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1 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 xiaoxia
|
93b3e896c8 |
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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# 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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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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),
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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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# 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")
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).first()
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qwen37_row = conn.execute(
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sa.text("SELECT id FROM ai_models WHERE model_key = 'qwen3.7-plus' AND deleted_at IS NULL LIMIT 1")
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).first()
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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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current_ia = conn.execute(
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sa.text(
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"SELECT primary_model_id, lite_model_id, fallback_model_id "
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"FROM ai_capability_configs WHERE capability_key = 'image_analysis'"
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)
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).first()
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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
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if current_fallback != qwen37_id:
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updates["fallback_model_id"] = qwen37_id
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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()"
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updates["cap_key"] = "image_analysis"
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conn.execute(
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sa.text(f"UPDATE ai_capability_configs SET {set_clause} WHERE capability_key = :cap_key"),
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updates,
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)
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def downgrade() -> None:
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pass
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@@ -1,153 +0,0 @@
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# -*- coding: utf-8 -*-
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"""101: 补齐 qwen-vl-plus 视觉模型并修正 image_analysis 绑定与 max_tokens.
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背景：
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- qwen-vl-plus 做图片识别时返回 JSON 约 500-600 tokens，旧硬编码
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max_tokens=350 导致 JSON 被截断、解析失败返回"未识别"。
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- 代码侧已移除硬编码，改由 capability 的 DB 配置决定 max_tokens。
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幂等：
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- qwen-vl-plus 已存在则不插入；
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- 仅当 image_analysis 当前 primary_model 不是 qwen-vl-plus 时才更新绑定，
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避免覆盖后台手动配置。
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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 = "101_qwen_vl_plus_and_max_tokens"
|
||||
down_revision = "100_fix_capability_model_bindings"
|
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branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
conn = op.get_bind()
|
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|
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models_table = conn.execute(sa.text("SELECT to_regclass('public.ai_models')")).scalar()
|
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if not models_table:
|
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return
|
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|
||||
caps_table = conn.execute(sa.text("SELECT to_regclass('public.ai_capability_configs')")).scalar()
|
||||
if not caps_table:
|
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return
|
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|
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# ── c. 补全其他 capability 的 max_tokens 默认值（幂等）──────────────────
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# 放在 image_analysis 特定逻辑之前，确保任何分支 return 都不会跳过本段。
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# 仅在当前值为 NULL 或过小 (<100) 时更新，不覆盖已有合理配置。
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# embedding / tts / voice_clone 不走 chat 接口，无需设置。
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default_max_tokens = {
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"intent_parsing": 500,
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"copy_fusion": 2500,
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"storyboard": 4000,
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"copy_review": 1000,
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"asset_classify": 500,
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"image_generation": 500,
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"video_generation": 500,
|
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}
|
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for cap_key, mt in default_max_tokens.items():
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conn.execute(
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sa.text(
|
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"UPDATE ai_capability_configs "
|
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"SET max_tokens = :mt, updated_at = now() "
|
||||
"WHERE capability_key = :key "
|
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"AND (max_tokens IS NULL OR max_tokens < 100)"
|
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),
|
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{"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'"
|
||||
)
|
||||
)
|
||||
@@ -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
|
||||
|
||||
@@ -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;
|
||||
|
||||
@@ -92,30 +92,22 @@ def _save_job(repo, job, session):
|
||||
session.commit()
|
||||
|
||||
|
||||
def _start_trust_chain_preheat(job_id: str, products: list[dict]) -> None:
|
||||
"""#2172/#2174/#2220 后台启动信任链预热（Seedream t2i 文生图人像），不阻塞调用方。
|
||||
def _start_trust_chain_preheat(job_id: str, portrait_descriptions: list[str]) -> None:
|
||||
"""#2172/#2174 后台启动信任链预热（Seedream t2i 文生图人像），不阻塞调用方。
|
||||
|
||||
#2220 修复：只对 has_person=True 的图（真人照片）生成 AI 人像替换，
|
||||
场景图/商品图/门店图保持原图不变，传给 Seedance 作为 reference_image 直接使用。
|
||||
#2174 重要：改为 t2i 文生图模式——用 VLM 分析出的人物外貌描述做 prompt，不传 reference_images，
|
||||
产物是方舟信任模型输出，Seedance 直接放行不触发肖像审核。
|
||||
i2i（传用户照片做 reference）产物不被信任，实测仍被 400 portrait_intercept 拦截。
|
||||
|
||||
预热结果写入 job.pre_trusted_images：与 products 等长的稀疏列表，
|
||||
人像位是 AI 图 URL，非人像位是 None（表示保留原图）。
|
||||
预热成功后把结果写入 job.pre_trusted_images，阶段3 渲染直接使用，省掉串行等待。
|
||||
预热失败静默（pre_trusted_images 保持 None），阶段3 会走 #2166 自动降级纯 t2v。
|
||||
"""
|
||||
# 构建人像位索引映射：person_indices[k] = products中第k个人像的位置
|
||||
_person_indices: list[int] = []
|
||||
_valid: list[str] = []
|
||||
for _i, _p in enumerate(products or []):
|
||||
if not isinstance(_p, dict):
|
||||
continue
|
||||
if not _p.get("has_person", False):
|
||||
continue
|
||||
_d = (_p.get("portrait_prompt") or "").strip()
|
||||
if not _d or "无人像" in _d or len(_d) < 10:
|
||||
continue
|
||||
_person_indices.append(_i)
|
||||
_valid.append(_d)
|
||||
# 过滤有效描述：非空且不是"无人像"
|
||||
_valid = [
|
||||
d for d in (portrait_descriptions or []) if d and isinstance(d, str) and "无人像" not in d and len(d) >= 10
|
||||
]
|
||||
if not _valid:
|
||||
logger.info("[trust-chain][preheat] 无有效人物描述（可能是纯商品/场景图），跳过预热 job=%s", job_id)
|
||||
logger.info("[trust-chain][preheat] 无有效人物描述（可能是纯商品图），跳过预热 job=%s", job_id)
|
||||
return
|
||||
# 判断是否是 doubao provider（DashScope/Wan 不需要信任链）
|
||||
try:
|
||||
@@ -141,33 +133,21 @@ def _start_trust_chain_preheat(job_id: str, products: list[dict]) -> None:
|
||||
|
||||
logger.info("[trust-chain][preheat] 后台t2i预热启动 job=%s n=%d", job_id, len(_valid))
|
||||
result = preheat_trust_chain(_valid, timeout=120)
|
||||
if result and len(result) == len(_valid):
|
||||
if result and len(result) >= 1:
|
||||
sess2, repo2, job2 = _get_repo_and_job(job_id)
|
||||
try:
|
||||
# #2220: 构建与 products 等长的稀疏列表，人像位放AI图URL，非人像位None
|
||||
_imgs2 = job2.images or []
|
||||
_sparse: list[str | None] = [None] * max(len(_imgs2), len(products or []))
|
||||
for _k, _url in enumerate(result):
|
||||
if _k < len(_person_indices):
|
||||
_sparse[_person_indices[_k]] = _url
|
||||
job2.pre_trusted_images = _sparse
|
||||
job2.pre_trusted_images = result
|
||||
repo2.update(job2)
|
||||
sess2.commit()
|
||||
logger.info(
|
||||
"[trust-chain][preheat] t2i预热完成并持久化 job=%s n_person=%d total=%d",
|
||||
"[trust-chain][preheat] t2i预热完成并持久化 job=%s n=%d",
|
||||
job_id,
|
||||
len(result),
|
||||
len(_sparse),
|
||||
)
|
||||
finally:
|
||||
sess2.close()
|
||||
else:
|
||||
logger.info(
|
||||
"[trust-chain][preheat] 预热失败或数量不匹配 job=%s got=%s expect=%d，阶段3现场跑兜底",
|
||||
job_id,
|
||||
len(result) if result else 0,
|
||||
len(_valid),
|
||||
)
|
||||
logger.info("[trust-chain][preheat] 预热失败 job=%s，阶段3现场跑兜底", job_id)
|
||||
except Exception as e:
|
||||
logger.warning("[trust-chain][preheat] 预热异常 job=%s err=%s", job_id, e, exc_info=True)
|
||||
|
||||
@@ -264,8 +244,8 @@ def _recover_stale_jobs() -> int:
|
||||
"SET status='failed', error_msg='任务执行超时，请重试', updated_at=:now "
|
||||
"WHERE status='running' "
|
||||
" AND started_at IS NOT NULL AND started_at < :cutoff_start "
|
||||
" AND updated_at < :cutoff_beat "
|
||||
" AND (heartbeat_at IS NULL OR heartbeat_at < :cutoff_beat)"
|
||||
" AND (heartbeat_at IS NULL OR heartbeat_at < :cutoff_beat) "
|
||||
" AND (heartbeat_at IS NOT NULL OR updated_at < :cutoff_beat)"
|
||||
)
|
||||
result = ssn.execute(sql, {"now": now, "cutoff_start": cutoff_start, "cutoff_beat": cutoff_beat})
|
||||
ssn.commit()
|
||||
@@ -288,7 +268,6 @@ def _recover_stale_jobs() -> int:
|
||||
|
||||
_DEFAULT_HARD_CONSTRAINTS = [
|
||||
"无字幕、无水印、无任何自动生成文字、无 logo",
|
||||
"严格还原参考图片中的真实场景、门店环境、商品陈列、人物外貌服装特征，不得凭空生成与参考图无关的人物、场景或物品",
|
||||
"同一人物全程保持一致的五官、发型、服装、身材，不得换脸或变形",
|
||||
"口播语音必须在指定时长内自然念完，语速自然，口型与语音同步",
|
||||
"画面流畅无闪烁、无多余肢体、无扭曲变形、无穿模",
|
||||
@@ -316,9 +295,9 @@ _DEFAULT_NEGATIVE_PROMPTS = [
|
||||
]
|
||||
|
||||
|
||||
def _empty_copy_result(duration: int = 15, ratio: str = "9:16", theme: str = "") -> dict:
|
||||
def _empty_copy_result(duration: int = 15, ratio: str = "9:16") -> dict:
|
||||
return {
|
||||
"overview": {"theme": theme or "好物推荐", "total_duration": duration, "aspect_ratio": ratio},
|
||||
"overview": {"theme": "好物推荐", "total_duration": duration, "aspect_ratio": ratio},
|
||||
"scene_and_lighting": "简洁明亮的室内场景，柔和自然光，产品主体清晰",
|
||||
"shots": [],
|
||||
"hard_constraints": list(_DEFAULT_HARD_CONSTRAINTS),
|
||||
@@ -344,7 +323,6 @@ def _vision_fallback(idx: int, reason: str, extra: dict | None = None) -> dict:
|
||||
"scene": "通用",
|
||||
"portrait_prompt": "无人像",
|
||||
"summary": "",
|
||||
"has_person": False,
|
||||
"_source": reason,
|
||||
}
|
||||
if extra:
|
||||
@@ -443,11 +421,11 @@ def _step_intent_parsing(job: ViralVideoJob, image_analysis: dict) -> dict:
|
||||
render_system_prompt,
|
||||
render_user_prompt,
|
||||
)
|
||||
from packages.shared.ai_router import ai_router
|
||||
from packages.shared.ai_client import get_doubao_client
|
||||
except ImportError:
|
||||
return {"intent": "推广产品", "key_messages": ["产品亮点"], "tone": "专业", "suggested_title": ""}
|
||||
|
||||
_llm_client = ai_router.get_llm_client("intent_parsing")
|
||||
_llm_client = get_doubao_client()
|
||||
if not _llm_client.is_available:
|
||||
return {"intent": "推广产品", "key_messages": ["产品亮点"], "tone": "专业", "suggested_title": ""}
|
||||
|
||||
@@ -471,15 +449,11 @@ def _step_intent_parsing(job: ViralVideoJob, image_analysis: dict) -> dict:
|
||||
|
||||
template = get_template("intent_parsing")
|
||||
system = render_system_prompt(template)
|
||||
marketing_purpose = getattr(job, "marketing_purpose", "") or "未指定"
|
||||
image_category_hint = _determine_theme(image_analysis, marketing_purpose)
|
||||
user = render_user_prompt(
|
||||
template,
|
||||
user_copy_text=job.user_copy_text or "（未提供，全由 AI 创作）",
|
||||
industry=job.industry or "未指定",
|
||||
image_analysis=products_summary or "- （无图片分析结果）",
|
||||
marketing_purpose=marketing_purpose,
|
||||
image_category_hint=image_category_hint,
|
||||
)
|
||||
|
||||
def _parse(raw: str) -> dict:
|
||||
@@ -503,31 +477,19 @@ def _step_intent_parsing(job: ViralVideoJob, image_analysis: dict) -> dict:
|
||||
"suggested_title": "",
|
||||
}
|
||||
|
||||
# #2220: 直接用 ai_router 获取 client，不再手动提取 model_key
|
||||
from packages.shared.ai_router import ai_router
|
||||
|
||||
_client_fast = ai_router.get_llm_client("intent_parsing", variant="primary")
|
||||
_client_pro = ai_router.get_llm_client("intent_parsing", variant="lite")
|
||||
_intent_deadline = time.time() + 60
|
||||
_seen_models: set[str] = set()
|
||||
for _client, _lbl in [(_client_fast, "fast"), (_client_pro, "pro-fallback")]:
|
||||
if not _client or not _client.is_available:
|
||||
continue
|
||||
if _client.model in _seen_models:
|
||||
logger.info("[爆款视频] 意图解析跳过重复模型 %s label=%s", _client.model, _lbl)
|
||||
continue
|
||||
_seen_models.add(_client.model)
|
||||
if time.time() > _intent_deadline:
|
||||
logger.warning("[爆款视频] 意图解析超过60s总预算，跳过 label=%s", _lbl)
|
||||
break
|
||||
_s = get_shared_settings()
|
||||
_fast = _s.doubao_fast_model
|
||||
_pro = _s.doubao_model
|
||||
for _m, _lbl in [(_fast, "fast"), (_pro, "pro-fallback")]:
|
||||
try:
|
||||
logger.info("[爆款视频] 意图解析 model=%s label=%s", _client.model, _lbl)
|
||||
raw = _client.chat_completion(
|
||||
logger.info("[爆款视频] 意图解析 model=%s label=%s", _m, _lbl)
|
||||
raw = _llm_client.chat_completion(
|
||||
[{"role": "system", "content": system}, {"role": "user", "content": user}],
|
||||
temperature=0.4,
|
||||
max_tokens=1024,
|
||||
timeout=20,
|
||||
)
|
||||
model=_m,
|
||||
timeout=60,
|
||||
) # #2180/#2215: 直接用 client.chat_completion 传 messages list，不再走 call_llm 字符串包装
|
||||
if not raw:
|
||||
continue
|
||||
parsed = _parse(raw)
|
||||
@@ -563,70 +525,6 @@ def _persona_style_hint(persona_id: str) -> str:
|
||||
return "【人设风格：未指定】亲切自然、像朋友分享好物"
|
||||
|
||||
|
||||
def _determine_theme(image_analysis: dict | None, marketing_purpose: str = "") -> str:
|
||||
"""根据图片分析结果和营销目的，智能推断默认主题。
|
||||
|
||||
门店类→门店探店/到店体验；商品图→好物分享/产品种草；
|
||||
人物图→穿搭/人物故事；场景图→场景氛围/空间体验。
|
||||
"""
|
||||
products = (image_analysis or {}).get("products", []) or []
|
||||
type_counts: dict[str, int] = {}
|
||||
for p in products:
|
||||
if not isinstance(p, dict):
|
||||
continue
|
||||
cat = (p.get("category") or "").strip()
|
||||
if any(
|
||||
k in cat
|
||||
for k in (
|
||||
"门店",
|
||||
"店铺",
|
||||
"餐饮",
|
||||
"美容",
|
||||
"美发",
|
||||
"养生",
|
||||
"健身",
|
||||
"酒店",
|
||||
"咖啡",
|
||||
"奶茶",
|
||||
"餐厅",
|
||||
"颈肩",
|
||||
"调理",
|
||||
)
|
||||
):
|
||||
type_counts["store"] = type_counts.get("store", 0) + 1
|
||||
elif any(k in cat for k in ("人物", "穿搭", "人像", "服装")):
|
||||
type_counts["person"] = type_counts.get("person", 0) + 1
|
||||
elif any(k in cat for k in ("场景", "空间", "环境", "非产品")):
|
||||
type_counts["scene"] = type_counts.get("scene", 0) + 1
|
||||
elif cat and cat not in ("无法判断", "非产品图", ""):
|
||||
type_counts["product"] = type_counts.get("product", 0) + 1
|
||||
src = p.get("_source") or ""
|
||||
if "store" in src:
|
||||
type_counts["store"] = type_counts.get("store", 0) + 1
|
||||
elif "person" in src:
|
||||
type_counts["person"] = type_counts.get("person", 0) + 1
|
||||
|
||||
dominant = max(type_counts, key=type_counts.get) if type_counts else "product"
|
||||
mp = (marketing_purpose or "").strip()
|
||||
|
||||
if any(k in mp for k in ("获客", "引流", "到店")):
|
||||
if dominant == "store":
|
||||
return "门店探店·到店体验"
|
||||
return "门店探店·到店体验"
|
||||
if any(k in mp for k in ("品牌", "宣传")):
|
||||
return "品牌故事·门店体验" if dominant == "store" else "品牌故事·产品展示"
|
||||
if any(k in mp for k in ("种草", "推荐")):
|
||||
return "穿搭分享·人物种草" if dominant == "person" else "好物分享·产品种草"
|
||||
|
||||
theme_map = {
|
||||
"store": "门店探店·到店体验",
|
||||
"person": "穿搭分享·人物故事",
|
||||
"scene": "空间体验·场景氛围",
|
||||
"product": "好物分享·产品种草",
|
||||
}
|
||||
return theme_map.get(dominant, "好物分享·产品种草")
|
||||
|
||||
|
||||
def _build_products_summary(image_analysis: dict) -> str:
|
||||
"""把 VLM 返回的商品分析结果拼给文案/分镜生成 prompt 用。
|
||||
优先用 summary（自然段落）；没有时用结构化字段兜底拼一段。"""
|
||||
@@ -743,36 +641,14 @@ def _fallback_script(job: ViralVideoJob) -> dict:
|
||||
"""脚本生成失败时的兜底脚本（极简但可用）。"""
|
||||
dur = max(5, min(30, int(getattr(job, "duration", 15) or 15)))
|
||||
ratio = getattr(job, "video_ratio", None) or "9:16"
|
||||
_ia = getattr(job, "image_analysis", None) or {}
|
||||
_mp = getattr(job, "marketing_purpose", "") or ""
|
||||
default_theme = _determine_theme(_ia, _mp)
|
||||
base = _empty_copy_result(dur, ratio, theme=default_theme)
|
||||
_voiceover_map = {
|
||||
"store": "带你探店！今天来到这家店，环境真的超棒，服务也很到位，推荐大家来体验一下。",
|
||||
"person": "哈喽，今天给大家分享我的日常穿搭，简单舒适又好看，你们觉得怎么样？",
|
||||
"scene": "带大家感受一下这个空间，氛围感拉满，真的很适合打卡体验。",
|
||||
"product": "你好，给大家分享一款我最近在用的好物，真的很不错，推荐你们也试试。",
|
||||
}
|
||||
_products = (_ia or {}).get("products", []) or []
|
||||
_dominant = "product"
|
||||
for p in _products:
|
||||
if not isinstance(p, dict):
|
||||
continue
|
||||
src = p.get("_source") or ""
|
||||
cat = p.get("category") or ""
|
||||
if "store" in src or any(k in cat for k in ("门店", "店铺", "餐饮", "美容", "颈肩", "调理")):
|
||||
_dominant = "store"
|
||||
break
|
||||
elif "person" in src or any(k in cat for k in ("人物", "穿搭", "人像")):
|
||||
_dominant = "person"
|
||||
break
|
||||
voiceover = job.user_copy_text or _voiceover_map.get(_dominant, _voiceover_map["product"])
|
||||
base = _empty_copy_result(dur, ratio)
|
||||
voiceover = job.user_copy_text or "你好，给大家分享一款我最近在用的好物，真的很不错，推荐你们也试试。"
|
||||
shots = [
|
||||
{
|
||||
"time_range": f"0-{dur}秒",
|
||||
"shot_type_angle_movement": "中景平视，缓慢推镜",
|
||||
"scene_and_dialogue": voiceover,
|
||||
"action_details": "自然展示，表情亲切，动作流畅",
|
||||
"scene_and_dialogue": "明亮室内，人物自然出镜，微笑着看向镜头。" + voiceover,
|
||||
"action_details": "人物手持产品自然展示，表情亲切，动作流畅",
|
||||
"audio_bgm": "轻快流行BGM",
|
||||
"transition": "结束",
|
||||
"reference_image_index": 0 if job.images else None,
|
||||
@@ -782,7 +658,7 @@ def _fallback_script(job: ViralVideoJob) -> dict:
|
||||
base["voiceover_script"] = voiceover
|
||||
base["final_copy"] = voiceover
|
||||
base["suggested_copy"] = voiceover
|
||||
base["title"] = default_theme
|
||||
base["title"] = "好物分享"
|
||||
return base
|
||||
|
||||
|
||||
@@ -800,18 +676,12 @@ def _validate_and_normalize_script(raw, job: ViralVideoJob) -> dict:
|
||||
ov = raw.get("overview")
|
||||
if isinstance(ov, dict):
|
||||
base["overview"] = {
|
||||
"theme": str(
|
||||
ov.get("theme")
|
||||
or _determine_theme(getattr(job, "image_analysis", None), getattr(job, "marketing_purpose", ""))
|
||||
),
|
||||
"theme": str(ov.get("theme") or "好物分享"),
|
||||
"total_duration": int(ov.get("total_duration") or dur),
|
||||
"aspect_ratio": str(ov.get("aspect_ratio") or ratio),
|
||||
}
|
||||
else:
|
||||
base["overview"]["theme"] = str(
|
||||
raw.get("title")
|
||||
or _determine_theme(getattr(job, "image_analysis", None), getattr(job, "marketing_purpose", ""))
|
||||
)
|
||||
base["overview"]["theme"] = str(raw.get("title") or "好物分享")
|
||||
|
||||
base["scene_and_lighting"] = str(raw.get("scene_and_lighting") or base["scene_and_lighting"])
|
||||
|
||||
@@ -898,11 +768,7 @@ def _script_from_xml(raw: str, job: ViralVideoJob) -> dict | None:
|
||||
base = _empty_copy_result(dur, ratio)
|
||||
if not raw:
|
||||
return None
|
||||
base["overview"]["theme"] = (
|
||||
xp.text_of(raw, "overview_theme")
|
||||
or xp.text_of(raw, "title")
|
||||
or _determine_theme(getattr(job, "image_analysis", None), getattr(job, "marketing_purpose", ""))
|
||||
)
|
||||
base["overview"]["theme"] = xp.text_of(raw, "overview_theme") or xp.text_of(raw, "title") or "好物分享"
|
||||
est = xp.attr_int(xp.text_of(raw, "estimated_duration"), 0)
|
||||
if est:
|
||||
base["overview"]["total_duration"] = est
|
||||
@@ -963,11 +829,11 @@ def _step_script_generation(job: ViralVideoJob, intent: dict, image_analysis: di
|
||||
GLOBAL_CONSTRAINTS,
|
||||
NEGATIVE_RULES,
|
||||
)
|
||||
from packages.shared.ai_router import ai_router
|
||||
from packages.shared.ai_client import get_doubao_client
|
||||
except ImportError:
|
||||
return _fallback_script(job)
|
||||
|
||||
_llm_client2 = ai_router.get_llm_client("storyboard")
|
||||
_llm_client2 = get_doubao_client()
|
||||
if not _llm_client2.is_available:
|
||||
return _fallback_script(job)
|
||||
|
||||
@@ -992,11 +858,8 @@ def _step_script_generation(job: ViralVideoJob, intent: dict, image_analysis: di
|
||||
system_tpl = system_tpl.replace("{global_constraints}", GLOBAL_CONSTRAINTS)
|
||||
system_tpl = system_tpl.replace("{negative_rules}", NEGATIVE_RULES)
|
||||
|
||||
marketing_purpose = getattr(job, "marketing_purpose", "") or "未指定"
|
||||
image_category_hint = _determine_theme(image_analysis, marketing_purpose)
|
||||
fusion_brief = (
|
||||
f"意图：{intent_str}\n关键信息：{key_msgs}\n调性：{tone}\n"
|
||||
f"营销目的：{marketing_purpose}\n建议主题方向：{image_category_hint}\n"
|
||||
f"用户原文：{job.user_copy_text or '（未提供）'}\n创作模式：{fusion_level}"
|
||||
)
|
||||
user = render_user_prompt(
|
||||
@@ -1007,14 +870,13 @@ def _step_script_generation(job: ViralVideoJob, intent: dict, image_analysis: di
|
||||
image_analysis=products_summary,
|
||||
)
|
||||
|
||||
def _try_gen(client, temp: float, max_tok: int, label: str, tmo: int = 25):
|
||||
if not client or not client.is_available:
|
||||
return None
|
||||
logger.info("[爆款视频] 编导脚本生成 model=%s label=%s timeout=%d", client.model, label, tmo)
|
||||
raw = client.chat_completion(
|
||||
def _try_gen(model: str, temp: float, max_tok: int, label: str, tmo: int = 25):
|
||||
logger.info("[爆款视频] 编导脚本生成 model=%s label=%s timeout=%d", model, label, tmo)
|
||||
raw = _llm_client2.chat_completion(
|
||||
[{"role": "system", "content": system_tpl}, {"role": "user", "content": user}],
|
||||
temperature=temp,
|
||||
max_tokens=max_tok,
|
||||
model=model,
|
||||
timeout=tmo,
|
||||
)
|
||||
if not raw:
|
||||
@@ -1045,35 +907,25 @@ def _step_script_generation(job: ViralVideoJob, intent: dict, image_analysis: di
|
||||
)
|
||||
return None if is_fallback else normalized
|
||||
|
||||
# #2220: 直接用 ai_router 获取 client，不再手动提取 model_key
|
||||
_client_fast = ai_router.get_llm_client("storyboard", variant="primary")
|
||||
_client_pro = ai_router.get_llm_client("storyboard", variant="lite")
|
||||
_script_fast_tmo = int(os.environ.get("VIRAL_VIDEO_SCRIPT_FAST_TIMEOUT", "90"))
|
||||
_script_pro_tmo = int(os.environ.get("VIRAL_VIDEO_SCRIPT_PRO_TIMEOUT", "60"))
|
||||
_script_deadline = time.time() + 180
|
||||
_s = get_shared_settings()
|
||||
_fast = _s.doubao_fast_model
|
||||
_pro = getattr(_s, "doubao_model", None) or _fast
|
||||
_script_fast_tmo = int(os.environ.get("VIRAL_VIDEO_SCRIPT_FAST_TIMEOUT", "150"))
|
||||
_script_pro_tmo = int(os.environ.get("VIRAL_VIDEO_SCRIPT_PRO_TIMEOUT", "150"))
|
||||
try:
|
||||
# #2233: fast_timeout=90s, pro_timeout=60s，总deadline 180s
|
||||
normalized = _try_gen(_client_fast, 0.8, 2500, "fast-first", tmo=_script_fast_tmo)
|
||||
# #2217: doubao-seed-2-1-pro生成长编导脚本高峰期>90s，上调到150s，支持ENV覆盖
|
||||
normalized = _try_gen(_fast, 0.8, 2500, "fast-first", tmo=_script_fast_tmo)
|
||||
if normalized is not None:
|
||||
return normalized
|
||||
if time.time() > _script_deadline:
|
||||
logger.warning("[爆款视频] 编导脚本超过180s总预算，使用兜底脚本")
|
||||
return _fallback_script(job)
|
||||
normalized = _try_gen(_client_fast, 0.6, 3200, "fast-retry", tmo=_script_fast_tmo)
|
||||
normalized = _try_gen(_fast, 0.6, 3200, "fast-retry", tmo=_script_fast_tmo)
|
||||
if normalized is not None:
|
||||
return normalized
|
||||
# 第三次：用 lite/pro 模型兜底，跳过与 primary 相同的模型
|
||||
if _client_pro and _client_pro.is_available:
|
||||
if _client_pro.model != _client_fast.model:
|
||||
if time.time() <= _script_deadline:
|
||||
normalized = _try_gen(_client_pro, 0.7, 3500, "pro-fallback", tmo=_script_pro_tmo)
|
||||
if normalized is not None:
|
||||
return normalized
|
||||
else:
|
||||
logger.warning("[爆款视频] 编导脚本超过180s总预算，跳过pro-fallback")
|
||||
else:
|
||||
logger.info("[爆款视频] pro-fallback模型与primary相同(%s)，跳过重复调用", _client_pro.model)
|
||||
logger.warning("[爆款视频] 编导脚本均未生成合格结果，使用兜底脚本")
|
||||
# 第三次：用主力模型兜底
|
||||
if _pro and _pro != _fast:
|
||||
normalized = _try_gen(_pro, 0.7, 3500, "pro-fallback", tmo=_script_pro_tmo)
|
||||
if normalized is not None:
|
||||
return normalized
|
||||
logger.warning("[爆款视频] 编导脚本三次都未生成合格结果，使用兜底脚本")
|
||||
return _fallback_script(job)
|
||||
except Exception as e:
|
||||
logger.warning("[爆款视频] 编导脚本生成异常: %s，使用兜底脚本", e, exc_info=True)
|
||||
@@ -1393,12 +1245,9 @@ def _step_render(job: ViralVideoJob, copy_result: dict, tts_audio_url: str | Non
|
||||
if u not in all_portrait_urls:
|
||||
all_portrait_urls.append(u)
|
||||
pti = getattr(job, "pre_trusted_images", None)
|
||||
# #2220: 稀疏列表模式（与images等长，None表示该位置保留原图）
|
||||
_n_total = len(all_portrait_urls)
|
||||
if pti and isinstance(pti, list) and len(pti) >= _n_total and _n_total > 0:
|
||||
pre_trusted = list(pti[:_n_total])
|
||||
_n_trusted = sum(1 for _x in pre_trusted if _x)
|
||||
logger.info("[爆款视频] 使用信任链预热结果 person=%d total=%d，跳过现场Seedream AI化", _n_trusted, _n_total)
|
||||
if pti and len(pti) == len(all_portrait_urls):
|
||||
pre_trusted = list(pti)
|
||||
logger.info("[爆款视频] 使用信任链预热结果 n=%d，跳过现场 Seedream AI 化", len(pre_trusted))
|
||||
elif all_portrait_urls and _mcfg.get("provider", "doubao") == "doubao":
|
||||
# #2183: 真·现场跑信任链——同步调用 Seedream t2i，拿到 AI 人像 URL 后再传 Seedance
|
||||
logger.info(
|
||||
@@ -1411,39 +1260,32 @@ def _step_render(job: ViralVideoJob, copy_result: dict, tts_audio_url: str | Non
|
||||
|
||||
_ia = getattr(job, "image_analysis", None) or {}
|
||||
_prods = (_ia.get("products") if isinstance(_ia, dict) else None) or []
|
||||
_live_person_idx: list[int] = []
|
||||
_live_pdescs: list[str] = []
|
||||
for _i, _pp in enumerate(_prods):
|
||||
if not isinstance(_pp, dict) or not _pp.get("has_person", False):
|
||||
continue
|
||||
_d = (_pp.get("portrait_prompt") or "").strip()
|
||||
if not _d or "无人像" in _d or len(_d) < 10:
|
||||
continue
|
||||
_live_person_idx.append(_i)
|
||||
_live_pdescs.append(_d)
|
||||
if _live_pdescs:
|
||||
_pdescs = []
|
||||
if _prods:
|
||||
_pdescs = [(pp.get("portrait_prompt") or "无人像") for pp in _prods]
|
||||
elif isinstance(_ia, dict):
|
||||
_pp0 = _ia.get("portrait_prompt") or "无人像"
|
||||
if _pp0 and _pp0 != "无人像":
|
||||
_pdescs = [_pp0]
|
||||
_valid = [d for d in _pdescs if d and isinstance(d, str) and "无人像" not in d and len(d) >= 10]
|
||||
if _valid:
|
||||
_t0 = time.time()
|
||||
_live_urls = preheat_trust_chain(_live_pdescs, timeout=120)
|
||||
if _live_urls and len(_live_urls) == len(_live_pdescs):
|
||||
# #2220: 构建稀疏列表，人像位替换AI图，非人像位保留None(ai_client里用原图)
|
||||
pre_trusted = [None] * len(all_portrait_urls)
|
||||
for _k, _u in enumerate(_live_urls):
|
||||
if _k < len(_live_person_idx):
|
||||
pre_trusted[_live_person_idx[_k]] = _u
|
||||
_live_urls = preheat_trust_chain(_valid, timeout=120)
|
||||
if _live_urls and len(_live_urls) == len(all_portrait_urls):
|
||||
pre_trusted = list(_live_urls)
|
||||
logger.info(
|
||||
"[爆款视频] 现场信任链t2i完成 %d张人像AI化 耗时%.1fs（共%d张图，其余保留原图）",
|
||||
len(_live_urls),
|
||||
"[爆款视频] 现场信任链t2i完成 %d张 耗时%.1fs，将用AI人像传Seedance",
|
||||
len(pre_trusted),
|
||||
time.time() - _t0,
|
||||
len(all_portrait_urls),
|
||||
)
|
||||
else:
|
||||
logger.warning(
|
||||
"[爆款视频] 现场信任链t2i返回不匹配 urls=%s n_person=%d，人像位原图传Seedance（可能触发400拦截）",
|
||||
"[爆款视频] 现场信任链t2i返回不匹配 urls=%s n_portraits=%d，回退原图+400降级纯t2v",
|
||||
_live_urls,
|
||||
len(_live_pdescs),
|
||||
len(all_portrait_urls),
|
||||
)
|
||||
else:
|
||||
logger.info("[爆款视频] 无有效人物描述（商品/场景图），无需AI化，直接传原图给Seedance")
|
||||
logger.info("[爆款视频] 无有效人物描述（可能是商品图），无需现场跑信任链")
|
||||
except Exception as _te:
|
||||
logger.warning("[爆款视频] 现场跑信任链异常: %s，回退原图+400降级纯t2v", _te, exc_info=True)
|
||||
|
||||
@@ -1566,8 +1408,13 @@ def run_viral_video_pipeline(self: Task, job_id: str) -> dict:
|
||||
if job.images:
|
||||
try:
|
||||
_products = (image_analysis or {}).get("products", []) or []
|
||||
# #2220: 直接传 products 列表，由 _start_trust_chain_preheat 内部按 has_person 筛选
|
||||
_start_trust_chain_preheat(job.id, _products)
|
||||
_portrait_descs = [(p.get("portrait_prompt") or "无人像") for p in _products] if _products else []
|
||||
# 兼容单图结果格式（非products列表）
|
||||
if not _portrait_descs and isinstance(image_analysis, dict):
|
||||
_pp = image_analysis.get("portrait_prompt") or "无人像"
|
||||
if _pp and _pp != "无人像":
|
||||
_portrait_descs = [_pp]
|
||||
_start_trust_chain_preheat(job.id, _portrait_descs)
|
||||
except Exception as _e:
|
||||
logger.warning("[爆款视频][阶段1] 启动信任链t2i预热失败: %s", _e)
|
||||
_save_job(repo, job, session)
|
||||
@@ -1739,8 +1586,12 @@ def run_viral_video_analyze(self: Task, job_id: str) -> dict:
|
||||
if job.images:
|
||||
try:
|
||||
_products = (image_analysis or {}).get("products", []) or []
|
||||
# #2220: 直接传 products 列表，由 _start_trust_chain_preheat 内部按 has_person 筛选
|
||||
_start_trust_chain_preheat(job.id, _products)
|
||||
_portrait_descs = [(p.get("portrait_prompt") or "无人像") for p in _products] if _products else []
|
||||
if not _portrait_descs and isinstance(image_analysis, dict):
|
||||
_pp = image_analysis.get("portrait_prompt") or "无人像"
|
||||
if _pp and _pp != "无人像":
|
||||
_portrait_descs = [_pp]
|
||||
_start_trust_chain_preheat(job.id, _portrait_descs)
|
||||
except Exception as _e:
|
||||
logger.warning("[爆款视频][阶段1] 启动信任链t2i预热失败: %s", _e)
|
||||
_save_job(repo, job, session)
|
||||
@@ -1828,7 +1679,7 @@ def run_viral_video_generate_copy(self: Task, job_id: str) -> dict:
|
||||
image_analysis = job.image_analysis or {"products": []}
|
||||
intent_result = _step_intent_parsing(job, image_analysis)
|
||||
job.intent_result = intent_result
|
||||
# #2218: 不在意图解析后单独落库，等 copy_result 生成后与 mark_copy_generated 一起原子写入
|
||||
_save_job(repo, job, session)
|
||||
_emit_progress(job_id, ViralVideoStage.INTENT_PARSING, 35.0, "意图解析完成")
|
||||
|
||||
# 阶段：编导脚本生成（核心耗时环节，已用快模型）
|
||||
@@ -1886,8 +1737,7 @@ def run_viral_video_generate_copy(self: Task, job_id: str) -> dict:
|
||||
except Retry:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error("[爆款视频][阶段2] 异常 job_id=%s: %s", job_id, e, exc_info=True)
|
||||
# #2218: 阶段2任何异常都标记为 failed（由 _mark_failed_and_notify 处理），前端提示重试
|
||||
logger.error("[爆款视频][阶段2] 异常: %s", e, exc_info=True)
|
||||
_mark_failed_and_notify(job_id, session, None, None, str(e), ViralVideoStage.SCRIPT_GENERATION)
|
||||
return {"ok": False, "job_id": job_id, "error": str(e)}
|
||||
finally:
|
||||
@@ -2046,26 +1896,16 @@ def _run_render_pipeline(job_id: str, session, repo, job) -> dict:
|
||||
阶段2 generate-copy 已把 LLM 深度审核后置，这里在 TTS 前做最终审核（不通过则自动重写1次）。
|
||||
所有阶段通过 _set_stage 持久化 current_stage/phase_message。
|
||||
"""
|
||||
image_analysis = job.image_analysis or {"products": []}
|
||||
|
||||
# #2218: render 流程严禁补生成意图+编导脚本。copy_result 必须由 generate-copy 提前准备好；
|
||||
# 若缺失说明 generate-copy 未完成或数据丢失，直接报错让用户重新点「生成文案」。
|
||||
# 如果没有 copy_result（旧数据/失败重试），现场补生成（意图+脚本，不走 LLM 审核，出片前会统一做）
|
||||
copy_result = job.copy_result
|
||||
_copy_src = "db"
|
||||
if not isinstance(copy_result, dict) or not copy_result:
|
||||
logger.error(
|
||||
"[爆款视频][阶段3] copy_result 为空或无效，无法进入渲染流程。job_id=%s status=%s intent_len=%d，请重新触发「生成文案」",
|
||||
job_id,
|
||||
job.status,
|
||||
len((job.intent_result or {}) if isinstance(job.intent_result, dict) else {}),
|
||||
)
|
||||
raise ValueError("文案数据缺失，请先点击「生成文案」完成文案生成后再生成视频")
|
||||
logger.info(
|
||||
"[爆款视频][阶段3] 进入渲染流程 job_id=%s copy_result_shots=%d copy_result_len=%d source=%s",
|
||||
job_id,
|
||||
len((copy_result.get("shots") or [])),
|
||||
len(str(copy_result)),
|
||||
_copy_src,
|
||||
)
|
||||
_set_stage(job, repo, session, ViralVideoStage.SCRIPT_GENERATION, "正在补生成编导脚本...")
|
||||
intent = job.intent_result or _step_intent_parsing(job, image_analysis)
|
||||
copy_result = _step_script_generation(job, intent, image_analysis)
|
||||
job.mark_copy_generated(copy_result)
|
||||
_save_job(repo, job, session)
|
||||
|
||||
# 出片前 LLM 深度合规审核（#2134 问题7：审核从阶段2后置到这里，不阻塞前端预览脚本）
|
||||
_set_stage(job, repo, session, ViralVideoStage.REVIEW, "正在进行出片前合规审核...")
|
||||
@@ -2078,18 +1918,12 @@ def _run_render_pipeline(job_id: str, session, repo, job) -> dict:
|
||||
if isinstance(rewritten, dict) and rewritten:
|
||||
copy_result = rewritten
|
||||
else:
|
||||
# #2218: 审核重写失败不再从意图解析重跑，直接报错让用户重新生成文案
|
||||
logger.error(
|
||||
"[爆款视频][阶段3] 合规审核未通过且自动重写失败 job_id=%s，终止渲染",
|
||||
job_id,
|
||||
)
|
||||
raise ValueError("文案合规审核未通过，请修改文案后重试或重新生成文案")
|
||||
intent = job.intent_result or _step_intent_parsing(job, image_analysis)
|
||||
copy_result = _step_script_generation(job, intent, image_analysis)
|
||||
_step_review(job, copy_result)
|
||||
job.copy_result = copy_result
|
||||
job.generated_copy_text = copy_result.get("voiceover_script", "") or ""
|
||||
_save_job(repo, job, session)
|
||||
except ValueError:
|
||||
# #2218: 审核未通过/文案缺失的业务异常，不继续出片，向上抛出
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.warning("[爆款视频][阶段3] 合规审核异常，继续出片: %s", e)
|
||||
_emit_progress(job_id, ViralVideoStage.REVIEW, 70.0, "合规审核完成")
|
||||
|
||||
@@ -170,7 +170,7 @@ def _is_v4_schema(fj: dict) -> bool:
|
||||
"""判断是v4嵌套schema还是旧扁平schema"""
|
||||
return (
|
||||
isinstance(fj.get("products"), list)
|
||||
or fj.get("type") in ("product", "store", "person", "scene", "other")
|
||||
or fj.get("type") in ("product", "store", "person", "other")
|
||||
or isinstance(fj.get("people"), dict)
|
||||
)
|
||||
|
||||
@@ -381,55 +381,9 @@ def assemble_result(idx: int, fast_json: dict | None, ocr_texts: list[str]) -> d
|
||||
ocr_texts = ocr_texts or []
|
||||
|
||||
if _is_v4_schema(fj):
|
||||
result = _assemble_v4(idx, fj, ocr_texts)
|
||||
return _assemble_v4(idx, fj, ocr_texts)
|
||||
else:
|
||||
result = _assemble_old(idx, fj, ocr_texts)
|
||||
return _apply_partial_fallback(result, fj)
|
||||
|
||||
|
||||
def _apply_partial_fallback(result: dict[str, Any], fj: dict) -> dict[str, Any]:
|
||||
"""partial（截断修复）产物的字段兜底：用已有碎片填充空字段，
|
||||
避免"无法判断"直接透传给下游。非partial产物原样返回。"""
|
||||
if not fj.get("_partial"):
|
||||
return result
|
||||
desc = str(fj.get("description") or "").strip()
|
||||
# 收集所有顶层标量碎片作为兜底素材
|
||||
fragments: list[str] = []
|
||||
for k in ("main_subject", "store_type", "scene_type", "description"):
|
||||
v = fj.get(k)
|
||||
if isinstance(v, str) and v.strip() and v != "无法判断":
|
||||
fragments.append(v.strip())
|
||||
for arr_k in ("environment_objects", "key_elements", "visual_elements"):
|
||||
arr = fj.get(arr_k) or []
|
||||
if isinstance(arr, list):
|
||||
for item in arr[:3]:
|
||||
if isinstance(item, str) and item.strip():
|
||||
fragments.append(item.strip())
|
||||
elif isinstance(item, dict):
|
||||
tv = item.get("text") or item.get("name")
|
||||
if tv:
|
||||
fragments.append(str(tv))
|
||||
frag_text = "；".join(fragments[:3])
|
||||
|
||||
if result.get("name") in ("未识别", "", None) and (desc or frag_text):
|
||||
result["name"] = (desc or fragments[0])[:30]
|
||||
if str(result.get("appearance", "")).startswith("无法判断"):
|
||||
if desc:
|
||||
result["appearance"] = desc[:200]
|
||||
elif frag_text:
|
||||
result["appearance"] = frag_text[:200]
|
||||
if result.get("key_features") in (["无法判断"], []) and (desc or fragments):
|
||||
kf = []
|
||||
if desc:
|
||||
kf.append(desc[:30])
|
||||
for f in fragments[:3]:
|
||||
if f not in kf:
|
||||
kf.append(f[:40])
|
||||
result["key_features"] = kf[:8]
|
||||
if result.get("summary") in ("未识别", "", None) and (desc or fragments):
|
||||
result["summary"] = (desc or fragments[0])[:40]
|
||||
result["_partial"] = True
|
||||
return result
|
||||
return _assemble_old(idx, fj, ocr_texts)
|
||||
|
||||
|
||||
def _assemble_v4(idx: int, fj: dict, ocr_texts: list[str]) -> dict[str, Any]:
|
||||
@@ -464,42 +418,9 @@ def _assemble_v4(idx: int, fj: dict, ocr_texts: list[str]) -> dict[str, Any]:
|
||||
|
||||
has_person = fj.get("has_person", False)
|
||||
|
||||
# 兼容老schema：无type字段或type不在已知枚举时，按has_person/products兜底
|
||||
_KNOWN_V4_TYPES = ("product", "store", "person", "scene", "other")
|
||||
if vtype not in _KNOWN_V4_TYPES:
|
||||
if has_person:
|
||||
vtype = "person"
|
||||
elif products:
|
||||
vtype = "product"
|
||||
else:
|
||||
vtype = "other"
|
||||
|
||||
# 人物信息提取辅助（scene/store分支有人物时追加到key_features）
|
||||
def _extract_person_features(source: dict) -> list[str]:
|
||||
parts = []
|
||||
for pk in ("outfit_style", "upper_wear", "lower_wear", "dress_wear", "outerwear", "pose", "expression"):
|
||||
pv = source.get(pk)
|
||||
if pv and str(pv) not in ("null", None, "无法判断") and len(str(pv)) <= 40:
|
||||
parts.append(f"人物:{pv}")
|
||||
for pk2 in ("shoes", "bag", "hairstyle"):
|
||||
pv2 = source.get(pk2)
|
||||
if pv2 and str(pv2) not in ("null", None) and len(str(pv2)) <= 40:
|
||||
parts.append(f"人物:{pv2}")
|
||||
return parts
|
||||
|
||||
# partial截断保护：声明了product但products数组没来得及输出时，
|
||||
# 按已返回的碎片字段改路由，避免直接掉到other丢信息
|
||||
if fj.get("_partial") and vtype == "product" and not products:
|
||||
if any(fj.get(k) for k in ("signage_details", "store_layout", "brand_signage", "store_type")):
|
||||
vtype = "store"
|
||||
elif any(fj.get(k) for k in ("key_elements", "main_subject", "scene_type", "spatial_layout")):
|
||||
vtype = "scene"
|
||||
else:
|
||||
vtype = "other"
|
||||
|
||||
# ── 人物类 ──
|
||||
if vtype == "person":
|
||||
# 取第一个人物信息（v5 schema人物信息在顶层）
|
||||
# 人物类
|
||||
if vtype == "person" or has_person:
|
||||
# 取第一个人物信息（v4 schema人物信息在顶层）
|
||||
person_info = fj
|
||||
# 兼容people嵌套
|
||||
ppl = fj.get("people")
|
||||
@@ -508,82 +429,58 @@ def _assemble_v4(idx: int, fj: dict, ocr_texts: list[str]) -> dict[str, Any]:
|
||||
has_person = True
|
||||
|
||||
portrait_prompt = _build_portrait_prompt_from_v4(person_info)
|
||||
outfit_style = person_info.get("outfit_style") or ""
|
||||
upper = person_info.get("upper_wear") or ""
|
||||
lower = person_info.get("lower_wear") or ""
|
||||
dress = person_info.get("dress_wear") or ""
|
||||
outer = person_info.get("outerwear") or ""
|
||||
if dress:
|
||||
name = str(dress)[:25]
|
||||
elif outer and upper:
|
||||
name = f"{outer}+{upper}"[:30]
|
||||
elif upper:
|
||||
name = (str(upper) + (f"+{lower}" if lower else ""))[:30]
|
||||
name = person_info.get("upper_wear") or "人物穿搭"
|
||||
if "连衣裙" in name:
|
||||
pass
|
||||
else:
|
||||
name = "人物穿搭"
|
||||
lower = person_info.get("lower_wear") or ""
|
||||
if lower:
|
||||
name = f"{name}+{lower}"
|
||||
brand = "无法判断"
|
||||
category = "人物穿搭"
|
||||
# appearance: 外套+上衣+下装/裙+鞋+包+发型+妆容
|
||||
app_parts = []
|
||||
for k in ("outerwear", "upper_wear", "lower_wear", "dress_wear", "shoes", "bag", "hairstyle", "makeup"):
|
||||
category = "服饰"
|
||||
outfit_parts = []
|
||||
for k in ("upper_wear", "lower_wear", "dress_color", "upper_color", "lower_color", "outfit_style"):
|
||||
v = person_info.get(k)
|
||||
if v and v not in ("null", None, "无明显妆容"):
|
||||
app_parts.append(str(v))
|
||||
appearance = "；".join(app_parts) if app_parts else "人像穿搭整体造型"
|
||||
# key_features: 服装+配饰+拍摄信息
|
||||
if v and v not in ("null", None):
|
||||
outfit_parts.append(str(v))
|
||||
appearance = "、".join(outfit_parts) if outfit_parts else "人像穿搭整体造型"
|
||||
# key_features: 穿搭特征+配饰
|
||||
kf = []
|
||||
for k in (
|
||||
"outfit_style",
|
||||
"upper_wear",
|
||||
"lower_wear",
|
||||
"dress_wear",
|
||||
"outerwear",
|
||||
"shoes",
|
||||
"bag",
|
||||
"upper_color",
|
||||
"lower_color",
|
||||
"hairstyle",
|
||||
"expression",
|
||||
"pose",
|
||||
"outfit_style",
|
||||
):
|
||||
v = person_info.get(k)
|
||||
if v and v not in ("null", None, "无法判断"):
|
||||
kf.append(str(v))
|
||||
acc = person_info.get("accessories") or []
|
||||
if isinstance(acc, list):
|
||||
for a in acc:
|
||||
if a and str(a) not in kf:
|
||||
kf.append(str(a))
|
||||
elif isinstance(acc, str) and acc:
|
||||
kf.append(acc)
|
||||
for k in ("shot_type", "camera_angle", "lighting", "atmosphere"):
|
||||
v = person_info.get(k)
|
||||
if v and v not in ("null", None):
|
||||
kf.append(str(v))
|
||||
env_obj = person_info.get("environment_objects") or []
|
||||
if not isinstance(env_obj, list):
|
||||
env_obj = [env_obj]
|
||||
for o in env_obj:
|
||||
if o and str(o) not in kf and len(str(o)) <= 40:
|
||||
kf.append(str(o))
|
||||
kf.extend(str(a) for a in acc if a)
|
||||
if text_on_package:
|
||||
kf.append(f"文字:{'/'.join(text_on_package[:3])}")
|
||||
kf = kf[:8] or ["无法判断"]
|
||||
summary = (outfit_style + " " if outfit_style and outfit_style not in name else "") + name[:25]
|
||||
if not summary.strip():
|
||||
kf.append(f"画面文字: {'/'.join(text_on_package[:3])}")
|
||||
kf = kf[:6] or ["无法判断"]
|
||||
summary = (person_info.get("outfit_style") or "") + (person_info.get("upper_wear") or "穿搭")
|
||||
if not summary or summary == "穿搭":
|
||||
summary = "人物穿搭"
|
||||
return {
|
||||
"name": name[:30],
|
||||
"brand": brand,
|
||||
"category": category,
|
||||
"appearance": appearance[:400],
|
||||
"appearance": appearance,
|
||||
"packaging": "人物形象无包装",
|
||||
"text_on_package": text_on_package,
|
||||
"key_features": kf,
|
||||
"scene": scene,
|
||||
"mood": mood,
|
||||
"portrait_prompt": portrait_prompt[:300],
|
||||
"summary": summary[:50],
|
||||
"_source": "v2_fast_json_v5",
|
||||
"has_person": True,
|
||||
"portrait_prompt": portrait_prompt,
|
||||
"summary": summary[:40],
|
||||
"_source": "v2_fast_json_v4",
|
||||
}
|
||||
|
||||
# 商品类
|
||||
@@ -624,15 +521,9 @@ def _assemble_v4(idx: int, fj: dict, ocr_texts: list[str]) -> dict[str, Any]:
|
||||
for s in sell[:2]:
|
||||
if s and len(str(s)) <= 30 and str(s) not in kf:
|
||||
kf.append(f"卖点:{s}")
|
||||
env_obj = fj.get("environment_objects") or []
|
||||
if not isinstance(env_obj, list):
|
||||
env_obj = [env_obj]
|
||||
for o in env_obj:
|
||||
if o and str(o) not in kf and len(str(o)) <= 40:
|
||||
kf.append(str(o))
|
||||
if text_on_package:
|
||||
kf.append(f"文字: {'/'.join(text_on_package[:3])}")
|
||||
kf = kf[:8] or ["无法判断"]
|
||||
kf = kf[:6] or ["无法判断"]
|
||||
portrait_prompt = _build_product_prompt_from_v4(main, fj)
|
||||
if brand != "无法判断" and brand not in name:
|
||||
summary = f"{brand} {name}"
|
||||
@@ -650,226 +541,59 @@ def _assemble_v4(idx: int, fj: dict, ocr_texts: list[str]) -> dict[str, Any]:
|
||||
"mood": mood,
|
||||
"portrait_prompt": portrait_prompt[:200],
|
||||
"summary": str(summary)[:60],
|
||||
"has_person": False,
|
||||
"_source": "v2_fast_json_v4",
|
||||
}
|
||||
|
||||
# 门店类
|
||||
# 门店类或其他
|
||||
if vtype == "store":
|
||||
store_type = fj.get("store_type") or "店铺"
|
||||
# brand 多级兜底：brand_signage → visible_text招牌文字 → text_on_package短词
|
||||
brand_raw = fj.get("brand_signage")
|
||||
if not brand_raw or brand_raw in ("无法判断", "", None):
|
||||
brand = None
|
||||
# 从visible_text找招牌文字（通常是位置含招牌/门头/背景的短词）
|
||||
for vt in visible_text:
|
||||
vt_str = vt.get("text") if isinstance(vt, dict) else str(vt)
|
||||
if not vt_str or len(vt_str) < 2 or len(vt_str) > 12:
|
||||
continue
|
||||
loc = (vt.get("location") or "") if isinstance(vt, dict) else ""
|
||||
if any(k in loc for k in ("招牌", "门头", "背景", "招牌墙")):
|
||||
brand = vt_str
|
||||
break
|
||||
# 从text_on_package找2-8字的短词（非描述性）
|
||||
if not brand:
|
||||
_desc_words = {"干净", "整洁", "温馨", "专业", "明亮", "舒适", "宽敞", "现代", "传统", "时尚"}
|
||||
for t in text_on_package:
|
||||
if 2 <= len(t) <= 8 and t not in _desc_words and not any(c in t for c in "的了是在我"):
|
||||
brand = t
|
||||
break
|
||||
if not brand:
|
||||
brand = "无法判断"
|
||||
else:
|
||||
brand = brand_raw
|
||||
# name兜底：store_type为空时用brand
|
||||
name = store_type if store_type != "店铺" else (brand if brand != "无法判断" else store_type)
|
||||
name = store_type
|
||||
brand = fj.get("brand_signage") or "无法判断"
|
||||
category = "门店场景"
|
||||
# appearance: store_layout + furnishings + 陈设色调
|
||||
visual = fj.get("visual_elements") or []
|
||||
if isinstance(visual, str):
|
||||
visual = [visual]
|
||||
atmosphere = fj.get("atmosphere") or mood
|
||||
appearance_parts = []
|
||||
if fj.get("store_layout"):
|
||||
appearance_parts.append(str(fj["store_layout"]))
|
||||
furnishings = fj.get("furnishings") or []
|
||||
if isinstance(furnishings, dict):
|
||||
_furn_vals = []
|
||||
for fk in ("materials", "furniture", "shelving", "seating"):
|
||||
fv = furnishings.get(fk)
|
||||
if isinstance(fv, list):
|
||||
_furn_vals.extend(str(x) for x in fv if x)
|
||||
elif isinstance(fv, str) and fv:
|
||||
_furn_vals.append(fv)
|
||||
if _furn_vals:
|
||||
appearance_parts.append("陈设：" + "、".join(_furn_vals[:4]))
|
||||
elif isinstance(furnishings, list) and furnishings:
|
||||
appearance_parts.append("陈设：" + "、".join(str(f) for f in furnishings[:4] if f))
|
||||
if visual:
|
||||
appearance_parts.append("、".join(str(v) for v in visual[:3]))
|
||||
if fj.get("cleanliness"):
|
||||
appearance_parts.append(str(fj["cleanliness"]))
|
||||
appearance = "；".join(appearance_parts) if appearance_parts else "门店环境"
|
||||
# key_features: 招牌细节+海报+外部物品+视觉元素+陈列商品+环境物件
|
||||
kf = []
|
||||
for arr_key in (
|
||||
"signage_details",
|
||||
"signage_posters",
|
||||
"exterior_items",
|
||||
"visual_elements",
|
||||
"products_on_display",
|
||||
"environment_objects",
|
||||
):
|
||||
arr = fj.get(arr_key) or []
|
||||
if not isinstance(arr, list):
|
||||
arr = [arr]
|
||||
for item in arr:
|
||||
if item and str(item) not in kf and len(str(item)) <= 40:
|
||||
kf.append(str(item))
|
||||
if isinstance(visual, list):
|
||||
kf.extend(str(v) for v in visual if v and len(str(v)) <= 30)
|
||||
prods_vis = fj.get("product_categories_visible") or []
|
||||
if isinstance(prods_vis, list):
|
||||
for c in prods_vis[:3]:
|
||||
if c and str(c) not in kf:
|
||||
kf.append(str(c))
|
||||
kf.extend(str(c) for c in prods_vis[:3] if c)
|
||||
promo = fj.get("promotion_elements") or []
|
||||
if isinstance(promo, list) and promo:
|
||||
kf.append("促销活动:" + "、".join(str(p) for p in promo[:2]))
|
||||
if has_person:
|
||||
for pf in _extract_person_features(fj):
|
||||
if pf not in kf:
|
||||
kf.append(pf)
|
||||
if text_on_package:
|
||||
kf.append("文字:" + "/".join(text_on_package[:3]))
|
||||
kf = kf[:8] or ["门店场景"]
|
||||
atmosphere = fj.get("atmosphere") or fj.get("mood") or mood
|
||||
# portrait_prompt: 品牌+store_type+主色+atmosphere+scene+核心视觉元素
|
||||
pieces = []
|
||||
if brand != "无法判断":
|
||||
pieces.append(brand)
|
||||
pieces.append(store_type)
|
||||
if color_names:
|
||||
pieces.append("、".join(color_names[:3]) + "配色")
|
||||
if atmosphere:
|
||||
pieces.append(atmosphere)
|
||||
if scene and scene != "通用":
|
||||
pieces.append(scene)
|
||||
visual = fj.get("visual_elements") or []
|
||||
if isinstance(visual, list) and visual:
|
||||
pieces.append("、".join(str(v) for v in visual[:3] if v))
|
||||
pieces.append("门店实拍")
|
||||
portrait_prompt = "，".join(p for p in pieces if p)
|
||||
if len(portrait_prompt) > 200:
|
||||
portrait_prompt = portrait_prompt[:200].rstrip("，")
|
||||
summary = f"{brand} {store_type}" if brand != "无法判断" else f"{store_type}场景"
|
||||
kf.append(f"文字: {'/'.join(text_on_package[:3])}")
|
||||
kf = kf[:6] or ["门店场景"]
|
||||
portrait_prompt = f"{brand if brand!='无法判断' else ''}{store_type}，{atmosphere}，{scene}场景，{('、'.join(color_names[:3])+'配色，') if color_names else ''}产品陈列丰富，门店实拍"
|
||||
portrait_prompt = portrait_prompt.strip("，")
|
||||
summary = f"{store_type}场景"
|
||||
return {
|
||||
"name": name[:30],
|
||||
"brand": str(brand)[:30],
|
||||
"category": category,
|
||||
"appearance": appearance[:300],
|
||||
"appearance": appearance[:200],
|
||||
"packaging": "门店场景无包装",
|
||||
"text_on_package": text_on_package,
|
||||
"key_features": kf,
|
||||
"scene": scene,
|
||||
"mood": atmosphere,
|
||||
"mood": atmosphere or mood,
|
||||
"portrait_prompt": portrait_prompt[:200],
|
||||
"summary": summary[:40],
|
||||
"has_person": bool(fj.get("has_person", False)),
|
||||
"_source": "v2_fast_json_v6_store",
|
||||
"_source": "v2_fast_json_v4",
|
||||
}
|
||||
|
||||
# 场景类（纯场景图，无产品/人物）
|
||||
if vtype == "scene":
|
||||
main_subject = fj.get("main_subject") or ""
|
||||
scene_type = fj.get("scene_type") or "场景图"
|
||||
name = main_subject or scene_type
|
||||
brand = "无法判断"
|
||||
category = scene_type or "场景图"
|
||||
# appearance: 空间布局 + 关键元素
|
||||
app_parts = []
|
||||
if fj.get("spatial_layout"):
|
||||
app_parts.append(str(fj["spatial_layout"]))
|
||||
key_elements = fj.get("key_elements") or []
|
||||
if not isinstance(key_elements, list):
|
||||
key_elements = [key_elements]
|
||||
if key_elements:
|
||||
app_parts.append("关键元素：" + "、".join(str(k) for k in key_elements[:4] if k))
|
||||
if fj.get("lighting"):
|
||||
app_parts.append("光线：" + str(fj["lighting"]))
|
||||
appearance = "；".join(app_parts) if app_parts else "场景环境"
|
||||
# key_features: key_elements + environment_objects（去重，最多8）
|
||||
kf = []
|
||||
for k in key_elements:
|
||||
if k and str(k) not in kf and len(str(k)) <= 40:
|
||||
kf.append(str(k))
|
||||
env_obj = fj.get("environment_objects") or []
|
||||
if not isinstance(env_obj, list):
|
||||
env_obj = [env_obj]
|
||||
for o in env_obj:
|
||||
if o and str(o) not in kf and len(str(o)) <= 40:
|
||||
kf.append(str(o))
|
||||
if has_person:
|
||||
for pf in _extract_person_features(fj):
|
||||
if pf not in kf:
|
||||
kf.append(pf)
|
||||
if text_on_package:
|
||||
kf.append("文字:" + "/".join(text_on_package[:3]))
|
||||
kf = kf[:8] or ["场景元素"]
|
||||
scene_name = fj.get("scene") or scene_type
|
||||
scene_mood = fj.get("mood") or mood
|
||||
packaging = "场景无包装"
|
||||
# portrait_prompt: main_subject + key_elements + lighting + atmosphere + composition + style
|
||||
pieces = []
|
||||
if main_subject:
|
||||
pieces.append(main_subject)
|
||||
if key_elements:
|
||||
pieces.append("、".join(str(k) for k in key_elements[:3] if k))
|
||||
if fj.get("lighting"):
|
||||
pieces.append(str(fj["lighting"]) + "光线")
|
||||
if scene_mood:
|
||||
pieces.append(scene_mood + "氛围")
|
||||
if fj.get("composition"):
|
||||
pieces.append(str(fj["composition"]))
|
||||
if fj.get("style"):
|
||||
pieces.append(str(fj["style"]))
|
||||
pieces.append("场景实拍，摄影级画质")
|
||||
portrait_prompt = "，".join(p for p in pieces if p)
|
||||
if len(portrait_prompt) > 200:
|
||||
portrait_prompt = portrait_prompt[:200].rstrip("，")
|
||||
summary = main_subject or scene_type
|
||||
return {
|
||||
"name": str(name)[:30],
|
||||
"brand": brand,
|
||||
"category": str(category)[:20],
|
||||
"appearance": appearance[:300],
|
||||
"packaging": packaging,
|
||||
"text_on_package": text_on_package,
|
||||
"key_features": kf,
|
||||
"scene": scene_name,
|
||||
"mood": scene_mood,
|
||||
"portrait_prompt": portrait_prompt[:200],
|
||||
"summary": str(summary)[:40],
|
||||
"has_person": bool(fj.get("has_person", False)),
|
||||
"_source": "v2_fast_json_v6_scene",
|
||||
}
|
||||
|
||||
# other 兜底：当 description 为空时，用 environment_objects/scene 拼基本描述
|
||||
desc = fj.get("description") or ""
|
||||
if not desc.strip():
|
||||
env_obj = fj.get("environment_objects") or []
|
||||
if not isinstance(env_obj, list):
|
||||
env_obj = [env_obj]
|
||||
env_names = [str(e) for e in env_obj if e and len(str(e)) <= 25][:5]
|
||||
scene_text = fj.get("scene") or ""
|
||||
if env_names:
|
||||
desc = f"{scene_text}场景中" + "、".join(env_names) if scene_text else "、".join(env_names)
|
||||
elif scene_text and scene_text != "通用":
|
||||
desc = f"{scene_text}场景"
|
||||
else:
|
||||
desc = "未识别"
|
||||
kf_other = []
|
||||
if desc != "未识别":
|
||||
kf_other.append(desc[:30])
|
||||
env_obj = fj.get("environment_objects") or []
|
||||
if not isinstance(env_obj, list):
|
||||
env_obj = [env_obj]
|
||||
for o in env_obj:
|
||||
if o and str(o) not in kf_other and len(str(o)) <= 40:
|
||||
kf_other.append(str(o))
|
||||
kf_other = kf_other[:8] or ["无法判断"]
|
||||
# other 兜底
|
||||
desc = fj.get("description") or "未识别"
|
||||
return {
|
||||
"name": desc[:30],
|
||||
"brand": "无法判断",
|
||||
@@ -877,12 +601,11 @@ def _assemble_v4(idx: int, fj: dict, ocr_texts: list[str]) -> dict[str, Any]:
|
||||
"appearance": desc[:200],
|
||||
"packaging": "无法判断",
|
||||
"text_on_package": text_on_package,
|
||||
"key_features": kf_other,
|
||||
"key_features": [desc[:30]] if desc != "未识别" else ["无法判断"],
|
||||
"scene": scene,
|
||||
"mood": mood,
|
||||
"portrait_prompt": f"{scene}，{mood}氛围，{desc}"[:200],
|
||||
"summary": desc[:40],
|
||||
"has_person": False,
|
||||
"_source": "v2_fast_json_v4_other",
|
||||
}
|
||||
|
||||
@@ -920,5 +643,4 @@ def _assemble_old(idx: int, fj: dict, ocr_texts: list[str]) -> dict[str, Any]:
|
||||
"portrait_prompt": portrait_prompt,
|
||||
"summary": summary,
|
||||
"_source": "v2_fast_json",
|
||||
"has_person": bool(fj.get("has_person", False)),
|
||||
}
|
||||
|
||||
@@ -23,10 +23,10 @@ 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"))
|
||||
_FAST_TIMEOUT = float(os.environ.get("VISION_V2_FAST_TIMEOUT", "15"))
|
||||
_FAST_JSON_TIMEOUT = float(os.environ.get("VISION_V2_FAST_JSON_TIMEOUT", "15"))
|
||||
_OCR_TIMEOUT = float(os.environ.get("VISION_V2_OCR_TIMEOUT", "6"))
|
||||
_PRO_TIMEOUT = float(os.environ.get("VISION_V2_PRO_TIMEOUT", "45"))
|
||||
_PRO_TIMEOUT = float(os.environ.get("VISION_V2_PRO_TIMEOUT", "30"))
|
||||
|
||||
_FALLBACK_RESULT = {
|
||||
"name": "未识别",
|
||||
@@ -58,29 +58,27 @@ def analyze_image_v2(idx: int, img_url: str) -> dict[str, Any]:
|
||||
|
||||
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
|
||||
with ThreadPoolExecutor(max_workers=2) as pool:
|
||||
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)
|
||||
|
||||
fast_elapsed = time.time() - t0
|
||||
|
||||
if fj_result:
|
||||
assembled = assembler.assemble_result(idx, fj_result, ocr_result)
|
||||
|
||||
@@ -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
|
||||
@@ -1,19 +1,21 @@
|
||||
# -*- 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。
|
||||
"""V2 兜底路径：qwen3.7-plus（阿里云百炼/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
|
||||
- 直接 httpx 直连 DashScope，不走 ai_client
|
||||
- enable_thinking=false + response_format=json_object
|
||||
- system prompt 优先读后台 viral_video_prompt_templates 配置，DB不可用时fallback到硬编码JSON schema
|
||||
- max_tokens 不传，使用 client 中 capability 的 DB 配置（避免硬编码截断 JSON）
|
||||
- timeout=30s
|
||||
- timeout=25s
|
||||
- API Key 从环境变量 DASHSCOPE_API_KEY 读取
|
||||
- 返回 dict 统一走 assembler.assemble_result 组装，与 fast 路径输出格式完全一致
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
from typing import Any
|
||||
|
||||
@@ -21,7 +23,14 @@ from . import _prompt, assembler
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_DEFAULT_TIMEOUT = 45
|
||||
_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1"
|
||||
_PRO_MODEL = "qwen3.7-plus"
|
||||
_DEFAULT_TIMEOUT = 30
|
||||
_DEFAULT_MAX_TOKENS = 800
|
||||
|
||||
|
||||
def _api_key() -> str | None:
|
||||
return os.environ.get("DASHSCOPE_API_KEY")
|
||||
|
||||
|
||||
def call_pro_vlm(
|
||||
@@ -29,81 +38,82 @@ def call_pro_vlm(
|
||||
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()
|
||||
import httpx
|
||||
|
||||
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)
|
||||
api_key = _api_key()
|
||||
if not api_key:
|
||||
logger.warning("[vision.v2] pro DASHSCOPE_API_KEY 未配置，跳过")
|
||||
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},
|
||||
],
|
||||
},
|
||||
]
|
||||
|
||||
payload: dict[str, Any] = {
|
||||
"model": _PRO_MODEL,
|
||||
"messages": [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image_url", "image_url": {"url": img_url}},
|
||||
{"type": "text", "text": user_prompt},
|
||||
],
|
||||
},
|
||||
],
|
||||
"temperature": 0.3,
|
||||
"max_tokens": _DEFAULT_MAX_TOKENS,
|
||||
"stream": False,
|
||||
"enable_thinking": False,
|
||||
"response_format": {"type": "json_object"},
|
||||
}
|
||||
try:
|
||||
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"),
|
||||
r = httpx.post(
|
||||
f"{_BASE_URL}/chat/completions",
|
||||
headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
|
||||
json=payload,
|
||||
timeout=timeout,
|
||||
)
|
||||
elapsed = time.time() - t0
|
||||
if r.status_code != 200:
|
||||
logger.warning("[vision.v2] pro HTTP %d elapsed=%.1fs body=%s", r.status_code, elapsed, r.text[:200])
|
||||
return None
|
||||
data = r.json()
|
||||
raw = (data.get("choices") or [{}])[0].get("message", {}).get("content")
|
||||
if not raw:
|
||||
logger.warning("[vision.v2] pro 返回空 elapsed=%.1fs", elapsed)
|
||||
return None
|
||||
usage = data.get("usage") or {}
|
||||
reasoning_tokens = usage.get("reasoning_tokens", 0)
|
||||
ctd = usage.get("completion_tokens_details") or {}
|
||||
if not reasoning_tokens:
|
||||
reasoning_tokens = ctd.get("reasoning_tokens", 0)
|
||||
logger.info(
|
||||
"[vision.v2] pro 完成 model=%s elapsed=%.1fs in=%d out=%d reasoning=%d",
|
||||
_PRO_MODEL,
|
||||
elapsed,
|
||||
usage.get("prompt_tokens", 0),
|
||||
usage.get("completion_tokens", 0),
|
||||
reasoning_tokens,
|
||||
)
|
||||
s = raw.strip()
|
||||
if s.startswith("```"):
|
||||
lines = s.split("\n")
|
||||
if lines and lines[0].startswith("```"):
|
||||
lines = lines[1:]
|
||||
if lines and lines[-1].strip().startswith("```"):
|
||||
lines = lines[:-1]
|
||||
s = "\n".join(lines).strip()
|
||||
lpos, rr = s.find("{"), s.rfind("}")
|
||||
if lpos >= 0 and rr > lpos:
|
||||
s = s[lpos : rr + 1]
|
||||
try:
|
||||
obj = json.loads(s)
|
||||
except json.JSONDecodeError:
|
||||
logger.warning("[vision.v2] pro JSON 解析失败 head=%s", raw[:200])
|
||||
return None
|
||||
if not isinstance(obj, dict):
|
||||
return None
|
||||
|
||||
# 通过assembler统一组装，兼容v4嵌套schema和旧扁平schema
|
||||
result = assembler.assemble_result(idx, obj, [])
|
||||
|
||||
@@ -1,20 +1,22 @@
|
||||
# -*- 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。
|
||||
"""V2 快速路径：qwen3.8-flash（阿里云百炼/DashScope）强约束 JSON-only 调用。
|
||||
|
||||
目标：替代"人体属性/商品检测/图像标签"三个火山不存在的专用云端 API。
|
||||
设计要点：
|
||||
- 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）
|
||||
- 直接用 httpx 发最小 payload 到 DashScope OpenAI 兼容 endpoint，不走 ai_client 包装
|
||||
- enable_thinking=false 关闭推理链（reasoning 是延迟主因）
|
||||
- response_format=json_object 强约束JSON输出
|
||||
- system prompt 优先读后台 viral_video_prompt_templates 配置，DB不可用时fallback到硬编码JSON schema
|
||||
- max_tokens 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）
|
||||
- max_tokens=350、temperature=0.1（稳定输出 JSON）
|
||||
- timeout=12s（失败由外层走 pro 兜底）
|
||||
- API Key 从环境变量 DASHSCOPE_API_KEY 读取
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
from typing import Any
|
||||
|
||||
@@ -22,99 +24,124 @@ from . import _prompt
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_DEFAULT_TIMEOUT = 20
|
||||
# DashScope OpenAI 兼容 endpoint
|
||||
_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1"
|
||||
_FAST_MODEL = "qwen3.8-flash"
|
||||
_DEFAULT_TIMEOUT = 15
|
||||
_DEFAULT_MAX_TOKENS = 350
|
||||
|
||||
|
||||
def _api_key() -> str | None:
|
||||
return os.environ.get("DASHSCOPE_API_KEY")
|
||||
|
||||
|
||||
def _strip_code_fence(s: str) -> str:
|
||||
s = s.strip()
|
||||
if s.startswith("```"):
|
||||
lines = s.split("\n")
|
||||
if lines and lines[0].startswith("```"):
|
||||
lines = lines[1:]
|
||||
if lines and lines[-1].strip().startswith("```"):
|
||||
lines = lines[:-1]
|
||||
s = "\n".join(lines).strip()
|
||||
return s
|
||||
|
||||
|
||||
def call_fast_json(
|
||||
img_url: str,
|
||||
*,
|
||||
timeout: int = _DEFAULT_TIMEOUT,
|
||||
max_tokens: int | None = None,
|
||||
max_tokens: int = _DEFAULT_MAX_TOKENS,
|
||||
) -> 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。
|
||||
"""
|
||||
"""调用 qwen3.8-flash 返回结构化 dict；失败/非 JSON 返回 None。"""
|
||||
t0 = time.time()
|
||||
import httpx
|
||||
|
||||
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)
|
||||
api_key = _api_key()
|
||||
if not api_key:
|
||||
logger.warning("[vision.v2] DASHSCOPE_API_KEY 未配置，跳过 fast_json")
|
||||
return None
|
||||
|
||||
system_prompt, user_prompt = _prompt.resolve_fast_prompt()
|
||||
|
||||
messages = [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image_url", "image_url": {"url": img_url}},
|
||||
{"type": "text", "text": user_prompt},
|
||||
],
|
||||
},
|
||||
]
|
||||
|
||||
url = f"{_BASE_URL}/chat/completions"
|
||||
payload: dict[str, Any] = {
|
||||
"model": _FAST_MODEL,
|
||||
"messages": [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image_url", "image_url": {"url": img_url}},
|
||||
{"type": "text", "text": user_prompt},
|
||||
],
|
||||
},
|
||||
],
|
||||
"temperature": 0.1,
|
||||
"max_tokens": max_tokens,
|
||||
"stream": False,
|
||||
"enable_thinking": False,
|
||||
"response_format": {"type": "json_object"},
|
||||
}
|
||||
try:
|
||||
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],
|
||||
)
|
||||
|
||||
resp = httpx.post(
|
||||
url,
|
||||
headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
|
||||
json=payload,
|
||||
timeout=timeout,
|
||||
)
|
||||
elapsed = time.time() - t0
|
||||
if obj is None:
|
||||
logger.warning("[vision.v2] fast_json 两次均未得到JSON elapsed=%.1fs", elapsed)
|
||||
if resp.status_code == 400 and "enable_thinking" in resp.text[:300].lower():
|
||||
logger.warning("[vision.v2] fast_json HTTP 400 thinking 参数不兼容，重试 elapsed=%.1fs", elapsed)
|
||||
payload.pop("enable_thinking", None)
|
||||
resp = httpx.post(
|
||||
url,
|
||||
headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
|
||||
json=payload,
|
||||
timeout=timeout,
|
||||
)
|
||||
elapsed = time.time() - t0
|
||||
if resp.status_code != 200:
|
||||
logger.warning(
|
||||
"[vision.v2] fast_json HTTP %d elapsed=%.1fs body=%s", resp.status_code, elapsed, resp.text[:200]
|
||||
)
|
||||
return None
|
||||
if obj.get("_partial"):
|
||||
logger.warning("[vision.v2] fast_json 返回截断JSON(partial) elapsed=%.1fs", elapsed)
|
||||
data = resp.json()
|
||||
raw = (data.get("choices") or [{}])[0].get("message", {}).get("content")
|
||||
if not raw:
|
||||
logger.warning("[vision.v2] fast_json 返回空 elapsed=%.1fs", elapsed)
|
||||
return None
|
||||
usage = data.get("usage") or {}
|
||||
reasoning_tokens = usage.get("reasoning_tokens", 0)
|
||||
ctd = usage.get("completion_tokens_details") or {}
|
||||
if not reasoning_tokens:
|
||||
reasoning_tokens = ctd.get("reasoning_tokens", 0)
|
||||
logger.info(
|
||||
"[vision.v2] fast_json 完成 model=%s elapsed=%.1fs has_person=%s type=%s",
|
||||
client.model,
|
||||
"[vision.v2] fast_json 完成 model=%s elapsed=%.1fs in=%d out=%d reasoning=%d",
|
||||
_FAST_MODEL,
|
||||
elapsed,
|
||||
usage.get("prompt_tokens", 0),
|
||||
usage.get("completion_tokens", 0),
|
||||
reasoning_tokens,
|
||||
)
|
||||
text = _strip_code_fence(raw)
|
||||
lpos, r = text.find("{"), text.rfind("}")
|
||||
if lpos >= 0 and r > lpos:
|
||||
text = text[lpos : r + 1]
|
||||
try:
|
||||
obj = json.loads(text)
|
||||
except json.JSONDecodeError:
|
||||
logger.warning("[vision.v2] fast_json JSON 解析失败 elapsed=%.1fs head=%s", elapsed, raw[:200])
|
||||
return None
|
||||
if not isinstance(obj, dict):
|
||||
logger.warning("[vision.v2] fast_json 非 dict: %s", type(obj))
|
||||
return None
|
||||
logger.info(
|
||||
"[vision.v2] fast_json 完成 elapsed=%.1fs has_person=%s has_product=%s category=%s",
|
||||
elapsed,
|
||||
obj.get("has_person"),
|
||||
obj.get("type"),
|
||||
obj.get("has_product"),
|
||||
obj.get("category"),
|
||||
)
|
||||
return obj
|
||||
except Exception as e:
|
||||
|
||||
@@ -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）
|
||||
|
||||
@@ -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>
|
||||
|
||||
@@ -55,14 +55,9 @@ _LOCATIONS = ["title", "hook", "body_points", "cta", "script_segments"]
|
||||
class Reviewer:
|
||||
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 ────────────────────────────────────────────────────────────
|
||||
@@ -101,7 +96,6 @@ class Reviewer:
|
||||
],
|
||||
temperature=0.2,
|
||||
max_tokens=1024,
|
||||
timeout=25,
|
||||
)
|
||||
if not raw:
|
||||
return None
|
||||
@@ -248,7 +242,6 @@ class Reviewer:
|
||||
],
|
||||
temperature=0.5,
|
||||
max_tokens=2048,
|
||||
timeout=25,
|
||||
)
|
||||
if not raw:
|
||||
return self._rule_fix(fusion, review)
|
||||
|
||||
+35
-27
@@ -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）。
|
||||
|
||||
@@ -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
|
||||
|
||||
+27
-106
@@ -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,
|
||||
@@ -317,24 +282,7 @@ 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:
|
||||
# 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=%d",
|
||||
@@ -367,21 +315,20 @@ 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 +368,14 @@ 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 +388,7 @@ 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 +590,28 @@ 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()
|
||||
@@ -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")
|
||||
|
||||
@@ -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()
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -137,18 +137,18 @@ V4_PERSON: dict[str, Any] = {
|
||||
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 r["category"] == "服饰"
|
||||
assert "T恤" in r["name"]
|
||||
assert "阔腿裤" in r["name"]
|
||||
assert "年轻女性" in r["portrait_prompt"]
|
||||
assert "项链" in r["portrait_prompt"]
|
||||
assert isinstance(r["key_features"], list) and len(r["key_features"]) <= 8
|
||||
assert isinstance(r["key_features"], list) and len(r["key_features"]) <= 6
|
||||
|
||||
|
||||
def test_assemble_v4_person_people_nested() -> None:
|
||||
fj = {"type": "person", "people": {**V4_PERSON, "has_person": True}}
|
||||
r = assembler.assemble_result(0, fj, [])
|
||||
assert r["category"] == "人物穿搭"
|
||||
assert r["category"] == "服饰"
|
||||
assert "年轻女性" in r["portrait_prompt"]
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user