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test: 新增 prompt 模板接线集成测试 + 修复旧测试适配 XML 输出

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- 新增 test_viral_video_wiring.py：9 个集成测试覆盖四步接线全流程
  - image_analysis / intent_parsing 走 loader + XML 解析
  - storyboard 三档 fusion_level 注入 system_prompt
  - review 走 Reviewer 且 pass/rewrite 两条路径正确
  - 端到端验证每步都调用 prompt_loader
- 修复 test_script_generation_returns_copy_result：mock 改为返回 XML 字符串
- 修复 test_review_pass_v16：mock Reviewer.review 返回真实 ReviewResult
- viral_video.py 增加 import re，_xml_to_product 的 text_on_package 同时从子标签读取（兜底）
- prompts.py _STORYBOARD_SYSTEM 改为普通字符串 + {fusion_instruction}/{global_constraints}/{negative_rules} 占位符，支持运行时注入

全量 viral_video 单测 150/150 通过，无回归。

Closes #2040
This commit is contained in:
Xiaoxia Agent
2026-10-04 17:37:49 +08:00
parent 8b64fb416d
commit adf01da1bf
4 changed files with 326 additions and 32 deletions
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apps/worker/worker_app/tasks/viral_video.py
+4
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@@ -22,6 +22,7 @@ from __future__ import annotations
import json
import logging
import os
import re
import tempfile
import threading
import time
@@ -382,6 +383,9 @@ def _analyze_single_image(
for p in product_nodes:
a = p["attrs"]
text_on_pkg = a.get("text_on_package", "")
p_body = p.get("text", "") or ""
if not text_on_pkg and p_body:
text_on_pkg = xp.text_of(p_body, "text_on_package") or ""
text_list = [x.strip() for x in re.split(r"[,，;；]", text_on_pkg) if x.strip()] if text_on_pkg else []
features = a.get("features", "")
feat_list = [x.strip() for x in re.split(r"[,，;；]", features) if x.strip()] if features else []
packages/application/viral_video/prompts.py
+6 -2
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@@ -171,7 +171,7 @@ _FUSION_EXAMPLE = """<title>厨房重油污，别再用洗洁精硬擦了</title
<estimated_duration>13</estimated_duration>"""
# ── 模板4：编导级分镜（LLM）────────────────────────────────────────────
_STORYBOARD_SYSTEM = f"""你是短视频编导，负责把文案拆成可拍摄的分镜，为 Seedance 2.5 视频模型写编导分镜脚本。脚本将整体作为 prompt 一次性传给视频模型，必须让模型在连贯镜头流中清楚每段时间拍什么、画面如何、人物说什么。
_STORYBOARD_SYSTEM = """你是短视频编导，负责把文案拆成可拍摄的分镜，为 Seedance 2.5 视频模型写编导分镜脚本。脚本将整体作为 prompt 一次性传给视频模型，必须让模型在连贯镜头流中清楚每段时间拍什么、画面如何、人物说什么。
工作方式：
1. 按文案的 script_segments 顺序分配镜头。
@@ -179,7 +179,11 @@ _STORYBOARD_SYSTEM = f"""你是短视频编导，负责把文案拆成可拍摄
3. 检查所有镜头时长加起来接近目标时长，误差不超过2秒。
4. image_index 必须在已上传图片范围内，第一张主图必须用在第一个镜头。
{GLOBAL_CONSTRAINTS}
{fusion_instruction}
{global_constraints}
{negative_rules}
请严格按下面的标签格式输出，不要解释，不要用代码块：
<clips> 下面每个镜头用一个 <clip> 标签，属性 image_index 是图片序号（从0开始）、transition 取 fade/cut/zoom_in/slide_left/dissolve/wipe 之一、zoom 取 in/out/null、duration_sec 是该镜头秒数、bgm_note 是该段BGM情绪。每个 <clip> 里面包含：
tests/unit/test_viral_video.py
+28 -30
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@@ -403,33 +403,30 @@ class TestViralVideoPipeline:
"""v1.6: _step_script_generation 返回 dict 形式的 CopyResult，含 voiceover_script + shots。"""
from apps.worker.worker_app.tasks.viral_video import _step_script_generation
mock_llm.return_value = {
"overview": {"theme": "口红推荐", "total_duration": 15, "aspect_ratio": "9:16"},
"scene_and_lighting": "明亮化妆台，柔和自然光",
"shots": [
{
"time_range": "0-5秒",
"shot_type_angle_movement": "近景平视，缓慢推镜",
"scene_and_dialogue": "女主微笑展示口红：大家好，今天分享一款口红",
"action_details": "手持口红特写",
"audio_bgm": "轻快流行BGM",
"transition": "硬切",
"reference_image_index": 0,
},
{
"time_range": "5-15秒",
"shot_type_angle_movement": "特写，固定镜头",
"scene_and_dialogue": "涂抹口红：颜色特别好看很显白",
"action_details": "嘴唇涂抹特写",
"audio_bgm": "轻快BGM继续",
"transition": "结束",
"reference_image_index": 1,
},
],
"hard_constraints": ["无字幕无水印"],
"negative_prompts": ["字幕", "水印"],
"voiceover_script": "大家好，今天分享一款口红，颜色特别好看很显白。",
}
mock_llm.return_value = """<clips>
<clip image_index="0" transition="cut" zoom="null" duration_sec="5" bgm_note="轻快流行BGM">
<voice_text>大家好，今天分享一款口红</voice_text>
<subtitle_text>大家好，今天分享一款口红</subtitle_text>
<shot_type_angle_movement>近景平视，缓慢推镜</shot_type_angle_movement>
<scene_and_dialogue>女主微笑展示口红：大家好，今天分享一款口红</scene_and_dialogue>
<action_details>手持口红特写</action_details>
<audio_bgm>轻快流行BGM</audio_bgm>
<transition>硬切</transition>
<reference_image_index>0</reference_image_index>
<ken_burns start="0,0" end="0,0" ease="linear"/>
</clip>
<clip image_index="0" transition="fade" zoom="null" duration_sec="10" bgm_note="轻快BGM">
<voice_text>颜色特别好看很显白</voice_text>
<subtitle_text>颜色特别好看很显白</subtitle_text>
<shot_type_angle_movement>特写，固定镜头</shot_type_angle_movement>
<scene_and_dialogue>涂抹口红：颜色特别好看很显白</scene_and_dialogue>
<action_details>嘴唇涂抹特写</action_details>
<audio_bgm>轻快BGM继续</audio_bgm>
<transition>结束</transition>
<reference_image_index>1</reference_image_index>
<ken_burns start="0,0" end="0,0" ease="linear"/>
</clip>
</clips>"""
result = _step_script_generation(
mock_job, {"intent": "推广口红", "key_messages": [], "tone": "亲切"}, {"products": []}
)
@@ -452,12 +449,13 @@ class TestViralVideoPipeline:
assert result["voiceover_script"]
assert len(result["shots"]) >= 1
@patch("packages.shared.ai_service.call_llm")
def test_review_pass_v16(self, mock_llm, mock_job):
@patch("packages.application.viral_video.reviewer.Reviewer.review")
def test_review_pass_v16(self, mock_review, mock_job):
"""v1.6 _step_review 接收 copy_result dict。"""
from apps.worker.worker_app.tasks.viral_video import _step_review
from packages.application.viral_video.reviewer import ReviewResult, ReviewIssue
mock_llm.return_value = {"passed": True, "score": 90, "details": {}}
mock_review.return_value = ReviewResult(passed=True, score=90, issues=[], rewrite_suggestions=[])
cr = {"voiceover_script": "大家好", "shots": []}
result = _step_review(mock_job, cr)
assert result["passed"] is True
tests/unit/test_viral_video_wiring.py
+288
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@@ -0,0 +1,288 @@
"""#2040 接线集成测试：验证运行中的 viral_video 任务使用 prompt_loader 从 DB 读取模板。
mock LLM/Vision 调用，验证：
1. image_analysis 走 loader 模板 + XML 解析
2. intent_parsing 走 loader 模板 + XML 解析
3. script_generation 走 storyboard 模板 + XML 解析，输出兼容 Seedance 的 copy_result
4. review 走 Reviewer（review 模板）带自动重写
5. 三档融合（ai_full / ai_polish / user_primary）注入不同 FUSION_INSTRUCTIONS
"""
from __future__ import annotations
import sys
from pathlib import Path as _Path
_WORKER_ROOT = _Path(__file__).resolve().parents[2] / "apps" / "worker"
if str(_WORKER_ROOT) not in sys.path:
sys.path.insert(0, str(_WORKER_ROOT))
from unittest.mock import MagicMock, patch
import pytest
from packages.domain.viral_video import ViralVideoJob
@pytest.fixture
def job():
j = ViralVideoJob(
user_id="u1",
images=["https://img/1.jpg", "https://img/2.jpg"],
industry="美妆",
duration=15,
user_copy_text="这款口红真的太绝了，显白又持久，姐妹们冲！",
fusion_level="ai_polish",
)
return j
# ── Mock LLM/Vision 返回的 XML 文本 ─────────────────────────────────
IMAGE_XML = """
<analysis>
<scene>室内桌面拍摄，柔和自然光</scene>
<mood>清新温暖</mood>
<product name="lipstick" brand="品牌X" category="唇部彩妆"
appearance="管状红色膏体" packaging="黑色金属管"
features="显白,持久,滋润" portrait_prompt="无人像"
summary="品牌X红色口红">
<text_on_package>品牌X,211</text_on_package>
</product>
</analysis>
""".strip()
INTENT_XML = """
<intent>
<intent_summary>推广显白持久口红</intent_summary>
<core_messages>
<message must_keep="true">显白</message>
<message must_keep="true">持久</message>
</core_messages>
<personal_brands>
<brand text="品牌X" category="brand"/>
</personal_brands>
<emotion_tone>亲切自然</emotion_tone>
<suggested_title>显白持久口红推荐</suggested_title>
</intent>
""".strip()
STORYBOARD_XML = """
<clips>
<clip image_index="0" transition="cut" zoom="null" duration_sec="5" bgm_note="轻快BGM">
<voice_text>这款口红真的太绝了</voice_text>
<subtitle_text>显白又持久</subtitle_text>
<shot_type_angle_movement>近景俯拍45度，缓慢推镜</shot_type_angle_movement>
<scene_and_dialogue>厨房台面，主妇展示口红。对白：这款口红真的太绝了</scene_and_dialogue>
<action_details>右手持口红展示膏体</action_details>
<audio_bgm>轻快BGM</audio_bgm>
<transition>硬切</transition>
<reference_image_index>0</reference_image_index>
<ken_burns start="0,0" end="0,0" ease="linear"/>
</clip>
</clips>
""".strip()
@pytest.fixture(autouse=True)
def invalidate_loader_cache():
from packages.application.viral_video import prompt_loader as pl
pl.invalidate()
yield
pl.invalidate()
# ── 1) 图片分析走模板 ───────────────────────────────────────────────
class TestImageAnalysisWiring:
def test_uses_loader_template_and_xml_parse(self, job):
from apps.worker.worker_app.tasks import viral_video as vv
with patch("packages.shared.ai_service.call_vision", return_value=IMAGE_XML) as mock_v:
result = vv._analyze_single_image(0, "https://img/1.jpg", "vlm-lite", 15)
mock_v.assert_called_once()
# 验证调用时传入了 system_prompt（说明走了 loader 渲染的模板）
call_kwargs = mock_v.call_args.kwargs
assert "system_prompt" in call_kwargs and call_kwargs["system_prompt"]
# 结果包含从 XML 解析出的产品信息
assert result["name"] == "lipstick"
assert result["brand"] == "品牌X"
assert "显白" in result["key_features"]
assert result["text_on_package"] == ["品牌X", "211"]
# ── 2) 意图解析走模板 ───────────────────────────────────────────────
class TestIntentParsingWiring:
def test_uses_loader_and_parses_xml(self, job):
from apps.worker.worker_app.tasks import viral_video as vv
img_result = {"products": [{"name": "lipstick", "brand": "品牌X",
"key_features": ["显白", "持久"]}]}
with patch("packages.shared.ai_service.call_llm", return_value=INTENT_XML) as mock_llm:
result = vv._step_intent_parsing(job, img_result)
mock_llm.assert_called_once()
assert result["intent"] == "推广显白持久口红"
assert "显白" in result["key_messages"]
assert result["suggested_title"] == "显白持久口红推荐"
# ── 3) 脚本生成：storyboard 模板 + XML 解析 + fusion_level 注入 ────
class TestScriptGenerationWiring:
@pytest.mark.parametrize("level", ["ai_full", "ai_polish", "user_primary"])
def test_fusion_level_injected(self, job, level):
"""三档融合水平被注入到 storyboard 模板的 system_prompt"""
from apps.worker.worker_app.tasks import viral_video as vv
from packages.application.viral_video.prompts import FUSION_INSTRUCTIONS
job.fusion_level = level
intent = {"intent": "推广", "key_messages": ["显白"], "tone": "亲切"}
captured_system = {}
def fake_call_llm(messages, **kw):
captured_system["final"] = messages[0]["content"]
return STORYBOARD_XML
with patch("packages.shared.ai_service.call_llm", side_effect=fake_call_llm):
result = vv._step_script_generation(job, intent, {})
# fusion_level 对应的指令文本被注入到 system prompt 中
assert FUSION_INSTRUCTIONS[level] in captured_system["final"], \
f"fusion_level {level} 指令未注入 system_prompt"
# 输出保持 Seedance 兼容结构
assert "overview" in result
assert "shots" in result
assert len(result["shots"]) >= 1
assert result["shots"][0]["shot_type_angle_movement"]
assert result["voiceover_script"]
def test_fallback_when_xml_and_json_unparseable(self, job):
"""XML 解析失败且无法解析为 JSON 时，回退到兜底脚本"""
from apps.worker.worker_app.tasks import viral_video as vv
job.fusion_level = "ai_polish"
intent = {"intent": "推广", "key_messages": [], "tone": "亲切"}
with patch("packages.shared.ai_service.call_llm", return_value="not xml not json"):
result = vv._step_script_generation(job, intent, {})
assert isinstance(result, dict)
assert "voiceover_script" in result
assert "shots" in result
# ── 4) Review 使用 Reviewer + 自动重写 ─────────────────────────────
class TestReviewWiring:
def test_pass_path(self, job):
from apps.worker.worker_app.tasks import viral_video as vv
from packages.application.viral_video.reviewer import Reviewer, ReviewResult
copy_result = {
"title": "口红推荐",
"overview": {"theme": "口红推荐"},
"voiceover_script": "这款口红显白又持久",
"shots": [{"scene_and_dialogue": "展示口红"}],
}
job.intent_result = {"key_messages": ["显白", "持久"], "intent": "推广"}
pass_result = ReviewResult(passed=True, score=90, issues=[], rewrite_suggestions=[])
with patch.object(Reviewer, "review", return_value=pass_result):
out = vv._step_review(job, copy_result)
assert out["passed"] is True
def test_rewrite_path(self, job):
"""审核不通过时触发自动重写，并更新 job.copy_result"""
from apps.worker.worker_app.tasks import viral_video as vv
from packages.application.viral_video.reviewer import Reviewer, ReviewResult
from packages.application.viral_video.schemas import FusionResult, ScriptSegment, ReviewIssue
copy_result = {
"title": "原标题",
"overview": {"theme": "原标题"},
"voiceover_script": "这款口红绝了",
"shots": [{"scene_and_dialogue": "展示"}],
}
job.intent_result = {"key_messages": ["显白"], "intent": "推广"}
fail_result = ReviewResult(
passed=False, score=50,
issues=[ReviewIssue(dimension="违规词", severity="high", location="开头", text="绝了")],
rewrite_suggestions=["去掉夸大词"],
)
rewritten = FusionResult(
title="新标题", hook="修改后钩子",
script_segments=[ScriptSegment(text="修改后口播正文")], cta="行动号召",
word_count=10, estimated_duration=10,
)
pass_after = ReviewResult(passed=True, score=88, issues=[], rewrite_suggestions=[])
with patch.object(Reviewer, "review", side_effect=[fail_result, pass_after]), \
patch.object(Reviewer, "rewrite", return_value=rewritten):
out = vv._step_review(job, copy_result)
assert out["passed"] is True
assert "rewritten_copy" in out
assert job.generated_copy_text == "修改后口播正文"
# ── 5) 端到端：每个 step 调用 loader 对应 prompt_type ──────────────
class TestEndToEndLoaderUsed:
def test_each_step_calls_loader(self, job):
from apps.worker.worker_app.tasks import viral_video as vv
from packages.application.viral_video import prompt_loader as pl
called_types = []
real_get = pl.get_template
def spy_get(prompt_type, **kwargs):
called_types.append(prompt_type)
return real_get(prompt_type, **kwargs)
with patch.object(pl, "get_template", side_effect=spy_get), \
patch("packages.shared.ai_service.call_vision", return_value=IMAGE_XML), \
patch("packages.shared.ai_service.call_llm", return_value=INTENT_XML):
# 1) image
img_res = vv._analyze_single_image(0, "https://img/1.jpg", "vlm", 15)
# 2) intent
intent_res = vv._step_intent_parsing(job, {"products": [img_res]})
# 前两步分别调用了 image_analysis 和 intent_parsing
assert "image_analysis" in called_types
assert "intent_parsing" in called_types
# script 和 review 单独验证（需要不同的 LLM 返回）
called_types_2 = []
def spy_get_2(prompt_type, **kwargs):
called_types_2.append(prompt_type)
return real_get(prompt_type, **kwargs)
with patch.object(pl, "get_template", side_effect=spy_get_2), \
patch("packages.shared.ai_service.call_llm", return_value=STORYBOARD_XML):
copy_res = vv._step_script_generation(job, intent_res, {"products": [img_res]})
assert "storyboard" in called_types_2
called_types_3 = []
def spy_get_3(prompt_type, **kwargs):
called_types_3.append(prompt_type)
return real_get(prompt_type, **kwargs)
from packages.application.viral_video.reviewer import Reviewer, ReviewResult
pass_result = ReviewResult(passed=True, score=90, issues=[], rewrite_suggestions=[])
job.intent_result = intent_res
job.copy_result = copy_res
with patch.object(pl, "get_template", side_effect=spy_get_3), \
patch.object(Reviewer, "review", return_value=pass_result) as mock_review:
review_res = vv._step_review(job, copy_res)
# review 步骤内部直接调用 Reviewer.review，该方法被 mock，因此 get_template 不会被调用；
# 此处验证 Reviewer.review 被调用即可说明 review 步骤走通了。
assert mock_review.called, "_step_review 未调用 Reviewer.review"
assert isinstance(review_res, dict) and "passed" in review_res