test(vision): update wiring tests for V2 batch analyze_images_v2 path

根因：上版同时传 thinking={type:disabled} + reasoning_effort=low，方舟返回400
Invalid combination of reasoning_effort and thinking type。降级重试分支
把两个参数都pop掉，模型回到默认thinking开启→响应9-12s超时，fast全败。

修复：
1. 只传 thinking={"type":"disabled"}，去掉互斥的 reasoning_effort
2. 400降级只pop thinking，保留精简payload重试（不pop多个）
3. 先单独验证 lite 单次HTTP 200且reasoning_tokens=0再跑E2E，省一轮部署

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

@@ -187,10 +187,10 @@ def call_pro_vlm(
         return None
 
     text = _strip_code_fence(raw)
-    l, r = text.find("{"), text.rfind("}")
-    if l >= 0 and r > l:
+    lb, rb = text.find("{"), text.rfind("}")
+    if lb >= 0 and rb > lb:
         try:
-            obj = json.loads(text[l : r + 1])
+            obj = json.loads(text[lb : rb + 1])
             if isinstance(obj, dict):
                 logger.info("[vision.vlm] 图片 #%d pro VLM JSON 完成 elapsed=%.1fs", idx, elapsed)
                 return {

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

@@ -117,14 +117,9 @@ def call_fast_json(
             "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"
+        # 关键：关闭 thinking（reasoning_tokens 是延迟主因，单次要10-12s）
+        # 方舟/豆包 Seed 2.x 支持 thinking={type:"disabled"}，且不要和 reasoning_effort 同时传（两者互斥会400）
+        payload["thinking"] = {"type": "disabled"}
 
         resp = httpx.post(
             url,
@@ -133,15 +128,12 @@ def call_fast_json(
             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():
+        # 400 说明模型不支持 thinking 参数（极少数旧模型），重试一次不带 thinking
+        if resp.status_code == 400:
+            body_preview = resp.text[:300].lower()
+            logger.warning("[vision.v2] fast_json HTTP 400 elapsed=%.1fs body=%s", elapsed, resp.text[:200])
+            if "thinking" in body_preview or "reasoning" in body_preview:
                 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"},
@@ -150,10 +142,15 @@ def call_fast_json(
                 )
                 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)
+                    logger.warning("[vision.v2] fast_json 降级重试 HTTP %d elapsed=%.1fs", resp.status_code, elapsed)
                     return None
             else:
                 return None
+        elif resp.status_code != 200:
+            logger.warning(
+                "[vision.v2] fast_json HTTP %d elapsed=%.1fs body=%s", resp.status_code, elapsed, resp.text[:200]
+            )
+            return None
         data = resp.json()
         raw = (data.get("choices") or [{}])[0].get("message", {}).get("content")
         if raw is None:
@@ -175,10 +172,10 @@ def call_fast_json(
 
         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("}")
-        if l >= 0 and r > l:
-            text = text[l : r + 1]
+        lb = text.find("{")
+        rb = text.rfind("}")
+        if lb >= 0 and rb > lb:
+            text = text[lb : rb + 1]
         try:
             obj = json.loads(text)
         except json.JSONDecodeError:

tests/unit/test_viral_video_wiring.py

@@ -97,21 +97,28 @@ def invalidate_loader_cache():
 
 
 class TestImageAnalysisWiring:
-    def test_uses_loader_template_and_xml_parse(self, job):
+    def test_v2_batch_analysis_returns_products(self, job):
+        """V2 路径：_step_image_analysis 批量调用 analyze_images_v2，返回 products。"""
         from apps.worker.worker_app.tasks import viral_video as vv
 
-        with patch("packages.shared.ai_service.call_vision", return_value=IMAGE_XML) as mock_v:
-            result = vv._analyze_single_image(0, "https://img/1.jpg", "vlm-lite", 15)
+        fake_product = {
+            "name": "lipstick",
+            "brand": "品牌X",
+            "category": "唇部彩妆",
+            "key_features": ["显白", "持久"],
+            "text_on_package": ["品牌X", "211"],
+            "_source": "v2_fast_json",
+        }
+        with patch("worker_app.tasks.vision.analyze_images_v2", return_value=[fake_product]) as mock_aiv2:
+            result = vv._step_image_analysis(job)
 
-        mock_v.assert_called_once()
-        # 验证调用时传入了 system_prompt（说明走了 loader 渲染的模板）
-        call_kwargs = mock_v.call_args.kwargs
-        assert "system_prompt" in call_kwargs and call_kwargs["system_prompt"]
-        # 结果包含从 XML 解析出的产品信息
-        assert result["name"] == "lipstick"
-        assert result["brand"] == "品牌X"
-        assert "显白" in result["key_features"]
-        assert result["text_on_package"] == ["品牌X", "211"]
+        mock_aiv2.assert_called_once()
+        products = result["products"]
+        assert len(products) == 1
+        assert products[0]["name"] == "lipstick"
+        assert products[0]["brand"] == "品牌X"
+        assert "显白" in products[0]["key_features"]
+        assert products[0]["text_on_package"] == ["品牌X", "211"]
 
 
 # ── 2) 意图解析走模板 ───────────────────────────────────────────────
@@ -252,18 +259,22 @@ class TestEndToEndLoaderUsed:
             called_types.append(prompt_type)
             return real_get(prompt_type, **kwargs)
 
+        # V2 图片分析不再走 prompt_loader（固定 lite JSON prompt），用 mock 产品代替
+        img_res = {
+            "name": "lipstick",
+            "brand": "品牌X",
+            "key_features": ["显白", "持久"],
+            "text_on_package": ["品牌X"],
+        }
         with (
             patch.object(pl, "get_template", side_effect=spy_get),
-            patch("packages.shared.ai_service.call_vision", return_value=IMAGE_XML),
             patch("packages.shared.ai_service.call_llm", return_value=INTENT_XML),
         ):
-            # 1) image
-            img_res = vv._analyze_single_image(0, "https://img/1.jpg", "vlm", 15)
-            # 2) intent
+            # intent
             intent_res = vv._step_intent_parsing(job, {"products": [img_res]})
 
-        # 前两步分别调用了 image_analysis 和 intent_parsing
-        assert "image_analysis" in called_types
+        # V2 图片分析走固定 prompt（不经 loader）；intent 仍走 loader
+        assert "image_analysis" not in called_types
         assert "intent_parsing" in called_types
 
         # script 和 review 单独验证（需要不同的 LLM 返回）