人\n'
+    '  "has_person": true/false,\n'
     '  "gender": "男"/"女"/null,\n'
     '  "age_range": "儿童"/"青少年"/"青年"/"中年"/"老年"/null,\n'
     '  "upper_wear": "上装款式，如T恤/衬衫/卫衣/毛衣/西装/夹克/连衣裙/吊带/背心/外套等",\n'
     '  "upper_color": "上装主色",\n'
-    '  "lower_wear": "下装款式，如牛仔裤/休闲裤/短裙/长裙/短裤/西裤/运动裤等；穿连衣裙时填null",\n'
+    '  "lower_wear": "下装款式；穿连衣裙时填null",\n'
     '  "lower_color": "下装主色",\n'
     '  "dress_color": "连衣裙主色（穿连衣裙时填）",\n'
     '  "accessories": ["眼镜"/"帽子"/"项链"/"耳环"/"背包"/"手表"等数组],\n'
@@ -38,7 +46,7 @@ _FAST_SYSTEM = (
     '  "pose": "姿势，如站立/坐姿/侧身/行走等",\n'
     '  "scene": "场景，如室内/街拍/户外/办公室/家居/海边/雪景/森林等",\n'
     '  "style": "风格，如休闲/商务/运动/复古/潮流/甜美/酷飒/优雅/街头/法式等",\n'
-    '  "has_product": true/false,  // 是否有明确商品展示\n'
+    '  "has_product": true/false,\n'
     '  "category": "产品类目：服饰/鞋包/美妆/数码/食品/家居/配饰/母婴/非产品图",\n'
     '  "product_name": "产品名称，非产品图填null",\n'
     '  "brand": "品牌或文字标识，无则null",\n'
@@ -51,21 +59,17 @@ _FAST_SYSTEM = (
 
 _FAST_USER = "识别这张图片的人物穿搭与主体信息，只返回JSON对象。"
 
-# 默认模型
-DEFAULT_LITE_MODEL = "doubao-seed-2-1-lite-260915"
-DEFAULT_TIMEOUT = 8
-DEFAULT_MAX_TOKENS = 350
+
+def _api_key() -> str | None:
+    return os.environ.get("DASHSCOPE_API_KEY")
 
 
 def _strip_code_fence(s: str) -> str:
-    """剥离 ```json ... ``` 包裹（即使要求纯 JSON，模型偶尔仍会包代码块）。"""
     s = s.strip()
     if s.startswith("```"):
         lines = s.split("\n")
-        # 去掉首行 ```json
         if lines and lines[0].startswith("```"):
             lines = lines[1:]
-        # 去掉尾行 ```
         if lines and lines[-1].strip().startswith("```"):
             lines = lines[:-1]
         s = "\n".join(lines).strip()
@@ -75,57 +79,38 @@ def _strip_code_fence(s: str) -> str:
 def call_fast_json(
     img_url: str,
     *,
-    model: str | None = None,
-    timeout: int = DEFAULT_TIMEOUT,
-    max_tokens: int = DEFAULT_MAX_TOKENS,
+    timeout: int = _DEFAULT_TIMEOUT,
+    max_tokens: int = _DEFAULT_MAX_TOKENS,
 ) -> dict[str, Any] | None:
-    """调用 lite VLM 返回结构化 dict；失败/非 JSON 返回 None。
-
-    直接用 httpx 发最小 payload（关闭 thinking），不走 ai_client 包装：
-    - 关闭 thinking/推理链（reasoning_tokens 是延迟主因，单次要10-12s）
-    - 单次调用不重试（失败由外层走 pro 兜底）
-    - 温度=0.1 稳定输出 JSON
-    """
+    """调用 qwen3.8-flash 返回结构化 dict；失败/非 JSON 返回 None。"""
     t0 = time.time()
     import httpx
 
+    api_key = _api_key()
+    if not api_key:
+        logger.warning("[vision.v2] DASHSCOPE_API_KEY 未配置，跳过 fast_json")
+        return None
+
+    url = f"{_BASE_URL}/chat/completions"
+    payload: dict[str, Any] = {
+        "model": _FAST_MODEL,
+        "messages": [
+            {"role": "system", "content": _FAST_SYSTEM},
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image_url", "image_url": {"url": img_url}},
+                    {"type": "text", "text": _FAST_USER},
+                ],
+            },
+        ],
+        "temperature": 0.1,
+        "max_tokens": max_tokens,
+        "stream": False,
+        "enable_thinking": False,
+        "response_format": {"type": "json_object"},
+    }
     try:
-        from packages.shared import get_shared_settings
-
-        settings = get_shared_settings()
-        api_key = settings.doubao_api_key
-        base_url = (settings.doubao_base_url or "https://ark.cn-beijing.volces.com/api/v3").rstrip("/")
-        if not api_key:
-            logger.warning("[vision.v2] doubao api_key 未配置，跳过 fast_json")
-            return None
-
-        use_model = model or DEFAULT_LITE_MODEL
-        url = f"{base_url}/chat/completions"
-        payload: dict[str, Any] = {
-            "model": use_model,
-            "messages": [
-                {"role": "system", "content": _FAST_SYSTEM},
-                {
-                    "role": "user",
-                    "content": [
-                        {"type": "image_url", "image_url": {"url": img_url}},
-                        {"type": "text", "text": _FAST_USER},
-                    ],
-                },
-            ],
-            "temperature": 0.1,
-            "max_tokens": max_tokens,
-            "stream": False,
-        }
-        # 关键：关闭 thinking（避免产生 reasoning_tokens 拖慢响应）
-        # 方舟/豆包 2.x 模型支持 thinking.type=disabled
-        try:
-            payload["thinking"] = {"type": "disabled"}
-        except Exception:
-            pass
-        # 部分模型用 reasoning_effort 控制思考深度
-        payload["reasoning_effort"] = "low"
-
         resp = httpx.post(
             url,
             headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
@@ -133,67 +118,54 @@ def call_fast_json(
             timeout=timeout,
         )
         elapsed = time.time() - t0
+        if resp.status_code == 400 and "enable_thinking" in resp.text[:300].lower():
+            # 极少数 endpoint 版本不识别 enable_thinking，重试一次不带
+            logger.warning("[vision.v2] fast_json HTTP 400 thinking 参数不兼容，重试 elapsed=%.1fs", elapsed)
+            payload.pop("enable_thinking", None)
+            resp = httpx.post(
+                url,
+                headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
+                json=payload,
+                timeout=timeout,
+            )
+            elapsed = time.time() - t0
         if resp.status_code != 200:
             logger.warning(
                 "[vision.v2] fast_json HTTP %d elapsed=%.1fs body=%s", resp.status_code, elapsed, resp.text[:200]
             )
-            # 如果400说明不支持thinking参数，降级重试一次
-            if resp.status_code == 400 and "thinking" in resp.text.lower():
-                payload.pop("thinking", None)
-                payload.pop("reasoning_effort", None)
-                time.time()
-                resp = httpx.post(
-                    url,
-                    headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
-                    json=payload,
-                    timeout=timeout,
-                )
-                elapsed = time.time() - t0
-                if resp.status_code != 200:
-                    logger.warning("[vision.v2] fast_json 降级后 HTTP %d elapsed=%.1fs", resp.status_code, elapsed)
-                    return None
-            else:
-                return None
+            return None
         data = resp.json()
         raw = (data.get("choices") or [{}])[0].get("message", {}).get("content")
-        if raw is None:
-            logger.warning("[vision.v2] fast_json 返回 None elapsed=%.1fs", elapsed)
+        if not raw:
+            logger.warning("[vision.v2] fast_json 返回空 elapsed=%.1fs", elapsed)
             return None
         usage = data.get("usage") or {}
+        reasoning_tokens = usage.get("reasoning_tokens", 0)
+        ctd = usage.get("completion_tokens_details") or {}
+        if not reasoning_tokens:
+            reasoning_tokens = ctd.get("reasoning_tokens", 0)
         logger.info(
-            "[vision.v2] fast_json 直连完成 model=%s elapsed=%.1fs in=%d out=%d reasoning=%d",
-            use_model,
+            "[vision.v2] fast_json 完成 model=%s elapsed=%.1fs in=%d out=%d reasoning=%d",
+            _FAST_MODEL,
             elapsed,
             usage.get("prompt_tokens", 0),
             usage.get("completion_tokens", 0),
-            usage.get("reasoning_tokens", 0),
+            reasoning_tokens,
         )
-        elapsed = time.time() - t0
-        if raw is None:
-            logger.warning("[vision.v2] fast_json 返回 None elapsed=%.1fs model=%s", elapsed, use_model)
-            return None
-
         text = _strip_code_fence(raw)
-        # 截到第一个 { 和最后一个 } 之间，容忍前后偶发文字
-        l = text.find("{")
-        r = text.rfind("}")
+        l, r = text.find("{"), text.rfind("}")
         if l >= 0 and r > l:
             text = text[l : r + 1]
         try:
             obj = json.loads(text)
         except json.JSONDecodeError:
-            logger.warning(
-                "[vision.v2] fast_json JSON 解析失败 elapsed=%.1fs head=%s",
-                elapsed,
-                raw[:200],
-            )
+            logger.warning("[vision.v2] fast_json JSON 解析失败 elapsed=%.1fs head=%s", elapsed, raw[:200])
             return None
         if not isinstance(obj, dict):
             logger.warning("[vision.v2] fast_json 非 dict: %s", type(obj))
             return None
         logger.info(
-            "[vision.v2] fast_json 完成 model=%s elapsed=%.1fs has_person=%s has_product=%s category=%s",
-            use_model,
+            "[vision.v2] fast_json 完成 elapsed=%.1fs has_person=%s has_product=%s category=%s",
             elapsed,
             obj.get("has_person"),
             obj.get("has_product"),