Compare commits
1 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Coze Agent
|
14078ab85c |
@@ -1,61 +0,0 @@
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"""功能计费积分字段（爆款/对口型/智能剪辑 DB 化计费）。
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给 gpu_lipsync_tasks / generation_tasks / lipsync_jobs 三张表加积分字段：
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- credits_prepaid: 提交任务时预扣积分
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- credits_cost: 最终结算积分
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- credits_transaction_id: 预扣流水 ID
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注意：feature_pricing_configs 配置表由 xiaoxia-admin 侧 migration 建立，
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本仓库只读，不在此创建。
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Revision ID: 096_feature_billing_fields
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Revises: 095_viral_video_prompt_templates
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Create Date: 2026-10-05
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"""
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import sqlalchemy as sa
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from alembic import op
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revision = "096_feature_billing_fields"
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down_revision = "095_viral_video_prompt_templates"
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branch_labels = None
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depends_on = None
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_TABLES = ("gpu_lipsync_tasks", "generation_tasks", "lipsync_jobs")
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_COLUMNS = (
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("credits_prepaid", sa.Float(), "0"),
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("credits_cost", sa.Float(), "0"),
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("credits_transaction_id", sa.String(36), ""),
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)
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def _table_exists(conn, name: str) -> bool:
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return name in sa.inspect(conn).get_table_names()
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def upgrade() -> None:
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conn = op.get_bind()
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for table in _TABLES:
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if not _table_exists(conn, table):
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continue
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existing = {c["name"] for c in sa.inspect(conn).get_columns(table)}
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for col_name, col_type, default in _COLUMNS:
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if col_name in existing:
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continue
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op.add_column(
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table,
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sa.Column(col_name, col_type, nullable=False, server_default=default),
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)
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def downgrade() -> None:
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conn = op.get_bind()
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for table in _TABLES:
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if not _table_exists(conn, table):
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continue
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existing = {c["name"] for c in sa.inspect(conn).get_columns(table)}
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for col_name, _col_type, _default in _COLUMNS:
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if col_name not in existing:
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continue
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op.drop_column(table, col_name)
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File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -1,222 +0,0 @@
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# -*- coding: utf-8 -*-
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"""099: AI 模型路由层 seed — 补齐缺失模型和能力配置.
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幂等：所有 INSERT 先检查存在性。
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- ai_models: 补齐 qwen3.7-plus, seedream, seedance, embedding, wan3.0 等
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- ai_capability_configs: 补齐 image_generation, video_generation, embedding
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- 更新已有 capability 的 lite_model_id
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"""
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import json
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import sqlalchemy as sa
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from alembic import op
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revision = "099_ai_model_router_seed"
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down_revision = "098_viral_video_image_analysis_v5"
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branch_labels = None
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depends_on = None
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def upgrade() -> None:
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conn = op.get_bind()
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# CI 环境下 ai_models 表可能尚未创建（由 ORM 自动建表，非 migration）
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# 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
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table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_models')")).scalar()
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if not table_check:
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# ai_models 表不存在，跳过所有 seed（CI 环境）
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return
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# ── 1. 补齐 ai_models 缺失记录 ────────────────────────────────────────────
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existing_models = {
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row[0]
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for row in conn.execute(
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sa.text("SELECT model_key FROM ai_models WHERE deleted_at IS NULL")
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).fetchall()
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}
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# 从已有 active 记录获取 API key（复用，不硬编码）
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dashscope_key_row = conn.execute(
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sa.text(
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"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"
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)
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).first()
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dashscope_key = dashscope_key_row[0] if dashscope_key_row else ""
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volcengine_key_row = conn.execute(
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sa.text(
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"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"
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)
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).first()
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volcengine_key = volcengine_key_row[0] if volcengine_key_row else ""
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new_models = [
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{
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"model_key": "qwen3.7-plus",
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"name": "通义千问3.7 Plus（VLM 兜底）",
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"provider": "dashscope",
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"api_key": dashscope_key,
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"api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1",
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"description": "阿里云百炼 Qwen3.7 Plus 多模态模型，用于 VLM 兜底分析",
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},
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{
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"model_key": "doubao-seedream-5-0-flash-260915",
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"name": "Seedream 5.0 Flash（图片生成）",
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"provider": "volcengine",
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"api_key": volcengine_key,
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"api_base": "https://ark.cn-beijing.volces.com/api/v3",
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"description": "火山引擎 Seedream 5.0 Flash 文生图模型",
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},
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{
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"model_key": "doubao-seedance-2-5-260628",
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"name": "Seedance 2.5（视频生成）",
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"provider": "volcengine",
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"api_key": volcengine_key,
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"api_base": "https://ark.cn-beijing.volces.com/api/v3",
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"description": "火山引擎 Seedance 2.5 图/文生视频模型",
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},
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{
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"model_key": "doubao-embedding-vision-251215",
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"name": "豆包多模态向量嵌入",
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"provider": "volcengine",
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"api_key": volcengine_key,
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"api_base": "https://ark.cn-beijing.volces.com/api/v3",
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"description": "火山引擎豆包多模态向量嵌入模型",
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},
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{
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"model_key": "wan3.0-video",
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"name": "Wan 3.0 视频生成",
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"provider": "dashscope",
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"api_key": dashscope_key,
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"api_base": "https://dashscope.aliyuncs.com/api/v1",
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"description": "阿里云百炼 Wan 3.0 视频生成模型",
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},
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{
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"model_key": "doubao-seed-2-1-pro-260915",
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"name": "豆包 Seed 2.1 Pro（高精度推理）",
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"provider": "volcengine",
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"api_key": volcengine_key,
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"api_base": "https://ark.cn-beijing.volces.com/api/v3",
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"description": "火山引擎豆包 Seed 2.1 Pro 深度思考+多模态",
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},
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]
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for m in new_models:
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if m["model_key"] not in existing_models:
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conn.execute(
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sa.text(
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"""
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INSERT INTO ai_models (id, name, provider, model_key, api_key, api_base, description, status, is_default, usage_today, created_at, updated_at)
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VALUES (gen_random_uuid()::text, :name, :provider, :model_key, :api_key, :api_base, :description, 'active', false, 0, now(), now())
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"""
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),
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m,
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)
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# ── 2. 补齐 ai_capability_configs 缺失项 ──────────────────────────────────
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cap_table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_capability_configs')")).scalar()
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if not cap_table_check:
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return
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existing_caps = {
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row[0]
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for row in conn.execute(
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sa.text("SELECT capability_key FROM ai_capability_configs")
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).fetchall()
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}
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def _get_model_id(model_key: str) -> str | None:
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row = conn.execute(
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sa.text(
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"SELECT id FROM ai_models WHERE model_key = :key AND deleted_at IS NULL AND status = 'active' LIMIT 1"
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),
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{"key": model_key},
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).first()
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return row[0] if row else None
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# image_generation
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if "image_generation" not in existing_caps:
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mid = _get_model_id("doubao-seedream-5-0-flash-260915")
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if mid:
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conn.execute(
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sa.text(
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"""
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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)
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VALUES (gen_random_uuid()::text, :ck, :cn, :pm, 60, 1, 2, :ep, true, now(), now())
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"""
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),
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{
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"ck": "image_generation",
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"cn": "图片生成（Seedream）",
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"pm": mid,
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"ep": json.dumps({"size": "1K"}),
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},
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)
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# video_generation
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if "video_generation" not in existing_caps:
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mid = _get_model_id("doubao-seedance-2-5-260628")
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fb_mid = _get_model_id("wan3.0-video")
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if mid:
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conn.execute(
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sa.text(
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"""
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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)
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VALUES (gen_random_uuid()::text, :ck, :cn, :pm, :fm, 600, 1, 1, :ep, true, now(), now())
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"""
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),
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{
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"ck": "video_generation",
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"cn": "视频生成（Seedance/Wan）",
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"pm": mid,
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"fm": fb_mid,
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"ep": json.dumps({}),
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},
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)
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# embedding
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if "embedding" not in existing_caps:
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mid = _get_model_id("doubao-embedding-vision-251215")
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if mid:
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conn.execute(
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sa.text(
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"""
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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)
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VALUES (gen_random_uuid()::text, :ck, :cn, :pm, 30, 2, 5, :ep, true, now(), now())
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"""
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),
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{
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"ck": "embedding",
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"cn": "向量嵌入",
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"pm": mid,
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"ep": json.dumps({}),
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},
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)
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# ── 3. 更新 image_analysis 的 lite_model_id ─────────────────────────────
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lite_model_id = _get_model_id("qwen3.8-flash")
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if lite_model_id:
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conn.execute(
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sa.text(
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"UPDATE ai_capability_configs SET lite_model_id = :lite WHERE capability_key = 'image_analysis' AND lite_model_id IS NULL"
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),
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{"lite": lite_model_id},
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)
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def downgrade() -> None:
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conn = op.get_bind()
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# 安全检查表是否存在
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table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_models')")).scalar()
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if not table_check:
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return
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conn.execute(
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sa.text("DELETE FROM ai_capability_configs WHERE capability_key IN ('image_generation', 'video_generation', 'embedding')")
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)
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conn.execute(
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sa.text(
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"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"
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)
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)
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@@ -1,107 +0,0 @@
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# -*- coding: utf-8 -*-
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"""100: 修正已有 capability 的模型绑定.
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幂等：仅当 primary_model_id 当前绑定到旧模型 (doubao-seed-1-6) 时才更新，
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避免覆盖用户在后台的自定义配置。
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- 更新 5 个 LLM capability (intent_parsing, copy_fusion, storyboard, copy_review, asset_classify)
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的 primary_model_id 从 doubao-seed-1-6 改为 doubao-seed-2-1-pro-260915
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- 更新 image_analysis 的 primary/lite/fallback 模型绑定
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"""
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import sqlalchemy as sa
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from alembic import op
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revision = "100_fix_capability_model_bindings"
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down_revision = "099_ai_model_router_seed"
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branch_labels = None
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depends_on = None
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def upgrade() -> None:
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conn = op.get_bind()
|
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|
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# Check tables exist
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table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_models')")).scalar()
|
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if not table_check:
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return
|
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config_table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_capability_configs')")).scalar()
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if not config_table_check:
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return
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# Look up model IDs by model_key (not hardcoded UUIDs)
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pro_model_row = conn.execute(
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sa.text(
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"SELECT id FROM ai_models WHERE model_key = 'doubao-seed-2-1-pro-260915' AND deleted_at IS NULL LIMIT 1"
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)
|
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).first()
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if not pro_model_row:
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return
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pro_model_id = pro_model_row[0]
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old_model_row = conn.execute(
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sa.text("SELECT id FROM ai_models WHERE model_key = 'doubao-seed-1-6-250615' LIMIT 1")
|
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).first()
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old_model_id = old_model_row[0] if old_model_row else None
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llm_capabilities = [
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"intent_parsing",
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"copy_fusion",
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"storyboard",
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"copy_review",
|
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"asset_classify",
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]
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|
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for cap_key in llm_capabilities:
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if old_model_id:
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conn.execute(
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sa.text(
|
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"UPDATE ai_capability_configs SET primary_model_id = :new_id, updated_at = NOW() "
|
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"WHERE capability_key = :cap_key AND primary_model_id = :old_id"
|
||||
),
|
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{"new_id": pro_model_id, "old_id": old_model_id, "cap_key": cap_key},
|
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)
|
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|
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# Update image_analysis
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qwen38_row = conn.execute(
|
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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:
|
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qwen38_id = qwen38_row[0]
|
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qwen37_id = qwen37_row[0]
|
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|
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current_ia = conn.execute(
|
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sa.text(
|
||||
"SELECT primary_model_id, lite_model_id, fallback_model_id "
|
||||
"FROM ai_capability_configs WHERE capability_key = 'image_analysis'"
|
||||
)
|
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).first()
|
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|
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if current_ia:
|
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current_primary, current_lite, current_fallback = current_ia
|
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updates = {}
|
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if current_primary != qwen38_id:
|
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updates["primary_model_id"] = qwen38_id
|
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if current_lite != qwen38_id:
|
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updates["lite_model_id"] = qwen38_id
|
||||
if current_fallback != qwen37_id:
|
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updates["fallback_model_id"] = qwen37_id
|
||||
|
||||
if updates:
|
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set_clause = ", ".join([f"{k} = :{k}" for k in updates.keys()])
|
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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
|
||||
@@ -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
|
||||
@@ -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
|
||||
@@ -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'"
|
||||
)
|
||||
)
|
||||
@@ -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"
|
||||
)
|
||||
)
|
||||
@@ -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。
|
||||
|
||||
@@ -280,18 +280,11 @@ 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 +341,6 @@ 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
|
||||
|
||||
@@ -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,
|
||||
)
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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()
|
||||
@@ -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:
|
||||
|
||||
@@ -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)
|
||||
|
||||
|
||||
Generated
-12
@@ -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",
|
||||
|
||||
@@ -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",
|
||||
|
||||
@@ -83,8 +83,6 @@ export interface ImageProductAnalysis {
|
||||
label_text?: string
|
||||
selling_points?: string
|
||||
image_index?: number
|
||||
/** v8: 用户端展示用的 markdown 描述（由提示词控制排版） */
|
||||
summary_markdown?: string
|
||||
}
|
||||
|
||||
export interface ImageAnalysisResult {
|
||||
@@ -134,8 +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 }>
|
||||
}
|
||||
|
||||
@@ -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;
|
||||
@@ -1973,99 +1948,3 @@
|
||||
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;
|
||||
}
|
||||
|
||||
@@ -1,6 +1,5 @@
|
||||
import React, { useCallback, useEffect, useRef, useState } from "react"
|
||||
import axios from "axios"
|
||||
import { marked } from "marked"
|
||||
import {
|
||||
PlusOutlined,
|
||||
CloseOutlined,
|
||||
@@ -31,6 +30,7 @@ import { uploadAssetDirect, getAssetLibraries, getAssetsByKind, type AssetItem }
|
||||
import { fetchPresetVoices } from "@/api/voices"
|
||||
import { getVoiceClones, getVoiceClonePreview } from "@/api/voice-clone"
|
||||
import type { VoiceClone } from "@/api/voice-clone"
|
||||
import DurationWheelPicker from "@/components/common/DurationWheelPicker"
|
||||
import {
|
||||
FUSION_LEVELS,
|
||||
STYLE_STRENGTHS,
|
||||
@@ -151,16 +151,6 @@ 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, "&").replace(/</g, "<")
|
||||
}
|
||||
}
|
||||
|
||||
/* ─────────── 常量 ─────────── */
|
||||
|
||||
const LANGUAGES = ["中文(普通话)", "粤语", "英语", "日语", "韩语"]
|
||||
@@ -234,7 +224,6 @@ const RATIOS = [
|
||||
{ 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 +296,6 @@ 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 +323,6 @@ 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 +358,6 @@ function copyResultToStoryboard(cr: CopyResult | null | undefined): Storyboard |
|
||||
hard_constraints: [],
|
||||
negative_prompts: [],
|
||||
voiceover_script: finalCopy,
|
||||
copy_display_markdown: cr.copy_display_markdown || "",
|
||||
}
|
||||
}
|
||||
|
||||
@@ -427,7 +412,6 @@ 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) => {
|
||||
@@ -1233,76 +1217,67 @@ const ViralVideoPage: React.FC = () => {
|
||||
<CheckCircleFilled style={{ color: "#10b981" }} />
|
||||
识别描述汇览
|
||||
</div>
|
||||
{products.map((p, i) =>
|
||||
p.summary_markdown ? (
|
||||
<div key={i} className="vv-recog-item vv-recog-md">
|
||||
<div
|
||||
className="vv-md-body"
|
||||
dangerouslySetInnerHTML={{ __html: renderMarkdown(p.summary_markdown) }}
|
||||
/>
|
||||
{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>
|
||||
) : (
|
||||
<div key={i} className="vv-recog-item">
|
||||
{featureText(p.key_features ?? p.features) && (
|
||||
<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>
|
||||
<span className="vv-recog-k">核心特征：</span>
|
||||
<span className="vv-recog-v">{featureText(p.key_features ?? p.features)}</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>
|
||||
),
|
||||
)}
|
||||
)}
|
||||
{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>
|
||||
)
|
||||
}
|
||||
@@ -1441,19 +1416,6 @@ 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">
|
||||
@@ -2471,12 +2433,12 @@ const ViralVideoPage: React.FC = () => {
|
||||
</div>
|
||||
<div className="vv-form-row">
|
||||
<label className="vv-label">文案视频时长</label>
|
||||
<Select
|
||||
className="vv-select"
|
||||
style={{ width: "100%" }}
|
||||
<DurationWheelPicker
|
||||
value={task.duration}
|
||||
min={15}
|
||||
max={30}
|
||||
step={1}
|
||||
onChange={(v) => setTask({ duration: v })}
|
||||
options={DURATIONS.map((n) => ({ value: n, label: `${n}秒` }))}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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,
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -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
|
||||
@@ -1,208 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""V2 prompt 解析：优先读后台 viral_video_prompt_templates 表（prompt_type='image_analysis'
|
||||
且 is_active=true），30s TTL 热加载；DB 无有效记录/异常时，fallback 到纯硬编码 JSON schema prompt。
|
||||
|
||||
规则（简单直接，不做字符串匹配判断）：
|
||||
- DB 有 is_active=true 的 image_analysis 记录（含种子版本和用户修改后的版本）：
|
||||
* system = DB.system_prompt（DB prompt 自带完整输出格式，不追加硬编码 schema，
|
||||
避免 DB 写 XML、调用强制 json_object 造成的格式冲突）
|
||||
* user = DB.user_prompt_template 渲染后使用；渲染后为空则用硬编码默认
|
||||
- DB 无记录/连接异常/返回空：system/user 全部用纯硬编码 JSON schema prompt
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import threading
|
||||
import time
|
||||
from typing import Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# ---- 纯硬编码 JSON schema（DB 无有效配置时全量使用） ----
|
||||
|
||||
_FAST_JSON_SCHEMA = (
|
||||
"你是图片结构化识别器。严格按下方 JSON schema 返回一个对象，不要任何解释、"
|
||||
"不要markdown、不要代码块、不要前后缀文字。字段值不确定时填 null 或空数组。\n"
|
||||
"{\n"
|
||||
' "has_person": true/false,\n'
|
||||
' "gender": "男"/"女"/null,\n'
|
||||
' "age_range": "儿童"/"青少年"/"青年"/"中年"/"老年"/null,\n'
|
||||
' "upper_wear": "上装款式，如T恤/衬衫/卫衣/毛衣/西装/夹克/连衣裙/吊带/背心/外套等",\n'
|
||||
' "upper_color": "上装主色",\n'
|
||||
' "lower_wear": "下装款式；穿连衣裙时填null",\n'
|
||||
' "lower_color": "下装主色",\n'
|
||||
' "dress_color": "连衣裙主色（穿连衣裙时填）",\n'
|
||||
' "accessories": ["眼镜"/"帽子"/"项链"/"耳环"/"背包"/"手表"等数组],\n'
|
||||
' "hairstyle": "发型，如短发/长发/马尾/卷发/丸子头/光头等",\n'
|
||||
' "expression": "表情，如微笑/严肃/酷/开心等",\n'
|
||||
' "pose": "姿势，如站立/坐姿/侧身/行走等",\n'
|
||||
' "scene": "场景，如室内/街拍/户外/办公室/家居/海边/雪景/森林等",\n'
|
||||
' "style": "风格，如休闲/商务/运动/复古/潮流/甜美/酷飒/优雅/街头/法式等",\n'
|
||||
' "has_product": true/false,\n'
|
||||
' "category": "产品类目：服饰/鞋包/美妆/数码/食品/家居/配饰/母婴/非产品图",\n'
|
||||
' "product_name": "产品名称，非产品图填null",\n'
|
||||
' "brand": "品牌或文字标识，无则null",\n'
|
||||
' "material": "材质，如棉质/牛仔/皮革/真丝/针织/涤纶等",\n'
|
||||
' "pattern": "图案，如纯色/条纹/波点/格子/印花/碎花/Logo等",\n'
|
||||
' "colors": ["主色数组"],\n'
|
||||
' "mood": "整体氛围/情绪，如清新/活力/高级/温暖/冷峻/甜美/复古等"\n'
|
||||
"}\n\n"
|
||||
"你必须只返回一个合法的JSON对象，不要输出任何其他文字、解释、XML标签或markdown。"
|
||||
)
|
||||
DEFAULT_FAST_USER = "识别这张图片的人物穿搭与主体信息，只返回JSON对象。"
|
||||
|
||||
_PRO_JSON_SCHEMA = (
|
||||
"你是图片分析专家。严格按下方 JSON schema 返回一个对象，不要解释、不要markdown、不要代码块、不要XML标签。\n"
|
||||
"{\n"
|
||||
' "has_person": true/false,\n'
|
||||
' "gender": "男"/"女"/null,\n'
|
||||
' "age_range": "儿童"/"青少年"/"青年"/"中年"/"老年"/null,\n'
|
||||
' "outfit": "整体穿着描述（含颜色款式）",\n'
|
||||
' "hair": "发型发色",\n'
|
||||
' "pose": "姿势",\n'
|
||||
' "expression": "表情",\n'
|
||||
' "scene": "场景",\n'
|
||||
' "mood": "氛围",\n'
|
||||
' "has_product": true/false,\n'
|
||||
' "category": "类目：服饰/鞋包/美妆/数码/食品/家居/配饰/母婴/非产品图",\n'
|
||||
' "product_name": "产品名，非产品图填null",\n'
|
||||
' "brand": "品牌，无则null",\n'
|
||||
' "key_features": ["核心特征数组，3-6个短语"]\n'
|
||||
"}\n\n"
|
||||
"你必须只返回一个合法的JSON对象，不要输出任何其他文字、解释、XML标签或markdown。"
|
||||
)
|
||||
DEFAULT_PRO_USER = "分析这张图片，返回符合schema的JSON。"
|
||||
|
||||
# 保留旧 JSON schema 追加文本作为常量（DB prompt 完全控制输出格式后不再使用，
|
||||
# 保留以便排查历史行为）。
|
||||
_FAST_JSON_APPEND = (
|
||||
"\n\n【输出格式要求】无论上文如何要求，最终你必须只返回一个合法的JSON对象，"
|
||||
"严格包含以下字段（字段值不确定时填null或空数组）：\n"
|
||||
"{\n"
|
||||
' "has_person": true/false,\n'
|
||||
' "gender": "男"/"女"/null,\n'
|
||||
' "age_range": "儿童"/"青少年"/"青年"/"中年"/"老年"/null,\n'
|
||||
' "upper_wear": "上装款式字符串",\n'
|
||||
' "upper_color": "上装主色",\n'
|
||||
' "lower_wear": "下装款式（穿连衣裙时填null）",\n'
|
||||
' "lower_color": "下装主色",\n'
|
||||
' "dress_color": "连衣裙主色（穿连衣裙时填）",\n'
|
||||
' "accessories": ["配饰数组"],\n'
|
||||
' "hairstyle": "发型",\n'
|
||||
' "expression": "表情",\n'
|
||||
' "pose": "姿势",\n'
|
||||
' "scene": "场景",\n'
|
||||
' "style": "风格",\n'
|
||||
' "has_product": true/false,\n'
|
||||
' "category": "产品类目：服饰/鞋包/美妆/数码/食品/家居/配饰/母婴/非产品图",\n'
|
||||
' "product_name": "产品名称，非产品图填null",\n'
|
||||
' "brand": "品牌或文字标识，无则null",\n'
|
||||
' "material": "材质",\n'
|
||||
' "pattern": "图案",\n'
|
||||
' "colors": ["主色数组"],\n'
|
||||
' "mood": "整体氛围"\n'
|
||||
"}\n"
|
||||
"不要输出任何其他文字、解释、XML标签或markdown。"
|
||||
)
|
||||
|
||||
_PRO_JSON_APPEND = (
|
||||
"\n\n【输出格式要求】无论上文如何要求，最终你必须只返回一个合法的JSON对象，"
|
||||
"严格包含以下字段（字段值不确定时填null或空数组）：\n"
|
||||
"{\n"
|
||||
' "has_person": true/false,\n'
|
||||
' "gender": "男"/"女"/null,\n'
|
||||
' "age_range": "儿童"/"青少年"/"青年"/"中年"/"老年"/null,\n'
|
||||
' "outfit": "整体穿着描述（含颜色款式）",\n'
|
||||
' "hair": "发型发色",\n'
|
||||
' "pose": "姿势",\n'
|
||||
' "expression": "表情",\n'
|
||||
' "scene": "场景",\n'
|
||||
' "mood": "氛围",\n'
|
||||
' "has_product": true/false,\n'
|
||||
' "category": "类目：服饰/鞋包/美妆/数码/食品/家居/配饰/母婴/非产品图",\n'
|
||||
' "product_name": "产品名，非产品图填null",\n'
|
||||
' "brand": "品牌，无则null",\n'
|
||||
' "key_features": ["核心特征3-6个短语"]\n'
|
||||
"}\n"
|
||||
"不要输出任何其他文字、解释、XML标签或markdown。"
|
||||
)
|
||||
|
||||
_cache_lock = threading.Lock()
|
||||
_cache: dict[str, tuple[float, Any]] = {}
|
||||
_CACHE_TTL = 30.0
|
||||
|
||||
|
||||
def _load_db_template() -> Any | None:
|
||||
"""直接查DB viral_video_prompt_templates 中 is_active=true 的 image_analysis 记录；
|
||||
DB不可达/无记录/异常返回None。
|
||||
复用 prompt_loader._load_from_db，它只查DB不做DEFAULT_TEMPLATES fallback，
|
||||
返回None表示DB无记录或异常。"""
|
||||
try:
|
||||
from packages.application.viral_video.prompt_loader import _load_from_db
|
||||
|
||||
return _load_from_db("image_analysis")
|
||||
except Exception as e:
|
||||
logger.warning("[vision.v2] 查询DB prompt配置失败: %s", e)
|
||||
return None
|
||||
|
||||
|
||||
def _render_user(tpl: Any | None, default_user: str) -> str:
|
||||
if not tpl:
|
||||
return default_user
|
||||
tpl_str = getattr(tpl, "user_prompt_template", "") or ""
|
||||
if not tpl_str.strip():
|
||||
return default_user
|
||||
rendered = tpl_str.replace("{image_count}", "1").replace("{industry}", "通用").replace("{image_urls}", "").strip()
|
||||
return rendered or default_user
|
||||
|
||||
|
||||
def resolve_fast_prompt() -> tuple[str, str]:
|
||||
return _resolve("fast")
|
||||
|
||||
|
||||
def resolve_pro_prompt() -> tuple[str, str]:
|
||||
return _resolve("pro")
|
||||
|
||||
|
||||
def _resolve(kind: str) -> tuple[str, str]:
|
||||
now = time.time()
|
||||
cache_key = f"prompt_{kind}"
|
||||
with _cache_lock:
|
||||
hit = _cache.get(cache_key)
|
||||
if hit and now - hit[0] < _CACHE_TTL:
|
||||
return hit[1]
|
||||
|
||||
default_sys = _FAST_JSON_SCHEMA if kind == "fast" else _PRO_JSON_SCHEMA
|
||||
default_user = DEFAULT_FAST_USER if kind == "fast" else DEFAULT_PRO_USER
|
||||
|
||||
sys_prompt = default_sys
|
||||
usr_prompt = default_user
|
||||
try:
|
||||
tpl = _load_db_template()
|
||||
if tpl is not None:
|
||||
db_sys = (getattr(tpl, "system_prompt", "") or "").strip()
|
||||
if db_sys:
|
||||
sys_prompt = db_sys # DB prompt自带完整输出格式，不追加硬编码schema避免冲突
|
||||
usr_prompt = _render_user(tpl, default_user)
|
||||
logger.info(
|
||||
"[vision.v2] 使用DB image_analysis prompt (kind=%s version=%s sys_len=%d)",
|
||||
kind,
|
||||
getattr(tpl, "version", "?"),
|
||||
len(db_sys),
|
||||
)
|
||||
else:
|
||||
logger.debug("[vision.v2] DB image_analysis system_prompt为空，使用默认JSON (kind=%s)", kind)
|
||||
else:
|
||||
logger.debug("[vision.v2] DB无image_analysis记录/不可达，使用默认JSON prompt (kind=%s)", kind)
|
||||
except Exception as e:
|
||||
logger.warning("[vision.v2] 解析DB prompt异常，使用默认: %s", e)
|
||||
|
||||
with _cache_lock:
|
||||
_cache[cache_key] = (now, (sys_prompt, usr_prompt))
|
||||
return sys_prompt, usr_prompt
|
||||
|
||||
|
||||
def invalidate_cache() -> None:
|
||||
with _cache_lock:
|
||||
_cache.clear()
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,146 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""V2 图片分析主路径：每图并行 OCR（火山MediaKit，未配置时自动跳过）+ qwen3.8-flash JSON VLM，
|
||||
失败时单次 qwen3.7-plus 兜底。
|
||||
|
||||
架构（灵应10-05确认）：
|
||||
- 唯一后端：阿里云百炼 DashScope，qwen3.8-flash 做快速路径、qwen3.7-plus 做兜底
|
||||
- 主力：单图2路并行（OCR + fast VLM），外层N图全并发（workers=8）
|
||||
- 兜底：单次 pro VLM 调用，无竞速/重试/复杂超时
|
||||
- 输出 dict 格式与旧版完全一致，下游零改动
|
||||
"""
|
||||
|
||||
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__)
|
||||
|
||||
# 超时（可通过环境变量覆盖）
|
||||
_IMG_WORKERS = int(os.environ.get("VISION_V2_IMG_WORKERS", "8"))
|
||||
_FAST_TIMEOUT = float(os.environ.get("VISION_V2_FAST_TIMEOUT", "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"))
|
||||
|
||||
_FALLBACK_RESULT = {
|
||||
"name": "未识别",
|
||||
"brand": "无法判断",
|
||||
"category": "非产品图",
|
||||
"appearance": "无法判断",
|
||||
"packaging": "无法判断",
|
||||
"text_on_package": [],
|
||||
"key_features": ["无法判断"],
|
||||
"scene": "通用",
|
||||
"mood": "",
|
||||
"portrait_prompt": "无法判断",
|
||||
"summary": "未识别",
|
||||
}
|
||||
|
||||
|
||||
def _is_usable(r: dict[str, Any]) -> bool:
|
||||
pp = (r.get("portrait_prompt") or "").strip()
|
||||
if pp and pp not in ("无人像", "无法判断", "未识别"):
|
||||
return True
|
||||
name = (r.get("name") or "").strip()
|
||||
if name and name not in ("未识别", "无法判断", "未知"):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
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] = []
|
||||
fast_elapsed = 0.0
|
||||
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:
|
||||
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) # 不等待未完成的线程，避免计时膨胀
|
||||
|
||||
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 pp=%s",
|
||||
idx,
|
||||
fast_elapsed,
|
||||
(assembled.get("portrait_prompt") or "")[:40],
|
||||
)
|
||||
return assembled
|
||||
|
||||
pro_t0 = time.time()
|
||||
pro_result = vlm_fallback.call_pro_vlm(img_url, idx, timeout=_PRO_TIMEOUT)
|
||||
if pro_result and _is_usable(pro_result):
|
||||
pro_result["_fallback_used"] = True
|
||||
pro_result["_fast_elapsed"] = round(fast_elapsed, 2)
|
||||
pro_result["_pro_elapsed"] = round(time.time() - pro_t0, 2)
|
||||
if ocr_result and not pro_result.get("text_on_package"):
|
||||
pro_result["text_on_package"] = ocr_result[:8]
|
||||
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)
|
||||
out = dict(_FALLBACK_RESULT)
|
||||
out["_source"] = "v2_all_failed"
|
||||
out["text_on_package"] = ocr_result[:8]
|
||||
out["_fast_elapsed"] = round(fast_elapsed, 2)
|
||||
return out
|
||||
|
||||
|
||||
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:
|
||||
logger.warning("[vision.v2] 图片 #%d future异常: %s", idx, e, exc_info=True)
|
||||
r = dict(_FALLBACK_RESULT)
|
||||
r["_source"] = "v2_future_exception"
|
||||
results[idx] = r
|
||||
|
||||
elapsed = time.time() - t0
|
||||
succ = sum(1 for r in results if r and _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]
|
||||
@@ -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
|
||||
@@ -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
|
||||
@@ -1,116 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""V2 兜底路径：image_analysis（默认 qwen-vl-plus 视觉模型，fallback qwen3.7-plus / DashScope）单图调用。
|
||||
|
||||
fast_json 超时/返回非 JSON/识别为空时，本路径单次调用兜底。
|
||||
设计要点：
|
||||
- 通过 ai_router.get_vision_client() 获取 DoubaoClient 实例，不再自己拼 httpx 请求
|
||||
- enable_thinking=False + response_format=json_object
|
||||
- system prompt 优先读后台 viral_video_prompt_templates 配置，DB不可用时fallback到硬编码JSON schema
|
||||
- max_tokens 不传，使用 client 中 capability 的 DB 配置（避免硬编码截断 JSON）
|
||||
- timeout=30s
|
||||
- 返回 dict 统一走 assembler.assemble_result 组装，与 fast 路径输出格式完全一致
|
||||
"""
|
||||
|
||||
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,
|
||||
*,
|
||||
timeout: int = _DEFAULT_TIMEOUT,
|
||||
max_tokens: int | None = None,
|
||||
) -> dict[str, Any] | None:
|
||||
"""max_tokens 默认 None：不显式传参，使用 client 内 capability 的 DB 配置。"""
|
||||
t0 = time.time()
|
||||
|
||||
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:
|
||||
logger.warning("[vision.v2] ai_router 获取失败: %s", e)
|
||||
return None
|
||||
|
||||
system_prompt, user_prompt = _prompt.resolve_pro_prompt()
|
||||
|
||||
messages = [
|
||||
{"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, # 图片已在 messages 中
|
||||
"temperature": 0.3,
|
||||
"timeout": timeout,
|
||||
"enable_thinking": False,
|
||||
"response_format": {"type": "json_object"},
|
||||
}
|
||||
# pro fallback：显式4000 tokens给复杂门店图留足空间
|
||||
call_kwargs["max_tokens"] = max_tokens if max_tokens is not None else 4000
|
||||
|
||||
from .json_utils import extract_json_object
|
||||
|
||||
raw = None
|
||||
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 list(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:
|
||||
logger.warning("[vision.v2] pro 返回空 outer=%s", _outer)
|
||||
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 返回截断JSON(partial) elapsed=%.1fs", elapsed)
|
||||
logger.info(
|
||||
"[vision.v2] pro 完成 model=%s elapsed=%.1fs type=%s",
|
||||
client.model,
|
||||
elapsed,
|
||||
obj.get("type"),
|
||||
)
|
||||
|
||||
# 通过assembler统一组装，兼容v4嵌套schema和旧扁平schema
|
||||
result = assembler.assemble_result(idx, obj, [])
|
||||
result["_source"] = "vlm_pro"
|
||||
result["_fallback_used"] = True
|
||||
return result
|
||||
except Exception as e:
|
||||
elapsed = time.time() - t0
|
||||
logger.warning("[vision.v2] pro 异常 elapsed=%.1fs err=%s", elapsed, e, exc_info=True)
|
||||
return None
|
||||
@@ -1,123 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""V2 快速路径：image_analysis capability（默认 qwen-vl-plus 视觉模型 / DashScope）强约束 JSON-only 调用。
|
||||
|
||||
目标：替代"人体属性/商品检测/图像标签"三个火山不存在的专用云端 API。
|
||||
设计要点：
|
||||
- 通过 ai_router.get_vision_client() 获取 DoubaoClient 实例，不再自己拼 httpx 请求
|
||||
- enable_thinking=False 关闭推理链（reasoning 是延迟主因）
|
||||
- response_format=json_object 强约束JSON输出
|
||||
- system prompt 优先读后台 viral_video_prompt_templates 配置，DB不可用时fallback到硬编码JSON schema
|
||||
- max_tokens 不传，使用 client 中 capability 的 DB 配置（避免硬编码截断 JSON）
|
||||
- temperature=0.1（稳定输出 JSON）
|
||||
- timeout=15s（失败由外层走 pro 兜底）
|
||||
"""
|
||||
|
||||
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:
|
||||
"""调用 vision client 返回结构化 dict；失败/非 JSON 返回 None。
|
||||
|
||||
max_tokens 默认 None：不显式传参，使用 client 内 capability 的 DB 配置；
|
||||
显式传入时作为覆盖。
|
||||
"""
|
||||
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:
|
||||
logger.warning("[vision.v2] ai_router 获取失败: %s", e)
|
||||
return None
|
||||
|
||||
system_prompt, user_prompt = _prompt.resolve_fast_prompt()
|
||||
|
||||
messages = [
|
||||
{"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, # 图片已在 messages 中
|
||||
"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
|
||||
|
||||
# 双重防护：第1次正常调用；第2次去掉json_object强约束（部分模型在该约束下
|
||||
# 反而幻觉），并加严格指令。解析全部走 json_utils，截断partial产物可用。
|
||||
from .json_utils import extract_json_object
|
||||
|
||||
raw = None
|
||||
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 = list(msgs2[1]["content"])
|
||||
cont = [dict(c) for c in cont]
|
||||
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:
|
||||
logger.warning("[vision.v2] fast_json 返回空 outer=%s", _outer)
|
||||
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 返回截断JSON(partial) elapsed=%.1fs", elapsed)
|
||||
logger.info(
|
||||
"[vision.v2] fast_json 完成 model=%s elapsed=%.1fs has_person=%s type=%s",
|
||||
client.model,
|
||||
elapsed,
|
||||
obj.get("has_person"),
|
||||
obj.get("type"),
|
||||
)
|
||||
return obj
|
||||
except Exception as e:
|
||||
elapsed = time.time() - t0
|
||||
logger.warning("[vision.v2] fast_json 异常 elapsed=%.1fs err=%s", elapsed, e, exc_info=True)
|
||||
return None
|
||||
@@ -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."""
|
||||
|
||||
@@ -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）
|
||||
|
||||
@@ -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
|
||||
@@ -110,12 +110,10 @@ _INTENT_SYSTEM = f"""你负责理解用户的营销意图。用户给的文案
|
||||
|
||||
_INTENT_USER = """用户原始文案：{user_copy_text}
|
||||
所属行业：{industry}
|
||||
营销目的：{marketing_purpose}
|
||||
图片分析结果（供参考）：
|
||||
{image_analysis}
|
||||
图片类型推断：{image_category_hint}
|
||||
|
||||
请理解用户意图，按标签格式输出。注意：theme和emotion_tone应与图片类型和营销目的匹配——门店类图片偏向"门店探店/到店体验"，商品图偏向"好物分享/产品种草"，人物图偏向"穿搭/人物故事"。"""
|
||||
请理解用户意图，按标签格式输出。"""
|
||||
|
||||
_INTENT_EXAMPLE = """<intent_summary>一款厨房去油污神器，喷一喷油污就掉</intent_summary>
|
||||
<core_messages>
|
||||
@@ -178,7 +176,7 @@ _FUSION_EXAMPLE = """<title>厨房重油污，别再用洗洁精硬擦了</title
|
||||
<segment duration_sec="4" image_index="0">39块钱625ml，厨房重油污的可以试一瓶</segment>
|
||||
</script_segments>
|
||||
<voiceover_script>这油污我真的忍很久了，用洗洁精擦半天都没用。后来换了这个大公鸡头油污净，喷上等几分钟，一擦就干净。39块钱625ml，厨房重油污的可以试一瓶。</voiceover_script>
|
||||
<overview_theme>厨房好物分享·产品种草</overview_theme>
|
||||
<overview_theme>厨房油污清洁好物分享</overview_theme>
|
||||
<scene_and_lighting>简洁明亮的厨房台面场景，自然光从窗户洒入，色调温暖柔和，突出产品白色瓶身与去油污对比效果。</scene_and_lighting>
|
||||
<word_count>58</word_count>
|
||||
<estimated_duration>13</estimated_duration>"""
|
||||
@@ -217,8 +215,6 @@ _STORYBOARD_USER = """目标时长：{duration}秒
|
||||
图片分析结果：
|
||||
{image_analysis}
|
||||
|
||||
重要：overview_theme 必须与图片实际内容和营销目的匹配。门店/餐饮/服务类图片用"门店探店·到店体验"；商品图用"好物分享·产品种草"；人物图用"穿搭分享·人物故事"；场景图用"空间体验·场景氛围"。不要对所有图片都使用"好物分享"。
|
||||
|
||||
请按标签格式输出分镜。"""
|
||||
|
||||
_STORYBOARD_EXAMPLE = """<clips>
|
||||
|
||||
@@ -53,19 +53,11 @@ _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
|
||||
from packages.shared.ai_client import get_doubao_client
|
||||
|
||||
client = ai_router.get_llm_client("copy_review")
|
||||
except Exception:
|
||||
from packages.shared.ai_client import get_doubao_client
|
||||
|
||||
client = get_doubao_client()
|
||||
client = get_doubao_client()
|
||||
self.client = client
|
||||
|
||||
# ── 审核 ────────────────────────────────────────────────────────────
|
||||
@@ -73,9 +65,8 @@ class Reviewer:
|
||||
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,
|
||||
passed=not local,
|
||||
issues=local,
|
||||
rewrite_suggestions=[],
|
||||
raw="",
|
||||
@@ -90,17 +81,6 @@ class Reviewer:
|
||||
)
|
||||
|
||||
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(
|
||||
@@ -116,7 +96,6 @@ class Reviewer:
|
||||
],
|
||||
temperature=0.2,
|
||||
max_tokens=1024,
|
||||
timeout=25,
|
||||
)
|
||||
if not raw:
|
||||
return None
|
||||
@@ -263,7 +242,6 @@ class Reviewer:
|
||||
],
|
||||
temperature=0.5,
|
||||
max_tokens=2048,
|
||||
timeout=25,
|
||||
)
|
||||
if not raw:
|
||||
return self._rule_fix(fusion, review)
|
||||
@@ -318,13 +296,10 @@ class Reviewer:
|
||||
|
||||
@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
|
||||
|
||||
+35
-56
@@ -80,46 +80,54 @@ 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-2-1-pro-260915" # 推理模型（Seed 2.1 Pro，深度思考+多模态；原 seed-1-6 已下线）
|
||||
doubao_fast_model: str = (
|
||||
"doubao-seed-2-1-pro-260915" # #2181: lite方舟侧100%超时，默认fast_model也走pro；方舟恢复lite后通过ENV DOUBAO_FAST_MODEL切回
|
||||
)
|
||||
doubao_base_url: str = "https://ark.cn-beijing.volces.com/api/v3"
|
||||
doubao_timeout: int = 45 # #2180: 方舟LLM高峰期响应6-8s，原30s太紧提到45s
|
||||
doubao_max_retries: int = 1 # #2180: timeout调大后一次调用就够，1次重试防偶发抖动；避免6次重试叠加到351s
|
||||
doubao_vision_model: str = (
|
||||
"doubao-seed-2-1-pro-260915" # 高精度视觉（Seed 2.1 Pro 原生多模态；原 vision-pro-250328 已下线）
|
||||
)
|
||||
doubao_vision_lite_model: str = (
|
||||
"doubao-seed-2-1-lite-260915" # 快速视觉（Seed 2.1 Lite 原生多模态；原 vision-lite-250315 不可用）
|
||||
)
|
||||
doubao_vision_use_lite: bool = True # #2188: lite恢复稳定，爆款视频默认lite-first提速（20-30s）
|
||||
doubao_embedding_model: str = "doubao-embedding-vision-251215" # 多模态向量化（原 large-text-240915 已 Retiring）
|
||||
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）
|
||||
doubao_image_model: str = (
|
||||
"doubao-seedream-5-0-flash-260915" # #2173: 信任链 Seedream 改 flash 模型（实测 pro 46.5s→flash 13s；pro AI化图仍被Seedance拦截）
|
||||
)
|
||||
doubao_image_size: str = "1K" # #2173: 1K 已足够做 Seedance 参考图，2K 在 flash 下也 22s，1K 13s
|
||||
doubao_image_timeout: int = 60 # #2173: flash+1K 通常15s内，给60s余量
|
||||
doubao_trust_chain_enabled: bool = (
|
||||
True # #2173: 信任链总开关；若Seedream产物仍被Seedance拦截，可配 False 关闭直接t2v降级
|
||||
)
|
||||
|
||||
# ── 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 +181,6 @@ 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(
|
||||
|
||||
@@ -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
|
||||
@@ -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
|
||||
|
||||
|
||||
@@ -191,45 +191,13 @@ 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 +206,14 @@ 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
|
||||
|
||||
+28
-108
@@ -170,35 +170,14 @@ 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
|
||||
@@ -211,13 +190,6 @@ class DoubaoClient:
|
||||
# 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)
|
||||
|
||||
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。"""
|
||||
if not self.is_available or not text or not text.strip():
|
||||
@@ -268,17 +240,16 @@ class DoubaoClient:
|
||||
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）
|
||||
max_tokens: 最大生成token数，默认1024
|
||||
|
||||
Returns:
|
||||
模型返回的文本内容，失败返回 None
|
||||
@@ -291,24 +262,18 @@ 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)
|
||||
payload: dict[str, Any] = {
|
||||
"model": model or self.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)
|
||||
|
||||
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)
|
||||
_req_timeout = timeout if timeout is not None else self.timeout
|
||||
response = httpx.post(
|
||||
url,
|
||||
headers=headers,
|
||||
@@ -317,33 +282,15 @@ class DoubaoClient:
|
||||
)
|
||||
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",
|
||||
"[doubao] chat_completion 完成 model=%s tokens_in=%d tokens_out=%d elapsed=%.1fs attempt=%d timeout=%d",
|
||||
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:
|
||||
@@ -367,21 +314,20 @@ class DoubaoClient:
|
||||
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。
|
||||
|
||||
@@ -421,19 +367,14 @@ 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)
|
||||
payload: dict[str, Any] = {
|
||||
"model": model or self.model,
|
||||
"model": model or self.vision_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)
|
||||
req_timeout = timeout or self.timeout
|
||||
last_error: Optional[Exception] = None
|
||||
_t0 = time.time()
|
||||
for attempt in range(self.max_retries + 1):
|
||||
@@ -446,24 +387,7 @@ class DoubaoClient:
|
||||
)
|
||||
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",
|
||||
@@ -665,32 +589,28 @@ class DoubaoClient:
|
||||
# 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)
|
||||
trusted_urls: list[str] = []
|
||||
if len(pre_trusted_images) >= 1:
|
||||
trusted_urls = list(pre_trusted_images)
|
||||
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,
|
||||
"[trust-chain] 使用预热t2i结果 %d 张，替换原参考图走 reference_image 模式（原n=%d）",
|
||||
len(trusted_urls),
|
||||
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 []
|
||||
if trust_chain_applied and trusted_urls:
|
||||
# 替换：原 image_url 用第一张 AI 图，ref_imgs 用剩余
|
||||
if image_url and trusted_urls:
|
||||
image_url = trusted_urls[0]
|
||||
ref_imgs = trusted_urls[1:] if len(trusted_urls) > 1 else []
|
||||
else:
|
||||
ref_imgs = merged
|
||||
ref_imgs = trusted_urls
|
||||
# ─────────────────────────────────────────────────────────────────
|
||||
|
||||
# 判断任务模式：
|
||||
|
||||
@@ -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
|
||||
@@ -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()
|
||||
@@ -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},
|
||||
|
||||
@@ -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()
|
||||
@@ -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 "doubao" in s.doubao_model
|
||||
assert s.doubao_timeout == 45 # #2180 默认提到45s
|
||||
assert s.doubao_max_retries == 3
|
||||
assert s.doubao_max_retries == 1
|
||||
|
||||
|
||||
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:
|
||||
|
||||
@@ -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_max_retries == 1
|
||||
assert "volces.com" in s.doubao_base_url
|
||||
|
||||
def test_default_empty_api_keys(self):
|
||||
"""API Key 默认空字符串"""
|
||||
|
||||
@@ -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")
|
||||
|
||||
@@ -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"] == " "
|
||||
@@ -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()
|
||||
|
||||
@@ -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
|
||||
@@ -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
|
||||
@@ -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"
|
||||
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -97,43 +97,21 @@ def invalidate_loader_cache():
|
||||
|
||||
|
||||
class TestImageAnalysisWiring:
|
||||
def test_step_image_analysis_uses_v2_batch_path(self, job):
|
||||
"""#2200/#2207 后图片分析走 V2 批处理（OCR+lite JSON 并行），
|
||||
_step_image_analysis 归一化 URL 后调用 analyze_images_v2。"""
|
||||
def test_uses_loader_template_and_xml_parse(self, job):
|
||||
from apps.worker.worker_app.tasks import viral_video as vv
|
||||
|
||||
fake_product = {
|
||||
"name": "lipstick",
|
||||
"brand": "品牌X",
|
||||
"category": "唇部彩妆",
|
||||
"key_features": ["显白", "持久"],
|
||||
"text_on_package": ["品牌X", "211"],
|
||||
"_source": "v2",
|
||||
}
|
||||
with patch.object(vv, "_normalize_image_url", side_effect=lambda raw, idx: raw):
|
||||
with patch(
|
||||
"worker_app.tasks.vision.analyze_images_v2",
|
||||
return_value=[fake_product, fake_product],
|
||||
create=True,
|
||||
) as mock_v2:
|
||||
result = vv._step_image_analysis(job)
|
||||
with patch("packages.shared.ai_service.call_vision", return_value=IMAGE_XML) as mock_v:
|
||||
result = vv._analyze_single_image(0, "https://img/1.jpg", "vlm-lite", 15)
|
||||
|
||||
mock_v2.assert_called_once()
|
||||
# 传入的是归一化后的图片 URL 列表
|
||||
assert mock_v2.call_args.args[0] == job.images
|
||||
products = result["products"]
|
||||
assert len(products) == 2
|
||||
assert products[0]["name"] == "lipstick"
|
||||
assert products[0]["brand"] == "品牌X"
|
||||
assert "显白" in products[0]["key_features"]
|
||||
assert products[0]["text_on_package"] == ["品牌X", "211"]
|
||||
|
||||
def test_step_image_analysis_empty_images(self, job):
|
||||
from apps.worker.worker_app.tasks import viral_video as vv
|
||||
|
||||
job.images = []
|
||||
result = vv._step_image_analysis(job)
|
||||
assert result == {"products": []}
|
||||
mock_v.assert_called_once()
|
||||
# 验证调用时传入了 system_prompt（说明走了 loader 渲染的模板）
|
||||
call_kwargs = mock_v.call_args.kwargs
|
||||
assert "system_prompt" in call_kwargs and call_kwargs["system_prompt"]
|
||||
# 结果包含从 XML 解析出的产品信息
|
||||
assert result["name"] == "lipstick"
|
||||
assert result["brand"] == "品牌X"
|
||||
assert "显白" in result["key_features"]
|
||||
assert result["text_on_package"] == ["品牌X", "211"]
|
||||
|
||||
|
||||
# ── 2) 意图解析走模板 ───────────────────────────────────────────────
|
||||
@@ -274,29 +252,18 @@ class TestEndToEndLoaderUsed:
|
||||
called_types.append(prompt_type)
|
||||
return real_get(prompt_type, **kwargs)
|
||||
|
||||
v2_product = {
|
||||
"name": "lipstick",
|
||||
"brand": "品牌X",
|
||||
"key_features": ["显白", "持久"],
|
||||
}
|
||||
with (
|
||||
patch.object(pl, "get_template", side_effect=spy_get),
|
||||
patch.object(vv, "_normalize_image_url", side_effect=lambda raw, idx: raw),
|
||||
patch(
|
||||
"worker_app.tasks.vision.analyze_images_v2",
|
||||
return_value=[v2_product],
|
||||
create=True,
|
||||
),
|
||||
patch("packages.shared.ai_service.call_vision", return_value=IMAGE_XML),
|
||||
patch("packages.shared.ai_service.call_llm", return_value=INTENT_XML),
|
||||
):
|
||||
# 1) image（V2 路径，不再经过 prompt_loader）
|
||||
img_step = vv._step_image_analysis(job)
|
||||
img_res = img_step["products"][0]
|
||||
# 2) intent（走 loader image_analysis? 否——intent_parsing 模板）
|
||||
# 1) image
|
||||
img_res = vv._analyze_single_image(0, "https://img/1.jpg", "vlm", 15)
|
||||
# 2) intent
|
||||
intent_res = vv._step_intent_parsing(job, {"products": [img_res]})
|
||||
|
||||
# V2 图片分析不再调用 loader；意图解析调用 intent_parsing 模板
|
||||
assert "image_analysis" not in called_types
|
||||
# 前两步分别调用了 image_analysis 和 intent_parsing
|
||||
assert "image_analysis" in called_types
|
||||
assert "intent_parsing" in called_types
|
||||
|
||||
# script 和 review 单独验证（需要不同的 LLM 返回）
|
||||
|
||||
@@ -1,291 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""vision v4 prompt / assembler 单元测试：
|
||||
|
||||
- assembler 正确识别 v4 嵌套 schema 与旧扁平 schema
|
||||
- v4 product/person/store/other 四类输出组装出下游必出字段
|
||||
- 旧扁平 schema 行为不变
|
||||
- _prompt._resolve：DB 有 active prompt 时原样使用（不追加硬编码 schema）；
|
||||
DB 无记录时回落到硬编码 JSON schema
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import types
|
||||
from typing import Any
|
||||
|
||||
import pytest
|
||||
from worker_app.tasks.vision import _prompt, assembler
|
||||
|
||||
REQUIRED_KEYS = {
|
||||
"name",
|
||||
"brand",
|
||||
"category",
|
||||
"appearance",
|
||||
"packaging",
|
||||
"text_on_package",
|
||||
"key_features",
|
||||
"scene",
|
||||
"mood",
|
||||
"portrait_prompt",
|
||||
"summary",
|
||||
"_source",
|
||||
}
|
||||
|
||||
|
||||
# ---------- schema 识别 ----------
|
||||
|
||||
|
||||
def test_is_v4_schema_products_list() -> None:
|
||||
assert assembler._is_v4_schema({"type": "product", "products": []})
|
||||
|
||||
|
||||
def test_is_v4_schema_type_only() -> None:
|
||||
assert assembler._is_v4_schema({"type": "person"})
|
||||
|
||||
|
||||
def test_is_v4_schema_people_dict() -> None:
|
||||
assert assembler._is_v4_schema({"people": {"has_person": True}})
|
||||
|
||||
|
||||
def test_is_not_v4_schema_flat() -> None:
|
||||
assert not assembler._is_v4_schema({"has_person": True, "upper_wear": "T恤"})
|
||||
|
||||
|
||||
# ---------- v4 product ----------
|
||||
|
||||
V4_PRODUCT: dict[str, Any] = {
|
||||
"type": "product",
|
||||
"scene": "白色背景产品图",
|
||||
"mood": "清新专业",
|
||||
"style": "商业产品摄影",
|
||||
"colors": [{"hex": "#E60012", "name": "亮红色", "coverage": 0.6}],
|
||||
"visible_text": [{"text": "OMO奥妙除菌除螨", "position": "瓶身正面"}],
|
||||
"products": [
|
||||
{
|
||||
"product_name": "OMO奥妙除菌除螨洗衣液",
|
||||
"brand": "OMO奥妙",
|
||||
"category": "洗护",
|
||||
"package_type": "瓶装",
|
||||
"package_color": "亮红色瓶身",
|
||||
"cap_type": "透明翻盖式按压瓶口",
|
||||
"body_shape": "带侧面握持把手的竖款瓶身",
|
||||
"label_design": "瓶身印十字盾牌图案",
|
||||
"product_features": ["亮红色瓶装", "按压式瓶口", "十字盾牌标签"],
|
||||
"key_selling_points": ["天然除菌除螨"],
|
||||
"position": "main",
|
||||
}
|
||||
],
|
||||
"has_person": False,
|
||||
}
|
||||
|
||||
|
||||
def test_assemble_v4_product_fields() -> None:
|
||||
r = assembler.assemble_result(0, V4_PRODUCT, ["OMO奥妙"])
|
||||
assert REQUIRED_KEYS <= set(r.keys())
|
||||
assert r["name"] == "OMO奥妙除菌除螨洗衣液"
|
||||
assert r["brand"] == "OMO奥妙"
|
||||
assert r["category"] == "洗护"
|
||||
assert "瓶装" in r["packaging"]
|
||||
assert isinstance(r["key_features"], list) and r["key_features"]
|
||||
assert any("除菌" in str(t) for t in r["text_on_package"])
|
||||
assert len(r["portrait_prompt"]) >= 10
|
||||
assert r["_source"] == "v2_fast_json_v4"
|
||||
|
||||
|
||||
def test_assemble_v4_product_multi_selects_main() -> None:
|
||||
fj = {
|
||||
"type": "product",
|
||||
"products": [
|
||||
{"product_name": "次要商品", "brand": "B"},
|
||||
{"product_name": "主商品", "brand": "A", "position": "main"},
|
||||
],
|
||||
}
|
||||
r = assembler.assemble_result(1, fj, [])
|
||||
assert r["name"] == "主商品"
|
||||
|
||||
|
||||
# ---------- v4 person ----------
|
||||
|
||||
V4_PERSON: dict[str, Any] = {
|
||||
"type": "person",
|
||||
"scene": "户外街拍",
|
||||
"mood": "自信",
|
||||
"style": "街拍",
|
||||
"colors": [],
|
||||
"visible_text": [],
|
||||
"has_person": True,
|
||||
"gender": "女",
|
||||
"age_range": "青年",
|
||||
"upper_wear": "白色V领短袖T恤",
|
||||
"upper_color": "白色",
|
||||
"lower_wear": "黑色高腰阔腿裤",
|
||||
"lower_color": "黑色",
|
||||
"dress_color": None,
|
||||
"accessories": ["银色项链"],
|
||||
"hairstyle": "黑色长直发",
|
||||
"expression": "自信",
|
||||
"pose": "侧身站立",
|
||||
"outfit_style": "休闲日常",
|
||||
"portrait_prompt": (
|
||||
"一位年轻女性，身穿白色V领短袖T恤、黑色高腰阔腿裤，佩戴银色项链，"
|
||||
"黑色长直发，神情自信，侧身站立，休闲日常风格，城市街拍场景"
|
||||
),
|
||||
"products": [],
|
||||
}
|
||||
|
||||
|
||||
def test_assemble_v4_person() -> None:
|
||||
r = assembler.assemble_result(0, V4_PERSON, [])
|
||||
assert REQUIRED_KEYS <= set(r.keys())
|
||||
assert r["category"] == "人物穿搭"
|
||||
assert r["_source"] == "v2_fast_json_v5"
|
||||
assert "T恤" in r["name"]
|
||||
assert "年轻女性" in r["portrait_prompt"]
|
||||
assert "项链" in r["portrait_prompt"]
|
||||
assert isinstance(r["key_features"], list) and len(r["key_features"]) <= 8
|
||||
|
||||
|
||||
def test_assemble_v4_person_people_nested() -> None:
|
||||
fj = {"type": "person", "people": {**V4_PERSON, "has_person": True}}
|
||||
r = assembler.assemble_result(0, fj, [])
|
||||
assert r["category"] == "人物穿搭"
|
||||
assert "年轻女性" in r["portrait_prompt"]
|
||||
|
||||
|
||||
# ---------- v4 store ----------
|
||||
|
||||
|
||||
def test_assemble_v4_store() -> None:
|
||||
fj = {
|
||||
"type": "store",
|
||||
"scene": "便利店内部",
|
||||
"mood": "日常便民",
|
||||
"style": "门店实拍",
|
||||
"store_type": "社区便利店",
|
||||
"store_layout": "纵深货架布局",
|
||||
"brand_signage": "全家FamilyMart",
|
||||
"visual_elements": ["红白主色调", "促销海报"],
|
||||
"product_categories_visible": ["饮料", "零食"],
|
||||
"promotion_elements": ["第二件半价海报"],
|
||||
"atmosphere": "亲民生活化",
|
||||
"has_person": False,
|
||||
}
|
||||
r = assembler.assemble_result(0, fj, [])
|
||||
assert REQUIRED_KEYS <= set(r.keys())
|
||||
assert r["name"] == "社区便利店"
|
||||
assert r["brand"] == "全家FamilyMart"
|
||||
assert r["category"] == "门店场景"
|
||||
assert any("饮料" in str(f) for f in r["key_features"])
|
||||
assert "门店实拍" in r["portrait_prompt"]
|
||||
|
||||
|
||||
# ---------- v4 other ----------
|
||||
|
||||
|
||||
def test_assemble_v4_other() -> None:
|
||||
fj = {"type": "other", "description": "海边日落风景", "scene": "海边", "mood": "宁静"}
|
||||
r = assembler.assemble_result(0, fj, [])
|
||||
assert REQUIRED_KEYS <= set(r.keys())
|
||||
assert r["name"] == "海边日落风景"
|
||||
assert r["category"] == "非产品图"
|
||||
|
||||
|
||||
# ---------- 旧扁平 schema 兼容 ----------
|
||||
|
||||
|
||||
def test_assemble_old_flat_person() -> None:
|
||||
fj = {
|
||||
"has_person": True,
|
||||
"gender": "男",
|
||||
"age_range": "中年",
|
||||
"upper_wear": "西装",
|
||||
"upper_color": "深灰色",
|
||||
"lower_wear": "西裤",
|
||||
"lower_color": "黑色",
|
||||
"accessories": ["手表"],
|
||||
"hairstyle": "短发",
|
||||
"expression": "严肃",
|
||||
"scene": "办公室",
|
||||
"style": "商务",
|
||||
"mood": "专业",
|
||||
}
|
||||
r = assembler.assemble_result(0, fj, [])
|
||||
assert REQUIRED_KEYS <= set(r.keys())
|
||||
assert "中年男性" in r["portrait_prompt"]
|
||||
assert r["_source"] == "v2_fast_json"
|
||||
|
||||
|
||||
def test_assemble_old_flat_product() -> None:
|
||||
fj = {
|
||||
"has_person": False,
|
||||
"product_name": "口红",
|
||||
"brand": "Dior",
|
||||
"category": "美妆",
|
||||
"colors": ["红色"],
|
||||
"scene": "通用",
|
||||
"style": "商业",
|
||||
"mood": "高级",
|
||||
}
|
||||
r = assembler.assemble_result(0, fj, ["Dior"])
|
||||
assert r["name"] == "口红"
|
||||
assert r["brand"] == "Dior"
|
||||
assert r["text_on_package"] == ["Dior"]
|
||||
|
||||
|
||||
def test_assemble_none_input() -> None:
|
||||
r = assembler.assemble_result(0, None, [])
|
||||
assert REQUIRED_KEYS <= set(r.keys())
|
||||
|
||||
|
||||
# ---------- _prompt 解析 ----------
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def _clear_prompt_cache() -> Any:
|
||||
_prompt.invalidate_cache()
|
||||
yield
|
||||
_prompt.invalidate_cache()
|
||||
|
||||
|
||||
def _fake_tpl(system_prompt: str = "v4 system prompt 只返回JSON") -> Any:
|
||||
return types.SimpleNamespace(
|
||||
system_prompt=system_prompt,
|
||||
user_prompt_template="分析 {image_count} 张图",
|
||||
version=4,
|
||||
)
|
||||
|
||||
|
||||
def test_resolve_uses_db_prompt_without_append(monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
monkeypatch.setattr(_prompt, "_load_db_template", lambda: _fake_tpl("DB_V4_PROMPT_XYZ"))
|
||||
sys_prompt, user_prompt = _prompt.resolve_fast_prompt()
|
||||
assert sys_prompt == "DB_V4_PROMPT_XYZ"
|
||||
assert "DB_V4_PROMPT_XYZ" not in _prompt._FAST_JSON_APPEND # sanity: 旧append是另一段文本
|
||||
assert "分析 1 张图" in user_prompt
|
||||
|
||||
|
||||
def test_resolve_pro_uses_db_prompt_without_append(monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
monkeypatch.setattr(_prompt, "_load_db_template", lambda: _fake_tpl("DB_V4_PRO_PROMPT"))
|
||||
sys_prompt, _ = _prompt.resolve_pro_prompt()
|
||||
assert sys_prompt == "DB_V4_PRO_PROMPT"
|
||||
assert "【输出格式要求】" not in sys_prompt
|
||||
|
||||
|
||||
def test_resolve_falls_back_when_no_db(monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
monkeypatch.setattr(_prompt, "_load_db_template", lambda: None)
|
||||
sys_prompt, user_prompt = _prompt.resolve_fast_prompt()
|
||||
assert sys_prompt == _prompt._FAST_JSON_SCHEMA
|
||||
assert user_prompt == _prompt.DEFAULT_FAST_USER
|
||||
|
||||
|
||||
def test_resolve_caches(monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
calls = {"n": 0}
|
||||
|
||||
def _load() -> Any:
|
||||
calls["n"] += 1
|
||||
return _fake_tpl("CACHED_PROMPT")
|
||||
|
||||
monkeypatch.setattr(_prompt, "_load_db_template", _load)
|
||||
s1, _ = _prompt.resolve_fast_prompt()
|
||||
s2, _ = _prompt.resolve_fast_prompt()
|
||||
assert s1 == s2 == "CACHED_PROMPT"
|
||||
assert calls["n"] == 1
|
||||
Reference in New Issue
Block a user