style(vision): rename ambiguous l/r vars to lb/rb (ruff E741)

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

alembic/versions/096_feature_billing_fields.py

@@ -1,61 +0,0 @@
-"""功能计费积分字段（爆款/对口型/智能剪辑 DB 化计费）。
-
-给 gpu_lipsync_tasks / generation_tasks / lipsync_jobs 三张表加积分字段：
-- credits_prepaid: 提交任务时预扣积分
-- credits_cost: 最终结算积分
-- credits_transaction_id: 预扣流水 ID
-
-注意：feature_pricing_configs 配置表由 xiaoxia-admin 侧 migration 建立，
-本仓库只读，不在此创建。
-
-Revision ID: 096_feature_billing_fields
-Revises: 095_viral_video_prompt_templates
-Create Date: 2026-10-05
-"""
-
-import sqlalchemy as sa
-
-from alembic import op
-
-revision = "096_feature_billing_fields"
-down_revision = "095_viral_video_prompt_templates"
-branch_labels = None
-depends_on = None
-
-_TABLES = ("gpu_lipsync_tasks", "generation_tasks", "lipsync_jobs")
-_COLUMNS = (
-    ("credits_prepaid", sa.Float(), "0"),
-    ("credits_cost", sa.Float(), "0"),
-    ("credits_transaction_id", sa.String(36), ""),
-)
-
-
-def _table_exists(conn, name: str) -> bool:
-    return name in sa.inspect(conn).get_table_names()
-
-
-def upgrade() -> None:
-    conn = op.get_bind()
-    for table in _TABLES:
-        if not _table_exists(conn, table):
-            continue
-        existing = {c["name"] for c in sa.inspect(conn).get_columns(table)}
-        for col_name, col_type, default in _COLUMNS:
-            if col_name in existing:
-                continue
-            op.add_column(
-                table,
-                sa.Column(col_name, col_type, nullable=False, server_default=default),
-            )
-
-
-def downgrade() -> None:
-    conn = op.get_bind()
-    for table in _TABLES:
-        if not _table_exists(conn, table):
-            continue
-        existing = {c["name"] for c in sa.inspect(conn).get_columns(table)}
-        for col_name, _col_type, _default in _COLUMNS:
-            if col_name not in existing:
-                continue
-            op.drop_column(table, col_name)

apps/api/app/api/routes/generation_tasks.py

@@ -44,7 +44,6 @@ from packages.application import (
     GetGenerationTaskUseCase,
     ListGeneratedVideosByTaskUseCase,
 )
-from packages.domain import feature_pricing_service
 from packages.domain.smart_match import smart_select_assets
 
 # #2035：文案关键词 → 素材分类 映射表（用于 smart_match category_match 维度）
@@ -164,6 +163,7 @@ def _infer_expected_categories(script_tags: set[str] | None) -> set[str] | None:
     return matched or None
 
 
+
 logger = logging.getLogger(__name__)
 
 router = APIRouter()
@@ -700,17 +700,6 @@ def create_generation_task(
         logger.info("画中画已下线，strategy_id %s → one_take", effective_strategy_id)
         effective_strategy_id = "one_take"
 
-    # ── smart_edit 计费预扣（全局 points 开关 + 功能开关均开才扣） ──
-    # 首期固定价：dynamic_cost=0，price=(0+fixed_cost)×multiplier，price_cap 封顶。
-    # 预览任务不扣费；按任务条数扣费，任一任务预扣失败（余额不足）整体拒绝。
-    smart_edit_charge = 0.0
-    charged_task_count = 0
-    if not request.is_preview and feature_pricing_service.is_feature_enabled("smart_edit"):
-        unit_credits, _bd = feature_pricing_service.calculate_price("smart_edit", 0.0)
-        if unit_credits > 0:
-            smart_edit_charge = round(unit_credits * count, 2)
-            charged_task_count = count
-
     # 批量生成（count>1）：每个变体必须走与单视频完全相同的独立选片流程（#1743/#1749）。
     # - 变体 0：clone 源 plan（不污染源 plan），变体 1..N-1 用 reselect_plan_for_variant
     #   完整重跑选片（素材级去重：fresh 优先 → 受控复用 overlap≤20% → 短素材禁复用）；
@@ -941,42 +930,6 @@ def create_generation_task(
             )
             # 变体序号写入 extra_meta（响应/排查时可辨识）
             task.extra_meta["variant_index"] = task_index
-
-            # smart_edit 逐条预扣（首期固定价，credits_cost=prepaid，不做结算）
-            task_txn_id = ""
-            if charged_task_count > 0:
-                from packages.domain.points_service import PointsService
-
-                unit_credits = round(smart_edit_charge / count, 2)
-                res = PointsService().deduct_points(
-                    user_id=user_id,
-                    amount=unit_credits,
-                    source="smart_edit",
-                    db=db,
-                    description="智能剪辑生成预扣",
-                    ref_id=task.id,
-                )
-                if not res.get("success"):
-                    # 余额不足：退还本次请求已扣积分后整体拒绝
-                    already_charged = round(unit_credits * task_index, 2)
-                    if already_charged > 0:
-                        PointsService().refund_points(
-                            user_id=user_id,
-                            amount=already_charged,
-                            source="smart_edit",
-                            db=db,
-                            ref_id=task.id,
-                            description="智能剪辑批量提交失败退回",
-                        )
-                    raise HTTPException(
-                        status_code=402,
-                        detail=(f"积分不足：智能剪辑每条需 {unit_credits:.2f} 积分，当前余额 {res.get('balance', 0)}"),
-                    )
-                task_txn_id = str(res.get("transaction_id") or "")
-                task.credits_prepaid = unit_credits
-                task.credits_cost = unit_credits
-                task.credits_transaction_id = task_txn_id
-                generation_task_repository.update(task)
             try:
                 # 兜底关联编辑计划：前端未传 source_edit_plan_id 时，
                 # 通过 template_id + user_id 在 DB 层直接查找最新的 plan。

apps/api/app/services/gpu_lipsync_service.py

@@ -228,8 +228,6 @@ class GpuLipsyncService:
         lipsync_job_id: str = "",
         user_id: str = "",
         project_id: str = "",
-        credits_prepaid: float = 0.0,
-        credits_transaction_id: str = "",
     ) -> GpuLipsyncTaskModel:
         task_id = str(uuid.uuid4())
         now = datetime.now(UTC)
@@ -242,8 +240,6 @@ class GpuLipsyncService:
             audio_url=audio_url,
             status="pending",
             attempt=0,
-            credits_prepaid=float(credits_prepaid or 0.0),
-            credits_transaction_id=str(credits_transaction_id or ""),
             created_at=now,
             updated_at=now,
         )

apps/api/app/services/lipsync_service.py

@@ -38,7 +38,6 @@ from sqlalchemy.orm import Session
 from packages.adapters.sqlalchemy_impl.models import LipsyncJobModel
 from packages.application.cosyvoice_service import CosyVoiceError
 from packages.config import get_api_settings
-from packages.domain import feature_pricing_service
 from packages.domain.sentence_timings import (
     compute_sentence_timings,
     probe_audio_duration,
@@ -369,8 +368,6 @@ class LipsyncService:
             lipsync_job_id=job.id,
             user_id=job.user_id,
             project_id=job.project_id,
-            credits_prepaid=float(getattr(job, "credits_prepaid", 0) or 0),
-            credits_transaction_id=str(getattr(job, "credits_transaction_id", "") or ""),
         )
         logger.info(
             "[lipsync] 已创建 GPU 任务（异步）: job_id=%s gpu_task=%s",
@@ -418,121 +415,6 @@ class LipsyncService:
             job.output_duration,
         )
 
-    # ── lip_sync 计费辅助 ────────────────────────────────────────────────
-
-    @staticmethod
-    def _estimate_duration(
-        *,
-        audio_duration: Optional[float] = None,
-        sentence_timings: Optional[list] = None,
-        script_text: str = "",
-    ) -> float:
-        """预估音频/成片秒数。
-
-        优先级：audio_duration（预合成前端已 ffprobe）> timings 末句 end_time >
-        脚本字数 / 5 字每秒 > 默认 10 秒。
-        """
-        if audio_duration and float(audio_duration) > 0:
-            return float(audio_duration)
-        if sentence_timings:
-            max_end = 0.0
-            for item in sentence_timings:
-                if isinstance(item, dict):
-                    end = item.get("end_time") or item.get("end") or 0.0
-                else:
-                    end = 0.0
-                try:
-                    max_end = max(max_end, float(end))
-                except (TypeError, ValueError):
-                    continue
-            if max_end > 0:
-                return max_end
-        text = (script_text or "").strip()
-        if text:
-            return max(1.0, len(text) / 5.0)
-        return 10.0
-
-    def _settle_lip_sync(self, job: LipsyncJobModel, actual_duration: float) -> None:
-        """按实际时长结算（首期只退不补：final < prepaid 退差额，> 不补）。
-
-        幂等：credits_cost 已 > 0 说明结算过，直接跳过。
-        结算失败不阻塞业务（结果已产出），仅记录日志。
-        """
-        try:
-            prepaid = float(getattr(job, "credits_prepaid", 0) or 0)
-            if prepaid <= 0:
-                return
-            if float(getattr(job, "credits_cost", 0) or 0) > 0:
-                return
-            feature_cfg = feature_pricing_service.get_feature_config("lip_sync")
-            unit_cost = float(feature_cfg.dynamic_unit_cost) if feature_cfg is not None else 0.0
-            duration = float(actual_duration or 0.0)
-            if duration <= 0:
-                duration = self._estimate_duration(
-                    sentence_timings=job.sentence_timings,
-                    script_text=job.script_text,
-                )
-            final_price, _bd = feature_pricing_service.calculate_price("lip_sync", duration * unit_cost)
-            final_price = round(float(final_price), 2)
-            job.credits_cost = final_price
-            if final_price < prepaid - 0.009:
-                refund = round(prepaid - final_price, 2)
-                from packages.domain.points_service import PointsService
-
-                res = PointsService().refund_points(
-                    user_id=job.user_id,
-                    amount=refund,
-                    source="lip_sync",
-                    db=self.db,
-                    ref_id=str(job.credits_transaction_id or job.id),
-                    description="对口型结算退费",
-                )
-                if not res.get("success"):
-                    logger.warning(
-                        "[lip_sync] 结算退费失败 job_id=%s refund=%.2f（不阻塞）",
-                        job.id,
-                        refund,
-                    )
-            # final > prepaid：首期只退不补，不补扣
-            self.db.commit()
-        except Exception:  # noqa: BLE001
-            logger.exception("[lip_sync] 结算异常 job_id=%s（不阻塞结果）", job.id)
-            try:
-                self.db.rollback()
-            except Exception:  # noqa: BLE001
-                pass
-
-    def _refund_lip_sync(self, job: LipsyncJobModel) -> None:
-        """任务失败/取消时全额退还预扣积分（credits_cost 已结算则退实际未消耗部分）。"""
-        try:
-            prepaid = float(getattr(job, "credits_prepaid", 0) or 0)
-            if prepaid <= 0:
-                return
-            txn_id = str(getattr(job, "credits_transaction_id", "") or "")
-            cost = float(getattr(job, "credits_cost", 0) or 0)
-            refund = round(prepaid - cost, 2) if cost > 0 else round(prepaid, 2)
-            if refund <= 0:
-                return
-            from packages.domain.points_service import PointsService
-
-            res = PointsService().refund_points(
-                user_id=job.user_id,
-                amount=refund,
-                source="lip_sync",
-                db=self.db,
-                ref_id=txn_id or job.id,
-                description="对口型失败/取消退款",
-            )
-            if res.get("success"):
-                job.credits_cost = prepaid  # 标记已全额退回，防重复退
-                self.db.commit()
-        except Exception:  # noqa: BLE001
-            logger.exception("[lip_sync] 退款异常 job_id=%s", job.id)
-            try:
-                self.db.rollback()
-            except Exception:  # noqa: BLE001
-                pass
-
     # ── 创建任务 ──────────────────────────────────────────────────────────
 
     def create_job(
@@ -584,35 +466,6 @@ class LipsyncService:
             if not isinstance(sentence_timings, list) or len(sentence_timings) == 0:
                 raise MediaKitError("预合成模式 sentence_timings 不能为空", code="InvalidInput")
 
-        # 0.5 lip_sync 计费预扣（全局 points 开关 + 功能开关均开才扣）
-        prepaid_credits = 0.0
-        prepaid_txn_id = ""
-        if feature_pricing_service.is_feature_enabled("lip_sync"):
-            est_duration = self._estimate_duration(
-                audio_duration=audio_duration,
-                sentence_timings=sentence_timings,
-                script_text=script_text,
-            )
-            feature_cfg = feature_pricing_service.get_feature_config("lip_sync")
-            unit_cost = float(feature_cfg.dynamic_unit_cost) if feature_cfg is not None else 0.0
-            dynamic_cost = est_duration * unit_cost
-            prepaid_credits, _bd = feature_pricing_service.calculate_price("lip_sync", dynamic_cost)
-            if prepaid_credits > 0:
-                from packages.domain.points_service import PointsService
-
-                res = PointsService().deduct_points(
-                    user_id=user_id,
-                    amount=prepaid_credits,
-                    source="lip_sync",
-                    db=self.db,
-                    description="对口型生成预扣",
-                )
-                if not res.get("success"):
-                    raise ValueError(
-                        f"积分不足：本次对口型需 {prepaid_credits:.2f} 积分，当前余额 {res.get('balance', 0)}"
-                    )
-                prepaid_txn_id = str(res.get("transaction_id") or "")
-
         # 1. 创建数据库记录
         job_id = str(uuid.uuid4())
         job = LipsyncJobModel(
@@ -629,8 +482,6 @@ class LipsyncService:
             emotion=emotion or "",
             # 音频直传（含预合成）直接进入 pending（后续同步改为 submitted）；TTS 模式进入 tts_processing
             status="tts_processing" if is_tts_mode else "pending",
-            credits_prepaid=prepaid_credits,
-            credits_transaction_id=prepaid_txn_id,
         )
         self.db.add(job)
         self.db.flush()
@@ -826,8 +677,6 @@ class LipsyncService:
                     job.completed_at = _now
                     job.updated_at = _now
                     self.db.commit()
-                    # lip_sync 超时全额退款
-                    self._refund_lip_sync(job)
             return job
 
         # 未提交的任务不轮询
@@ -853,8 +702,6 @@ class LipsyncService:
                 job.completed_at = datetime.now(UTC)
                 job.updated_at = datetime.now(UTC)
                 self.db.commit()
-                # lip_sync 结算（只退不补）
-                self._settle_lip_sync(job, float(job.output_duration or 0.0))
                 # 异步转存自家 OSS
                 try:
                     from app.tasks.lipsync_tts import persist_output_video_task
@@ -872,8 +719,6 @@ class LipsyncService:
                 job.error_message = error.get("message", "任务执行失败")
                 job.error_code = error.get("code", "TaskFailed")
                 job.completed_at = datetime.now(UTC)
-                # lip_sync 失败全额退款（先退款再统一 commit）
-                self._refund_lip_sync(job)
             else:
                 # 中间状态（running/processing/queued 等）同步到 DB，避免前端永远卡在 submitted
                 if isinstance(mk_status, str) and mk_status:
@@ -967,8 +812,6 @@ class LipsyncService:
             job.status = "cancelled"
             job.updated_at = datetime.now(UTC)
             self.db.commit()
-            # lip_sync 取消全额退款
-            self._refund_lip_sync(job)
             self.db.refresh(job)
 
         return job

apps/api/app/tasks/lipsync_gpu.py

@@ -104,7 +104,6 @@ def lipsync_gpu_process_async(self, job_id: str, user_id: str, gpu_task_id: str)
             job.updated_at = datetime.now(UTC)
             db.commit()
             logger.info("[lipsync_gpu_async] GPU 任务已被用户取消: job_id=%s", job_id)
-            _refund_lip_sync(db, job)
             return
 
         if final_task.status != "done":
@@ -142,7 +141,6 @@ def lipsync_gpu_process_async(self, job_id: str, user_id: str, gpu_task_id: str)
             job_id,
             job.output_duration,
         )
-        _settle_lip_sync(db, job, final_task)
     except Exception as exc:
         logger.exception("[lipsync_gpu_async] 异常: job_id=%s err=%s", job_id, exc)
         try:
@@ -159,33 +157,6 @@ def lipsync_gpu_process_async(self, job_id: str, user_id: str, gpu_task_id: str)
         db.close()
 
 
-def _settle_lip_sync(db: Session, job: LipsyncJobModel, gpu_task) -> None:
-    """GPU 成功后结算：同步 credits_cost 到 gpu 任务并按实际时长多退少不补。"""
-    try:
-        from app.services.lipsync_service import LipsyncService
-
-        # GPU 任务表先同步结算结果（标记用）
-        LipsyncService._settle_lip_sync(job, float(getattr(gpu_task, "result_duration", 0) or 0.0))
-        gpu_task.credits_cost = float(job.credits_cost or 0.0)
-        db.commit()
-    except Exception:  # noqa: BLE001
-        logger.exception("[lipsync_gpu_async] lip_sync 结算异常 job_id=%s（不阻塞）", job.id)
-        try:
-            db.rollback()
-        except Exception:  # noqa: BLE001
-            pass
-
-
-def _refund_lip_sync(db: Session, job: LipsyncJobModel) -> None:
-    """GPU 取消/失败路径全额退款。"""
-    try:
-        from app.services.lipsync_service import LipsyncService
-
-        LipsyncService(db)._refund_lip_sync(job)
-    except Exception:  # noqa: BLE001
-        logger.exception("[lipsync_gpu_async] lip_sync 退款异常 job_id=%s", job.id)
-
-
 def _fallback_to_mediakit(db: Session, job: LipsyncJobModel) -> None:
     """GPU 失败时回退到 MediaKit 云端渲染。"""
     try:

apps/worker/worker_app/tasks/generation.py

@@ -387,41 +387,6 @@ BATCH_RENDER_SIMILARITY_LIMIT = 0.20
 """批次内成片查重相似度阈值：超过则重选独立 plan 重渲一次（20%）。"""
 
 
-def _refund_smart_edit_prepaid(task_id: str) -> None:
-    """智能剪辑任务最终失败时退还预扣积分（幂等）。"""
-    session = SessionLocal()
-    try:
-        from packages.adapters.sqlalchemy_impl.generation_task_repository import (
-            SQLAlchemyGenerationTaskRepository,
-        )
-        from packages.domain.points_service import PointsService
-
-        repo = SQLAlchemyGenerationTaskRepository(session)
-        task = repo.get(task_id)
-        if not task:
-            return
-        prepaid = float(getattr(task, "credits_prepaid", 0) or 0)
-        if prepaid <= 0:
-            return
-        txn_id = getattr(task, "credits_transaction_id", "") or ""
-        res = PointsService().refund_points(
-            user_id=task.user_id,
-            amount=prepaid,
-            source="smart_edit",
-            db=session,
-            ref_id=task.id,
-            related_transaction_id=txn_id or None,
-            description="智能剪辑任务失败退回",
-        )
-        task.credits_cost = 0.0
-        task.credits_prepaid = 0.0
-        repo.update(task)
-        if not res.get("success"):
-            logger.warning("[task_id=%s] 失败退积分未成功: %s", task_id, res)
-    finally:
-        session.close()
-
-
 def should_rerender_for_batch_dedup(*, batch_id: str, render_attempt: int, batch_similarity) -> bool:
     """批次内查重后判定是否需要重选 plan 重渲。
 
@@ -1202,10 +1167,6 @@ def generate_video(self, task_id: str) -> dict:
                 "mark_failed",
                 error_message="source_edit_plan_id is required. Please create a preview task first.",
             )
-            try:
-                _refund_smart_edit_prepaid(task_id)
-            except Exception:
-                logger.warning("[task_id=%s] 失败退积分异常", task_id, exc_info=True)
             return {
                 "status": "failed",
                 "task_id": task_id,
@@ -1244,7 +1205,6 @@ def generate_video(self, task_id: str) -> dict:
         )
 
         # ── 自动重试逻辑 ──────────────────────────────────────────────────
-        will_retry = False
         try:
             from packages.adapters.sqlalchemy_impl.generation_task_repository import (
                 SQLAlchemyGenerationTaskRepository,
@@ -1257,7 +1217,6 @@ def generate_video(self, task_id: str) -> dict:
                 if _task and _task.auto_retry_enabled and _task.auto_retry_max > 0:
                     current_retry = _task.retry_count or 0
                     if current_retry < _task.auto_retry_max:
-                        will_retry = True
                         logger.info(
                             "[task_id=%s] 触发自动重试: 当前重试次数=%d, 最大重试次数=%d",
                             task_id,
@@ -1291,13 +1250,6 @@ def generate_video(self, task_id: str) -> dict:
                 exc_info=True,
             )
 
-        # 最终失败（不再重试）：退还 smart_edit 预扣积分
-        if not will_retry:
-            try:
-                _refund_smart_edit_prepaid(task_id)
-            except Exception:
-                logger.warning("[task_id=%s] 失败退积分异常", task_id, exc_info=True)
-
         return {
             "status": "failed",
             "task_id": task_id,

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+qwen3.8-flash并行 + qwen3.7-plus兜底）
+  1. _step_image_analysis   图片分析（V2: OCR+lite VLM并行 + pro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，未配置时自动跳过）+ qwen3.8-flash 强约束 JSON，每图2路并行，目标<3s；
+      - 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；
       - 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 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。
+      - 兜底：fast 结果不可用时单次调用 doubao-seed-2.1-pro VLM（简单、无竞速）。
     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。
     """
@@ -383,8 +383,26 @@ 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}")]}
 
-    # 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)
+    # 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
+
     return {"products": list(results)}
 
 
@@ -421,14 +439,10 @@ def _step_intent_parsing(job: ViralVideoJob, image_analysis: dict) -> dict:
             render_system_prompt,
             render_user_prompt,
         )
-        from packages.shared.ai_client import get_doubao_client
+        from packages.shared.ai_service import call_llm
     except ImportError:
         return {"intent": "推广产品", "key_messages": ["产品亮点"], "tone": "专业", "suggested_title": ""}
 
-    _llm_client = get_doubao_client()
-    if not _llm_client.is_available:
-        return {"intent": "推广产品", "key_messages": ["产品亮点"], "tone": "专业", "suggested_title": ""}
-
     products_summary = ""
     products = (image_analysis or {}).get("products", []) or []
     for p in products:
@@ -483,13 +497,13 @@ def _step_intent_parsing(job: ViralVideoJob, image_analysis: dict) -> dict:
     for _m, _lbl in [(_fast, "fast"), (_pro, "pro-fallback")]:
         try:
             logger.info("[爆款视频] 意图解析 model=%s label=%s", _m, _lbl)
-            raw = _llm_client.chat_completion(
+            raw = call_llm(
                 [{"role": "system", "content": system}, {"role": "user", "content": user}],
                 temperature=0.4,
                 max_tokens=1024,
                 model=_m,
                 timeout=60,
-            )  # #2180/#2215: 直接用 client.chat_completion 传 messages list，不再走 call_llm 字符串包装
+            )  # #2180: 意图解析 LLM 实测需更长响应，原25s太紧
             if not raw:
                 continue
             parsed = _parse(raw)
@@ -829,14 +843,10 @@ def _step_script_generation(job: ViralVideoJob, intent: dict, image_analysis: di
             GLOBAL_CONSTRAINTS,
             NEGATIVE_RULES,
         )
-        from packages.shared.ai_client import get_doubao_client
+        from packages.shared.ai_service import call_llm
     except ImportError:
         return _fallback_script(job)
 
-    _llm_client2 = get_doubao_client()
-    if not _llm_client2.is_available:
-        return _fallback_script(job)
-
     products_summary = _build_products_summary(image_analysis)
     dur = max(5, min(30, int(getattr(job, "duration", 15) or 15)))
 
@@ -872,7 +882,7 @@ def _step_script_generation(job: ViralVideoJob, intent: dict, image_analysis: di
 
     def _try_gen(model: str, temp: float, max_tok: int, label: str, tmo: int = 25):
         logger.info("[爆款视频] 编导脚本生成 model=%s label=%s timeout=%d", model, label, tmo)
-        raw = _llm_client2.chat_completion(
+        raw = call_llm(
             [{"role": "system", "content": system_tpl}, {"role": "user", "content": user}],
             temperature=temp,
             max_tokens=max_tok,

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

@@ -1,208 +0,0 @@
-# -*- coding: utf-8 -*-
-"""V2 prompt 解析：优先读后台 viral_video_prompt_templates 表（prompt_type='image_analysis'
-且 is_active=true），30s TTL 热加载；DB 无有效记录/异常时，fallback 到纯硬编码 JSON schema prompt。
-
-规则（简单直接，不做字符串匹配判断）：
-- DB 有 is_active=true 的 image_analysis 记录（含种子版本和用户修改后的版本）：
-  * system = DB.system_prompt（DB prompt 自带完整输出格式，不追加硬编码 schema，
-    避免 DB 写 XML、调用强制 json_object 造成的格式冲突）
-  * user   = DB.user_prompt_template 渲染后使用；渲染后为空则用硬编码默认
-- DB 无记录/连接异常/返回空：system/user 全部用纯硬编码 JSON schema prompt
-"""
-
-from __future__ import annotations
-
-import logging
-import threading
-import time
-from typing import Any
-
-logger = logging.getLogger(__name__)
-
-# ---- 纯硬编码 JSON schema（DB 无有效配置时全量使用） ----
-
-_FAST_JSON_SCHEMA = (
-    "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,\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；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'
-    '  "hairstyle": "发型，如短发/长发/马尾/卷发/丸子头/光头等",\n'
-    '  "expression": "表情，如微笑/严肃/酷/开心等",\n'
-    '  "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,\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'
-    '  "material": "材质，如棉质/牛仔/皮革/真丝/针织/涤纶等",\n'
-    '  "pattern": "图案，如纯色/条纹/波点/格子/印花/碎花/Logo等",\n'
-    '  "colors": ["主色数组"],\n'
-    '  "mood": "整体氛围/情绪，如清新/活力/高级/温暖/冷峻/甜美/复古等"\n'
-    "}\n\n"
-    "你必须只返回一个合法的JSON对象，不要输出任何其他文字、解释、XML标签或markdown。"
-)
-DEFAULT_FAST_USER = "识别这张图片的人物穿搭与主体信息，只返回JSON对象。"
-
-_PRO_JSON_SCHEMA = (
-    "你是图片分析专家。严格按下方 JSON schema 返回一个对象，不要解释、不要markdown、不要代码块、不要XML标签。\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": "整体穿着描述（含颜色款式）",\n'
-    '  "hair": "发型发色",\n'
-    '  "pose": "姿势",\n'
-    '  "expression": "表情",\n'
-    '  "scene": "场景",\n'
-    '  "mood": "氛围",\n'
-    '  "has_product": true/false,\n'
-    '  "category": "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，非产品图填null",\n'
-    '  "brand": "品牌，无则null",\n'
-    '  "key_features": ["核心特征数组，3-6个短语"]\n'
-    "}\n\n"
-    "你必须只返回一个合法的JSON对象，不要输出任何其他文字、解释、XML标签或markdown。"
-)
-DEFAULT_PRO_USER = "分析这张图片，返回符合schema的JSON。"
-
-# 保留旧 JSON schema 追加文本作为常量（DB prompt 完全控制输出格式后不再使用，
-# 保留以便排查历史行为）。
-_FAST_JSON_APPEND = (
-    "\n\n【输出格式要求】无论上文如何要求，最终你必须只返回一个合法的JSON对象，"
-    "严格包含以下字段（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'
-    '  "upper_wear": "上装款式字符串",\n'
-    '  "upper_color": "上装主色",\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": ["配饰数组"],\n'
-    '  "hairstyle": "发型",\n'
-    '  "expression": "表情",\n'
-    '  "pose": "姿势",\n'
-    '  "scene": "场景",\n'
-    '  "style": "风格",\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'
-    '  "material": "材质",\n'
-    '  "pattern": "图案",\n'
-    '  "colors": ["主色数组"],\n'
-    '  "mood": "整体氛围"\n'
-    "}\n"
-    "不要输出任何其他文字、解释、XML标签或markdown。"
-)
-
-_PRO_JSON_APPEND = (
-    "\n\n【输出格式要求】无论上文如何要求，最终你必须只返回一个合法的JSON对象，"
-    "严格包含以下字段（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": "整体穿着描述（含颜色款式）",\n'
-    '  "hair": "发型发色",\n'
-    '  "pose": "姿势",\n'
-    '  "expression": "表情",\n'
-    '  "scene": "场景",\n'
-    '  "mood": "氛围",\n'
-    '  "has_product": true/false,\n'
-    '  "category": "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，非产品图填null",\n'
-    '  "brand": "品牌，无则null",\n'
-    '  "key_features": ["核心特征3-6个短语"]\n'
-    "}\n"
-    "不要输出任何其他文字、解释、XML标签或markdown。"
-)
-
-_cache_lock = threading.Lock()
-_cache: dict[str, tuple[float, Any]] = {}
-_CACHE_TTL = 30.0
-
-
-def _load_db_template() -> Any | None:
-    """直接查DB viral_video_prompt_templates 中 is_active=true 的 image_analysis 记录；
-    DB不可达/无记录/异常返回None。
-    复用 prompt_loader._load_from_db，它只查DB不做DEFAULT_TEMPLATES fallback，
-    返回None表示DB无记录或异常。"""
-    try:
-        from packages.application.viral_video.prompt_loader import _load_from_db
-
-        return _load_from_db("image_analysis")
-    except Exception as e:
-        logger.warning("[vision.v2] 查询DB prompt配置失败: %s", e)
-        return None
-
-
-def _render_user(tpl: Any | None, default_user: str) -> str:
-    if not tpl:
-        return default_user
-    tpl_str = getattr(tpl, "user_prompt_template", "") or ""
-    if not tpl_str.strip():
-        return default_user
-    rendered = tpl_str.replace("{image_count}", "1").replace("{industry}", "通用").replace("{image_urls}", "").strip()
-    return rendered or default_user
-
-
-def resolve_fast_prompt() -> tuple[str, str]:
-    return _resolve("fast")
-
-
-def resolve_pro_prompt() -> tuple[str, str]:
-    return _resolve("pro")
-
-
-def _resolve(kind: str) -> tuple[str, str]:
-    now = time.time()
-    cache_key = f"prompt_{kind}"
-    with _cache_lock:
-        hit = _cache.get(cache_key)
-        if hit and now - hit[0] < _CACHE_TTL:
-            return hit[1]
-
-    default_sys = _FAST_JSON_SCHEMA if kind == "fast" else _PRO_JSON_SCHEMA
-    default_user = DEFAULT_FAST_USER if kind == "fast" else DEFAULT_PRO_USER
-
-    sys_prompt = default_sys
-    usr_prompt = default_user
-    try:
-        tpl = _load_db_template()
-        if tpl is not None:
-            db_sys = (getattr(tpl, "system_prompt", "") or "").strip()
-            if db_sys:
-                sys_prompt = db_sys  # DB prompt自带完整输出格式，不追加硬编码schema避免冲突
-                usr_prompt = _render_user(tpl, default_user)
-                logger.info(
-                    "[vision.v2] 使用DB image_analysis prompt (kind=%s version=%s sys_len=%d)",
-                    kind,
-                    getattr(tpl, "version", "?"),
-                    len(db_sys),
-                )
-            else:
-                logger.debug("[vision.v2] DB image_analysis system_prompt为空，使用默认JSON (kind=%s)", kind)
-        else:
-            logger.debug("[vision.v2] DB无image_analysis记录/不可达，使用默认JSON prompt (kind=%s)", kind)
-    except Exception as e:
-        logger.warning("[vision.v2] 解析DB prompt异常，使用默认: %s", e)
-
-    with _cache_lock:
-        _cache[cache_key] = (now, (sys_prompt, usr_prompt))
-    return sys_prompt, usr_prompt
-
-
-def invalidate_cache() -> None:
-    with _cache_lock:
-        _cache.clear()

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

@@ -1,8 +1,5 @@
 # -*- coding: utf-8 -*-
-"""把 fast_json VLM 输出 + OCR 文本组装为下游兼容的 product dict。
-
-v4 schema: DB prompt完全控制输出格式，可能是v4嵌套schema(type/products/people/store_info)
-或旧扁平schema(has_person/upper_wear/product_name/brand等)。assembler兼容两种格式。
+"""把 fast_json VLM 输出 + OCR 文本组装为与旧 _normalize() 完全一致的 dict。
 
 目标：下游（信任链t2i/intent_parsing/script_generation）零改动。
 必出字段：name, brand, category, appearance, packaging, text_on_package,
@@ -13,16 +10,29 @@ from __future__ import annotations
 
 from typing import Any
 
+# ---------- portrait_prompt 模板 ----------
+# 目标：60-100 字的人物穿搭描述，用于 Seedream 纯文生图。要求具体、风格化、视觉细节丰富。
+# 旧 VLM 输出格式参考："一位25岁左右的亚洲女性，身穿白色V领短袖T恤，黑色高腰阔腿裤，
+# 搭配银色项链，长发披肩，表情自信，街拍风格，阳光明媚的城市街头"
+
 
 def _join_parts(*parts: str | None) -> str:
     return "".join(p for p in parts if p)
 
 
-_AGE_PREFIX = {"青年": "年轻", "中年": "中年", "老年": "老年"}
+_AGE_PREFIX = {
+    "青年": "年轻",
+    "中年": "中年",
+    "老年": "老年",
+}
+# gender 后缀
 _GENDER_WORD = {"男": "男性", "女": "女性"}
 
 
-def _person_subject(gender: str, age: str) -> str:
+def _person_subject(fj: dict[str, Any]) -> str:
+    """人物主语：年轻女性 / 中年男性 / 少女 / 小男孩 / 人物 等。"""
+    gender = fj.get("gender") or ""
+    age = fj.get("age_range") or ""
     gw = _GENDER_WORD.get(gender, "")
     if age == "儿童":
         if gender == "女":
@@ -42,147 +52,8 @@ def _person_subject(gender: str, age: str) -> str:
     return f"{prefix}人物" if prefix else "人物"
 
 
-def _build_wear_from_v4(p: dict) -> str:
-    """v4 person schema: upper_wear/upper_color/lower_wear/lower_color/dress_color"""
-    upper = p.get("upper_wear") or ""
-    upper_color = p.get("upper_color") or ""
-    lower = p.get("lower_wear") or ""
-    lower_color = p.get("lower_color") or ""
-    dress_color = p.get("dress_color") or ""
-    is_dress = ("连衣裙" in upper) or ("裙" in upper and not lower)
-    if is_dress:
-        c = dress_color or upper_color
-        return f"身穿{c}{upper}" if c else f"身穿{upper}"
-    parts = []
-    if upper:
-        up = f"{upper_color}{upper}" if upper_color else upper
-        parts.append(f"上身{up}")
-    if lower:
-        lo = f"{lower_color}{lower}" if lower_color else lower
-        parts.append(f"下身{lo}")
-    return "，".join(parts)
-
-
-def _build_portrait_prompt_from_v4(p: dict) -> str:
-    """v4 person: 直接用portrait_prompt字段；没有就拼"""
-    direct = p.get("portrait_prompt")
-    if direct and len(direct) >= 10:
-        return direct
-    subject = _person_subject(p.get("gender", ""), p.get("age_range", ""))
-    wear = _build_wear_from_v4(p)
-    acc = p.get("accessories") or []
-    if isinstance(acc, str):
-        acc = [acc]
-    acc_str = "，佩戴" + "、".join(str(a) for a in acc if a) if acc else ""
-    hair = p.get("hairstyle") or ""
-    expr = p.get("expression") or ""
-    pose = p.get("pose") or ""
-    style = p.get("outfit_style") or p.get("style") or ""
-    scene = p.get("scene") or ""
-    mood = p.get("mood") or ""
-    details = []
-    if hair:
-        details.append(hair)
-    if expr and expr not in ("自然", "平静"):
-        details.append(f"神情{expr}")
-    if pose and pose not in ("站立",):
-        details.append(pose)
-    style_parts = []
-    if style:
-        style_parts.append(style)
-    if mood:
-        style_parts.append(mood)
-    if scene and scene not in ("通用",):
-        style_parts.append(scene)
-    pieces = [f"一位{subject}"]
-    if wear:
-        pieces.append(wear)
-    if acc_str:
-        pieces.append(acc_str.lstrip("，"))
-    if details:
-        pieces.append("，".join(details))
-    pieces.append(("".join(style_parts) + "风格") if style_parts else "人像写真")
-    full = "，".join(p for p in pieces if p)
-    if len(full) < 40:
-        full += "，自然光线下人像特写，画面清晰"
-    if len(full) > 120:
-        full = full[:120].rstrip("，") + "。"
-    return full
-
-
-def _build_product_prompt_from_v4(prod: dict, top: dict) -> str:
-    """v4 product: 拼商品视觉描述prompt（用于AI生图参考）"""
-    name = prod.get("product_name") or "商品"
-    brand = prod.get("brand") or ""
-    lead = f"{brand} {name}" if brand and brand not in name else name
-    pkg_color = prod.get("package_color") or ""
-    pkg_type = prod.get("package_type") or ""
-    cap = prod.get("cap_type") or ""
-    body = prod.get("body_shape") or ""
-    features = prod.get("product_features") or []
-    sell = prod.get("key_selling_points") or []
-    colors = top.get("colors") or []
-    style = top.get("style") or ""
-    scene = top.get("scene") or ""
-    mood = top.get("mood") or ""
-
-    parts = [lead]
-    desc = []
-    if pkg_color:
-        desc.append(pkg_color)
-    if pkg_type:
-        desc.append(pkg_type)
-    if cap and len(desc) < 3:
-        desc.append(f"配{cap}")
-    if body and len(desc) < 3:
-        desc.append(body)
-    if desc:
-        parts.append("，".join(desc))
-    if features:
-        core = [str(f) for f in features[:3] if f and len(str(f)) <= 25]
-        if core:
-            parts.append("；".join(core))
-    if sell:
-        s = [str(x) for x in sell[:2] if x]
-        if s:
-            parts.append("突出" + "、".join(s))
-    cnames = []
-    for cc in colors:
-        if isinstance(cc, dict) and cc.get("name"):
-            cnames.append(cc["name"])
-        elif isinstance(cc, str):
-            cnames.append(cc)
-    cnames = cnames[:3]
-    if cnames:
-        parts.append("、".join(cnames) + "主色")
-    if style:
-        parts.append(style)
-    if mood:
-        parts.append(mood)
-    if scene and not any(k in scene for k in ("白色背景", "纯色", "通用")):
-        parts.append(scene)
-    parts.append("产品特写，画面清晰")
-    prompt = "，".join(p for p in parts if p)
-    return prompt if len(prompt) >= 10 else "产品展示图，特写镜头"
-
-
-def _is_v4_schema(fj: dict) -> bool:
-    """判断是v4嵌套schema还是旧扁平schema"""
-    return (
-        isinstance(fj.get("products"), list)
-        or fj.get("type") in ("product", "store", "person", "other")
-        or isinstance(fj.get("people"), dict)
-    )
-
-
-# ---------- 旧扁平schema兼容（保留原逻辑） ----------
-
-
-def _person_subject_old(fj: dict) -> str:
-    return _person_subject(fj.get("gender", ""), fj.get("age_range", ""))
-
-
-def _build_wear_sentence_old(fj: dict) -> str:
+def _build_wear_sentence(fj: dict[str, Any]) -> str:
+    """穿搭段：上装+下装/连衣裙，带颜色+材质+图案。"""
     upper = fj.get("upper_wear") or ""
     upper_color = fj.get("upper_color") or ""
     lower = fj.get("lower_wear") or ""
@@ -190,6 +61,7 @@ def _build_wear_sentence_old(fj: dict) -> str:
     dress_color = fj.get("dress_color") or ""
     material = fj.get("material") or ""
     pattern = fj.get("pattern") or ""
+
     is_dress = ("连衣裙" in upper) or ("裙" in upper and not lower)
     if is_dress:
         c = dress_color or upper_color
@@ -199,22 +71,25 @@ def _build_wear_sentence_old(fj: dict) -> str:
         if pattern and pattern not in wear and pattern != "纯色":
             wear += f"，{pattern}图案"
         return f"身穿{wear}"
-    parts = []
+
+    parts: list[str] = []
     if upper:
         up = f"{upper_color}{upper}" if upper_color else upper
         if material and material not in up:
             up = f"{material}{up}"
         if pattern and pattern != "纯色" and pattern not in up:
             up += f"（{pattern}）"
-        parts.append(f"上身{up}")
+        parts.append(f"上身{up}" if up else "")
     if lower:
         lo = f"{lower_color}{lower}" if lower_color else lower
-        parts.append(f"下身{lo}")
+        parts.append(f"下身{lo}" if lo else "")
     return "，".join(p for p in parts if p)
 
 
-def _build_portrait_prompt_old(fj: dict) -> str:
+def _build_portrait_prompt(fj: dict[str, Any]) -> str:
+    """组装最终 portrait_prompt（目标 60-100 字，用于 Seedream 纯文生图）。"""
     if not fj.get("has_person"):
+        # 非人像：用商品+场景+mood 拼一段
         name = fj.get("product_name") or "商品"
         brand = fj.get("brand") or ""
         colors = fj.get("colors") or []
@@ -226,15 +101,7 @@ def _build_portrait_prompt_old(fj: dict) -> str:
             pieces.append(brand)
         pieces.append(name)
         if colors:
-            cnames = []
-            for c in colors:
-                if isinstance(c, dict):
-                    cnames.append(c.get("name", ""))
-                elif isinstance(c, str):
-                    cnames.append(c)
-            cnames = [c for c in cnames if c][:3]
-            if cnames:
-                pieces.append("、".join(cnames) + "配色")
+            pieces.append("、".join(colors[:3]) + "配色")
         if style:
             pieces.append(style + "风格")
         if mood:
@@ -244,32 +111,40 @@ def _build_portrait_prompt_old(fj: dict) -> str:
         pieces.append("产品特写")
         prompt = "，".join(p for p in pieces if p)
         return prompt if len(prompt) >= 10 else "产品展示图，特写镜头"
-    subject = _person_subject_old(fj)
-    wear = _build_wear_sentence_old(fj)
+
+    subject = _person_subject(fj)
+    wear = _build_wear_sentence(fj)
+
     accessories = fj.get("accessories") or []
     if isinstance(accessories, str):
         accessories = [accessories]
-    acc_str = "，佩戴" + "、".join(str(a) for a in accessories if a) if accessories else ""
+    acc_str = ""
+    if accessories:
+        acc_str = "，佩戴" + "、".join(str(a) for a in accessories if a)
+
     hairstyle = fj.get("hairstyle") or ""
     expression = fj.get("expression") or ""
     pose = fj.get("pose") or ""
     style = fj.get("style") or ""
     scene = fj.get("scene") or ""
     mood = fj.get("mood") or ""
-    detail_parts = []
+
+    detail_parts: list[str] = []
     if hairstyle:
         detail_parts.append(hairstyle)
     if expression and expression not in ("自然", "平静"):
         detail_parts.append(f"神情{expression}")
     if pose and pose not in ("站立",):
         detail_parts.append(pose)
-    style_parts = []
+
+    style_parts: list[str] = []
     if style:
         style_parts.append(style)
     if mood:
         style_parts.append(mood)
     if scene and scene not in ("通用",):
         style_parts.append(scene)
+
     pieces = [f"一位{subject}"]
     if wear:
         pieces.append(wear)
@@ -277,40 +152,53 @@ def _build_portrait_prompt_old(fj: dict) -> str:
         pieces.append(acc_str.lstrip("，"))
     if detail_parts:
         pieces.append("，".join(detail_parts))
-    pieces.append("".join(style_parts) + "风格" if style_parts else "人像写真")
+    if style_parts:
+        # 风格词之间不用逗号，用空格紧凑
+        pieces.append("".join(style_parts) + "风格")
+    else:
+        pieces.append("人像写真")
+
     full = "，".join(p for p in pieces if p)
+    # 过短补充镜头词
     if len(full) < 40:
         full += "，自然光线下人像特写，画面清晰"
+    # 过长截断
     if len(full) > 120:
         full = full[:120].rstrip("，") + "。"
     return full
 
 
-def _infer_name_old(fj: dict, ocr_texts: list[str]) -> str:
+# ---------- 商品字段 ----------
+
+
+def _infer_name(fj: dict[str, Any], ocr_texts: list[str]) -> str:
     pname = fj.get("product_name")
     if pname and pname != "未识别":
         return str(pname)
+    # 人物图 → name 用穿搭主件
     if fj.get("has_person"):
         up = fj.get("upper_wear") or ""
         if "连衣裙" in up:
             return up
         return up or "人物穿搭"
     if ocr_texts:
+        # 商品名可能是 OCR 最长的一行（品牌/产品名）
         return max(ocr_texts, key=len)
     return "未识别"
 
 
-def _infer_brand_old(fj: dict, ocr_texts: list[str]) -> str:
+def _infer_brand(fj: dict[str, Any], ocr_texts: list[str]) -> str:
     brand = fj.get("brand")
     if brand:
         return str(brand)
+    # OCR 里短的、纯字母/汉字短串可能是 brand
     for t in ocr_texts:
         if 1 < len(t) <= 12:
             return t
     return "无法判断"
 
 
-def _infer_category_old(fj: dict) -> str:
+def _infer_category(fj: dict[str, Any]) -> str:
     cat = fj.get("category")
     if cat:
         return str(cat)
@@ -319,19 +207,27 @@ def _infer_category_old(fj: dict) -> str:
     return "非产品图"
 
 
-def _build_appearance_old(fj: dict) -> str:
-    parts = []
-    for key in ("upper_color", "upper_wear", "material", "pattern"):
+def _build_appearance(fj: dict[str, Any]) -> str:
+    """外观描述：颜色+款式+材质+图案 拼成一段。"""
+    parts: list[str] = []
+    for key, _label in [
+        ("upper_color", "主色"),
+        ("upper_wear", "款式"),
+        ("material", "材质"),
+        ("pattern", "图案"),
+    ]:
         v = fj.get(key)
         if v and v not in ("无法判断", "未知", "纯色"):
             parts.append(str(v))
     if not parts:
-        return "人像穿搭整体造型" if fj.get("has_person") else "无法判断"
+        if fj.get("has_person"):
+            return "人像穿搭整体造型"
+        return "无法判断"
     return "、".join(parts)
 
 
-def _build_key_features_old(fj: dict, ocr_texts: list[str]) -> list[str]:
-    feats = []
+def _build_key_features(fj: dict[str, Any], ocr_texts: list[str]) -> list[str]:
+    feats: list[str] = []
     for key in (
         "upper_wear",
         "lower_wear",
@@ -352,7 +248,9 @@ def _build_key_features_old(fj: dict, ocr_texts: list[str]) -> list[str]:
             feats.append(v)
     if ocr_texts:
         feats.append(f"画面文字: {'/'.join(ocr_texts[:3])}")
-    out, seen = [], set()
+    # 去重
+    out: list[str] = []
+    seen: set[str] = set()
     for f in feats:
         f = f.strip()
         if f and f not in seen and len(f) <= 30:
@@ -361,275 +259,27 @@ def _build_key_features_old(fj: dict, ocr_texts: list[str]) -> list[str]:
     return out[:6] if out else ["无法判断"]
 
 
-def _flatten_colors(c) -> list[str]:
-    """colors可能是字符串数组或[{hex,name,coverage}]，统一返回名字数组"""
-    if not c:
-        return []
-    out = []
-    for item in c:
-        if isinstance(item, dict):
-            n = item.get("name")
-            if n:
-                out.append(n)
-        elif isinstance(item, str):
-            out.append(item)
-    return out
-
-
-def assemble_result(idx: int, fast_json: dict | None, ocr_texts: list[str]) -> dict[str, Any]:
+def assemble_result(
+    idx: int,
+    fast_json: dict[str, Any] | None,
+    ocr_texts: list[str],
+) -> dict[str, Any]:
+    """把 fast_json 结果 + OCR 文本组装成下游兼容的 product dict。"""
     fj = fast_json or {}
     ocr_texts = ocr_texts or []
 
-    if _is_v4_schema(fj):
-        return _assemble_v4(idx, fj, ocr_texts)
-    else:
-        return _assemble_old(idx, fj, ocr_texts)
-
-
-def _assemble_v4(idx: int, fj: dict, ocr_texts: list[str]) -> dict[str, Any]:
-    """v4嵌套schema → 下游product dict"""
-    vtype = fj.get("type") or "other"
-    products = fj.get("products") or []
+    portrait_prompt = _build_portrait_prompt(fj)
+    name = _infer_name(fj, ocr_texts)
+    brand = _infer_brand(fj, ocr_texts)
+    category = _infer_category(fj)
+    appearance = _build_appearance(fj)
+    key_features = _build_key_features(fj, ocr_texts)
     scene = fj.get("scene") or "通用"
     mood = fj.get("mood") or ""
-    colors = fj.get("colors") or []
-    visible_text = fj.get("visible_text") or []
-    color_names = _flatten_colors(colors)
-
-    # 合并OCR文字和visible_text
-    pkg_texts = []
-    for vt in visible_text:
-        if isinstance(vt, dict):
-            t = vt.get("text")
-            if t:
-                pkg_texts.append(str(t))
-        elif isinstance(vt, str):
-            pkg_texts.append(vt)
-    pkg_texts.extend(ocr_texts[:5])
-    # 去重
-    seen_t = set()
-    text_on_package = []
-    for t in pkg_texts:
-        t = str(t).strip()
-        if t and t not in seen_t and len(t) <= 50:
-            seen_t.add(t)
-            text_on_package.append(t)
-    text_on_package = text_on_package[:8]
-
-    has_person = fj.get("has_person", False)
-
-    # 人物类
-    if vtype == "person" or has_person:
-        # 取第一个人物信息（v4 schema人物信息在顶层）
-        person_info = fj
-        # 兼容people嵌套
-        ppl = fj.get("people")
-        if isinstance(ppl, dict) and ppl.get("has_person"):
-            person_info = {**fj, **ppl}
-            has_person = True
-
-        portrait_prompt = _build_portrait_prompt_from_v4(person_info)
-        name = person_info.get("upper_wear") or "人物穿搭"
-        if "连衣裙" in name:
-            pass
-        else:
-            lower = person_info.get("lower_wear") or ""
-            if lower:
-                name = f"{name}+{lower}"
-        brand = "无法判断"
-        category = "服饰"
-        outfit_parts = []
-        for k in ("upper_wear", "lower_wear", "dress_color", "upper_color", "lower_color", "outfit_style"):
-            v = person_info.get(k)
-            if v and v not in ("null", None):
-                outfit_parts.append(str(v))
-        appearance = "、".join(outfit_parts) if outfit_parts else "人像穿搭整体造型"
-        # key_features: 穿搭特征+配饰
-        kf = []
-        for k in (
-            "upper_wear",
-            "lower_wear",
-            "upper_color",
-            "lower_color",
-            "hairstyle",
-            "expression",
-            "pose",
-            "outfit_style",
-        ):
-            v = person_info.get(k)
-            if v and v not in ("null", None, "无法判断"):
-                kf.append(str(v))
-        acc = person_info.get("accessories") or []
-        if isinstance(acc, list):
-            kf.extend(str(a) for a in acc if a)
-        if text_on_package:
-            kf.append(f"画面文字: {'/'.join(text_on_package[:3])}")
-        kf = kf[:6] or ["无法判断"]
-        summary = (person_info.get("outfit_style") or "") + (person_info.get("upper_wear") or "穿搭")
-        if not summary or summary == "穿搭":
-            summary = "人物穿搭"
-        return {
-            "name": name[:30],
-            "brand": brand,
-            "category": category,
-            "appearance": appearance,
-            "packaging": "人物形象无包装",
-            "text_on_package": text_on_package,
-            "key_features": kf,
-            "scene": scene,
-            "mood": mood,
-            "portrait_prompt": portrait_prompt,
-            "summary": summary[:40],
-            "_source": "v2_fast_json_v4",
-        }
-
-    # 商品类
-    if vtype == "product" and products:
-        # 主商品（第一个position=main或第一个）
-        main = products[0]
-        for p in products:
-            if p.get("position") == "main":
-                main = p
-                break
-        name = main.get("product_name") or "未识别"
-        brand = main.get("brand") or "无法判断"
-        category = main.get("category") or "非产品图"
-        # appearance: 包装外观
-        app_parts = []
-        for k in ("package_color", "package_type", "cap_type", "body_shape", "label_design"):
-            v = main.get(k)
-            if v and v not in ("null", None):
-                app_parts.append(str(v))
-        appearance = "；".join(app_parts) if app_parts else "无法判断"
-        # packaging: 包装信息（直接用package_type+package_color）
-        pkg_parts = []
-        if main.get("package_type"):
-            pkg_parts.append(str(main["package_type"]))
-        if main.get("package_color"):
-            pkg_parts.append(str(main["package_color"]))
-        if main.get("cap_type"):
-            pkg_parts.append(f"配{main['cap_type']}")
-        packaging = "，".join(pkg_parts) if pkg_parts else "无法判断"
-        # key_features: product_features字段
-        feats = main.get("product_features") or []
-        if not isinstance(feats, list):
-            feats = [str(feats)]
-        kf = [str(f) for f in feats if f and len(str(f)) <= 40][:6]
-        # 补充卖点
-        sell = main.get("key_selling_points") or []
-        if isinstance(sell, list):
-            for s in sell[:2]:
-                if s and len(str(s)) <= 30 and str(s) not in kf:
-                    kf.append(f"卖点:{s}")
-        if text_on_package:
-            kf.append(f"文字: {'/'.join(text_on_package[:3])}")
-        kf = kf[:6] or ["无法判断"]
-        portrait_prompt = _build_product_prompt_from_v4(main, fj)
-        if brand != "无法判断" and brand not in name:
-            summary = f"{brand} {name}"
-        else:
-            summary = name
-        return {
-            "name": str(name)[:50],
-            "brand": str(brand)[:30],
-            "category": str(category)[:20],
-            "appearance": appearance[:200],
-            "packaging": packaging[:100],
-            "text_on_package": text_on_package,
-            "key_features": kf,
-            "scene": scene,
-            "mood": mood,
-            "portrait_prompt": portrait_prompt[:200],
-            "summary": str(summary)[:60],
-            "_source": "v2_fast_json_v4",
-        }
-
-    # 门店类或其他
-    if vtype == "store":
-        store_type = fj.get("store_type") or "店铺"
-        name = store_type
-        brand = fj.get("brand_signage") or "无法判断"
-        category = "门店场景"
-        visual = fj.get("visual_elements") or []
-        if isinstance(visual, str):
-            visual = [visual]
-        atmosphere = fj.get("atmosphere") or mood
-        appearance_parts = []
-        if fj.get("store_layout"):
-            appearance_parts.append(str(fj["store_layout"]))
-        if visual:
-            appearance_parts.append("、".join(str(v) for v in visual[:3]))
-        if fj.get("cleanliness"):
-            appearance_parts.append(str(fj["cleanliness"]))
-        appearance = "；".join(appearance_parts) if appearance_parts else "门店环境"
-        kf = []
-        if isinstance(visual, list):
-            kf.extend(str(v) for v in visual if v and len(str(v)) <= 30)
-        prods_vis = fj.get("product_categories_visible") or []
-        if isinstance(prods_vis, list):
-            kf.extend(str(c) for c in prods_vis[:3] if c)
-        promo = fj.get("promotion_elements") or []
-        if isinstance(promo, list) and promo:
-            kf.append("促销活动:" + "、".join(str(p) for p in promo[:2]))
-        if text_on_package:
-            kf.append(f"文字: {'/'.join(text_on_package[:3])}")
-        kf = kf[:6] or ["门店场景"]
-        portrait_prompt = f"{brand if brand!='无法判断' else ''}{store_type}，{atmosphere}，{scene}场景，{('、'.join(color_names[:3])+'配色，') if color_names else ''}产品陈列丰富，门店实拍"
-        portrait_prompt = portrait_prompt.strip("，")
-        summary = f"{store_type}场景"
-        return {
-            "name": name[:30],
-            "brand": str(brand)[:30],
-            "category": category,
-            "appearance": appearance[:200],
-            "packaging": "门店场景无包装",
-            "text_on_package": text_on_package,
-            "key_features": kf,
-            "scene": scene,
-            "mood": atmosphere or mood,
-            "portrait_prompt": portrait_prompt[:200],
-            "summary": summary[:40],
-            "_source": "v2_fast_json_v4",
-        }
-
-    # other 兜底
-    desc = fj.get("description") or "未识别"
-    return {
-        "name": desc[:30],
-        "brand": "无法判断",
-        "category": "非产品图",
-        "appearance": desc[:200],
-        "packaging": "无法判断",
-        "text_on_package": text_on_package,
-        "key_features": [desc[:30]] if desc != "未识别" else ["无法判断"],
-        "scene": scene,
-        "mood": mood,
-        "portrait_prompt": f"{scene}，{mood}氛围，{desc}"[:200],
-        "summary": desc[:40],
-        "_source": "v2_fast_json_v4_other",
-    }
-
-
-def _assemble_old(idx: int, fj: dict, ocr_texts: list[str]) -> dict[str, Any]:
-    """旧扁平schema（兼容存量prompt或pro兜底输出）"""
-    portrait_prompt = _build_portrait_prompt_old(fj)
-    name = _infer_name_old(fj, ocr_texts)
-    brand = _infer_brand_old(fj, ocr_texts)
-    category = _infer_category_old(fj)
-    appearance = _build_appearance_old(fj)
-    key_features = _build_key_features_old(fj, ocr_texts)
-    scene = fj.get("scene") or "通用"
-    mood = fj.get("mood") or ""
-    packaging = "无法判断"
+    packaging = "无法判断"  # 包装细节专用API无，保留占位
     text_on_package = ocr_texts[:8]
-    if fj.get("has_person"):
-        up = fj.get("upper_wear") or "穿搭"
-        style = fj.get("style") or ""
-        summary = f"{style}{up}" if style and style not in up else up
-    elif brand != "无法判断" and name != brand:
-        summary = f"{brand} {name}"
-    else:
-        summary = name
+    summary = _build_summary(fj, name, brand, category)
+
     return {
         "name": name,
         "brand": brand,
@@ -644,3 +294,14 @@ def _assemble_old(idx: int, fj: dict, ocr_texts: list[str]) -> dict[str, Any]:
         "summary": summary,
         "_source": "v2_fast_json",
     }
+
+
+def _build_summary(fj: dict, name: str, brand: str, category: str) -> str:
+    if fj.get("has_person"):
+        up = fj.get("upper_wear") or "穿搭"
+        style = fj.get("style") or ""
+        base = f"{style}{up}" if style and style not in up else up
+        return base
+    if brand != "无法判断" and name != brand:
+        return f"{brand} {name}"
+    return name

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

@@ -1,11 +1,9 @@
 # -*- coding: utf-8 -*-
-"""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。
+"""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。
 
-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
+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
 - 输出 dict 格式与旧版完全一致，下游零改动
 """
 
@@ -21,12 +19,12 @@ from . import assembler, ocr_volc, vlm_fallback, vlm_fast_json
 
 logger = logging.getLogger(__name__)
 
-# 超时（可通过环境变量覆盖）
+# 可通过环境变量调参（有默认值，无需配置即可跑）
 _IMG_WORKERS = int(os.environ.get("VISION_V2_IMG_WORKERS", "8"))
-_FAST_TIMEOUT = float(os.environ.get("VISION_V2_FAST_TIMEOUT", "15"))
-_FAST_JSON_TIMEOUT = float(os.environ.get("VISION_V2_FAST_JSON_TIMEOUT", "15"))
+_FAST_TIMEOUT = float(os.environ.get("VISION_V2_FAST_TIMEOUT", "8"))
+_FAST_JSON_TIMEOUT = float(os.environ.get("VISION_V2_FAST_JSON_TIMEOUT", "8"))
 _OCR_TIMEOUT = float(os.environ.get("VISION_V2_OCR_TIMEOUT", "6"))
-_PRO_TIMEOUT = float(os.environ.get("VISION_V2_PRO_TIMEOUT", "30"))
+_PRO_TIMEOUT = float(os.environ.get("VISION_V2_PRO_TIMEOUT", "45"))
 
 _FALLBACK_RESULT = {
     "name": "未识别",
@@ -44,6 +42,7 @@ _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
@@ -54,8 +53,10 @@ 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:
@@ -73,6 +74,7 @@ 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()
@@ -80,6 +82,7 @@ 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):
@@ -92,6 +95,7 @@ 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):
@@ -103,6 +107,7 @@ 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"
@@ -112,18 +117,13 @@ 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 pro_timeout=%.0fs",
-        len(img_urls),
-        workers,
-        _FAST_TIMEOUT,
-        _PRO_TIMEOUT,
-    )
+    logger.info("[vision.v2] 开始图片分析 n=%d workers=%d fast_timeout=%.0fs", len(img_urls), workers, _FAST_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

@@ -1,126 +1,226 @@
 # -*- coding: utf-8 -*-
-"""V2 兜底路径：qwen3.7-plus（阿里云百炼/DashScope）单图调用。
+"""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。
 
-fast_json 超时/返回非 JSON/识别为空时，本路径单次调用兜底。
-设计要点：
-- 直接 httpx 直连 DashScope，不走 ai_client
-- enable_thinking=false + response_format=json_object
-- system prompt 优先读后台 viral_video_prompt_templates 配置，DB不可用时fallback到硬编码JSON schema
-- timeout=25s
-- API Key 从环境变量 DASHSCOPE_API_KEY 读取
-- 返回 dict 统一走 assembler.assemble_result 组装，与 fast 路径输出格式完全一致
+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。
 """
 
 from __future__ import annotations
 
 import json
 import logging
-import os
+import re
 import time
 from typing import Any
 
-from . import _prompt, assembler
-
 logger = logging.getLogger(__name__)
 
-_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1"
-_PRO_MODEL = "qwen3.7-plus"
-_DEFAULT_TIMEOUT = 30
-_DEFAULT_MAX_TOKENS = 800
+DEFAULT_PRO_MODEL = "doubao-seed-2-1-pro-260915"
+DEFAULT_TIMEOUT = 45
+DEFAULT_MAX_TOKENS = 800
 
 
-def _api_key() -> str | None:
-    return os.environ.get("DASHSCOPE_API_KEY")
+def _strip_code_fence(s: str) -> str:
+    s = s.strip()
+    if s.startswith("```"):
+        lines = s.split("\n")
+        if lines and lines[0].startswith("```"):
+            lines = lines[1:]
+        if lines and lines[-1].strip().startswith("```"):
+            lines = lines[:-1]
+        s = "\n".join(lines).strip()
+    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")
+        parts.append(hair)
+        if outfit:
+            parts.append(f"身着{outfit}")
+        if pose:
+            parts.append(f"姿态{pose}")
+        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",
+    }
 
 
 def call_pro_vlm(
     img_url: str,
     idx: int,
     *,
-    timeout: int = _DEFAULT_TIMEOUT,
+    model: str | None = None,
+    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。"""
     t0 = time.time()
-    import httpx
-
-    api_key = _api_key()
-    if not api_key:
-        logger.warning("[vision.v2] pro DASHSCOPE_API_KEY 未配置，跳过")
+    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)
         return None
 
-    system_prompt, user_prompt = _prompt.resolve_pro_prompt()
-
-    payload: dict[str, Any] = {
-        "model": _PRO_MODEL,
-        "messages": [
-            {"role": "system", "content": system_prompt},
-            {
-                "role": "user",
-                "content": [
-                    {"type": "image_url", "image_url": {"url": img_url}},
-                    {"type": "text", "text": user_prompt},
-                ],
-            },
-        ],
-        "temperature": 0.3,
-        "max_tokens": _DEFAULT_MAX_TOKENS,
-        "stream": False,
-        "enable_thinking": False,
-        "response_format": {"type": "json_object"},
-    }
     try:
-        r = httpx.post(
-            f"{_BASE_URL}/chat/completions",
-            headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
-            json=payload,
-            timeout=timeout,
-        )
-        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 {}
-        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] pro 完成 model=%s elapsed=%.1fs in=%d out=%d reasoning=%d",
-            _PRO_MODEL,
-            elapsed,
-            usage.get("prompt_tokens", 0),
-            usage.get("completion_tokens", 0),
-            reasoning_tokens,
-        )
-        s = raw.strip()
-        if s.startswith("```"):
-            lines = s.split("\n")
-            if lines and lines[0].startswith("```"):
-                lines = lines[1:]
-            if lines and lines[-1].strip().startswith("```"):
-                lines = lines[:-1]
-            s = "\n".join(lines).strip()
-        lpos, rr = s.find("{"), s.rfind("}")
-        if lpos >= 0 and rr > lpos:
-            s = s[lpos : rr + 1]
-        try:
-            obj = json.loads(s)
-        except json.JSONDecodeError:
-            logger.warning("[vision.v2] pro JSON 解析失败 head=%s", raw[:200])
-            return None
-        if not isinstance(obj, dict):
-            return None
+        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
 
-        # 通过assembler统一组装，兼容v4嵌套schema和旧扁平schema
-        result = assembler.assemble_result(idx, obj, [])
-        result["_source"] = "vlm_pro"
+    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,
+            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)
+    lb, rb = text.find("{"), text.rfind("}")
+    if lb >= 0 and rb > lb:
+        try:
+            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 {
+                    "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)
+        logger.info(
+            "[vision.vlm] 图片 #%d pro VLM XML 完成 elapsed=%.2fs pp=%s",
+            idx,
+            elapsed,
+            (result.get("portrait_prompt") or "")[:40],
+        )
         return result
     except Exception as e:
-        elapsed = time.time() - t0
-        logger.warning("[vision.v2] pro 异常 elapsed=%.1fs err=%s", elapsed, e, exc_info=True)
+        logger.warning("[vision.vlm] 图片 #%d 解析失败 elapsed=%.1fs err=%s head=%s", idx, elapsed, e, raw[:200])
         return None

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

@@ -1,46 +1,71 @@
 # -*- coding: utf-8 -*-
-"""V2 快速路径：qwen3.8-flash（阿里云百炼/DashScope）强约束 JSON-only 调用。
+"""doubao-seed-2.1-lite 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）
-- response_format=json_object 强约束JSON输出
-- system prompt 优先读后台 viral_video_prompt_templates 配置，DB不可用时fallback到硬编码JSON schema
-- 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
+- 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）
 """
 
 from __future__ import annotations
 
 import json
 import logging
-import os
 import time
 from typing import Any
 
-from . import _prompt
-
 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 = 15
-_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'
+    '  "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_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'
+    '  "hairstyle": "发型，如短发/长发/马尾/卷发/丸子头/光头等",\n'
+    '  "expression": "表情，如微笑/严肃/酷/开心等",\n'
+    '  "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'
+    '  "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'
+    '  "material": "材质，如棉质/牛仔/皮革/真丝/针织/涤纶等",\n'
+    '  "pattern": "图案，如纯色/条纹/波点/格子/印花/碎花/Logo等",\n'
+    '  "colors": ["主色数组"],\n'
+    '  "mood": "整体氛围/情绪，如清新/活力/高级/温暖/冷峻/甜美/复古等"\n'
+    "}"
+)
 
+_FAST_USER = "识别这张图片的人物穿搭与主体信息，只返回JSON对象。"
 
-def _api_key() -> str | None:
-    return os.environ.get("DASHSCOPE_API_KEY")
+# 默认模型
+DEFAULT_LITE_MODEL = "doubao-seed-2-1-lite-260915"
+DEFAULT_TIMEOUT = 8
+DEFAULT_MAX_TOKENS = 350
 
 
 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()
@@ -50,40 +75,52 @@ def _strip_code_fence(s: str) -> str:
 def call_fast_json(
     img_url: str,
     *,
-    timeout: int = _DEFAULT_TIMEOUT,
-    max_tokens: int = _DEFAULT_MAX_TOKENS,
+    model: str | None = None,
+    timeout: int = DEFAULT_TIMEOUT,
+    max_tokens: int = DEFAULT_MAX_TOKENS,
 ) -> dict[str, Any] | None:
-    """调用 qwen3.8-flash 返回结构化 dict；失败/非 JSON 返回 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
+    """
     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
-
-    system_prompt, user_prompt = _prompt.resolve_fast_prompt()
-
-    url = f"{_BASE_URL}/chat/completions"
-    payload: dict[str, Any] = {
-        "model": _FAST_MODEL,
-        "messages": [
-            {"role": "system", "content": system_prompt},
-            {
-                "role": "user",
-                "content": [
-                    {"type": "image_url", "image_url": {"url": img_url}},
-                    {"type": "text", "text": user_prompt},
-                ],
-            },
-        ],
-        "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（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,
             headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
@@ -91,53 +128,69 @@ def call_fast_json(
             timeout=timeout,
         )
         elapsed = time.time() - t0
-        if resp.status_code == 400 and "enable_thinking" in resp.text[:300].lower():
-            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:
+        # 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)
+                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 not raw:
-            logger.warning("[vision.v2] fast_json 返回空 elapsed=%.1fs", elapsed)
+        if raw is None:
+            logger.warning("[vision.v2] fast_json 返回 None 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 完成 model=%s elapsed=%.1fs in=%d out=%d reasoning=%d",
-            _FAST_MODEL,
+            "[vision.v2] fast_json 直连完成 model=%s elapsed=%.1fs in=%d out=%d reasoning=%d",
+            use_model,
             elapsed,
             usage.get("prompt_tokens", 0),
             usage.get("completion_tokens", 0),
-            reasoning_tokens,
+            usage.get("reasoning_tokens", 0),
         )
+        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)
-        lpos, r = text.find("{"), text.rfind("}")
-        if lpos >= 0 and r > lpos:
-            text = text[lpos : r + 1]
+        # 截到第一个 { 和最后一个 } 之间，容忍前后偶发文字
+        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:
-            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 elapsed=%.1fs has_person=%s has_product=%s category=%s",
+            "[vision.v2] fast_json 完成 model=%s elapsed=%.1fs has_person=%s has_product=%s category=%s",
+            use_model,
             elapsed,
             obj.get("has_person"),
             obj.get("has_product"),

packages/adapters/sqlalchemy_impl/models.py

@@ -335,10 +335,6 @@ class GenerationTaskModel(Base):
     bgm_config = Column(JSON, nullable=False, default=dict)
     extra_meta = Column("metadata", JSON, nullable=False, default=dict)
     logs = Column(Text, nullable=False, default="[]", server_default="[]")
-    # 功能计费（smart_edit）：预扣积分 / 最终积分 / 预扣流水 ID
-    credits_prepaid = Column(Float, nullable=False, default=0.0, server_default="0")
-    credits_cost = Column(Float, nullable=False, default=0.0, server_default="0")
-    credits_transaction_id = Column(String(36), nullable=False, default="", server_default="")
     created_at = Column(DateTime, nullable=False, default=lambda: datetime.now(UTC))
     updated_at = Column(
         DateTime,
@@ -731,11 +727,6 @@ class LipsyncJobModel(Base):
     # 精确句子时间戳（TTS 合成后由 silencedetect 计算，用于 B-roll 精确定位）
     sentence_timings = Column(JSON, nullable=True)  # list[{index,text,start_time,end_time}]
 
-    # 功能计费（lip_sync）：预扣积分 / 最终积分 / 预扣流水 ID
-    credits_prepaid = Column(Float, nullable=False, default=0.0, server_default="0")
-    credits_cost = Column(Float, nullable=False, default=0.0, server_default="0")
-    credits_transaction_id = Column(String(36), nullable=False, default="", server_default="")
-
     # 时间戳
     submitted_at = Column(DateTime, nullable=True)
     completed_at = Column(DateTime, nullable=True)
@@ -914,11 +905,6 @@ class GpuLipsyncTaskModel(Base):
     # 心跳：worker 最近一次 poll/result 的时间，用于判定 worker 失联
     last_heartbeat_at = Column(DateTime, nullable=True)
 
-    # 功能计费（lip_sync）：预扣积分 / 最终积分 / 预扣流水 ID
-    credits_prepaid = Column(Float, nullable=False, default=0.0, server_default="0")
-    credits_cost = Column(Float, nullable=False, default=0.0, server_default="0")
-    credits_transaction_id = Column(String(36), nullable=False, default="", server_default="")
-
 
 class GpuWorkerModel(Base):
     """GPU Worker 注册表 — 反向轮询模式下用于心跳与监控."""

packages/domain/feature_pricing_service.py

@@ -1,376 +0,0 @@
-"""功能计费配置服务：从 feature_pricing_configs 读配置，300 秒 TTL 内存缓存。
-
-配置表由 xiaoxia-admin 侧维护（同库 PostgreSQL），本服务只读。
-DB 不可用 / 表不存在 / 无数据时自动回落到内置兜底配置，保证业务不崩。
-
-计费公式：最终积分 = (动态成本 + 固定成本) × 利润系数，price_cap 封顶。
-启用条件：全局 points_enabled 总开关 AND 功能 is_enabled 同时为 true。
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-import threading
-import time
-from dataclasses import dataclass, field
-from typing import Optional
-
-import sqlalchemy as sa
-
-from packages.adapters.sqlalchemy_impl import session as _session_mod
-
-logger = logging.getLogger(__name__)
-
-CACHE_TTL_SECONDS = 300.0
-
-# ── 爆款视频兜底模型单价（与旧硬编码表/现状一致；DB 不可用时使用） ───────
-# 结构：models[model_key][resolution]["true"/"false"] = 单价
-# token 模式：元/百万输出 tokens；per_second 模式：元/秒
-# 注意：仅 seedance-2.5 配置 true（图生视频）单价；其余模型只有 false，
-# 精确 key 缺失时由 points_rules 回落到 seedance-2.5/false（与旧现状一致）。
-_FALLBACK_VIRAL_MODEL_PRICING: dict = {
-    "seedance-2.5": {
-        "480p": {"false": 70.0, "true": 42.0},
-        "720p": {"false": 70.0, "true": 42.0},
-        "1080p": {"false": 77.0, "true": 46.0},
-    },
-    "seedance-2.0": {
-        "480p": {"false": 46.0},
-        "720p": {"false": 46.0},
-        "1080p": {"false": 51.0},
-        "4k": {"false": 80.0},
-    },
-    "seedance-2.0-fast": {
-        "480p": {"false": 28.0},
-        "720p": {"false": 28.0},
-    },
-    "seedance-2.0-mini": {
-        "480p": {"false": 9.2},
-        "720p": {"false": 9.2},
-    },
-    "wan-3.0": {
-        "480p": {"false": 0.3},
-        "720p": {"false": 0.6},
-        "1080p": {"false": 1.2},
-    },
-}
-
-
-@dataclass
-class FeatureConfig:
-    """功能计费配置快照。"""
-
-    feature_key: str
-    name: str = ""
-    emoji: str = ""
-    is_enabled: bool = False
-    fixed_cost: float = 0.0
-    profit_multiplier: float = 1.0
-    dynamic_unit_cost: float = 0.0
-    billing_mode: str = "model_based"
-    price_cap: float = 0.0
-    model_pricing: dict = field(default_factory=dict)
-    description: str = ""
-
-
-# ── 进程内缓存：(loaded_monotonic, {feature_key: FeatureConfig}) ──────────
-_lock = threading.Lock()
-_cache: Optional[tuple[float, dict[str, FeatureConfig]]] = None
-
-
-def _fallback_configs() -> dict[str, FeatureConfig]:
-    """内置兜底配置：爆款启用（与现状一致），其余两个关闭。"""
-    return {
-        "viral_video": FeatureConfig(
-            feature_key="viral_video",
-            name="爆款视频",
-            emoji="🎬",
-            is_enabled=True,
-            fixed_cost=0.15,
-            profit_multiplier=1.3,
-            dynamic_unit_cost=0.0,
-            billing_mode="model_based",
-            price_cap=0.0,
-            model_pricing=json.loads(json.dumps(_FALLBACK_VIRAL_MODEL_PRICING)),
-            description="爆款视频动态定价（兜底配置）",
-        ),
-        "lip_sync": FeatureConfig(
-            feature_key="lip_sync",
-            name="对口型",
-            emoji="🎙️",
-            is_enabled=False,
-            fixed_cost=0.0,
-            profit_multiplier=1.0,
-            dynamic_unit_cost=0.0,
-            billing_mode="per_second",
-            price_cap=0.0,
-            description="对口型计费（兜底配置，默认关闭）",
-        ),
-        "smart_edit": FeatureConfig(
-            feature_key="smart_edit",
-            name="智能剪辑",
-            emoji="✂️",
-            is_enabled=False,
-            fixed_cost=0.0,
-            profit_multiplier=1.0,
-            dynamic_unit_cost=0.0,
-            billing_mode="model_based",
-            price_cap=0.0,
-            description="智能剪辑固定价计费（兜底配置，默认关闭）",
-        ),
-    }
-
-
-_lazy_session = None
-
-
-def _get_session():
-    """优先用全局 SessionLocal（worker）；否则按应用配置懒建同步引擎（api）。"""
-    global _lazy_session
-    if _session_mod.SessionLocal is not None:
-        return _session_mod.SessionLocal()
-    if _lazy_session is not None:
-        return _lazy_session()
-    try:
-        from packages.config import get_shared_settings
-
-        url = str(get_shared_settings().database_url)
-    except Exception:  # noqa: BLE001
-        return None
-    if not url:
-        return None
-    url = url.replace("postgresql+asyncpg://", "postgresql+psycopg://")
-    if url.startswith("postgresql://"):
-        url = url.replace("postgresql://", "postgresql+psycopg://")
-    engine = sa.create_engine(url, pool_pre_ping=True, pool_size=2, max_overflow=2)
-    from sqlalchemy.orm import sessionmaker
-
-    _lazy_session = sessionmaker(bind=engine)
-    return _lazy_session()
-
-
-def _parse_model_pricing(raw) -> dict:
-    """解析 model_pricing_json（Text JSON），空/失败 → {}。"""
-    if raw is None:
-        return {}
-    if isinstance(raw, dict):
-        return raw
-    text = str(raw).strip()
-    if not text:
-        return {}
-    try:
-        data = json.loads(text)
-    except (ValueError, TypeError):
-        logger.warning("model_pricing_json 解析失败，按空配置处理: %r", text[:200])
-        return {}
-    return data if isinstance(data, dict) else {}
-
-
-def _to_float(value, default: float = 0.0) -> float:
-    try:
-        if value is None:
-            return default
-        return float(value)
-    except (TypeError, ValueError):
-        return default
-
-
-def _load_all() -> dict[str, FeatureConfig]:
-    """SELECT * FROM feature_pricing_configs，返回 {feature_key: FeatureConfig}。
-
-    表不存在 / DB 异常由调用方捕获并回落兜底配置。
-    """
-    session = None
-    try:
-        session = _get_session()
-        if session is None:
-            raise RuntimeError("no db session available")
-        sql = sa.text("""
-            SELECT feature_key, name, emoji, is_enabled, fixed_cost,
-                   profit_multiplier, dynamic_unit_cost, billing_mode,
-                   price_cap, model_pricing_json, description
-              FROM feature_pricing_configs
-            """)
-        rows = session.execute(sql).mappings().all()
-        configs: dict[str, FeatureConfig] = {}
-        for row in rows:
-            key = str(row["feature_key"] or "").strip()
-            if not key:
-                continue
-            configs[key] = FeatureConfig(
-                feature_key=key,
-                name=str(row["name"] or key),
-                emoji=str(row["emoji"] or ""),
-                is_enabled=bool(row["is_enabled"]),
-                fixed_cost=_to_float(row["fixed_cost"]),
-                profit_multiplier=_to_float(row["profit_multiplier"], 1.0),
-                dynamic_unit_cost=_to_float(row["dynamic_unit_cost"]),
-                billing_mode=str(row["billing_mode"] or "model_based"),
-                price_cap=_to_float(row["price_cap"]),
-                model_pricing=_parse_model_pricing(row["model_pricing_json"]),
-                description=str(row["description"] or ""),
-            )
-        return configs
-    finally:
-        if session is not None:
-            try:
-                session.close()
-            except Exception:  # noqa: BLE001
-                pass
-
-
-def _get_cache() -> dict[str, FeatureConfig]:
-    """TTL 内返回缓存，否则重新 load；DB 异常/表不存在时返回内置兜底配置。"""
-    global _cache
-    now = time.monotonic()
-    with _lock:
-        if _cache is not None and now - _cache[0] < CACHE_TTL_SECONDS:
-            return _cache[1]
-
-    try:
-        loaded = _load_all()
-    except Exception:  # noqa: BLE001 - 表不存在/DB 不可用时静默回落
-        logger.info("feature_pricing_configs 读取失败，使用内置兜底配置", exc_info=True)
-        return _fallback_configs()
-
-    # DB 可用但表为空：同样回落兜底（保证爆款现状不被改变）
-    if not loaded:
-        fallback = _fallback_configs()
-        with _lock:
-            _cache = (now, fallback)
-        return fallback
-
-    # 以兜底为底（DB 未配置的 feature_key 仍有兜底），DB 行覆盖
-    merged = _fallback_configs()
-    merged.update(loaded)
-    with _lock:
-        _cache = (now, merged)
-    return merged
-
-
-def get_feature_config(feature_key: str) -> Optional[FeatureConfig]:
-    """获取指定功能配置，未知 key 返回 None。"""
-    key = str(feature_key or "").strip()
-    if not key:
-        return None
-    return _get_cache().get(key)
-
-
-def _global_points_enabled() -> bool:
-    """全局积分总开关（兼容 api / worker 运行时），取不到时默认关闭。"""
-    try:
-        from packages.shared import get_shared_settings
-
-        return bool(get_shared_settings().points_enabled)
-    except Exception:  # noqa: BLE001
-        pass
-    try:
-        from app.config import settings
-
-        return bool(getattr(settings, "points_enabled", False))
-    except Exception:  # noqa: BLE001
-        return False
-
-
-def is_feature_enabled(feature_key: str) -> bool:
-    """功能是否启用并扣费：全局 points_enabled AND 功能 is_enabled。"""
-    cfg = get_feature_config(feature_key)
-    if cfg is None:
-        return False
-    return bool(cfg.is_enabled) and _global_points_enabled()
-
-
-def calculate_price(feature_key: str, dynamic_cost: float = 0.0) -> tuple[float, dict]:
-    """按公式计算最终积分并返回明细。
-
-    price = (dynamic_cost + fixed_cost) × profit_multiplier
-    price_cap > 0 时封顶（取 min）。
-    功能未启用 → (0.0, breakdown{is_enabled: False, charged: False})。
-    """
-    cfg = get_feature_config(feature_key)
-    dynamic = max(0.0, _to_float(dynamic_cost))
-    if cfg is None or not cfg.is_enabled:
-        return 0.0, {
-            "feature_key": feature_key,
-            "is_enabled": False,
-            "charged": False,
-            "dynamic_cost": dynamic,
-            "fixed_cost": 0.0,
-            "profit_multiplier": 1.0,
-            "price_cap": 0.0,
-            "final_price": 0.0,
-        }
-
-    fixed = max(0.0, cfg.fixed_cost)
-    multiplier = cfg.profit_multiplier if cfg.profit_multiplier > 0 else 1.0
-    raw_price = (dynamic + fixed) * multiplier
-    cap = cfg.price_cap if cfg.price_cap and cfg.price_cap > 0 else 0.0
-    final_price = min(raw_price, cap) if cap else raw_price
-    final_price = round(float(final_price), 2)
-    breakdown = {
-        "feature_key": cfg.feature_key,
-        "is_enabled": True,
-        "charged": True,
-        "dynamic_cost": round(dynamic, 4),
-        "fixed_cost": float(fixed),
-        "profit_multiplier": float(multiplier),
-        "price_cap": float(cap),
-        "raw_price": round(float(raw_price), 4),
-        "final_price": final_price,
-    }
-    return final_price, breakdown
-
-
-def lookup_model_price(
-    model_pricing: dict,
-    model_key: str,
-    resolution: str,
-    has_video_input: bool,
-) -> Optional[float]:
-    """从 model_pricing dict 取模型单价，兼容两种常见 JSON 结构。
-
-    1. 嵌套：{model: {resolution: {"true"/"false": price}}}
-       （内层 bool key 也兼容直接 bool / 省略）
-    2. 扁平：{"model|resolution|true_or_false": price}
-       （分隔符支持 | / : / , / 空格；bool 段可省略）
-    取不到返回 None。
-    """
-    if not isinstance(model_pricing, dict):
-        return None
-    model = str(model_key or "").strip()
-    res = str(resolution or "").strip()
-    flag = "true" if has_video_input else "false"
-
-    # 1. 嵌套
-    model_node = model_pricing.get(model)
-    if isinstance(model_node, dict):
-        res_node = model_node.get(res)
-        if isinstance(res_node, dict):
-            # 精确 bool key 命中才返回；不做“只有一个值就取”的模糊匹配
-            # （否则缺失 true 时会错误地取到 false 价，破坏旧版回落规则）
-            if flag in res_node:
-                return _to_float(res_node[flag]) if res_node[flag] is not None else None
-            if has_video_input in res_node:
-                val = res_node[has_video_input]
-                return _to_float(val) if val is not None else None
-        elif isinstance(res_node, (int, float)):
-            return float(res_node)
-
-    # 2. 扁平
-    for sep in ("|", ":", ",", " "):
-        for key in (
-            f"{model}{sep}{res}{sep}{flag}",
-            f"{model}{sep}{res}",
-        ):
-            if key in model_pricing:
-                value = model_pricing[key]
-                return _to_float(value) if value is not None else None
-    return None
-
-
-def refresh_feature_configs() -> None:
-    """清空缓存（下次读取重新 load DB；测试/admin 改配置后可手动调）。"""
-    global _cache
-    with _lock:
-        _cache = None

packages/domain/points_rules.py

@@ -2,21 +2,17 @@
 
 v1.6.1: 按产品决策，智能混剪/AI数字人/AI配音/抖音解析/改写/标题/封面 全部免费，
 仅保留声音克隆合成（voice_clone_synth）的扣点逻辑；声音克隆训练保持免费。
-爆款视频（viral_video）走动态定价，计费参数 DB 化（feature_pricing_configs，
-见 feature_pricing_service），calculate_viral_video_credits 从配置读取单价/
-固定成本/利润系数/封顶，DB 不可用时回落兜底配置。
+爆款视频（viral_video）走动态定价，见本文件 VIRAL_VIDEO_MODEL_PRICES + calculate_viral_video_credits。
 """
 
 from __future__ import annotations
 
 import math
 
-from packages.domain import feature_pricing_service
-
-# ============ 爆款视频动态定价 ============
-# 单价/固定成本/利润系数已 DB 化（feature_pricing_configs，feature_key=viral_video），
-# 由 feature_pricing_service 读取（300s 缓存），DB 不可用时回落内置兜底配置。
-# 以下三个常量仅为向后兼容保留（旧引用方/兜底场景），值取自兜底配置。
+# ============ 爆款视频动态定价 (#2151) ============
+# key = (model_id, resolution, has_video_input)，单位：
+# - billing_mode=token: 元/百万tokens（输出）
+# - billing_mode=per_second: 元/秒（视频时长）
 VIRAL_VIDEO_MODEL_PRICES: dict[tuple[str, str, bool], float] = {
     ("seedance-2.5", "480p", False): 70.0,
     ("seedance-2.5", "720p", False): 70.0,
@@ -37,9 +33,9 @@ VIRAL_VIDEO_MODEL_PRICES: dict[tuple[str, str, bool], float] = {
     ("wan-3.0", "1080p", False): 1.2,
 }
 
-# 固定成本（元）：VLM 分析 + LLM 文案 + TTS + OSS + 服务器（兜底默认值）
+# 固定成本（元）：VLM 分析 + LLM 文案 + TTS + OSS + 服务器
 VIRAL_VIDEO_FIXED_COST = 0.15
-# 利润系数（兜底默认值）
+# 利润系数
 VIRAL_VIDEO_PROFIT_MULTIPLIER = 1.3
 # Seedance 输出帧率
 VIRAL_VIDEO_FPS = 24
@@ -226,22 +222,17 @@ def calculate_viral_video_credits_with_breakdown(
 ) -> tuple[float, dict]:
     """计算爆款视频所需积分（1 积分 = 1 元），并返回计费公式明细。
 
-    单价/固定成本/利润系数/封顶从 feature_pricing_configs（viral_video）读取；
-    DB 不可用时回落与现状一致的内置兜底配置。
-
     公式:
         tokens = duration * width * height * fps / 1024
         video_cost = tokens / 1_000_000 * model_token_price
         total = round((video_cost + fixed_cost) * profit_multiplier, 2)
-        price_cap > 0 时封顶取 min
     若传入 actual_tokens 则用它替代计算值。
 
     Returns:
         (credits, breakdown) 二元组：
         - credits: 四舍五入保留两位小数的最终积分
         - breakdown: dict，包含 tokens / video_cost / fixed_cost / profit_multiplier /
-          model_price / width / height / fps / feature_enabled / charged / price_cap
-          字段，便于前端展示计费明细。功能关闭时 credits=0、charged=False。
+          model_price / width / height / fps 字段，便于前端展示计费明细。
     """
     w = max(1, int(width or 1))
     h = max(1, int(height or 1))
@@ -251,36 +242,11 @@ def calculate_viral_video_credits_with_breakdown(
     cfg = get_viral_video_model_config(prefix)
     res_key = _infer_resolution_key(w, h)
     billing = cfg.get("billing_mode", "token")
-    dur = max(1, int(duration_seconds or 15))
-
-    # ── 从 DB 配置（兜底内置）取计费参数 ──
-    feature_cfg = feature_pricing_service.get_feature_config("viral_video")
-    # 注意：此处 feature_enabled 只表示“功能自身开关”，不并入全局 points_enabled
-    # 总开关（保持与旧版计费函数行为一致：价格照常计算）。全局总开关由业务层
-    # （route/worker）通过 feature_pricing_service.is_feature_enabled 统一把关。
-    feature_enabled = bool(feature_cfg.is_enabled) if feature_cfg is not None else True
-    model_pricing = feature_cfg.model_pricing if feature_cfg is not None else {}
-    fixed_cost = float(feature_cfg.fixed_cost) if feature_cfg is not None else float(VIRAL_VIDEO_FIXED_COST)
-    multiplier = (
-        float(feature_cfg.profit_multiplier)
-        if feature_cfg is not None and feature_cfg.profit_multiplier > 0
-        else float(VIRAL_VIDEO_PROFIT_MULTIPLIER)
-    )
-    price_cap = float(feature_cfg.price_cap) if feature_cfg is not None else 0.0
-
-    # 单价：优先配置 dict；复刻旧版回落规则——精确 key 取不到时，回落
-    # seedance-2.5 同分辨率 False 单价；最终兜底 70.0。
-    price = feature_pricing_service.lookup_model_price(model_pricing, prefix, res_key, bool(has_video_input))
-    if price is None:
-        # 配置表未命中：先尝试配置里的 seedance-2.5/False
-        if prefix != "seedance-2.5" or bool(has_video_input):
-            price = feature_pricing_service.lookup_model_price(model_pricing, "seedance-2.5", res_key, False)
-    if price is None:
-        key = (prefix, res_key, bool(has_video_input))
-        price = VIRAL_VIDEO_MODEL_PRICES.get(key)
+    key = (prefix, res_key, bool(has_video_input))
+    price = VIRAL_VIDEO_MODEL_PRICES.get(key)
     if price is None:
         price = VIRAL_VIDEO_MODEL_PRICES.get(("seedance-2.5", res_key, False), 70.0)
-
+    dur = max(1, int(duration_seconds or 15))
     if billing == "per_second":
         tokens = 0.0
         video_cost = dur * float(price)
@@ -293,40 +259,13 @@ def calculate_viral_video_credits_with_breakdown(
         video_cost = tokens / 1_000_000.0 * float(price)
         billing_unit = "token"
 
-    if not feature_enabled:
-        # 功能关闭（is_enabled=false 或全局 points 关闭）：不扣费，明细照旧返回
-        credits = 0.0
-        raw_total = (video_cost + fixed_cost) * multiplier
-        breakdown = {
-            "tokens": float(tokens),
-            "video_cost": float(video_cost),
-            "fixed_cost": float(fixed_cost),
-            "profit_multiplier": float(multiplier),
-            "price_cap": float(price_cap or 0.0),
-            "model_price": float(price),
-            "model_key": prefix,
-            "billing_mode": billing,
-            "billing_unit": billing_unit,
-            "width": int(w),
-            "height": int(h),
-            "fps": int(effective_fps),
-            "duration": dur,
-            "feature_enabled": False,
-            "charged": False,
-            "raw_price": round(float(raw_total), 4),
-        }
-        return credits, breakdown
-
-    total = (video_cost + fixed_cost) * multiplier
-    if price_cap and price_cap > 0:
-        total = min(total, price_cap)
+    total = (video_cost + VIRAL_VIDEO_FIXED_COST) * VIRAL_VIDEO_PROFIT_MULTIPLIER
     credits = round(float(total), 2)
     breakdown = {
         "tokens": float(tokens),
         "video_cost": float(video_cost),
-        "fixed_cost": float(fixed_cost),
-        "profit_multiplier": float(multiplier),
-        "price_cap": float(price_cap or 0.0),
+        "fixed_cost": float(VIRAL_VIDEO_FIXED_COST),
+        "profit_multiplier": float(VIRAL_VIDEO_PROFIT_MULTIPLIER),
         "model_price": float(price),
         "model_key": prefix,
         "billing_mode": billing,
@@ -335,8 +274,6 @@ def calculate_viral_video_credits_with_breakdown(
         "height": int(h),
         "fps": int(effective_fps),
         "duration": dur,
-        "feature_enabled": True,
-        "charged": True,
     }
     return credits, breakdown
 

tests/unit/test_feature_billing_integration.py

@@ -1,221 +0,0 @@
-"""功能计费改造测试：爆款读配置、对口型/智能剪辑预扣逻辑。
-
-策略：
-- 爆款：通过修改缓存中的 FeatureConfig（multiplier/model_pricing）验证价格随配置变化
-- lip_sync / smart_edit：直接测 LipsyncService 的预扣/结算/退款辅助方法，
-  PointsService 用 mock，避免依赖真实积分账户。
-"""
-
-from __future__ import annotations
-
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from packages.domain import feature_pricing_service as fps
-from packages.domain.feature_pricing_service import FeatureConfig, refresh_feature_configs
-
-
-@pytest.fixture(autouse=True)
-def _reset_cache():
-    refresh_feature_configs()
-    yield
-    refresh_feature_configs()
-
-
-def _seed_cache(configs: dict) -> None:
-    import time
-
-    fps._cache = (time.monotonic(), configs)
-
-
-class TestViralVideoReadsConfig:
-    def test_multiplier_change_changes_price(self):
-        """配置里 multiplier 改大后，爆款价格随之变大（证明不再读死常量）。"""
-        from packages.domain.points_rules import calculate_viral_video_credits
-
-        # 基线兜底
-        base = calculate_viral_video_credits(15, 1280, 720)
-        assert base == 29.68
-
-        fallback = fps._fallback_configs()
-        vv = fallback["viral_video"]
-        vv.profit_multiplier = 2.0
-        _seed_cache(fallback)
-
-        changed = calculate_viral_video_credits(15, 1280, 720)
-        assert changed > base
-        # 精确校验：video_cost 相同，仅系数从 1.3 → 2.0
-        _, bd = __import__(
-            "packages.domain.points_rules", fromlist=["calculate_viral_video_credits_with_breakdown"]
-        ).calculate_viral_video_credits_with_breakdown(15, 1280, 720)
-        assert bd["profit_multiplier"] == 2.0
-
-    def test_model_price_from_config(self):
-        """model_pricing 改单价后，token 成本按新单价计算。"""
-        from packages.domain.points_rules import calculate_viral_video_credits_with_breakdown
-
-        fallback = fps._fallback_configs()
-        vv = fallback["viral_video"]
-        # seedance-2.5/720p/false 从 70 改成 100
-        vv.model_pricing["seedance-2.5"]["720p"]["false"] = 100.0
-        _seed_cache(fallback)
-
-        _, bd = calculate_viral_video_credits_with_breakdown(15, 1280, 720)
-        assert bd["model_price"] == 100.0
-
-    def test_price_cap_from_config(self):
-        from packages.domain.points_rules import calculate_viral_video_credits_with_breakdown
-
-        fallback = fps._fallback_configs()
-        vv = fallback["viral_video"]
-        vv.price_cap = 5.0
-        _seed_cache(fallback)
-
-        credits, bd = calculate_viral_video_credits_with_breakdown(15, 1280, 720)
-        assert credits == 5.0
-        assert bd["price_cap"] == 5.0
-
-    def test_disabled_feature_returns_zero_credits(self):
-        """功能 is_enabled=false 时计费函数返回 0（纯计费层语义）。"""
-        from packages.domain.points_rules import calculate_viral_video_credits_with_breakdown
-
-        fallback = fps._fallback_configs()
-        fallback["viral_video"].is_enabled = False
-        _seed_cache(fallback)
-
-        credits, bd = calculate_viral_video_credits_with_breakdown(15, 1280, 720)
-        assert credits == 0.0
-        assert bd["feature_enabled"] is False
-        assert bd["charged"] is False
-
-
-class TestLipSyncPricing:
-    def _make_service(self):
-        from app.services.lipsync_service import LipsyncService
-
-        svc = LipsyncService.__new__(LipsyncService)
-        svc.db = MagicMock()
-        return svc
-
-    def _lip_cfg(self, **kw):
-        base = dict(
-            feature_key="lip_sync",
-            name="对口型",
-            is_enabled=True,
-            fixed_cost=0.1,
-            profit_multiplier=1.0,
-            dynamic_unit_cost=0.05,
-            billing_mode="per_second",
-            price_cap=0.0,
-            model_pricing={},
-            description="",
-        )
-        base.update(kw)
-        return FeatureConfig(**base)
-
-    def test_estimate_duration_from_script(self):
-        svc = self._make_service()
-        # 10 个字 / 5 = 2 秒，下限 1
-        assert svc._estimate_duration(script_text="一二三四五六七八九十") == 2.0
-        # 无任何信息 → 默认 10 秒
-        assert svc._estimate_duration() == 10.0
-
-    def test_calculate_lipsync_price_per_second(self):
-        _seed_cache({"lip_sync": self._lip_cfg()})
-        price, bd = fps.calculate_price("lip_sync", dynamic_cost=20.0 * 0.05)
-        # dynamic 1.0 + fixed 0.1 = 1.1
-        assert price == 1.1
-        assert bd["charged"] is True
-
-    def test_settle_refunds_overcharge(self):
-        """实际时长短 → 只退不补，退还差额。"""
-        svc = self._make_service()
-        _seed_cache({"lip_sync": self._lip_cfg()})
-
-        job = MagicMock()
-        job.credits_prepaid = 2.0
-        job.credits_cost = 0.0  # 未结算
-        job.user_id = "u1"
-        job.credits_transaction_id = "txn-old"
-
-        with patch("packages.domain.points_service.PointsService") as MockPS:
-            inst = MockPS.return_value
-            inst.refund_points.return_value = {"success": True}
-            svc._settle_lip_sync(job, actual_duration=10.0)
-
-        # final: (10*0.05 + 0.1)*1.0 = 0.6；退 2.0-0.6=1.4
-        assert round(job.credits_cost, 2) == 0.6
-        inst.refund_points.assert_called_once()
-        kwargs = inst.refund_points.call_args.kwargs
-        assert kwargs["amount"] == 1.4
-
-    def test_settle_no_refund_when_longer(self):
-        """首期只退不补：实际更贵不补扣。"""
-        svc = self._make_service()
-        _seed_cache({"lip_sync": self._lip_cfg()})
-
-        job = MagicMock()
-        job.credits_prepaid = 0.5
-        job.credits_cost = 0.0
-
-        with patch("packages.domain.points_service.PointsService") as MockPS:
-            inst = MockPS.return_value
-            svc._settle_lip_sync(job, actual_duration=60.0)
-
-        assert round(job.credits_cost, 2) > 0.5
-        inst.refund_points.assert_not_called()
-
-    def test_refund_on_failure_full(self):
-        svc = self._make_service()
-        job = MagicMock()
-        job.credits_prepaid = 3.0
-        job.credits_cost = 0.0
-        job.user_id = "u1"
-        job.credits_transaction_id = "t1"
-
-        with patch("packages.domain.points_service.PointsService") as MockPS:
-            inst = MockPS.return_value
-            inst.refund_points.return_value = {"success": True}
-            svc._refund_lip_sync(job)
-
-        kwargs = inst.refund_points.call_args.kwargs
-        assert kwargs["amount"] == 3.0
-
-
-class TestSmartEditFixedPrice:
-    def test_fixed_price_formula(self):
-        """首期固定价：dynamic=0，price=fixed*multiplier，cap 封顶。"""
-        cfg = FeatureConfig(
-            feature_key="smart_edit",
-            name="智能剪辑",
-            is_enabled=True,
-            fixed_cost=2.0,
-            profit_multiplier=1.5,
-            billing_mode="model_based",
-            price_cap=0.0,
-        )
-        _seed_cache({"smart_edit": cfg})
-        price, bd = fps.calculate_price("smart_edit", dynamic_cost=0.0)
-        # (0+2)*1.5 = 3.0
-        assert price == 3.0
-        assert bd["dynamic_cost"] == 0.0
-
-    def test_fixed_price_with_cap(self):
-        cfg = FeatureConfig(
-            feature_key="smart_edit",
-            is_enabled=True,
-            fixed_cost=10.0,
-            profit_multiplier=2.0,
-            price_cap=8.0,
-        )
-        _seed_cache({"smart_edit": cfg})
-        price, _ = fps.calculate_price("smart_edit", dynamic_cost=0.0)
-        assert price == 8.0
-
-    def test_disabled_smart_edit_free(self):
-        cfg = FeatureConfig(feature_key="smart_edit", is_enabled=False, fixed_cost=2.0)
-        _seed_cache({"smart_edit": cfg})
-        price, bd = fps.calculate_price("smart_edit", dynamic_cost=0.0)
-        assert price == 0.0
-        assert bd["charged"] is False

tests/unit/test_feature_pricing_service.py

@@ -1,235 +0,0 @@
-"""feature_pricing_service 单元测试。
-
-覆盖：
-- 300s TTL 内存缓存（命中不重复 load / 过期重新 load / refresh 强制刷新）
-- calculate_price 公式 (dynamic+fixed)*multiplier、price_cap 封顶、round
-- disabled / 未知 key 返回 0
-- DB 异常 / 空表 → 内置兜底配置（爆款启用且价格与现状一致）
-- lookup_model_price 嵌套/扁平结构与旧版回落语义
-"""
-
-from __future__ import annotations
-
-import time
-
-import pytest
-
-from packages.domain import feature_pricing_service as fps
-from packages.domain.feature_pricing_service import (
-    CACHE_TTL_SECONDS,
-    FeatureConfig,
-    calculate_price,
-    get_feature_config,
-    is_feature_enabled,
-    lookup_model_price,
-    refresh_feature_configs,
-)
-
-
-@pytest.fixture(autouse=True)
-def _reset_cache():
-    """每个用例前后清空模块缓存，避免相互污染。"""
-    refresh_feature_configs()
-    yield
-    refresh_feature_configs()
-
-
-def _cfg(key="x", **kw) -> FeatureConfig:
-    base = dict(
-        feature_key=key,
-        name=key,
-        is_enabled=True,
-        fixed_cost=0.2,
-        profit_multiplier=2.0,
-        dynamic_unit_cost=0.0,
-        billing_mode="per_second",
-        price_cap=0.0,
-        model_pricing={},
-        description="",
-    )
-    base.update(kw)
-    return FeatureConfig(**base)
-
-
-class TestCacheTTL:
-    def test_cache_hit_avoids_reload(self, monkeypatch):
-        """TTL 内第二次读取不再调 _load_all。"""
-        calls = {"n": 0}
-
-        def fake_load():
-            calls["n"] += 1
-            return {"x": _cfg()}
-
-        monkeypatch.setattr(fps, "_load_all", fake_load)
-        get_feature_config("x")
-        get_feature_config("x")
-        get_feature_config("x")
-        assert calls["n"] == 1
-
-    def test_expired_cache_reloads(self, monkeypatch):
-        """超过 TTL 后重新 load。"""
-        calls = {"n": 0}
-
-        def fake_load():
-            calls["n"] += 1
-            return {"x": _cfg()}
-
-        monkeypatch.setattr(fps, "_load_all", fake_load)
-        get_feature_config("x")
-        assert calls["n"] == 1
-
-        # 把缓存时间戳回拨到 TTL 之前
-        ts, data = fps._cache
-        fps._cache = (ts - CACHE_TTL_SECONDS - 1, data)
-        get_feature_config("x")
-        assert calls["n"] == 2
-
-    def test_refresh_forces_reload(self, monkeypatch):
-        calls = {"n": 0}
-
-        def fake_load():
-            calls["n"] += 1
-            return {"x": _cfg()}
-
-        monkeypatch.setattr(fps, "_load_all", fake_load)
-        get_feature_config("x")
-        refresh_feature_configs()
-        get_feature_config("x")
-        assert calls["n"] == 2
-
-    def test_ttl_constant_is_300(self):
-        assert CACHE_TTL_SECONDS == 300.0
-
-
-class TestCalculatePrice:
-    def test_basic_formula(self, monkeypatch):
-        # (dynamic 1.0 + fixed 0.2) * 2.0 = 2.4
-        monkeypatch.setattr(fps, "_load_all", lambda: {"x": _cfg(dynamic_unit_cost=1.0)})
-        price, bd = calculate_price("x", dynamic_cost=1.0)
-        assert price == 2.4
-        assert bd["dynamic_cost"] == 1.0
-        assert bd["fixed_cost"] == 0.2
-        assert bd["profit_multiplier"] == 2.0
-        assert bd["final_price"] == 2.4
-        assert bd["charged"] is True
-
-    def test_price_cap_clamps(self, monkeypatch):
-        # raw = (1+0.2)*2 = 2.4，cap=1.0 → 1.0
-        monkeypatch.setattr(fps, "_load_all", lambda: {"x": _cfg(price_cap=1.0)})
-        price, bd = calculate_price("x", dynamic_cost=1.0)
-        assert price == 1.0
-        assert bd["price_cap"] == 1.0
-
-    def test_no_cap_keeps_raw(self, monkeypatch):
-        # cap=0 视为不封顶
-        monkeypatch.setattr(fps, "_load_all", lambda: {"x": _cfg(price_cap=0.0)})
-        price, _ = calculate_price("x", dynamic_cost=1.0)
-        assert price == 2.4
-
-    def test_rounded_two_decimals(self, monkeypatch):
-        monkeypatch.setattr(
-            fps,
-            "_load_all",
-            lambda: {"x": _cfg(fixed_cost=0.1, profit_multiplier=1.0)},
-        )
-        price, _ = calculate_price("x", dynamic_cost=1.0 / 3.0)
-        # 0.3333... + 0.1 = 0.4333 → 0.43
-        assert price == 0.43
-
-    def test_negative_dynamic_treated_as_zero(self, monkeypatch):
-        monkeypatch.setattr(fps, "_load_all", lambda: {"x": _cfg()})
-        price, _ = calculate_price("x", dynamic_cost=-5.0)
-        # (0 + 0.2) * 2 = 0.4
-        assert price == 0.4
-
-    def test_disabled_returns_zero(self, monkeypatch):
-        monkeypatch.setattr(fps, "_load_all", lambda: {"x": _cfg(is_enabled=False)})
-        price, bd = calculate_price("x", dynamic_cost=1.0)
-        assert price == 0.0
-        assert bd["is_enabled"] is False
-        assert bd["charged"] is False
-
-    def test_unknown_key_returns_zero(self, monkeypatch):
-        monkeypatch.setattr(fps, "_load_all", lambda: {"x": _cfg()})
-        price, bd = calculate_price("nope", dynamic_cost=1.0)
-        assert price == 0.0
-        assert bd["charged"] is False
-
-
-class TestDBFailureFallback:
-    def test_load_exception_uses_fallback(self, monkeypatch):
-        def boom():
-            raise RuntimeError("table does not exist")
-
-        monkeypatch.setattr(fps, "_load_all", boom)
-        cfg = get_feature_config("viral_video")
-        assert cfg is not None
-        assert cfg.is_enabled is True
-        assert cfg.fixed_cost == 0.15
-        assert cfg.profit_multiplier == 1.3
-
-    def test_empty_table_uses_fallback(self, monkeypatch):
-        monkeypatch.setattr(fps, "_load_all", lambda: {})
-        assert get_feature_config("viral_video").is_enabled is True
-        assert get_feature_config("lip_sync").is_enabled is False
-        assert get_feature_config("smart_edit").is_enabled is False
-
-    def test_fallback_viral_price_matches_current(self, monkeypatch):
-        """兜底爆款价格与旧硬编码现状一致：seedance-2.5/720p/false=70。"""
-        monkeypatch.setattr(fps, "_load_all", lambda: {})
-        from packages.domain.points_rules import calculate_viral_video_credits
-
-        # 默认全局开关关闭，但纯计费函数价格照常算
-        assert calculate_viral_video_credits(15, 1280, 720) == 29.68
-
-    def test_db_row_overrides_fallback(self, monkeypatch):
-        monkeypatch.setattr(
-            fps,
-            "_load_all",
-            lambda: {"viral_video": _cfg("viral_video", fixed_cost=0.5, profit_multiplier=2.0, price_cap=50.0)},
-        )
-        cfg = get_feature_config("viral_video")
-        assert cfg.fixed_cost == 0.5
-        assert cfg.profit_multiplier == 2.0
-        assert cfg.price_cap == 50.0
-
-
-class TestIsFeatureEnabled:
-    def test_disabled_feature(self, monkeypatch):
-        monkeypatch.setattr(fps, "_load_all", lambda: {"x": _cfg(is_enabled=False)})
-        assert is_feature_enabled("x") is False
-
-    def test_global_switch_off_blocks_enabled_feature(self, monkeypatch):
-        monkeypatch.setattr(fps, "_load_all", lambda: {"x": _cfg(is_enabled=True)})
-        monkeypatch.setattr(fps, "_global_points_enabled", lambda: False)
-        assert is_feature_enabled("x") is False
-
-    def test_both_switches_on(self, monkeypatch):
-        monkeypatch.setattr(fps, "_load_all", lambda: {"x": _cfg(is_enabled=True)})
-        monkeypatch.setattr(fps, "_global_points_enabled", lambda: True)
-        assert is_feature_enabled("x") is True
-
-
-class TestLookupModelPrice:
-    NESTED = {
-        "seedance-2.5": {
-            "720p": {"false": 70.0, "true": 42.0},
-        },
-        "wan-3.0": {"480p": {"false": 0.3}},
-    }
-
-    def test_nested_exact_hit(self):
-        assert lookup_model_price(self.NESTED, "seedance-2.5", "720p", False) == 70.0
-        assert lookup_model_price(self.NESTED, "seedance-2.5", "720p", True) == 42.0
-
-    def test_missing_bool_key_returns_none(self):
-        # wan-3.0/480p 只有 false，请求 true → None（由调用方回落）
-        assert lookup_model_price(self.NESTED, "wan-3.0", "480p", True) is None
-
-    def test_unknown_model_returns_none(self):
-        assert lookup_model_price(self.NESTED, "nope", "720p", False) is None
-
-    def test_flat_structure(self):
-        flat = {"m|720p|false": 12.5}
-        assert lookup_model_price(flat, "m", "720p", False) == 12.5
-        assert lookup_model_price(flat, "m", "720p", True) is None

tests/unit/test_viral_video_wiring.py

@@ -97,43 +97,21 @@ def invalidate_loader_cache():
 
 
 class TestImageAnalysisWiring:
-    def test_step_image_analysis_uses_v2_batch_path(self, job):
-        """#2200/#2207 后图片分析走 V2 批处理（OCR+lite JSON 并行），
-        _step_image_analysis 归一化 URL 后调用 analyze_images_v2。"""
+    def test_uses_loader_template_and_xml_parse(self, job):
         from apps.worker.worker_app.tasks import viral_video as vv
 
-        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",
-        }
-        with patch.object(vv, "_normalize_image_url", side_effect=lambda raw, idx: raw):
-            with patch(
-                "worker_app.tasks.vision.analyze_images_v2",
-                return_value=[fake_product, fake_product],
-                create=True,
-            ) as mock_v2:
-                result = vv._step_image_analysis(job)
+        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)
 
-        mock_v2.assert_called_once()
-        # 传入的是归一化后的图片 URL 列表
-        assert mock_v2.call_args.args[0] == job.images
-        products = result["products"]
-        assert len(products) == 2
-        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"]
-
-    def test_step_image_analysis_empty_images(self, job):
-        from apps.worker.worker_app.tasks import viral_video as vv
-
-        job.images = []
-        result = vv._step_image_analysis(job)
-        assert result == {"products": []}
+        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"]
 
 
 # ── 2) 意图解析走模板 ───────────────────────────────────────────────
@@ -274,29 +252,18 @@ class TestEndToEndLoaderUsed:
             called_types.append(prompt_type)
             return real_get(prompt_type, **kwargs)
 
-        v2_product = {
-            "name": "lipstick",
-            "brand": "品牌X",
-            "key_features": ["显白", "持久"],
-        }
         with (
             patch.object(pl, "get_template", side_effect=spy_get),
-            patch.object(vv, "_normalize_image_url", side_effect=lambda raw, idx: raw),
-            patch(
-                "worker_app.tasks.vision.analyze_images_v2",
-                return_value=[v2_product],
-                create=True,
-            ),
+            patch("packages.shared.ai_service.call_vision", return_value=IMAGE_XML),
             patch("packages.shared.ai_service.call_llm", return_value=INTENT_XML),
         ):
-            # 1) image（V2 路径，不再经过 prompt_loader）
-            img_step = vv._step_image_analysis(job)
-            img_res = img_step["products"][0]
-            # 2) intent（走 loader image_analysis? 否——intent_parsing 模板）
+            # 1) image
+            img_res = vv._analyze_single_image(0, "https://img/1.jpg", "vlm", 15)
+            # 2) intent
             intent_res = vv._step_intent_parsing(job, {"products": [img_res]})
 
-        # V2 图片分析不再调用 loader；意图解析调用 intent_parsing 模板
-        assert "image_analysis" not in called_types
+        # 前两步分别调用了 image_analysis 和 intent_parsing
+        assert "image_analysis" in called_types
         assert "intent_parsing" in called_types
 
         # script 和 review 单独验证（需要不同的 LLM 返回）

tests/unit/test_vision_v4_prompt.py

@@ -1,291 +0,0 @@
-# -*- coding: utf-8 -*-
-"""vision v4 prompt / assembler 单元测试：
-
-- assembler 正确识别 v4 嵌套 schema 与旧扁平 schema
-- v4 product/person/store/other 四类输出组装出下游必出字段
-- 旧扁平 schema 行为不变
-- _prompt._resolve：DB 有 active prompt 时原样使用（不追加硬编码 schema）；
-  DB 无记录时回落到硬编码 JSON schema
-"""
-
-from __future__ import annotations
-
-import types
-from typing import Any
-
-import pytest
-from worker_app.tasks.vision import _prompt, assembler
-
-REQUIRED_KEYS = {
-    "name",
-    "brand",
-    "category",
-    "appearance",
-    "packaging",
-    "text_on_package",
-    "key_features",
-    "scene",
-    "mood",
-    "portrait_prompt",
-    "summary",
-    "_source",
-}
-
-
-# ---------- schema 识别 ----------
-
-
-def test_is_v4_schema_products_list() -> None:
-    assert assembler._is_v4_schema({"type": "product", "products": []})
-
-
-def test_is_v4_schema_type_only() -> None:
-    assert assembler._is_v4_schema({"type": "person"})
-
-
-def test_is_v4_schema_people_dict() -> None:
-    assert assembler._is_v4_schema({"people": {"has_person": True}})
-
-
-def test_is_not_v4_schema_flat() -> None:
-    assert not assembler._is_v4_schema({"has_person": True, "upper_wear": "T恤"})
-
-
-# ---------- v4 product ----------
-
-V4_PRODUCT: dict[str, Any] = {
-    "type": "product",
-    "scene": "白色背景产品图",
-    "mood": "清新专业",
-    "style": "商业产品摄影",
-    "colors": [{"hex": "#E60012", "name": "亮红色", "coverage": 0.6}],
-    "visible_text": [{"text": "OMO奥妙除菌除螨", "position": "瓶身正面"}],
-    "products": [
-        {
-            "product_name": "OMO奥妙除菌除螨洗衣液",
-            "brand": "OMO奥妙",
-            "category": "洗护",
-            "package_type": "瓶装",
-            "package_color": "亮红色瓶身",
-            "cap_type": "透明翻盖式按压瓶口",
-            "body_shape": "带侧面握持把手的竖款瓶身",
-            "label_design": "瓶身印十字盾牌图案",
-            "product_features": ["亮红色瓶装", "按压式瓶口", "十字盾牌标签"],
-            "key_selling_points": ["天然除菌除螨"],
-            "position": "main",
-        }
-    ],
-    "has_person": False,
-}
-
-
-def test_assemble_v4_product_fields() -> None:
-    r = assembler.assemble_result(0, V4_PRODUCT, ["OMO奥妙"])
-    assert REQUIRED_KEYS <= set(r.keys())
-    assert r["name"] == "OMO奥妙除菌除螨洗衣液"
-    assert r["brand"] == "OMO奥妙"
-    assert r["category"] == "洗护"
-    assert "瓶装" in r["packaging"]
-    assert isinstance(r["key_features"], list) and r["key_features"]
-    assert any("除菌" in str(t) for t in r["text_on_package"])
-    assert len(r["portrait_prompt"]) >= 10
-    assert r["_source"] == "v2_fast_json_v4"
-
-
-def test_assemble_v4_product_multi_selects_main() -> None:
-    fj = {
-        "type": "product",
-        "products": [
-            {"product_name": "次要商品", "brand": "B"},
-            {"product_name": "主商品", "brand": "A", "position": "main"},
-        ],
-    }
-    r = assembler.assemble_result(1, fj, [])
-    assert r["name"] == "主商品"
-
-
-# ---------- v4 person ----------
-
-V4_PERSON: dict[str, Any] = {
-    "type": "person",
-    "scene": "户外街拍",
-    "mood": "自信",
-    "style": "街拍",
-    "colors": [],
-    "visible_text": [],
-    "has_person": True,
-    "gender": "女",
-    "age_range": "青年",
-    "upper_wear": "白色V领短袖T恤",
-    "upper_color": "白色",
-    "lower_wear": "黑色高腰阔腿裤",
-    "lower_color": "黑色",
-    "dress_color": None,
-    "accessories": ["银色项链"],
-    "hairstyle": "黑色长直发",
-    "expression": "自信",
-    "pose": "侧身站立",
-    "outfit_style": "休闲日常",
-    "portrait_prompt": (
-        "一位年轻女性，身穿白色V领短袖T恤、黑色高腰阔腿裤，佩戴银色项链，"
-        "黑色长直发，神情自信，侧身站立，休闲日常风格，城市街拍场景"
-    ),
-    "products": [],
-}
-
-
-def test_assemble_v4_person() -> None:
-    r = assembler.assemble_result(0, V4_PERSON, [])
-    assert REQUIRED_KEYS <= set(r.keys())
-    assert r["category"] == "服饰"
-    assert "T恤" in r["name"]
-    assert "阔腿裤" in r["name"]
-    assert "年轻女性" in r["portrait_prompt"]
-    assert "项链" in r["portrait_prompt"]
-    assert isinstance(r["key_features"], list) and len(r["key_features"]) <= 6
-
-
-def test_assemble_v4_person_people_nested() -> None:
-    fj = {"type": "person", "people": {**V4_PERSON, "has_person": True}}
-    r = assembler.assemble_result(0, fj, [])
-    assert r["category"] == "服饰"
-    assert "年轻女性" in r["portrait_prompt"]
-
-
-# ---------- v4 store ----------
-
-
-def test_assemble_v4_store() -> None:
-    fj = {
-        "type": "store",
-        "scene": "便利店内部",
-        "mood": "日常便民",
-        "style": "门店实拍",
-        "store_type": "社区便利店",
-        "store_layout": "纵深货架布局",
-        "brand_signage": "全家FamilyMart",
-        "visual_elements": ["红白主色调", "促销海报"],
-        "product_categories_visible": ["饮料", "零食"],
-        "promotion_elements": ["第二件半价海报"],
-        "atmosphere": "亲民生活化",
-        "has_person": False,
-    }
-    r = assembler.assemble_result(0, fj, [])
-    assert REQUIRED_KEYS <= set(r.keys())
-    assert r["name"] == "社区便利店"
-    assert r["brand"] == "全家FamilyMart"
-    assert r["category"] == "门店场景"
-    assert any("饮料" in str(f) for f in r["key_features"])
-    assert "门店实拍" in r["portrait_prompt"]
-
-
-# ---------- v4 other ----------
-
-
-def test_assemble_v4_other() -> None:
-    fj = {"type": "other", "description": "海边日落风景", "scene": "海边", "mood": "宁静"}
-    r = assembler.assemble_result(0, fj, [])
-    assert REQUIRED_KEYS <= set(r.keys())
-    assert r["name"] == "海边日落风景"
-    assert r["category"] == "非产品图"
-
-
-# ---------- 旧扁平 schema 兼容 ----------
-
-
-def test_assemble_old_flat_person() -> None:
-    fj = {
-        "has_person": True,
-        "gender": "男",
-        "age_range": "中年",
-        "upper_wear": "西装",
-        "upper_color": "深灰色",
-        "lower_wear": "西裤",
-        "lower_color": "黑色",
-        "accessories": ["手表"],
-        "hairstyle": "短发",
-        "expression": "严肃",
-        "scene": "办公室",
-        "style": "商务",
-        "mood": "专业",
-    }
-    r = assembler.assemble_result(0, fj, [])
-    assert REQUIRED_KEYS <= set(r.keys())
-    assert "中年男性" in r["portrait_prompt"]
-    assert r["_source"] == "v2_fast_json"
-
-
-def test_assemble_old_flat_product() -> None:
-    fj = {
-        "has_person": False,
-        "product_name": "口红",
-        "brand": "Dior",
-        "category": "美妆",
-        "colors": ["红色"],
-        "scene": "通用",
-        "style": "商业",
-        "mood": "高级",
-    }
-    r = assembler.assemble_result(0, fj, ["Dior"])
-    assert r["name"] == "口红"
-    assert r["brand"] == "Dior"
-    assert r["text_on_package"] == ["Dior"]
-
-
-def test_assemble_none_input() -> None:
-    r = assembler.assemble_result(0, None, [])
-    assert REQUIRED_KEYS <= set(r.keys())
-
-
-# ---------- _prompt 解析 ----------
-
-
-@pytest.fixture(autouse=True)
-def _clear_prompt_cache() -> Any:
-    _prompt.invalidate_cache()
-    yield
-    _prompt.invalidate_cache()
-
-
-def _fake_tpl(system_prompt: str = "v4 system prompt 只返回JSON") -> Any:
-    return types.SimpleNamespace(
-        system_prompt=system_prompt,
-        user_prompt_template="分析 {image_count} 张图",
-        version=4,
-    )
-
-
-def test_resolve_uses_db_prompt_without_append(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(_prompt, "_load_db_template", lambda: _fake_tpl("DB_V4_PROMPT_XYZ"))
-    sys_prompt, user_prompt = _prompt.resolve_fast_prompt()
-    assert sys_prompt == "DB_V4_PROMPT_XYZ"
-    assert "DB_V4_PROMPT_XYZ" not in _prompt._FAST_JSON_APPEND  # sanity: 旧append是另一段文本
-    assert "分析 1 张图" in user_prompt
-
-
-def test_resolve_pro_uses_db_prompt_without_append(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(_prompt, "_load_db_template", lambda: _fake_tpl("DB_V4_PRO_PROMPT"))
-    sys_prompt, _ = _prompt.resolve_pro_prompt()
-    assert sys_prompt == "DB_V4_PRO_PROMPT"
-    assert "【输出格式要求】" not in sys_prompt
-
-
-def test_resolve_falls_back_when_no_db(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(_prompt, "_load_db_template", lambda: None)
-    sys_prompt, user_prompt = _prompt.resolve_fast_prompt()
-    assert sys_prompt == _prompt._FAST_JSON_SCHEMA
-    assert user_prompt == _prompt.DEFAULT_FAST_USER
-
-
-def test_resolve_caches(monkeypatch: pytest.MonkeyPatch) -> None:
-    calls = {"n": 0}
-
-    def _load() -> Any:
-        calls["n"] += 1
-        return _fake_tpl("CACHED_PROMPT")
-
-    monkeypatch.setattr(_prompt, "_load_db_template", _load)
-    s1, _ = _prompt.resolve_fast_prompt()
-    s2, _ = _prompt.resolve_fast_prompt()
-    assert s1 == s2 == "CACHED_PROMPT"
-    assert calls["n"] == 1