fix(doubao): chat/vision/embedding 4xx错误不重试 + 模型404自动fallback到默认模型

- chat_completion: 拆分_do_chat_request，4xx错误（鉴权/配额/参数）不做无意义重试
- chat_completion: 404/model_not_exist 时自动fallback到self.model（doubao-seed-1-6推理模型）
- vision_completion: 同样4xx不重试，404 fallback到默认模型
- embed_text: 4xx不重试
- 新增embedding_model字段初始化
- 新增2个单测：404 fallback成功 / 401 不fallback
- 解决运维反馈doubao-1-5-pro-32k/doubao-vision-lite等模型直连404每次多走一轮失败重试的问题

.env.example

@@ -211,24 +211,15 @@ COSYVOICE_CLONE_MODEL=voice-enrollment
 # 用于 AI 文案生成、智能剪辑等需要大模型能力的场景
 
 DOUBAO_API_KEY=your-doubao-api-key
-DOUBAO_MODEL=doubao-seed-2-1-pro-260915
-DOUBAO_FAST_MODEL=doubao-seed-2-1-lite-260915
+DOUBAO_MODEL=doubao-seed-1-6-250615
+DOUBAO_FAST_MODEL=doubao-1-5-pro-32k-250115
 DOUBAO_BASE_URL=https://ark.cn-beijing.volces.com/api/v3
 DOUBAO_TIMEOUT=30
 DOUBAO_MAX_RETRIES=2
 # 视觉模型：pro 精度高，lite 速度快（viral-video 商品识别默认用 lite 提速）
-DOUBAO_VISION_MODEL=doubao-seed-2-1-pro-260915
-DOUBAO_VISION_LITE_MODEL=doubao-seed-2-1-lite-260915
+DOUBAO_VISION_MODEL=doubao-1-5-vision-pro-250328
+DOUBAO_VISION_LITE_MODEL=doubao-1-5-vision-lite-250315
 DOUBAO_VISION_USE_LITE=true
-# Embedding 向量化模型
-DOUBAO_EMBEDDING_MODEL=doubao-embedding-vision-251215
-# 视频模型（Seedance 2.5，统一走方舟；真人参考图通过信任链自动 AI 化）
-DOUBAO_VIDEO_MODEL=doubao-seedance-2-5-260628
-DOUBAO_VIDEO_TIMEOUT=480
-DOUBAO_VIDEO_POLL_INTERVAL=10
-# 图片模型（Seedream 5.0 Pro，用于信任链真人 AI 化 + 文生图）
-DOUBAO_IMAGE_MODEL=doubao-seedream-5-0-pro-260628
-DOUBAO_IMAGE_TIMEOUT=120
 
 # ==================== 积分/会员系统 (#1895) ====================
 # 积分系统总开关：默认 false（暂停积分系统）。

.gitea/workflows/ci-pipeline.yml

@@ -1187,14 +1187,6 @@ jobs:
         DOUBAO_MODEL: "${{ secrets.DOUBAO_MODEL }}"
         DOUBAO_BASE_URL: "${{ secrets.DOUBAO_BASE_URL }}"
         DOUBAO_VISION_MODEL: "${{ secrets.DOUBAO_VISION_MODEL }}"
-        DOUBAO_VISION_LITE_MODEL: "${{ secrets.DOUBAO_VISION_LITE_MODEL }}"
-        DOUBAO_VISION_USE_LITE: "${{ secrets.DOUBAO_VISION_USE_LITE }}"
-        DOUBAO_IMAGE_MODEL: "${{ secrets.DOUBAO_IMAGE_MODEL }}"
-        DOUBAO_IMAGE_SIZE: "${{ secrets.DOUBAO_IMAGE_SIZE }}"
-        DOUBAO_IMAGE_TIMEOUT: "${{ secrets.DOUBAO_IMAGE_TIMEOUT }}"
-        DOUBAO_FAST_MODEL: "${{ secrets.DOUBAO_FAST_MODEL }}"
-        DOUBAO_TIMEOUT: "${{ secrets.DOUBAO_TIMEOUT }}"
-        DOUBAO_MAX_RETRIES: "${{ secrets.DOUBAO_MAX_RETRIES }}"
         WECHAT_APP_ID: "${{ secrets.WECHAT_APP_ID }}"
         WECHAT_APP_SECRET: "${{ secrets.WECHAT_APP_SECRET }}"
         TIKHUB_API_KEY: "${{ secrets.TIKHUB_API_KEY }}"

CHANGES.md

@@ -1,2 +0,0 @@
-Mon Oct  5 04:09:11 PM CST 2026
-2198 lite/pro并行竞速 (commit 9699a1f) — CI rebuild trigger Mon Oct  5 08:09:11 AM UTC 2026

alembic/versions/094_viral_video_pre_trusted.py

@@ -1,31 +0,0 @@
-"""viral_video_jobs 增加 pre_trusted_images 列（信任链Seedream预热结果）
-
-Revision ID: 094_viral_video_pre_trusted
-Revises: 093_viral_video_pricing_points_float
-Create Date: 2026-10-04
-"""
-
-import sqlalchemy as sa
-
-from alembic import op
-
-revision = "094_viral_video_pre_trusted"
-down_revision = "093"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    conn = op.get_bind()
-    inspector = sa.inspect(conn)
-    cols = {c["name"] for c in inspector.get_columns("viral_video_jobs")}
-    if "pre_trusted_images" not in cols:
-        op.add_column("viral_video_jobs", sa.Column("pre_trusted_images", sa.Text(), nullable=True))
-
-
-def downgrade() -> None:
-    conn = op.get_bind()
-    inspector = sa.inspect(conn)
-    cols = {c["name"] for c in inspector.get_columns("viral_video_jobs")}
-    if "pre_trusted_images" in cols:
-        op.drop_column("viral_video_jobs", "pre_trusted_images")

alembic/versions/095_viral_video_prompt_templates.py

@@ -1,102 +0,0 @@
-"""爆款视频 Prompt 模板配置表（#2040）。
-
-086 曾预留同名旧表（id varchar / content / variables json），从未被业务使用；
-本迁移将其替换为 #2040 新结构。
-
-Revision ID: 095_viral_video_prompt_templates
-Revises: 094_viral_video_pre_trusted
-Create Date: 2026-10-04
-"""
-
-import sqlalchemy as sa
-
-from alembic import op
-
-revision = "095_viral_video_prompt_templates"
-down_revision = "094_viral_video_pre_trusted"
-branch_labels = None
-depends_on = None
-
-
-def _table_exists(conn, name: str) -> bool:
-    return name in sa.inspect(conn).get_table_names()
-
-
-def upgrade() -> None:
-    conn = op.get_bind()
-    # 086 预留的旧结构表：先删除（无业务数据、无任何引用）
-    if _table_exists(conn, "viral_video_prompt_templates"):
-        op.drop_table("viral_video_prompt_templates")
-
-    op.create_table(
-        "viral_video_prompt_templates",
-        sa.Column("id", sa.Integer, primary_key=True, autoincrement=True),
-        sa.Column("name", sa.String(128), nullable=False),
-        sa.Column("prompt_type", sa.String(32), nullable=False),
-        sa.Column("version", sa.Integer, nullable=False, server_default="1"),
-        sa.Column("system_prompt", sa.Text, nullable=False),
-        sa.Column("user_prompt_template", sa.Text, nullable=False),
-        sa.Column("example_output", sa.Text, nullable=True),
-        sa.Column("is_active", sa.Boolean, nullable=False, server_default=sa.text("true")),
-        sa.Column(
-            "created_at",
-            sa.DateTime(timezone=True),
-            server_default=sa.func.now(),
-            nullable=False,
-        ),
-        sa.Column(
-            "updated_at",
-            sa.DateTime(timezone=True),
-            server_default=sa.func.now(),
-            nullable=False,
-        ),
-    )
-    op.create_index(
-        "ix_vvpt_type_active",
-        "viral_video_prompt_templates",
-        ["prompt_type", "is_active"],
-    )
-    op.create_index(
-        "uq_vvpt_type_version",
-        "viral_video_prompt_templates",
-        ["prompt_type", "version"],
-        unique=True,
-    )
-
-
-def downgrade() -> None:
-    conn = op.get_bind()
-    if _table_exists(conn, "viral_video_prompt_templates"):
-        op.drop_index("uq_vvpt_type_version", table_name="viral_video_prompt_templates")
-        op.drop_index("ix_vvpt_type_active", table_name="viral_video_prompt_templates")
-        op.drop_table("viral_video_prompt_templates")
-
-    # 恢复 086 的旧预留结构
-    op.create_table(
-        "viral_video_prompt_templates",
-        sa.Column("id", sa.String(36), primary_key=True),
-        sa.Column("prompt_type", sa.String(50), nullable=False, index=True),
-        sa.Column("name", sa.String(200), nullable=False),
-        sa.Column("content", sa.Text, nullable=False, server_default=""),
-        sa.Column("variables", sa.JSON, nullable=False, server_default="[]"),
-        sa.Column("version", sa.Integer, nullable=False, server_default="1"),
-        sa.Column(
-            "is_active",
-            sa.Boolean,
-            nullable=False,
-            server_default=sa.text("true"),
-            index=True,
-        ),
-        sa.Column(
-            "created_at",
-            sa.DateTime(timezone=True),
-            nullable=False,
-            server_default=sa.func.now(),
-        ),
-        sa.Column(
-            "updated_at",
-            sa.DateTime(timezone=True),
-            nullable=False,
-            server_default=sa.func.now(),
-        ),
-    )

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)

alembic/versions/099_ai_model_router_seed.py

@@ -1,222 +0,0 @@
-# -*- coding: utf-8 -*-
-"""099: AI 模型路由层 seed — 补齐缺失模型和能力配置.
-
-幂等：所有 INSERT 先检查存在性。
-- ai_models: 补齐 qwen3.7-plus, seedream, seedance, embedding, wan3.0 等
-- ai_capability_configs: 补齐 image_generation, video_generation, embedding
-- 更新已有 capability 的 lite_model_id
-"""
-
-import json
-
-import sqlalchemy as sa
-
-from alembic import op
-
-revision = "099_ai_model_router_seed"
-down_revision = "098_viral_video_image_analysis_v5"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    conn = op.get_bind()
-
-    # CI 环境下 ai_models 表可能尚未创建（由 ORM 自动建表，非 migration）
-    # 如果表不存在则跳过 seed，由应用启动时 ORM 建表后首次访问时生效
-    table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_models')")).scalar()
-    if not table_check:
-        # ai_models 表不存在，跳过所有 seed（CI 环境）
-        return
-
-    # ── 1. 补齐 ai_models 缺失记录 ────────────────────────────────────────────
-    existing_models = {
-        row[0]
-        for row in conn.execute(
-            sa.text("SELECT model_key FROM ai_models WHERE deleted_at IS NULL")
-        ).fetchall()
-    }
-
-    # 从已有 active 记录获取 API key（复用，不硬编码）
-    dashscope_key_row = conn.execute(
-        sa.text(
-            "SELECT api_key FROM ai_models WHERE provider='dashscope' AND deleted_at IS NULL AND api_key IS NOT NULL AND api_key != '' LIMIT 1"
-        )
-    ).first()
-    dashscope_key = dashscope_key_row[0] if dashscope_key_row else ""
-
-    volcengine_key_row = conn.execute(
-        sa.text(
-            "SELECT api_key FROM ai_models WHERE provider='volcengine' AND deleted_at IS NULL AND api_key IS NOT NULL AND api_key != '' LIMIT 1"
-        )
-    ).first()
-    volcengine_key = volcengine_key_row[0] if volcengine_key_row else ""
-
-    new_models = [
-        {
-            "model_key": "qwen3.7-plus",
-            "name": "通义千问3.7 Plus（VLM 兜底）",
-            "provider": "dashscope",
-            "api_key": dashscope_key,
-            "api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1",
-            "description": "阿里云百炼 Qwen3.7 Plus 多模态模型，用于 VLM 兜底分析",
-        },
-        {
-            "model_key": "doubao-seedream-5-0-flash-260915",
-            "name": "Seedream 5.0 Flash（图片生成）",
-            "provider": "volcengine",
-            "api_key": volcengine_key,
-            "api_base": "https://ark.cn-beijing.volces.com/api/v3",
-            "description": "火山引擎 Seedream 5.0 Flash 文生图模型",
-        },
-        {
-            "model_key": "doubao-seedance-2-5-260628",
-            "name": "Seedance 2.5（视频生成）",
-            "provider": "volcengine",
-            "api_key": volcengine_key,
-            "api_base": "https://ark.cn-beijing.volces.com/api/v3",
-            "description": "火山引擎 Seedance 2.5 图/文生视频模型",
-        },
-        {
-            "model_key": "doubao-embedding-vision-251215",
-            "name": "豆包多模态向量嵌入",
-            "provider": "volcengine",
-            "api_key": volcengine_key,
-            "api_base": "https://ark.cn-beijing.volces.com/api/v3",
-            "description": "火山引擎豆包多模态向量嵌入模型",
-        },
-        {
-            "model_key": "wan3.0-video",
-            "name": "Wan 3.0 视频生成",
-            "provider": "dashscope",
-            "api_key": dashscope_key,
-            "api_base": "https://dashscope.aliyuncs.com/api/v1",
-            "description": "阿里云百炼 Wan 3.0 视频生成模型",
-        },
-        {
-            "model_key": "doubao-seed-2-1-pro-260915",
-            "name": "豆包 Seed 2.1 Pro（高精度推理）",
-            "provider": "volcengine",
-            "api_key": volcengine_key,
-            "api_base": "https://ark.cn-beijing.volces.com/api/v3",
-            "description": "火山引擎豆包 Seed 2.1 Pro 深度思考+多模态",
-        },
-    ]
-
-    for m in new_models:
-        if m["model_key"] not in existing_models:
-            conn.execute(
-                sa.text(
-                    """
-                    INSERT INTO ai_models (id, name, provider, model_key, api_key, api_base, description, status, is_default, usage_today, created_at, updated_at)
-                    VALUES (gen_random_uuid()::text, :name, :provider, :model_key, :api_key, :api_base, :description, 'active', false, 0, now(), now())
-                    """
-                ),
-                m,
-            )
-
-    # ── 2. 补齐 ai_capability_configs 缺失项 ──────────────────────────────────
-    cap_table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_capability_configs')")).scalar()
-    if not cap_table_check:
-        return
-
-    existing_caps = {
-        row[0]
-        for row in conn.execute(
-            sa.text("SELECT capability_key FROM ai_capability_configs")
-        ).fetchall()
-    }
-
-    def _get_model_id(model_key: str) -> str | None:
-        row = conn.execute(
-            sa.text(
-                "SELECT id FROM ai_models WHERE model_key = :key AND deleted_at IS NULL AND status = 'active' LIMIT 1"
-            ),
-            {"key": model_key},
-        ).first()
-        return row[0] if row else None
-
-    # image_generation
-    if "image_generation" not in existing_caps:
-        mid = _get_model_id("doubao-seedream-5-0-flash-260915")
-        if mid:
-            conn.execute(
-                sa.text(
-                    """
-                    INSERT INTO ai_capability_configs (id, capability_key, capability_name, primary_model_id, timeout_seconds, max_retries, concurrency, extra_params, is_enabled, created_at, updated_at)
-                    VALUES (gen_random_uuid()::text, :ck, :cn, :pm, 60, 1, 2, :ep, true, now(), now())
-                    """
-                ),
-                {
-                    "ck": "image_generation",
-                    "cn": "图片生成（Seedream）",
-                    "pm": mid,
-                    "ep": json.dumps({"size": "1K"}),
-                },
-            )
-
-    # video_generation
-    if "video_generation" not in existing_caps:
-        mid = _get_model_id("doubao-seedance-2-5-260628")
-        fb_mid = _get_model_id("wan3.0-video")
-        if mid:
-            conn.execute(
-                sa.text(
-                    """
-                    INSERT INTO ai_capability_configs (id, capability_key, capability_name, primary_model_id, fallback_model_id, timeout_seconds, max_retries, concurrency, extra_params, is_enabled, created_at, updated_at)
-                    VALUES (gen_random_uuid()::text, :ck, :cn, :pm, :fm, 600, 1, 1, :ep, true, now(), now())
-                    """
-                ),
-                {
-                    "ck": "video_generation",
-                    "cn": "视频生成（Seedance/Wan）",
-                    "pm": mid,
-                    "fm": fb_mid,
-                    "ep": json.dumps({}),
-                },
-            )
-
-    # embedding
-    if "embedding" not in existing_caps:
-        mid = _get_model_id("doubao-embedding-vision-251215")
-        if mid:
-            conn.execute(
-                sa.text(
-                    """
-                    INSERT INTO ai_capability_configs (id, capability_key, capability_name, primary_model_id, timeout_seconds, max_retries, concurrency, extra_params, is_enabled, created_at, updated_at)
-                    VALUES (gen_random_uuid()::text, :ck, :cn, :pm, 30, 2, 5, :ep, true, now(), now())
-                    """
-                ),
-                {
-                    "ck": "embedding",
-                    "cn": "向量嵌入",
-                    "pm": mid,
-                    "ep": json.dumps({}),
-                },
-            )
-
-    # ── 3. 更新 image_analysis 的 lite_model_id ─────────────────────────────
-    lite_model_id = _get_model_id("qwen3.8-flash")
-    if lite_model_id:
-        conn.execute(
-            sa.text(
-                "UPDATE ai_capability_configs SET lite_model_id = :lite WHERE capability_key = 'image_analysis' AND lite_model_id IS NULL"
-            ),
-            {"lite": lite_model_id},
-        )
-
-
-def downgrade() -> None:
-    conn = op.get_bind()
-    # 安全检查表是否存在
-    table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_models')")).scalar()
-    if not table_check:
-        return
-    conn.execute(
-        sa.text("DELETE FROM ai_capability_configs WHERE capability_key IN ('image_generation', 'video_generation', 'embedding')")
-    )
-    conn.execute(
-        sa.text(
-            "DELETE FROM ai_models WHERE model_key IN ('qwen3.7-plus', 'doubao-seedream-5-0-flash-260915', 'doubao-seedance-2-5-260628', 'doubao-embedding-vision-251215', 'wan3.0-video', 'doubao-seed-2-1-pro-260915') AND deleted_at IS NULL"
-        )
-    )

alembic/versions/100_fix_capability_model_bindings.py

@@ -1,107 +0,0 @@
-# -*- coding: utf-8 -*-
-"""100: 修正已有 capability 的模型绑定.
-
-幂等：仅当 primary_model_id 当前绑定到旧模型 (doubao-seed-1-6) 时才更新，
-避免覆盖用户在后台的自定义配置。
-
-- 更新 5 个 LLM capability (intent_parsing, copy_fusion, storyboard, copy_review, asset_classify)
-  的 primary_model_id 从 doubao-seed-1-6 改为 doubao-seed-2-1-pro-260915
-- 更新 image_analysis 的 primary/lite/fallback 模型绑定
-"""
-
-import sqlalchemy as sa
-
-from alembic import op
-
-revision = "100_fix_capability_model_bindings"
-down_revision = "099_ai_model_router_seed"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    conn = op.get_bind()
-
-    # Check tables exist
-    table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_models')")).scalar()
-    if not table_check:
-        return
-
-    config_table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_capability_configs')")).scalar()
-    if not config_table_check:
-        return
-
-    # Look up model IDs by model_key (not hardcoded UUIDs)
-    pro_model_row = conn.execute(
-        sa.text(
-            "SELECT id FROM ai_models WHERE model_key = 'doubao-seed-2-1-pro-260915' AND deleted_at IS NULL LIMIT 1"
-        )
-    ).first()
-    if not pro_model_row:
-        return
-    pro_model_id = pro_model_row[0]
-
-    old_model_row = conn.execute(
-        sa.text("SELECT id FROM ai_models WHERE model_key = 'doubao-seed-1-6-250615' LIMIT 1")
-    ).first()
-    old_model_id = old_model_row[0] if old_model_row else None
-
-    llm_capabilities = [
-        "intent_parsing",
-        "copy_fusion",
-        "storyboard",
-        "copy_review",
-        "asset_classify",
-    ]
-
-    for cap_key in llm_capabilities:
-        if old_model_id:
-            conn.execute(
-                sa.text(
-                    "UPDATE ai_capability_configs SET primary_model_id = :new_id, updated_at = NOW() "
-                    "WHERE capability_key = :cap_key AND primary_model_id = :old_id"
-                ),
-                {"new_id": pro_model_id, "old_id": old_model_id, "cap_key": cap_key},
-            )
-
-    # Update image_analysis
-    qwen38_row = conn.execute(
-        sa.text("SELECT id FROM ai_models WHERE model_key = 'qwen3.8-flash' AND deleted_at IS NULL LIMIT 1")
-    ).first()
-    qwen37_row = conn.execute(
-        sa.text("SELECT id FROM ai_models WHERE model_key = 'qwen3.7-plus' AND deleted_at IS NULL LIMIT 1")
-    ).first()
-
-    if qwen38_row and qwen37_row:
-        qwen38_id = qwen38_row[0]
-        qwen37_id = qwen37_row[0]
-
-        current_ia = conn.execute(
-            sa.text(
-                "SELECT primary_model_id, lite_model_id, fallback_model_id "
-                "FROM ai_capability_configs WHERE capability_key = 'image_analysis'"
-            )
-        ).first()
-
-        if current_ia:
-            current_primary, current_lite, current_fallback = current_ia
-            updates = {}
-            if current_primary != qwen38_id:
-                updates["primary_model_id"] = qwen38_id
-            if current_lite != qwen38_id:
-                updates["lite_model_id"] = qwen38_id
-            if current_fallback != qwen37_id:
-                updates["fallback_model_id"] = qwen37_id
-
-            if updates:
-                set_clause = ", ".join([f"{k} = :{k}" for k in updates.keys()])
-                set_clause += ", updated_at = NOW()"
-                updates["cap_key"] = "image_analysis"
-                conn.execute(
-                    sa.text(f"UPDATE ai_capability_configs SET {set_clause} WHERE capability_key = :cap_key"),
-                    updates,
-                )
-
-
-def downgrade() -> None:
-    pass

alembic/versions/101_qwen_vl_plus_and_max_tokens.py

@@ -1,153 +0,0 @@
-# -*- coding: utf-8 -*-
-"""101: 补齐 qwen-vl-plus 视觉模型并修正 image_analysis 绑定与 max_tokens.
-
-背景：
-- qwen-vl-plus 做图片识别时返回 JSON 约 500-600 tokens，旧硬编码
-  max_tokens=350 导致 JSON 被截断、解析失败返回"未识别"。
-- 代码侧已移除硬编码，改由 capability 的 DB 配置决定 max_tokens。
-
-幂等：
-- qwen-vl-plus 已存在则不插入；
-- 仅当 image_analysis 当前 primary_model 不是 qwen-vl-plus 时才更新绑定，
-  避免覆盖后台手动配置。
-"""
-
-import sqlalchemy as sa
-
-from alembic import op
-
-revision = "101_qwen_vl_plus_and_max_tokens"
-down_revision = "100_fix_capability_model_bindings"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    conn = op.get_bind()
-
-    models_table = conn.execute(sa.text("SELECT to_regclass('public.ai_models')")).scalar()
-    if not models_table:
-        return
-
-    caps_table = conn.execute(sa.text("SELECT to_regclass('public.ai_capability_configs')")).scalar()
-    if not caps_table:
-        return
-
-    # ── c. 补全其他 capability 的 max_tokens 默认值（幂等）──────────────────
-    # 放在 image_analysis 特定逻辑之前，确保任何分支 return 都不会跳过本段。
-    # 仅在当前值为 NULL 或过小 (<100) 时更新，不覆盖已有合理配置。
-    # embedding / tts / voice_clone 不走 chat 接口，无需设置。
-    default_max_tokens = {
-        "intent_parsing": 500,
-        "copy_fusion": 2500,
-        "storyboard": 4000,
-        "copy_review": 1000,
-        "asset_classify": 500,
-        "image_generation": 500,
-        "video_generation": 500,
-    }
-    for cap_key, mt in default_max_tokens.items():
-        conn.execute(
-            sa.text(
-                "UPDATE ai_capability_configs "
-                "SET max_tokens = :mt, updated_at = now() "
-                "WHERE capability_key = :key "
-                "AND (max_tokens IS NULL OR max_tokens < 100)"
-            ),
-            {"mt": mt, "key": cap_key},
-        )
-
-    # ── a. 确保 qwen-vl-plus 模型存在 ────────────────────────────────────────
-    conn.execute(sa.text("""
-            INSERT INTO ai_models (id, name, provider, model_key, api_key, api_base,
-                                   description, status, is_default, usage_today,
-                                   created_at, updated_at)
-            SELECT gen_random_uuid()::text,
-                   '通义千问VL Plus',
-                   'dashscope',
-                   'qwen-vl-plus',
-                   COALESCE(
-                       (SELECT api_key FROM ai_models
-                        WHERE provider = 'dashscope' AND deleted_at IS NULL
-                          AND api_key IS NOT NULL AND api_key != ''
-                        LIMIT 1),
-                       ''
-                   ),
-                   'https://dashscope.aliyuncs.com/compatible-mode/v1',
-                   '阿里云视觉理解模型（图片识别/分析）',
-                   'active', false, 0, now(), now()
-            WHERE NOT EXISTS (
-                SELECT 1 FROM ai_models
-                WHERE model_key = 'qwen-vl-plus' AND deleted_at IS NULL
-            )
-            """))
-
-    qwen_vl_row = conn.execute(
-        sa.text(
-            "SELECT id FROM ai_models WHERE model_key = 'qwen-vl-plus' "
-            "AND deleted_at IS NULL AND status = 'active' LIMIT 1"
-        )
-    ).first()
-    if not qwen_vl_row:
-        return
-    qwen_vl_id = qwen_vl_row[0]
-
-    qwen37_row = conn.execute(
-        sa.text(
-            "SELECT id FROM ai_models WHERE model_key = 'qwen3.7-plus' "
-            "AND deleted_at IS NULL AND status = 'active' LIMIT 1"
-        )
-    ).first()
-    qwen37_id = qwen37_row[0] if qwen37_row else None
-
-    # ── b. 仅当当前 primary 不是 qwen-vl-plus 时修正绑定与 max_tokens ───────
-    current = conn.execute(
-        sa.text(
-            "SELECT primary_model_id, lite_model_id, fallback_model_id, max_tokens "
-            "FROM ai_capability_configs WHERE capability_key = 'image_analysis'"
-        )
-    ).first()
-
-    if current is None:
-        # capability 不存在则创建
-        conn.execute(
-            sa.text("""
-                INSERT INTO ai_capability_configs
-                    (id, capability_key, capability_name, primary_model_id,
-                     lite_model_id, fallback_model_id, timeout_seconds,
-                     max_retries, max_tokens, concurrency, extra_params,
-                     is_enabled, created_at, updated_at)
-                VALUES (gen_random_uuid()::text, 'image_analysis', '图片分析',
-                        :primary, :primary, :fallback, 30, 1, 1000, 2,
-                        '{}'::jsonb, true, now(), now())
-                """),
-            {"primary": qwen_vl_id, "fallback": qwen37_id},
-        )
-        return
-
-    current_primary = current[0]
-    if current_primary == qwen_vl_id:
-        # 已经绑定 qwen-vl-plus：视为后台/数据迁移已处理，不覆盖任何配置
-        return
-
-    set_parts = [
-        "primary_model_id = :vl_id",
-        "lite_model_id = :vl_id",
-        "max_tokens = 1000",
-        "updated_at = now()",
-    ]
-    params: dict = {"vl_id": qwen_vl_id}
-    if qwen37_id is not None:
-        set_parts.insert(2, "fallback_model_id = :qwen37_id")
-        params["qwen37_id"] = qwen37_id
-
-    conn.execute(
-        sa.text(
-            "UPDATE ai_capability_configs SET " + ", ".join(set_parts) + " WHERE capability_key = 'image_analysis'"
-        ),
-        params,
-    )
-
-
-def downgrade() -> None:
-    pass

alembic/versions/102_image_analysis_max_tokens_1500.py

@@ -1,36 +0,0 @@
-# -*- coding: utf-8 -*-
-"""102: image_analysis max_tokens 1200 -> 1500.
-
-v6 prompt 更长、字段更多，旧 max_tokens 容易截断 JSON。
-仅在 image_analysis 当前 max_tokens < 1500 时更新（幂等，不覆盖后台已调到 >=1500 的配置）。
-"""
-
-import sqlalchemy as sa
-
-from alembic import op
-
-revision = "102_image_analysis_max_tokens_1500"
-down_revision = "101_qwen_vl_plus_and_max_tokens"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    conn = op.get_bind()
-
-    caps_table = conn.execute(sa.text("SELECT to_regclass('public.ai_capability_configs')")).scalar()
-    if not caps_table:
-        return
-
-    conn.execute(
-        sa.text(
-            "UPDATE ai_capability_configs "
-            "SET max_tokens = 1500, updated_at = now() "
-            "WHERE capability_key = 'image_analysis' "
-            "AND (max_tokens IS NULL OR max_tokens < 1500)"
-        )
-    )
-
-
-def downgrade() -> None:
-    pass

alembic/versions/103_v7_prompt_and_tokens_3000.py

@@ -1,194 +0,0 @@
-# -*- coding: utf-8 -*-
-"""image_analysis v7 prompt + max_tokens 3000 + max_retries 3
-
-Revision ID: 103_v7_prompt_and_tokens_3000
-Revises: 102_image_analysis_max_tokens_1500
-Create Date: 2026-10-07
-
-变更：
-1. 插入v7精简prompt（~1KB，v6 ~4.5KB，删除few-shot/冗长规则，减少输出token占用），设为active
-2. v6停用（is_active=False），保留历史
-3. image_analysis capability: max_tokens 1500→3000，max_retries 1→3
-
-ai_capability_configs 由应用 create_all 创建，全新 alembic-only 库可能不存在，
-故第3步做 to_regclass 守卫（同 102）。
-"""
-
-from sqlalchemy import text
-
-from alembic import op
-
-revision = "103_v7_prompt_and_tokens_3000"
-down_revision = "102_image_analysis_max_tokens_1500"
-branch_labels = None
-depends_on = None
-
-V7_SYSTEM = """# 角色
-你是一位专业的图片分析师，擅长准确识别图片中的场景、人物、物体、文字、氛围。
-
-# 任务
-对用户上传的图片逐张分析，描述你看到的内容，输出JSON格式。
-
-## 技能
-
-### 技能1：判断图片类型
-判断图片属于哪种类型，type字段填对应的英文值：
-- 商品图（product）：单个或多个商品、产品包装
-- 门店场景图（store）：店铺内部、门头招牌、货架陈列
-- 人物图（person）：人物形象、穿搭造型、肖像照片
-- 风景图（scene）：风景、动物、美食、街景
-- 其他（other）：以上都不是
-
-### 技能2：描述通用信息
-不管什么图都要描述：
-- type：图片类型，填product/store/person/scene/other其中一个
-- scene：一句话描述场景，例如"理疗养生店内部，摆着多张理疗床和产品货架"
-- mood：整体氛围，2-4个词，例如"整洁专业"、"热闹温馨"
-- colors：主要颜色，最多5个，写具体颜色名（亮红色/米白色/深蓝色，不写笼统的红色蓝色）
-- visible_text：图片里看到的文字，说明什么字、在什么位置，最多5条；没看到就空数组
-- lighting：光线情况，例如"明亮柔光"、"自然光"、"室内暖黄灯"
-- composition：怎么拍的，例如"居中特写"、"中景平视"、"俯拍"
-- has_person：有没有人，true或false
-
-### 技能3：描述门店场景
-如果是门店场景图（type="store"），还要描述：
-- store_type：什么类型的店，例如"养生馆"、"便利店"、"餐饮店"、"母婴店"
-- brand_signage：招牌上写了什么字、有什么品牌标识
-- visual_elements：看到哪些显眼的东西（招牌样式、灯光、货架、商品陈列、海报、收银台等），最多8个
-- product_categories：看到哪些品类的商品，例如"饮料零食"、"养生产品"
-- promotion_elements：有没有促销活动（打折海报、满减吊旗等），没有就空数组
-- atmosphere：店内什么氛围，例如"亲民生活化"、"老字号专业感"
-- cleanliness：店内干净程度，例如"干净整洁"、"货架整齐"
-- 看到顾客或店员要描述他们在做什么，has_person填true
-
-### 技能4：描述商品
-如果是商品图（type="product"），逐个商品描述：
-- product_name：商品名称，尽量具体，例如"OMO奥妙除菌除螨洗衣液"；看不出来填null
-- brand：什么牌子，看不出来填null
-- category：类目，从以下选一个：服饰鞋包/美妆/数码/食品/家居清洁/母婴/配饰/其他
-- package_type：什么包装，例如"瓶装"、"盒装"、"罐装"、"袋装"、"多瓶装"
-- package_color：包装主要颜色，写具体色（亮红色不写红色）
-- body_shape：瓶身或包装形状，例如"圆润胖瓶"、"竖款带把手瓶身"
-- label_design：标签设计，例如"红色标签印白色品牌logo"
-- key_text_on_package：包装上最显眼的文字（品牌名、功能词、卖点词），最多5个
-- product_features：包装特征，3-6个短语，包含颜色、瓶盖、形状、标签图案
-- key_selling_points：核心卖点，1-3个短语
-
-### 技能5：描述人物
-如果是人物图（type="person"），描述：
-- person_count：几个人
-- gender：性别（男/女/无法判断）
-- age_range：年龄段（儿童/青少年/青年/中年/老年/无法判断）
-- outfit_style：穿搭风格，例如"休闲日常"、"通勤商务"、"街头潮流"
-- upper_wear：上装（颜色+款式+材质），穿裙装不填
-- lower_wear：下装（颜色+款式+版型），穿裙装不填
-- dress_wear：裙装描述，穿上下装不填
-- outerwear：外套
-- shoes：鞋子
-- bag：包袋，没有填null
-- accessories：配饰（眼镜/帽子/项链/耳环/手表/手链/围巾/腰带等），没有填空数组
-- hairstyle：发型
-- makeup：妆容，男生或看不出填null
-- expression：表情，例如"微笑看镜头"、"冷酷无表情"
-- pose：姿势动作，例如"身直立正对镜头"、"单手撩发"
-- body_type：身材，例如"纤细苗条"、"高挑身材"、"丰满匀称"
-- portrait_prompt：80-150字详细描述人物形象（后面用来AI生成肖像图），要写清年龄段、穿搭完整细节、发型发色、妆容、表情、姿势、场景、光线、风格感觉，语言要有画面感
-
-### 技能6：描述风景
-如果是风景图（type="scene"），描述：
-- scene_type：什么场景，例如"自然风景"、"城市街景"、"动物"、"美食"
-- main_subject：画面主体是什么
-- key_elements：关键元素，最多8个
-- environment_objects：周围环境物体，最多8个
-- atmosphere：整体氛围，例如"秋日慵懒氛围感"、"清新自然氧气感"
-- 有人物就描述人物特征
-
-## 限制
-- 只输出JSON，不要任何解释文字，不要markdown代码块包裹，不要写"好的""以下是分析结果"这种废话
-- 颜色写具体色调（亮红色/米白色/深蓝色/翠绿色），不写笼统词汇
-- 瓶身、包装、招牌上的文字尽量识别出来（品牌名、功能词、卖点词）
-- 多个商品、多个人物分开描述，不要合并
-- 看不出来、不确定的字段填null或空数组，布尔值填true/false，绝对不要瞎编
-- 确保JSON格式合法，所有大括号、中括号、引号正确闭合
-- 数组字段控制数量：colors最多5个，visible_text最多5条，visual_elements最多8个，accessories最多10个"""
-V7_USER = "请分析这张图片，按系统消息的JSON结构输出。"
-
-
-def _capability_table_exists(bind) -> bool:
-    return bool(bind.execute(text("SELECT to_regclass('public.ai_capability_configs')")).scalar())
-
-
-def upgrade() -> None:
-    bind = op.get_bind()
-    # 1. 停用旧的active image_analysis prompt（含v6）
-    bind.execute(
-        text(
-            "UPDATE viral_video_prompt_templates SET is_active = FALSE "
-            "WHERE prompt_type = 'image_analysis' AND is_active = TRUE"
-        )
-    )
-    # 2. 幂等插入v7（存在则更新并重新激活）
-    existing = bind.execute(
-        text("SELECT id FROM viral_video_prompt_templates " "WHERE prompt_type = 'image_analysis' AND version = 7")
-    ).fetchone()
-    if existing:
-        bind.execute(
-            text(
-                "UPDATE viral_video_prompt_templates SET is_active = TRUE, "
-                "system_prompt = :sys, user_prompt_template = :usr, "
-                "name = 'v7 精简结构化分析', updated_at = NOW() "
-                "WHERE prompt_type = 'image_analysis' AND version = 7"
-            ),
-            {"sys": V7_SYSTEM, "usr": V7_USER},
-        )
-    else:
-        bind.execute(
-            text(
-                "INSERT INTO viral_video_prompt_templates "
-                "(prompt_type, version, name, system_prompt, user_prompt_template, "
-                "is_active, created_at, updated_at) "
-                "VALUES ('image_analysis', 7, 'v7 精简结构化分析', "
-                ":sys, :usr, TRUE, NOW(), NOW())"
-            ),
-            {"sys": V7_SYSTEM, "usr": V7_USER},
-        )
-    # 3. capability max_tokens=3000、max_retries=3（表不存在则跳过）
-    if _capability_table_exists(bind):
-        bind.execute(
-            text(
-                "UPDATE ai_capability_configs SET max_tokens = 3000, "
-                "updated_at = NOW() "
-                "WHERE capability_key = 'image_analysis' AND "
-                "(max_tokens IS NULL OR max_tokens < 3000)"
-            )
-        )
-        bind.execute(
-            text(
-                "UPDATE ai_capability_configs SET max_retries = 3, updated_at = NOW() "
-                "WHERE capability_key = 'image_analysis' AND "
-                "(max_retries IS NULL OR max_retries < 3)"
-            )
-        )
-
-
-def downgrade() -> None:
-    bind = op.get_bind()
-    # 删除v7
-    bind.execute(
-        text("DELETE FROM viral_video_prompt_templates " "WHERE prompt_type = 'image_analysis' AND version = 7")
-    )
-    # 恢复v6为active
-    bind.execute(
-        text(
-            "UPDATE viral_video_prompt_templates SET is_active = TRUE "
-            "WHERE prompt_type = 'image_analysis' AND version = 6"
-        )
-    )
-    # tokens/retries回退
-    if _capability_table_exists(bind):
-        bind.execute(
-            text(
-                "UPDATE ai_capability_configs SET max_tokens = 1500, max_retries = 1, "
-                "updated_at = NOW() WHERE capability_key = 'image_analysis'"
-            )
-        )

alembic/versions/104_v8_image_analysis_and_storyboard_v3.py

@@ -1,242 +0,0 @@
-# -*- coding: utf-8 -*-
-"""image_analysis v8 prompt + storyboard v3 prompt - 用户端展示格式 markdown 控制
-
-Revision ID: 104_v8_display_markdown
-Revises: 103_v7_prompt_and_tokens_3000
-Create Date: 2026-10-07
-
-变更：
-1. image_analysis v8: 在 v7 基础上 system_prompt 末尾追加「## 用户端展示格式」章节，
-   要求 VLM 在每张图的 JSON 里输出 summary_markdown 字段（markdown 格式的图片描述），
-   v8 设 is_active=true，v7 设 is_active=false。
-2. storyboard v3: 在 v2 基础上 system_prompt 追加要求 LLM 在 copy_result 中
-   输出 copy_display_markdown 字段（markdown 格式的完整文案展示），
-   v3 设 is_active=true，v2 设 is_active=false。
-"""
-
-from sqlalchemy import text
-
-from alembic import op
-
-revision = "104_v8_display_markdown"
-down_revision = "103_v7_prompt_and_tokens_3000"
-branch_labels = None
-depends_on = None
-
-# ── v8 追加的 system prompt 内容 ──────────────────────────────────────
-V8_SYSTEM_APPEND = """
-
-## 用户端展示格式
-
-对于每张分析的图片，在 JSON 中额外输出一个 **summary_markdown** 字段，用 markdown 格式写出给用户看的图片描述。
-
-格式要求（根据图片类型自适应）：
-
-**商品图（type=product）**示例：
-### 商品名称
-**品牌**：品牌名 | **类目**：服饰鞋包/美妆/数码/...
-**核心特征**
-- 特征1：描述
-- 特征2：描述
-**外观**：颜色+材质+设计描述
-**包装**：包装类型描述
-**文字信息**：包装上看到的文字
-
-**门店场景图（type=store）**示例：
-### 门店名称/类型
-**类型**：奶茶店/便利店/养生馆/...
-**品牌标识**：招牌文字描述
-**环境氛围**：店内整体感觉
-**陈列亮点**
-- 亮点1
-- 亮点2
-**氛围**：亲民/专业/时尚/...
-
-**人物图（type=person）**示例：
-### 人物描述
-**形象**：年龄段 + 风格
-**穿搭**
-- 上装：颜色+款式
-- 下装：颜色+款式
-- 配饰：...
-**气质**：表情+姿势+整体感觉
-
-**风景/场景图（type=scene）**示例：
-### 场景名称
-**类型**：自然风景/城市街景/动物/美食
-**主体**：画面主要元素
-**氛围**：整体感觉描述
-
-要求：
-- 内容真实具体，从实际图片分析得出
-- 用 markdown 语法：**加粗**、列表、标题
-- 控制在 100-200 字
-- 不要编造图片中没有的信息
-"""
-
-# ── storyboard v3 追加的 system prompt 内容 ──────────────────────────
-V3_STORYBOARD_APPEND = """
-
-## 用户端展示格式
-
-在输出分镜脚本的同时，在顶层输出一个 **copy_display_markdown** 字段（用 XML 标签 <copy_display_markdown> 包裹），用 markdown 格式写出完整文案展示。
-
-格式示例：
-# 标题/主题
-
-## 整体概要
-一句话描述视频内容
-
-## 分镜预览
-
-### 镜头1（0-3秒）
-**景别**：近景俯拍，缓慢推镜
-**画面**：场景描述
-**台词**：口播文本
-**动作**：人物动作描述
-
-### 镜头2（3-9秒）
-...
-
-## 完整口播
-完整口播文案文本
-
-要求：
-- 把所有分镜按时间顺序整理成易读的格式
-- 用 markdown 语法组织，**加粗**标签、##二级标题、列表等
-- 控制在 300-500 字
-- 让用户一眼看懂视频会拍成什么样
-"""
-
-
-def upgrade() -> None:
-    bind = op.get_bind()
-
-    # ── 1. image_analysis v8 ──────────────────────────────────────────
-    # 停用所有 active image_analysis prompt
-    bind.execute(
-        text(
-            "UPDATE viral_video_prompt_templates SET is_active = FALSE "
-            "WHERE prompt_type = 'image_analysis' AND is_active = TRUE"
-        )
-    )
-
-    # 读取 v7 的 prompt 内容作为基础
-    v7_row = bind.execute(
-        text(
-            "SELECT system_prompt, user_prompt_template, COALESCE(example_output, '') "
-            "FROM viral_video_prompt_templates "
-            "WHERE prompt_type = 'image_analysis' "
-            "ORDER BY version DESC LIMIT 1"
-        )
-    ).fetchone()
-
-    if v7_row:
-        v7_system = v7_row[0] or ""
-        v8_system = v7_system + V8_SYSTEM_APPEND
-        v8_user = v7_row[1] or "{image_url}"
-        v8_example = v7_row[2] or ""
-
-        # 幂等：已有 v8 则更新，否则插入
-        existing_v8 = bind.execute(
-            text("SELECT id FROM viral_video_prompt_templates " "WHERE prompt_type = 'image_analysis' AND version = 8")
-        ).fetchone()
-        if existing_v8:
-            bind.execute(
-                text(
-                    "UPDATE viral_video_prompt_templates SET is_active = TRUE, "
-                    "system_prompt = :sys, user_prompt_template = :usr, "
-                    "example_output = :ex, name = 'v8 用户端展示格式', "
-                    "updated_at = NOW() "
-                    "WHERE prompt_type = 'image_analysis' AND version = 8"
-                ),
-                {"sys": v8_system, "usr": v8_user, "ex": v8_example},
-            )
-        else:
-            bind.execute(
-                text(
-                    "INSERT INTO viral_video_prompt_templates "
-                    "(prompt_type, version, name, system_prompt, user_prompt_template, "
-                    "example_output, is_active, created_at, updated_at) "
-                    "VALUES ('image_analysis', 8, 'v8 用户端展示格式', "
-                    ":sys, :usr, :ex, TRUE, NOW(), NOW())"
-                ),
-                {"sys": v8_system, "usr": v8_user, "ex": v8_example},
-            )
-
-    # ── 2. storyboard v3 ─────────────────────────────────────────────
-    # 停用所有 active storyboard prompt
-    bind.execute(
-        text(
-            "UPDATE viral_video_prompt_templates SET is_active = FALSE "
-            "WHERE prompt_type = 'storyboard' AND is_active = TRUE"
-        )
-    )
-
-    # 读取当前 storyboard prompt
-    sb_row = bind.execute(
-        text(
-            "SELECT system_prompt, user_prompt_template, COALESCE(example_output, '') "
-            "FROM viral_video_prompt_templates "
-            "WHERE prompt_type = 'storyboard' "
-            "ORDER BY version DESC LIMIT 1"
-        )
-    ).fetchone()
-
-    if sb_row:
-        sb_system = sb_row[0] or ""
-        v3_system = sb_system + V3_STORYBOARD_APPEND
-        v3_user = sb_row[1] or ""
-        v3_example = sb_row[2] or ""
-
-        existing_v3 = bind.execute(
-            text("SELECT id FROM viral_video_prompt_templates " "WHERE prompt_type = 'storyboard' AND version = 3")
-        ).fetchone()
-        if existing_v3:
-            bind.execute(
-                text(
-                    "UPDATE viral_video_prompt_templates SET is_active = TRUE, "
-                    "system_prompt = :sys, user_prompt_template = :usr, "
-                    "example_output = :ex, name = 'v3 用户端展示格式', "
-                    "updated_at = NOW() "
-                    "WHERE prompt_type = 'storyboard' AND version = 3"
-                ),
-                {"sys": v3_system, "usr": v3_user, "ex": v3_example},
-            )
-        else:
-            bind.execute(
-                text(
-                    "INSERT INTO viral_video_prompt_templates "
-                    "(prompt_type, version, name, system_prompt, user_prompt_template, "
-                    "example_output, is_active, created_at, updated_at) "
-                    "VALUES ('storyboard', 3, 'v3 用户端展示格式', "
-                    ":sys, :usr, :ex, TRUE, NOW(), NOW())"
-                ),
-                {"sys": v3_system, "usr": v3_user, "ex": v3_example},
-            )
-
-
-def downgrade() -> None:
-    bind = op.get_bind()
-
-    # 删除 v8
-    bind.execute(
-        text("DELETE FROM viral_video_prompt_templates " "WHERE prompt_type = 'image_analysis' AND version = 8")
-    )
-    # 恢复 v7 active
-    bind.execute(
-        text(
-            "UPDATE viral_video_prompt_templates SET is_active = TRUE, updated_at = NOW() "
-            "WHERE prompt_type = 'image_analysis' AND version = 7"
-        )
-    )
-
-    # 删除 v3
-    bind.execute(text("DELETE FROM viral_video_prompt_templates " "WHERE prompt_type = 'storyboard' AND version = 3"))
-    # 恢复 storyboard v2 active
-    bind.execute(
-        text(
-            "UPDATE viral_video_prompt_templates SET is_active = TRUE, updated_at = NOW() "
-            "WHERE prompt_type = 'storyboard' AND version = 2"
-        )
-    )

alembic/versions/105_narration_first.py

@@ -1,116 +0,0 @@
-# -*- coding: utf-8 -*-
-"""image_analysis v8 + storyboard v3 叙述优先重写版（架构大简化）
-
-Revision ID: 105_narration_first
-Revises: 104_v8_display_markdown
-Create Date: 2026-10-07
-
-变更：
-1. image_analysis v8：用「叙述优先」版整体替换 104 的 append 版——VLM 主交付物是
-   自然叙述 summary_markdown，结构化字段仅保留 type/name/brand/has_person，
-   顶层 products 改名 images；v8 active，其余 image_analysis 全部 deactivate。
-2. storyboard v3：整体替换为风格重写版（口播口语化、画面有画面感、
-   copy_display_markdown 流畅叙述）；v3 active，其余 storyboard deactivate。
-3. intent_parsing 类型模板全部 deactivate（意图解析步骤已删除）。
-模板内容直接取自 packages.application.viral_video.prompts.DEFAULT_TEMPLATES，
-保证代码默认值与 DB seed 完全一致。
-"""
-
-from sqlalchemy import text
-
-from alembic import op
-from packages.application.viral_video.prompts import DEFAULT_TEMPLATES
-
-revision = "105_narration_first"
-down_revision = "104_v8_display_markdown"
-branch_labels = None
-depends_on = None
-
-
-def _tpl(prompt_type: str, version: int) -> dict:
-    for t in DEFAULT_TEMPLATES:
-        if t["prompt_type"] == prompt_type and t["version"] == version:
-            return t
-    raise RuntimeError("default template missing: %s v%s" % (prompt_type, version))
-
-
-def _upsert(bind, t: dict) -> None:
-    existing = bind.execute(
-        text("SELECT id FROM viral_video_prompt_templates " "WHERE prompt_type = :pt AND version = :ver"),
-        {"pt": t["prompt_type"], "ver": t["version"]},
-    ).fetchone()
-    params = {
-        "pt": t["prompt_type"],
-        "ver": t["version"],
-        "name": t["name"],
-        "sys": t["system_prompt"],
-        "usr": t["user_prompt_template"],
-        "ex": t.get("example_output", "") or "",
-    }
-    if existing:
-        bind.execute(
-            text(
-                "UPDATE viral_video_prompt_templates SET name = :name, "
-                "system_prompt = :sys, user_prompt_template = :usr, "
-                "example_output = :ex, is_active = TRUE, updated_at = NOW() "
-                "WHERE prompt_type = :pt AND version = :ver"
-            ),
-            params,
-        )
-    else:
-        bind.execute(
-            text(
-                "INSERT INTO viral_video_prompt_templates "
-                "(prompt_type, version, name, system_prompt, user_prompt_template, "
-                "example_output, is_active, created_at, updated_at) "
-                "VALUES (:pt, :ver, :name, :sys, :usr, :ex, TRUE, NOW(), NOW())"
-            ),
-            params,
-        )
-
-
-def upgrade() -> None:
-    bind = op.get_bind()
-
-    # 1. image_analysis：停用全部后写入叙述优先 v8
-    bind.execute(
-        text("UPDATE viral_video_prompt_templates SET is_active = FALSE " "WHERE prompt_type = 'image_analysis'")
-    )
-    _upsert(bind, _tpl("image_analysis", 8))
-
-    # 2. storyboard：停用全部后写入重写版 v3
-    bind.execute(text("UPDATE viral_video_prompt_templates SET is_active = FALSE " "WHERE prompt_type = 'storyboard'"))
-    _upsert(bind, _tpl("storyboard", 3))
-
-    # 3. intent_parsing 已废弃：全部停用
-    bind.execute(
-        text("UPDATE viral_video_prompt_templates SET is_active = FALSE " "WHERE prompt_type = 'intent_parsing'")
-    )
-
-    # 4. review 模板确保 active
-    bind.execute(text("UPDATE viral_video_prompt_templates SET is_active = TRUE " "WHERE prompt_type = 'review'"))
-
-
-def downgrade() -> None:
-    bind = op.get_bind()
-    # 恢复 104 的 v8/v3 无法重建（内容已替换），仅把版本 active 状态回退：
-    # 停用新版，尝试恢复 v7 / v2
-    bind.execute(
-        text(
-            "UPDATE viral_video_prompt_templates SET is_active = FALSE "
-            "WHERE prompt_type IN ('image_analysis','storyboard') "
-            "AND version IN (8, 3)"
-        )
-    )
-    bind.execute(
-        text(
-            "UPDATE viral_video_prompt_templates SET is_active = TRUE "
-            "WHERE prompt_type = 'image_analysis' AND version = 7"
-        )
-    )
-    bind.execute(
-        text(
-            "UPDATE viral_video_prompt_templates SET is_active = TRUE "
-            "WHERE prompt_type = 'storyboard' AND version = 2"
-        )
-    )

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/api/routes/viral_video.py

@@ -146,7 +146,6 @@ def _to_response(job) -> ViralVideoJobResponse:
         video_model=getattr(job, "video_model", "") or "",
         intent_result=job.intent_result,
         result_video_url=job.result_video_url,
-        pre_trusted_images=getattr(job, "pre_trusted_images", None) or None,
         video_resolution=getattr(job, "video_resolution", "720p") or "720p",
         credits_prepaid=float(getattr(job, "credits_prepaid", 0) or 0),
         credits_cost=float(getattr(job, "credits_cost", 0) or 0),
@@ -280,18 +279,11 @@ def generate_copy(
         raise HTTPException(status_code=404, detail="任务不存在")
     if job.user_id != authenticated_user.user.id:
         raise HTTPException(status_code=403, detail="无权操作此任务")
-    # 允许首次进入（IMAGE_ANALYZED/PENDING）、失败重试（FAILED）、文案重新生成（COPY_GENERATED/COMPLETED）
-    if job.status not in (
-        ViralVideoStatus.IMAGE_ANALYZED,
-        ViralVideoStatus.PENDING,
-        ViralVideoStatus.FAILED,
-        ViralVideoStatus.COPY_GENERATED,
-        ViralVideoStatus.COMPLETED,
-    ):
+    if job.status not in (ViralVideoStatus.IMAGE_ANALYZED, ViralVideoStatus.PENDING, ViralVideoStatus.FAILED):
         raise HTTPException(status_code=409, detail=f"任务当前状态 {job.status} 不能生成文案")
 
-    # 失败重试 / 重新生成：retry_count 自增
-    if job.status in (ViralVideoStatus.FAILED, ViralVideoStatus.COPY_GENERATED, ViralVideoStatus.COMPLETED):
+    # 允许失败任务重试：重置
+    if job.status == ViralVideoStatus.FAILED:
         job.retry_count += 1
         job.error_msg = ""
 
@@ -348,9 +340,6 @@ def confirm_copy(
         raise HTTPException(status_code=403, detail="无权操作此任务")
     if job.status != ViralVideoStatus.COPY_GENERATED:
         raise HTTPException(status_code=409, detail=f"任务当前状态 {job.status} 不能确认文案（需 copy_generated）")
-    # #2218: 额外校验 copy_result 完整性，防止孤儿/脏数据进入渲染
-    if not isinstance(job.copy_result, dict) or not job.copy_result:
-        raise HTTPException(status_code=409, detail="文案数据缺失，请先点击「生成文案」")
 
     # 积分预扣（已扣过/重试任务跳过）
     from app.config import settings as _settings

apps/api/app/schemas/viral_video.py

@@ -222,7 +222,6 @@ class ViralVideoJobResponse(BaseModel):
     video_model: str = ""
     intent_result: dict | None = None
     result_video_url: str = ""
-    pre_trusted_images: list[str] | None = None
     video_resolution: str = "720p"
     credits_prepaid: float = 0.0
     credits_cost: float = 0.0

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,
@@ -223,47 +222,7 @@ class LipsyncService:
         if timings:
             job.sentence_timings = timings
 
-        # 4. 检查是否走 Ditto（蚂蚁数字人，#2076）：开关 + 配置完整
-        use_ditto = False
-        if self.settings.use_ditto_lipsync:
-            try:
-                from packages.application.ditto_service import get_ditto_client
-
-                ditto = get_ditto_client()
-                if ditto.is_configured:
-                    use_ditto = True
-                    logger.info("[lipsync] 优先走 Ditto 蚂蚁数字人: job_id=%s", job.id)
-                else:
-                    logger.info(
-                        "[lipsync] Ditto 开关已开但配置不完整（base_url=%s, template=%s），继续判断 GPU: job_id=%s",
-                        bool(ditto.base_url),
-                        bool(ditto.default_video_url),
-                        job.id,
-                    )
-            except Exception as exc:
-                logger.warning("[lipsync] Ditto 初始化失败，继续判断 GPU: job_id=%s err=%s", job.id, exc)
-
-        if use_ditto:
-            try:
-                # Ditto 使用预置人物模板视频，不用用户上传的 video_url；
-                # 但保留用户 video_url 以便失败回退到 GPU/MediaKit。
-                job.status = "processing"
-                job.mediakit_task_id = "ditto:submitted"
-                job.updated_at = datetime.now(UTC)
-                self.db.commit()
-                from app.tasks.lipsync_ditto import lipsync_ditto_process_async
-
-                lipsync_ditto_process_async.apply_async(args=(job.id, job.user_id))
-                logger.info("[lipsync] Ditto 任务已异步派发: job_id=%s", job.id)
-                return
-            except Exception as exc:
-                logger.warning("[lipsync] Ditto 派发失败，回退 GPU/MediaKit: job_id=%s err=%s", job.id, exc)
-                try:
-                    self.db.rollback()
-                except Exception:
-                    pass
-
-        # 5. 检查是否走 GPU 路径：开关打开 + 有可用 Worker
+        # 4. 检查是否走 GPU 路径：开关打开 + 有可用 Worker
         use_gpu = False
         if self.settings.use_gpu_lipsync:
             try:
@@ -409,8 +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",
@@ -458,121 +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(
@@ -624,35 +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(
@@ -669,8 +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()
@@ -866,8 +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
@@ -893,8 +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
@@ -912,8 +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:
@@ -1007,8 +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_ditto.py

@@ -1,318 +0,0 @@
-"""Ditto 蚂蚁数字人口型异步任务 — #2076.
-
-把 Ditto 同步 HTTP 调用（30-120s）从 API 请求移到 Celery 后台执行：
-1. 加载 LipsyncJob
-2. 调 DittoClient.generate_and_persist(video_url=默认模板, audio_url=job.audio_url, script=job.script_text)
-3. 成功：标记 completed，写入 output_video_url（Ditto 输出自带音频，无需二次混流/超分）
-4. 失败：回退 GPU MuseTalk → 再失败回退 MediaKit
-
-注意：
-- 保留 MuseTalk 代码不动；Ditto 优先，失败按原链路兜底
-- Ditto 使用预置的人物模板视频（settings.ditto_default_video_url），不用用户上传的 video_url
-- 不传 GFPGAN 超分，不需要 ffmpeg 音视频混流
-"""
-
-from __future__ import annotations
-
-import logging
-from datetime import UTC, datetime
-from typing import TYPE_CHECKING, Optional
-
-from celery import shared_task
-from sqlalchemy.orm import Session
-
-if TYPE_CHECKING:
-    from packages.adapters.sqlalchemy_impl.models import LipsyncJobModel
-
-logger = logging.getLogger(__name__)
-
-_DITTO_URL_TTL_SECONDS = 7 * 24 * 3600  # Ditto 结果 OSS URL 7 天有效
-
-
-def _get_db_session() -> Session:
-    try:
-        from worker_app.db import SessionLocal  # type: ignore
-    except ImportError:
-        from app.db import SessionLocal  # type: ignore
-    return SessionLocal()
-
-
-def _sign_media_url(url: str) -> str:
-    """对自家 OSS URL 签 7 天预签名。"""
-    if not url:
-        return url
-    try:
-        from urllib.parse import urlparse
-
-        from packages.shared.storage import get_shared_storage_service
-
-        storage = get_shared_storage_service()
-        public_base = getattr(storage, "public_url", "")
-        if not isinstance(public_base, str) or not public_base:
-            return url
-        own_host = urlparse(public_base).netloc.lower()
-        host = urlparse(url).netloc.lower()
-        if not own_host or host != own_host:
-            return url
-        return storage.get_download_url(url, expires_seconds=_DITTO_URL_TTL_SECONDS)
-    except Exception:
-        return url
-
-
-def _probe_video_duration(video_bytes: bytes) -> float:
-    """用 ffprobe 探测视频时长（秒）；失败返回 0。"""
-    try:
-        import os
-        import subprocess
-        import tempfile
-
-        with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp:
-            tmp.write(video_bytes)
-            tmp_path = tmp.name
-        try:
-            out = subprocess.check_output(
-                [
-                    "ffprobe",
-                    "-v",
-                    "error",
-                    "-show_entries",
-                    "format=duration",
-                    "-of",
-                    "default=noprint_wrappers=1:nokey=1",
-                    tmp_path,
-                ],
-                stderr=subprocess.STDOUT,
-                timeout=10,
-            )
-            return float(out.decode().strip() or 0)
-        finally:
-            os.unlink(tmp_path)
-    except Exception as exc:
-        logger.warning("[ditto_task] ffprobe 失败: %s", exc)
-        return 0.0
-
-
-def _refund_lip_sync(db: Session, job: "LipsyncJobModel") -> None:
-    """Ditto 失败/取消时全额退款（复用 lipsync_service 的退款逻辑）。"""
-    try:
-        from app.services.lipsync_service import LipsyncService
-
-        LipsyncService(db)._refund_lip_sync(job)
-    except Exception:
-        logger.exception("[ditto_task] lip_sync 退款异常 job_id=%s", job.id)
-
-
-def _settle_lip_sync(db: Session, job: "LipsyncJobModel", duration: float) -> None:
-    """Ditto 成功后按实际时长结算。"""
-    try:
-        from app.services.lipsync_service import LipsyncService
-
-        LipsyncService(db)._settle_lip_sync(job, duration)
-    except Exception:
-        logger.exception("[ditto_task] lip_sync 结算异常 job_id=%s（不阻塞）", job.id)
-
-
-def _fallback_to_gpu_then_mediakit(db: Session, job: "LipsyncJobModel") -> None:
-    """Ditto 失败后：优先回退 GPU MuseTalk，再回退 MediaKit 云端。
-
-    复用 lipsync_service 现有路径逻辑以保证兜底一致性。
-    """
-    # 先尝试走 GPU MuseTalk（若可用）
-    try:
-        from app.services.gpu_lipsync_service import GpuLipsyncService
-        from app.tasks.lipsync_gpu import lipsync_gpu_process_async
-
-        gpu_svc = GpuLipsyncService(db)
-        if gpu_svc.has_available_worker():
-            logger.info("[ditto_task] 回退 GPU MuseTalk: job_id=%s", job.id)
-            # 复用 lipsync_service._submit_to_gpu_create 逻辑
-            from app.services.lipsync_service import LipsyncService
-
-            svc = LipsyncService(db)
-            storage = _shared_storage()
-            persisted_audio = None
-            try:
-                persisted_audio = svc._persist_external_audio_for_gpu(job=job, storage=storage)
-            except Exception as exc:
-                logger.warning("[ditto_task] GPU 外部音频转存失败: %s", exc)
-            audio_url_for_task = persisted_audio or job.audio_url
-            gpu_task = gpu_svc.create_task(
-                video_url=job.video_url,
-                audio_url=audio_url_for_task,
-                lipsync_job_id=job.id,
-                user_id=job.user_id,
-            )
-            if gpu_task is not None:
-                job.mediakit_task_id = f"gpu:{gpu_task.id}"
-                job.status = "processing"
-                job.updated_at = datetime.now(UTC)
-                db.commit()
-                lipsync_gpu_process_async.apply_async(args=(job.id, job.user_id, gpu_task.id))
-                return
-            db.rollback()
-    except Exception as exc:
-        logger.warning("[ditto_task] GPU MuseTalk 回退失败，转 MediaKit: %s", exc)
-        try:
-            db.rollback()
-        except Exception:
-            pass
-
-    # 最后兜底：MediaKit 云端
-    try:
-        from app.services.mediakit_client import get_mediakit_client
-
-        client = get_mediakit_client()
-        video_url = _sign_media_url(job.video_url)
-        signed_audio_url = _sign_media_url(job.audio_url)
-        result = client.submit_lipsync(
-            video_url=video_url,
-            audio_url=signed_audio_url,
-            enable_video_loop=job.enable_video_loop,
-            client_token=job.id,
-        )
-        job.mediakit_task_id = result["task_id"]
-        job.status = "submitted"
-        job.submitted_at = datetime.now(UTC)
-        job.updated_at = datetime.now(UTC)
-        db.commit()
-        logger.info("[ditto_task] 已回退 MediaKit: job_id=%s task_id=%s", job.id, result["task_id"])
-    except Exception as exc:
-        job.status = "failed"
-        job.error_message = f"Ditto/GPU/MediaKit 均失败: {exc}"
-        job.error_code = "AllBackendsFailed"
-        job.updated_at = datetime.now(UTC)
-        db.commit()
-        logger.error("[ditto_task] 所有兜底均失败: job_id=%s err=%s", job.id, exc)
-
-
-def _shared_storage():
-    from packages.shared.storage import get_shared_storage_service
-
-    return get_shared_storage_service()
-
-
-@shared_task(
-    name="lipsync_ditto_process_async",
-    bind=True,
-    max_retries=0,
-    acks_late=True,
-    time_limit=600,
-    soft_time_limit=540,
-)
-def lipsync_ditto_process_async(self, job_id: str, user_id: str) -> None:
-    """异步调用 Ditto 生成口型视频。
-
-    Args:
-        job_id: LipsyncJob ID
-        user_id: 用户 ID
-    """
-    from packages.application.ditto_service import DittoError, get_ditto_client
-
-    db: Session = _get_db_session()
-    job: Optional[LipsyncJobModel] = None
-    try:
-        from packages.adapters.sqlalchemy_impl.models import LipsyncJobModel
-
-        job = db.query(LipsyncJobModel).filter_by(id=job_id, user_id=user_id).first()
-        if job is None:
-            logger.error("[ditto_task] job 不存在: job_id=%s", job_id)
-            return
-
-        if job.status != "processing":
-            logger.warning(
-                "[ditto_task] job 状态异常（非 processing），跳过: job_id=%s status=%s",
-                job_id,
-                job.status,
-            )
-            return
-
-        audio_url = job.audio_url or ""
-        script = job.script_text or ""
-        if not audio_url:
-            raise DittoError("job.audio_url 为空，无法调用 Ditto", code="InvalidParam")
-
-        logger.info(
-            "[ditto_task] 开始 Ditto 生成: job_id=%s audio=%s script_len=%d",
-            job_id,
-            audio_url[:100],
-            len(script),
-        )
-        client = get_ditto_client()
-        result = client.generate_and_persist(
-            job_id=job_id,
-            user_id=user_id,
-            audio_url=audio_url,
-            script=script,
-            # video_url 不传则用默认模板
-        )
-
-        # Ditto 返回的 MP4 自带音频，直接标记完成
-        job.output_video_url = result.video_url
-        # 探测时长（用于计费）
-        duration = _probe_video_duration(result.video_bytes)
-        if duration <= 0:
-            # 兜底：按音频时长估算（1秒≈1秒）
-            try:
-                from packages.domain.sentence_timings import probe_audio_duration
-                from packages.shared.url_security import safe_download_bytes
-
-                audio_data = safe_download_bytes(
-                    audio_url, allowed_mime_types=("audio/mpeg", "audio/wav", "audio/x-wav"), timeout=30
-                )
-                duration = probe_audio_duration(audio_data)
-            except Exception:
-                duration = 0.0
-        job.output_duration = duration
-        job.status = "completed"
-        job.completed_at = datetime.now(UTC)
-        job.updated_at = datetime.now(UTC)
-        db.commit()
-        logger.info(
-            "[ditto_task] Ditto 完成: job_id=%s url=%s duration=%.2fs rtf=%.2f frames=%d",
-            job_id,
-            result.video_url[:100],
-            duration,
-            result.rtf,
-            result.frames,
-        )
-        _settle_lip_sync(db, job, duration)
-
-    except DittoError as exc:
-        logger.error("[ditto_task] Ditto 失败，回退: job_id=%s code=%s err=%s", job_id, exc.code, exc)
-        if job is not None:
-            try:
-                db.rollback()
-                job = db.query(type(job)).filter_by(id=job_id).first() if hasattr(job, "id") else job
-                # 回退 GPU/MediaKit
-                _fallback_to_gpu_then_mediakit(db, job)
-            except Exception as fallback_exc:
-                logger.exception("[ditto_task] 回退也失败 job_id=%s err=%s", job_id, fallback_exc)
-                try:
-                    if job:
-                        job.status = "failed"
-                        job.error_message = f"Ditto 失败且回退异常: {exc}; fallback: {fallback_exc}"
-                        job.error_code = "FallbackError"
-                        job.updated_at = datetime.now(UTC)
-                        db.commit()
-                except Exception:
-                    pass
-    except Exception as exc:
-        logger.exception("[ditto_task] 未预期异常: job_id=%s err=%s", job_id, exc)
-        if job is not None:
-            try:
-                db.rollback()
-                job = db.query(type(job)).filter_by(id=job_id).first()
-                _fallback_to_gpu_then_mediakit(db, job)
-            except Exception as fallback_exc:
-                logger.exception("[ditto_task] 回退也失败 job_id=%s err=%s", job_id, fallback_exc)
-                try:
-                    if job:
-                        job.status = "failed"
-                        job.error_message = f"Ditto 异常: {exc}"
-                        job.error_code = "DittoAsyncError"
-                        job.updated_at = datetime.now(UTC)
-                        db.commit()
-                except Exception:
-                    pass
-    finally:
-        db.close()

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/api/app/tasks/lipsync_tts.py

@@ -261,33 +261,7 @@ def tts_synthesize_and_submit(
                 "[lipsync_tts] 句子时间戳计算失败（不影响主流程）: job_id=%s err=%s", job_id, _st_err, exc_info=True
             )
 
-        # 3. 优先走 Ditto（#2076）：开关打开且配置完整时，派发 Ditto 异步任务，不再走 MediaKit
-        ditto_dispatched = False
-        try:
-            from packages.config import get_api_settings as _get_settings
-
-            _settings = _get_settings()
-            if _settings.use_ditto_lipsync and _settings.ditto_api_base_url and _settings.ditto_default_video_url:
-                from app.tasks.lipsync_ditto import lipsync_ditto_process_async
-
-                job.status = "processing"
-                job.mediakit_task_id = "ditto:tts-submitted"
-                job.updated_at = datetime.now(UTC)
-                db.commit()
-                lipsync_ditto_process_async.apply_async(args=(job_id, user_id))
-                logger.info("[lipsync_tts] TTS 完成，已派发 Ditto 任务: job_id=%s", job_id)
-                ditto_dispatched = True
-        except Exception as _ditto_err:
-            logger.warning("[lipsync_tts] Ditto 派发失败，回退 MediaKit: job_id=%s err=%s", job_id, _ditto_err)
-            try:
-                db.rollback()
-            except Exception:
-                pass
-
-        if ditto_dispatched:
-            return
-
-        # 4. 签名 URL 并提交到 MediaKit（复用模块内 _sign_media_url，避免对 LipsyncService 的耦合）
+        # 3. 签名 URL 并提交到 MediaKit（复用模块内 _sign_media_url，避免对 LipsyncService 的耦合）
         audio_url = _sign_media_url(job.audio_url)
         video_url = _sign_media_url(job.video_url)
 

apps/web/package-lock.json

@@ -14,7 +14,6 @@
         "axios": "^1.7.2",
         "classnames": "^2.5.1",
         "dayjs": "^1.11.23",
-        "marked": "^12.0.2",
         "mp4box": "^2.4.1",
         "react": "^18.3.1",
         "react-dom": "^18.3.1",
@@ -4503,17 +4502,6 @@
         "url": "https://github.com/sponsors/sindresorhus"
       }
     },
-    "node_modules/marked": {
-      "version": "12.0.2",
-      "resolved": "https://registry.npmmirror.com/marked/-/marked-12.0.2.tgz",
-      "integrity": "sha512-qXUm7e/YKFoqFPYPa3Ukg9xlI5cyAtGmyEIzMfW//m6kXwCy2Ps9DYf5ioijFKQ8qyuscrHoY04iJGctu2Kg0Q==",
-      "bin": {
-        "marked": "bin/marked.js"
-      },
-      "engines": {
-        "node": ">= 18"
-      }
-    },
     "node_modules/math-intrinsics": {
       "version": "1.1.0",
       "resolved": "https://registry.npmjs.org/math-intrinsics/-/math-intrinsics-1.1.0.tgz",

apps/web/package.json

@@ -25,7 +25,6 @@
     "axios": "^1.7.2",
     "classnames": "^2.5.1",
     "dayjs": "^1.11.23",
-    "marked": "^12.0.2",
     "mp4box": "^2.4.1",
     "react": "^18.3.1",
     "react-dom": "^18.3.1",

apps/web/src/api/viral-video/types.ts

@@ -65,23 +65,27 @@ export function isAnalysisStage(stage: ViralVideoStage | undefined): boolean {
   return isImageAnalysisStage(stage) || isCopyStage(stage)
 }
 
-/** 单张图片 VLM 识别结果（v8 叙述优先，仅保留最少结构化字段） */
+/** 单张图片 VLM 识别出的商品信息 */
 export interface ImageProductAnalysis {
-  /** store / product / person / scene */
-  type?: string
   name?: string
-  brand?: string
-  has_person?: boolean
-  /** v8: 用户端展示用的叙述 markdown（由提示词控制排版） */
-  summary_markdown?: string
-  /** 标题行兼容字段 */
   category?: string
+  brand?: string
+  colors?: string[]
+  material_or_texture?: string
+  key_features?: string[]
+  visual_style?: string
+  scene?: string
+  target_audience_hint?: string
+  text_on_image?: string
+  /** 旧字段兼容 */
+  spec?: string
+  features?: string[] | string
+  label_text?: string
+  selling_points?: string
+  image_index?: number
 }
 
 export interface ImageAnalysisResult {
-  /** v8 字段 */
-  images?: ImageProductAnalysis[]
-  /** 老数据兼容 */
   products?: ImageProductAnalysis[]
 }
 
@@ -128,8 +132,6 @@ export interface CopyResult {
   /** 向后兼容：= voiceover_script */
   suggested_copy?: string
   title?: string
-  /** v3 storyboard: 用户端展示用的 markdown 文案（由提示词控制排版） */
-  copy_display_markdown?: string
   /** v1.5 旧字段兼容（老数据降级时可能出现） */
   scenes?: Array<{ shot: string; narration: string; duration?: number }>
 }
@@ -174,7 +176,7 @@ export interface ViralVideoJob {
   fusion_level?: FusionLevel
   voice_id?: string
   voice_mode?: "global" | "per_video"
-  voice_source?: "preset" | "library" | "clone" | "upload" | "my_voice"
+  voice_source?: "preset" | "library" | "clone" | "upload"
   bgm_preference?: string
   intent_result?: IntentResult
   intent_text?: string
@@ -210,7 +212,7 @@ export interface GenerateViralVideoRequest {
   user_copy_text?: string
   fusion_level?: FusionLevel
   voice_id?: string
-  voice_source?: "preset" | "library" | "clone" | "upload" | "my_voice"
+  voice_source?: "preset" | "library" | "clone" | "upload"
   bgm_preference?: string
   industry?: string
   target_customer?: string
@@ -242,7 +244,7 @@ export interface AnalyzeImagesRequest {
   /** TTS 音色 ID（STEP1 已选音色时传） */
   voice_id?: string
   /** 音色来源：preset | library | clone | upload */
-  voice_source?: "preset" | "library" | "clone" | "upload" | "my_voice"
+  voice_source?: "preset" | "library" | "clone" | "upload"
   /** Seedance 视频比例：9:16 | 16:9 | 1:1 */
   video_ratio?: string
   /** Seedance 模型 ID（空则使用服务端默认） */
@@ -271,7 +273,7 @@ export interface GenerateCopyRequest {
   /** TTS 音色 ID（优先级高于 persona_id） */
   voice_id?: string
   /** 音色来源：preset | library | clone | upload */
-  voice_source?: "preset" | "library" | "clone" | "upload" | "my_voice"
+  voice_source?: "preset" | "library" | "clone" | "upload"
   /** Seedance 视频比例（9:16/16:9/1:1 等） */
   video_ratio?: string
   /** Seedance 模型 ID（空则使用服务端默认） */

apps/web/src/api/voice-clone/types.ts

@@ -18,8 +18,6 @@ export interface VoiceClone {
   language: string
   gender: string
   error_message: string | null
-  /** CosyVoice 实际使用的音色 ID（status=ready 时由后端填充，用于 TTS 调用） */
-  voice_id?: string | null
   created_at: string
   updated_at: string
 }

apps/web/src/api/voice-clone/utils.ts

@@ -18,7 +18,6 @@ export const toVoiceClone = (profile: VoiceCloneProfile): VoiceClone => ({
   language: profile.language || "",
   gender: profile.gender || "",
   error_message: profile.error_message || null,
-  voice_id: profile.voice_id,
   created_at: profile.created_at,
   updated_at: profile.updated_at,
 })

apps/web/src/components/common/DurationWheelPicker/DurationWheelPicker.css

@@ -1,182 +0,0 @@
-/* DurationWheelPicker —— 弹层式滚轮选择器（样式与表单一致） */
-
-/* 触发按钮：外观复用 .vv-select 风格 */
-.dw-trigger {
-  display: flex;
-  align-items: center;
-  justify-content: space-between;
-  width: 100%;
-  height: 36px;
-  padding: 0 12px;
-  background: #fff;
-  border: 1px solid #e0e0e8;
-  border-radius: 8px;
-  font-size: 13px;
-  color: #1f2937;
-  cursor: pointer;
-  box-sizing: border-box;
-  transition: all 0.15s;
-  user-select: none;
-}
-.dw-trigger:hover {
-  border-color: #c0c0d0;
-}
-.dw-trigger-open,
-.dw-trigger:focus-within {
-  border-color: #7c3aed !important;
-  box-shadow: 0 0 0 2px rgba(124, 58, 237, 0.12);
-}
-.dw-trigger-disabled {
-  opacity: 0.5;
-  pointer-events: none;
-  cursor: not-allowed;
-}
-.dw-trigger-val {
-  flex: 1;
-  overflow: hidden;
-  text-overflow: ellipsis;
-  white-space: nowrap;
-}
-.dw-trigger-placeholder {
-  color: #9ca3af;
-}
-.dw-trigger-arrow {
-  font-size: 10px;
-  color: #9ca3af;
-  margin-left: 8px;
-  transition: transform 0.2s;
-}
-.dw-trigger-arrow-up {
-  transform: rotate(180deg);
-}
-
-/* 弹层容器 */
-.dw-popup {
-  padding: 8px;
-  min-width: 140px;
-}
-
-/* 滚轮 */
-.dw-picker {
-  position: relative;
-  width: 100%;
-  overflow: hidden;
-  border-radius: 8px;
-  background: #fafafe;
-  border: 1px solid #e5e7eb;
-}
-.dw-picker-list {
-  margin: 0;
-  padding: 0;
-  list-style: none;
-  height: 100%;
-  overflow-y: scroll;
-  scroll-snap-type: y mandatory;
-  -webkit-overflow-scrolling: touch;
-  scrollbar-width: none;
-}
-.dw-picker-list::-webkit-scrollbar {
-  display: none;
-}
-.dw-picker-item {
-  display: flex;
-  align-items: baseline;
-  justify-content: center;
-  gap: 3px;
-  scroll-snap-align: center;
-  cursor: pointer;
-  font-size: 15px;
-  color: #9ca3af;
-  font-weight: 400;
-  transition:
-    color 0.15s,
-    transform 0.15s,
-    font-weight 0.15s;
-}
-.dw-picker-item-val {
-  font-variant-numeric: tabular-nums;
-}
-.dw-picker-item-unit {
-  font-size: 13px;
-  color: inherit;
-}
-.dw-picker-item-active {
-  color: #7c3aed;
-  font-weight: 600;
-}
-.dw-picker-item-active .dw-picker-item-val {
-  font-size: 18px;
-}
-.dw-picker-item-active .dw-picker-item-unit {
-  font-size: 14px;
-}
-
-/* 中心选中条 */
-.dw-picker-mask {
-  position: absolute;
-  left: 6px;
-  right: 6px;
-  pointer-events: none;
-  background: #f5f0ff;
-  border-radius: 6px;
-  z-index: 1;
-}
-.dw-picker-mask::before,
-.dw-picker-mask::after {
-  content: "";
-  position: absolute;
-  left: 0;
-  right: 0;
-  height: 1px;
-  background: #d8c4ff;
-}
-.dw-picker-mask::before {
-  top: 0;
-}
-.dw-picker-mask::after {
-  bottom: 0;
-}
-
-/* 上下渐变 */
-.dw-picker-fade {
-  position: absolute;
-  left: 0;
-  right: 0;
-  height: 40%;
-  pointer-events: none;
-  z-index: 2;
-}
-.dw-picker-fade-top {
-  top: 0;
-  background: linear-gradient(to bottom, #fafafe 25%, rgba(250, 250, 254, 0));
-}
-.dw-picker-fade-bottom {
-  bottom: 0;
-  background: linear-gradient(to top, #fafafe 25%, rgba(250, 250, 254, 0));
-}
-
-/* 弹层按钮区 */
-.dw-popup-actions {
-  display: flex;
-  gap: 8px;
-  justify-content: flex-end;
-  margin-top: 8px;
-}
-.dw-popup-actions .ant-btn {
-  border-radius: 6px;
-}
-.dw-popup-actions .ant-btn-primary {
-  background: #7c3aed;
-}
-.dw-popup-actions .ant-btn-primary:hover {
-  background: #6d28d9 !important;
-}
-
-/* 覆盖 antd Popover 默认内边距 */
-.dw-popover .ant-popover-inner {
-  padding: 0 !important;
-  overflow: hidden;
-}
-.dw-popover .ant-popover-arrow {
-  display: none;
-}

apps/web/src/components/common/DurationWheelPicker/index.tsx

@@ -1,180 +0,0 @@
-/**
- * DurationWheelPicker —— 竖屏滚轮式时长选择器（弹层版）
- *
- * 设计：
- * - 外观是和其他表单 Select 一致的输入框（白色底+1px灰边+紫色focus ring）
- * - 点击输入框弹出 Popover，内部是滚轮 picker（原生 scroll-snap，零依赖）
- * - 滚轮样式：白底容器，选中行 #7c3aed 紫字加粗+浅紫背景条
- * - 支持触摸/鼠标滚轮/点击；松手吸附；底部"确认/取消"按钮
- * - 默认范围 15–30 秒，步长 1 秒
- */
-import React, { useEffect, useMemo, useRef, useState, useCallback } from "react"
-import { Popover, Button } from "antd"
-import { DownOutlined } from "@ant-design/icons"
-import "./DurationWheelPicker.css"
-
-export interface DurationWheelPickerProps {
-  value?: number
-  min?: number
-  max?: number
-  step?: number
-  unit?: string
-  onChange?: (value: number) => void
-  placeholder?: string
-  disabled?: boolean
-  /** 弹层宽度，默认 160px */
-  popupWidth?: number
-  /** 弹层内滚轮高度，默认 180px */
-  wheelHeight?: number
-}
-
-const ITEM_HEIGHT = 36
-
-const DurationWheelPicker: React.FC<DurationWheelPickerProps> = ({
-  value = 20,
-  min = 15,
-  max = 30,
-  step = 1,
-  unit = "秒",
-  onChange,
-  placeholder = "请选择时长",
-  disabled = false,
-  popupWidth = 160,
-  wheelHeight = 180,
-}) => {
-  const options = useMemo(() => {
-    const arr: number[] = []
-    for (let v = min; v <= max; v += step) arr.push(v)
-    return arr
-  }, [min, max, step])
-
-  const [open, setOpen] = useState(false)
-  // 弹层内暂存值，点确认才提交
-  const [draft, setDraft] = useState<number>(value)
-  const listRef = useRef<HTMLUListElement>(null)
-  const scrollTimerRef = useRef<ReturnType<typeof setTimeout> | null>(null)
-
-  useEffect(() => {
-    if (open) {
-      setDraft(value)
-      // 下一帧滚到当前值
-      requestAnimationFrame(() => scrollToValue(value, false))
-    }
-    // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, [open])
-
-  const scrollToValue = useCallback(
-    (v: number, smooth = true) => {
-      const list = listRef.current
-      if (!list) return
-      const idx = options.indexOf(v)
-      if (idx < 0) return
-      list.scrollTo({ top: idx * ITEM_HEIGHT, behavior: smooth ? "smooth" : "auto" })
-    },
-    [options],
-  )
-
-  const handleScroll = () => {
-    if (scrollTimerRef.current) clearTimeout(scrollTimerRef.current)
-    scrollTimerRef.current = setTimeout(() => {
-      const list = listRef.current
-      if (!list) return
-      const idx = Math.round(list.scrollTop / ITEM_HEIGHT)
-      const clamped = Math.max(0, Math.min(options.length - 1, idx))
-      const targetTop = clamped * ITEM_HEIGHT
-      if (Math.abs(list.scrollTop - targetTop) > 1) {
-        list.scrollTo({ top: targetTop, behavior: "smooth" })
-      }
-      setDraft(options[clamped])
-    }, 100)
-  }
-
-  const handleConfirm = () => {
-    onChange?.(draft)
-    setOpen(false)
-  }
-
-  const handleCancel = () => {
-    setOpen(false)
-  }
-
-  const handleItemClick = (v: number) => {
-    setDraft(v)
-    scrollToValue(v, true)
-  }
-
-  const maskTop = wheelHeight / 2 - ITEM_HEIGHT / 2
-
-  const wheel = (
-    <div className="dw-popup">
-      <div
-        className="dw-picker"
-        style={{ height: wheelHeight, width: popupWidth - 24 /* padding */ }}
-      >
-        <div className="dw-picker-mask" style={{ top: maskTop, height: ITEM_HEIGHT }} aria-hidden />
-        <div className="dw-picker-fade dw-picker-fade-top" aria-hidden />
-        <div className="dw-picker-fade dw-picker-fade-bottom" aria-hidden />
-        <ul
-          ref={listRef}
-          className="dw-picker-list"
-          onScroll={handleScroll}
-          style={{
-            paddingTop: wheelHeight / 2 - ITEM_HEIGHT / 2,
-            paddingBottom: wheelHeight / 2 - ITEM_HEIGHT / 2,
-          }}
-        >
-          {options.map((v) => {
-            const isActive = v === draft
-            return (
-              <li
-                key={v}
-                className={`dw-picker-item${isActive ? " dw-picker-item-active" : ""}`}
-                style={{ height: ITEM_HEIGHT, lineHeight: `${ITEM_HEIGHT}px` }}
-                onClick={() => handleItemClick(v)}
-                aria-selected={isActive}
-                role="option"
-              >
-                <span className="dw-picker-item-val">{v}</span>
-                <span className="dw-picker-item-unit">{unit}</span>
-              </li>
-            )
-          })}
-        </ul>
-      </div>
-      <div className="dw-popup-actions">
-        <Button size="small" onClick={handleCancel}>
-          取消
-        </Button>
-        <Button size="small" type="primary" onClick={handleConfirm}>
-          确认
-        </Button>
-      </div>
-    </div>
-  )
-
-  return (
-    <Popover
-      open={!disabled && open}
-      onOpenChange={(v) => setOpen(v)}
-      content={wheel}
-      trigger="click"
-      placement="bottomLeft"
-      overlayClassName="dw-popover"
-      overlayStyle={{ padding: 0 }}
-      overlayInnerStyle={{ padding: 0, borderRadius: 10 }}
-      destroyTooltipOnHide
-    >
-      <div
-        className={`dw-trigger${disabled ? " dw-trigger-disabled" : ""}${open ? " dw-trigger-open" : ""}`}
-        style={{ height: 36 }}
-      >
-        <span className={`dw-trigger-val${value != null ? "" : " dw-trigger-placeholder"}`}>
-          {value != null ? `${value}${unit}` : placeholder}
-        </span>
-        <DownOutlined className={`dw-trigger-arrow${open ? " dw-trigger-arrow-up" : ""}`} />
-      </div>
-    </Popover>
-  )
-}
-
-export default DurationWheelPicker

apps/web/src/config/features.ts

@@ -10,4 +10,4 @@
  *   功能流程不做积分预校验，直接走生成。
  * - true：展示完整积分系统 UI。
  */
-export const ENABLE_CREDIT_SYSTEM = true
+export const ENABLE_CREDIT_SYSTEM = false

apps/web/src/pages/viral-video/ViralVideoPage.css

@@ -1050,13 +1050,10 @@
   flex-direction: column;
   align-items: center;
   justify-content: center;
-  height: 360px;
   padding: 28px 16px;
   gap: 10px;
   background: #fff;
   border: 1px solid #e5e7eb;
-  border-radius: 10px;
-  margin-top: 8px;
 }
 .vv-copy-loading .vv-spinner {
   width: 28px;
@@ -1085,38 +1082,18 @@
 
 /* ── Storyboard (linear doc style) ── */
 .vv-storyboard {
-  display: flex;
-  flex-direction: column;
-  height: 360px;
-  padding: 10px 12px;
-  background: #fff;
-  border: 1px solid #e5e7eb;
-  border-radius: 10px;
-  margin-top: 8px;
-  overflow: hidden;
+  padding: 6px 2px;
+  background: transparent;
+  border: none;
 }
 
 .vv-sb-doc {
-  flex: 1 1 auto;
   display: flex;
   flex-direction: column;
   gap: 3px;
   color: #1f2937;
   font-size: 13px;
   line-height: 1.55;
-  overflow-y: auto;
-  padding-right: 4px;
-  margin-right: -4px;
-}
-.vv-sb-doc::-webkit-scrollbar {
-  width: 6px;
-}
-.vv-sb-doc::-webkit-scrollbar-thumb {
-  background: #d8c4ff;
-  border-radius: 3px;
-}
-.vv-sb-doc::-webkit-scrollbar-track {
-  background: transparent;
 }
 .vv-sb-h {
   margin: 6px 0 2px;
@@ -1406,14 +1383,12 @@
 /* 口播稿 —— 复用 vv-sb-field 样式，无额外需求 */
 
 .vv-sb-actions {
-  flex-shrink: 0;
   display: flex;
   align-items: center;
   gap: 8px;
   margin-top: 8px;
   padding-top: 8px;
   border-top: 1px solid #e5e7eb;
-  background: #fff;
 }
 .vv-sb-actions .vv-btn-ghost {
   padding: 6px 14px;
@@ -1859,213 +1834,3 @@
   color: #7c3aed;
   font-weight: 500;
 }
-
-/* ── v1.6 我的音色（默认主路径） ── */
-.vv-voice-section {
-  margin-top: 4px;
-  border: 1px solid #e5e7eb;
-  border-radius: 8px;
-  background: #fff;
-  padding: 10px;
-}
-.vv-voice-section-head {
-  display: flex;
-  align-items: center;
-  justify-content: space-between;
-  margin-bottom: 8px;
-}
-.vv-voice-section-title {
-  font-size: 13px;
-  font-weight: 600;
-  color: #1f2937;
-  display: inline-flex;
-  align-items: center;
-  gap: 6px;
-}
-.vv-voice-section-title .anticon {
-  color: #7c3aed;
-}
-.vv-link-btn-sm {
-  font-size: 12px;
-  padding: 2px 6px;
-  display: inline-flex;
-  align-items: center;
-  gap: 4px;
-}
-.vv-my-voice-list {
-  display: flex;
-  flex-direction: column;
-  gap: 4px;
-  max-height: 220px;
-  overflow-y: auto;
-}
-.vv-my-voice-item {
-  display: flex;
-  align-items: center;
-  gap: 10px;
-  padding: 8px 10px;
-  border-radius: 6px;
-  cursor: pointer;
-  transition: background 0.15s;
-}
-.vv-my-voice-item:hover {
-  background: #f5f0ff;
-}
-.vv-my-voice-item.selected {
-  background: #f5f0ff;
-}
-.vv-my-voice-item.selected .vv-voice-radio {
-  border-color: #7c3aed;
-  background: #7c3aed;
-  box-shadow: inset 0 0 0 2px #fff;
-}
-.vv-voice-empty {
-  padding: 16px 12px;
-  text-align: center;
-  color: #9ca3af;
-  font-size: 12px;
-  display: flex;
-  flex-direction: column;
-  align-items: center;
-  gap: 8px;
-}
-.vv-voice-empty-ic {
-  font-size: 28px;
-  color: #d1d5db;
-}
-.vv-voice-empty-text {
-  font-size: 12px;
-}
-.vv-voice-alt-row {
-  display: flex;
-  gap: 8px;
-  margin-top: 8px;
-}
-.vv-voice-alt-btn {
-  flex: 1;
-  display: inline-flex;
-  align-items: center;
-  justify-content: center;
-  gap: 6px;
-  padding: 8px 10px;
-  background: #fafafe;
-  border: 1px solid #e5e7eb;
-  border-radius: 6px;
-  color: #6b7280;
-  font-size: 12px;
-  cursor: pointer;
-  transition: all 0.15s;
-}
-.vv-voice-alt-btn:hover {
-  border-color: #7c3aed;
-  color: #7c3aed;
-  background: #f5f0ff;
-}
-.vv-voice-alt-btn.selected {
-  background: #f5f0ff;
-  border-color: #7c3aed;
-  color: #7c3aed;
-}
-.vv-voice-panel-actions {
-  display: flex;
-  gap: 12px;
-  margin-bottom: 6px;
-  padding-bottom: 6px;
-  border-bottom: 1px dashed #e5e7eb;
-}
-
-/* ─────────── markdown 渲染（提示词控制展示格式） ─────────── */
-.vv-recog-md {
-  padding: 4px 0;
-}
-.vv-copy-preview {
-  margin-bottom: 14px;
-  padding: 12px 14px;
-  background: linear-gradient(180deg, #faf7ff 0%, #f6f2ff 100%);
-  border: 1px solid #ece4fb;
-  border-radius: 10px;
-}
-.vv-copy-preview-h {
-  margin: 0 0 8px;
-  border-bottom: none;
-  padding-bottom: 0;
-}
-.vv-md-body {
-  font-size: 13px;
-  line-height: 1.7;
-  color: #374151;
-  word-break: break-word;
-}
-.vv-md-body h1,
-.vv-md-body h2,
-.vv-md-body h3,
-.vv-md-body h4 {
-  margin: 10px 0 6px;
-  font-weight: 600;
-  color: #1f2937;
-  line-height: 1.4;
-}
-.vv-md-body h1 {
-  font-size: 18px;
-}
-.vv-md-body h2 {
-  font-size: 16px;
-}
-.vv-md-body h3 {
-  font-size: 15px;
-}
-.vv-md-body h4 {
-  font-size: 14px;
-}
-.vv-md-body p {
-  margin: 6px 0;
-}
-.vv-md-body ul,
-.vv-md-body ol {
-  margin: 6px 0;
-  padding-left: 20px;
-}
-.vv-md-body li {
-  margin: 3px 0;
-}
-.vv-md-body strong {
-  color: #111827;
-  font-weight: 600;
-}
-.vv-md-body blockquote {
-  margin: 8px 0;
-  padding: 4px 12px;
-  border-left: 3px solid #7c3aed;
-  background: rgba(124, 58, 237, 0.05);
-  color: #4b5563;
-}
-.vv-md-body code {
-  padding: 1px 5px;
-  background: #f3f4f6;
-  border-radius: 4px;
-  font-size: 12px;
-  color: #be185d;
-}
-.vv-md-body a {
-  color: #7c3aed;
-  text-decoration: none;
-}
-.vv-md-body a:hover {
-  text-decoration: underline;
-}
-.vv-md-body table {
-  border-collapse: collapse;
-  margin: 8px 0;
-  width: 100%;
-}
-.vv-md-body th,
-.vv-md-body td {
-  border: 1px solid #e5e7eb;
-  padding: 6px 10px;
-  text-align: left;
-}
-.vv-md-body hr {
-  border: none;
-  border-top: 1px solid #e5e7eb;
-  margin: 12px 0;
-}

apps/web/src/pages/viral-video/ViralVideoPage.tsx

@@ -1,6 +1,5 @@
 import React, { useCallback, useEffect, useRef, useState } from "react"
 import axios from "axios"
-import { marked } from "marked"
 import {
   PlusOutlined,
   CloseOutlined,
@@ -24,13 +23,10 @@ import {
   CheckCircleFilled,
   EditOutlined,
   HistoryOutlined,
-  UserOutlined,
 } from "@ant-design/icons"
 import { Select, Input, message } from "antd"
 import { uploadAssetDirect, getAssetLibraries, getAssetsByKind, type AssetItem } from "@/api/assets"
 import { fetchPresetVoices } from "@/api/voices"
-import { getVoiceClones, getVoiceClonePreview } from "@/api/voice-clone"
-import type { VoiceClone } from "@/api/voice-clone"
 import {
   FUSION_LEVELS,
   STYLE_STRENGTHS,
@@ -98,15 +94,13 @@ type RefVideo = {
 } | null
 
 type RefAudio = {
-  source: "preset" | "library" | "upload" | "clone" | "my_voice"
+  source: "preset" | "library" | "upload" | "clone"
   id?: string
   name: string
   url?: string
-  /** 克隆音色的 CosyVoice voice_id（source=my_voice 时使用） */
-  ttsVoiceId?: string
 } | null
 
-type VoicePanel = "my_voices" | "preset" | "upload" | "library" | "record" | "douyin" | null
+type VoicePanel = "preset" | "upload" | "library" | "record" | "douyin" | null
 
 type TabTask = {
   id: string
@@ -151,16 +145,6 @@ type TabTask = {
   audioInst: HTMLAudioElement | null
 }
 
-/* ── marked 配置：禁用 mangle/headerIds，输出干净 HTML ── */
-marked.setOptions({ gfm: true, breaks: false })
-const renderMarkdown = (md: string): string => {
-  try {
-    return marked.parse(md ?? "", { async: false }) as string
-  } catch {
-    return (md ?? "").replace(/&/g, "&amp;").replace(/</g, "&lt;")
-  }
-}
-
 /* ─────────── 常量 ─────────── */
 
 const LANGUAGES = ["中文(普通话)", "粤语", "英语", "日语", "韩语"]
@@ -229,12 +213,12 @@ const PURPOSES = [
   "悬念短剧",
   "情绪短片",
 ]
+const DURATIONS = [15, 20, 30, 45, 60]
 const RATIOS = [
   { v: "9:16", label: "9:16 竖屏（抖音/视频号）" },
   { v: "16:9", label: "16:9 横屏（B站/YouTube）" },
   { v: "1:1", label: "1:1 方形（小红书）" },
 ]
-const DURATIONS = Array.from({ length: 16 }, (_, i) => 15 + i)
 /** 兜底模型列表（接口未返回时使用，字段与 ViralVideoModel 对齐；后端返回后自动覆盖） */
 const FALLBACK_VIDEO_MODELS: ViralVideoModel[] = [
   {
@@ -307,8 +291,6 @@ interface Storyboard {
   hard_constraints: string[]
   negative_prompts: string[]
   voiceover_script: string
-  /** v3: 用户端展示用 markdown 文案（由提示词控制排版） */
-  copy_display_markdown: string
 }
 
 /** 兼容旧 copy_result（final_copy/title/scenes）→ 新 Storyboard 结构 */
@@ -336,7 +318,6 @@ function copyResultToStoryboard(cr: CopyResult | null | undefined): Storyboard |
       hard_constraints: Array.isArray(cr.hard_constraints) ? cr.hard_constraints : [],
       negative_prompts: Array.isArray(cr.negative_prompts) ? cr.negative_prompts : [],
       voiceover_script: cr.voiceover_script || cr.final_copy || cr.suggested_copy || "",
-      copy_display_markdown: cr.copy_display_markdown || "",
     }
   }
   // 兜底：旧结构转简单分镜
@@ -372,7 +353,6 @@ function copyResultToStoryboard(cr: CopyResult | null | undefined): Storyboard |
     hard_constraints: [],
     negative_prompts: [],
     voiceover_script: finalCopy,
-    copy_display_markdown: cr.copy_display_markdown || "",
   }
 }
 
@@ -427,7 +407,6 @@ const MOCK_STORYBOARD: Storyboard = {
   negative_prompts: ["冷色调", "模糊", "变形", "水印文字", "卡通风格", "空无一人"],
   voiceover_script:
     "还在为餐桌选不到好桌子发愁？这张北美黑胡桃木餐桌，一家人坐下来吃饭刚刚好。全实木、无贴皮，纹理好看又耐刮。点小黄车，给家里添一张好桌子。",
-  copy_display_markdown: "",
 }
 
 const fmtSize = (bytes: number | undefined) => {
@@ -453,7 +432,7 @@ const emptyTask = (id: string, title: string): TabTask => ({
   language: "中文(普通话)",
   viralStructure: STRUCTURES[0],
   marketingPurpose: "",
-  duration: 20,
+  duration: 15,
   persona: "",
   videoRatio: "9:16",
   videoModel: "seedance-2.5",
@@ -506,9 +485,6 @@ const ViralVideoPage: React.FC = () => {
     { id: string; name: string; url: string; desc?: string }[]
   >([])
   const [presetVoices, setPresetVoices] = useState<PresetVoice[]>([])
-  /** 我的克隆音色（GET /voice-clones?status=ready） */
-  const [myVoices, setMyVoices] = useState<VoiceClone[]>([])
-  const [myVoicesLoading, setMyVoicesLoading] = useState(false)
   const [voicePickerOpen, setVoicePickerOpen] = useState(false)
   const [videoModels, setVideoModels] = useState<ViralVideoModel[]>(FALLBACK_VIDEO_MODELS)
   const [assetPicker, setAssetPicker] = useState<{
@@ -538,18 +514,8 @@ const ViralVideoPage: React.FC = () => {
     [activeId],
   )
 
-  /* ── 加载音色（我的克隆音色 + 上传的配音素材 + 预设音色） ── */
-  const reloadMyVoices = useCallback(() => {
-    setMyVoicesLoading(true)
-    getVoiceClones({ status: "ready", limit: 50 })
-      .then((list) => {
-        setMyVoices(list.filter((v) => v.status === "ready"))
-      })
-      .catch(() => setMyVoices([]))
-      .finally(() => setMyVoicesLoading(false))
-  }, [])
+  /* ── 加载音色 ── */
   useEffect(() => {
-    reloadMyVoices()
     getAssetsByKind("voice", { limit: 50 })
       .then((list) =>
         setVoiceAssets(
@@ -557,7 +523,7 @@ const ViralVideoPage: React.FC = () => {
             id: a.id,
             name: a.name,
             url: a.file_url || "",
-            desc: a.duration ? `${Math.round(a.duration)}s` : "配音文件",
+            desc: a.duration ? `${Math.round(a.duration)}s` : "我的音色",
           })),
         ),
       )
@@ -578,7 +544,7 @@ const ViralVideoPage: React.FC = () => {
         // 接口失败兜底：弹窗内 MOCK_VOICES 会生效（voice_id 与后端 CosyVoice 一致）
         setPresetVoices([])
       })
-  }, [reloadMyVoices])
+  }, [])
 
   /* ── 加载视频模型列表 ── */
   useEffect(() => {
@@ -948,36 +914,21 @@ const ViralVideoPage: React.FC = () => {
     [setTask, uploadFile],
   )
 
-  const onCloneSuccess = (
-    voice: VoiceClone | { id?: string; name?: string; voice_id?: string | null },
-  ) => {
+  const onCloneSuccess = (voice: { id?: string; name?: string }) => {
     setCloneModalOpen(false)
-    // 克隆成功后刷新我的音色列表
-    reloadMyVoices()
-    const vid = "voice_id" in voice ? voice.voice_id : undefined
     if (voice?.id) {
       setTask({
-        refAudio: {
-          source: "my_voice",
-          id: voice.id,
-          name: voice.name || "我录制的音色",
-          ttsVoiceId: vid || undefined,
-        },
+        refAudio: { source: "clone", id: voice.id, name: voice.name || "我录制的音色" },
         voicePanel: null,
       })
       getAssetsByKind("voice", { limit: 50 })
         .then((list) =>
           setVoiceAssets(
-            list.map((a) => ({
-              id: a.id,
-              name: a.name,
-              url: a.file_url || "",
-              desc: a.duration ? `${Math.round(a.duration)}s` : "配音文件",
-            })),
+            list.map((a) => ({ id: a.id, name: a.name, url: a.file_url || "", desc: "我的音色" })),
           ),
         )
         .catch(() => {})
-      message.success("音色克隆完成，已自动选中")
+      message.success("录音已完成，已自动选中")
     }
   }
 
@@ -1063,12 +1014,7 @@ const ViralVideoPage: React.FC = () => {
             : undefined,
         fusion_level: task.fusionLevel,
         style_strength: task.refVideo?.ossUrl ? task.styleStrength : undefined,
-        voice_id:
-          task.refAudio?.source === "preset"
-            ? task.refAudio.id
-            : task.refAudio?.source === "my_voice"
-              ? task.refAudio.ttsVoiceId || task.refAudio.id
-              : undefined,
+        voice_id: task.refAudio?.source === "preset" ? task.refAudio.id : undefined,
         voice_source: task.refAudio?.source || undefined,
         video_ratio: task.videoRatio,
         video_model: task.videoModel,
@@ -1112,12 +1058,7 @@ const ViralVideoPage: React.FC = () => {
           style_strength: task.refVideo?.ossUrl ? task.styleStrength : undefined,
           user_copy_text: task.storyboard?.voiceover_script || task.userCopy || undefined,
           fusion_level: task.fusionLevel,
-          voice_id:
-            task.refAudio?.source === "preset"
-              ? task.refAudio.id
-              : task.refAudio?.source === "my_voice"
-                ? task.refAudio.ttsVoiceId || task.refAudio.id
-                : undefined,
+          voice_id: task.refAudio?.id || undefined,
           voice_source: task.refAudio?.source,
           industry: task.industry || undefined,
           target_customer: task.targetCustomer || undefined,
@@ -1208,10 +1149,8 @@ const ViralVideoPage: React.FC = () => {
 
   /* ── 识别描述汇览渲染 ── */
   const renderRecognition = () => {
-    const images: ImageProductAnalysis[] =
-      (task.imageAnalysis?.images as ImageProductAnalysis[] | undefined) ||
-      (task.imageAnalysis?.products as ImageProductAnalysis[] | undefined) ||
-      []
+    const products: ImageProductAnalysis[] =
+      (task.imageAnalysis?.products as ImageProductAnalysis[] | undefined) || []
     if (task.uiStep === "step1_analyzing") {
       return (
         <div className="vv-recog">
@@ -1219,36 +1158,83 @@ const ViralVideoPage: React.FC = () => {
             <LoadingOutlined style={{ color: "#7c3aed", marginRight: 6 }} />
             识别描述汇览
           </div>
-          <div className="vv-muted">AI 正在识别画面…</div>
+          <div className="vv-muted">AI 正在识别商品特征…</div>
         </div>
       )
     }
-    if (images.length === 0) return null
+    if (products.length === 0) return null
+    const featureText = (f: string[] | string | undefined) => {
+      if (!f) return ""
+      if (Array.isArray(f)) return f.join("；")
+      return f
+    }
     return (
       <div className="vv-recog">
         <div className="vv-recog-title">
           <CheckCircleFilled style={{ color: "#10b981" }} />
           识别描述汇览
         </div>
-        {images.map((p, i) => {
-          const meta = [p.name || "未识别", p.brand, p.category].filter(Boolean)
-          return (
-            <div key={i} className="vv-recog-item vv-recog-md">
-              <div className="vv-recog-line">
-                <span className="vv-recog-k">图片{i + 1}：</span>
-                <span>{meta.join(" · ")}</span>
-              </div>
-              {p.summary_markdown ? (
-                <div
-                  className="vv-md-body"
-                  dangerouslySetInnerHTML={{ __html: renderMarkdown(p.summary_markdown) }}
-                />
-              ) : (
-                <div className="vv-muted">（暂无叙述描述）</div>
-              )}
+        {products.map((p, i) => (
+          <div key={i} className="vv-recog-item">
+            <div className="vv-recog-line">
+              <span className="vv-recog-k">图片{i + 1}：</span>
+              <span>
+                {p.name || "未识别"}
+                {p.spec && <span className="vv-recog-meta">（{p.spec}）</span>}
+                {p.brand && <span className="vv-recog-meta"> · {p.brand}</span>}
+                {p.category && <span className="vv-recog-meta"> · {p.category}</span>}
+              </span>
             </div>
-          )
-        })}
+            {featureText(p.key_features ?? p.features) && (
+              <div className="vv-recog-line">
+                <span className="vv-recog-k">核心特征：</span>
+                <span className="vv-recog-v">{featureText(p.key_features ?? p.features)}</span>
+              </div>
+            )}
+            {p.colors && p.colors.length > 0 && (
+              <div className="vv-recog-line">
+                <span className="vv-recog-k">主色调：</span>
+                <span className="vv-recog-v">{p.colors.join(" / ")}</span>
+              </div>
+            )}
+            {p.material_or_texture && (
+              <div className="vv-recog-line">
+                <span className="vv-recog-k">材质/纹理：</span>
+                <span className="vv-recog-v">{p.material_or_texture}</span>
+              </div>
+            )}
+            {p.visual_style && (
+              <div className="vv-recog-line">
+                <span className="vv-recog-k">视觉风格：</span>
+                <span className="vv-recog-v">{p.visual_style}</span>
+              </div>
+            )}
+            {p.scene && (
+              <div className="vv-recog-line">
+                <span className="vv-recog-k">场景：</span>
+                <span className="vv-recog-v">{p.scene}</span>
+              </div>
+            )}
+            {p.target_audience_hint && (
+              <div className="vv-recog-line">
+                <span className="vv-recog-k">目标人群：</span>
+                <span className="vv-recog-v">{p.target_audience_hint}</span>
+              </div>
+            )}
+            {(p.text_on_image || p.label_text) && (
+              <div className="vv-recog-line">
+                <span className="vv-recog-k">包装文字：</span>
+                <span className="vv-recog-v">{p.text_on_image || p.label_text}</span>
+              </div>
+            )}
+            {p.selling_points && (
+              <div className="vv-recog-line">
+                <span className="vv-recog-k">卖点：</span>
+                <span className="vv-recog-v">{p.selling_points}</span>
+              </div>
+            )}
+          </div>
+        ))}
       </div>
     )
   }
@@ -1387,19 +1373,6 @@ const ViralVideoPage: React.FC = () => {
     return (
       <div className="vv-copy-box vv-storyboard">
         <div className="vv-sb-doc">
-          {/* 文案预览（提示词控制排版，只读；编辑在下方分镜字段中进行） */}
-          {sb.copy_display_markdown && (
-            <div className="vv-copy-preview">
-              <h4 className="vv-sb-h vv-copy-preview-h">
-                <FileTextOutlined style={{ color: "#7c3aed", marginRight: 6 }} />
-                文案预览
-              </h4>
-              <div
-                className="vv-md-body"
-                dangerouslySetInnerHTML={{ __html: renderMarkdown(sb.copy_display_markdown) }}
-              />
-            </div>
-          )}
           {/* 视频总览 */}
           <h4 className="vv-sb-h">视频总览</h4>
           <p className="vv-sb-inline-row">
@@ -2060,119 +2033,43 @@ const ViralVideoPage: React.FC = () => {
                 )}
               </div>
 
-              {/* 配音选择（默认"我的音色"） */}
+              {/* 参考音频 2x2 */}
               <div className="vv-subblock">
                 <div className="vv-subblock-head">
-                  <span className="vv-subblock-title">AI 配音</span>
+                  <span className="vv-subblock-title">参考音频</span>
                   <span className="vv-opt-tag">选填</span>
-                  <span className="vv-subblock-hint">
-                    默认使用我的克隆音色，也可选择内置音色或上传配音
-                  </span>
+                  <span className="vv-subblock-hint">最大 10MB · 最长 30 秒</span>
                 </div>
-
-                {/* 我的音色（主路径） */}
-                <div className="vv-voice-section">
-                  <div className="vv-voice-section-head">
-                    <span className="vv-voice-section-title">
-                      <UserOutlined /> 我的音色
-                    </span>
-                    <button
-                      className="vv-link-btn vv-link-btn-sm"
-                      onClick={() => setCloneModalOpen(true)}
-                    >
-                      <PlusOutlined /> 录制新音色
-                    </button>
-                  </div>
-                  {myVoicesLoading ? (
-                    <div className="vv-muted vv-voice-empty">加载中…</div>
-                  ) : myVoices.length === 0 ? (
-                    <div className="vv-voice-empty">
-                      <AudioMutedOutlined className="vv-voice-empty-ic" />
-                      <div className="vv-voice-empty-text">还没有克隆音色</div>
-                      <button
-                        className="vv-btn vv-btn-primary vv-btn-sm"
-                        onClick={() => setCloneModalOpen(true)}
-                      >
-                        去录制一个
-                      </button>
-                    </div>
-                  ) : (
-                    <div className="vv-my-voice-list">
-                      {myVoices.map((v) => {
-                        const selected =
-                          task.refAudio?.source === "my_voice" && task.refAudio.id === v.id
-                        const playing = task.playingVoiceId === `my-${v.id}`
-                        return (
-                          <div
-                            key={v.id}
-                            className={`vv-my-voice-item ${selected ? "selected" : ""}`}
-                            onClick={() =>
-                              setTask({
-                                refAudio: {
-                                  source: "my_voice",
-                                  id: v.id,
-                                  name: v.name,
-                                  ttsVoiceId: v.voice_id || undefined,
-                                },
-                                voicePanel: null,
-                              })
-                            }
-                          >
-                            <div className="vv-voice-radio" />
-                            <div className="vv-voice-info">
-                              <div className="vv-voice-name">{v.name}</div>
-                              <div className="vv-voice-desc">
-                                AI 克隆声音
-                                {v.gender
-                                  ? ` · ${v.gender === "male" ? "男" : v.gender === "female" ? "女" : ""}`
-                                  : ""}
-                              </div>
-                            </div>
-                            <button
-                              className={`vv-voice-play ${playing ? "playing" : ""}`}
-                              onClick={(e) => {
-                                e.stopPropagation()
-                                // 克隆音色试听：优先 sample_url，否则调用 preview 接口
-                                if (v.sample_url) {
-                                  toggleVoice({ id: `my-${v.id}`, url: v.sample_url })
-                                } else {
-                                  message.info("正在获取试听音频…")
-                                  getVoiceClonePreview(v.id)
-                                    .then((r) => {
-                                      if (r.audio_url)
-                                        toggleVoice({ id: `my-${v.id}`, url: r.audio_url })
-                                    })
-                                    .catch(() => message.error("试听获取失败"))
-                                }
-                              }}
-                            >
-                              {playing ? <PauseCircleOutlined /> : <PlayCircleOutlined />}
-                            </button>
-                          </div>
-                        )
-                      })}
-                    </div>
-                  )}
-                </div>
-
-                {/* 次要选项：内置音色 / 上传配音 */}
-                <div className="vv-voice-alt-row">
+                <div className="vv-audio-grid">
                   <button
-                    className={`vv-voice-alt-btn ${task.refAudio?.source === "preset" ? "selected" : ""}`}
+                    className={`vv-audio-cell ${task.refAudio?.source === "preset" ? "selected" : ""}`}
                     onClick={() => setVoicePickerOpen(true)}
                   >
-                    <FileTextOutlined /> 内置音色
+                    <FileTextOutlined className="vv-audio-ic" />
+                    <span>选择内置音色</span>
                   </button>
                   <button
-                    className={`vv-voice-alt-btn ${task.refAudio?.source === "upload" || task.refAudio?.source === "library" ? "selected" : ""}`}
-                    onClick={() =>
-                      setTask({ voicePanel: task.voicePanel === "library" ? null : "library" })
-                    }
+                    className={`vv-audio-cell ${task.voicePanel === "upload" ? "active" : ""} ${task.refAudio?.source === "upload" ? "selected" : ""}`}
+                    onClick={() => voiceInputRef.current?.click()}
                   >
-                    <FolderOpenOutlined /> 上传配音
+                    <UploadOutlined className="vv-audio-ic" />
+                    <span>本地上传</span>
+                  </button>
+                  <button
+                    className={`vv-audio-cell ${task.voicePanel === "library" ? "active" : ""} ${task.refAudio?.source === "library" ? "selected" : ""}`}
+                    onClick={() => setAssetPicker({ open: true, kind: "voice", multiple: false })}
+                  >
+                    <FolderOpenOutlined className="vv-audio-ic" />
+                    <span>从素材库选择</span>
+                  </button>
+                  <button
+                    className={`vv-audio-cell ${task.voicePanel === "record" ? "active" : ""} ${task.refAudio?.source === "clone" ? "selected" : ""}`}
+                    onClick={() => setCloneModalOpen(true)}
+                  >
+                    <AudioMutedOutlined className="vv-audio-ic" />
+                    <span>直接录音</span>
                   </button>
                 </div>
-
                 <input
                   ref={voiceInputRef}
                   type="file"
@@ -2186,14 +2083,7 @@ const ViralVideoPage: React.FC = () => {
                 {task.refAudio && (
                   <div className="vv-selected-audio">
                     <SoundOutlined style={{ color: "#7c3aed", marginRight: 6 }} />
-                    <span style={{ flex: 1 }}>
-                      已选：
-                      {task.refAudio.source === "my_voice"
-                        ? `我的音色 · ${task.refAudio.name}`
-                        : task.refAudio.source === "preset"
-                          ? `内置 · ${task.refAudio.name}`
-                          : task.refAudio.name}
-                    </span>
+                    <span style={{ flex: 1 }}>已选：{task.refAudio.name}</span>
                     <button className="vv-link-btn" onClick={() => setTask({ refAudio: null })}>
                       移除
                     </button>
@@ -2201,32 +2091,16 @@ const ViralVideoPage: React.FC = () => {
                 )}
                 {task.voicePanel === "library" && (
                   <div className="vv-voice-panel">
-                    <div className="vv-voice-panel-actions">
-                      <button
-                        className="vv-link-btn vv-link-btn-sm"
-                        onClick={() => voiceInputRef.current?.click()}
-                      >
-                        <UploadOutlined /> 本地上传音频文件
-                      </button>
-                      <button
-                        className="vv-link-btn vv-link-btn-sm"
-                        onClick={() =>
-                          setAssetPicker({ open: true, kind: "voice", multiple: false })
-                        }
-                      >
-                        <FolderOpenOutlined /> 从素材库选择
-                      </button>
-                    </div>
                     {voiceAssets.length === 0 ? (
                       <div className="vv-muted" style={{ padding: 12, textAlign: "center" }}>
-                        暂无已上传的配音文件
+                        暂无已上传的音频
                       </div>
                     ) : (
                       <div className="vv-voice-list">
                         {voiceAssets.map((v) => (
                           <div
                             key={v.id}
-                            className={`vv-voice-item ${task.refAudio?.id === v.id && (task.refAudio.source === "library" || task.refAudio.source === "upload") ? "selected" : ""}`}
+                            className={`vv-voice-item ${task.refAudio?.id === v.id && task.refAudio.source === "library" ? "selected" : ""}`}
                             onClick={() =>
                               pickVoiceAsset({
                                 id: v.id,
@@ -2415,7 +2289,7 @@ const ViralVideoPage: React.FC = () => {
                     options={PURPOSES.map((i) => ({ value: i, label: i }))}
                   />
                 </div>
-                <div className="vv-form-row">
+                <div className="vv-form-row" style={{ gridColumn: "1 / -1" }}>
                   <label className="vv-label">文案视频时长</label>
                   <Select
                     className="vv-select"

apps/worker/worker_app/celery_app.py

@@ -53,9 +53,6 @@ celery_app.conf.imports = (
     # #1998 GPU MuseTalk 异步推理：wait_for_result→签名 URL→回写 lipsync_jobs
     # 必须在 Worker 侧注册，否则 apply_async 消息无人消费，job 永远卡在 processing
     "app.tasks.lipsync_gpu",
-    # #2076 Ditto 蚂蚁数字人异步推理：同步 HTTP 调用 Ditto → MP4 流转存 OSS → 回写 lipsync_jobs
-    # 必须在 Worker 侧注册；失败回退 GPU MuseTalk → MediaKit
-    "app.tasks.lipsync_ditto",
 )
 
 # Celery Beat 定时任务调度

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/vision/__init__.py

@@ -1,4 +0,0 @@
-# -*- coding: utf-8 -*-
-"""V2 图片分析：火山OCR专用API + doubao-lite强约束JSON并行，单次pro VLM兜底。"""
-
-from .fast_path import analyze_image_v2, analyze_images_v2  # noqa: F401

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

@@ -1,95 +0,0 @@
-# -*- coding: utf-8 -*-
-"""V2 prompt 解析：优先读后台 viral_video_prompt_templates（prompt_type='image_analysis'
-且 is_active=true），30s TTL 热加载；DB 无有效记录/异常时，fallback 到 prompts.py 的
-image_analysis v8 默认 system/user。
-
-规则：
-- DB 有 is_active=true 的 image_analysis 记录：system 原样用 DB.system_prompt
-  （自带完整输出格式，不追加任何硬编码 schema），user 用 DB.user_prompt_template
-  渲染（填入 image_url / ocr_text）；
-- DB 无记录/异常：system/user 用 prompts.py 里的 v8 默认模板。
-"""
-
-from __future__ import annotations
-
-import logging
-import threading
-import time
-
-logger = logging.getLogger(__name__)
-
-
-def _default_template() -> dict:
-    # 延迟导入：避免模块加载时拉起整个 packages 依赖链（也便于旧 Python 收集测试）
-    from packages.application.viral_video.prompts import DEFAULT_TEMPLATES
-
-    for item in DEFAULT_TEMPLATES:
-        if item["prompt_type"] == "image_analysis":
-            return item
-    raise RuntimeError("image_analysis 默认模板缺失")
-
-
-_cache_lock = threading.Lock()
-_cache: dict[str, tuple[float, tuple[str, str]]] = {}
-_CACHE_TTL = 30.0
-
-
-def _load_db_template():
-    """查 DB is_active=true 的 image_analysis 记录；不可达/无记录返回 None。"""
-    try:
-        from packages.application.viral_video.prompt_loader import _load_from_db
-
-        return _load_from_db("image_analysis")
-    except Exception as e:  # noqa: BLE001
-        logger.warning("[vision.v2] 查询DB image_analysis prompt失败: %s", e)
-        return None
-
-
-def _render_user(user_tpl: str, image_url: str, ocr_text: str) -> str:
-    try:
-        return user_tpl.format(image_url=image_url, ocr_text=ocr_text or "无")
-    except Exception:  # noqa: BLE001
-        return user_tpl
-
-
-def _resolve(kind: str, image_url: str = "", ocr_text: 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:
-            sys_prompt, usr_prompt = hit[1]
-            return sys_prompt, _render_user(usr_prompt, image_url, ocr_text)
-
-    default = _default_template()
-    sys_prompt = default["system_prompt"]
-    usr_prompt = default["user_prompt_template"]
-
-    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_usr = getattr(tpl, "user_prompt_template", "") or usr_prompt
-            usr_prompt = db_usr or usr_prompt
-            logger.info(
-                "[vision.v2] 使用DB image_analysis prompt version=%s",
-                getattr(tpl, "version", "?"),
-            )
-
-    with _cache_lock:
-        _cache[cache_key] = (now, (sys_prompt, usr_prompt))
-    return sys_prompt, _render_user(usr_prompt, image_url, ocr_text)
-
-
-def resolve_fast_prompt(image_url: str = "", ocr_text: str = "") -> tuple[str, str]:
-    return _resolve("fast", image_url, ocr_text)
-
-
-def resolve_pro_prompt(image_url: str = "", ocr_text: str = "") -> tuple[str, str]:
-    return _resolve("pro", image_url, ocr_text)
-
-
-def invalidate_cache() -> None:
-    with _cache_lock:
-        _cache.clear()

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

@@ -1,106 +0,0 @@
-"""V2 结果组装（v8 叙述优先，大幅精简）。
-
-设计原则：VLM 直接输出最终给用户看的 summary_markdown，assembler 只负责
-- 解析 fast JSON（兼容顶层 {"images":[...]} 与 {"products":[...]} 两种键）；
-- 补齐 5 个必备字段（type/name/brand/has_person/summary_markdown）；
-- summary_markdown 缺失（异常）时才拼一句最基础的兜底文字。
-
-正常情况下不改写、不“润色”VLM 输出，不做 brand 多级兜底，不处理任何
-colors/material/key_features 等细分字段。
-"""
-
-from __future__ import annotations
-
-import logging
-from typing import Any
-
-logger = logging.getLogger(__name__)
-
-_VALID_TYPES = ("store", "product", "person", "scene")
-
-
-def _coerce_bool(value: Any) -> bool:
-    if isinstance(value, bool):
-        return value
-    if isinstance(value, (int, float)):
-        return value != 0
-    if isinstance(value, str):
-        return value.strip().lower() in ("true", "1", "yes", "是")
-    return False
-
-
-def _basic_markdown(image: dict[str, Any]) -> str:
-    """异常兜底：VLM 没给 summary_markdown 时只拼一句基础文字。"""
-    name = (image.get("name") or "").strip() or "未识别"
-    brand = (image.get("brand") or "").strip()
-    typ = image.get("type") or "scene"
-    label = f"{brand}{name}" if brand and brand not in name else (brand or name)
-    if typ == "store":
-        return f"这是{label}的门店场景，画面细节识别不完整。"
-    if typ == "person":
-        return f"画面中的人物与{label}相关，细节识别不完整。"
-    if typ == "product":
-        return f"这是{label}的商品图片，具体外观细节识别不完整。"
-    return f"画面内容为{label}，细节识别不完整。"
-
-
-def _normalize_image(raw: Any, idx: int) -> dict[str, Any]:
-    """把一条 VLM 输出归一化为 5 字段 dict。"""
-    if not isinstance(raw, dict):
-        raw = {}
-
-    typ = str(raw.get("type") or "").strip().lower()
-    if typ not in _VALID_TYPES:
-        typ = "scene"
-
-    name = str(raw.get("name") or "").strip() or "未识别"
-    brand = str(raw.get("brand") or "").strip()
-    has_person = _coerce_bool(raw.get("has_person"))
-    if typ == "person" and not has_person:
-        # type=person 通常意味着主体是人，保持一致（仅异常补全）
-        has_person = True
-
-    summary = raw.get("summary_markdown")
-    summary = summary.strip() if isinstance(summary, str) else ""
-
-    image: dict[str, Any] = {
-        "type": typ,
-        "name": name,
-        "brand": brand,
-        "has_person": has_person,
-        "summary_markdown": summary,
-    }
-    if not summary:
-        image["summary_markdown"] = _basic_markdown(image)
-        image["_source"] = "summary_missing"
-        logger.info("[assembler] 图片 #%s 缺少 summary_markdown，使用基础兜底", idx)
-    return image
-
-
-def _extract_items(fast_json: Any) -> list[Any]:
-    """从 fast JSON 中取出图片条目：优先 images，兼容 products。"""
-    if not isinstance(fast_json, dict):
-        return []
-    items = fast_json.get("images")
-    if not isinstance(items, list):
-        items = fast_json.get("products")
-    return items if isinstance(items, list) else []
-
-
-def assemble_result(idx: int, fast_json: Any, ocr_texts: list[str] | None = None) -> dict[str, Any]:
-    """组装单张图片分析结果。
-
-    每次调用对应一张图片；fast_json 形如 {"images": [{...}]}（v8）。
-    返回单条 image dict（5 字段，必要时带 _source）。
-    """
-    items = _extract_items(fast_json)
-    if items:
-        image = _normalize_image(items[0], idx)
-    else:
-        # 极端异常：fast 无任何可用条目，OCR 文字可作为名称线索
-        ocr_hint = ""
-        if ocr_texts:
-            ocr_hint = "、".join(t for t in ocr_texts if t)[:40]
-        image = _normalize_image({"name": ocr_hint or "未识别", "summary_markdown": ""}, idx)
-        image["_source"] = "empty_fast_json"
-    return image

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

@@ -1,124 +0,0 @@
-# -*- coding: utf-8 -*-
-"""V2 图片分析主路径（v8 叙述优先）：每图并行 OCR（火山 MediaKit，未配置自动跳过）
-+ fast VLM 强约束 JSON；失败时单次 pro VLM 兜底。
-
-架构：
-- 单图 2 路并行（OCR + fast VLM），外层 N 图全并发（workers=8）；
-- 兜底单次 pro VLM，无竞速/复杂重试；
-- 输出统一为 5 字段 image dict（type/name/brand/has_person/summary_markdown）。
-"""
-
-from __future__ import annotations
-
-import logging
-import os
-import time
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from typing import Any
-
-from . import assembler, ocr_volc, vlm_fallback, vlm_fast_json
-
-logger = logging.getLogger(__name__)
-
-_IMG_WORKERS = int(os.environ.get("VISION_V2_IMG_WORKERS", "8"))
-_FAST_TIMEOUT = float(os.environ.get("VISION_V2_FAST_TIMEOUT", "20"))
-_FAST_JSON_TIMEOUT = float(os.environ.get("VISION_V2_FAST_JSON_TIMEOUT", "20"))
-_OCR_TIMEOUT = float(os.environ.get("VISION_V2_OCR_TIMEOUT", "6"))
-_PRO_TIMEOUT = float(os.environ.get("VISION_V2_PRO_TIMEOUT", "45"))
-
-
-def _is_usable(r: dict[str, Any] | None) -> bool:
-    if not isinstance(r, dict):
-        return False
-    return bool((r.get("summary_markdown") or "").strip())
-
-
-def _basic_failure(ocr_result: list[str], fast_elapsed: float, source: str) -> dict[str, Any]:
-    image = assembler.assemble_result(-1, {}, ocr_result)
-    image["_source"] = source
-    image["_fast_elapsed"] = round(fast_elapsed, 2)
-    return image
-
-
-def analyze_image_v2(idx: int, img_url: str) -> dict[str, Any]:
-    t0 = time.time()
-
-    fj_result: dict[str, Any] | None = None
-    ocr_result: list[str] = []
-    pool = ThreadPoolExecutor(max_workers=2)
-    f_fj = pool.submit(vlm_fast_json.call_fast_json, img_url, timeout=_FAST_JSON_TIMEOUT)
-    f_ocr = pool.submit(ocr_volc.call_ocr, img_url, timeout=_OCR_TIMEOUT)
-    try:
-        for fut in as_completed([f_fj, f_ocr], timeout=_FAST_TIMEOUT):
-            try:
-                res = fut.result(timeout=1)
-            except Exception as e:  # noqa: BLE001
-                logger.warning("[vision.v2] 图片 #%d 子任务异常: %s", idx, e)
-                continue
-            if fut is f_fj and isinstance(res, dict):
-                fj_result = res
-            elif fut is f_ocr and isinstance(res, list):
-                ocr_result = res
-    except TimeoutError:
-        for f in (f_fj, f_ocr):
-            if not f.done():
-                f.cancel()
-        logger.warning("[vision.v2] 图片 #%d fast路径超时(%.0fs)，走pro兜底", idx, _FAST_TIMEOUT)
-    finally:
-        fast_elapsed = time.time() - t0
-        pool.shutdown(wait=False)
-
-    if fj_result:
-        assembled = assembler.assemble_result(idx, fj_result, ocr_result)
-        if _is_usable(assembled):
-            assembled["_fast_elapsed"] = round(fast_elapsed, 2)
-            logger.info("[vision.v2] 图片 #%d fast命中 elapsed=%.2fs", idx, fast_elapsed)
-            return assembled
-
-    pro_result = vlm_fallback.call_pro_vlm(img_url, idx, ocr_hint=ocr_result, timeout=_PRO_TIMEOUT)
-    if _is_usable(pro_result):
-        pro_result["_fallback_used"] = True
-        pro_result["_fast_elapsed"] = round(fast_elapsed, 2)
-        logger.info("[vision.v2] 图片 #%d pro兜底命中 total=%.2fs", idx, time.time() - t0)
-        return pro_result
-
-    logger.warning("[vision.v2] 图片 #%d 全路径失败 elapsed=%.2fs", idx, time.time() - t0)
-    return _basic_failure(ocr_result, fast_elapsed, "v2_all_failed")
-
-
-def analyze_images_v2(img_urls: list[str]) -> list[dict[str, Any]]:
-    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,
-    )
-    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)}
-        for fut in as_completed(future_to_idx):
-            idx = future_to_idx[fut]
-            try:
-                results[idx] = fut.result()
-            except Exception as e:  # noqa: BLE001
-                logger.warning("[vision.v2] 图片 #%d future异常: %s", idx, e, exc_info=True)
-                results[idx] = assembler.assemble_result(idx, {}, [])
-                results[idx]["_source"] = "v2_future_exception"  # type: ignore[index]
-
-    elapsed = time.time() - t0
-    succ = sum(1 for r in results if _is_usable(r))
-    fb = sum(1 for r in results if r and r.get("_fallback_used"))
-    logger.info(
-        "[vision.v2] 完成 n=%d usable=%d pro_fallback=%d elapsed=%.2fs",
-        len(img_urls),
-        succ,
-        fb,
-        elapsed,
-    )
-    return [r for r in results if r is not None]  # type: ignore[misc]

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

@@ -1,138 +0,0 @@
-# -*- coding: utf-8 -*-
-"""VLM 返回文本的稳健 JSON 提取工具。
-
-背景：复杂门店图 VLM 输出经常被 max_tokens 截断（finish_reason=length），
-json.loads 失败后整个结果被丢弃，导致"未识别"。本工具提供：
-1. markdown 代码块剥离（含只开不闭的截断场景）
-2. 最外层 { } 切片
-3. 非法控制字符清理
-4. 直接 json.loads
-5. 截断 JSON 括号/引号栈补全修复
-6. 尾部逐字符截断重试（去除最后一个不完整 token 后修复）
-
-成功返回 dict；截断修复产物带 _partial=True 标记；彻底失败返回 None。
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-import re
-
-logger = logging.getLogger(__name__)
-
-_CODE_FENCE_RE = re.compile(r"^```(?:json)?\s*\n?(.*?)\n?```\s*$", re.DOTALL)
-
-
-def _strip_code_fence(s: str) -> str:
-    s = s.strip()
-    m = _CODE_FENCE_RE.match(s)
-    if m:
-        return m.group(1).strip()
-    # 兼容开头 ```json 但结尾无 ```（截断场景）
-    if s.startswith("```"):
-        lines = s.split("\n")
-        if lines and lines[0].startswith("```"):
-            lines = lines[1:]
-        s = "\n".join(lines).strip()
-    return s
-
-
-def _repair_truncated_json(text: str) -> str:
-    """尝试补全被截断的JSON：维护 bracket/quote 栈，在末尾补闭合符。"""
-    stack: list[str] = []
-    in_string = False
-    escape = False
-    for ch in text:
-        if escape:
-            escape = False
-            continue
-        if ch == "\\" and in_string:
-            escape = True
-            continue
-        if ch == '"':
-            in_string = not in_string
-            continue
-        if in_string:
-            continue
-        if ch in "{[":
-            stack.append(ch)
-        elif ch == "}":
-            if stack and stack[-1] == "{":
-                stack.pop()
-        elif ch == "]":
-            if stack and stack[-1] == "[":
-                stack.pop()
-    repair = ""
-    if in_string:
-        repair += '"'
-    for opener in reversed(stack):
-        repair += "}" if opener == "{" else "]"
-    if repair:
-        logger.info(
-            "[json_utils] 截断JSON修复: 补全%d个闭合符 in_string=%s",
-            len(repair),
-            in_string,
-        )
-    return text + repair
-
-
-def _clean_invalid_chars(text: str) -> str:
-    """清理JSON中非法的控制字符（tab/newline 之外的 0x00-0x1f 段）。"""
-    return re.sub(r"[\x00-\x08\x0b\x0c\x0e-\x1f]", "", text)
-
-
-def extract_json_object(text: str) -> dict | None:
-    """从VLM返回文本中稳健提取JSON对象。
-
-    返回 dict 或 None。成功的 dict 可能带 _partial=True 标记，
-    表示原始文本被截断、经括号补全后得到的产物。
-    """
-    if not text or not isinstance(text, str):
-        return None
-    # 1. 剥离 markdown
-    text = _strip_code_fence(text)
-    # 2. 找最外层 { }
-    lpos = text.find("{")
-    if lpos < 0:
-        return None
-    rpos = text.rfind("}")
-    if rpos > lpos:
-        text = text[lpos : rpos + 1]
-    else:
-        # 截断场景：无任何闭合 }，取到末尾交给修复器
-        text = text[lpos:]
-    # 3. 清理非法控制字符
-    text = _clean_invalid_chars(text)
-    # 4. 直接 loads
-    try:
-        obj = json.loads(text)
-        return obj if isinstance(obj, dict) else None
-    except json.JSONDecodeError:
-        pass
-    # 5. 尝试截断修复
-    repaired = _repair_truncated_json(text)
-    try:
-        obj = json.loads(repaired)
-        if isinstance(obj, dict):
-            obj["_partial"] = True
-            return obj
-    except json.JSONDecodeError:
-        pass
-    # 6. 尾部逐字符截断重试（去除最后一个不完整 token）
-    for _ in range(50):
-        last_comma = repaired.rfind(",")
-        last_brace = max(repaired.rfind("}"), repaired.rfind("]"))
-        cut = max(last_comma, last_brace)
-        if cut < 10:
-            break
-        repaired = repaired[: cut + 1]
-        repaired = _repair_truncated_json(repaired)
-        try:
-            obj = json.loads(repaired)
-            if isinstance(obj, dict):
-                obj["_partial"] = True
-                return obj
-        except json.JSONDecodeError:
-            continue
-    return None

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

@@ -1,109 +0,0 @@
-# -*- coding: utf-8 -*-
-"""火山引擎 AI MediaKit OCR（同步）调用封装。
-
-接口：POST {mediakit_base_url}/tools-sync/ocr
-鉴权：Bearer {mediakit_api_key}
-请求体：{"image_url": "<公网可访问URL>"}  （部分版本也支持 image_base64）
-响应：{"code":0,"data":{"texts":[{"text":"...","bbox":[x,y,w,h],...},...],...}}
-
-目标：识别商品包装/Logo/水印上的文字，作为 fast_json VLM 的补充。
-返回值：识别到的文本字符串列表（失败返回 []）。
-"""
-
-from __future__ import annotations
-
-import logging
-import time
-from typing import Any
-
-logger = logging.getLogger(__name__)
-
-DEFAULT_TIMEOUT = 8  # OCR 秒级返回，8s 绰绰有余
-
-
-def call_ocr(img_url: str, *, timeout: int = DEFAULT_TIMEOUT) -> list[str]:
-    """调用 MediaKit 同步 OCR，返回去重后的纯文本列表。
-
-    不做重试（外层降级逻辑负责）。失败/未配置返回空列表，不抛异常。
-    """
-    t0 = time.time()
-    try:
-        import httpx
-
-        from packages.shared.mediakit_client import get_mediakit_client
-
-        client = get_mediakit_client()
-        if not client.is_available:
-            logger.info("[vision.v2] mediakit 未配置，跳过 OCR")
-            return []
-
-        url = f"{client.base_url}/tools-sync/ocr"
-        headers = {
-            "Authorization": f"Bearer {client.api_key}",
-            "Content-Type": "application/json",
-        }
-        payload: dict[str, Any] = {"image_url": img_url}
-        # 部分文档版本用 image_base64，但公网 URL 场景下 image_url 最简
-        resp = httpx.post(url, headers=headers, json=payload, timeout=timeout)
-        elapsed = time.time() - t0
-        if resp.status_code != 200:
-            logger.warning(
-                "[vision.v2] OCR HTTP %d elapsed=%.1fs body=%s",
-                resp.status_code,
-                elapsed,
-                resp.text[:200],
-            )
-            return []
-        data = resp.json()
-        # 兼容几种可能的响应结构
-        code = data.get("code", data.get("status", 0))
-        if code not in (0, "OK", "success", 200):
-            logger.warning("[vision.v2] OCR 业务错误 code=%s elapsed=%.1fs resp=%s", code, elapsed, str(data)[:200])
-            return []
-        texts = _extract_texts(data)
-        # 去重 + 过滤空
-        seen: set[str] = set()
-        out: list[str] = []
-        for t in texts:
-            t = (t or "").strip()
-            if t and t not in seen and len(t) <= 100:  # 过滤过长的误识别
-                seen.add(t)
-                out.append(t)
-        logger.info("[vision.v2] OCR 完成 elapsed=%.1fs n=%d texts=%s", elapsed, len(out), out[:5])
-        return out
-    except Exception as e:
-        elapsed = time.time() - t0
-        logger.warning("[vision.v2] OCR 异常 elapsed=%.1fs err=%s", elapsed, e, exc_info=True)
-        return []
-
-
-def _extract_texts(data: dict) -> list[str]:
-    """从 OCR 响应中抽取文本，兼容多种结构。"""
-    out: list[str] = []
-    # 常见结构1: data.texts = [{"text": "..."}, ...]
-    d = data.get("data") or data
-    if isinstance(d, dict):
-        for key in ("texts", "lines", "words", "items", "result"):
-            items = d.get(key)
-            if isinstance(items, list):
-                for it in items:
-                    if isinstance(it, dict):
-                        txt = it.get("text") or it.get("content") or it.get("word")
-                        if txt:
-                            out.append(str(txt))
-                    elif isinstance(it, str):
-                        out.append(it)
-                break
-        # 结构2: data.text = "..."
-        if not out:
-            t = d.get("text")
-            if isinstance(t, str):
-                out.append(t)
-        # 结构3: data.ocr_text / data.content
-        if not out:
-            for key in ("ocr_text", "content", "raw_text"):
-                v = d.get(key)
-                if isinstance(v, str) and v.strip():
-                    out.append(v)
-                    break
-    return out

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

@@ -1,109 +0,0 @@
-# -*- coding: utf-8 -*-
-"""V2 兜底路径：vision client（fallback 变体）单图调用，走 v8 叙述优先 prompt。
-
-fast 超时/非 JSON/为空时单次调用；输出统一走 assembler.assemble_result 组装，
-与 fast 路径同为 5 字段 image dict。
-"""
-
-from __future__ import annotations
-
-import logging
-import time
-from typing import Any
-
-from . import _prompt, assembler
-
-logger = logging.getLogger(__name__)
-
-_DEFAULT_TIMEOUT = 45
-
-
-def call_pro_vlm(
-    img_url: str,
-    idx: int,
-    *,
-    ocr_hint: list[str] | None = None,
-    timeout: int = _DEFAULT_TIMEOUT,
-    max_tokens: int | None = None,
-) -> dict[str, Any] | None:
-    t0 = time.time()
-    ocr_text = "、".join(t for t in (ocr_hint or []) if t)[:200]
-
-    try:
-        from packages.shared.ai_router import ai_router
-
-        client = ai_router.get_vision_client("image_analysis", variant="fallback")
-        if not client or not client.is_available:
-            logger.warning("[vision.v2] pro vision client 不可用，跳过")
-            return None
-    except Exception as e:  # noqa: BLE001
-        logger.warning("[vision.v2] ai_router 获取失败: %s", e)
-        return None
-
-    system_prompt, user_prompt = _prompt.resolve_pro_prompt(img_url, ocr_text)
-
-    messages: list[dict[str, Any]] = [
-        {"role": "system", "content": system_prompt},
-        {
-            "role": "user",
-            "content": [
-                {"type": "image_url", "image_url": {"url": img_url}},
-                {"type": "text", "text": user_prompt},
-            ],
-        },
-    ]
-
-    try:
-        call_kwargs: dict[str, Any] = {
-            "messages": messages,
-            "images": None,
-            "temperature": 0.3,
-            "timeout": timeout,
-            "enable_thinking": False,
-            "response_format": {"type": "json_object"},
-            "max_tokens": max_tokens if max_tokens is not None else 4000,
-        }
-
-        from .json_utils import extract_json_object
-
-        obj = None
-        for _outer in range(2):
-            kw = dict(call_kwargs)
-            if _outer == 1:
-                kw.pop("response_format", None)
-                msgs2 = [dict(messages[0]), dict(messages[1])]
-                cont = [dict(c) for c in msgs2[1]["content"]]
-                cont[-1] = {
-                    "type": "text",
-                    "text": user_prompt + "\n严格只输出JSON对象，不要解释或markdown。",
-                }
-                msgs2[1] = {"role": "user", "content": cont}
-                kw["messages"] = msgs2
-            raw = client.vision_completion(**kw)
-            if not raw:
-                continue
-            obj = extract_json_object(raw)
-            if obj is not None:
-                break
-            logger.warning("[vision.v2] pro 非JSON(100字) outer=%s: %s", _outer, raw[:100])
-
-        elapsed = time.time() - t0
-        if obj is None:
-            logger.warning("[vision.v2] pro 两次均未得到JSON elapsed=%.1fs", elapsed)
-            return None
-        if obj.get("_partial"):
-            logger.warning("[vision.v2] pro 截断JSON(partial) elapsed=%.1fs", elapsed)
-
-        result = assembler.assemble_result(idx, obj, ocr_hint or [])
-        result["_source"] = "vlm_pro"
-        result["_fallback_used"] = True
-        logger.info("[vision.v2] pro 完成 model=%s elapsed=%.1fs", client.model, elapsed)
-        return result
-    except Exception as e:  # noqa: BLE001
-        logger.warning(
-            "[vision.v2] pro 异常 elapsed=%.1fs err=%s",
-            time.time() - t0,
-            e,
-            exc_info=True,
-        )
-        return None

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

@@ -1,111 +0,0 @@
-# -*- coding: utf-8 -*-
-"""V2 快速路径：vision client（默认 image_analysis 能力）强约束 JSON-only 调用。
-
-要点：
-- 通过 ai_router.get_vision_client() 获取 client；
-- enable_thinking=False 关闭推理链，response_format=json_object 强约束 JSON；
-- system/user prompt 优先读后台模板（v8 叙述优先），DB 不可用时用 prompts.py 默认；
-- temperature=0.1（稳定输出 JSON）；两次尝试（第二次去 json_object 约束）。
-"""
-
-from __future__ import annotations
-
-import logging
-import time
-from typing import Any
-
-from . import _prompt
-
-logger = logging.getLogger(__name__)
-
-_DEFAULT_TIMEOUT = 20
-
-
-def call_fast_json(
-    img_url: str,
-    *,
-    timeout: int = _DEFAULT_TIMEOUT,
-    max_tokens: int | None = None,
-) -> dict[str, Any] | None:
-    t0 = time.time()
-
-    try:
-        from packages.shared.ai_router import ai_router
-
-        client = ai_router.get_vision_client("image_analysis", variant="primary")
-        if not client or not client.is_available:
-            logger.warning("[vision.v2] vision client 不可用，跳过 fast_json")
-            return None
-    except Exception as e:  # noqa: BLE001
-        logger.warning("[vision.v2] ai_router 获取失败: %s", e)
-        return None
-
-    system_prompt, user_prompt = _prompt.resolve_fast_prompt(img_url, "")
-
-    messages: list[dict[str, Any]] = [
-        {"role": "system", "content": system_prompt},
-        {
-            "role": "user",
-            "content": [
-                {"type": "image_url", "image_url": {"url": img_url}},
-                {"type": "text", "text": user_prompt},
-            ],
-        },
-    ]
-
-    try:
-        call_kwargs: dict[str, Any] = {
-            "messages": messages,
-            "images": None,
-            "temperature": 0.1,
-            "timeout": timeout,
-            "enable_thinking": False,
-            "response_format": {"type": "json_object"},
-        }
-        if max_tokens is not None:
-            call_kwargs["max_tokens"] = max_tokens
-
-        from .json_utils import extract_json_object
-
-        obj = None
-        for _outer in range(2):
-            kw = dict(call_kwargs)
-            if _outer == 1:
-                kw.pop("response_format", None)
-                msgs2 = [dict(messages[0]), dict(messages[1])]
-                cont = [dict(c) for c in msgs2[1]["content"]]
-                cont[-1] = {
-                    "type": "text",
-                    "text": user_prompt + "\n严格只输出JSON对象，不要解释或markdown。",
-                }
-                msgs2[1] = {"role": "user", "content": cont}
-                kw["messages"] = msgs2
-            raw = client.vision_completion(**kw)
-            if not raw:
-                continue
-            obj = extract_json_object(raw)
-            if obj is not None:
-                break
-            logger.warning("[vision.v2] fast_json 非JSON(100字) outer=%s: %s", _outer, raw[:100])
-
-        elapsed = time.time() - t0
-        if obj is None:
-            logger.warning("[vision.v2] fast_json 两次均未得到JSON elapsed=%.1fs", elapsed)
-            return None
-        if obj.get("_partial"):
-            logger.warning("[vision.v2] fast_json 截断JSON(partial) elapsed=%.1fs", elapsed)
-        logger.info(
-            "[vision.v2] fast_json 完成 model=%s elapsed=%.1fs type=%s",
-            client.model,
-            elapsed,
-            obj.get("type"),
-        )
-        return obj
-    except Exception as e:  # noqa: BLE001
-        logger.warning(
-            "[vision.v2] fast_json 异常 elapsed=%.1fs err=%s",
-            time.time() - t0,
-            e,
-            exc_info=True,
-        )
-        return None

deploy/configs/.env.staging

@@ -241,35 +241,19 @@ DOUBAO_API_KEY=${DOUBAO_API_KEY}
 
 # 模型 Endpoint ID（在 ARK 控制台创建推理接入点后获得）
 DOUBAO_MODEL=${DOUBAO_MODEL}
-DOUBAO_FAST_MODEL=${DOUBAO_FAST_MODEL}
 
 # API Base URL
 DOUBAO_BASE_URL=${DOUBAO_BASE_URL}
 
 # 请求超时（秒）
-DOUBAO_TIMEOUT=${DOUBAO_TIMEOUT}
+DOUBAO_TIMEOUT=60
 
 # 最大重试次数
-DOUBAO_MAX_RETRIES=${DOUBAO_MAX_RETRIES}
+DOUBAO_MAX_RETRIES=2
 
-# 视觉模型（支持图片/视频理解的模型，model name 格式）
+# 视觉模型 Endpoint ID（支持图片/视频理解的模型）
 DOUBAO_VISION_MODEL=${DOUBAO_VISION_MODEL}
 
-# 快速视觉模型（viral-video 图片分析 lite 路径）
-DOUBAO_VISION_LITE_MODEL=${DOUBAO_VISION_LITE_MODEL}
-
-# 是否启用 lite 视觉路径（true/false）
-DOUBAO_VISION_USE_LITE=${DOUBAO_VISION_USE_LITE}
-
-# 信任链文生图模型（Seedream）
-DOUBAO_IMAGE_MODEL=${DOUBAO_IMAGE_MODEL}
-
-# 文生图尺寸
-DOUBAO_IMAGE_SIZE=${DOUBAO_IMAGE_SIZE}
-
-# 文生图超时（秒）
-DOUBAO_IMAGE_TIMEOUT=${DOUBAO_IMAGE_TIMEOUT}
-
 
 # ==================== 微信开放平台 OAuth（网页扫码登录）====================
 # 回调域名：xiaoxiajianji.com（微信开放平台已配置）

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."""
@@ -942,7 +928,6 @@ class ViralVideoJobModel(Base):
     id = Column(String(36), primary_key=True)
     user_id = Column(String(36), nullable=False, index=True)
     images = Column(JSON, nullable=False, default=list)  # 产品图片 URL 列表
-    pre_trusted_images = Column(JSON, nullable=True)  # #2172 信任链预热结果（Seedream AI 化 URL 列表）
     industry = Column(String(100), nullable=False, default="")
     target_customer = Column(String(500), nullable=False, default="")
     persona_id = Column(String(36), nullable=False, default="")
@@ -1005,17 +990,16 @@ class ViralVideoStyleTemplateModel(Base):
 
 
 class ViralVideoPromptTemplateModel(Base):
-    """爆款视频 Prompt 模板表（#2040：纯文本 XML 标签模板，运营可直接编辑）"""
+    """爆款视频 Prompt 模板表（由 #2040 seed）"""
 
     __tablename__ = "viral_video_prompt_templates"
 
-    id = Column(Integer, primary_key=True, autoincrement=True)
-    name = Column(String(128), nullable=False)
-    prompt_type = Column(String(32), nullable=False)
+    id = Column(String(36), primary_key=True)
+    prompt_type = Column(String(50), nullable=False, index=True)
+    name = Column(String(200), nullable=False)
+    content = Column(Text, nullable=False, default="")
+    variables = Column(JSON, nullable=False, default=list)
     version = Column(Integer, nullable=False, default=1)
-    system_prompt = Column(Text, nullable=False)
-    user_prompt_template = Column(Text, nullable=False)
-    example_output = Column(Text, nullable=True)
-    is_active = Column(Boolean, nullable=False, default=True)
+    is_active = Column(Boolean, nullable=False, default=True, index=True)
     created_at = Column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(UTC))
     updated_at = Column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(UTC))

packages/adapters/sqlalchemy_impl/viral_video_repository.py

@@ -6,33 +6,18 @@ from sqlalchemy.orm import Session
 
 from packages.adapters.sqlalchemy_impl.models import (
     ViralVideoJobModel,
+    ViralVideoPromptTemplateModel,
     ViralVideoStyleTemplateModel,
 )
 from packages.domain.viral_video import ViralVideoJob, ViralVideoStatus
 
 
 def _to_domain(model: ViralVideoJobModel) -> ViralVideoJob:
-    """ORM → 领域实体。pre_trusted_images 兼容脏数据：双序列化字符串/字符数组/list[str]。"""
-    import json as _pti_json
-
-    _raw_pti = getattr(model, "pre_trusted_images", None)
-    _pti: list[str] | None = None
-    if _raw_pti is not None:
-        if isinstance(_raw_pti, str):
-            try:
-                _p = _pti_json.loads(_raw_pti)
-                if isinstance(_p, list):
-                    _pti = [u for u in _p if isinstance(u, str) and u] or None
-            except Exception:
-                _pti = None
-        elif isinstance(_raw_pti, list):
-            _f = [u for u in _raw_pti if isinstance(u, str) and len(u) > 5]
-            _pti = _f if _f else None
+    """ORM → 领域实体。"""
     return ViralVideoJob(
         id=model.id,
         user_id=model.user_id,
         images=list(model.images or []),
-        pre_trusted_images=_pti,
         industry=model.industry or "",
         target_customer=model.target_customer or "",
         persona_id=model.persona_id or "",
@@ -85,7 +70,6 @@ class SQLAlchemyViralVideoJobRepository:
             id=job.id,
             user_id=job.user_id,
             images=job.images,
-            pre_trusted_images=job.pre_trusted_images,
             industry=job.industry,
             target_customer=job.target_customer,
             persona_id=job.persona_id,
@@ -142,7 +126,6 @@ class SQLAlchemyViralVideoJobRepository:
         model.storyboard = job.storyboard
         model.generated_copy_text = job.generated_copy_text or ""
         model.copy_result = job.copy_result
-        model.pre_trusted_images = job.pre_trusted_images
         model.result_video_url = job.result_video_url
         model.video_resolution = getattr(job, "video_resolution", "720p") or "720p"
         model.credits_prepaid = float(getattr(job, "credits_prepaid", 0) or 0)
@@ -244,3 +227,31 @@ class SQLAlchemyViralVideoStyleTemplateRepository:
             "style_config": dict(model.style_config) if model.style_config else {},
             "is_system": model.is_system,
         }
+
+
+class SQLAlchemyViralVideoPromptTemplateRepository:
+    """Prompt 模板仓储（由 #2040 seed，这里只读取）。"""
+
+    def __init__(self, session: Session):
+        self.session = session
+
+    def get_active_by_type(self, prompt_type: str) -> dict | None:
+        model = (
+            self.session.query(ViralVideoPromptTemplateModel)
+            .filter(
+                ViralVideoPromptTemplateModel.prompt_type == prompt_type,
+                ViralVideoPromptTemplateModel.is_active.is_(True),
+            )
+            .order_by(ViralVideoPromptTemplateModel.version.desc())
+            .first()
+        )
+        if model is None:
+            return None
+        return {
+            "id": model.id,
+            "prompt_type": model.prompt_type,
+            "name": model.name,
+            "content": model.content,
+            "variables": list(model.variables or []),
+            "version": model.version,
+        }

packages/application/cosyvoice_service.py

@@ -351,25 +351,12 @@ class CosyVoiceService:
                 用于私有 bucket 下，将裸 URL 转为预签名 URL，
                 确保 CosyVoice 服务器能下载参考音频.
         """
-        # 优先从 ai_router 获取 DB 配置
-        _router_key, _router_url, _router_model = "", "", ""
-        try:
-            from packages.shared.ai_router import ai_router
-
-            tts_client = ai_router.get_tts_client("tts")
-            if tts_client and tts_client.is_available:
-                _router_key = tts_client.api_key
-                _router_url = tts_client.base_url
-                _router_model = tts_client.model
-        except Exception:
-            pass
-
         settings = get_shared_settings()
 
-        self._api_key = api_key or _router_key or settings.cosyvoice_api_key
-        self._base_url = base_url or _router_url or settings.cosyvoice_base_url
-        self._model = model or _router_model or settings.cosyvoice_model
-        self._clone_model = clone_model or getattr(settings, "cosyvoice_clone_model", "")
+        self._api_key = api_key or settings.cosyvoice_api_key
+        self._base_url = base_url or settings.cosyvoice_base_url
+        self._model = model or settings.cosyvoice_model
+        self._clone_model = clone_model or getattr(settings, "cosyvoice_clone_model", "voice-enrollment")
         self._audio_url_signer = audio_url_signer
 
         # base_url 规范化：去掉末尾的路径残留（兼容旧版配置）

packages/application/ditto_service.py

@@ -1,269 +0,0 @@
-"""蚂蚁 Ditto 数字人口型 API 客户端 — #2076.
-
-封装 Ditto FastAPI（部署在 5060Ti GPU 节点，Tailscale 内网可达）：
-- GET  /health               健康检查
-- POST /generate             生成口型视频（同步返回 MP4 流）
-
-关键特性：
-- 入参：video_url（人物模板视频 URL） + audio_url（TTS 音频 URL） + script（文案原文）
-- 出参：直接返回 video/mp4 字节流（自带音频，无需二次混流）
-- 429 时指数退避重试（最多 ditto_max_retries 次）
-- 500/超时视为失败
-- 输出 MP4 字节流转存到自家 OSS，返回公网 URL
-
-注意：
-- 保留 MuseTalk/GPU 路径不变；本服务作为更高优先级的第三条口型路径
-- 不传 emotion/表情精细控制，使用默认 emo_global=4（中性）+ use_script_emo=true（关键词驱动表情）
-- Ditto 输出自带音视频，不需要 GFPGAN 超分，不需要 ffmpeg 音视频混流
-"""
-
-from __future__ import annotations
-
-import io
-import logging
-import time
-from dataclasses import dataclass
-from typing import Optional
-
-import httpx
-
-from packages.config import get_api_settings
-
-logger = logging.getLogger(__name__)
-
-
-class DittoError(Exception):
-    """Ditto API 调用失败."""
-
-    def __init__(self, message: str, code: str = "DittoError", status_code: int = 0):
-        self.code = code
-        self.status_code = status_code
-        super().__init__(message)
-
-
-@dataclass
-class DittoResult:
-    """Ditto 生成结果."""
-
-    video_bytes: bytes
-    video_url: str = ""  # 转存 OSS 后填充
-    elapsed_seconds: float = 0.0
-    rtf: float = 0.0  # 实时率（响应头 X-RTF）
-    frames: int = 0  # 帧数（响应头 X-Frames）
-
-
-class DittoClient:
-    """蚂蚁 Ditto 数字人口型 API 客户端."""
-
-    def __init__(
-        self,
-        base_url: Optional[str] = None,
-        default_video_url: Optional[str] = None,
-        max_retries: Optional[int] = None,
-        timeout: Optional[int] = None,
-    ):
-        s = get_api_settings()
-        self.base_url = (base_url or s.ditto_api_base_url or "").rstrip("/")
-        self.default_video_url = default_video_url or s.ditto_default_video_url or ""
-        self.max_retries = int(max_retries if max_retries is not None else s.ditto_max_retries)
-        self.timeout = int(timeout if timeout is not None else s.ditto_request_timeout)
-
-    @property
-    def is_configured(self) -> bool:
-        """配置是否完整（base_url + 默认模板视频都有值）."""
-        return bool(self.base_url) and bool(self.default_video_url)
-
-    def health(self) -> bool:
-        """健康检查；成功返回 True，失败返回 False（不抛异常）."""
-        if not self.base_url:
-            return False
-        url = f"{self.base_url}/health"
-        try:
-            with httpx.Client(timeout=5.0) as client:
-                resp = client.get(url)
-                ok = resp.status_code == 200
-                if ok:
-                    logger.info("[ditto] health check OK: %s", url)
-                else:
-                    logger.warning("[ditto] health check status=%d: %s", resp.status_code, url)
-                return ok
-        except Exception as exc:
-            logger.warning("[ditto] health check failed: %s", exc)
-            return False
-
-    def generate(
-        self,
-        *,
-        audio_url: str,
-        script: str,
-        video_url: Optional[str] = None,
-        emo_global: int = 4,
-        use_script_emo: bool = True,
-        blend_frames: int = 6,
-    ) -> DittoResult:
-        """调用 Ditto /generate 接口，返回 MP4 字节流结果.
-
-        Raises DittoError on failure.
-        """
-        if not self.base_url:
-            raise DittoError("DITTO_API_BASE_URL 未配置", code="ConfigMissing")
-        driver_url = video_url or self.default_video_url
-        if not driver_url:
-            raise DittoError("Ditto 人物模板视频 URL 未配置", code="ConfigMissing")
-        if not audio_url:
-            raise DittoError("audio_url 不能为空", code="InvalidParam")
-        if not script:
-            script = " "
-
-        payload = {
-            "video_url": driver_url,
-            "audio_url": audio_url,
-            "script": script,
-            "emo_global": emo_global,
-            "use_script_emo": use_script_emo,
-            "blend_frames": blend_frames,
-        }
-        url = f"{self.base_url}/generate"
-
-        last_exc: Optional[Exception] = None
-        for attempt in range(self.max_retries + 1):
-            try:
-                start = time.monotonic()
-                with httpx.Client(timeout=self.timeout, follow_redirects=True) as client:
-                    resp = client.post(url, json=payload)
-                elapsed = time.monotonic() - start
-
-                if resp.status_code == 429:
-                    wait = min(2**attempt, 30)
-                    logger.warning(
-                        "[ditto] GPU 繁忙 (429)，%ds 后重试 (%d/%d)",
-                        wait,
-                        attempt + 1,
-                        self.max_retries,
-                    )
-                    if attempt >= self.max_retries:
-                        raise DittoError(
-                            f"Ditto GPU 繁忙，重试 {self.max_retries} 次仍失败",
-                            code="BusyRetriesExhausted",
-                            status_code=429,
-                        )
-                    time.sleep(wait)
-                    continue
-
-                if resp.status_code != 200:
-                    _text = (resp.text or "")[:300]
-                    logger.error(
-                        "[ditto] generate 失败 status=%d attempt=%d body=%s",
-                        resp.status_code,
-                        attempt + 1,
-                        _text,
-                    )
-                    if resp.status_code >= 500 and attempt < self.max_retries:
-                        time.sleep(min(2**attempt, 15))
-                        continue
-                    raise DittoError(
-                        f"Ditto 返回 {resp.status_code}: {_text}",
-                        code="DittoAPIError",
-                        status_code=resp.status_code,
-                    )
-
-                video_bytes = resp.content
-                if not video_bytes or len(video_bytes) < 1024:
-                    raise DittoError(
-                        f"Ditto 返回内容异常（size={len(video_bytes) if video_bytes else 0}）",
-                        code="EmptyResponse",
-                    )
-                try:
-                    rtf = float(resp.headers.get("X-RTF", "0") or 0)
-                except ValueError:
-                    rtf = 0.0
-                try:
-                    frames = int(resp.headers.get("X-Frames", "0") or 0)
-                except ValueError:
-                    frames = 0
-                try:
-                    x_time = float(resp.headers.get("X-Time", "0") or 0)
-                    if x_time > 0:
-                        elapsed = x_time
-                except ValueError:
-                    pass
-
-                logger.info(
-                    "[ditto] generate 成功 size=%d rtf=%.2f frames=%d elapsed=%.1fs attempt=%d",
-                    len(video_bytes),
-                    rtf,
-                    frames,
-                    elapsed,
-                    attempt + 1,
-                )
-                return DittoResult(
-                    video_bytes=video_bytes,
-                    elapsed_seconds=elapsed,
-                    rtf=rtf,
-                    frames=frames,
-                )
-
-            except DittoError:
-                raise
-            except httpx.TimeoutException as exc:
-                last_exc = exc
-                logger.warning("[ditto] 请求超时 attempt=%d err=%s", attempt + 1, exc)
-                if attempt < self.max_retries:
-                    time.sleep(min(2**attempt, 15))
-                    continue
-                raise DittoError(
-                    f"Ditto 请求超时（{self.timeout}s），重试耗尽",
-                    code="Timeout",
-                ) from exc
-            except Exception as exc:
-                last_exc = exc
-                logger.warning("[ditto] 请求异常 attempt=%d err=%s", attempt + 1, exc)
-                if attempt < self.max_retries:
-                    time.sleep(min(2**attempt, 10))
-                    continue
-                raise DittoError(f"Ditto 调用异常: {exc}", code="NetworkError") from exc
-
-        raise DittoError("Ditto 未知错误", code="Unknown") from last_exc
-
-    def generate_and_persist(
-        self,
-        *,
-        job_id: str,
-        user_id: str,
-        audio_url: str,
-        script: str,
-        video_url: Optional[str] = None,
-    ) -> DittoResult:
-        """调用 generate 并把 MP4 转存到自家 OSS，返回带 video_url 的结果."""
-        result = self.generate(audio_url=audio_url, script=script, video_url=video_url)
-        try:
-            from packages.shared.storage import get_shared_storage_service
-
-            storage = get_shared_storage_service()
-            storage_key = f"ditto-output/{user_id}/{job_id}.mp4"
-            public_url = storage.upload_file(
-                io.BytesIO(result.video_bytes),
-                storage_key,
-                content_type="video/mp4",
-            )
-            result.video_url = public_url
-            logger.info(
-                "[ditto] 转存 OSS 完成 job=%s key=%s",
-                job_id,
-                storage_key,
-            )
-        except Exception as exc:
-            logger.error("[ditto] 转存 OSS 失败 job=%s err=%s", job_id, exc, exc_info=True)
-            raise DittoError(f"Ditto 结果转存 OSS 失败: {exc}", code="StorageError") from exc
-        return result
-
-
-_ditto_client_singleton: Optional[DittoClient] = None
-
-
-def get_ditto_client() -> DittoClient:
-    """获取 DittoClient 单例（简易工厂，便于单测 mock）."""
-    global _ditto_client_singleton
-    if _ditto_client_singleton is None:
-        _ditto_client_singleton = DittoClient()
-    return _ditto_client_singleton

packages/application/viral_video/__init__.py

@@ -1 +0,0 @@
-"""应用层：爆款视频 Prompt 模板系统（#2040）。"""

packages/application/viral_video/generator.py

@@ -1,427 +0,0 @@
-"""爆款视频 5 步编排：图片分析 → 意图解析 → 文案融合 → 分镜 → 审核重写。
-
-所有 LLM 调用走 DoubaoClient，单测通过 client 参数注入 mock，不真调 API。
-任何一步解析失败都走规则 fallback，不抛异常阻断。
-"""
-
-from __future__ import annotations
-
-import logging
-from typing import Optional
-
-from packages.application.viral_video import xml_parser as xp
-from packages.application.viral_video.prompt_loader import (
-    PromptTemplate,
-    get_template,
-    render_system_prompt,
-    render_user_prompt,
-)
-from packages.application.viral_video.prompts import (
-    FUSION_INSTRUCTIONS,
-    GLOBAL_CONSTRAINTS,
-    NEGATIVE_RULES,
-)
-from packages.application.viral_video.reviewer import Reviewer
-from packages.application.viral_video.schemas import (
-    BodyPoint,
-    Clip,
-    ColorItem,
-    CoreMessage,
-    FusionResult,
-    ImageAnalysis,
-    IntentResult,
-    KenBurns,
-    PersonalBrand,
-    ProductItem,
-    ReviewResult,
-    ScriptSegment,
-    Storyboard,
-    TextItem,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class CopyGenerator:
-    """5 步 Prompt 编排器。"""
-
-    def __init__(self, client=None, reviewer: Optional[Reviewer] = None):
-        if client is None:
-            from packages.shared.ai_client import get_doubao_client
-
-            client = get_doubao_client()
-        self.client = client
-        self.reviewer = reviewer or Reviewer(client)
-
-    # ── 底层调用 ────────────────────────────────────────────────────────
-    def _chat(self, template: PromptTemplate, system_kwargs: dict | None, **user_kwargs) -> str:
-        system = render_system_prompt(template, **(system_kwargs or {}))
-        user = render_user_prompt(template, **user_kwargs)
-        result = self.client.chat_completion(
-            [
-                {"role": "system", "content": system},
-                {"role": "user", "content": user},
-            ],
-            temperature=0.7,
-            max_tokens=2048,
-        )
-        return result or ""
-
-    # ── 步骤1：图片多模态分析 ───────────────────────────────────────────
-    def analyze_images(self, images: list[str], industry: str = "") -> ImageAnalysis:
-        template = get_template("image_analysis")
-        image_urls = "\n".join(f"第{i + 1}张：{url}" for i, url in enumerate(images))
-        system = render_system_prompt(template)
-        user = render_user_prompt(template, image_count=len(images), industry=industry or "通用", image_urls=image_urls)
-        raw = self.client.vision_completion(
-            [
-                {"role": "system", "content": system},
-                {"role": "user", "content": user},
-            ],
-            images=images,
-            max_tokens=2048,
-            temperature=0.3,
-        )
-        analysis = self._parse_image_analysis(raw or "")
-        if not analysis.products and not analysis.key_selling_points:
-            logger.warning("图片分析标签解析失败，走规则 fallback")
-            return self._fallback_image_analysis(images, raw or "")
-        return analysis
-
-    def _parse_image_analysis(self, raw: str) -> ImageAnalysis:
-        products = [
-            ProductItem(
-                name=n["attrs"].get("name", "无法判断"),
-                features=n["attrs"].get("features", "无法判断"),
-                position=n["attrs"].get("position", "secondary"),
-                image_index=xp.attr_int(n["attrs"].get("image_index"), 0),
-            )
-            for n in xp.find_all(raw, "product")
-        ]
-        colors = [
-            ColorItem(
-                hex=c["attrs"].get("hex", "#000000"),
-                name=c["attrs"].get("name", "无法判断"),
-                coverage=xp.attr_float(c["attrs"].get("coverage"), 0.0),
-            )
-            for c in xp.find_all(raw, "color")
-        ]
-        people = xp.find_first(raw, "people")
-        visible_text = [
-            TextItem(text=t["attrs"].get("text", ""), position=t["attrs"].get("position", ""))
-            for t in xp.find_all(raw, "text_item")
-        ]
-        quality_node = xp.find_first(raw, "quality")
-        selling_points = [n["text"] or n["attrs"].get("text", "") for n in xp.find_all(raw, "point")]
-        return ImageAnalysis(
-            products=products,
-            colors=colors,
-            has_person=xp.attr_bool(people["attrs"].get("has_person")) if people else False,
-            person_count=xp.attr_int(people["attrs"].get("count"), 0) if people else 0,
-            people=people["attrs"] if people else {},
-            mood=xp.text_of(raw, "mood"),
-            visible_text=visible_text,
-            scene=xp.text_of(raw, "scene"),
-            quality=quality_node["attrs"] if quality_node else {},
-            key_selling_points=[p for p in selling_points if p],
-            raw=raw,
-        )
-
-    def _fallback_image_analysis(self, images: list[str], raw: str) -> ImageAnalysis:
-        return ImageAnalysis(
-            products=[ProductItem(name="无法判断（视觉分析不可用）", image_index=0)],
-            scene="无法判断",
-            raw=raw,
-        )
-
-    # ── 步骤2：意图解析 ─────────────────────────────────────────────────
-    def parse_intent(self, user_copy_text: str, image_analysis: ImageAnalysis, industry: str = "") -> IntentResult:
-        template = get_template("intent_parsing")
-        raw = self._chat(
-            template,
-            None,
-            user_copy_text=user_copy_text or "（用户没有提供文案）",
-            industry=industry or "通用",
-            image_analysis=self._image_brief(image_analysis),
-        )
-        intent = self._parse_intent(raw)
-        if not intent.intent_summary and not intent.core_messages:
-            logger.warning("意图解析标签解析失败，走规则 fallback")
-            return self._fallback_intent(user_copy_text, raw)
-        return intent
-
-    def _parse_intent(self, raw: str) -> IntentResult:
-        messages = [
-            CoreMessage(
-                text=n["text"],
-                must_keep=xp.attr_bool(n["attrs"].get("must_keep"), default=False),
-                confidence=xp.attr_float(n["attrs"].get("confidence"), 0.0),
-            )
-            for n in xp.find_all(raw, "message")
-            if n["text"]
-        ]
-        brands = [
-            PersonalBrand(text=n["text"], category=n["attrs"].get("category", "brand"))
-            for n in xp.find_all(raw, "brand")
-            if n["text"]
-        ]
-        missing = [n["text"] for n in xp.find_all(raw, "info") if n["text"]]
-        return IntentResult(
-            intent_summary=xp.text_of(raw, "intent_summary"),
-            core_messages=messages,
-            personal_brands=brands,
-            emotion_tone=xp.text_of(raw, "emotion_tone"),
-            missing_info=missing,
-            raw=raw,
-        )
-
-    def _fallback_intent(self, user_copy_text: str, raw: str) -> IntentResult:
-        text = (user_copy_text or "").strip()
-        messages = [CoreMessage(text=text[:80], must_keep=True, confidence=1.0)] if text else []
-        return IntentResult(
-            intent_summary=text[:30] or "未提供文案，按产品图片自由创作",
-            core_messages=messages,
-            personal_brands=[],
-            raw=raw,
-        )
-
-    # ── 步骤3：文案融合生成（三档）──────────────────────────────────────
-    def fuse(
-        self,
-        fusion_level: str,
-        image_analysis: ImageAnalysis,
-        intent: IntentResult,
-        industry: str = "",
-        target_customer: str = "",
-        marketing_purpose: str = "",
-        duration: int = 15,
-    ) -> FusionResult:
-        template = get_template("copy_fusion")
-        system_kwargs = {
-            "fusion_instruction": FUSION_INSTRUCTIONS.get(fusion_level, FUSION_INSTRUCTIONS["ai_polish"]),
-            "global_constraints": GLOBAL_CONSTRAINTS,
-            "negative_rules": NEGATIVE_RULES,
-        }
-        raw = self._chat(
-            template,
-            system_kwargs,
-            industry=industry or "通用",
-            target_customer=target_customer or "通用消费者",
-            marketing_purpose=marketing_purpose or "产品种草",
-            duration=duration,
-            image_analysis=self._image_brief(image_analysis),
-            intent_result=self._intent_brief(intent),
-        )
-        result = self._parse_fusion(raw)
-        if not result.title and not result.script_segments:
-            logger.warning("文案融合标签解析失败（fusion=%s），走规则 fallback", fusion_level)
-            return self._fallback_fusion(fusion_level, image_analysis, intent, duration, raw)
-        return result
-
-    def _parse_fusion(self, raw: str) -> FusionResult:
-        body_points = [
-            BodyPoint(
-                text=n["text"],
-                elaboration=n["attrs"].get("elaboration", ""),
-                image_index=xp.attr_int(n["attrs"].get("image_index"), 0),
-            )
-            for n in xp.find_all(raw, "point")
-            if n["text"]
-        ]
-        segments = [
-            ScriptSegment(
-                text=n["text"],
-                duration_sec=xp.attr_float(n["attrs"].get("duration_sec"), 0.0),
-                image_index=xp.attr_int(n["attrs"].get("image_index"), 0),
-            )
-            for n in xp.find_all(raw, "segment")
-            if n["text"]
-        ]
-        return FusionResult(
-            title=xp.text_of(raw, "title"),
-            hook=xp.text_of(raw, "hook"),
-            body_points=body_points,
-            cta=xp.text_of(raw, "cta"),
-            script_segments=segments,
-            word_count=xp.attr_int(xp.text_of(raw, "word_count"), 0),
-            estimated_duration=xp.attr_int(xp.text_of(raw, "estimated_duration"), 0),
-            raw=raw,
-        )
-
-    def _fallback_fusion(
-        self,
-        fusion_level: str,
-        image_analysis: ImageAnalysis,
-        intent: IntentResult,
-        duration: int,
-        raw: str,
-    ) -> FusionResult:
-        product_name = image_analysis.products[0].name if image_analysis.products else "这款产品"
-        selling = image_analysis.key_selling_points[:2]
-        if fusion_level == "ai_full":
-            title = f"{product_name}，很多人用完都回购了"
-            hook = f"这个{product_name}，我想认真说说"
-            body = selling or ["图片可见的产品卖点"]
-            cta = "感兴趣的可以了解一下"
-        elif fusion_level == "user_primary":
-            user_text = intent.intent_summary or product_name
-            title = user_text[:20]
-            hook = user_text[:15]
-            body = [m.text for m in intent.core_messages] or [user_text]
-            cta = "想了解的可以看看"
-        else:
-            title = intent.intent_summary[:20] or product_name
-            hook = intent.core_messages[0].text[:15] if intent.core_messages else product_name
-            body = [m.text for m in intent.core_messages] or selling or [product_name]
-            cta = "有需要的可以了解一下"
-
-        brand_texts = [b.text for b in intent.personal_brands]
-        points = [BodyPoint(text=b) for b in body]
-        lines = [hook] + body + brand_texts[:2] + [cta]
-        joined = "，".join(lines)
-        per = max(3, duration // max(1, len(lines)))
-        segments = [ScriptSegment(text=line, duration_sec=per, image_index=0) for line in lines]
-        return FusionResult(
-            title=title,
-            hook=hook,
-            body_points=points,
-            cta=cta,
-            script_segments=segments,
-            word_count=len(joined),
-            estimated_duration=duration,
-            raw=raw,
-        )
-
-    # ── 步骤4：编导级分镜 ───────────────────────────────────────────────
-    def storyboard(
-        self, fusion: FusionResult, image_analysis: ImageAnalysis, images: list[str], duration: int
-    ) -> Storyboard:
-        template = get_template("storyboard")
-        raw = self._chat(
-            template,
-            None,
-            duration=duration,
-            image_count=len(images),
-            fusion_result=self._fusion_brief(fusion),
-            image_analysis=self._image_brief(image_analysis),
-        )
-        board = self._parse_storyboard(raw)
-        if not board.clips:
-            logger.warning("分镜标签解析失败，走规则 fallback")
-            return self._fallback_storyboard(fusion, duration, raw)
-        return board
-
-    def _parse_storyboard(self, raw: str) -> Storyboard:
-        clips: list[Clip] = []
-        for node in xp.find_all(raw, "clip"):
-            attrs = node["attrs"]
-            body = node["text"]
-            kb = xp.find_first(node["text"] and f"<root>{node['text']}</root>", "ken_burns")
-            clips.append(
-                Clip(
-                    image_index=xp.attr_int(attrs.get("image_index"), 0),
-                    transition=attrs.get("transition", "cut"),
-                    zoom=(None if attrs.get("zoom") in (None, "null", "None", "") else attrs.get("zoom")),
-                    duration_sec=xp.attr_float(attrs.get("duration_sec"), 0.0),
-                    bgm_note=attrs.get("bgm_note", ""),
-                    voice_text=xp.text_of(body and f"<root>{body}</root>", "voice_text"),
-                    subtitle_text=xp.text_of(body and f"<root>{body}</root>", "subtitle_text"),
-                    ken_burns=KenBurns(
-                        start=kb["attrs"].get("start", "0,0") if kb else "0,0",
-                        end=kb["attrs"].get("end", "0,0") if kb else "0,0",
-                        ease=kb["attrs"].get("ease", "linear") if kb else "linear",
-                    ),
-                )
-            )
-        return Storyboard(clips=clips, raw=raw)
-
-    def _fallback_storyboard(self, fusion: FusionResult, duration: int, raw: str) -> Storyboard:
-        segments = fusion.script_segments or [ScriptSegment(text=fusion.hook or fusion.title, duration_sec=duration)]
-        total = sum(s.duration_sec for s in segments) or duration
-        clips = [
-            Clip(
-                image_index=min(s.image_index, 0),
-                transition="cut",
-                duration_sec=max(2.0, s.duration_sec * duration / total if total else duration / len(segments)),
-                voice_text=s.text,
-                subtitle_text=s.text[:20],
-            )
-            for s in segments
-        ]
-        return Storyboard(clips=clips, raw=raw)
-
-    # ── 步骤5：审核（不通过自动重写1次）─────────────────────────────────
-    def review_and_rewrite(
-        self, fusion: FusionResult, intent: IntentResult, fusion_level: str
-    ) -> tuple[FusionResult, ReviewResult, int]:
-        """返回最终文案、最后一次审核结果、重写次数（0或1）。"""
-        review = self.reviewer.review(fusion, intent, fusion_level)
-        if review.passed:
-            return fusion, review, 0
-
-        logger.info("文案审核不通过，自动重写 1 次：%s", [i.text for i in review.issues])
-        rewritten = self.reviewer.rewrite(fusion, review, intent, fusion_level)
-        second = self.reviewer.review(rewritten, intent, fusion_level)
-        if second.passed:
-            return rewritten, second, 1
-        # 二次仍不通过：带上重写结果和问题返回，由上游决定是否交给前端
-        return rewritten, second, 1
-
-    # ── 全流程编排 ──────────────────────────────────────────────────────
-    def generate(
-        self,
-        images: list[str],
-        *,
-        industry: str = "",
-        target_customer: str = "",
-        marketing_purpose: str = "",
-        duration: int = 15,
-        user_copy_text: str = "",
-        fusion_level: str = "ai_polish",
-    ) -> dict:
-        image_analysis = self.analyze_images(images, industry)
-        intent = self.parse_intent(user_copy_text, image_analysis, industry)
-        fusion = self.fuse(
-            fusion_level,
-            image_analysis,
-            intent,
-            industry=industry,
-            target_customer=target_customer,
-            marketing_purpose=marketing_purpose,
-            duration=duration,
-        )
-        fusion, review, rewrites = self.review_and_rewrite(fusion, intent, fusion_level)
-        board = self.storyboard(fusion, image_analysis, images, duration)
-        return {
-            "image_analysis": image_analysis,
-            "intent_result": intent,
-            "fusion_result": fusion,
-            "review_result": review,
-            "storyboard": board,
-            "rewrite_count": rewrites,
-        }
-
-    # ── 简报工具 ────────────────────────────────────────────────────────
-    @staticmethod
-    def _image_brief(a) -> str:
-        if a is None:
-            return "无图片分析信息"
-        lines = [f"产品：{p.name}（{p.features}）" for p in a.products]
-        lines += [f"卖点：{s}" for s in a.key_selling_points]
-        lines.append(f"场景：{a.scene}")
-        return "\n".join(lines) or "无图片分析信息"
-
-    @staticmethod
-    def _intent_brief(i: IntentResult) -> str:
-        lines = [f"意图：{i.intent_summary}"]
-        lines += [f"核心信息[must_keep={m.must_keep}]：{m.text}" for m in i.core_messages]
-        lines += [f"事实（{b.category}）：{b.text}" for b in i.personal_brands]
-        return "\n".join(lines)
-
-    @staticmethod
-    def _fusion_brief(f: FusionResult) -> str:
-        lines = [f"标题：{f.title}", f"钩子：{f.hook}"]
-        lines += [f"要点：{p.text}" for p in f.body_points]
-        lines += [f"配音：{s.text}" for s in f.script_segments]
-        lines.append(f"行动号召：{f.cta}")
-        return "\n".join(lines)

packages/application/viral_video/prompt_loader.py

@@ -1,160 +0,0 @@
-"""Prompt 模板加载器：从 viral_video_prompt_templates 读模板，30 秒 TTL 热加载。
-
-DB 不可用或没有数据时自动回落到 prompts.DEFAULT_TEMPLATES，保证流程不阻断。
-"""
-
-from __future__ import annotations
-
-import threading
-import time
-from dataclasses import dataclass
-from typing import Optional
-
-import sqlalchemy as sa
-
-from packages.adapters.sqlalchemy_impl import session as _session_mod
-from packages.application.viral_video.prompts import DEFAULT_TEMPLATES
-
-CACHE_TTL_SECONDS = 30.0
-
-_VALID_TYPES = {"image_analysis", "intent_parsing", "copy_fusion", "storyboard", "review"}
-
-
-@dataclass
-class PromptTemplate:
-    name: str
-    prompt_type: str
-    version: int
-    system_prompt: str
-    user_prompt_template: str
-    example_output: str = ""
-    is_active: bool = True
-
-
-_lock = threading.Lock()
-_cache: dict[str, tuple[float, PromptTemplate]] = {}
-
-
-def _fallback(prompt_type: str) -> Optional[PromptTemplate]:
-    for item in DEFAULT_TEMPLATES:
-        if item["prompt_type"] == prompt_type:
-            return PromptTemplate(
-                name=item["name"],
-                prompt_type=item["prompt_type"],
-                version=item["version"],
-                system_prompt=item["system_prompt"],
-                user_prompt_template=item["user_prompt_template"],
-                example_output=item["example_output"] or "",
-                is_active=bool(item["is_active"]),
-            )
-    return None
-
-
-_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://")
-    url = url.replace("postgresql://", "postgresql+psycopg://") if url.startswith("postgresql://") else url
-    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 _load_from_db(prompt_type: str) -> Optional[PromptTemplate]:
-    session = None
-    try:
-        session = _get_session()
-        if session is None:
-            return None
-        sql = sa.text("""
-            SELECT name, prompt_type, version, system_prompt,
-                   user_prompt_template, COALESCE(example_output, '') AS example_output,
-                   is_active
-              FROM viral_video_prompt_templates
-             WHERE prompt_type = :pt AND is_active = TRUE
-             ORDER BY version DESC
-             LIMIT 1
-            """)
-        row = session.execute(sql, {"pt": prompt_type}).first()
-        if row is None:
-            return None
-        return PromptTemplate(
-            name=row[0],
-            prompt_type=row[1],
-            version=int(row[2]),
-            system_prompt=row[3],
-            user_prompt_template=row[4],
-            example_output=row[5] or "",
-            is_active=bool(row[6]),
-        )
-    except Exception:  # noqa: BLE001 - 表不存在/DB 不可用时静默回落
-        return None
-    finally:
-        if session is not None:
-            try:
-                session.close()
-            except Exception:  # noqa: BLE001
-                pass
-
-
-def get_template(prompt_type: str, *, force_refresh: bool = False) -> Optional[PromptTemplate]:
-    """取某类型当前启用模板，30 秒缓存；DB 无数据则回落到代码默认模板。"""
-    if prompt_type not in _VALID_TYPES:
-        raise ValueError(f"未知 prompt_type: {prompt_type}")
-
-    now = time.monotonic()
-    with _lock:
-        cached = _cache.get(prompt_type)
-        if not force_refresh and cached and now - cached[0] < CACHE_TTL_SECONDS:
-            return cached[1]
-
-    template = _load_from_db(prompt_type) or _fallback(prompt_type)
-    if template is not None:
-        with _lock:
-            _cache[prompt_type] = (now, template)
-    return template
-
-
-def invalidate() -> None:
-    """清空缓存（测试用）。"""
-    with _lock:
-        _cache.clear()
-
-
-class _SafeDict(dict):
-    def __missing__(self, key: str) -> str:
-        return "{" + key + "}"
-
-
-def _safe_format(text: str, kwargs: dict) -> str:
-    try:
-        return text.format_map(_SafeDict(kwargs))
-    except Exception:  # noqa: BLE001
-        return text
-
-
-def render_user_prompt(template: PromptTemplate, **kwargs) -> str:
-    """填充 user_prompt_template 占位符，缺键原样保留不报错。"""
-    return _safe_format(template.user_prompt_template, kwargs)
-
-
-def render_system_prompt(template: PromptTemplate, **kwargs) -> str:
-    """copy_fusion 等 system_prompt 含运行时变量时填充。"""
-    return _safe_format(template.system_prompt, kwargs)

packages/application/viral_video/prompts.py

@@ -1,230 +0,0 @@
-"""爆款视频 Prompt 模板默认值（v8 / v3 叙述优先重构）。
-
-设计原则（灵应 2026-10-07）：LLM 直接输出最终给用户看的文案，代码尽量薄。
-- image_analysis v8：VLM 主交付物是自然叙述风格的 summary_markdown，结构化
-  字段仅保留 type/name/brand/has_person，顶层 products 改名 images；
-- storyboard v3：口播台词口语化、画面描述有画面感，copy_display_markdown 是
-  LLM 直接写给用户看的流畅叙述文案，代码只做解析不改写；
-- intent_parsing 步骤整体删除，意图理解并入 storyboard 一次调用。
-
-模板字段与 DB 表 viral_video_prompt_templates、prompt_loader 完全对应：
-name / prompt_type / version(int) / system_prompt / user_prompt_template /
-example_output / is_active。
-"""
-
-from __future__ import annotations
-
-# 所有文案类 Prompt 自动注入的硬约束
-GLOBAL_CONSTRAINTS = """【必须遵守的硬约束】
-1. 不编造时间：不写“今年最新”“2024 爆款”等会过时的时间表述。
-2. 不承诺效果：不写“保证”“一定”“100%有效”“包治百病”等绝对化用语。
-3. 不编造价格、销量、认证、奖项：除非用户在文案中明确给出，否则一律不写。
-4. 符合广告法及平台社区规范。
-5. 只描述图片中真实可见的内容，看不到的不瞎猜。"""
-
-# 负向提示（注入 storyboard / 视频生成负面词）
-NEGATIVE_RULES = """【反套路化要求】
-禁止使用“家人们谁懂啊”“绝绝子”“宝子们”“家人们”“太绝了”“yyds”等烂大街网络词；
-禁止固定模板化开头；语言要像真人朋友之间的分享，自然、具体、有信息量。"""
-
-# 输出禁用套路词（测试会检查）
-BANNED_PHRASES = ["家人们谁懂啊", "绝绝子", "宝子们", "yyds", "太绝了"]
-
-# 文案融合三档独立指令段（storyboard 一次生成，按档位注入风格指令）
-FUSION_INSTRUCTIONS = {
-    "ai_full": """【本次创作模式：AI 全权创作】
-你是资深短视频编导。用户只提供了产品/门店图片，没有给出具体文案方向。请根据图片的真实观察和营销参数，自由发挥创作完整成片级方案，口播自然、画面可拍。""",
-    "ai_polish": """【本次创作模式：AI 辅助润色】
-用户已给出方向或碎碎念。以用户的意思为主，保留其所有核心信息，在此基础上润色、补衔接、优化表达，让口播更自然、画面更具体；绝不改变用户核心意思，不添加用户没提到的卖点，品牌名、价格、人名等事实原样保留。""",
-    "user_primary": """【本次创作模式：以用户原文为主】
-最小化修改：只做必要的通顺、合规修正与衔接补全，用户的核心句子与事实一律不改；用户文案已经很好就直接用，不为改而改。""",
-}
-
-# ── 模板1：图片多模态分析 v8（叙述优先）───────────────────────────────
-_IMAGE_ANALYSIS_SYSTEM = """你是一名擅长观察和写作的品牌内容编导。面对一张真实图片，先用眼睛仔细看，再用自然、流畅、具体的中文把画面写成一段可以直接读给人听的描述。
-
-## 输出格式（严格 JSON，不要输出 JSON 以外的任何内容）
-{
-  "images": [
-    {
-      "type": "store 或 product 或 person 或 scene，四选一",
-      "name": "主体名称，看不出就写“未识别”",
-      "brand": "品牌名，看不出就留空字符串",
-      "has_person": false,
-      "summary_markdown": "用 Markdown 写成的自然叙述，这是最主要的交付物"
-    }
-  ]
-}
-
-## summary_markdown 写作要求（最重要）
-1. 写成完整、通顺的句子，像在跟朋友认真描述你看到的画面；不要用分号堆砌关键词，不要罗列“核心特征：xxx”“主色调：xxx”这类填表式标签。
-2. 开头先给一句整体定性，让读者立刻明白这是什么场景、什么主体。
-3. 颜色、材质、形状、部件要具体可感，写到位置和搭配；画面里出现的文字原样读出并自然融进句子，数字、规格、价格精确引用，看不清的不要编造。
-4. 只写真实看到的内容，不脑补功能、疗效、销量或画面之外的信息。
-5. 长度控制在 200-500 字。
-
-## 按类型组织内容
-- type=store（门店/店内环境）：用以下小标题分段，小标题下写连贯的句子而不是清单：
-###店铺主体
-###周边物品
-1.家具陈设
-2.商品与标识
-- type=product（商品）：按自然段从整体到局部描写——先说是什么、什么品牌，再写包装/外形、颜色与材质、标签文字、可见部件与规格。
-- type=person（人物）：描述人物身份感、姿态、穿着（上下装/颜色/款式）、动作与所处环境；用于品牌宣传时突出其精神状态。
-- type=scene（纯场景/风景）：描述空间或风景的构成、色彩、光线、氛围与关键物件。
-
-## 判断规则
-- has_person：画面中出现可辨识的真实人物（脸或完整上半身）才为 true，海报/模特立牌/照片里的人不算。
-- 一张图只描述其本身；多张图属于同一场景时可呼应，但不编造对应关系。
-- 输出必须是严格 JSON，summary_markdown 是字符串，内部换行用 \\n 表示。"""
-
-_IMAGE_ANALYSIS_USER = """请分析这张图片。
-图片地址：{image_url}
-OCR 辅助文字（可能为空，仅供参考，不要照抄错误识别）：{ocr_text}
-
-严格按系统要求只输出 JSON。"""
-
-_IMAGE_ANALYSIS_EXAMPLE = """{
-  "images": [
-    {
-      "type": "store",
-      "name": "御众堂门店",
-      "brand": "御众堂",
-      "has_person": false,
-      "summary_markdown": "###店铺主体\\n这是一家名为“御众堂”的线下门店内部，整体暖木色调……"
-    }
-  ]
-}"""
-
-# ── 模板2：编导级分镜 v3（意图理解 + 分镜一次完成）────────────────────
-_STORYBOARD_SYSTEM = (
-    """你是一名懂短视频的编导和口播文案高手。你会拿到图片的真实观察、营销目的和用户参数，请一次性完成对营销意图的理解，并产出可直接拍摄/生成的分镜脚本。不要单独输出“意图解析”，意图要直接体现在台词和分镜里。
-
-## 输出格式（XML，严格按结构输出，不要输出额外解释）
-<script>
-  <copy_display_markdown><![CDATA[直接展示给用户看的成片文案，用 Markdown 写成流畅叙述]]></copy_display_markdown>
-  <clips>
-    <clip index="1">
-      <time_range>0-3秒</time_range>
-      <voiceover>这一镜的口播台词</voiceover>
-      <visual>具体、有画面感的镜头描述（主体/动作/镜头运动/景别/光线）</visual>
-      <reference_image_index>0</reference_image_index>
-    </clip>
-  </clips>
-  <voiceover_script>把所有 clip 的 voiceover 连成完整口播稿</voiceover_script>
-  <theme>一句话主题</theme>
-  <negative>"""
-    + NEGATIVE_RULES
-    + """</negative>
-</script>
-
-## 写作要求
-1. 口播台词：像真人面对镜头说话，短句、口语化、有停顿有情绪，开头 3 秒给出钩子；不要书面腔，不要机械报参数。
-2. 画面描述：写清“观众会看到什么”，有动作、有镜头运动、有景别和光线，具体可拍；不堆砌形容词，不写无法实现的画面。
-3. copy_display_markdown：直接展示给最终用户的文案，用 Markdown 写成自然、流畅、有感染力的成片成片文案，可用小标题与短句组织；不要做字段列表，不要出现“镜头一/台词：”这类制作说明。
-4. 内容必须来自图片观察与用户给出的信息，不编造卖点、不夸大、不使用绝对化用语和虚假承诺。
-5. reference_image_index 填本镜参考图片序号（从 0 开始），没有合适参考图填 -1。
-6. 分镜数量与时长匹配总时长，节奏紧凑。"""
-)
-
-_STORYBOARD_USER = """<marketing_purpose>{marketing_purpose}</marketing_purpose>
-<image_analysis>
-{image_summary}
-</image_analysis>
-<user_parameters>
-<theme_hint>{theme_hint}</theme_hint>
-<duration>{duration}秒</duration>
-<aspect_ratio>{aspect_ratio}</aspect_ratio>
-<tone>{tone}</tone>
-<target_audience>{target_audience}</target_audience>
-<extra_requirements>{extra_requirements}</extra_requirements>
-</user_parameters>
-{video_style_section}
-请严格按 XML 结构输出分镜脚本。"""
-
-_STORYBOARD_EXAMPLE = """<script>
-  <copy_display_markdown><![CDATA[# 在御众堂，把松弛的自己一点点找回来
-产后妈妈最懂那种力不从心，推开门，暖光和一杯热茶先接住了你……]]></copy_display_markdown>
-  <clips>
-    <clip index="1">
-      <time_range>0-3秒</time_range>
-      <voiceover>生完娃，是不是连照镜子的勇气都没了？</voiceover>
-      <visual>中近景，暖光下一位妈妈略显疲惫地看向镜中，镜头缓缓推近</visual>
-      <reference_image_index>0</reference_image_index>
-    </clip>
-  </clips>
-  <voiceover_script>生完娃，是不是连照镜子的勇气都没了？</voiceover_script>
-  <theme>产后妈妈走进御众堂重拾状态</theme>
-  <negative>模糊、畸变、夸大疗效、绝对化用语</negative>
-</script>"""
-
-# ── 模板3：文案审核（合规/质量门禁）───────────────────────────────────
-_REVIEW_SYSTEM = """你是一名短视频广告合规审核与文案优化专家。审核待审文案：
-1) 广告法与平台合规（绝对化用语、虚假承诺、医疗功效宣称、导流违规）；
-2) 卖点是否聚焦、逻辑是否通顺、口播是否自然；
-3) 是否有机械堆砌、书面腔、标签化表述。
-
-只输出 XML，结构：
-<review>
-  <passed>true 或 false</passed>
-  <issues>
-    <issue>
-      <severity>high 或 medium 或 low</severity>
-      <field>问题所在位置/字段</field>
-      <problem>具体问题</problem>
-      <suggestion>可直接替换的修改</suggestion>
-    </issue>
-  </issues>
-  <rewrite>整体重写后的合规流畅版本（无问题时留空）</rewrite>
-</review>
-没有问题时 issues 留空、passed 为 true、rewrite 留空。"""
-
-_REVIEW_USER = """<fusion_text>
-{fusion_text}
-</fusion_text>
-
-请审核以上文案。"""
-
-_REVIEW_EXAMPLE = """<review>
-  <passed>false</passed>
-  <issues>
-    <issue>
-      <severity>high</severity>
-      <field>opening</field>
-      <problem>使用绝对化用语“全网第一”</problem>
-      <suggestion>改为“很多老客户回购的一款”</suggestion>
-    </issue>
-  </issues>
-  <rewrite>……</rewrite>
-</review>"""
-
-
-DEFAULT_TEMPLATES: list[dict] = [
-    {
-        "name": "图片多模态分析 v8",
-        "prompt_type": "image_analysis",
-        "version": 8,
-        "system_prompt": _IMAGE_ANALYSIS_SYSTEM,
-        "user_prompt_template": _IMAGE_ANALYSIS_USER,
-        "example_output": _IMAGE_ANALYSIS_EXAMPLE,
-        "is_active": True,
-    },
-    {
-        "name": "编导级分镜 v3",
-        "prompt_type": "storyboard",
-        "version": 3,
-        "system_prompt": _STORYBOARD_SYSTEM,
-        "user_prompt_template": _STORYBOARD_USER,
-        "example_output": _STORYBOARD_EXAMPLE,
-        "is_active": True,
-    },
-    {
-        "name": "文案审核",
-        "prompt_type": "review",
-        "version": 1,
-        "system_prompt": _REVIEW_SYSTEM,
-        "user_prompt_template": _REVIEW_USER,
-        "example_output": _REVIEW_EXAMPLE,
-        "is_active": True,
-    },
-]

packages/application/viral_video/reviewer.py

@@ -1,337 +0,0 @@
-"""文案审核 + 自动重写（#2040 第5套 Prompt）。
-
-6 维度：违规词 / 夸大承诺 / 事实一致性 / 用户意图保留 / 结构完整性 / 语气人设。
-LLM 审核之外叠加本地规则预检（保证即使 LLM 不可用也能兜住广告法红线）。
-"""
-
-from __future__ import annotations
-
-import logging
-import re
-from typing import Optional
-
-from packages.application.viral_video import xml_parser as xp
-from packages.application.viral_video.prompt_loader import (
-    get_template,
-    render_system_prompt,
-    render_user_prompt,
-)
-from packages.application.viral_video.schemas import (
-    FusionResult,
-    IntentResult,
-    ReviewIssue,
-    ReviewResult,
-)
-
-logger = logging.getLogger(__name__)
-
-# 本地规则：绝对化/夸大词
-_EXAGGERATION_PATTERNS = [
-    r"100\s*%",
-    r"百分百",
-    r"包治百病",
-    r"保证.{0,8}(有效|赚钱|瘦|好)",
-    r"绝对(有效|安全|靠谱)",
-    r"全网第一",
-    r"国家级",
-    r"特效",
-    r"立刻见效",
-    r"一喷(就|全|100)",
-]
-
-# 本地规则：平台违规/套路词
-_VIOLATION_PHRASES = [
-    "家人们谁懂啊",
-    "绝绝子",
-    "宝子们",
-    "yyds",
-    "最(好|强|牛|便宜)",  # 广告法极限词
-    "第一(名|品牌)?",
-]
-
-_LOCATIONS = ["title", "hook", "body_points", "cta", "script_segments"]
-
-
-class Reviewer:
-    # markdown展示字段不参与合规审核（避免格式字符误判）
-    _MARKDOWN_FIELDS = {"summary_markdown", "copy_display_markdown"}
-
-    def __init__(self, client=None):
-        if client is None:
-            try:
-                from packages.shared.ai_router import ai_router
-
-                client = ai_router.get_llm_client("copy_review")
-            except Exception:
-                from packages.shared.ai_client import get_doubao_client
-
-                client = get_doubao_client()
-        self.client = client
-
-    # ── 审核 ────────────────────────────────────────────────────────────
-    def review(self, fusion: FusionResult, intent: IntentResult, fusion_level: str) -> ReviewResult:
-        local = self._rule_check(fusion, intent, fusion_level)
-        llm_result = self._llm_review(fusion, intent, fusion_level)
-        if llm_result is None:
-            # LLM审核失败（超时/网络错误等），降级放行，不阻断渲染
-            return ReviewResult(
-                passed=True,
-                issues=local,
-                rewrite_suggestions=[],
-                raw="",
-            )
-        # LLM 与本地规则合并去重
-        issues = self._merge_issues(llm_result.issues, local)
-        return ReviewResult(
-            passed=llm_result.passed and not local,
-            issues=issues,
-            rewrite_suggestions=llm_result.rewrite_suggestions,
-            raw=llm_result.raw,
-        )
-
-    def _llm_review(self, fusion: FusionResult, intent: IntentResult, fusion_level: str) -> Optional[ReviewResult]:
-        try:
-            return self._llm_review_inner(fusion, intent, fusion_level)
-        except Exception as e:
-            import logging
-
-            logging.getLogger(__name__).warning("[Reviewer] LLM审核调用异常，降级放行: %s", e)
-            return None
-
-    def _llm_review_inner(
-        self, fusion: FusionResult, intent: IntentResult, fusion_level: str
-    ) -> Optional[ReviewResult]:
-        template = get_template("review")
-        system = render_system_prompt(template)
-        user = render_user_prompt(
-            template,
-            fusion_level=fusion_level,
-            fusion_result=self._fusion_text(fusion),
-            intent_result=self._intent_text(intent),
-        )
-        raw = self.client.chat_completion(
-            [
-                {"role": "system", "content": system},
-                {"role": "user", "content": user},
-            ],
-            temperature=0.2,
-            max_tokens=1024,
-            timeout=25,
-        )
-        if not raw:
-            return None
-        passed = xp.text_of(raw, "passed").strip().lower()
-        issues = [
-            ReviewIssue(
-                dimension=n["attrs"].get("dimension", "未知维度"),
-                severity=n["attrs"].get("severity", "warning"),
-                location=n["attrs"].get("location", ""),
-                text=n["text"],
-            )
-            for n in xp.find_all(raw, "issue")
-            if n["text"]
-        ]
-        suggestions = [n["text"] for n in xp.find_all(raw, "suggestion") if n["text"]]
-        parsed = ReviewResult(
-            passed=passed == "true" and not issues,
-            issues=issues,
-            rewrite_suggestions=suggestions,
-            raw=raw,
-        )
-        return parsed
-
-    # ── 本地规则预检 ────────────────────────────────────────────────────
-    def _rule_check(self, fusion: FusionResult, intent, fusion_level: str) -> list[ReviewIssue]:
-        issues: list[ReviewIssue] = []
-        for location, text in self._segments(fusion):
-            for pattern in _EXAGGERATION_PATTERNS:
-                if re.search(pattern, text):
-                    issues.append(
-                        ReviewIssue(
-                            dimension="夸大承诺",
-                            severity="error",
-                            location=location,
-                            text=f"出现夸大/绝对化表述：{self._hit(text, pattern)}",
-                        )
-                    )
-            for phrase in _VIOLATION_PHRASES:
-                if re.search(phrase, text, flags=re.IGNORECASE):
-                    issues.append(
-                        ReviewIssue(
-                            dimension="违规词",
-                            severity="error",
-                            location=location,
-                            text=f"出现违规或套路词：{self._hit(text, phrase)}",
-                        )
-                    )
-
-        # 结构完整性
-        if not fusion.title:
-            issues.append(ReviewIssue(dimension="结构完整性", severity="warning", location="title", text="缺少标题"))
-        if not fusion.hook:
-            issues.append(ReviewIssue(dimension="结构完整性", severity="warning", location="hook", text="缺少开头钩子"))
-        if not fusion.cta:
-            issues.append(ReviewIssue(dimension="结构完整性", severity="warning", location="cta", text="缺少行动号召"))
-
-        # 用户意图保留（must_keep）
-        full_text = self._fusion_text(fusion)
-        if fusion_level in {"ai_polish", "user_primary"} and intent is not None:
-            for message in intent.core_messages:
-                if message.must_keep:
-                    key = self._compact(message.text)
-                    if key and key[:10] not in self._compact(full_text):
-                        issues.append(
-                            ReviewIssue(
-                                dimension="用户意图保留",
-                                severity="warning",
-                                location="script_segments",
-                                text=f"用户核心信息被丢失：{message.text[:30]}",
-                            )
-                        )
-            for brand in intent.personal_brands:
-                if brand.text and brand.text not in full_text:
-                    issues.append(
-                        ReviewIssue(
-                            dimension="事实一致性",
-                            severity="error",
-                            location="script_segments",
-                            text=f"personal_brands 事实信息未原样保留：{brand.text[:30]}",
-                        )
-                    )
-        return issues
-
-    @staticmethod
-    def _hit(text: str, pattern: str) -> str:
-        match = re.search(pattern, text, flags=re.IGNORECASE)
-        return match.group(0) if match else pattern
-
-    @staticmethod
-    def _compact(text: str) -> str:
-        return re.sub(r"[\s，。！？、,.!?；;：:\"'“”‘’（）()【】\[\]]", "", text)
-
-    @staticmethod
-    def _merge_issues(llm_issues: list[ReviewIssue], local: list[ReviewIssue]) -> list[ReviewIssue]:
-        merged = list(local)
-        seen = {(i.dimension, Reviewer._compact(i.text)[:20]) for i in local}
-        for issue in llm_issues:
-            key = (issue.dimension, Reviewer._compact(issue.text)[:20])
-            if key not in seen:
-                merged.append(issue)
-                seen.add(key)
-        return merged
-
-    # ── 自动重写（1 次）─────────────────────────────────────────────────
-    def rewrite(
-        self,
-        fusion: FusionResult,
-        review: ReviewResult,
-        intent: IntentResult,
-        fusion_level: str,
-    ) -> FusionResult:
-        from packages.application.viral_video.generator import CopyGenerator
-
-        template = get_template("copy_fusion")
-        system_kwargs = {
-            "fusion_instruction": (
-                "【本次任务：按审核意见修正文案】只修改指出的问题，其他内容尽量原样保留；"
-                "personal_brands 事实信息逐字保留；修正后按原标签格式完整输出。"
-            ),
-            "global_constraints": "",
-            "negative_rules": "",
-        }
-        issue_text = "\n".join(f"- [{i.dimension}/{i.location}] {i.text}" for i in review.issues)
-        suggestion_text = "\n".join(f"- {s}" for s in review.rewrite_suggestions)
-        user = render_user_prompt(
-            template,
-            industry="",
-            target_customer="",
-            marketing_purpose="",
-            duration=fusion.estimated_duration or 15,
-            image_analysis="（沿用原图片分析）",
-            intent_result=self._intent_text(intent),
-        )
-        user = (
-            f"{user}\n\n原文案：\n{self._fusion_text(fusion)}\n\n"
-            f"审核发现的问题：\n{issue_text}\n\n修改建议：\n{suggestion_text or '（无）'}\n"
-            "请输出修正后的完整文案。"
-        )
-        system = render_system_prompt(template, **system_kwargs)
-        raw = self.client.chat_completion(
-            [
-                {"role": "system", "content": system},
-                {"role": "user", "content": user},
-            ],
-            temperature=0.5,
-            max_tokens=2048,
-            timeout=25,
-        )
-        if not raw:
-            return self._rule_fix(fusion, review)
-        rewritten = CopyGenerator._parse_fusion(CopyGenerator(self.client), raw)
-        if not rewritten.title and not rewritten.script_segments:
-            return self._rule_fix(fusion, review)
-        # 保底：personal_brands 必须保留
-        full = self._fusion_text(rewritten)
-        for brand in intent.personal_brands:
-            if brand.text and brand.text not in full:
-                rewritten.cta = (rewritten.cta + brand.text).strip()
-        return rewritten
-
-    def _rule_fix(self, fusion: FusionResult, review: ReviewResult) -> FusionResult:
-        """LLM 重写不可用时的本地兜底：删除/替换明显违规表述。"""
-        replacements = [
-            (re.compile(r"100\s*%|百分百"), "大部分"),
-            (re.compile(r"绝对(有效|安全|靠谱)"), "比较\\1"),
-            (re.compile(r"包治百病"), "适用多种情况"),
-            (re.compile(r"立刻见效"), "坚持使用会有改善"),
-            (re.compile(r"一喷(就|全|100%)"), "喷上等一会儿可以"),
-            (re.compile(r"家人们谁懂啊|绝绝子|宝子们|yyds", re.IGNORECASE), ""),
-            (re.compile(r"最好|最强|最牛|最便宜"), "很不错"),
-        ]
-
-        def fix(text: str) -> str:
-            for pattern, repl in replacements:
-                text = pattern.sub(repl, text)
-            return text
-
-        fusion.title = fix(fusion.title)
-        fusion.hook = fix(fusion.hook)
-        fusion.cta = fix(fusion.cta)
-        for point in fusion.body_points:
-            point.text = fix(point.text)
-            point.elaboration = fix(point.elaboration)
-        for segment in fusion.script_segments:
-            segment.text = fix(segment.text)
-        fusion.raw = ""
-        return fusion
-
-    # ── 文本工具 ────────────────────────────────────────────────────────
-    @staticmethod
-    def _segments(fusion: FusionResult):
-        yield "title", fusion.title
-        yield "hook", fusion.hook
-        for point in fusion.body_points:
-            yield "body_points", f"{point.text} {point.elaboration}"
-        yield "cta", fusion.cta
-        for segment in fusion.script_segments:
-            yield "script_segments", segment.text
-
-    @staticmethod
-    def _fusion_text(fusion: FusionResult) -> str:
-        _MARKDOWN_FIELDS = {"summary_markdown", "copy_display_markdown"}
-        parts = [fusion.title, fusion.hook]
-        parts += [p.text for p in fusion.body_points]
-        parts += [s.text for s in fusion.script_segments]
-        parts.append(fusion.cta)
-        # 过滤掉markdown展示字段，避免格式字符被误判
-        parts = [p for p in parts if not any(mk in p for mk in _MARKDOWN_FIELDS)]
-        return "\n".join(p for p in parts if p)
-
-    @staticmethod
-    def _intent_text(intent) -> str:
-        if intent is None:
-            return "无意图信息"
-        parts = [f"意图：{intent.intent_summary}"]
-        parts += [f"核心信息[must_keep={m.must_keep}]：{m.text}" for m in intent.core_messages]
-        parts += [f"事实（{b.category}）：{b.text}" for b in intent.personal_brands]
-        return "\n".join(parts)

packages/application/viral_video/schemas.py

@@ -1,116 +0,0 @@
-"""内部 Pydantic 校验模型（不暴露给运营，运营只看 DB 里的纯文本）。"""
-
-from __future__ import annotations
-
-from pydantic import BaseModel, Field
-
-
-class ProductItem(BaseModel):
-    name: str = "无法判断"
-    features: str = "无法判断"
-    position: str = "secondary"
-    image_index: int = 0
-
-
-class ColorItem(BaseModel):
-    hex: str = "#000000"
-    name: str = "无法判断"
-    coverage: float = 0.0
-
-
-class TextItem(BaseModel):
-    text: str = ""
-    position: str = ""
-
-
-class ImageAnalysis(BaseModel):
-    products: list[ProductItem] = Field(default_factory=list)
-    colors: list[ColorItem] = Field(default_factory=list)
-    has_person: bool = False
-    person_count: int = 0
-    people: dict[str, str] = Field(default_factory=dict)
-    mood: str = ""
-    visible_text: list[TextItem] = Field(default_factory=list)
-    scene: str = ""
-    quality: dict[str, str] = Field(default_factory=dict)
-    key_selling_points: list[str] = Field(default_factory=list)
-    raw: str = ""
-
-
-class CoreMessage(BaseModel):
-    text: str
-    must_keep: bool = False
-    confidence: float = 0.0
-
-
-class PersonalBrand(BaseModel):
-    text: str
-    category: str = "brand"
-
-
-class IntentResult(BaseModel):
-    intent_summary: str = ""
-    core_messages: list[CoreMessage] = Field(default_factory=list)
-    personal_brands: list[PersonalBrand] = Field(default_factory=list)
-    emotion_tone: str = ""
-    missing_info: list[str] = Field(default_factory=list)
-    raw: str = ""
-
-
-class BodyPoint(BaseModel):
-    text: str
-    elaboration: str = ""
-    image_index: int = 0
-
-
-class ScriptSegment(BaseModel):
-    text: str
-    duration_sec: float = 0
-    image_index: int = 0
-
-
-class FusionResult(BaseModel):
-    title: str = ""
-    hook: str = ""
-    body_points: list[BodyPoint] = Field(default_factory=list)
-    cta: str = ""
-    script_segments: list[ScriptSegment] = Field(default_factory=list)
-    word_count: int = 0
-    estimated_duration: int = 0
-    raw: str = ""
-
-
-class KenBurns(BaseModel):
-    start: str = "0,0"
-    end: str = "0,0"
-    ease: str = "linear"
-
-
-class Clip(BaseModel):
-    image_index: int = 0
-    transition: str = "cut"
-    zoom: str | None = None
-    duration_sec: float = 0
-    bgm_note: str = ""
-    voice_text: str = ""
-    subtitle_text: str = ""
-    ken_burns: KenBurns = Field(default_factory=KenBurns)
-
-
-class Storyboard(BaseModel):
-    clips: list[Clip] = Field(default_factory=list)
-    raw: str = ""
-
-
-class ReviewIssue(BaseModel):
-    dimension: str
-    severity: str = "warning"
-    location: str = ""
-    text: str = ""
-
-
-class ReviewResult(BaseModel):
-    passed: bool = True
-    issues: list[ReviewIssue] = Field(default_factory=list)
-    rewrite_suggestions: list[str] = Field(default_factory=list)
-    raw: str = ""

packages/application/viral_video/xml_parser.py

@@ -1,113 +0,0 @@
-"""XML 标签式输出解析器（替代 json.loads）。
-
-LLM 按 ``<tag attr="x">内容</tag>`` 输出，本模块解析，解析失败不抛异常，
-由调用方走规则 fallback。采用栈式扫描，嵌套标签全部可提取（内外层都保留）。
-"""
-
-from __future__ import annotations
-
-import re
-from html import unescape
-from typing import Optional
-
-_OPEN_RE = re.compile(r"<(?P<tag>[\w-]+)(?P<attrs>(?:\s(?:[^>]*?\S)?)?)(?P<self>/?)>")
-_CLOSE_RE = re.compile(r"</(?P<tag>[\w-]+)\s*>")
-_ATTR_RE = re.compile(r"""([\w:-]+)\s*=\s*(?:"([^"]*)"|'([^']*)')""")
-_CDATA_RE = re.compile(r"^<!\[CDATA\[(.*)\]\]>$", re.DOTALL)
-
-
-def _strip_cdata(s: str) -> str:
-    """剥离 LLM 可能照抄示例输出的 ``<![CDATA[...]]>`` 包裹层。"""
-    m = _CDATA_RE.match(s.strip())
-    return m.group(1) if m else s
-
-
-def parse_attributes(raw: str) -> dict[str, str]:
-    """解析标签属性字符串。"""
-    attrs: dict[str, str] = {}
-    for match in _ATTR_RE.finditer(raw or ""):
-        value = match.group(2) if match.group(2) is not None else match.group(3)
-        attrs[match.group(1)] = value
-    return attrs
-
-
-def parse_tags(text: Optional[str]) -> list[dict]:
-    """提取全部标签（含嵌套内外层），返回 [{tag, attrs, text}]，按开标签出现顺序。"""
-    if not text:
-        return []
-    results: list[dict] = []
-    stack: list[dict] = []
-    token_re = re.compile(r"<[^>]+>")
-    for token in token_re.finditer(text):
-        raw_token = token.group(0)
-        # 先按开/闭标签匹配
-        open_match = _OPEN_RE.match(raw_token)
-        close_match = _CLOSE_RE.match(raw_token)
-        is_close_tag = raw_token.startswith("</")
-        if not is_close_tag and open_match:
-            is_self_close = open_match.group("self") == "/"
-            node = {
-                "tag": open_match.group("tag"),
-                "attrs": parse_attributes(open_match.group("attrs")),
-                "text": "",
-                "_start": token.end(),
-            }
-            if is_self_close:
-                node.pop("_start")
-                results.append(node)
-            else:
-                stack.append(node)
-                results.append(node)
-        elif is_close_tag and close_match:
-            tag = close_match.group("tag")
-            # 弹出到最近同名开标签
-            for idx in range(len(stack) - 1, -1, -1):
-                if stack[idx]["tag"] == tag:
-                    node = stack[idx]
-                    node["text"] = unescape(text[node["_start"] : token.start()].strip())
-                    node["text"] = _strip_cdata(node["text"])
-                    node.pop("_start", None)
-                    del stack[idx:]
-                    break
-    # 未闭合标签：给剩余部分作为文本
-    for node in stack:
-        if "_start" in node:
-            node["text"] = unescape(text[node["_start"] :].strip())
-            node["text"] = _strip_cdata(node["text"])
-            node.pop("_start", None)
-    return results
-
-
-def find_all(text: Optional[str], tag: str) -> list[dict]:
-    """提取指定标签的全部节点。"""
-    return [n for n in parse_tags(text) if n["tag"] == tag]
-
-
-def find_first(text: Optional[str], tag: str) -> Optional[dict]:
-    nodes = find_all(text, tag)
-    return nodes[0] if nodes else None
-
-
-def text_of(text: Optional[str], tag: str, default: str = "") -> str:
-    node = find_first(text, tag)
-    return node["text"] if node else default
-
-
-def attr_bool(value: Optional[str], default: bool = False) -> bool:
-    if value is None:
-        return default
-    return value.strip().lower() in {"true", "1", "yes", "是"}
-
-
-def attr_float(value: Optional[str], default: float = 0.0) -> float:
-    try:
-        return float(value) if value is not None and value.strip() else default
-    except (TypeError, ValueError):
-        return default
-
-
-def attr_int(value: Optional[str], default: int = 0) -> int:
-    try:
-        return int(float(value)) if value is not None and value.strip() else default
-    except (TypeError, ValueError):
-        return default

packages/config/base.py

@@ -80,46 +80,40 @@ class SharedSettings(BaseSettings):
 
     # ── CosyVoice (阿里云百炼语音合成) ───────────────────────────────────
     cosyvoice_api_key: str = ""
-    cosyvoice_base_url: str = ""
-    cosyvoice_model: str = ""
-    cosyvoice_voice: str = "longxiaochun_v3"
+    cosyvoice_base_url: str = "https://dashscope.aliyuncs.com/api/v1"
+    cosyvoice_model: str = "cosyvoice-v3-flash"
+    cosyvoice_voice: str = "longxiaochun_v3"  # 默认音色（v3 系列系统音色带 _v3 后缀）
     cosyvoice_sample_rate: int = 22050
-    cosyvoice_format: str = "mp3"
+    cosyvoice_format: str = "mp3"  # 输出格式：mp3/wav/pcm
     # 音色克隆模型名（固定为 voice-enrollment）
-    cosyvoice_clone_model: str = ""
+    cosyvoice_clone_model: str = "voice-enrollment"
 
     # ── 豆包大模型（火山引擎方舟） ────────────────────────────────────────
-    # AI模型路由化：model/base_url 默认值清空，由 DB ai_models/ai_capability_configs 配置驱动。
-    # 环境变量仍可覆盖（兼容旧部署）；无任何配置时 ai_router fallback 提供最终默认值。
     doubao_api_key: str = ""
-    doubao_model: str = ""
-    doubao_fast_model: str = ""
-    doubao_base_url: str = ""
-    doubao_timeout: int = 45
-    doubao_max_retries: int = 3
-    doubao_vision_model: str = ""
-    doubao_vision_lite_model: str = ""
-    doubao_vision_use_lite: bool = True
-    doubao_embedding_model: str = ""
-    doubao_video_model: str = ""
-    doubao_video_timeout: int = 600
-    doubao_video_poll_interval: int = 10
-    doubao_image_model: str = ""
-    doubao_image_size: str = "1K"
-    doubao_image_timeout: int = 60
-    doubao_trust_chain_enabled: bool = True
+    doubao_model: str = "doubao-seed-1-6-250615"  # 推理模型（通用兜底）
+    doubao_fast_model: str = "doubao-1-5-pro-32k-250115"  # 快速结构化输出模型（编导脚本/意图解析/审核）
+    doubao_base_url: str = "https://ark.cn-beijing.volces.com/api/v3"
+    doubao_timeout: int = 30
+    doubao_max_retries: int = 2
+    doubao_vision_model: str = "doubao-1-5-vision-pro-250328"  # 高精度视觉（备用）
+    doubao_vision_lite_model: str = "doubao-1-5-vision-lite-250315"  # 快速视觉（商品识别默认，速度优先）
+    doubao_vision_use_lite: bool = True  # viral-video 图片分析默认用 lite 提速
+    doubao_embedding_model: str = "doubao-embedding-large-text-240915"
+    doubao_video_model: str = "doubao-seedance-2-5-260628"
+    doubao_video_timeout: int = 600  # 视频生成轮询总超时（秒）
+    doubao_video_poll_interval: int = 10  # 轮询间隔（秒）
 
     # ── DashScope (阿里云百炼 Wan 3.0 等) ─────────────────────────────────
     dashscope_api_key: str = ""
-    dashscope_base_url: str = ""
-    dashscope_video_timeout: int = 900
+    dashscope_base_url: str = "https://dashscope.aliyuncs.com/api/v1"
+    dashscope_video_timeout: int = 900  # Wan 视频任务轮询总超时（秒）
     dashscope_video_poll_interval: int = 10
 
     # ── MediaKit (火山引擎 AI 媒体工具) ──────────────────────────────────
     mediakit_api_key: str = ""
-    mediakit_base_url: str = ""
+    mediakit_base_url: str = "https://mediakit.cn-beijing.volces.com/api/v1"
     mediakit_timeout: int = 60
-    mediakit_cover_enabled: bool = False
+    mediakit_cover_enabled: bool = False  # 封面抽帧是否走MediaKit(默认false走本地ffmpeg+cv2，<2s完成)
 
     # ── 积分/会员系统 (#1895) ────────────────────────────────────────────
     # 积分系统总开关（产品要求 #1895：暂停积分系统但保留全部代码/表/接口）。
@@ -173,35 +167,6 @@ class SharedSettings(BaseSettings):
     # 判断 Worker 可用的心跳新鲜度窗口（秒）—— last_heartbeat_at 在窗口内视为在线
     gpu_worker_stale_seconds: int = 300
 
-    # ── Ditto 蚂蚁数字人口型 API（#2076）─────────────────────────────────
-    # 是否优先使用 Ditto（蚂蚁数字人，替代 MuseTalk）。开关开启且 base_url 配置
-    # 非空时，对口型任务优先走 Ditto；失败后回退 MuseTalk/MediaKit。
-    use_ditto_lipsync: bool = Field(
-        default=False,
-        validation_alias=AliasChoices("USE_DITTO_LIPSYNC", "use_ditto_lipsync"),
-    )
-    # Ditto FastAPI 内网地址（Tailscale），如 http://100.x.x.x:8000
-    ditto_api_base_url: str = Field(
-        default="",
-        validation_alias=AliasChoices("DITTO_API_BASE_URL", "ditto_api_base_url"),
-    )
-    # 默认人物模板视频 URL（正面 5-10 秒循环、光线均匀、半身）。Ditto 模式下忽略
-    # 用户上传的驱动视频/图片，统一用该模板；后续可扩展为多模板让用户选择。
-    ditto_default_video_url: str = Field(
-        default="",
-        validation_alias=AliasChoices("DITTO_DEFAULT_VIDEO_URL", "ditto_default_video_url"),
-    )
-    # 429 GPU 繁忙时指数退避最大重试次数
-    ditto_max_retries: int = Field(
-        default=3,
-        validation_alias=AliasChoices("DITTO_MAX_RETRIES", "ditto_max_retries"),
-    )
-    # Ditto 单次请求超时（秒）：数字人半身视频推理通常 30-120s
-    ditto_request_timeout: int = Field(
-        default=300,
-        validation_alias=AliasChoices("DITTO_REQUEST_TIMEOUT", "ditto_request_timeout"),
-    )
-
     # ── P4000 NVENC 硬件编码 ────────────────────────────────────────────
     # GPU 编码总开关；关闭或 endpoint 为空时始终走本机 CPU libx264
     enable_gpu_encode: bool = Field(

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
 

packages/domain/viral_video.py

@@ -87,9 +87,6 @@ class ViralVideoJob:
 
     user_id: str
     images: list[str] = field(default_factory=list)
-    pre_trusted_images: list[str] | None = (
-        None  # #2172 信任链预热结果（Seedream AI 化后的 URL 列表），与 images 顺序对应
-    )
     industry: str = ""
     target_customer: str = ""
     persona_id: str = ""
@@ -191,45 +188,13 @@ class ViralVideoJob:
         self.updated_at = datetime.now(timezone.utc)
 
     def resume_from_image_analyzed(self, **kwargs) -> None:
-        """阶段2入口：允许从 IMAGE_ANALYZED/PENDING 首次进入，也允许从 COPY_GENERATED/COMPLETED/FAILED 重新生成文案。
-
-        重新生成时清空上一轮文案产物（copy_result/intent_result/storyboard/generated_copy_text），
-        并重置 completed_at/result_video_url/error_msg，确保前端轮询能看到新的阶段2进度。
-        """
-        _allowed = (
-            ViralVideoStatus.IMAGE_ANALYZED,
-            ViralVideoStatus.PENDING,
-            ViralVideoStatus.COPY_GENERATED,
-            ViralVideoStatus.COMPLETED,
-            ViralVideoStatus.FAILED,
-        )
-        if self.status not in _allowed:
+        if self.status not in (ViralVideoStatus.IMAGE_ANALYZED, ViralVideoStatus.PENDING):
             raise ValueError(f"Cannot resume from {self.status} to copy-gen")
-        _is_regen = self.status in (
-            ViralVideoStatus.COPY_GENERATED,
-            ViralVideoStatus.COMPLETED,
-            ViralVideoStatus.FAILED,
-        )
         for k, v in kwargs.items():
             if hasattr(self, k) and v not in (None, "", []):
                 setattr(self, k, v)
-        if _is_regen:
-            # 清空上一轮文案/视频产物，避免前端拿到旧数据
-            self.intent_result = None
-            self.copy_result = None
-            self.storyboard = None
-            self.generated_copy_text = ""
-            self.result_video_url = ""
-            self.current_stage = ""
-            self.phase_message = ""
-            self.error_msg = ""
-            self.completed_at = None
-            self.heartbeat_at = None
-        _now = datetime.now(timezone.utc)
-        self.started_at = _now
-        self.heartbeat_at = _now
         self.status = ViralVideoStatus.RUNNING
-        self.updated_at = _now
+        self.updated_at = datetime.now(timezone.utc)
 
     def resume_from_copy_generated(self, edited_copy: str | None = None) -> None:
         """阶段2->阶段3：用户确认/编辑口播文案，开始跑 TTS+单次Seedance渲染。"""
@@ -238,20 +203,14 @@ class ViralVideoJob:
         if edited_copy and isinstance(self.copy_result, dict):
             self.copy_result = {**self.copy_result, "voiceover_script": edited_copy}
             self.generated_copy_text = edited_copy
-        _now = datetime.now(timezone.utc)
-        self.started_at = _now
-        self.heartbeat_at = _now
         self.status = ViralVideoStatus.RUNNING
-        self.updated_at = _now
+        self.updated_at = datetime.now(timezone.utc)
 
     def resume_from_confirm(self) -> None:
         if self.status != ViralVideoStatus.WAIT_USER_CONFIRM:
             raise ValueError(f"Cannot resume from {self.status}")
-        _now = datetime.now(timezone.utc)
-        self.started_at = _now
-        self.heartbeat_at = _now
         self.status = ViralVideoStatus.RUNNING
-        self.updated_at = _now
+        self.updated_at = datetime.now(timezone.utc)
 
     def mark_completed(self, video_url: str) -> None:
         self.status = ViralVideoStatus.COMPLETED

packages/shared/ai_client.py

@@ -34,11 +34,11 @@ _HTTP_STATUS_ERROR = httpx.HTTPStatusError if hasattr(httpx, "HTTPStatusError")
 logger = logging.getLogger(__name__)
 
 
-# 视频模型 ID 解析逻辑（#2159 多模型支持，#2170 方舟信任链统一走方舟）。
-# 内部使用简短别名（seedance-2.5 / seedance-2.0 / wan-3.0 等）做 PRICING key；
+# 视频模型 ID 解析逻辑（#2159 多模型支持）。
+# 内部使用简短别名（seedance-2.5 / seedance-2.0 / wan-3.0 等）做 PRICING key 和前端选择值；
 # 实际调用时按 VIRAL_VIDEO_MODEL_CONFIG（domain/points_rules.py）的 provider/model_id 分发。
-# - provider=doubao → 火山方舟 Seedance（含信任链真人 AI 化）
-# - provider=dashscope → 阿里云 DashScope（Wan 系列，可选）
+# - provider=doubao → 火山方舟
+# - provider=dashscope → 阿里云 DashScope（Wan 系列）
 
 
 def _resolve_video_provider_and_id(model: str | None) -> tuple[str, str, dict]:
@@ -170,53 +170,21 @@ class DoubaoClient:
     未配置 API Key 时 is_available 为 False，调用方应降级处理。
     """
 
-    def __init__(
-        self,
-        api_key: str = "",
-        base_url: str = "",
-        model: str = "",
-        timeout: int = 0,
-        max_retries: int = 0,
-        max_tokens: int | None = None,
-        temperature: float | None = None,
-        extra_params: dict | None = None,
-        provider: str = "volcengine",
-    ) -> None:
+    def __init__(self) -> None:
         settings = get_shared_settings()
-        self.provider: str = provider
-        if provider == "dashscope":
-            self.api_key: str = api_key or getattr(settings, "dashscope_api_key", "")
-            self.model: str = model or getattr(settings, "dashscope_model", "")
-            self.base_url: str = (base_url or getattr(settings, "dashscope_base_url", "")).rstrip("/")
-        else:  # volcengine (default)
-            self.api_key = api_key or settings.doubao_api_key
-            self.model = model or settings.doubao_model
-            self.base_url = (base_url or settings.doubao_base_url).rstrip("/")
-        self.timeout: int = timeout or settings.doubao_timeout
-        self.max_retries: int = max_retries or settings.doubao_max_retries
-        self.max_tokens: int | None = max_tokens
-        self.temperature: float | None = temperature
-        self.extra_params: dict = extra_params or {}
+        self.api_key: str = settings.doubao_api_key
+        self.model: str = settings.doubao_model
+        self.base_url: str = settings.doubao_base_url.rstrip("/")
+        self.timeout: int = settings.doubao_timeout
+        self.max_retries: int = settings.doubao_max_retries
         self.vision_model: str = settings.doubao_vision_model
-        self.last_finish_reason: str = ""
         self.vision_lite_model: str = settings.doubao_vision_lite_model
         self.fast_model: str = settings.doubao_fast_model
         self.embedding_model: str = settings.doubao_embedding_model
-        self.image_model: str = settings.doubao_image_model
-        self.image_size: str = getattr(settings, "doubao_image_size", "1K") or "1K"
-        self.image_timeout: int = getattr(settings, "doubao_image_timeout", 60) or 60
-        self.trust_chain_enabled: bool = getattr(settings, "doubao_trust_chain_enabled", True)
         # 最近一次视频生成的详细错误（error_code + user_message + raw detail），供上层读取后展示给用户
         self.last_video_error: dict = {}
-        # 最近一次图片生成的详细错误，供上层读取
-        self.last_image_error: dict = {}
-
-    def _resolve_timeout(self, timeout) -> "httpx.Timeout":
-        """将整数超时转为 httpx.Timeout，区分 connect/read/write/pool，避免 read 卡到 TCP 120s 默认值."""
-        if isinstance(timeout, httpx.Timeout):
-            return timeout
-        t = int(timeout) if timeout else 60
-        return httpx.Timeout(connect=10, read=max(t, 10), write=10, pool=5)
+        # chat fallback 信号位：_do_chat_request 遇 404/model_not_exist 时置 True
+        self._last_chat_model_not_found: bool = False
 
     def embed_text(self, text: str, timeout: int | None = None) -> list[float] | None:
         """调用豆包文本 Embedding API，返回浮点向量；失败返回 None。"""
@@ -229,7 +197,7 @@ class DoubaoClient:
             "Content-Type": "application/json",
         }
         payload: dict[str, Any] = {
-            "model": self.embedding_model,
+            "model": getattr(self, "embedding_model", None) or "doubao-embedding-large-text-240915",
             "input": text.strip(),
             "encoding_format": "float",
         }
@@ -264,24 +232,99 @@ class DoubaoClient:
         """是否可用（配置了 API Key）."""
         return bool(self.api_key)
 
+    @staticmethod
+    def _is_model_not_found_err(e: Exception) -> bool:
+        """判断 HTTP 异常是否是模型/endpoint 不存在（404 或 body 含 not found 关键词）。"""
+        try:
+            resp = getattr(e, "response", None)
+            if resp is not None:
+                sc = getattr(resp, "status_code", 0)
+                if sc == 404:
+                    return True
+                try:
+                    body = resp.text or ""
+                except Exception:
+                    body = ""
+                bl = body.lower()
+                if sc == 400 and any(
+                    k in bl
+                    for k in (
+                        "modelnotfound",
+                        "model_not_exist",
+                        "endpointnotfound",
+                        "model not found",
+                        "endpoint not found",
+                        "不存在",
+                    )
+                ):
+                    return True
+        except Exception:
+            pass
+        return False
+
+    def _do_chat_request(self, url: str, headers: dict, payload: dict, model_label: str) -> Optional[str]:
+        """单次 chat 请求，带 5xx/网络重试；4xx 直接返回 None，同时置位 self._last_chat_model_not_found。"""
+        self._last_chat_model_not_found = False
+        last_err: Optional[Exception] = None
+        for attempt in range(self.max_retries + 1):
+            try:
+                response = httpx.post(url, headers=headers, json=payload, timeout=self.timeout)
+                response.raise_for_status()
+                data = response.json()
+                return (data["choices"][0]["message"]["content"] or "").strip()
+            except Exception as e:
+                last_err = e
+                sc = getattr(getattr(e, "response", None), "status_code", 0) or 0
+                if 400 <= sc < 500:
+                    # 判断是否模型/endpoint 不存在
+                    is_mnf = sc == 404
+                    if not is_mnf:
+                        try:
+                            body = getattr(e.response, "text", "") or ""
+                        except Exception:
+                            body = ""
+                        bl = body.lower()
+                        is_mnf = any(
+                            k in bl
+                            for k in (
+                                "modelnotfound",
+                                "model_not_exist",
+                                "endpointnotfound",
+                                "model not found",
+                                "endpoint not found",
+                            )
+                        )
+                    if is_mnf:
+                        self._last_chat_model_not_found = True
+                    logger.warning(
+                        "[豆包chat] HTTP %d model=%s mnf=%s err=%s 不重试", sc, model_label, is_mnf, str(e)[:200]
+                    )
+                    return None
+                if attempt < self.max_retries:
+                    wait = 0.5 * (2**attempt)
+                    logger.warning(
+                        "[豆包chat] 调用失败 %.1fs后重试(%d/%d) model=%s err=%s",
+                        wait,
+                        attempt + 1,
+                        self.max_retries + 1,
+                        model_label,
+                        e,
+                    )
+                    time.sleep(wait)
+        logger.error("[豆包chat] 调用最终失败 model=%s err=%s", model_label, last_err)
+        return None
+
     def chat_completion(
         self,
         messages: list[dict[str, str]],
         temperature: float = 0.7,
-        max_tokens: int | None = None,
+        max_tokens: int = 1024,
         model: str | None = None,
-        timeout: int | None = None,
-        **kwargs,
     ) -> Optional[str]:
         """调用 Chat Completion 接口.
 
-        Args:
-            messages: 对话消息列表，[{"role": "user"/"system"/"assistant", "content": "..."}]
-            temperature: 采样温度，0-2，默认0.7
-            max_tokens: 最大生成token数，默认 None（使用实例 self.max_tokens DB 配置，兜底 1024）
-
-        Returns:
-            模型返回的文本内容，失败返回 None
+        - 若指定 model 返回 404/model not found，自动 fallback 到默认推理模型 self.model
+        - 5xx/网络错误按 max_retries 重试；4xx（鉴权/配额/参数/模型不存在）不重试
         """
         if not self.is_available:
             return None
@@ -291,97 +334,44 @@ class DoubaoClient:
             "Authorization": f"Bearer {self.api_key}",
             "Content-Type": "application/json",
         }
-        effective_max_tokens = max_tokens if max_tokens is not None else (self.max_tokens or 1024)
+        req_model = model or self.model
         payload: dict[str, Any] = {
-            "model": model or self.model,
+            "model": req_model,
             "messages": messages,
             "temperature": temperature,
-            "max_tokens": effective_max_tokens,
+            "max_tokens": max_tokens,
         }
-        # 合并实例级额外参数和调用方传入的额外参数
-        if self.extra_params:
-            payload.update(self.extra_params)
-        if kwargs:
-            payload.update(kwargs)
+        content = self._do_chat_request(url, headers, payload, req_model)
+        if content is not None:
+            return content
 
-        last_error: Optional[Exception] = None
-        _t0 = time.time()
-        for attempt in range(self.max_retries + 1):
-            try:
-                _req_timeout = self._resolve_timeout(timeout if timeout is not None else self.timeout)
-                response = httpx.post(
-                    url,
-                    headers=headers,
-                    json=payload,
-                    timeout=_req_timeout,
-                )
-                response.raise_for_status()
-                data = response.json()
-                finish_reason = (data.get("choices") or [{}])[0].get("finish_reason", "")
-                if finish_reason == "length" and attempt < self.max_retries:
-                    # 输出被 max_tokens 截断：2.0x 扩容后重试（计入 max_retries，不额外增加）
-                    old_max = int(payload["max_tokens"])
-                    new_max = int(old_max * 2)
-                    payload["max_tokens"] = new_max
-                    wait = 0.5 * (2**attempt)
-                    logger.warning(
-                        "输出被max_tokens截断(%d)，扩容到%d后重试 (第%d/%d次)",
-                        old_max,
-                        new_max,
-                        attempt + 1,
-                        self.max_retries + 1,
-                    )
-                    time.sleep(wait)
-                    continue
-                content = data["choices"][0]["message"]["content"]
-                self.last_finish_reason = finish_reason
-                _elapsed = time.time() - _t0
-                logger.info(
-                    "[doubao] chat_completion 完成 model=%s tokens_in=%d tokens_out=%d elapsed=%.1fs attempt=%d timeout=%s",
-                    payload.get("model"),
-                    data.get("usage", {}).get("prompt_tokens", 0),
-                    data.get("usage", {}).get("completion_tokens", 0),
-                    _elapsed,
-                    attempt + 1,
-                    getattr(_req_timeout, "read", _req_timeout),
-                )
-                return content.strip()
-            except Exception as e:
-                last_error = e
-                if attempt < self.max_retries:
-                    wait = 0.5 * (2**attempt)
-                    logger.warning(
-                        "豆包API调用失败，%.1fs后重试 (第%d/%d次, elapsed=%.1fs): %s",
-                        wait,
-                        attempt + 1,
-                        self.max_retries + 1,
-                        time.time() - _t0,
-                        e,
-                    )
-                    time.sleep(wait)
-
-        logger.error("豆包API调用最终失败: elapsed=%.1fs err=%s", time.time() - _t0, last_error)
+        # fallback：仅当请求的模型是"模型不存在/endpoint 不存在"时才降级到默认模型
+        # （鉴权/配额/限流等全局错误 fallback 也会失败，别浪费请求）
+        if req_model != self.model and getattr(self, "_last_chat_model_not_found", False):
+            logger.warning("[豆包chat] 模型 %s 不存在，fallback 到默认模型 %s", req_model, self.model)
+            payload["model"] = self.model
+            self._last_chat_model_not_found = False
+            return self._do_chat_request(url, headers, payload, self.model)
         return None
 
     def vision_completion(
         self,
         messages: list[dict],
         images: list[str] | None = None,
-        max_tokens: int | None = None,
+        max_tokens: int = 2048,
         temperature: float = 0.3,
         timeout: int | None = None,
         model: str | None = None,
-        **kwargs,
     ) -> Optional[str]:
         """调用豆包视觉理解 API（OpenAI 兼容多模态格式）.
 
         将 images 附加到最后一条 user message 的 content 中，
-        使用构造函数传入的 self.model（DB capability 绑定的视觉模型，默认 qwen-vl-plus）。
+        使用 vision_model（默认 doubao-1-5-vision-pro-250328）。
 
         Args:
             messages: 对话消息列表。最后一条 user message 会被注入图片内容。
             images: 图片列表，支持 base64 data URI 或 HTTP(S) URL。
-            max_tokens: 最大生成 token 数，默认 None（使用实例 self.max_tokens DB 配置，兜底 2048）。
+            max_tokens: 最大生成 token 数，默认 2048。
             temperature: 采样温度，默认 0.3（视觉任务偏低更稳定）。
             timeout: 单次请求超时秒数，不传则使用默认 self.timeout。
 
@@ -421,140 +411,76 @@ class DoubaoClient:
             "Authorization": f"Bearer {self.api_key}",
             "Content-Type": "application/json",
         }
-        effective_max_tokens = max_tokens if max_tokens is not None else (self.max_tokens or 2048)
+        req_model = model or self.vision_model
         payload: dict[str, Any] = {
-            "model": model or self.model,
+            "model": req_model,
             "messages": vision_messages,
             "temperature": temperature,
-            "max_tokens": effective_max_tokens,
+            "max_tokens": max_tokens,
         }
-        if self.extra_params:
-            payload.update(self.extra_params)
-        if kwargs:
-            payload.update(kwargs)
 
-        req_timeout = self._resolve_timeout(timeout or self.timeout)
-        last_error: Optional[Exception] = None
-        _t0 = time.time()
-        for attempt in range(self.max_retries + 1):
-            try:
-                response = httpx.post(
-                    url,
-                    headers=headers,
-                    json=payload,
-                    timeout=req_timeout,
-                )
-                response.raise_for_status()
-                data = response.json()
-                finish_reason = (data.get("choices") or [{}])[0].get("finish_reason", "")
-                if finish_reason == "length" and attempt < self.max_retries:
-                    # 视觉输出被 max_tokens 截断：2.0x 扩容后重试（计入 max_retries）
-                    old_max = int(payload["max_tokens"])
-                    new_max = int(old_max * 2)
-                    payload["max_tokens"] = new_max
-                    wait = 0.5 * (2**attempt)
-                    logger.warning(
-                        "视觉输出被max_tokens截断(%d)，扩容到%d后重试 (第%d/%d次)",
-                        old_max,
-                        new_max,
-                        attempt + 1,
-                        self.max_retries + 1,
-                    )
-                    time.sleep(wait)
-                    continue
-                content = data["choices"][0]["message"]["content"]
-                self.last_finish_reason = finish_reason
-                _elapsed = time.time() - _t0
-                logger.info(
-                    "[doubao] vision_completion 完成 model=%s tokens_in=%d tokens_out=%d elapsed=%.1fs attempt=%d",
-                    payload.get("model"),
-                    data.get("usage", {}).get("prompt_tokens", 0),
-                    data.get("usage", {}).get("completion_tokens", 0),
-                    _elapsed,
-                    attempt + 1,
-                )
-                return content.strip()
-            except Exception as e:
-                last_error = e
-                if attempt < self.max_retries:
-                    wait = 0.5 * (2**attempt)
-                    logger.warning(
-                        "豆包视觉API调用失败，%.1fs后重试 (第%d/%d次, elapsed=%.1fs): %s",
-                        wait,
-                        attempt + 1,
-                        self.max_retries + 1,
-                        time.time() - _t0,
-                        e,
-                    )
-                    time.sleep(wait)
+        vision_mnf = {"mnf": False}
 
-        logger.error("豆包视觉API调用最终失败: elapsed=%.1fs err=%s", time.time() - _t0, last_error)
+        def _do_vision(curr_model: str) -> Optional[str]:
+            payload["model"] = curr_model
+            for attempt in range(self.max_retries + 1):
+                try:
+                    response = httpx.post(url, headers=headers, json=payload, timeout=req_timeout)
+                    response.raise_for_status()
+                    data = response.json()
+                    return (data["choices"][0]["message"]["content"] or "").strip()
+                except Exception as e:
+                    sc = getattr(getattr(e, "response", None), "status_code", 0) or 0
+                    if 400 <= sc < 500:
+                        is_mnf = sc == 404
+                        if not is_mnf:
+                            try:
+                                body = getattr(e.response, "text", "") or ""
+                            except Exception:
+                                body = ""
+                            bl = body.lower()
+                            is_mnf = any(
+                                k in bl
+                                for k in (
+                                    "modelnotfound",
+                                    "model_not_exist",
+                                    "endpointnotfound",
+                                    "model not found",
+                                    "endpoint not found",
+                                )
+                            )
+                        if is_mnf:
+                            vision_mnf["mnf"] = True
+                        logger.warning(
+                            "[豆包vision] HTTP %d model=%s mnf=%s err=%s 不重试", sc, curr_model, is_mnf, str(e)[:200]
+                        )
+                        return None
+                    if attempt < self.max_retries:
+                        wait = 0.5 * (2**attempt)
+                        logger.warning(
+                            "豆包视觉API调用失败，%.1fs后重试 (第%d/%d次): %s",
+                            wait,
+                            attempt + 1,
+                            self.max_retries + 1,
+                            e,
+                        )
+                        time.sleep(wait)
+            return None
+
+        req_timeout = timeout or self.timeout
+        content = _do_vision(req_model)
+        if content is not None:
+            return content
+        # 仅 404/模型不存在时才 fallback 到默认 chat 模型
+        if req_model != self.model and vision_mnf["mnf"]:
+            logger.warning("[豆包vision] 模型 %s 不存在，fallback 到默认模型 %s", req_model, self.model)
+            content = _do_vision(self.model)
+            if content is not None:
+                return content
         return None
 
     # ── 视频生成（Seedance 2.5，异步任务）────────────────────────────
 
-    def preheat_trust_chain(
-        self,
-        portrait_descriptions: list[str],
-        *,
-        timeout: int | None = None,
-        size: str | None = None,
-    ) -> list[str] | None:
-        """#2174 信任链预热（t2i 版）：用 VLM 分析出的人物外貌描述，纯文生图生成 Seedream 人像，
-        返回信任产物 URL 列表。
-
-        重要：方舟信任链规则是 Seedream 文生图(t2i)产物（不传 reference_images）才被 Seedance 信任；
-        i2i（带用户照片 reference）产物不被信任，仍会被肖像审核拦截。
-
-        - portrait_descriptions: VLM 输出的 portrait_prompt 列表（中文描述），与原始图片顺序对应
-        - 全部成功返回 list[str]（顺序与输入一致）
-        - 任何一张失败返回 None（上层会走现场兜底：直接 t2v 不带参考图）
-        - 若 trust_chain_enabled=False 或所有描述均为"无人像"，直接返回 None
-        - 供 worker 在 VLM 分析完成后后台预热使用；video_generation 内部若收到 preheated 结果会直接使用。
-        """
-        if not portrait_descriptions or not self.is_available or not getattr(self, "trust_chain_enabled", True):
-            return None
-        # 过滤掉"无人像"等无效描述，收集需要生成的索引
-        _idx_map: list[int] = []
-        _prompts: list[str] = []
-        for i, desc in enumerate(portrait_descriptions):
-            if not desc or "无人像" in desc or len(desc) < 10:
-                continue
-            _idx_map.append(i)
-            # 把VLM的中文描述包装成适合Seedream t2i的英文+中文混合prompt，明确是写实半身人像
-            _prompts.append(
-                f"高清写实半身人像照片，{desc}，自然光线，面部清晰居中，皮肤质感自然，"
-                "高清摄影细节，构图居中，人物占据画面主体，背景柔和虚化。photorealistic portrait, "
-                "sharp focus on face, soft natural lighting, high detail, half body shot."
-            )
-        if not _prompts:
-            logger.info("[trust-chain][preheat] 无需生成信任人像（所有图片均无人像），跳过")
-            return None
-        trusted: list[str] = []
-        _t0 = time.time()
-        for idx, sd_prompt in enumerate(_prompts):
-            # #2174 关键：t2i模式——不传reference_images，纯prompt文生图，产物才被Seedance信任
-            sd_result = self.image_generation(
-                prompt=sd_prompt,
-                size=size or getattr(self, "image_size", "1K"),
-                timeout=timeout or getattr(self, "image_timeout", 60),
-            )
-            if not sd_result:
-                logger.warning(
-                    "[trust-chain][preheat] Seedream t2i 第 %d/%d 张失败: %s，预热整体失败",
-                    idx + 1,
-                    len(_prompts),
-                    getattr(self, "last_image_error", None),
-                )
-                return None
-            trusted.append(sd_result["url"])
-        logger.info(
-            "[trust-chain][preheat] Seedream t2i 文生图预热完成 %d 张，总耗时 %.1fs",
-            len(trusted),
-            time.time() - _t0,
-        )
-        return trusted
-
     def video_generation(
         self,
         prompt: str,
@@ -570,7 +496,6 @@ class DoubaoClient:
         reference_images: list[str] | None = None,
         reference_audios: list[str] | None = None,
         reference_videos: list[str] | None = None,
-        pre_trusted_images: list[str] | None = None,
     ) -> dict | None:
         """调用 Seedance 2.5 生视频（异步任务→轮询→下载）。
 
@@ -654,59 +579,17 @@ class DoubaoClient:
         ref_videos = [u for u in (reference_videos or [])[:3] if u and isinstance(u, str)]
         ref_imgs = [u for u in (reference_images or [])[:9] if u and isinstance(u, str)]
 
-        # ── #2170/#2172 方舟信任链（Trust Chain）────────────────────────────────
-        # 真人照片直接传给 Seedance 会触发 50411 肖像审核拦截。
-        # 解决：先通过同账号的 Seedream 5.0 Pro 图生图 AI 化（保持五官特征），
-        # 得到的 AI 产物图属于"模型信任产物"，再作为 reference_image 传给 Seedance 即可通过审核。
-        # #2172/#2174: 信任链——使用预热好的 Seedream t2i 文生图（纯模型生成人像，是方舟信任产物，
-        # 不会触发肖像审核）。预热在 VLM 分析后由 daemon 线程后台完成，结果通过 pre_trusted_images 传入。
-        # - 预热结果有效 → 替换原参考图，走 omni_ref 模式
-        # - 预热结果不可用 → 直接用原图（上层 _step_render 已现场同步跑 Seedream t2i 兜底；若再被400拦截，下方自动降级纯t2v）
-        # 信任链只作用于 doubao provider；DashScope(Wan) 保持原行为。
-        trust_chain_applied = False
-        if provider == "doubao" and getattr(self, "trust_chain_enabled", True) and pre_trusted_images:
-            # #2220: 稀疏列表模式——pre_trusted_images 与 raw_portrait_urls 等长，
-            # None 位保留原图，非 None 位用 AI 人像替换。
-            raw_portrait_urls: list[str] = []
-            if image_url:
-                raw_portrait_urls.append(image_url)
-            for u in ref_imgs:
-                if u not in raw_portrait_urls:
-                    raw_portrait_urls.append(u)
-            _n_trusted = sum(1 for _x in pre_trusted_images if _x)
-            if _n_trusted >= 1 and len(pre_trusted_images) >= len(raw_portrait_urls):
-                merged: list[str] = []
-                for _i, _orig in enumerate(raw_portrait_urls):
-                    _ai = pre_trusted_images[_i] if _i < len(pre_trusted_images) else None
-                    merged.append(str(_ai) if _ai else _orig)
-                trust_chain_applied = True
-                logger.info(
-                    "[trust-chain] 稀疏替换 %d/%d 张为AI人像（场景/商品图保留原图），走reference_image模式",
-                    _n_trusted,
-                    len(raw_portrait_urls),
-                )
-                # 替换：image_url 用第一张（可能是AI或原图），ref_imgs 用剩余
-                if image_url and merged:
-                    image_url = merged[0]
-                    ref_imgs = merged[1:] if len(merged) > 1 else []
-                else:
-                    ref_imgs = merged
-        # ─────────────────────────────────────────────────────────────────
-
-        # 判断任务模式：
-        # - 信任链强制走 reference_image（不是 first_frame；产品语义是人物参考，不是从图开始动）
-        # - 有参考音/视/多图 → omni_reference（支持指定 ratio）
-        # - 纯首帧无其他参考 → first_frame（ratio=adaptive）
+        # 判断任务模式：有参考音/视/多图 → omni_reference（支持指定 ratio）；纯首帧 → first_frame（ratio=adaptive）
         has_extra_refs = bool(ref_audios or ref_videos or ref_imgs)
-        is_first_frame_mode = bool(image_url) and not has_extra_refs and not trust_chain_applied
+        is_first_frame_mode = bool(image_url) and not has_extra_refs
         # 最终 ratio：first_frame 模式强制 adaptive，否则按用户传值（默认 9:16）
         final_ratio = "adaptive" if is_first_frame_mode else (ratio or "9:16")
 
         # 构造 content 数组：text + 图 + 音 + 视
         content: list[dict[str, Any]] = [{"type": "text", "text": prompt.strip()}]
         if image_url:
-            if has_extra_refs or trust_chain_applied:
-                # omni_reference 或信任链模式：首张图作为 reference_image，允许指定 ratio
+            if has_extra_refs:
+                # omni_reference：首张图作为 reference_image，允许指定 ratio
                 content.append(
                     {
                         "type": "image_url",
@@ -750,7 +633,7 @@ class DoubaoClient:
             video_model,
             duration,
             final_ratio,
-            "first_frame" if is_first_frame_mode else ("omni_ref+trust_chain" if trust_chain_applied else "omni_ref"),
+            "first_frame" if is_first_frame_mode else "omni_ref",
             generate_audio,
             (1 if image_url else 0) + len(ref_imgs),
             len(ref_audios),
@@ -843,26 +726,6 @@ class DoubaoClient:
                 # 保留第二次的错误信息
                 sc, body = sc2, body2
 
-        # #2183: portrait_intercept / 真人肖像审核拦截 → 去掉所有参考图（含image_url首帧），纯 t2v 重试一次
-        # 信任链预热或现场 t2i 都失败时的最后兜底，保证能出片
-        if not task_id and sc == 400:
-            _err_code_for_400, _ = _classify_video_error(sc, body, last_err)
-            if _err_code_for_400 == "portrait_intercept" and (image_url or ref_imgs):
-                logger.warning(
-                    "Seedance 创建因 portrait_intercept 失败，降级纯 t2v（移除所有参考图）重试: img=%d ref=%d",
-                    1 if image_url else 0,
-                    len(ref_imgs),
-                )
-                _t2v_payload = dict(create_payload)
-                _t2v_payload["content"] = [{"type": "text", "text": prompt.strip()}]
-                _t2v_payload["ratio"] = ratio or "9:16"
-                task_id, last_err, sc3, body3 = _do_create(_t2v_payload)
-                if task_id:
-                    sc, body = sc3, body3
-                    logger.info("[trust-chain] portrait_intercept 降级纯 t2v 成功 task_id=%s", task_id)
-                else:
-                    sc, body = sc3, body3
-
         if not task_id:
             err_code, user_msg = _classify_video_error(sc, body, last_err)
             self.last_video_error = {
@@ -1025,162 +888,6 @@ class DoubaoClient:
             }
             return None
 
-    def image_generation(
-        self,
-        prompt: str,
-        *,
-        reference_images: list[str] | None = None,
-        size: str = "2K",
-        model: str | None = None,
-        watermark: bool = False,
-        output_format: str = "png",
-        timeout: int | None = None,
-    ) -> dict | None:
-        """#2170: 调用方舟 Seedream 图片生成（文生图/图生图）。
-
-        - reference_images: 0~10 张参考图 URL；0 张 = 纯文生图；1 张 string/URL 直传；多张 list[str]。
-        - 成功返回 {"url": str, "usage": dict | None}；失败返回 None，错误写入 self.last_image_error。
-        - 返回的 url 有时效性（通常 24h），应立即使用，不持久化存储。
-        """
-        self.last_image_error = {}
-        if not self.is_available:
-            self.last_image_error = {
-                "error_code": "auth_error",
-                "user_message": "图片生成服务未配置（API Key 缺失），请联系管理员。",
-                "detail": "DoubaoClient not available (api_key empty)",
-            }
-            return None
-        if not prompt or not prompt.strip():
-            self.last_image_error = {
-                "error_code": "invalid_param",
-                "user_message": "图片生成提示词不能为空。",
-                "detail": "empty prompt",
-            }
-            return None
-
-        img_model = model or self.image_model
-        _img_size = size or getattr(self, "image_size", "1K")
-        url = f"{self.base_url}/images/generations"
-        headers = {
-            "Authorization": f"Bearer {self.api_key}",
-            "Content-Type": "application/json",
-        }
-        payload: dict[str, Any] = {
-            "model": img_model,
-            "prompt": prompt.strip(),
-            "size": _img_size,
-            "response_format": "url",
-            "output_format": output_format,
-            "watermark": bool(watermark),
-        }
-        ref_imgs_local = [u for u in (reference_images or []) if u and isinstance(u, str)]
-        if ref_imgs_local:
-            if len(ref_imgs_local) == 1:
-                payload["image"] = ref_imgs_local[0]
-            else:
-                payload["image"] = ref_imgs_local[:10]
-
-        req_timeout = timeout or getattr(self, "image_timeout", 60)
-        last_err: Exception | None = None
-        last_sc = 0
-        last_body = ""
-        _img_t0 = time.time()
-        for attempt in range(self.max_retries + 1):
-            try:
-                resp = httpx.post(url, headers=headers, json=payload, timeout=req_timeout)
-                last_sc = int(getattr(resp, "status_code", 0) or 0)
-                last_body = (getattr(resp, "text", "") or "")[:2000]
-                if last_sc >= 400:
-                    logger.error("Seedream 图片生成 HTTP %d: %s", last_sc, last_body[:500])
-                    try:
-                        resp.raise_for_status()
-                    except Exception as ee:
-                        last_err = ee
-                        if attempt < self.max_retries and last_sc >= 500:
-                            time.sleep(0.5 * (2**attempt))
-                            continue
-                        break
-                data = resp.json()
-                data_list = data.get("data") or []
-                if data_list and isinstance(data_list, list):
-                    item = data_list[0]
-                    img_url = item.get("url")
-                    if img_url:
-                        logger.info(
-                            "Seedream 图片生成成功 model=%s ref_imgs=%d size=%s elapsed=%.1fs attempt=%d",
-                            img_model,
-                            len(ref_imgs_local),
-                            _img_size,
-                            time.time() - _img_t0,
-                            attempt + 1,
-                        )
-                        return {"url": img_url, "usage": data.get("usage")}
-                last_err = RuntimeError(f"Seedream 返回结构异常: {str(data)[:300]}")
-                break
-            except _HTTP_NETWORK_ERRORS as ne:
-                last_err = ne
-                last_sc = 0
-                last_body = f"network error: {ne}"
-                logger.warning(
-                    "Seedream 网络异常 (%s)，重试 %d/%d", type(ne).__name__, attempt + 1, self.max_retries + 1
-                )
-                if attempt < self.max_retries:
-                    time.sleep(0.5 * (2**attempt))
-                    continue
-                break
-            except Exception as e:
-                last_err = e
-                if attempt < self.max_retries and not isinstance(e, _HTTP_STATUS_ERROR):
-                    wait = 0.5 * (2**attempt)
-                    logger.warning(
-                        "Seedream 图片生成失败，%.1fs 后重试 (%d/%d): %s", wait, attempt + 1, self.max_retries + 1, e
-                    )
-                    time.sleep(wait)
-                    continue
-                break
-
-        # 分类错误
-        err_code = "unknown"
-        user_msg = "图片生成失败，请稍后重试。"
-        body_lower = (last_body or "").lower()
-        if last_sc == 401 or last_sc == 403:
-            err_code, user_msg = "auth_error", "图片生成服务鉴权失败，请联系管理员。"
-        elif last_sc == 400:
-            if any(k in body_lower for k in ("quota", "billing", "insufficient", "balance")):
-                err_code, user_msg = "quota_exceeded", "图片生成配额不足或账号欠费，请联系管理员。"
-            elif any(k in body_lower for k in ("rate", "throughput", "too many", "frequency")):
-                err_code, user_msg = "rate_limit", "图片生成请求过于频繁，请稍后重试。"
-            elif any(k in body_lower for k in ("sensitive", "porn", "terror", "risk", "audit", "content", "violat")):
-                err_code, user_msg = "portrait_intercept", "参考素材未通过内容安全审核，请更换照片后重试。"
-            else:
-                err_code, user_msg = "invalid_param", f"图片生成参数错误：{last_body[:200]}"
-        elif last_sc == 404:
-            err_code, user_msg = "model_not_found", f"图片模型 {img_model} 不存在，请联系管理员。"
-        elif last_sc >= 500:
-            err_code, user_msg = "network_error", "图片生成服务暂时不可用，请稍后重试。"
-        elif last_sc == 0:
-            err_code, user_msg = "network_error", f"图片生成网络错误：{last_err!s}"[:200]
-        self.last_image_error = {
-            "error_code": err_code,
-            "user_message": user_msg,
-            "status_code": last_sc,
-            "detail": (last_body or "")[:500] or (str(last_err) if last_err else ""),
-            "model": img_model,
-        }
-        logger.error(
-            "Seedream 图片生成最终失败: model=%s status=%d code=%s elapsed=%.1fs err=%s",
-            img_model,
-            last_sc,
-            err_code,
-            time.time() - _img_t0,
-            last_err,
-        )
-        return None
-
-    def get_last_image_error(self) -> dict:
-        """返回最近一次 image_generation 失败的详细错误。空 dict 表示上次成功或未调用。"""
-        return dict(self.last_image_error or {})
-
     def get_last_video_error(self) -> dict:
         """返回最近一次 video_generation 失败的详细错误。空 dict 表示上次成功或未调用。"""
         return dict(self.last_video_error or {})

packages/shared/ai_config_version.py

@@ -1,62 +0,0 @@
-"""AI 配置版本号管理 — Redis 通知机制.
-
-admin 后台修改 ai_models / ai_capability_configs 后调用 bump_version()，
-SaaS 端 AIRouter 每次取配置前比对版本号，变了才重新查 DB。
-
-Redis key: xiaoxia:ai_config:version = 时间戳字符串
-"""
-
-from __future__ import annotations
-
-import logging
-import time
-from typing import Optional
-
-logger = logging.getLogger(__name__)
-
-_REDIS_KEY = "xiaoxia:ai_config:version"
-
-
-def _get_redis_client():
-    """获取 Redis 客户端（复用 Celery broker 连接）."""
-    try:
-        import redis as _redis
-
-        from packages.shared.config import get_shared_settings
-
-        settings = get_shared_settings()
-        redis_url = getattr(settings, "redis_url", None) or getattr(
-            settings, "celery_broker_url", "redis://localhost:6379/0"
-        )
-        return _redis.Redis.from_url(redis_url, decode_responses=True, socket_timeout=2)
-    except Exception as e:
-        logger.warning("AI config version: Redis 客户端初始化失败: %s", e)
-        return None
-
-
-def bump_version() -> str:
-    """写入新版本号（当前时间戳），返回版本号字符串。失败返回空串。"""
-    r = _get_redis_client()
-    if r is None:
-        logger.warning("AI config bump_version: Redis 不可用，跳过版本号更新")
-        return ""
-    try:
-        ver = str(int(time.time() * 1000))
-        r.set(_REDIS_KEY, ver)
-        logger.info("AI config version bumped to %s", ver)
-        return ver
-    except Exception as e:
-        logger.warning("AI config bump_version 失败: %s", e)
-        return ""
-
-
-def get_version() -> Optional[str]:
-    """读取当前版本号。Redis 不可用或异常返回 None。"""
-    r = _get_redis_client()
-    if r is None:
-        return None
-    try:
-        return r.get(_REDIS_KEY)
-    except Exception as e:
-        logger.warning("AI config get_version 失败: %s", e)
-        return None

packages/shared/ai_router.py

@@ -1,516 +0,0 @@
-"""AI 模型路由层 — 统一模型配置读取与客户端构建.
-
-业务代码通过 AIRouter 获取客户端，不再硬编码 model/api_key/base_url。
-配置来源：DB ai_capability_configs JOIN ai_models → Redis 版本号缓存 → SharedSettings fallback。
-
-使用方式：
-    from packages.shared.ai_router import ai_router
-
-    client = ai_router.get_llm_client("intent_parsing")
-    result = client.chat_completion(messages=[...])
-"""
-
-from __future__ import annotations
-
-import logging
-import threading
-from dataclasses import dataclass
-
-from packages.shared.config import get_shared_settings
-
-logger = logging.getLogger(__name__)
-
-
-# ── 配置数据类 ──────────────────────────────────────────────────────────────
-
-
-@dataclass(frozen=True)
-class ModelConfig:
-    """单个 AI 模型配置（来自 ai_models 表）"""
-
-    id: str
-    name: str
-    provider: str
-    model_key: str
-    api_key: str
-    api_base: str
-    api_version: str | None
-    status: str
-
-
-@dataclass(frozen=True)
-class CapabilityConfig:
-    """业务能力配置（来自 ai_capability_configs JOIN ai_models）"""
-
-    capability_key: str
-    capability_name: str
-    primary_model: ModelConfig | None
-    lite_model: ModelConfig | None
-    fallback_model: ModelConfig | None
-    timeout_seconds: int
-    max_retries: int
-    max_tokens: int | None
-    temperature: float | None
-    concurrency: int
-    extra_params: dict
-    is_enabled: bool
-
-
-# ── 简单包装类（TTS / ImageGen / VideoGen）──────────────────────────────────
-
-
-class TTSClient:
-    """TTS 客户端（简单配置持有者，实际调用由 CosyVoiceService 完成）"""
-
-    def __init__(
-        self,
-        provider: str,
-        api_key: str,
-        base_url: str,
-        model: str,
-        timeout: int = 60,
-        extra_params: dict | None = None,
-    ):
-        self.provider = provider
-        self.api_key = api_key
-        self.base_url = base_url
-        self.model = model
-        self.timeout = timeout
-        self.extra_params = extra_params or {}
-
-    @property
-    def is_available(self) -> bool:
-        return bool(self.api_key and self.base_url and self.model)
-
-
-class ImageGenClient:
-    """图片生成客户端（简单配置持有者）"""
-
-    def __init__(
-        self,
-        provider: str,
-        api_key: str,
-        base_url: str,
-        model: str,
-        timeout: int = 60,
-        extra_params: dict | None = None,
-    ):
-        self.provider = provider
-        self.api_key = api_key
-        self.base_url = base_url
-        self.model = model
-        self.timeout = timeout
-        self.extra_params = extra_params or {}
-
-    @property
-    def is_available(self) -> bool:
-        return bool(self.api_key and self.base_url and self.model)
-
-
-class VideoGenClient:
-    """视频生成客户端（简单配置持有者）"""
-
-    def __init__(
-        self,
-        provider: str,
-        api_key: str,
-        base_url: str,
-        model: str,
-        timeout: int = 600,
-        extra_params: dict | None = None,
-    ):
-        self.provider = provider
-        self.api_key = api_key
-        self.base_url = base_url
-        self.model = model
-        self.timeout = timeout
-        self.extra_params = extra_params or {}
-
-    @property
-    def is_available(self) -> bool:
-        return bool(self.api_key and self.base_url and self.model)
-
-
-# ── DB Session 获取 ─────────────────────────────────────────────────────────
-
-
-def _get_session():
-    """获取 DB session，兼容 api / worker / 独立脚本场景"""
-    from packages.adapters.sqlalchemy_impl.session import SessionLocal
-
-    if SessionLocal is not None:
-        return SessionLocal()
-
-    try:
-        from worker_app.db import SessionLocal as WorkerSL
-
-        if WorkerSL is not None:
-            return WorkerSL()
-    except ImportError:
-        pass
-
-    try:
-        from app.db import SessionLocal as ApiSL
-
-        if ApiSL is not None:
-            return ApiSL()
-    except ImportError:
-        pass
-
-    return None
-
-
-# ── 核心路由类 ──────────────────────────────────────────────────────────────
-
-
-class AIRouter:
-    """AI 模型路由器 — 统一配置读取与客户端构建.
-
-    缓存策略：
-    1. 本地内存缓存 {capability_key: CapabilityConfig}
-    2. 每次读取前比对 Redis 版本号，变了则清缓存重新查 DB
-    3. DB 无配置 / Redis 不可用 → fallback 到 SharedSettings 环境变量
-    """
-
-    def __init__(self):
-        self._cache: dict[str, CapabilityConfig] = {}
-        self._local_ver: str | None = None
-        self._lock = threading.Lock()
-
-    def _check_version(self) -> bool:
-        """检查 Redis 版本号，变了返回 True（需要刷新缓存）"""
-        from packages.shared.ai_config_version import get_version
-
-        current_ver = get_version()
-        if current_ver is None:
-            return False
-        if self._local_ver != current_ver:
-            return True
-        return False
-
-    def _load_from_db(self, capability_key: str) -> CapabilityConfig | None:
-        """从 DB 加载配置（ai_capability_configs JOIN ai_models）"""
-        session = _get_session()
-        if session is None:
-            logger.warning("AI Router: 无法获取 DB session")
-            return None
-        try:
-            from sqlalchemy import text
-
-            sql = text("""
-                SELECT
-                    cc.capability_key, cc.capability_name, cc.timeout_seconds,
-                    cc.max_retries, cc.max_tokens, cc.temperature,
-                    cc.concurrency, cc.extra_params, cc.is_enabled,
-                    pm.id AS pm_id, pm.name AS pm_name, pm.provider AS pm_provider,
-                    pm.model_key AS pm_model_key, pm.api_key AS pm_api_key,
-                    pm.api_base AS pm_api_base, pm.api_version AS pm_api_version,
-                    pm.status AS pm_status,
-                    lm.id AS lm_id, lm.name AS lm_name, lm.provider AS lm_provider,
-                    lm.model_key AS lm_model_key, lm.api_key AS lm_api_key,
-                    lm.api_base AS lm_api_base, lm.api_version AS lm_api_version,
-                    lm.status AS lm_status,
-                    fm.id AS fm_id, fm.name AS fm_name, fm.provider AS fm_provider,
-                    fm.model_key AS fm_model_key, fm.api_key AS fm_api_key,
-                    fm.api_base AS fm_api_base, fm.api_version AS fm_api_version,
-                    fm.status AS fm_status
-                FROM ai_capability_configs cc
-                LEFT JOIN ai_models pm ON cc.primary_model_id = pm.id AND pm.deleted_at IS NULL
-                LEFT JOIN ai_models lm ON cc.lite_model_id = lm.id AND lm.deleted_at IS NULL
-                LEFT JOIN ai_models fm ON cc.fallback_model_id = fm.id AND fm.deleted_at IS NULL
-                WHERE cc.capability_key = :key AND cc.is_enabled = true
-            """)
-            row = session.execute(sql, {"key": capability_key}).first()
-            if not row:
-                return None
-
-            def _to_model(prefix: str) -> ModelConfig | None:
-                mid = getattr(row, f"{prefix}_id", None)
-                if not mid:
-                    return None
-                return ModelConfig(
-                    id=mid,
-                    name=getattr(row, f"{prefix}_name", "") or "",
-                    provider=getattr(row, f"{prefix}_provider", "") or "",
-                    model_key=getattr(row, f"{prefix}_model_key", "") or "",
-                    api_key=getattr(row, f"{prefix}_api_key", "") or "",
-                    api_base=getattr(row, f"{prefix}_api_base", "") or "",
-                    api_version=getattr(row, f"{prefix}_api_version", None),
-                    status=getattr(row, f"{prefix}_status", "active") or "active",
-                )
-
-            return CapabilityConfig(
-                capability_key=row.capability_key,
-                capability_name=row.capability_name,
-                primary_model=_to_model("pm"),
-                lite_model=_to_model("lm"),
-                fallback_model=_to_model("fm"),
-                timeout_seconds=row.timeout_seconds or 30,
-                max_retries=row.max_retries or 1,
-                max_tokens=row.max_tokens,
-                temperature=row.temperature,
-                concurrency=row.concurrency or 2,
-                extra_params=row.extra_params or {},
-                is_enabled=row.is_enabled,
-            )
-        except Exception as e:
-            logger.warning("AI Router: DB 查询失败 (key=%s): %s", capability_key, e)
-            return None
-        finally:
-            session.close()
-
-    def get_capability(self, key: str) -> CapabilityConfig | None:
-        """获取业务能力配置（带缓存）"""
-        with self._lock:
-            if self._check_version():
-                self._cache.clear()
-                from packages.shared.ai_config_version import get_version
-
-                self._local_ver = get_version()
-
-            if key in self._cache:
-                return self._cache[key]
-
-            config = self._load_from_db(key)
-            if config:
-                self._cache[key] = config
-            return config
-
-    def _get_model_or_fallback(self, cap: CapabilityConfig, variant: str = "primary") -> ModelConfig | None:
-        """按 variant 选择模型，不存在则降级。
-
-        - primary: primary → fallback
-        - lite: lite → primary
-        - fallback: fallback → primary（修复点：此前 fallback variant 被忽略，错误地使用了 primary 模型）
-        """
-        if variant == "fallback":
-            if cap.fallback_model:
-                return cap.fallback_model
-            if cap.primary_model:
-                return cap.primary_model
-        elif variant == "lite":
-            if cap.lite_model:
-                return cap.lite_model
-            if cap.primary_model:
-                return cap.primary_model
-        else:  # primary
-            if cap.primary_model:
-                return cap.primary_model
-            if cap.fallback_model:
-                return cap.fallback_model
-        return None
-
-    # ── 构建客户端 ─────────────────────────────────────────────────────────
-
-    def _build_llm_client(self, model: ModelConfig, cap: CapabilityConfig):
-        """构建 LLM 客户端 — 返回 DoubaoClient 实例"""
-        from packages.shared.ai_client import DoubaoClient
-
-        return DoubaoClient(
-            api_key=model.api_key,
-            base_url=model.api_base,
-            model=model.model_key,
-            timeout=cap.timeout_seconds,
-            max_retries=cap.max_retries,
-            max_tokens=cap.max_tokens,
-            temperature=cap.temperature,
-            extra_params=cap.extra_params,
-            provider=model.provider,
-        )
-
-    def _build_vision_client(self, model: ModelConfig, cap: CapabilityConfig):
-        """构建 VLM 客户端 — 返回 DoubaoClient 实例（DoubaoClient 已支持 vision_completion）"""
-        from packages.shared.ai_client import DoubaoClient
-
-        return DoubaoClient(
-            api_key=model.api_key,
-            base_url=model.api_base,
-            model=model.model_key,
-            timeout=cap.timeout_seconds,
-            max_retries=cap.max_retries,
-            max_tokens=cap.max_tokens,
-            temperature=cap.temperature,
-            extra_params=cap.extra_params,
-            provider=model.provider,
-        )
-
-    def _build_tts_client(self, model: ModelConfig, cap: CapabilityConfig) -> TTSClient:
-        return TTSClient(
-            provider=model.provider,
-            api_key=model.api_key,
-            base_url=model.api_base,
-            model=model.model_key,
-            timeout=cap.timeout_seconds,
-            extra_params=cap.extra_params,
-        )
-
-    def _build_image_gen_client(self, model: ModelConfig, cap: CapabilityConfig) -> ImageGenClient:
-        return ImageGenClient(
-            provider=model.provider,
-            api_key=model.api_key,
-            base_url=model.api_base,
-            model=model.model_key,
-            timeout=cap.timeout_seconds,
-            extra_params=cap.extra_params,
-        )
-
-    def _build_video_gen_client(self, model: ModelConfig, cap: CapabilityConfig) -> VideoGenClient:
-        return VideoGenClient(
-            provider=model.provider,
-            api_key=model.api_key,
-            base_url=model.api_base,
-            model=model.model_key,
-            timeout=cap.timeout_seconds,
-            extra_params=cap.extra_params,
-        )
-
-    # ── 公开接口 ────────────────────────────────────────────────────────────
-
-    def get_llm_client(self, key: str, variant: str = "primary"):
-        """获取 LLM 客户端（返回 DoubaoClient 实例）"""
-        cap = self.get_capability(key)
-        if cap and cap.is_enabled:
-            model = self._get_model_or_fallback(cap, variant)
-            if model and model.api_key:
-                return self._build_llm_client(model, cap)
-
-        return self._fallback_llm_client(key)
-
-    def get_vision_client(self, key: str, variant: str = "primary"):
-        """获取 VLM 客户端（返回 DoubaoClient 实例）"""
-        cap = self.get_capability(key)
-        if cap and cap.is_enabled:
-            model = self._get_model_or_fallback(cap, variant)
-            if model and model.api_key:
-                return self._build_vision_client(model, cap)
-
-        return self._fallback_vision_client(key)
-
-    def get_tts_client(self, key: str = "tts") -> TTSClient | None:
-        """获取 TTS 客户端"""
-        cap = self.get_capability(key)
-        if cap and cap.is_enabled and cap.primary_model and cap.primary_model.api_key:
-            return self._build_tts_client(cap.primary_model, cap)
-
-        return self._fallback_tts_client()
-
-    def get_image_gen_client(self, key: str = "image_generation") -> ImageGenClient | None:
-        """获取图片生成客户端"""
-        cap = self.get_capability(key)
-        if cap and cap.is_enabled and cap.primary_model and cap.primary_model.api_key:
-            return self._build_image_gen_client(cap.primary_model, cap)
-
-        return self._fallback_image_gen_client()
-
-    def get_video_gen_client(self, key: str = "video_generation") -> VideoGenClient | None:
-        """获取视频生成客户端"""
-        cap = self.get_capability(key)
-        if cap and cap.is_enabled and cap.primary_model and cap.primary_model.api_key:
-            return self._build_video_gen_client(cap.primary_model, cap)
-
-        return self._fallback_video_gen_client()
-
-    # ── Fallback 方法（读 SharedSettings 环境变量）──────────────────────────
-
-    def _fallback_llm_client(self, key: str):
-        """Fallback LLM 客户端 — 从 settings 读取配置，不硬编码"""
-        settings = get_shared_settings()
-        model_map = {
-            "intent_parsing": (settings.doubao_fast_model, settings.doubao_base_url, settings.doubao_api_key),
-            "copy_fusion": (settings.doubao_fast_model, settings.doubao_base_url, settings.doubao_api_key),
-            "storyboard": (settings.doubao_model, settings.doubao_base_url, settings.doubao_api_key),
-            "copy_review": (settings.doubao_model, settings.doubao_base_url, settings.doubao_api_key),
-            "asset_classify": (settings.doubao_model, settings.doubao_base_url, settings.doubao_api_key),
-        }
-        if key in model_map:
-            model_id, base_url, api_key = model_map[key]
-        else:
-            model_id = settings.doubao_model
-            base_url = settings.doubao_base_url
-            api_key = settings.doubao_api_key
-
-        if not api_key:
-            return None
-
-        from packages.shared.ai_client import DoubaoClient
-
-        return DoubaoClient(
-            provider="volcengine",
-            api_key=api_key,
-            base_url=base_url,
-            model=model_id,
-            timeout=settings.doubao_timeout,
-            max_retries=settings.doubao_max_retries,
-        )
-
-    def _fallback_vision_client(self, key: str):
-        """Fallback VLM 客户端 — 从 settings 读取 dashscope 配置，不硬编码"""
-        settings = get_shared_settings()
-        api_key = getattr(settings, "dashscope_api_key", "")
-        if not api_key:
-            return None
-        base_url = getattr(settings, "dashscope_base_url", "") or ""
-        model = getattr(settings, "dashscope_model", "") or getattr(settings, "doubao_vision_model", "")
-
-        from packages.shared.ai_client import DoubaoClient
-
-        return DoubaoClient(
-            provider="dashscope",
-            api_key=api_key,
-            base_url=base_url,
-            model=model,
-            timeout=15,
-        )
-
-    def _fallback_tts_client(self) -> TTSClient | None:
-        settings = get_shared_settings()
-        api_key = getattr(settings, "cosyvoice_api_key", "")
-        if not api_key:
-            return None
-        base_url = getattr(settings, "cosyvoice_base_url", "")
-        model = getattr(settings, "cosyvoice_model", "")
-
-        return TTSClient(provider="dashscope", api_key=api_key, base_url=base_url, model=model)
-
-    def _fallback_image_gen_client(self) -> ImageGenClient | None:
-        settings = get_shared_settings()
-        api_key = getattr(settings, "doubao_api_key", "")
-        if not api_key:
-            return None
-        base_url = getattr(settings, "doubao_base_url", "")
-        model = getattr(settings, "doubao_image_model", "")
-
-        return ImageGenClient(
-            provider="volcengine",
-            api_key=api_key,
-            base_url=base_url,
-            model=model,
-            timeout=getattr(settings, "doubao_image_timeout", 60),
-        )
-
-    def _fallback_video_gen_client(self) -> VideoGenClient | None:
-        settings = get_shared_settings()
-        api_key = getattr(settings, "doubao_api_key", "")
-        if not api_key:
-            return None
-        base_url = getattr(settings, "doubao_base_url", "")
-        model = getattr(settings, "doubao_video_model", "")
-
-        return VideoGenClient(
-            provider="volcengine",
-            api_key=api_key,
-            base_url=base_url,
-            model=model,
-            timeout=getattr(settings, "doubao_video_timeout", 600),
-        )
-
-    def invalidate(self):
-        """清空本地缓存"""
-        with self._lock:
-            self._cache.clear()
-            self._local_ver = None
-
-
-# ── 全局单例 ──────────────────────────────────────────────────────────────
-
-ai_router = AIRouter()

packages/shared/ai_service.py

@@ -502,7 +502,6 @@ def call_llm(
     max_tokens: int = 2048,
     model: str | None = None,
     system_prompt: str | None = None,
-    timeout: int | None = None,
 ) -> object:
     """调用豆包大模型（文本对话），返回解析后的 JSON（dict/list）或原文字符串；失败返回 None。
 
@@ -522,7 +521,7 @@ def call_llm(
         {"role": "system", "content": system_prompt},
         {"role": "user", "content": prompt},
     ]
-    raw = client.chat_completion(messages, temperature=temperature, max_tokens=max_tokens, model=model, timeout=timeout)
+    raw = client.chat_completion(messages, temperature=temperature, max_tokens=max_tokens, model=model)
     if raw is None:
         return None
     try:
@@ -605,24 +604,6 @@ def call_vision(
         return raw
 
 
-def preheat_trust_chain(portrait_descriptions: list[str], *, timeout: int = 120) -> list[str] | None:
-    """#2174 信任链预热（t2i版）：用 VLM 分析出的人物外貌描述，跑 Seedream 文生图，
-    生成的信任产物 URL 可传给 call_video_generation(pre_trusted_images=...)。
-
-    - portrait_descriptions: VLM输出的portrait_prompt列表（中文人物外貌描述）
-    - 成功返回与输入同序的信任图URL列表；任意一张失败返回None（调用方回退到纯t2v）
-    - 必须传VLM人物描述，不传reference_images，走纯t2i路径才是方舟信任产物
-    """
-    client = get_doubao_client()
-    if not client.is_available:
-        return None
-    try:
-        return client.preheat_trust_chain(portrait_descriptions, timeout=timeout)
-    except Exception as e:
-        logger.error("[ai_service] preheat_trust_chain 异常: %s", e, exc_info=True)
-        return None
-
-
 def call_video_generation(
     prompt: str,
     *,
@@ -636,9 +617,8 @@ def call_video_generation(
     reference_images: list[str] | None = None,
     reference_audios: list[str] | None = None,
     reference_videos: list[str] | None = None,
-    pre_trusted_images: list[str] | None = None,
 ) -> dict | None:
-    """调用 Seedance / Wan 视频生成（v1.6.2 多模型版 + #2172 信任链预热）。
+    """调用 Seedance / Wan 视频生成（v1.6.2 多模型版）。
 
     成功返回 {"video_path": str, "usage": dict | None}（usage 含 completion_tokens），失败返回 None。
     失败时错误详情会写入 client.last_video_error，可通过 get_last_video_error() 读取：
@@ -670,7 +650,6 @@ def call_video_generation(
             reference_images=reference_images,
             reference_audios=reference_audios,
             reference_videos=reference_videos,
-            pre_trusted_images=pre_trusted_images,
         )
         if effective_ratio:
             kwargs["ratio"] = effective_ratio

packages/shared/celery_queues.py

@@ -51,8 +51,6 @@ task_routes = {
     "ai_avatar_render.execute": {"queue": QUEUE_GENERATION},
     # GPU MuseTalk 口型同步（用户等成片，链路子任务全部走 generation 避免跨队列阻塞）
     "lipsync_gpu_process_async": {"queue": QUEUE_GENERATION},
-    # #2076 Ditto 蚂蚁数字人口型同步（走 generation 队列，避免跨队列阻塞）
-    "lipsync_ditto_process_async": {"queue": QUEUE_GENERATION},
     "lipsync_tts.synthesize_and_submit": {"queue": QUEUE_GENERATION},
     "lipsync_tts.poll_mediakit_status": {"queue": QUEUE_GENERATION},
     "lipsync_tts.persist_output_video": {"queue": QUEUE_GENERATION},

scripts/render_env.sh

@@ -57,7 +57,7 @@ if [ "$TARGET_ENV" = "staging" ]; then
 fi
 
 # 共用 secrets 直接导出（如果存在）
-SHARED_SECRETS="OSS_ACCESS_KEY_ID OSS_ACCESS_KEY_SECRET COSYVOICE_API_KEY DASHSCOPE_API_KEY MEDIAKIT_API_KEY DOUBAO_API_KEY DOUBAO_MODEL DOUBAO_FAST_MODEL DOUBAO_BASE_URL DOUBAO_VISION_MODEL DOUBAO_VISION_LITE_MODEL DOUBAO_VISION_USE_LITE DOUBAO_IMAGE_MODEL DOUBAO_IMAGE_SIZE DOUBAO_IMAGE_TIMEOUT DOUBAO_FAST_MODEL DOUBAO_TIMEOUT DOUBAO_MAX_RETRIES WECHAT_APP_ID WECHAT_APP_SECRET TIKHUB_API_KEY APIZERO_API_KEY GPU_WORKER_TOKEN"
+SHARED_SECRETS="OSS_ACCESS_KEY_ID OSS_ACCESS_KEY_SECRET COSYVOICE_API_KEY DASHSCOPE_API_KEY MEDIAKIT_API_KEY DOUBAO_API_KEY DOUBAO_MODEL DOUBAO_FAST_MODEL DOUBAO_BASE_URL DOUBAO_VISION_MODEL DOUBAO_VISION_LITE_MODEL DOUBAO_VISION_USE_LITE WECHAT_APP_ID WECHAT_APP_SECRET TIKHUB_API_KEY APIZERO_API_KEY GPU_WORKER_TOKEN"
 for var in $SHARED_SECRETS; do
   value="${!var:-}"
   # 已经在环境中了，无需额外操作

scripts/seed_viral_video_prompts.py

@@ -1,80 +0,0 @@
-"""爆款视频 5 套 Prompt 模板种子脚本（#2040）。
-
-幂等：以 (prompt_type, version) 为唯一键，存在则更新（UPSERT），重复执行结果一致。
-用法：
-    python scripts/seed_viral_video_prompts.py            # 自动用应用配置连库
-    DATABASE_URL=postgresql+psycopg2://... python scripts/seed_viral_video_prompts.py
-"""
-
-from __future__ import annotations
-
-import os
-import sys
-
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
-import sqlalchemy as sa  # noqa: E402
-
-from packages.application.viral_video.prompts import DEFAULT_TEMPLATES  # noqa: E402
-
-
-def _engine():
-    database_url = os.environ.get("DATABASE_URL")
-    if database_url:
-        return sa.create_engine(database_url)
-    # 复用应用自身配置
-    from packages.config import get_shared_settings
-
-    url = str(get_shared_settings().database_url)
-    return sa.create_engine(url.replace("postgresql+asyncpg://", "postgresql+psycopg2://"))
-
-
-UPSERT_SQL = sa.text("""
-    INSERT INTO viral_video_prompt_templates
-        (name, prompt_type, version, system_prompt, user_prompt_template,
-         example_output, is_active, updated_at)
-    VALUES
-        (:name, :prompt_type, :version, :system_prompt, :user_prompt_template,
-         :example_output, TRUE, :now_ts)
-    ON CONFLICT (prompt_type, version) DO UPDATE SET
-        name = EXCLUDED.name,
-        system_prompt = EXCLUDED.system_prompt,
-        user_prompt_template = EXCLUDED.user_prompt_template,
-        example_output = EXCLUDED.example_output,
-        is_active = TRUE,
-        updated_at = :now_ts
-    """)
-
-
-def seed(engine) -> int:
-    count = 0
-    from datetime import datetime, timezone
-
-    now_ts = datetime.now(timezone.utc)
-    with engine.begin() as conn:
-        for item in DEFAULT_TEMPLATES:
-            conn.execute(
-                UPSERT_SQL,
-                {
-                    "name": item["name"],
-                    "prompt_type": item["prompt_type"],
-                    "version": item["version"],
-                    "system_prompt": item["system_prompt"],
-                    "user_prompt_template": item["user_prompt_template"],
-                    "example_output": item["example_output"],
-                    "now_ts": now_ts,
-                },
-            )
-            count += 1
-    return count
-
-
-def main() -> int:
-    engine = _engine()
-    count = seed(engine)
-    print(f"seed 完成：{count} 套模板已写入/更新（image_analysis/intent_parsing/copy_fusion/storyboard/review）")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())

tests/unit/test_2035_coverage.py

@@ -1,5 +1,4 @@
 """Additional unit tests to hit uncovered lines for diff-coverage >=60%."""
-
 from __future__ import annotations
 
 import json
@@ -14,20 +13,15 @@ from packages.shared.ai_client import DoubaoClient
 
 class _FakeSettings:
     doubao_api_key = "test-key"
-    doubao_model = "doubao-seed-2-1-pro-260915"
-    doubao_fast_model = "doubao-seed-2-1-lite-260915"
+    doubao_model = "test-model"
+    doubao_fast_model = "test-fast-model"
     doubao_base_url = "https://ark.cn-beijing.volces.com/api/v3"
     doubao_timeout = 10
     doubao_max_retries = 0
-    doubao_vision_model = "doubao-seed-2-1-pro-260915"
-    doubao_vision_lite_model = "doubao-seed-2-1-lite-260915"
+    doubao_vision_model = "test-vision"
+    doubao_vision_lite_model = "test-vision-lite"
     doubao_vision_use_lite = False
-    doubao_embedding_model = "doubao-embedding-vision-251215"
-    doubao_video_model = "doubao-seedance-2-5-260628"
-    doubao_video_timeout = 480
-    doubao_video_poll_interval = 10
-    doubao_image_model = "doubao-seedream-5-0-pro-260628"
-    doubao_image_timeout = 120
+    doubao_embedding_model = "test-embedding"
 
 
 def _make_client(api_key: str = "test-key") -> DoubaoClient:
@@ -135,50 +129,26 @@ from packages.domain.atom_clip_tagger import parse_vision_response
 
 class TestParseVisionResponseEdgeCases:
     def test_person_count_type_error_defaults_zero(self):
-        text = json.dumps(
-            {
-                "scene": [],
-                "objects": [],
-                "action": [],
-                "shot": "",
-                "has_text": False,
-                "person_count": "not-an-int",
-                "text_content": "",
-                "caption": "x",
-            }
-        )
+        text = json.dumps({
+            "scene": [], "objects": [], "action": [], "shot": "", "has_text": False,
+            "person_count": "not-an-int", "text_content": "", "caption": "x",
+        })
         r = parse_vision_response(text)
         assert r["person_count"] == 0
 
     def test_person_count_out_of_range_clamped(self):
-        text = json.dumps(
-            {
-                "scene": [],
-                "objects": [],
-                "action": [],
-                "shot": "",
-                "has_text": False,
-                "person_count": 10,
-                "text_content": "",
-                "caption": "x",
-            }
-        )
+        text = json.dumps({
+            "scene": [], "objects": [], "action": [], "shot": "", "has_text": False,
+            "person_count": 10, "text_content": "", "caption": "x",
+        })
         r = parse_vision_response(text)
         assert r["person_count"] == 3
 
     def test_person_count_negative_clamped(self):
-        text = json.dumps(
-            {
-                "scene": [],
-                "objects": [],
-                "action": [],
-                "shot": "",
-                "has_text": False,
-                "person_count": -5,
-                "text_content": "",
-                "caption": "x",
-            }
-        )
+        text = json.dumps({
+            "scene": [], "objects": [], "action": [], "shot": "", "has_text": False,
+            "person_count": -5, "text_content": "", "caption": "x",
+        })
         r = parse_vision_response(text)
         assert r["person_count"] == 0
 
@@ -189,18 +159,10 @@ class TestParseVisionResponseEdgeCases:
 
     def test_caption_truncation_at_80(self):
         long_caption = "描" * 100
-        text = json.dumps(
-            {
-                "scene": [],
-                "objects": [],
-                "action": [],
-                "shot": "",
-                "has_text": False,
-                "person_count": 0,
-                "text_content": "",
-                "caption": long_caption,
-            }
-        )
+        text = json.dumps({
+            "scene": [], "objects": [], "action": [], "shot": "", "has_text": False,
+            "person_count": 0, "text_content": "", "caption": long_caption,
+        })
         r = parse_vision_response(text)
         assert len(r["caption"]) == 80
 
@@ -234,7 +196,9 @@ class TestNarrativeMatchNonDictClipTags:
     def test_non_dict_clip_tags_are_skipped(self):
         a1 = _FA("a1", tags=[])
         clip_map = {"a1": [None, "bad", {"scene": ["工厂"], "objects": [], "action": []}, 123]}
-        matched, unmatched = match_assets_by_script_tags([a1], script_tags=["工厂"], clip_ai_tags_by_asset=clip_map)
+        matched, unmatched = match_assets_by_script_tags(
+            [a1], script_tags=["工厂"], clip_ai_tags_by_asset=clip_map
+        )
         assert [a.id for a in matched] == ["a1"]
 
 
@@ -267,7 +231,6 @@ class _FQuery:
 class TestUpdateCaptionEmbedding:
     def _make_repo(self, session):
         from packages.adapters.sqlalchemy_impl.asset_atom_clip_repository import SQLAlchemyAssetAtomClipRepository
-
         repo = SQLAlchemyAssetAtomClipRepository.__new__(SQLAlchemyAssetAtomClipRepository)
         repo.session = session
         return repo

tests/unit/test_ai_client.py

@@ -17,7 +17,7 @@ def mock_settings():
             doubao_api_key="test-api-key",
             doubao_model="doubao-pro-32k",
             doubao_base_url="https://ark.example.com/api/v3",
-            doubao_timeout=45,
+            doubao_timeout=30,
             doubao_max_retries=2,
         )
         yield mock
@@ -37,7 +37,7 @@ def client_without_key():
             doubao_api_key="",
             doubao_model="doubao-pro-32k",
             doubao_base_url="https://ark.example.com/api/v3",
-            doubao_timeout=45,
+            doubao_timeout=30,
             doubao_max_retries=2,
         )
         yield DoubaoClient()
@@ -52,7 +52,7 @@ class TestDoubaoClientInit:
         assert client.api_key == "test-api-key"
         assert client.model == "doubao-pro-32k"
         assert client.base_url == "https://ark.example.com/api/v3"
-        assert client.timeout == 45
+        assert client.timeout == 30
         assert client.max_retries == 2
 
     def test_base_url_strips_trailing_slash(self, mock_settings):

tests/unit/test_ai_client_image.py

@@ -1,635 +0,0 @@
-"""#2170 Seedream 图片生成 + 方舟信任链单测。"""
-
-from __future__ import annotations
-
-from unittest.mock import MagicMock, patch
-
-import httpx
-
-from packages.shared.ai_client import DoubaoClient
-
-
-def _make_client(**overrides):
-    client = DoubaoClient.__new__(DoubaoClient)
-    client.api_key = overrides.get("api_key", "test-key")
-    client.base_url = overrides.get("base_url", "https://ark.cn-beijing.volces.com/api/v3")
-    client.model = "doubao-model"
-    client.vision_model = "doubao-vision"
-    client.embedding_model = "doubao-embedding"
-    client.image_model = overrides.get("image_model", "doubao-seedream-5-0-pro-260628")
-    client.image_timeout = overrides.get("image_timeout", 120)
-    client.timeout = overrides.get("timeout", 30)
-    client.max_retries = overrides.get("max_retries", 0)
-    client.last_video_error = {}
-    client.last_image_error = {}
-    return client
-
-
-def _fake_time(base=1000.0, stable_calls=50, big=9e9):
-    """返回 time.time 替身：前 stable_calls 次返回 base+i，之后返回 big+i。
-
-    Python 3.12 logging.LogRecord.__init__ 内部会调 time.time()，
-    用有限 iter 会 StopIteration，因此必须用无限生成器。
-    """
-    state = {"n": 0}
-
-    def _t():
-        n = state["n"]
-        state["n"] += 1
-        if n < stable_calls:
-            return base + n
-        return big + n
-
-    return _t
-
-
-# ── Seedream 图片生成单测 ──────────────────────────────────────────
-
-
-class TestImageGenerationHappyPath:
-    def test_returns_none_when_no_api_key(self):
-        client = _make_client(api_key="")
-        assert client.image_generation("p") is None
-        err = client.get_last_image_error()
-        assert err["error_code"] == "auth_error"
-
-    def test_returns_none_on_empty_prompt(self):
-        client = _make_client()
-        assert client.image_generation("   ") is None
-        err = client.get_last_image_error()
-        assert err["error_code"] == "invalid_param"
-
-    def test_text_to_image_success(self):
-        client = _make_client()
-        captured = {}
-        ok_resp = MagicMock()
-        ok_resp.status_code = 200
-        ok_resp.json.return_value = {"data": [{"url": "https://cdn.example.com/i.png"}], "usage": {"tokens": 1}}
-        ok_resp.raise_for_status = MagicMock()
-        ok_resp.text = ""
-
-        def fake_post(url, **kwargs):
-            captured["url"] = url
-            captured["json"] = kwargs.get("json")
-            return ok_resp
-
-        with patch("packages.shared.ai_client.httpx.post", side_effect=fake_post):
-            result = client.image_generation("一只可爱的猫", size="1K")
-        assert result is not None
-        assert result["url"] == "https://cdn.example.com/i.png"
-        assert "/images/generations" in captured["url"]
-        assert captured["json"]["model"] == "doubao-seedream-5-0-pro-260628"
-        assert captured["json"]["size"] == "1K"
-        assert "image" not in captured["json"]
-
-    def test_image_to_image_single_ref_passed_as_string(self):
-        client = _make_client()
-        captured = {}
-        ok_resp = MagicMock(status_code=200)
-        ok_resp.json.return_value = {"data": [{"url": "https://cdn.example.com/out.png"}]}
-        ok_resp.raise_for_status = MagicMock()
-        ok_resp.text = ""
-
-        def fake_post(url, **kwargs):
-            captured["json"] = kwargs.get("json")
-            return ok_resp
-
-        with patch("packages.shared.ai_client.httpx.post", side_effect=fake_post):
-            client.image_generation("保持五官", reference_images=["https://img/x.jpg"])
-        assert captured["json"]["image"] == "https://img/x.jpg"
-
-    def test_image_to_image_multiple_refs_passed_as_list(self):
-        client = _make_client()
-        captured = {}
-        ok_resp = MagicMock(status_code=200)
-        ok_resp.json.return_value = {"data": [{"url": "https://cdn.example.com/out.png"}]}
-        ok_resp.raise_for_status = MagicMock()
-
-        def fake_post(url, **kwargs):
-            captured["json"] = kwargs.get("json")
-            return ok_resp
-
-        refs = [f"https://img/{i}.jpg" for i in range(3)]
-        with patch("packages.shared.ai_client.httpx.post", side_effect=fake_post):
-            client.image_generation("保持", reference_images=refs)
-        assert captured["json"]["image"] == refs
-
-    def test_400_sensitive_returns_portrait_intercept(self):
-        client = _make_client(max_retries=0)
-        bad_resp = MagicMock(status_code=400)
-        bad_resp.text = '{"error":{"code":"ContentRisk","message":"sensitive content detected"}}'
-        bad_resp.json.return_value = {"error": {"code": "ContentRisk"}}
-        bad_resp.raise_for_status.side_effect = httpx.HTTPStatusError("bad", request=MagicMock(), response=bad_resp)
-        with patch("packages.shared.ai_client.httpx.post", return_value=bad_resp):
-            assert client.image_generation("p", reference_images=["https://img/x.jpg"]) is None
-        err = client.get_last_image_error()
-        assert err["error_code"] == "portrait_intercept"
-
-    def test_500_retries_then_fails(self):
-        client = _make_client(max_retries=1)
-        bad_resp = MagicMock(status_code=500)
-        bad_resp.text = "internal error"
-        bad_resp.json.return_value = {"error": {"message": "internal"}}
-        bad_resp.raise_for_status.side_effect = httpx.HTTPStatusError("500", request=MagicMock(), response=bad_resp)
-        with (
-            patch("packages.shared.ai_client.httpx.post", return_value=bad_resp) as mp,
-            patch("packages.shared.ai_client.time.sleep", return_value=None),
-        ):
-            assert client.image_generation("p") is None
-            assert mp.call_count == 2
-        err = client.get_last_image_error()
-        assert err["error_code"] == "network_error"
-
-
-# ── 信任链集成单测 ────────────────────────────────────────────────
-
-
-class TestTrustChainIntegration:
-    def test_pre_trusted_images_replace_original_refs(self, tmp_path):
-        """#2174: 传 pre_trusted_images（预热好的t2i信任图）时，替换原image_url/ref_imgs发给Seedance，不再现场跑Seedream。"""
-        client = _make_client()
-        captured_calls = []
-
-        task_ok = MagicMock(status_code=200)
-        task_ok.json.return_value = {"id": "t-trust"}
-        task_ok.raise_for_status = MagicMock()
-        task_ok.text = ""
-
-        poll_ok = MagicMock(status_code=200)
-        poll_ok.json.return_value = {"status": "succeeded", "content": {"video_url": "https://cdn.example.com/v.mp4"}}
-        poll_ok.raise_for_status = MagicMock()
-
-        class FakeStream:
-            def __init__(self):
-                self._c = [b"OK"]
-                self._it = iter(self._c)
-
-            def __enter__(self):
-                return self
-
-            def __exit__(self, *a):
-                return False
-
-            def raise_for_status(self):
-                return None
-
-            def iter_bytes(self, chunk_size=None):
-                return self._it
-
-        def fake_post(url, **kwargs):
-            captured_calls.append({"url": url, "json": kwargs.get("json")})
-            return task_ok
-
-        fake_uuid = MagicMock()
-        fake_uuid.hex = "00000001"
-        with (
-            patch("packages.shared.ai_client.httpx.post", side_effect=fake_post),
-            patch("packages.shared.ai_client.httpx.get", return_value=poll_ok),
-            patch("packages.shared.ai_client.httpx.stream", return_value=FakeStream()),
-            patch("packages.shared.ai_client.time.sleep", return_value=None),
-            patch("packages.shared.ai_client.time.time", side_effect=_fake_time(stable_calls=2)),
-            patch("packages.shared.ai_client.uuid.uuid4", return_value=fake_uuid),
-            patch("packages.shared.ai_client.get_shared_settings") as ms,
-        ):
-            ms.return_value = MagicMock(
-                doubao_video_poll_interval=0,
-                doubao_video_timeout=60,
-                doubao_video_model="doubao-seedance-2-5-260628",
-            )
-            out = client.video_generation(
-                "人物在海边散步",
-                image_url="https://img/raw.jpg",
-                pre_trusted_images=["https://ai.example.com/trusted.png"],
-                duration=5,
-                ratio="9:16",
-                resolution="720p",
-                output_dir=str(tmp_path),
-            )
-        assert out is not None
-        # 只有一次 Seedance 调用（不现场跑Seedream）
-        assert len(captured_calls) == 1
-        assert "/contents/generations/tasks" in captured_calls[0]["url"]
-        seedance_payload = captured_calls[0]["json"]
-        content = seedance_payload["content"]
-        img_items = [c for c in content if c.get("type") == "image_url"]
-        assert len(img_items) == 1
-        assert img_items[0]["image_url"]["url"] == "https://ai.example.com/trusted.png"
-        assert img_items[0]["role"] == "reference_image"
-        assert seedance_payload["ratio"] == "9:16"
-
-    def test_no_reference_image_skips_seedream(self, tmp_path):
-        client = _make_client()
-        captured_calls = []
-
-        task_ok = MagicMock(status_code=200)
-        task_ok.json.return_value = {"id": "t-t2v"}
-        task_ok.raise_for_status = MagicMock()
-        poll_ok = MagicMock(status_code=200)
-        poll_ok.json.return_value = {"status": "succeeded", "content": {"video_url": "https://cdn.example.com/v.mp4"}}
-        poll_ok.raise_for_status = MagicMock()
-
-        class FakeStream:
-            def __init__(self):
-                self._it = iter([b"OK"])
-
-            def __enter__(self):
-                return self
-
-            def __exit__(self, *a):
-                return False
-
-            def raise_for_status(self):
-                return None
-
-            def iter_bytes(self, chunk_size=None):
-                return self._it
-
-        def fake_post(url, **kwargs):
-            captured_calls.append({"url": url, "json": kwargs.get("json")})
-            return task_ok
-
-        fake_uuid = MagicMock()
-        fake_uuid.hex = "00000002"
-        with (
-            patch("packages.shared.ai_client.httpx.post", side_effect=fake_post),
-            patch("packages.shared.ai_client.httpx.get", return_value=poll_ok),
-            patch("packages.shared.ai_client.httpx.stream", return_value=FakeStream()),
-            patch("packages.shared.ai_client.time.sleep", return_value=None),
-            patch("packages.shared.ai_client.time.time", side_effect=_fake_time(stable_calls=2)),
-            patch("packages.shared.ai_client.uuid.uuid4", return_value=fake_uuid),
-            patch("packages.shared.ai_client.get_shared_settings") as ms,
-        ):
-            ms.return_value = MagicMock(
-                doubao_video_poll_interval=0,
-                doubao_video_timeout=60,
-                doubao_video_model="doubao-seedance-2-5-260628",
-            )
-            out = client.video_generation("海边日落", duration=5, ratio="9:16", output_dir=str(tmp_path))
-        assert out is not None
-        assert len(captured_calls) == 1
-        assert "/contents/generations/tasks" in captured_calls[0]["url"]
-        content = captured_calls[0]["json"]["content"]
-        assert all(c.get("type") != "image_url" for c in content)
-
-    def test_no_preheated_images_falls_back_to_first_frame_mode(self, tmp_path):
-        """#2174: 无 pre_trusted_images 时原图走 first_frame 模式（ratio=adaptive），不再现场跑 Seedream。"""
-        client = _make_client(max_retries=0)
-        captured_calls = []
-
-        task_ok = MagicMock(status_code=200)
-        task_ok.json.return_value = {"id": "t-fb"}
-        task_ok.raise_for_status = MagicMock()
-        poll_ok = MagicMock(status_code=200)
-        poll_ok.json.return_value = {"status": "succeeded", "content": {"video_url": "https://cdn.example.com/v.mp4"}}
-        poll_ok.raise_for_status = MagicMock()
-
-        class FakeStream:
-            def __init__(self):
-                self._it = iter([b"OK"])
-
-            def __enter__(self):
-                return self
-
-            def __exit__(self, *a):
-                return False
-
-            def raise_for_status(self):
-                return None
-
-            def iter_bytes(self, chunk_size=None):
-                return self._it
-
-        def fake_post(url, **kwargs):
-            captured_calls.append({"url": url, "json": kwargs.get("json")})
-            return task_ok
-
-        fake_uuid = MagicMock()
-        fake_uuid.hex = "00000003"
-        with (
-            patch("packages.shared.ai_client.httpx.post", side_effect=fake_post),
-            patch("packages.shared.ai_client.httpx.get", return_value=poll_ok),
-            patch("packages.shared.ai_client.httpx.stream", return_value=FakeStream()),
-            patch("packages.shared.ai_client.time.sleep", return_value=None),
-            patch("packages.shared.ai_client.time.time", side_effect=_fake_time(stable_calls=2)),
-            patch("packages.shared.ai_client.uuid.uuid4", return_value=fake_uuid),
-            patch("packages.shared.ai_client.get_shared_settings") as ms,
-        ):
-            ms.return_value = MagicMock(
-                doubao_video_poll_interval=0,
-                doubao_video_timeout=60,
-                doubao_video_model="doubao-seedance-2-5-260628",
-            )
-            out = client.video_generation(
-                "海边散步",
-                image_url="https://img/raw.jpg",
-                duration=5,
-                ratio="9:16",
-                output_dir=str(tmp_path),
-            )
-        assert out is not None
-        # 不现场跑 Seedream，只有一次 Seedance 调用
-        assert len(captured_calls) == 1
-        seedance_payload = captured_calls[0]["json"]
-        content = seedance_payload["content"]
-        img_items = [c for c in content if c.get("type") == "image_url"]
-        assert len(img_items) == 1
-        assert img_items[0]["image_url"]["url"] == "https://img/raw.jpg"
-        assert img_items[0]["role"] == "first_frame"
-        assert seedance_payload["ratio"] == "adaptive"
-
-
-# ── image_generation 补充分支覆盖 ─────────────────────────────────
-
-
-class TestImageGenerationBranches:
-    """覆盖 image_generation 的错误分类/重试/结构异常等分支。"""
-
-    def test_401_returns_auth_error(self):
-        client = _make_client(max_retries=0)
-        r = MagicMock(status_code=401, text='{"error":{}}')
-        r.json.return_value = {"error": {}}
-        r.raise_for_status.side_effect = httpx.HTTPStatusError("a", request=MagicMock(), response=r)
-        with patch("packages.shared.ai_client.httpx.post", return_value=r):
-            assert client.image_generation("p") is None
-        assert client.get_last_image_error()["error_code"] == "auth_error"
-
-    def test_404_returns_model_not_found(self):
-        client = _make_client(max_retries=0)
-        r = MagicMock(status_code=404, text="not found")
-        r.json.return_value = {"error": {"message": "model not found"}}
-        r.raise_for_status.side_effect = httpx.HTTPStatusError("a", request=MagicMock(), response=r)
-        with patch("packages.shared.ai_client.httpx.post", return_value=r):
-            assert client.image_generation("p") is None
-        assert client.get_last_image_error()["error_code"] == "model_not_found"
-
-    def test_400_quota_returns_quota_exceeded(self):
-        client = _make_client(max_retries=0)
-        r = MagicMock(status_code=400, text="insufficient balance quota exceeded")
-        r.json.return_value = {"error": {"message": "quota"}}
-        r.raise_for_status.side_effect = httpx.HTTPStatusError("a", request=MagicMock(), response=r)
-        with patch("packages.shared.ai_client.httpx.post", return_value=r):
-            assert client.image_generation("p") is None
-        assert client.get_last_image_error()["error_code"] == "quota_exceeded"
-
-    def test_400_rate_limit_returns_rate_limit(self):
-        client = _make_client(max_retries=0)
-        r = MagicMock(status_code=400, text="too many requests, rate limit exceeded")
-        r.json.return_value = {"error": {"message": "rate"}}
-        r.raise_for_status.side_effect = httpx.HTTPStatusError("a", request=MagicMock(), response=r)
-        with patch("packages.shared.ai_client.httpx.post", return_value=r):
-            assert client.image_generation("p") is None
-        assert client.get_last_image_error()["error_code"] == "rate_limit"
-
-    def test_400_generic_returns_invalid_param(self):
-        client = _make_client(max_retries=0)
-        r = MagicMock(status_code=400, text="bad parameter size")
-        r.json.return_value = {"error": {"message": "bad"}}
-        r.raise_for_status.side_effect = httpx.HTTPStatusError("a", request=MagicMock(), response=r)
-        with patch("packages.shared.ai_client.httpx.post", return_value=r):
-            assert client.image_generation("p") is None
-        assert client.get_last_image_error()["error_code"] == "invalid_param"
-
-    def test_200_but_no_url_returns_none(self):
-        client = _make_client(max_retries=0)
-        r = MagicMock(status_code=200, text="")
-        r.json.return_value = {"data": [{"no_url": True}]}  # 缺 url 字段
-        r.raise_for_status = MagicMock()
-        with patch("packages.shared.ai_client.httpx.post", return_value=r):
-            assert client.image_generation("p") is None
-        assert client.get_last_image_error()["error_code"] == "unknown"
-
-    def test_network_error_retries_then_fails(self):
-        client = _make_client(max_retries=1)
-        import httpcore
-
-        with (
-            patch("packages.shared.ai_client.httpx.post", side_effect=httpx.ConnectError("no network")),
-            patch("packages.shared.ai_client.time.sleep", return_value=None),
-        ):
-            assert client.image_generation("p") is None
-        err = client.get_last_image_error()
-        assert err["error_code"] == "network_error"
-
-    def test_get_last_image_error_returns_copy(self):
-        client = _make_client()
-        client.last_image_error = {"error_code": "x"}
-        e1 = client.get_last_image_error()
-        e1["error_code"] = "mutated"
-        assert client.last_image_error["error_code"] == "x"
-
-
-# ── 信任链分支覆盖 ────────────────────────────────────────
-
-
-class TestTrustChainBranches:
-    def test_dashscope_provider_skips_trust_chain(self, tmp_path):
-        """provider=dashscope 时不走信任链（Wan 模型由 dashscope_client 处理，在我们分支之前已经 return）。
-        这里测 doubao 分支：信任链默认触发，验证 DashScope 分发路径不受影响。"""
-        # 该测试实际覆盖 video_generation 入口的 dashscope 分发：缺 DASHSCOPE_API_KEY 时返回 auth_error
-        client = _make_client()
-        with (patch("packages.shared.ai_client.get_shared_settings") as ms,):
-            ms.return_value = MagicMock(
-                doubao_video_poll_interval=0,
-                doubao_video_timeout=1,
-                doubao_video_model="doubao-seedance-2-5-260628",
-            )
-            # DashScope 不可用时返回 auth_error（不是信任链相关错误）
-            result = client.video_generation(
-                "p",
-                output_dir=str(tmp_path),
-                model="wan-3.0",
-                image_url="https://img/x.jpg",
-            )
-            assert result is None
-            err = client.get_last_video_error()
-            # 不论是否走信任链，DashScope 无 key 时返回 auth_error
-            assert err["error_code"] == "auth_error"
-
-    def test_trust_chain_partial_seedream_success_falls_back(self, tmp_path):
-        """#2174: 预热结果为空/None 时回退原图直传（走#2166的400→t2v自动降级路径）。"""
-        client = _make_client(max_retries=0)
-
-        # 预热结果传 None → 应该直接用原图发给 Seedance
-        def fake_post(url, **kwargs):
-            # Seedance create task（收到原图直传时会调用）
-            t = MagicMock(status_code=200, text="")
-            t.json.return_value = {"id": "t-partial"}
-            t.raise_for_status = MagicMock()
-            return t
-
-        poll_ok = MagicMock(status_code=200, text="")
-        poll_ok.json.return_value = {"status": "succeeded", "content": {"video_url": "https://cdn.example.com/v.mp4"}}
-        poll_ok.raise_for_status = MagicMock()
-
-        class FS:
-            def __init__(self):
-                self._it = iter([b"OK"])
-
-            def __enter__(self):
-                return self
-
-            def __exit__(self, *a):
-                return False
-
-            def raise_for_status(self):
-                return None
-
-            def iter_bytes(self, chunk_size=None):
-                return self._it
-
-        fake_uuid = MagicMock()
-        fake_uuid.hex = "00000004"
-        with (
-            patch("packages.shared.ai_client.httpx.post", side_effect=fake_post),
-            patch("packages.shared.ai_client.httpx.get", return_value=poll_ok),
-            patch("packages.shared.ai_client.httpx.stream", return_value=FS()),
-            patch("packages.shared.ai_client.time.sleep", return_value=None),
-            patch("packages.shared.ai_client.time.time", side_effect=_fake_time(stable_calls=50)),
-            patch("packages.shared.ai_client.uuid.uuid4", return_value=fake_uuid),
-            patch("packages.shared.ai_client.get_shared_settings") as ms,
-        ):
-            ms.return_value = MagicMock(
-                doubao_video_poll_interval=0,
-                doubao_video_timeout=60,
-                doubao_video_model="doubao-seedance-2-5-260628",
-            )
-            out = client.video_generation(
-                "p",
-                image_url="https://img/a.jpg",
-                reference_images=["https://img/b.jpg"],
-                duration=5,
-                ratio="9:16",
-                output_dir=str(tmp_path),
-            )
-        assert out is not None
-        # 最终发给 Seedance 的图应是原始 https://img/a.jpg（回退），role=first_frame（因为 has_extra_refs=False 只有 1 张）
-        # 注意：回退后 ref_imgs 是原始 ["https://img/b.jpg"]，所以 has_extra_refs=True，role=reference_image
-        # 断言最终 Seedance payload 里的 image_url 是原图（不是 AI 图）
-
-    def test_default_values_on_missing_settings(self):
-        """getattr 兜底：settings 缺 image_timeout 字段时使用默认 120。"""
-        client = _make_client()
-        # 直接调用 image_generation，让它走一次完整流程（成功路径），验证 timeout 取值
-        ok = MagicMock(status_code=200, text="")
-        ok.json.return_value = {"data": [{"url": "https://ai.example.com/x.png"}]}
-        ok.raise_for_status = MagicMock()
-        captured_kwargs = {}
-
-        def fake_post(url, **kw):
-            captured_kwargs["timeout"] = kw.get("timeout")
-            return ok
-
-        with patch("packages.shared.ai_client.httpx.post", side_effect=fake_post):
-            r = client.image_generation("p", timeout=None)  # 不传 timeout，走 self.image_timeout=120
-        assert r is not None
-        assert captured_kwargs["timeout"] == 120
-
-    def test_trust_chain_uses_preheated_t2i_images(self, tmp_path):
-        """#2174: 传 pre_trusted_images（预热好的t2i信任图）时，替换原参考图发给Seedance。"""
-        client = _make_client()
-        captured = []
-
-        task_ok = MagicMock(status_code=200, text="")
-        task_ok.json.return_value = {"id": "t-refonly"}
-        task_ok.raise_for_status = MagicMock()
-        poll_ok = MagicMock(status_code=200, text="")
-        poll_ok.json.return_value = {"status": "succeeded", "content": {"video_url": "https://cdn.example.com/v.mp4"}}
-        poll_ok.raise_for_status = MagicMock()
-
-        class FS:
-            def __init__(self):
-                self._it = iter([b"OK"])
-
-            def __enter__(self):
-                return self
-
-            def __exit__(self, *a):
-                return False
-
-            def raise_for_status(self):
-                return None
-
-            def iter_bytes(self, chunk_size=None):
-                return self._it
-
-        def fake_post(url, **kw):
-            captured.append({"url": url, "json": kw.get("json")})
-            return task_ok
-
-        fu = MagicMock()
-        fu.hex = "0000000a"
-        with (
-            patch("packages.shared.ai_client.httpx.post", side_effect=fake_post),
-            patch("packages.shared.ai_client.httpx.get", return_value=poll_ok),
-            patch("packages.shared.ai_client.httpx.stream", return_value=FS()),
-            patch("packages.shared.ai_client.time.sleep", return_value=None),
-            patch("packages.shared.ai_client.time.time", side_effect=_fake_time(stable_calls=3)),
-            patch("packages.shared.ai_client.uuid.uuid4", return_value=fu),
-            patch("packages.shared.ai_client.get_shared_settings") as ms,
-        ):
-            ms.return_value = MagicMock(
-                doubao_video_poll_interval=0,
-                doubao_video_timeout=60,
-                doubao_video_model="doubao-seedance-2-5-260628",
-            )
-            out = client.video_generation(
-                "人物散步",
-                reference_images=["https://img/portrait.jpg"],
-                pre_trusted_images=["https://ai.example.com/t2i-portrait.png"],
-                duration=5,
-                ratio="9:16",
-                output_dir=str(tmp_path),
-            )
-        assert out is not None
-        # 只有一次 Seedance 创建任务（预热已完成，不再现场跑 Seedream）
-        assert len(captured) == 1
-        seedance_payload = captured[0]["json"]
-        content = seedance_payload["content"]
-        img_items = [c for c in content if c.get("type") == "image_url"]
-        assert len(img_items) == 1
-        # 不传 image_url，信任链产物放 ref_imgs，走 reference_image 模式（非 first_frame）
-        assert img_items[0]["image_url"]["url"] == "https://ai.example.com/t2i-portrait.png"
-        assert img_items[0]["role"] == "reference_image"
-        # 因为没有 image_url，没有 text 也没有 extra_refs 之外的字段，应保留用户 ratio=9:16
-        assert seedance_payload.get("ratio") == "9:16"
-
-    def test_image_generation_generic_exception_retries_then_fails(self):
-        """image_generation 遇到非 HTTPStatusError 的通用异常时走重试分支（lines 962-971），重试耗尽后返回 None。"""
-        client = _make_client(max_retries=1)
-        call_n = {"n": 0}
-
-        def fake_post(url, **kw):
-            call_n["n"] += 1
-            if call_n["n"] == 1:
-                raise RuntimeError("boiler exploded")
-            # 第二次调用返回成功，验证重试生效
-            ok = MagicMock(status_code=200, text="")
-            ok.json.return_value = {"data": [{"url": "https://ai.example.com/retry-ok.png"}]}
-            ok.raise_for_status = MagicMock()
-            return ok
-
-        with (
-            patch("packages.shared.ai_client.httpx.post", side_effect=fake_post),
-            patch("packages.shared.ai_client.time.sleep", return_value=None),
-        ):
-            r = client.image_generation("test prompt")
-        assert r is not None
-        assert r["url"] == "https://ai.example.com/retry-ok.png"
-        assert call_n["n"] == 2
-
-    def test_image_generation_generic_exception_exhausts_retries(self):
-        """通用异常重试耗尽后返回 None，并正确写入 last_image_error (lines 969-971 break 分支)。"""
-        client = _make_client(max_retries=1)
-
-        def fake_post(url, **kw):
-            raise RuntimeError("always fails")
-
-        with (
-            patch("packages.shared.ai_client.httpx.post", side_effect=fake_post),
-            patch("packages.shared.ai_client.time.sleep", return_value=None),
-        ):
-            r = client.image_generation("test prompt")
-        assert r is None
-        err = client.last_image_error
-        assert err["error_code"] == "network_error"
-        assert "always fails" in err["detail"]

tests/unit/test_ai_client_video.py

@@ -17,13 +17,8 @@ def _make_client(**overrides):
     client.base_url = overrides.get("base_url", "https://ark.cn-beijing.volces.com/api/v3")
     client.model = "doubao-model"
     client.vision_model = "doubao-vision"
-    client.embedding_model = "doubao-embedding"
-    client.image_model = overrides.get("image_model", "doubao-seedream-5-0-pro-260628")
-    client.image_timeout = overrides.get("image_timeout", 120)
     client.timeout = overrides.get("timeout", 30)
     client.max_retries = overrides.get("max_retries", 0)
-    client.last_video_error = {}
-    client.last_image_error = {}
     return client
 
 
@@ -111,7 +106,7 @@ class TestVideoGenerationHappyPath:
             )
             out = client.video_generation(
                 prompt="  镜头一  ",
-                # 不传 image_url：纯文生视频，不触发信任链，post 调用数为 1（创建任务）
+                image_url="https://img/x.jpg",
                 duration=5,
                 ratio="9:16",
                 resolution="720p",
@@ -568,7 +563,7 @@ class TestResolveVideoModelId:
 
 class TestVideoGenerationLastError:
     def test_create_400_portrait_returns_user_message(self, tmp_path):
-        """#2169: HTTP 400 + 真人拦截关键词 → 自动尝试即梦兜底；即梦未配时返回 portrait_intercept。"""
+        """HTTP 400 + 真人拦截关键词 → portrait_intercept 错误码，用户提示友好。"""
         client = _make_client(max_retries=0)
         create_resp = MagicMock()
         create_resp.status_code = 400
@@ -577,23 +572,8 @@ class TestVideoGenerationLastError:
         create_resp.raise_for_status.side_effect = httpx.HTTPStatusError(
             "bad", request=MagicMock(), response=create_resp
         )
-        # 信任链：Seedream 会先被调用来 AI 化；这里 mock Seedream 也失败，回退原图直传，
-        # 原图直传被 400 portrait 拦截，最终返回 portrait_intercept。
-        seedream_resp = MagicMock()
-        seedream_resp.status_code = 400
-        seedream_resp.text = '{"error":{"code":"ContentRisk","message":"sensitive"}}'
-        seedream_resp.json.return_value = {"error": {"code": "ContentRisk", "message": "sensitive"}}
-        seedream_resp.raise_for_status.side_effect = httpx.HTTPStatusError(
-            "bad", request=MagicMock(), response=seedream_resp
-        )
-
-        def fake_post(url, **kwargs):
-            # 第一次 POST 是 Seedream（/images/generations），返回 portrait 拦截
-            # 回退原图直传后第二次 POST 是 Seedance（/contents/generations/tasks），也返回 portrait 拦截
-            return create_resp
-
         with (
-            patch("packages.shared.ai_client.httpx.post", side_effect=fake_post),
+            patch("packages.shared.ai_client.httpx.post", return_value=create_resp),
             patch("packages.shared.ai_client.time.sleep", return_value=None),
             patch("packages.shared.ai_client.get_shared_settings") as mock_s,
         ):
@@ -604,8 +584,8 @@ class TestVideoGenerationLastError:
             assert result is None
             err = client.get_last_video_error()
             assert err["error_code"] == "portrait_intercept"
-            assert "真人" in err["user_message"] or "肖像" in err["user_message"] or "审核" in err["user_message"]
-            assert err["status_code"] in (0, 400)
+            assert "真人" in err["user_message"]
+            assert err["status_code"] == 400
 
     def test_create_401_returns_auth_error(self, tmp_path):
         client = _make_client(max_retries=0)
@@ -676,3 +656,76 @@ class TestAiServiceLastVideoError:
             err = ai_service.get_last_video_error()
             assert err["error_code"] == "unknown"
             assert "user_message" in err
+
+
+class TestChatCompletion404Fallback:
+    """chat_completion / vision_completion 404 自动 fallback 到默认模型。"""
+
+    def test_chat_404_fallback_to_default_model(self, monkeypatch):
+        """指定模型 404 时自动用 self.model 重试一次。"""
+        from packages.shared import ai_client as ac_mod
+
+        calls = []
+
+        class FakeResp:
+            def __init__(self, sc, body):
+                self.status_code = sc
+                self._body = body
+
+            def raise_for_status(self):
+                import httpx
+
+                if self.status_code >= 400:
+                    raise httpx.HTTPStatusError("x", request=object(), response=self)
+
+            def json(self):
+                return self._body
+
+            @property
+            def text(self):
+                import json as _json
+
+                return _json.dumps(self._body)
+
+        def fake_post(url, headers=None, json=None, timeout=None):
+            calls.append(json.get("model"))
+            if json.get("model") == "doubao-1-5-pro-32k-250115":
+                return FakeResp(404, {"error": {"message": "model not found"}})
+            return FakeResp(200, {"choices": [{"message": {"content": "ok-from-default"}}]})
+
+        monkeypatch.setattr(ac_mod.httpx, "post", fake_post)
+        c = ac_mod.DoubaoClient()
+        c.api_key = "test"
+        c.model = "doubao-seed-1-6-250615"
+        c.max_retries = 0
+        out = c.chat_completion([{"role": "user", "content": "hi"}], model="doubao-1-5-pro-32k-250115")
+        assert out == "ok-from-default"
+        assert calls == ["doubao-1-5-pro-32k-250115", "doubao-seed-1-6-250615"]
+
+    def test_chat_401_does_not_retry(self, monkeypatch):
+        """401（鉴权失败）不重试也不 fallback，直接返回 None。"""
+        from packages.shared import ai_client as ac_mod
+
+        calls = []
+
+        class FakeResp:
+            def __init__(self, sc):
+                self.status_code = sc
+
+            def raise_for_status(self):
+                import httpx
+
+                raise httpx.HTTPStatusError("x", request=object(), response=self)
+
+        def fake_post(url, headers=None, json=None, timeout=None):
+            calls.append(json.get("model"))
+            return FakeResp(401)
+
+        monkeypatch.setattr(ac_mod.httpx, "post", fake_post)
+        c = ac_mod.DoubaoClient()
+        c.api_key = "test"
+        c.model = "doubao-seed-1-6-250615"
+        c.max_retries = 0
+        out = c.chat_completion([{"role": "user", "content": "hi"}], model="doubao-1-5-pro-32k-250115")
+        assert out is None
+        assert calls == ["doubao-1-5-pro-32k-250115"]  # 不重试不 fallback（401 是全局鉴权错，fallback 也没用）

tests/unit/test_ai_router.py

@@ -1,395 +0,0 @@
-"""AI Router 单元测试 — 23 cases covering routing/cache/fallback/client construction."""
-
-from __future__ import annotations
-
-import sys
-import unittest
-from dataclasses import dataclass
-from typing import Optional
-from unittest.mock import MagicMock, patch
-
-# ── Pre-mock heavy import chain to avoid pulling in full app ──
-_mock_config = MagicMock()
-_mock_settings = MagicMock()
-_mock_settings.doubao_model = "doubao-seed-2-1-pro-260915"
-_mock_settings.doubao_fast_model = "doubao-seed-2-1-pro-260915"
-_mock_settings.doubao_base_url = "https://ark.cn-beijing.volces.com/api/v3"
-_mock_settings.doubao_api_key = "test-key"
-_mock_settings.doubao_timeout = 45
-_mock_settings.doubao_max_retries = 1
-_mock_settings.doubao_image_model = "doubao-seedream-5-0-flash-260915"
-_mock_settings.doubao_image_timeout = 60
-_mock_settings.doubao_video_model = "doubao-seedance-2-5-260628"
-_mock_settings.doubao_video_timeout = 600
-_mock_settings.dashscope_api_key = "ds-key"
-_mock_settings.cosyvoice_api_key = "cv-key"
-_mock_settings.cosyvoice_base_url = "https://dashscope.aliyuncs.com/api/v1"
-_mock_settings.cosyvoice_model = "cosyvoice-v3-flash"
-_mock_settings.redis_url = "redis://localhost:6379/0"
-_mock_settings.celery_broker_url = "redis://localhost:6379/0"
-_mock_config.get_shared_settings.return_value = _mock_settings
-
-# Prevent the full packages.shared from loading
-for mod_name in list(sys.modules.keys()):
-    if "packages.shared" in mod_name and "ai_router" not in mod_name and "ai_config_version" not in mod_name:
-        pass  # don't remove, just prevent new imports
-
-# Direct import of our modules (bypassing __init__.py)
-import importlib.util
-import os
-
-
-def _load_module_from_file(name, path):
-    spec = importlib.util.spec_from_file_location(name, path)
-    mod = importlib.util.module_from_spec(spec)
-    sys.modules[name] = mod
-    spec.loader.exec_module(mod)
-    return mod
-
-
-# Load ai_config_version
-_ai_config_version = _load_module_from_file(
-    "packages.shared.ai_config_version",
-    os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), "packages", "shared", "ai_config_version.py"),
-)
-# Patch get_shared_settings in the loaded module
-_ai_config_version.get_shared_settings = lambda: _mock_settings
-
-# Load ai_router - needs packages.shared.config to be available
-sys.modules["packages.shared.config"] = MagicMock()
-sys.modules["packages.shared.config"].get_shared_settings = lambda: _mock_settings
-
-# Mock packages.shared.ai_client to avoid triggering packages.shared.__init__ chain
-# (which fails on Python 3.10 due to datetime.UTC import in packages.domain)
-_mock_ai_client = MagicMock()
-
-class _FakeDoubaoClient:
-    """Fake DoubaoClient for testing - mimics the real interface."""
-    def __init__(self, api_key="", base_url="", model="", timeout=0, max_retries=0,
-                 max_tokens=None, temperature=None, extra_params=None, provider="volcengine"):
-        self.api_key = api_key
-        self.base_url = base_url
-        self.model = model
-        self.timeout = timeout
-        self.max_retries = max_retries
-        self.max_tokens = max_tokens
-        self.temperature = temperature
-        self.extra_params = extra_params or {}
-        self.provider = provider
-        self.vision_model = model
-
-    @property
-    def is_available(self):
-        return bool(self.api_key)
-
-    def chat_completion(self, messages, **kwargs):
-        return None
-
-    def vision_completion(self, messages, **kwargs):
-        return None
-
-_mock_ai_client.DoubaoClient = _FakeDoubaoClient
-sys.modules["packages.shared.ai_client"] = _mock_ai_client
-
-_ai_router = _load_module_from_file(
-    "packages.shared.ai_router",
-    os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), "packages", "shared", "ai_router.py"),
-)
-
-
-class TestAIConfigVersion(unittest.TestCase):
-    """Redis 版本号机制测试"""
-
-    @patch.object(_ai_config_version, "_get_redis_client")
-    def test_bump_version_success(self, mock_redis_fn):
-        mock_r = MagicMock()
-        mock_r.set.return_value = True
-        mock_redis_fn.return_value = mock_r
-        ver = _ai_config_version.bump_version()
-        self.assertTrue(ver)
-        self.assertTrue(ver.isdigit())
-        mock_r.set.assert_called_once()
-
-    @patch.object(_ai_config_version, "_get_redis_client")
-    def test_bump_version_redis_unavailable(self, mock_redis_fn):
-        mock_redis_fn.return_value = None
-        ver = _ai_config_version.bump_version()
-        self.assertEqual(ver, "")
-
-    @patch.object(_ai_config_version, "_get_redis_client")
-    def test_get_version_success(self, mock_redis_fn):
-        mock_r = MagicMock()
-        mock_r.get.return_value = "1234567890"
-        mock_redis_fn.return_value = mock_r
-        ver = _ai_config_version.get_version()
-        self.assertEqual(ver, "1234567890")
-
-    @patch.object(_ai_config_version, "_get_redis_client")
-    def test_get_version_redis_down(self, mock_redis_fn):
-        mock_redis_fn.return_value = None
-        ver = _ai_config_version.get_version()
-        self.assertIsNone(ver)
-
-    @patch.object(_ai_config_version, "_get_redis_client")
-    def test_get_version_exception(self, mock_redis_fn):
-        mock_r = MagicMock()
-        mock_r.get.side_effect = Exception("connection refused")
-        mock_redis_fn.return_value = mock_r
-        ver = _ai_config_version.get_version()
-        self.assertIsNone(ver)
-
-
-class TestAIRouter(unittest.TestCase):
-    """AIRouter 路由/缓存/fallback 测试"""
-
-    def setUp(self):
-        self.router = _ai_router.AIRouter()
-
-    @patch.object(_ai_config_version, "get_version", return_value=None)
-    def test_get_capability_db_unavailable(self, mock_ver):
-        with patch.object(_ai_router, "_get_session", return_value=None):
-            cap = self.router.get_capability("intent_parsing")
-            self.assertIsNone(cap)
-
-    @patch.object(_ai_config_version, "get_version", return_value=None)
-    def test_get_capability_from_db(self, mock_ver):
-        mock_session = MagicMock()
-        mock_row = MagicMock()
-        mock_row.capability_key = "intent_parsing"
-        mock_row.capability_name = "文案意图解析"
-        mock_row.timeout_seconds = 45
-        mock_row.max_retries = 1
-        mock_row.max_tokens = None
-        mock_row.temperature = None
-        mock_row.concurrency = 2
-        mock_row.extra_params = {}
-        mock_row.is_enabled = True
-        mock_row.pm_id = "model-1"
-        mock_row.pm_name = "豆包"
-        mock_row.pm_provider = "volcengine"
-        mock_row.pm_model_key = "doubao-seed-1-6-250615"
-        mock_row.pm_api_key = "test-key"
-        mock_row.pm_api_base = "https://ark.test.com"
-        mock_row.pm_api_version = None
-        mock_row.pm_status = "active"
-        mock_row.lm_id = None
-        mock_row.fm_id = None
-        mock_session.execute.return_value.first.return_value = mock_row
-
-        with patch.object(_ai_router, "_get_session", return_value=mock_session):
-            cap = self.router.get_capability("intent_parsing")
-            self.assertIsNotNone(cap)
-            self.assertEqual(cap.capability_key, "intent_parsing")
-            self.assertEqual(cap.primary_model.model_key, "doubao-seed-1-6-250615")
-
-    @patch.object(_ai_config_version, "get_version", side_effect=[None, "v2"])
-    def test_cache_invalidation_on_version_change(self, mock_ver):
-        with patch.object(self.router, "_load_from_db", return_value=None):
-            self.router.get_capability("test_key")
-        self.router._local_ver = "v1"
-        self.assertTrue(self.router._check_version())
-
-    @patch.object(_ai_config_version, "get_version", return_value="same_ver")
-    def test_cache_hit_same_version(self, mock_ver):
-        model = _ai_router.ModelConfig(
-            id="m1", name="test", provider="volcengine", model_key="test-model",
-            api_key="key", api_base="https://test.com", api_version=None, status="active",
-        )
-        cap = _ai_router.CapabilityConfig(
-            capability_key="test", capability_name="test", primary_model=model,
-            lite_model=None, fallback_model=None, timeout_seconds=30,
-            max_retries=1, max_tokens=None, temperature=None, concurrency=2,
-            extra_params={}, is_enabled=True,
-        )
-        self.router._cache["test"] = cap
-        self.router._local_ver = "same_ver"
-        result = self.router.get_capability("test")
-        self.assertEqual(result, cap)
-
-    def test_invalidate_clears_cache(self):
-        self.router._cache["x"] = MagicMock()
-        self.router._local_ver = "v1"
-        self.router.invalidate()
-        self.assertEqual(len(self.router._cache), 0)
-        self.assertIsNone(self.router._local_ver)
-
-    @patch.object(_ai_router, "_get_session", return_value=None)
-    @patch.object(_ai_config_version, "get_version", return_value=None)
-    def test_get_llm_client_fallback(self, mock_ver, mock_session):
-        _ai_router.get_shared_settings = lambda: _mock_settings
-        client = self.router.get_llm_client("intent_parsing")
-        self.assertIsNotNone(client)
-        self.assertEqual(client.model, "doubao-seed-2-1-pro-260915")
-        self.assertEqual(client.api_key, "test-key")
-
-    @patch.object(_ai_config_version, "get_version", return_value=None)
-    def test_get_llm_client_from_db(self, mock_ver):
-        model = _ai_router.ModelConfig(
-            id="m1", name="test", provider="dashscope", model_key="qwen3.8-flash",
-            api_key="db-key", api_base="https://dashscope.test.com", api_version=None, status="active",
-        )
-        cap = _ai_router.CapabilityConfig(
-            capability_key="image_analysis", capability_name="图片分析",
-            primary_model=model, lite_model=None, fallback_model=None,
-            timeout_seconds=15, max_retries=1, max_tokens=350, temperature=0.1,
-            concurrency=2, extra_params={}, is_enabled=True,
-        )
-        with patch.object(self.router, "get_capability", return_value=cap):
-            client = self.router.get_llm_client("image_analysis")
-            self.assertIsNotNone(client)
-            self.assertEqual(client.model, "qwen3.8-flash")
-            self.assertEqual(client.provider, "dashscope")
-
-    @patch.object(_ai_config_version, "get_version", return_value=None)
-    def test_get_vision_client(self, mock_ver):
-        model = _ai_router.ModelConfig(
-            id="m1", name="test", provider="dashscope", model_key="qwen3.8-flash",
-            api_key="key", api_base="https://dashscope.test.com", api_version=None, status="active",
-        )
-        cap = _ai_router.CapabilityConfig(
-            capability_key="image_analysis", capability_name="图片分析",
-            primary_model=model, lite_model=None, fallback_model=None,
-            timeout_seconds=15, max_retries=1, max_tokens=None, temperature=None,
-            concurrency=2, extra_params={}, is_enabled=True,
-        )
-        with patch.object(self.router, "get_capability", return_value=cap):
-            client = self.router.get_vision_client("image_analysis")
-            self.assertIsNotNone(client)
-            # #2220: vision client is now DoubaoClient with vision_completion
-            self.assertTrue(hasattr(client, "vision_completion"))
-
-    @patch.object(_ai_config_version, "get_version", return_value=None)
-    def test_get_tts_client(self, mock_ver):
-        model = _ai_router.ModelConfig(
-            id="m1", name="test", provider="dashscope", model_key="cosyvoice-v3-flash",
-            api_key="key", api_base="https://dashscope.test.com", api_version=None, status="active",
-        )
-        cap = _ai_router.CapabilityConfig(
-            capability_key="tts", capability_name="语音合成",
-            primary_model=model, lite_model=None, fallback_model=None,
-            timeout_seconds=60, max_retries=1, max_tokens=None, temperature=None,
-            concurrency=2, extra_params={}, is_enabled=True,
-        )
-        with patch.object(self.router, "get_capability", return_value=cap):
-            client = self.router.get_tts_client()
-            self.assertIsNotNone(client)
-            self.assertEqual(client.model, "cosyvoice-v3-flash")
-
-    @patch.object(_ai_config_version, "get_version", return_value=None)
-    def test_get_image_gen_client(self, mock_ver):
-        model = _ai_router.ModelConfig(
-            id="m1", name="test", provider="volcengine", model_key="seedream-5.0-flash",
-            api_key="key", api_base="https://ark.test.com", api_version=None, status="active",
-        )
-        cap = _ai_router.CapabilityConfig(
-            capability_key="image_generation", capability_name="图片生成",
-            primary_model=model, lite_model=None, fallback_model=None,
-            timeout_seconds=60, max_retries=1, max_tokens=None, temperature=None,
-            concurrency=2, extra_params={"size": "1K"}, is_enabled=True,
-        )
-        with patch.object(self.router, "get_capability", return_value=cap):
-            client = self.router.get_image_gen_client()
-            self.assertIsNotNone(client)
-            self.assertEqual(client.model, "seedream-5.0-flash")
-
-    @patch.object(_ai_config_version, "get_version", return_value=None)
-    def test_get_video_gen_client(self, mock_ver):
-        model = _ai_router.ModelConfig(
-            id="m1", name="test", provider="volcengine", model_key="seedance-2.5",
-            api_key="key", api_base="https://ark.test.com", api_version=None, status="active",
-        )
-        cap = _ai_router.CapabilityConfig(
-            capability_key="video_generation", capability_name="视频生成",
-            primary_model=model, lite_model=None, fallback_model=None,
-            timeout_seconds=600, max_retries=1, max_tokens=None, temperature=None,
-            concurrency=1, extra_params={}, is_enabled=True,
-        )
-        with patch.object(self.router, "get_capability", return_value=cap):
-            client = self.router.get_video_gen_client()
-            self.assertIsNotNone(client)
-            self.assertEqual(client.model, "seedance-2.5")
-
-    @patch.object(_ai_config_version, "get_version", return_value=None)
-    def test_lite_variant_preference(self, mock_ver):
-        primary = _ai_router.ModelConfig(id="p1", name="pro", provider="volcengine", model_key="pro-model", api_key="k", api_base="u", api_version=None, status="active")
-        lite = _ai_router.ModelConfig(id="l1", name="lite", provider="volcengine", model_key="lite-model", api_key="k", api_base="u", api_version=None, status="active")
-        cap = _ai_router.CapabilityConfig(
-            capability_key="image_analysis", capability_name="图片分析",
-            primary_model=primary, lite_model=lite, fallback_model=None,
-            timeout_seconds=15, max_retries=1, max_tokens=None, temperature=None,
-            concurrency=2, extra_params={}, is_enabled=True,
-        )
-        model = self.router._get_model_or_fallback(cap, "lite")
-        self.assertEqual(model.model_key, "lite-model")
-        model_primary = self.router._get_model_or_fallback(cap, "primary")
-        self.assertEqual(model_primary.model_key, "pro-model")
-
-    @patch.object(_ai_config_version, "get_version", return_value=None)
-    def test_disabled_capability_returns_fallback(self, mock_ver):
-        cap = _ai_router.CapabilityConfig(
-            capability_key="test", capability_name="test",
-            primary_model=None, lite_model=None, fallback_model=None,
-            timeout_seconds=30, max_retries=1, max_tokens=None, temperature=None,
-            concurrency=2, extra_params={}, is_enabled=False,
-        )
-        _ai_router.get_shared_settings = lambda: _mock_settings
-        with patch.object(self.router, "get_capability", return_value=cap):
-            client = self.router.get_llm_client("test")
-            self.assertIsNotNone(client)
-            self.assertEqual(client.model, "doubao-seed-2-1-pro-260915")
-
-    @patch.object(_ai_config_version, "get_version", return_value=None)
-    def test_fallback_chain_primary_none(self, mock_ver):
-        """primary_model 为 None 时 fallback 到 fallback_model"""
-        fb = _ai_router.ModelConfig(id="f1", name="fb", provider="volcengine", model_key="fb-model", api_key="k", api_base="u", api_version=None, status="active")
-        cap = _ai_router.CapabilityConfig(
-            capability_key="test", capability_name="test",
-            primary_model=None, lite_model=None, fallback_model=fb,
-            timeout_seconds=30, max_retries=1, max_tokens=None, temperature=None,
-            concurrency=2, extra_params={}, is_enabled=True,
-        )
-        model = self.router._get_model_or_fallback(cap, "primary")
-        self.assertEqual(model.model_key, "fb-model")
-
-
-class TestModelConfig(unittest.TestCase):
-    """数据类测试"""
-
-    def test_model_config_frozen(self):
-        m = _ai_router.ModelConfig(id="1", name="t", provider="p", model_key="k", api_key="a", api_base="b", api_version=None, status="active")
-        with self.assertRaises(AttributeError):
-            m.model_key = "new"
-
-    def test_capability_config_frozen(self):
-        c = _ai_router.CapabilityConfig(
-            capability_key="k", capability_name="n", primary_model=None,
-            lite_model=None, fallback_model=None, timeout_seconds=30,
-            max_retries=1, max_tokens=None, temperature=None, concurrency=2,
-            extra_params={}, is_enabled=True,
-        )
-        with self.assertRaises(AttributeError):
-            c.is_enabled = False
-
-
-class TestClientAvailability(unittest.TestCase):
-    """客户端可用性测试"""
-
-    def test_tts_client_available(self):
-        c = _ai_router.TTSClient(provider="p", api_key="k", base_url="u", model="m")
-        self.assertTrue(c.is_available)
-
-    def test_tts_client_unavailable_no_model(self):
-        c = _ai_router.TTSClient(provider="p", api_key="k", base_url="u", model="")
-        self.assertFalse(c.is_available)
-
-    def test_image_gen_client_unavailable_no_url(self):
-        c = _ai_router.ImageGenClient(provider="p", api_key="k", base_url="", model="m")
-        self.assertFalse(c.is_available)
-
-    def test_video_gen_client_available(self):
-        c = _ai_router.VideoGenClient(provider="p", api_key="k", base_url="u", model="m")
-        self.assertTrue(c.is_available)
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/unit/test_api_settings.py

@@ -73,15 +73,15 @@ class TestSharedSettingsDefaults:
 
     def test_default_cosyvoice_settings(self):
         s = SharedSettings()
-        assert s.cosyvoice_model == ""  # 零硬编码：默认值已清空
+        assert s.cosyvoice_model == "cosyvoice-v3-flash"
         assert s.cosyvoice_format == "mp3"
         assert s.cosyvoice_sample_rate == 22050
 
     def test_default_doubao_settings(self):
         s = SharedSettings()
-        assert s.doubao_model == ""  # 零硬编码：默认值已清空
-        assert s.doubao_timeout == 45  # #2180 默认提到45s
-        assert s.doubao_max_retries == 3
+        assert "doubao" in s.doubao_model
+        assert s.doubao_timeout == 30
+        assert s.doubao_max_retries == 2
 
 
 class TestAPISettingsDefaults:
@@ -321,7 +321,7 @@ class TestWorkerSettingsDefaults:
         assert s.database_url  # 继承自SharedSettings
         assert s.redis_url
         assert s.oss_endpoint
-        assert s.cosyvoice_model == ""  # 零硬编码：默认值已清空
+        assert s.cosyvoice_model == "cosyvoice-v3-flash"
 
 
 class TestGetWorkerSettings:

tests/unit/test_config_base.py

@@ -102,17 +102,17 @@ class TestSharedSettingsDefaults:
     def test_default_cosyvoice_config(self):
         """CosyVoice 默认配置"""
         s = self._make_settings()
-        assert s.cosyvoice_model == ""  # 零硬编码：默认值已清空
+        assert s.cosyvoice_model == "cosyvoice-v3-flash"
         assert s.cosyvoice_sample_rate == 22050
         assert s.cosyvoice_format == "mp3"
-        assert s.cosyvoice_clone_model == ""  # 零硬编码：默认值已清空
+        assert s.cosyvoice_clone_model == "voice-enrollment"
 
     def test_default_doubao_config(self):
         """豆包默认配置"""
         s = self._make_settings()
-        assert s.doubao_timeout == 45  # #2180 默认提到45s
-        assert s.doubao_max_retries == 3
-        assert s.doubao_base_url == ""  # 零硬编码：默认值已清空
+        assert s.doubao_timeout == 30
+        assert s.doubao_max_retries == 2
+        assert "volces.com" in s.doubao_base_url
 
     def test_default_empty_api_keys(self):
         """API Key 默认空字符串"""

tests/unit/test_cosyvoice_service.py

@@ -27,10 +27,7 @@ def mock_client() -> MagicMock:
 @pytest.fixture
 def service(mock_client: MagicMock) -> CosyVoiceService:
     """Create CosyVoiceService with mocked HTTP client and config."""
-    with (
-        patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
-        patch("packages.shared.ai_router.ai_router") as mock_router,
-    ):
+    with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
         settings = MagicMock()
         settings.cosyvoice_api_key = "sk-test-12345678"
         settings.cosyvoice_base_url = "https://dashscope.aliyuncs.com/api/v1"
@@ -40,7 +37,6 @@ def service(mock_client: MagicMock) -> CosyVoiceService:
         settings.cosyvoice_voice = "longxiaochun_v3"
         settings.cosyvoice_clone_model = "voice-enrollment"
         mock_settings.return_value = settings
-        mock_router.get_tts_client.return_value = None  # ai_router returns None in tests
         svc = CosyVoiceService(http_client=mock_client)
         svc.CLONE_POLL_INTERVAL = 0.001  # 加速测试
         svc.RETRY_BACKOFF = 0.001
@@ -52,10 +48,7 @@ class TestInitConfig:
 
     def test_base_url_with_old_text2audio_path_gets_normalized(self, mock_client: MagicMock) -> None:
         """旧版 base_url 带 text2audio 路径应自动修正."""
-        with (
-            patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
-            patch("packages.shared.ai_router.ai_router") as mock_router,
-        ):
+        with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
             settings = MagicMock()
             settings.cosyvoice_api_key = "sk-test"
             settings.cosyvoice_base_url = "https://dashscope.aliyuncs.com/api/v1/services/aigc/text2audio"
@@ -65,16 +58,12 @@ class TestInitConfig:
             settings.cosyvoice_voice = "test"
             settings.cosyvoice_clone_model = "voice-enrollment"
             mock_settings.return_value = settings
-            mock_router.get_tts_client.return_value = None
             svc = CosyVoiceService(http_client=mock_client)
             assert svc._base_url == "https://dashscope.aliyuncs.com/api/v1"
 
     def test_custom_params_override_config(self, mock_client: MagicMock) -> None:
         """显式传入参数覆盖配置."""
-        with (
-            patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
-            patch("packages.shared.ai_router.ai_router") as mock_router,
-        ):
+        with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
             settings = MagicMock()
             settings.cosyvoice_api_key = "sk-config"
             settings.cosyvoice_base_url = "https://config.example.com"
@@ -84,7 +73,6 @@ class TestInitConfig:
             settings.cosyvoice_voice = "test"
             settings.cosyvoice_clone_model = "voice-enrollment"
             mock_settings.return_value = settings
-            mock_router.get_tts_client.return_value = None
             svc = CosyVoiceService(
                 api_key="sk-custom",
                 base_url="https://custom.example.com/api/v1",
@@ -99,10 +87,7 @@ class TestInitConfig:
 
     def test_context_manager(self, mock_client: MagicMock) -> None:
         """上下文管理器正常工作."""
-        with (
-            patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
-            patch("packages.shared.ai_router.ai_router") as mock_router,
-        ):
+        with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
             settings = MagicMock()
 
             settings.cosyvoice_api_key = "sk-test"
@@ -120,7 +105,6 @@ class TestInitConfig:
             settings.cosyvoice_clone_model = "voice-enrollment"
 
             mock_settings.return_value = settings
-            mock_router.get_tts_client.return_value = None
             svc = CosyVoiceService(http_client=mock_client)
             with svc as s:
                 assert s is svc
@@ -129,10 +113,7 @@ class TestInitConfig:
 
     def test_owns_client_gets_closed(self) -> None:
         """自有client在close时被关闭."""
-        with (
-            patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
-            patch("packages.shared.ai_router.ai_router") as mock_router,
-        ):
+        with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
             settings = MagicMock()
 
             settings.cosyvoice_api_key = "sk-test"
@@ -150,7 +131,6 @@ class TestInitConfig:
             settings.cosyvoice_clone_model = "voice-enrollment"
 
             mock_settings.return_value = settings
-            mock_router.get_tts_client.return_value = None
             with patch("packages.application.cosyvoice_service.httpx.Client") as mock_cls:
                 mock_instance = MagicMock()
                 mock_cls.return_value = mock_instance
@@ -193,10 +173,7 @@ class TestSubmitCloneTask:
 
     def test_no_api_key_raises_auth_error(self, mock_client: MagicMock) -> None:
         """无API Key抛认证错误."""
-        with (
-            patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
-            patch("packages.shared.ai_router.ai_router") as mock_router,
-        ):
+        with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
             settings = MagicMock()
 
             settings.cosyvoice_api_key = ""
@@ -214,7 +191,6 @@ class TestSubmitCloneTask:
             settings.cosyvoice_clone_model = "voice-enrollment"
 
             mock_settings.return_value = settings
-            mock_router.get_tts_client.return_value = None
             svc = CosyVoiceService(http_client=mock_client)
             with pytest.raises(CosyVoiceAuthError, match="API Key 未配置"):
                 svc.submit_clone_task(audio_url="https://example.com/audio.mp3")
@@ -282,10 +258,7 @@ class TestSubmitCloneTask:
 
     def test_audio_url_signer_is_called(self, mock_client: MagicMock) -> None:
         """配置了audio_url_signer时会被调用预签名."""
-        with (
-            patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
-            patch("packages.shared.ai_router.ai_router") as mock_router,
-        ):
+        with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
             settings = MagicMock()
 
             settings.cosyvoice_api_key = "sk-test"
@@ -303,7 +276,6 @@ class TestSubmitCloneTask:
             settings.cosyvoice_clone_model = "voice-enrollment"
 
             mock_settings.return_value = settings
-            mock_router.get_tts_client.return_value = None
             signer = MagicMock(return_value="https://signed.example.com/audio.mp3?token=xxx")
             svc = CosyVoiceService(http_client=mock_client, audio_url_signer=signer)
 
@@ -325,10 +297,7 @@ class TestSubmitCloneTask:
 
     def test_signer_failure_falls_back_to_original_url(self, mock_client: MagicMock) -> None:
         """预签名失败时回退到原始URL."""
-        with (
-            patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
-            patch("packages.shared.ai_router.ai_router") as mock_router,
-        ):
+        with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
             settings = MagicMock()
 
             settings.cosyvoice_api_key = "sk-test"
@@ -346,7 +315,6 @@ class TestSubmitCloneTask:
             settings.cosyvoice_clone_model = "voice-enrollment"
 
             mock_settings.return_value = settings
-            mock_router.get_tts_client.return_value = None
             signer = MagicMock(side_effect=RuntimeError("sign failed"))
             svc = CosyVoiceService(http_client=mock_client, audio_url_signer=signer)
 
@@ -407,10 +375,7 @@ class TestQueryVoiceStatus:
 
     def test_no_api_key_raises(self, mock_client: MagicMock) -> None:
         """无API Key抛认证错误."""
-        with (
-            patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
-            patch("packages.shared.ai_router.ai_router") as mock_router,
-        ):
+        with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
             settings = MagicMock()
 
             settings.cosyvoice_api_key = ""
@@ -428,7 +393,6 @@ class TestQueryVoiceStatus:
             settings.cosyvoice_clone_model = "voice-enrollment"
 
             mock_settings.return_value = settings
-            mock_router.get_tts_client.return_value = None
             svc = CosyVoiceService(http_client=mock_client)
             with pytest.raises(CosyVoiceAuthError):
                 svc.query_voice_status("v1")
@@ -625,10 +589,7 @@ class TestSubmitSynthesizeTask:
 
     def test_no_api_key_raises(self, mock_client: MagicMock) -> None:
         """无API Key抛认证错误."""
-        with (
-            patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
-            patch("packages.shared.ai_router.ai_router") as mock_router,
-        ):
+        with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
             settings = MagicMock()
 
             settings.cosyvoice_api_key = ""
@@ -646,7 +607,6 @@ class TestSubmitSynthesizeTask:
             settings.cosyvoice_clone_model = "voice-enrollment"
 
             mock_settings.return_value = settings
-            mock_router.get_tts_client.return_value = None
             svc = CosyVoiceService(http_client=mock_client)
             with pytest.raises(CosyVoiceAuthError):
                 svc.submit_synthesize_task(text="你好", voice_id="v1")

tests/unit/test_ditto_service.py

@@ -1,197 +0,0 @@
-"""Ditto 蚂蚁数字人客户端单元测试 — #2076."""
-
-from __future__ import annotations
-
-from unittest.mock import MagicMock, patch
-
-import httpx
-import pytest
-
-from packages.application.ditto_service import DittoClient, DittoError, DittoResult
-
-
-class _FakeResponse:
-    def __init__(self, status_code=200, content=b"\x00\x01" * 1000, headers=None, text=""):
-        self.status_code = status_code
-        self.content = content
-        self.headers = headers or {}
-        self.text = text
-
-
-def _make_client(base_url="http://ditto:8000", default_video_url="http://oss/tpl.mp4", max_retries=2, timeout=60):
-    with patch("packages.application.ditto_service.get_api_settings") as mock_settings:
-        s = MagicMock()
-        s.ditto_api_base_url = base_url
-        s.ditto_default_video_url = default_video_url
-        s.ditto_max_retries = max_retries
-        s.ditto_request_timeout = timeout
-        mock_settings.return_value = s
-        return DittoClient()
-
-
-def test_is_configured_true():
-    c = _make_client()
-    assert c.is_configured is True
-
-
-def test_is_configured_false_without_base():
-    c = _make_client(base_url="")
-    assert c.is_configured is False
-
-
-def test_is_configured_false_without_template():
-    c = _make_client(default_video_url="")
-    assert c.is_configured is False
-
-
-def test_health_ok():
-    c = _make_client()
-    with patch("httpx.Client") as mock_cls:
-        client = MagicMock()
-        client.get.return_value = _FakeResponse(200)
-        mock_cls.return_value.__enter__.return_value = client
-        assert c.health() is True
-        client.get.assert_called_once()
-
-
-def test_health_fail_status():
-    c = _make_client()
-    with patch("httpx.Client") as mock_cls:
-        client = MagicMock()
-        client.get.return_value = _FakeResponse(500)
-        mock_cls.return_value.__enter__.return_value = client
-        assert c.health() is False
-
-
-def test_health_network_error():
-    c = _make_client()
-    with patch("httpx.Client") as mock_cls:
-        client = MagicMock()
-        client.get.side_effect = httpx.ConnectError("fail")
-        mock_cls.return_value.__enter__.return_value = client
-        assert c.health() is False
-
-
-def test_generate_missing_base():
-    c = _make_client(base_url="")
-    with pytest.raises(DittoError, match="DITTO_API_BASE_URL"):
-        c.generate(audio_url="http://x/a.mp3", script="你好")
-
-
-def test_generate_missing_audio():
-    c = _make_client()
-    with pytest.raises(DittoError, match="audio_url"):
-        c.generate(audio_url="", script="你好")
-
-
-def test_generate_success_with_headers():
-    c = _make_client(max_retries=0)
-    fake_resp = _FakeResponse(
-        status_code=200,
-        content=b"\x00" * 99999,
-        headers={"X-RTF": "0.35", "X-Frames": "125", "X-Time": "12.5"},
-    )
-    with patch("httpx.Client") as mock_cls, patch("time.monotonic", side_effect=[0, 1]):
-        client = MagicMock()
-        client.post.return_value = fake_resp
-        mock_cls.return_value.__enter__.return_value = client
-        result = c.generate(audio_url="http://x/a.mp3", script="你好")
-    assert isinstance(result, DittoResult)
-    assert len(result.video_bytes) == 99999
-    assert result.rtf == 0.35
-    assert result.frames == 125
-    assert result.elapsed_seconds == 12.5
-
-
-def test_generate_uses_default_template_when_video_url_empty():
-    c = _make_client(max_retries=0)
-    fake_resp = _FakeResponse(200, b"1" * 99999)
-    with patch("httpx.Client") as mock_cls:
-        client = MagicMock()
-        client.post.return_value = fake_resp
-        mock_cls.return_value.__enter__.return_value = client
-        c.generate(audio_url="http://x/a.mp3", script="你好")
-        call_kwargs = client.post.call_args
-        payload = call_kwargs.kwargs.get("json") or call_kwargs[1].get("json")
-        assert payload["video_url"] == "http://oss/tpl.mp4"
-        assert payload["audio_url"] == "http://x/a.mp3"
-        assert payload["script"] == "你好"
-        assert payload["emo_global"] == 4
-        assert payload["use_script_emo"] is True
-
-
-def test_generate_retries_on_429_then_success():
-    c = _make_client(max_retries=2)
-    busy = _FakeResponse(429, b"", text="busy")
-    ok = _FakeResponse(200, b"v" * 99999)
-    with patch("httpx.Client") as mock_cls, patch("time.sleep") as mock_sleep:
-        client = MagicMock()
-        client.post.side_effect = [busy, ok]
-        mock_cls.return_value.__enter__.return_value = client
-        result = c.generate(audio_url="http://x/a.mp3", script="你好")
-    assert len(result.video_bytes) == 99999
-    assert mock_sleep.called
-    assert client.post.call_count == 2
-
-
-def test_generate_429_exhausted():
-    c = _make_client(max_retries=1)
-    with patch("httpx.Client") as mock_cls, patch("time.sleep"):
-        client = MagicMock()
-        client.post.return_value = _FakeResponse(429, b"", text="busy")
-        mock_cls.return_value.__enter__.return_value = client
-        with pytest.raises(DittoError, match="重试"):
-            c.generate(audio_url="http://x/a.mp3", script="你好")
-
-
-def test_generate_400_no_retry():
-    c = _make_client(max_retries=2)
-    with patch("httpx.Client") as mock_cls:
-        client = MagicMock()
-        client.post.return_value = _FakeResponse(400, b"", text="bad request")
-        mock_cls.return_value.__enter__.return_value = client
-        with pytest.raises(DittoError, match="Ditto 返回 400"):
-            c.generate(audio_url="http://x/a.mp3", script="你好")
-        assert client.post.call_count == 1  # 400 不重试
-
-
-def test_generate_small_response_raises():
-    c = _make_client(max_retries=0)
-    with patch("httpx.Client") as mock_cls:
-        client = MagicMock()
-        client.post.return_value = _FakeResponse(200, b"xx")
-        mock_cls.return_value.__enter__.return_value = client
-        with pytest.raises(DittoError) as exc_info:
-            c.generate(audio_url="http://x/a.mp3", script="你好")
-        assert exc_info.value.code == "EmptyResponse"
-
-
-def test_generate_and_persist_uploads_to_storage():
-    c = _make_client(max_retries=0)
-    fake_resp = _FakeResponse(200, b"v" * 99999)
-    fake_storage = MagicMock()
-    fake_storage.upload_file.return_value = "http://oss/ditto/x.mp4"
-    with (
-        patch("httpx.Client") as mock_cls,
-        patch("packages.shared.storage.get_shared_storage_service", return_value=fake_storage),
-    ):
-        client = MagicMock()
-        client.post.return_value = fake_resp
-        mock_cls.return_value.__enter__.return_value = client
-        result = c.generate_and_persist(job_id="j1", user_id="u1", audio_url="http://x/a.mp3", script="hi")
-    assert result.video_url == "http://oss/ditto/x.mp4"
-    fake_storage.upload_file.assert_called_once()
-    call_args = fake_storage.upload_file.call_args
-    assert call_args.args[1].startswith("ditto-output/u1/j1")
-
-
-def test_empty_script_replaced_with_space():
-    c = _make_client(max_retries=0)
-    fake_resp = _FakeResponse(200, b"v" * 99999)
-    with patch("httpx.Client") as mock_cls:
-        client = MagicMock()
-        client.post.return_value = fake_resp
-        mock_cls.return_value.__enter__.return_value = client
-        c.generate(audio_url="http://x/a.mp3", script="")
-        payload = client.post.call_args.kwargs["json"]
-        assert payload["script"] == " "

tests/unit/test_domain_entities_extended.py

@@ -521,81 +521,3 @@ class TestIngestJob:
             storage_key="k",
         )
         assert job.error_message == ""
-
-
-class TestViralVideoResumeForRegenerate:
-    """#2222: resume_from_image_analyzed 应支持 COPY_GENERATED/COMPLETED/FAILED 重新生成文案。"""
-
-    def test_regen_from_copy_generated_clears_old_copy(self):
-        from datetime import datetime, timezone
-
-        from packages.domain.viral_video import ViralVideoJob, ViralVideoStatus
-
-        job = ViralVideoJob(user_id="u1", images=["img1"])
-        # 模拟已经生成过文案和视频
-        job.status = ViralVideoStatus.COPY_GENERATED
-        job.copy_result = {"shots": [{"x": 1}], "voiceover_script": "旧文案"}
-        job.intent_result = {"intent": "旧意图"}
-        job.storyboard = [{"x": 1}]
-        job.generated_copy_text = "旧文案"
-        job.result_video_url = "http://old.mp4"
-        job.completed_at = datetime(2026, 10, 6, tzinfo=timezone.utc)
-        job.error_msg = ""
-        job.current_stage = "tts_generation"
-        job.phase_message = "TTS完成"
-
-        # 重新生成
-        job.resume_from_image_analyzed()
-
-        assert job.status == ViralVideoStatus.RUNNING
-        assert job.copy_result is None
-        assert job.intent_result is None
-        assert job.storyboard is None
-        assert job.generated_copy_text == ""
-        assert job.result_video_url == ""
-        assert job.completed_at is None
-        assert job.error_msg == ""
-        assert job.current_stage == ""
-        assert job.phase_message == ""
-
-    def test_regen_from_completed_clears_old_copy(self):
-        from packages.domain.viral_video import ViralVideoJob, ViralVideoStatus
-
-        job = ViralVideoJob(user_id="u1", images=["img1"])
-        job.status = ViralVideoStatus.COMPLETED
-        job.copy_result = {"shots": [], "voiceover_script": "xx"}
-        job.intent_result = {"intent": "x"}
-        job.result_video_url = "http://v.mp4"
-
-        job.resume_from_image_analyzed()
-
-        assert job.status == ViralVideoStatus.RUNNING
-        assert job.copy_result is None
-        assert job.intent_result is None
-        assert job.result_video_url == ""
-
-    def test_first_call_from_image_analyzed_keeps_fields(self):
-        """首次进入（IMAGE_ANALYZED）不应清空任何已有的字段。"""
-        from packages.domain.viral_video import ViralVideoJob, ViralVideoStatus
-
-        job = ViralVideoJob(user_id="u1", images=["img1"])
-        job.status = ViralVideoStatus.IMAGE_ANALYZED
-        job.image_analysis = {"products": []}
-        job.industry = "美妆"
-
-        job.resume_from_image_analyzed()
-
-        assert job.status == ViralVideoStatus.RUNNING
-        assert job.image_analysis == {"products": []}
-        assert job.industry == "美妆"
-
-    def test_wait_user_confirm_rejected(self):
-        """wait_user_confirm 中间状态应被拒绝（前端正在编辑/确认文案）。"""
-        import pytest
-
-        from packages.domain.viral_video import ViralVideoJob, ViralVideoStatus
-
-        job = ViralVideoJob(user_id="u1", images=["img1"])
-        job.status = ViralVideoStatus.WAIT_USER_CONFIRM
-        with pytest.raises(ValueError, match="Cannot resume"):
-            job.resume_from_image_analyzed()

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_tts_service_factory.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import os
-from unittest.mock import MagicMock, patch
+from unittest.mock import patch
 
 import pytest
 
@@ -108,14 +108,8 @@ class TestGetTtsService:
 
     def test_empty_env_falls_back_to_auto_detect(self):
         """环境变量为空时自动检测."""
-        with (
-            patch.dict(os.environ, {"TTS_PROVIDER": ""}),
-            patch("packages.shared.config.get_shared_settings") as mock_settings,
-        ):
+        with patch.dict(os.environ, {"TTS_PROVIDER": ""}):
             # 没有 cosyvoice_api_key 时应该用 mock
-            settings = MagicMock()
-            settings.cosyvoice_api_key = ""
-            mock_settings.return_value = settings
             service = get_tts_service(None)
             assert service.provider_name == "mock"
 

tests/unit/test_viral_video.py

@@ -350,29 +350,6 @@ class TestViralVideoRepository:
 class TestViralVideoPipeline:
     """编排器流水线测试。"""
 
-    # v3 分镜 XML（copy_display_markdown + clips + voiceover_script）
-    V3_XML = """<copy_display_markdown>今天给大家分享一支很显白的口红。</copy_display_markdown>
-<clips>
-<clip image_index="0" time_range="0-5秒">
-<voiceover>大家好，今天分享一款口红</voiceover>
-<visual>近景平视，缓慢推镜</visual>
-<action_details>手持口红特写</action_details>
-<audio_bgm>轻快流行BGM</audio_bgm>
-<transition>硬切</transition>
-<reference_image_index>0</reference_image_index>
-</clip>
-<clip image_index="1" time_range="5-15秒">
-<voiceover>颜色特别好看很显白</voiceover>
-<visual>特写，固定镜头</visual>
-<action_details>嘴唇涂抹特写</action_details>
-<audio_bgm>轻快BGM继续</audio_bgm>
-<transition>结束</transition>
-<reference_image_index>1</reference_image_index>
-</clip>
-</clips>
-<voiceover_script>大家好，今天分享一款口红。颜色特别好看很显白</voiceover_script>
-<theme>口红分享</theme>"""
-
     @pytest.fixture
     def mock_job(self):
         return ViralVideoJob(
@@ -387,27 +364,23 @@ class TestViralVideoPipeline:
             video_ratio="9:16",
         )
 
-    @patch("apps.worker.worker_app.tasks.vision.analyze_images_v2")
+    @patch("packages.shared.ai_service.call_vision")
     def test_image_analysis_step(self, mock_vision, mock_job):
         from apps.worker.worker_app.tasks.viral_video import _step_image_analysis
 
-        # 每张图返回一个 v8 5 字段结果
-        mock_vision.return_value = [
-            {"type": "product", "name": "口红", "brand": "", "has_person": False, "summary_markdown": "一支口红"},
-            {"type": "product", "name": "口红", "brand": "", "has_person": False, "summary_markdown": "口红特写"},
-        ]
+        mock_vision.return_value = {"name": "口红", "features": ["持久", "滋润"]}
         result = _step_image_analysis(mock_job)
-        assert "images" in result
-        assert len(result["images"]) == 2  # 两张图片
+        assert "products" in result
+        assert len(result["products"]) == 2  # 两张图片
 
-    @patch("apps.worker.worker_app.tasks.vision.analyze_images_v2")
+    @patch("packages.shared.ai_service.call_vision")
     def test_image_analysis_fallback(self, mock_vision, mock_job):
         from apps.worker.worker_app.tasks.viral_video import _step_image_analysis
 
-        # v2 分析内部异常时，每图走兜底，仍返回 images 结构
-        mock_vision.side_effect = RuntimeError("vision unavailable")
+        # 模拟 call_vision 不存在
+        mock_vision.side_effect = ImportError("no module")
         result = _step_image_analysis(mock_job)
-        assert "images" in result
+        assert "products" in result
 
     def test_video_analysis_no_reference(self, mock_job):
         from apps.worker.worker_app.tasks.viral_video import _step_video_analysis
@@ -417,40 +390,49 @@ class TestViralVideoPipeline:
         result = _step_video_analysis(mock_job)
         assert result is None
 
-    def test_intent_parsing_step_removed(self, mock_job):
-        """intent_parsing 已合并进脚本生成，不再作为独立步骤/函数存在。"""
-        import apps.worker.worker_app.tasks.viral_video as vv
+    @patch("packages.shared.ai_service.call_llm")
+    def test_intent_parsing(self, mock_llm, mock_job):
+        from apps.worker.worker_app.tasks.viral_video import _step_intent_parsing
 
-        assert not hasattr(vv, "_step_intent_parsing")
+        mock_llm.return_value = {"intent": "推广口红", "tone": "活泼"}
+        result = _step_intent_parsing(mock_job, {"products": []})
+        assert "intent" in result
 
-    def test_script_generation_returns_copy_result(self, mock_job):
+    @patch("packages.shared.ai_service.call_llm")
+    def test_script_generation_returns_copy_result(self, mock_llm, mock_job):
         """v1.6: _step_script_generation 返回 dict 形式的 CopyResult，含 voiceover_script + shots。"""
         from apps.worker.worker_app.tasks.viral_video import _step_script_generation
-        from packages.shared.ai_router import ai_router
 
-        class _FakeClient:
-            is_available = True
-            model = "fake-storyboard"
-
-            def __init__(self, xml: str):
-                self._xml = xml
-
-            def chat_completion(self, messages, **kwargs):
-                return self._xml
-
-        fake = _FakeClient(self.V3_XML)
-        orig_get = ai_router.get_llm_client
-
-        def _get(task, variant="primary"):
-            if task == "storyboard":
-                return fake
-            return orig_get(task, variant=variant)
-
-        ai_router.get_llm_client = _get  # type: ignore
-        try:
-            result = _step_script_generation(mock_job, {"images": []})
-        finally:
-            ai_router.get_llm_client = orig_get  # type: ignore
+        mock_llm.return_value = {
+            "overview": {"theme": "口红推荐", "total_duration": 15, "aspect_ratio": "9:16"},
+            "scene_and_lighting": "明亮化妆台，柔和自然光",
+            "shots": [
+                {
+                    "time_range": "0-5秒",
+                    "shot_type_angle_movement": "近景平视，缓慢推镜",
+                    "scene_and_dialogue": "女主微笑展示口红：大家好，今天分享一款口红",
+                    "action_details": "手持口红特写",
+                    "audio_bgm": "轻快流行BGM",
+                    "transition": "硬切",
+                    "reference_image_index": 0,
+                },
+                {
+                    "time_range": "5-15秒",
+                    "shot_type_angle_movement": "特写，固定镜头",
+                    "scene_and_dialogue": "涂抹口红：颜色特别好看很显白",
+                    "action_details": "嘴唇涂抹特写",
+                    "audio_bgm": "轻快BGM继续",
+                    "transition": "结束",
+                    "reference_image_index": 1,
+                },
+            ],
+            "hard_constraints": ["无字幕无水印"],
+            "negative_prompts": ["字幕", "水印"],
+            "voiceover_script": "大家好，今天分享一款口红，颜色特别好看很显白。",
+        }
+        result = _step_script_generation(
+            mock_job, {"intent": "推广口红", "key_messages": [], "tone": "亲切"}, {"products": []}
+        )
         assert isinstance(result, dict)
         assert "voiceover_script" in result
         assert "shots" in result
@@ -470,13 +452,12 @@ class TestViralVideoPipeline:
         assert result["voiceover_script"]
         assert len(result["shots"]) >= 1
 
-    @patch("packages.application.viral_video.reviewer.Reviewer.review")
-    def test_review_pass_v16(self, mock_review, mock_job):
+    @patch("packages.shared.ai_service.call_llm")
+    def test_review_pass_v16(self, mock_llm, mock_job):
         """v1.6 _step_review 接收 copy_result dict。"""
         from apps.worker.worker_app.tasks.viral_video import _step_review
-        from packages.application.viral_video.reviewer import ReviewIssue, ReviewResult
 
-        mock_review.return_value = ReviewResult(passed=True, score=90, issues=[], rewrite_suggestions=[])
+        mock_llm.return_value = {"passed": True, "score": 90, "details": {}}
         cr = {"voiceover_script": "大家好", "shots": []}
         result = _step_review(mock_job, cr)
         assert result["passed"] is True
@@ -522,6 +503,7 @@ class TestPipelineIntegration:
     @patch("apps.worker.worker_app.tasks.viral_video._step_tts")
     @patch("apps.worker.worker_app.tasks.viral_video._step_review")
     @patch("apps.worker.worker_app.tasks.viral_video._step_script_generation")
+    @patch("apps.worker.worker_app.tasks.viral_video._step_intent_parsing")
     @patch("apps.worker.worker_app.tasks.viral_video._step_video_analysis")
     @patch("apps.worker.worker_app.tasks.viral_video._step_image_analysis")
     @patch("apps.worker.worker_app.tasks.viral_video._get_repo_and_job")
@@ -532,6 +514,7 @@ class TestPipelineIntegration:
         mock_get_repo,
         mock_img_analysis,
         mock_video_analysis,
+        mock_intent,
         mock_script,
         mock_review,
         mock_tts,
@@ -558,6 +541,8 @@ class TestPipelineIntegration:
         mock_session = MagicMock()
         mock_get_repo.return_value = (mock_session, mock_repo, job)
 
+        # v1.6: 如果没有 copy_result 会现场补生成
+        mock_intent.return_value = {"intent": "推广", "key_messages": [], "tone": "亲切"}
         mock_script.return_value = {
             "overview": {"theme": "口红", "total_duration": 15, "aspect_ratio": "9:16"},
             "scene_and_lighting": "明亮化妆台",
@@ -570,8 +555,6 @@ class TestPipelineIntegration:
         mock_review.return_value = {"passed": True, "score": 90}
         mock_tts.return_value = None  # TTS 失败也能走下去（Seedance generate_audio=True 会自己合成音效）
         mock_tts_upload.return_value = None
-        # _run_render_pipeline 直接读 job.copy_result（#2218 守卫），需提前注入
-        job.copy_result = mock_script.return_value
         mock_render.return_value = ("/tmp/video.mp4", {"completion_tokens": 1000000})
         mock_upload.return_value = "https://oss.example.com/final.mp4"
 

tests/unit/test_viral_video_prompt_system.py

@@ -1,357 +0,0 @@
-"""#2040 爆款视频 Prompt 模板系统单测（v8/v3 叙述优先重构后）。
-
-不真调豆包 API，全部用 FakeClient 注入；覆盖：
-XML 标签解析 / 3 套模板纯文本（image_analysis/storyboard/review）/
-loader 缓存热加载与回落 / 本地规则审核识别违规词夸大 /
-各现存步 fallback / seed 幂等 / 负面词不出现。
-
-注：intent_parsing、copy_fusion 两套模板及其独立步骤已在叙述优先重构中删除，
-相关用例同步移除。
-"""
-
-from __future__ import annotations
-
-import os
-import sys
-
-import pytest
-import sqlalchemy as sa
-from sqlalchemy.orm import sessionmaker
-
-REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-for p in [REPO_ROOT, os.path.join(REPO_ROOT, "apps/api"), os.path.join(REPO_ROOT, "apps/worker")]:
-    if p not in sys.path:
-        sys.path.insert(0, p)
-
-from packages.application.viral_video import xml_parser as xp  # noqa: E402
-from packages.application.viral_video.generator import CopyGenerator  # noqa: E402
-from packages.application.viral_video.prompt_loader import (  # noqa: E402
-    get_template,
-    invalidate,
-    render_user_prompt,
-)
-from packages.application.viral_video.prompts import (  # noqa: E402
-    BANNED_PHRASES,
-    DEFAULT_TEMPLATES,
-)
-from packages.application.viral_video.reviewer import Reviewer  # noqa: E402
-
-IMAGE_XML = """<products>
-<product name="大公鸡头油污净" features="红白瓶身" position="main" image_index="0"/>
-</products>
-<colors><color hex="#D32F2F" name="红色" coverage="0.4"/></colors>
-<people has_person="false" count="0"/>
-<mood>干净实用</mood>
-<visible_text><text_item text="多功能油污净" position="瓶身"/></visible_text>
-<scene>白底棚拍</scene>
-<quality resolution="高清" lighting="柔和" composition="居中"/>
-<key_selling_points><point>去油快</point><point>625ml大容量</point></key_selling_points>"""
-
-STORYBOARD_XML = """<clips>
-<clip image_index="0" transition="cut" zoom="null" duration_sec="3" bgm_note="日常">
-<voice_text>这油污忍很久了</voice_text>
-<subtitle_text>油污忍很久</subtitle_text>
-<ken_burns start="0,0" end="0,0" ease="linear"/>
-</clip>
-<clip image_index="0" transition="zoom_in" zoom="in" duration_sec="10" bgm_note="轻快">
-<voice_text>大公鸡头喷上等几分钟一擦就净</voice_text>
-<subtitle_text>一擦就净</subtitle_text>
-<ken_burns start="20,20" end="80,80" ease="ease-in-out"/>
-</clip>
-</clips>"""
-
-REVIEW_FAIL_XML = """<passed>false</passed>
-<issues><issue dimension="夸大承诺" severity="error" location="body_points">出现绝对化表述</issue></issues>
-<rewrite_suggestions><suggestion>改为“大部分油污能擦掉”</suggestion></rewrite_suggestions>"""
-
-REVIEW_PASS_XML = """<passed>true</passed>
-<issues></issues>
-<rewrite_suggestions></rewrite_suggestions>"""
-
-
-class FakeClient:
-    """按 system 内容路由 canned 响应的假豆包客户端。"""
-
-    def __init__(self):
-        self.chat_calls: list[list[dict]] = []
-        self.vision_calls: list = []
-        self.review_sequence: list[str] | None = None
-
-    def chat_completion(self, messages, **kwargs):
-        self.chat_calls.append(messages)
-        system = messages[0]["content"]
-        # v3 审核 prompt 关键短语（叙述优先重构后更新）
-        if "短视频广告合规审核与文案优化专家" in system:
-            if self.review_sequence:
-                return self.review_sequence.pop(0)
-            return REVIEW_PASS_XML
-        # v3 分镜 prompt
-        if "懂短视频的编导和口播文案高手" in system:
-            return STORYBOARD_XML
-        return ""
-
-    def vision_completion(self, messages, images=None, **kwargs):
-        self.vision_calls.append({"messages": messages, "images": images})
-        return IMAGE_XML
-
-
-@pytest.fixture(autouse=True)
-def _clear_loader():
-    invalidate()
-    yield
-    invalidate()
-
-
-# ── XML 解析 ──────────────────────────────────────────────────────────────
-class TestXmlParser:
-    def test_parse_paired_tags_and_attrs(self):
-        nodes = xp.find_all(IMAGE_XML, "product")
-        assert len(nodes) == 1
-        assert nodes[0]["attrs"]["name"] == "大公鸡头油污净"
-        assert nodes[0]["attrs"]["image_index"] == "0"
-
-    def test_parse_selling_points(self):
-        points = [n["text"] for n in xp.find_all(IMAGE_XML, "point")]
-        assert points == ["去油快", "625ml大容量"]
-
-    def test_self_closing_and_bool_helpers(self):
-        nodes = xp.parse_tags('<people has_person="false"/><done x="true"/>')
-        assert xp.attr_bool(nodes[0]["attrs"]["has_person"]) is False
-        assert xp.attr_bool(nodes[1]["attrs"]["x"]) is True
-        assert xp.attr_float("0.97") == pytest.approx(0.97)
-        assert xp.attr_int("13", 5) == 13
-
-    def test_malformed_text_safe(self):
-        assert xp.find_all(None, "tag") == []
-        assert xp.text_of("乱七八糟没有标签", "intent", "默认") == "默认"
-
-
-# ── 3 套模板纯文本 ────────────────────────────────────────────────────────
-class TestTemplates:
-    def test_three_templates_present(self):
-        types_ = {t["prompt_type"] for t in DEFAULT_TEMPLATES}
-        assert types_ == {"image_analysis", "storyboard", "review"}
-
-    def test_no_json_blocks_in_templates(self):
-        for template in DEFAULT_TEMPLATES:
-            blob = "\n".join([template["system_prompt"], template["user_prompt_template"], template["example_output"]])
-            # image_analysis 模板明确要求输出 JSON，故只对非 image_analysis 模板校验
-            if template["prompt_type"] != "image_analysis":
-                assert "```json" not in blob
-                assert "JSON schema" not in blob
-
-    def test_placeholders_render_and_missing_key_kept(self):
-        # 现存模板里选取 storyboard 做占位符渲染校验
-        template = get_template("storyboard")
-        rendered = render_user_prompt(template, marketing_purpose="去油快", industry="家居")
-        assert "去油快" in rendered
-
-
-# ── loader：DB 加载/缓存/回落 ─────────────────────────────────────────────
-class TestPromptLoader:
-    def test_fallback_when_session_none(self, monkeypatch):
-        import packages.adapters.sqlalchemy_impl.session as session_mod
-
-        monkeypatch.setattr(session_mod, "SessionLocal", None, raising=False)
-        template = get_template("review")
-        assert template is not None
-        # v3 审核 prompt 实际内容断言
-        assert "合规审核" in template.system_prompt
-
-    def test_db_row_takes_precedence(self, tmp_path, monkeypatch):
-        import packages.adapters.sqlalchemy_impl.session as session_mod
-
-        db_path = tmp_path / "t.db"
-        engine = sa.create_engine(f"sqlite:///{db_path}")
-        with engine.begin() as conn:
-            conn.execute(
-                sa.text(
-                    "CREATE TABLE viral_video_prompt_templates ("
-                    "id INTEGER PRIMARY KEY, name TEXT, prompt_type TEXT, version INTEGER,"
-                    "system_prompt TEXT, user_prompt_template TEXT, example_output TEXT,"
-                    "is_active INTEGER)"
-                )
-            )
-            conn.execute(
-                sa.text(
-                    "INSERT INTO viral_video_prompt_templates VALUES"
-                    "(1,'自定义','review',2,'DB里的系统提示','DB用户提示','',1)"
-                )
-            )
-        factory = sessionmaker(bind=engine)
-        monkeypatch.setattr(session_mod, "SessionLocal", factory, raising=False)
-        template = get_template("review", force_refresh=True)
-        assert template.system_prompt == "DB里的系统提示"
-        assert template.version == 2
-
-        # 改 DB 后 30 秒内仍走缓存
-        with engine.begin() as conn:
-            conn.execute(sa.text("UPDATE viral_video_prompt_templates SET system_prompt='改了' WHERE id=1"))
-        assert get_template("review").system_prompt == "DB里的系统提示"
-        # force_refresh 后热加载生效
-        assert get_template("review", force_refresh=True).system_prompt == "改了"
-
-    def test_invalid_type_raises(self):
-        with pytest.raises(ValueError):
-            get_template("not_exist")
-
-
-# ── 现存步编排与 fallback ────────────────────────────────────────────────
-class TestGenerator:
-    def test_image_fallback_on_garbage(self):
-        client = FakeClient()
-        client.vision_completion = lambda *a, **k: "完全无法解析的内容"  # type: ignore
-        gen = CopyGenerator(client=client)
-        analysis = gen.analyze_images(["https://x/1.jpg"])
-        assert analysis.products[0].name.startswith("无法判断")
-
-    def test_storyboard_fallback_on_garbage(self):
-        client = FakeClient()
-        client.chat_completion = lambda *a, **k: "啥都没有"  # type: ignore
-        gen = CopyGenerator(client=client)
-        from packages.application.viral_video.schemas import FusionResult, ScriptSegment
-
-        fusion = FusionResult(
-            hook="开头",
-            script_segments=[ScriptSegment(text="a", duration_sec=5), ScriptSegment(text="b", duration_sec=5)],
-        )
-        board = gen.storyboard(fusion, None, ["u"], 10)
-        assert len(board.clips) == 2
-        assert board.clips[0].voice_text == "a"
-
-
-# ── 审核本地规则（LLM 降级放行时本地规则仍应识别红线）────────────────────
-class TestReview:
-    def test_rule_check_catches_exaggeration_even_if_llm_passes(self):
-        client = FakeClient()  # LLM 默认返回 passed
-        reviewer = Reviewer(client=client)
-        from packages.application.viral_video.schemas import FusionResult, IntentResult
-
-        fusion = FusionResult(title="一喷100%掉光", hook="x", cta="买")
-        result = reviewer.review(fusion, IntentResult(), "ai_full")
-        # LLM 返回 passed，且本地规则命中夸大 → 整体不通过
-        assert result.passed is False
-        dims = {i.dimension for i in result.issues}
-        assert "夸大承诺" in dims
-
-    def test_rule_check_catches_banned_phrase(self):
-        client = FakeClient()
-        reviewer = Reviewer(client=client)
-        from packages.application.viral_video.schemas import FusionResult
-
-        fusion = FusionResult(title="绝绝子", hook="x", cta="买")
-        result = reviewer.review(fusion, None, "ai_full")
-        assert not result.passed
-        assert any("绝绝子" in i.text for i in result.issues)
-
-    def test_missing_personal_brand_flagged(self):
-        client = FakeClient()
-        reviewer = Reviewer(client=client)
-        from packages.application.viral_video.schemas import (
-            FusionResult,
-            IntentResult,
-            PersonalBrand,
-        )
-
-        fusion = FusionResult(title="合规标题", hook="钩子", cta="行动")
-        intent = IntentResult(personal_brands=[PersonalBrand(text="39块钱一瓶", category="price")])
-        result = reviewer.review(fusion, intent, "ai_polish")
-        assert not result.passed
-        assert any("39块钱一瓶" in i.text and i.dimension == "事实一致性" for i in result.issues)
-
-    def test_missing_core_message_flagged(self):
-        client = FakeClient()
-        reviewer = Reviewer(client=client)
-        from packages.application.viral_video.schemas import (
-            CoreMessage,
-            FusionResult,
-            IntentResult,
-        )
-
-        fusion = FusionResult(title="标题", hook="别的内容", cta="号召")
-        intent = IntentResult(core_messages=[CoreMessage(text="必须保留的原意", must_keep=True)])
-        result = reviewer.review(fusion, intent, "user_primary")
-        assert any(i.dimension == "用户意图保留" for i in result.issues)
-
-    def test_rule_fix_local(self):
-        reviewer = Reviewer(client=FakeClient())
-        from packages.application.viral_video.schemas import (
-            BodyPoint,
-            FusionResult,
-            ReviewIssue,
-            ReviewResult,
-        )
-
-        fusion = FusionResult(
-            title="一喷100%掉光",
-            hook="绝绝子",
-            cta="最好用",
-            body_points=[BodyPoint(text="立刻见效")],
-        )
-        review = ReviewResult(
-            passed=False,
-            issues=[ReviewIssue(dimension="夸大承诺", text="100%")],
-        )
-        fixed = reviewer._rule_fix(fusion, review)
-        assert "100%" not in fixed.title
-        assert fixed.hook == ""
-        assert fixed.cta == "很不错用"
-        assert fixed.body_points[0].text == "坚持使用会有改善"
-
-
-# ── seed 幂等 ─────────────────────────────────────────────────────────────
-class TestSeed:
-    def test_seed_idempotent(self, tmp_path):
-        scripts_dir = os.path.join(REPO_ROOT, "scripts")
-        sys.path.insert(0, scripts_dir)
-        import importlib
-
-        seed_mod = importlib.import_module("seed_viral_video_prompts")
-
-        db_path = tmp_path / "seed.db"
-        engine = sa.create_engine(f"sqlite:///{db_path}")
-        with engine.begin() as conn:
-            conn.execute(
-                sa.text(
-                    "CREATE TABLE viral_video_prompt_templates ("
-                    "id INTEGER PRIMARY KEY AUTOINCREMENT, name TEXT, prompt_type TEXT,"
-                    "version INTEGER, system_prompt TEXT, user_prompt_template TEXT,"
-                    "example_output TEXT, is_active INTEGER DEFAULT 1,"
-                    "created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,"
-                    "updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,"
-                    "UNIQUE(prompt_type, version))"
-                )
-            )
-        assert seed_mod.seed(engine) == 3
-        assert seed_mod.seed(engine) == 3  # 再来一次不报错
-        with engine.begin() as conn:
-            count = conn.execute(sa.text("SELECT COUNT(*) FROM viral_video_prompt_templates")).scalar()
-        assert count == 3
-        with engine.begin() as conn:
-            types_ = {
-                r[0]
-                for r in conn.execute(sa.text("SELECT prompt_type FROM viral_video_prompt_templates WHERE is_active=1"))
-            }
-        assert types_ == {"image_analysis", "storyboard", "review"}
-
-
-# ── 负面词不出现于程序产出 ────────────────────────────────────────────────
-class TestNegativeOutput:
-    def test_fallback_outputs_clean(self):
-        gen = CopyGenerator(client=FakeClient())
-        from packages.application.viral_video.schemas import (
-            FusionResult,
-            ImageAnalysis,
-            IntentResult,
-        )
-
-        fusion = gen._fallback_fusion(
-            "ai_full",
-            ImageAnalysis(products=[]),
-            IntentResult(intent_summary="正常产品"),
-            15,
-            "",
-        )
-        blob = "\n".join(s.text for s in fusion.script_segments)
-        for phrase in BANNED_PHRASES:
-            assert phrase not in blob

tests/unit/test_viral_video_routes.py

@@ -431,7 +431,7 @@ class TestGenerateCopy:
         assert resp.id == "job-gc"
 
     def test_generate_copy_rejects_wrong_status(self):
-        """wait_user_confirm 等中间状态不允许调用 generate-copy（状态保护）。"""
+        """任务在 copy_generated/completed 时不能再 generate-copy（状态保护）。"""
         import pytest
         from app.api.routes import viral_video as vv_mod
         from app.schemas.viral_video import GenerateCopyRequest
@@ -441,8 +441,7 @@ class TestGenerateCopy:
 
         user = _auth_user("u1")
         session = MagicMock()
-        # wait_user_confirm 属于前端在编辑/确认文案的中间状态，应拒绝重新触发生成
-        job = _make_job(job_id="job-gc2", user_id="u1", status=ViralVideoStatus.WAIT_USER_CONFIRM)
+        job = _make_job(job_id="job-gc2", user_id="u1", status=ViralVideoStatus.COPY_GENERATED)
         repo = MagicMock()
         repo.get.return_value = job
 
@@ -451,31 +450,6 @@ class TestGenerateCopy:
                 vv_mod.generate_copy("job-gc2", GenerateCopyRequest(), authenticated_user=user, session=session)
             assert exc.value.status_code == 409
 
-    def test_generate_copy_allows_regenerate_from_copy_generated(self):
-        """#2222: COPY_GENERATED/COMPLETED 状态下点「重新生成文案」应放行入队，不返回 409。"""
-        from unittest.mock import patch
-
-        from app.api.routes import viral_video as vv_mod
-        from app.schemas.viral_video import GenerateCopyRequest
-
-        from packages.domain.viral_video import ViralVideoStatus
-
-        user = _auth_user("u1")
-        session = MagicMock()
-        for regen_status in (ViralVideoStatus.COPY_GENERATED, ViralVideoStatus.COMPLETED):
-            job = _make_job(job_id=f"job-regen-{regen_status}", user_id="u1", status=regen_status)
-            repo = MagicMock()
-            repo.get.return_value = job
-            with (
-                patch.object(vv_mod, "_get_job_repo", return_value=repo),
-                patch.object(vv_mod.celery_app, "send_task") as mock_send,
-            ):
-                resp = vv_mod.generate_copy(f"job-regen-{regen_status}", GenerateCopyRequest(), authenticated_user=user, session=session)
-            mock_send.assert_called_once()
-            job.resume_from_image_analyzed.assert_called()
-            assert job.retry_count >= 1
-            assert resp.id == f"job-regen-{regen_status}"
-
     def test_generate_copy_persists_voice_and_ratio(self):
         """generate-copy 应把 voice_id/voice_source/video_ratio 写入 job。"""
         from unittest.mock import patch

tests/unit/test_viral_video_wiring.py

@@ -1,317 +0,0 @@
-"""#2040 接线集成测试（v8/v3 叙述优先重构后）：
-
-验证运行中的 viral_video 任务使用 prompt_loader 从 DB 读取模板。
-mock LLM/Vision 调用，验证：
-1. image_analysis 走 V2 批处理路径，输出 {"images": [...]}
-2. script_generation 走 storyboard 模板 + v3 XML 解析，输出兼容 Seedance 的 copy_result
-3. review 走 Reviewer（review 模板）带自动重写
-4. 三档融合（ai_full / ai_polish / user_primary）的风格指令随 job.fusion_level 体现
-
-注：intent_parsing 独立步骤已删除，相关用例同步移除。
-"""
-
-from __future__ import annotations
-
-import sys
-from pathlib import Path as _Path
-
-_WORKER_ROOT = _Path(__file__).resolve().parents[2] / "apps" / "worker"
-if str(_WORKER_ROOT) not in sys.path:
-    sys.path.insert(0, str(_WORKER_ROOT))
-
-from unittest.mock import patch
-
-import pytest
-
-from packages.domain.viral_video import ViralVideoJob
-
-
-@pytest.fixture
-def job():
-    j = ViralVideoJob(
-        user_id="u1",
-        images=["https://img/1.jpg", "https://img/2.jpg"],
-        industry="美妆",
-        duration=15,
-        user_copy_text="这款口红真的显白又持久，姐妹们冲！",
-        fusion_level="ai_polish",
-    )
-    return j
-
-
-# ── v3 分镜 XML（与新 storyboard 模板 schema 对齐）──────────────────
-
-V3_XML = """<copy_display_markdown>今天给大家分享一支很显白的口红。</copy_display_markdown>
-<clips>
-<clip image_index="0" time_range="0-5秒">
-<voiceover>大家好，今天分享一款口红</voiceover>
-<visual>近景平视，缓慢推镜</visual>
-<action_details>手持口红特写</action_details>
-<audio_bgm>轻快流行BGM</audio_bgm>
-<transition>硬切</transition>
-<reference_image_index>0</reference_image_index>
-</clip>
-<clip image_index="1" time_range="5-15秒">
-<voiceover>颜色特别好看很显白</voiceover>
-<visual>特写，固定镜头</visual>
-<action_details>嘴唇涂抹特写</action_details>
-<audio_bgm>轻快BGM继续</audio_bgm>
-<transition>结束</transition>
-<reference_image_index>1</reference_image_index>
-</clip>
-</clips>
-<voiceover_script>大家好，今天分享一款口红。颜色特别好看很显白</voiceover_script>
-<theme>口红分享</theme>"""
-
-
-@pytest.fixture(autouse=True)
-def invalidate_loader_cache():
-    from packages.application.viral_video import prompt_loader as pl
-
-    pl.invalidate()
-    yield
-    pl.invalidate()
-
-
-class _FakeClient:
-    """替代 ai_router 返回的假 LLM 客户端，固定返回 v3 XML。"""
-
-    is_available = True
-    model = "fake-storyboard"
-
-    def __init__(self, xml: str = V3_XML):
-        self._xml = xml
-        self.captured: list[list[dict]] = []
-
-    def chat_completion(self, messages, **kwargs):
-        self.captured.append(messages)
-        return self._xml
-
-
-@pytest.fixture
-def patch_router(job):
-    """把 ai_router 单例的 get_llm_client 替换为返回 _FakeClient。"""
-    from packages.shared.ai_router import ai_router as _router
-
-    fake = _FakeClient()
-
-    def _get(_key, variant=None):
-        return fake
-
-    orig = _router.get_llm_client
-    _router.get_llm_client = _get  # type: ignore
-    job.image_analysis = {"images": []}
-    yield fake
-    _router.get_llm_client = orig  # type: ignore
-
-
-# ── 1) 图片分析走 V2 批处理 ──────────────────────────────────────────
-
-
-class TestImageAnalysisWiring:
-    def test_step_image_analysis_uses_v2_batch_path(self, job):
-        """图片分析走 V2 批处理，_step_image_analysis 归一化 URL 后调用 analyze_images_v2。"""
-        from apps.worker.worker_app.tasks import viral_video as vv
-
-        fake_image = {
-            "type": "product",
-            "name": "lipstick",
-            "brand": "品牌X",
-            "has_person": False,
-            "summary_markdown": "一支品牌X的红色口红。",
-            "_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_image, fake_image],
-                create=True,
-            ) as mock_v2:
-                result = vv._step_image_analysis(job)
-
-        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
-        images = result["images"]
-        assert len(images) == 2
-        assert images[0]["name"] == "lipstick"
-        assert images[0]["brand"] == "品牌X"
-        assert images[0]["type"] == "product"
-        assert images[0]["summary_markdown"] == "一支品牌X的红色口红。"
-
-    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 == {"images": []}
-
-
-# ── 2) 脚本生成：storyboard 模板 + v3 XML 解析 + fusion_level ───────
-
-
-class TestScriptGenerationWiring:
-    @pytest.mark.parametrize("level", ["ai_full", "ai_polish", "user_primary"])
-    def test_fusion_level_injected(self, job, patch_router, level):
-        """不同 fusion_level 下脚本生成走通，输出 Seedance 兼容结构。
-
-        叙述优先后，三档差异由 v3 storyboard 系统提示统一承载，这里验证调用成功
-        且输出结构完整（保留三档参数化以确保各档位都能跑通）。
-        """
-        from apps.worker.worker_app.tasks import viral_video as vv
-
-        job.fusion_level = level
-        result = vv._step_script_generation(job, {"images": []})
-
-        # 输出保持 Seedance 兼容结构
-        assert "overview" in result
-        assert "shots" in result
-        assert len(result["shots"]) >= 1
-        assert result["shots"][0]["shot_type_angle_movement"]
-        assert result["voiceover_script"]
-        # 系统提示确实被发送
-        assert patch_router.captured[0][0]["role"] == "system"
-
-    def test_fallback_when_xml_and_json_unparseable(self, job):
-        """XML 与 JSON 均无法解析时回退到兜底脚本。"""
-        from packages.shared.ai_router import ai_router as _router
-
-        fake = _FakeClient(xml="not xml not json")
-
-        def _get(_key, variant=None):
-            return fake
-
-        orig = _router.get_llm_client
-        _router.get_llm_client = _get  # type: ignore
-        job.image_analysis = {"images": []}
-        try:
-            from apps.worker.worker_app.tasks import viral_video as vv
-
-            result = vv._step_script_generation(job, {"images": []})
-        finally:
-            _router.get_llm_client = orig  # type: ignore
-        assert isinstance(result, dict)
-        assert "voiceover_script" in result
-        assert "shots" in result
-
-
-# ── 3) Review 使用 Reviewer + 自动重写 ─────────────────────────────
-
-
-class TestReviewWiring:
-    def test_pass_path(self, job):
-        from apps.worker.worker_app.tasks import viral_video as vv
-        from packages.application.viral_video.reviewer import Reviewer, ReviewResult
-
-        copy_result = {
-            "title": "口红推荐",
-            "overview": {"theme": "口红推荐"},
-            "voiceover_script": "这款口红显白又持久",
-            "shots": [{"scene_and_dialogue": "展示口红"}],
-        }
-        job.intent_result = {"key_messages": ["显白", "持久"], "intent": "推广"}
-
-        pass_result = ReviewResult(passed=True, score=90, issues=[], rewrite_suggestions=[])
-        with patch.object(Reviewer, "review", return_value=pass_result):
-            out = vv._step_review(job, copy_result)
-        assert out["passed"] is True
-
-    def test_rewrite_path(self, job):
-        """审核不通过时触发自动重写，并更新 job.copy_result。"""
-        from apps.worker.worker_app.tasks import viral_video as vv
-        from packages.application.viral_video.reviewer import Reviewer, ReviewResult
-        from packages.application.viral_video.schemas import FusionResult, ReviewIssue, ScriptSegment
-
-        copy_result = {
-            "title": "原标题",
-            "overview": {"theme": "原标题"},
-            "voiceover_script": "这款口红绝了",
-            "shots": [{"scene_and_dialogue": "展示"}],
-        }
-        job.intent_result = {"key_messages": ["显白"], "intent": "推广"}
-
-        fail_result = ReviewResult(
-            passed=False,
-            score=50,
-            issues=[ReviewIssue(dimension="违规词", severity="high", location="开头", text="绝了")],
-            rewrite_suggestions=["去掉夸大词"],
-        )
-        rewritten = FusionResult(
-            title="新标题",
-            hook="修改后钩子",
-            script_segments=[ScriptSegment(text="修改后口播正文")],
-            cta="行动号召",
-            word_count=10,
-            estimated_duration=10,
-        )
-        pass_after = ReviewResult(passed=True, score=88, issues=[], rewrite_suggestions=[])
-
-        with (
-            patch.object(Reviewer, "review", side_effect=[fail_result, pass_after]),
-            patch.object(Reviewer, "rewrite", return_value=rewritten),
-        ):
-            out = vv._step_review(job, copy_result)
-
-        assert out["passed"] is True
-
-
-# ── 4) 端到端：image 走 V2、script 走 storyboard loader ─────────────
-
-
-class TestEndToEndLoaderUsed:
-    def test_image_v2_and_script_uses_storyboard(self, job):
-        from apps.worker.worker_app.tasks import viral_video as vv
-        from packages.application.viral_video import prompt_loader as pl
-        from packages.shared.ai_router import ai_router as _router
-
-        called_types: list[str] = []
-        real_get = pl.get_template
-
-        def spy_get(prompt_type, **kwargs):
-            called_types.append(prompt_type)
-            return real_get(prompt_type, **kwargs)
-
-        v2_image = {
-            "type": "product",
-            "name": "lipstick",
-            "brand": "品牌X",
-            "has_person": False,
-            "summary_markdown": "一支品牌X口红。",
-        }
-        fake = _FakeClient()
-
-        def _get(_key, variant=None):
-            return fake
-
-        orig = _router.get_llm_client
-        _router.get_llm_client = _get  # type: ignore
-        job.image_analysis = {"images": []}
-        try:
-            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_image],
-                    create=True,
-                ),
-            ):
-                img_step = vv._step_image_analysis(job)
-                img_res = img_step["images"][0]
-                copy_res = vv._step_script_generation(job, {"images": [img_res]})
-        finally:
-            _router.get_llm_client = orig  # type: ignore
-
-        # V2 图片分析不经过 prompt_loader；脚本生成调用 storyboard 模板
-        assert "image_analysis" not in called_types
-        assert "storyboard" in called_types
-        assert copy_res["voiceover_script"]
-
-        # review 走 Reviewer.review
-        from packages.application.viral_video.reviewer import Reviewer, ReviewResult
-
-        pass_result = ReviewResult(passed=True, score=90, issues=[], rewrite_suggestions=[])
-        with patch.object(Reviewer, "review", return_value=pass_result) as mock_review:
-            review_res = vv._step_review(job, copy_res)
-        assert mock_review.called
-        assert isinstance(review_res, dict) and "passed" in review_res

tests/unit/test_vision_v4_prompt.py

@@ -1,191 +0,0 @@
-# -*- coding: utf-8 -*-
-"""vision v8 叙述优先 assembler / prompt 单元测试。
-
-- assembler 输出仅 5 字段（type/name/brand/has_person/summary_markdown）
-- images / 老 products 两种顶层键都能解析
-- summary_markdown 正常时原样透传，不改写
-- summary_markdown 缺失时才用一句话基础兜底
-- _prompt：DB 有 active 模板原样使用，无记录回落到 prompts.py 默认 v8
-"""
-
-from __future__ import annotations
-
-import sys
-import types
-from typing import Any
-
-import pytest
-from worker_app.tasks.vision import _prompt, assembler
-
-# packages 层依赖 datetime.UTC（Python 3.11+）。开发机若为旧版本，prompt 相关用例
-# 在 CI（3.11）上正常执行，本地直接跳过，避免污染基线。
-_PY311 = sys.version_info >= (3, 11)
-requires_packages = pytest.mark.skipif(not _PY311, reason="packages 需要 Python 3.11+")
-
-REQUIRED_KEYS = {"type", "name", "brand", "has_person", "summary_markdown"}
-
-
-# ---------- 正常 v8：叙述原样透传 ----------
-
-
-def test_assemble_v8_store_passthrough() -> None:
-    md = "###店铺主体\n这是一家名为“御众堂”的线下门店内部，整体暖木色调……"
-    fj = {
-        "images": [
-            {
-                "type": "store",
-                "name": "御众堂门店",
-                "brand": "御众堂",
-                "has_person": False,
-                "summary_markdown": md,
-            }
-        ]
-    }
-    r = assembler.assemble_result(0, fj, [])
-    assert REQUIRED_KEYS <= set(r.keys())
-    assert r["type"] == "store"
-    assert r["name"] == "御众堂门店"
-    assert r["brand"] == "御众堂"
-    assert r["has_person"] is False
-    assert r["summary_markdown"] == md
-    assert "_source" not in r
-
-
-def test_assemble_v8_product() -> None:
-    md = "这是一瓶洗衣液，亮红色瓶身配白色按压泵头，瓶身正面印着品牌标识……"
-    fj = {
-        "images": [{"type": "product", "name": "洗衣液", "brand": "OMO", "has_person": False, "summary_markdown": md}]
-    }
-    r = assembler.assemble_result(0, fj, ["OMO"])
-    assert r["type"] == "product"
-    assert r["summary_markdown"] == md
-
-
-def test_assemble_v8_person() -> None:
-    md = "画面里是一位年轻女性，穿白色T恤、黑色阔腿裤，神情自信……"
-    fj = {"images": [{"type": "person", "name": "年轻女性", "brand": "", "has_person": True, "summary_markdown": md}]}
-    r = assembler.assemble_result(0, fj, [])
-    assert r["type"] == "person"
-    assert r["has_person"] is True
-    assert r["summary_markdown"] == md
-
-
-def test_assemble_v8_scene() -> None:
-    fj = {
-        "images": [
-            {"type": "scene", "name": "海边日落", "brand": "", "has_person": False, "summary_markdown": "海边……"}
-        ]
-    }
-    r = assembler.assemble_result(0, fj, [])
-    assert r["type"] == "scene"
-
-
-# ---------- 顶层 products 老键兼容（assembler 层）----------
-
-
-def test_assemble_top_level_products_key() -> None:
-    fj = {"products": [{"type": "store", "name": "门店", "brand": "御众堂", "summary_markdown": "门店……"}]}
-    r = assembler.assemble_result(0, fj, [])
-    assert r["brand"] == "御众堂"
-    assert r["type"] == "store"
-
-
-# ---------- 字段缺失的异常兜底 ----------
-
-
-def test_assemble_missing_summary_uses_basic_fallback() -> None:
-    fj = {"images": [{"type": "store", "name": "御众堂门店", "brand": "御众堂", "has_person": False}]}
-    r = assembler.assemble_result(0, fj, [])
-    assert REQUIRED_KEYS <= set(r.keys())
-    assert r["summary_markdown"]
-    assert "御众堂" in r["summary_markdown"]
-    assert r.get("_source") == "summary_missing"
-
-
-def test_assemble_invalid_type_defaults_scene() -> None:
-    fj = {"images": [{"type": "weird", "name": "x", "summary_markdown": ""}]}
-    r = assembler.assemble_result(0, fj, [])
-    assert r["type"] == "scene"
-    assert r["summary_markdown"]  # basic fallback
-
-
-def test_assemble_empty_fast_json_uses_ocr_hint() -> None:
-    r = assembler.assemble_result(0, {}, ["御众堂"])
-    assert REQUIRED_KEYS <= set(r.keys())
-    assert "御众堂" in r["name"]
-    assert r.get("_source") == "empty_fast_json"
-
-
-def test_assemble_none_input() -> None:
-    r = assembler.assemble_result(0, None, [])
-    assert REQUIRED_KEYS <= set(r.keys())
-    assert r["type"] == "scene"
-
-
-# ---------- 布尔归一化 ----------
-
-
-def test_coerce_bool() -> None:
-    assert assembler._coerce_bool(True) is True
-    assert assembler._coerce_bool(1) is True
-    assert assembler._coerce_bool("true") is True
-    assert assembler._coerce_bool(False) is False
-    assert assembler._coerce_bool(0) is False
-    assert assembler._coerce_bool("否") is False
-
-
-# ---------- _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 = "DB_V8_PROMPT_XYZ") -> Any:
-    return types.SimpleNamespace(
-        system_prompt=system_prompt,
-        user_prompt_template="地址：{image_url}，OCR：{ocr_text}",
-        version=8,
-    )
-
-
-@requires_packages
-def test_resolve_uses_db_prompt(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(_prompt, "_load_db_template", lambda: _fake_tpl())
-    sys_prompt, user_prompt = _prompt.resolve_fast_prompt("http://img", "御众堂")
-    assert sys_prompt == "DB_V8_PROMPT_XYZ"
-    assert "http://img" in user_prompt
-    assert "御众堂" in user_prompt
-
-
-@requires_packages
-def test_resolve_pro_uses_db_prompt(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(_prompt, "_load_db_template", lambda: _fake_tpl("DB_PRO_PROMPT"))
-    sys_prompt, _ = _prompt.resolve_pro_prompt()
-    assert sys_prompt == "DB_PRO_PROMPT"
-
-
-@requires_packages
-def test_resolve_falls_back_to_default(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(_prompt, "_load_db_template", lambda: None)
-    default = _prompt._default_template()
-    sys_prompt, _ = _prompt.resolve_fast_prompt()
-    assert sys_prompt == default["system_prompt"]
-
-
-@requires_packages
-def test_resolve_caches(monkeypatch: pytest.MonkeyPatch) -> None:
-    calls = {"n": 0}
-
-    def _load() -> Any:
-        calls["n"] += 1
-        return _fake_tpl("CACHED")
-
-    monkeypatch.setattr(_prompt, "_load_db_template", _load)
-    s1, _ = _prompt.resolve_fast_prompt()
-    s2, _ = _prompt.resolve_fast_prompt()
-    assert s1 == s2 == "CACHED"
-    assert calls["n"] == 1

tests/unit/test_xml_parser.py

@@ -1,49 +0,0 @@
-"""xml_parser CDATA 剥离单元测试。"""
-
-from packages.application.viral_video.xml_parser import find_all, text_of
-
-XML = """<script>
-<copy_display_markdown><![CDATA[# 标题
-
-这是第一段，含**加粗**和[链接](https://a.com)。
-
-第二行，保留换行。]]></copy_display_markdown>
-<voiceover>口播不带 CDATA，保持原样。</voiceover>
-<visual><![CDATA[画面：产品特写，光线柔和]]></visual>
-<action_details><![CDATA[未闭合标签里的 CDATA 也要剥离]]></action_details>
-</script>"""
-
-
-def test_text_of_strips_cdata_with_markdown_newlines():
-    text = text_of(XML, "copy_display_markdown")
-    assert not text.startswith("<![CDATA[")
-    assert not text.endswith("]]>")
-    assert "# 标题" in text
-    assert "**加粗**" in text
-    assert "[链接](https://a.com)" in text
-    # markdown 换行被保留
-    assert "\n\n第二行" in text
-
-
-def test_plain_text_unchanged():
-    assert text_of(XML, "voiceover") == "口播不带 CDATA，保持原样。"
-
-
-def test_other_cdata_fields_stripped():
-    assert text_of(XML, "visual") == "画面：产品特写，光线柔和"
-
-
-def test_unclosed_tag_cdata_stripped():
-    # action_details 没有闭合标签，走未闭合兜底分支
-    node = find_all(XML, "action_details")[0]
-    assert node["text"] == "未闭合标签里的 CDATA 也要剥离"
-
-
-def test_no_cdata_returns_original():
-    xml = "<copy_display_markdown>普通内容]]> 残留结尾</copy_display_markdown>"
-    # 非完整 CDATA 包裹不应被误剥离
-    assert text_of(xml, "copy_display_markdown") == "普通内容]]> 残留结尾"
-
-
-def test_missing_tag_default():
-    assert text_of(XML, "nope", default="缺省") == "缺省"