starmen1265
5983e95352
- Modified SRTProcessor to read enable_semantic_segmentation from KBSettings - Changed constructor parameter default from False to None (None = read from config) - Added config priority: constructor parameter > KBSettings > default (True) - Added logging for config status (INFO level) - Added comprehensive tests for config integration (TestConfigIntegration) - Added tests for output format compatibility (TestOutputFormatBenefits) - Fixed MockAIClient to accept disable_reasoning parameter - All 34 tests passing Requirements satisfied: - CONFIG-01: SRTProcessor reads from KBSettings - CONFIG-02: Disabled config uses rule-based segmentation - OUTPUT-01: LLM segmentation outputs H2 titles - OUTPUT-02: title=null skips title output - OUTPUT-03: Rule-based segmentation outputs "## 内容" Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
936 lines
32 KiB
Python
936 lines
32 KiB
Python
"""Tests for SRT processing module
|
||
|
||
Tests for:
|
||
- SRT parser (parse_srt, parse_srt_file)
|
||
- Semantic segmenter (SemanticSegmenter)
|
||
- ASR corrector (ASRCorrector)
|
||
- SRTProcessor (pipeline processor)
|
||
"""
|
||
|
||
import asyncio
|
||
import json
|
||
import os
|
||
|
||
import pytest
|
||
from pathlib import Path
|
||
from dataclasses import dataclass
|
||
from typing import List, Optional
|
||
from unittest.mock import Mock, patch
|
||
|
||
|
||
# =============================================================================
|
||
# Test Fixtures
|
||
# =============================================================================
|
||
|
||
@pytest.fixture
|
||
def sample_srt_content() -> str:
|
||
"""标准 SRT 格式内容"""
|
||
return """1
|
||
00:00:00,000 --> 00:00:05,000
|
||
第一句字幕内容
|
||
|
||
2
|
||
00:00:05,000 --> 00:00:10,000
|
||
第二句字幕内容
|
||
|
||
3
|
||
00:00:10,000 --> 00:00:15,000
|
||
第三句字幕内容
|
||
"""
|
||
|
||
|
||
@pytest.fixture
|
||
def sample_srt_with_special_chars() -> str:
|
||
"""包含特殊字符的 SRT 内容"""
|
||
return """1
|
||
00:00:00,000 --> 00:00:05,000
|
||
你好，世界！这是测试。
|
||
|
||
2
|
||
00:00:05,000 --> 00:00:10,000
|
||
包含特殊字符：<>&"'
|
||
|
||
3
|
||
00:00:10,000 --> 00:00:15,000
|
||
多行
|
||
字幕内容
|
||
"""
|
||
|
||
|
||
@pytest.fixture
|
||
def invalid_srt_content() -> str:
|
||
"""格式错误的 SRT 内容"""
|
||
return "这不是有效的 SRT 格式"
|
||
|
||
|
||
@pytest.fixture
|
||
def empty_content() -> str:
|
||
"""空内容"""
|
||
return ""
|
||
|
||
|
||
@pytest.fixture
|
||
def sample_srt_file(tmp_path: Path, sample_srt_content: str) -> Path:
|
||
"""创建临时 SRT 文件"""
|
||
srt_file = tmp_path / "test.srt"
|
||
srt_file.write_text(sample_srt_content, encoding="utf-8")
|
||
return srt_file
|
||
|
||
|
||
# =============================================================================
|
||
# Test 1: SRT Parser - parse_srt
|
||
# =============================================================================
|
||
|
||
class TestParseSrt:
|
||
"""测试 SRT 解析功能"""
|
||
|
||
def test_parse_standard_srt(self, sample_srt_content: str):
|
||
"""Test 1: 标准的 SRT 格式能被正确解析为 Segment 列表"""
|
||
from kb_workflow.srt.parser import parse_srt, Segment
|
||
|
||
segments = parse_srt(sample_srt_content)
|
||
|
||
assert len(segments) == 3
|
||
assert all(isinstance(s, Segment) for s in segments)
|
||
|
||
# 验证第一个 segment
|
||
assert segments[0].id == 1
|
||
assert segments[0].start_time == 0.0
|
||
assert segments[0].end_time == 5.0
|
||
assert segments[0].text == "第一句字幕内容"
|
||
|
||
# 验证最后一个 segment
|
||
assert segments[2].id == 3
|
||
assert segments[2].start_time == 10.0
|
||
assert segments[2].end_time == 15.0
|
||
assert segments[2].text == "第三句字幕内容"
|
||
|
||
def test_parse_srt_with_special_chars(self, sample_srt_with_special_chars: str):
|
||
"""Test 2: 包含特殊字符的字幕能正确处理"""
|
||
from kb_workflow.srt.parser import parse_srt
|
||
|
||
segments = parse_srt(sample_srt_with_special_chars)
|
||
|
||
assert len(segments) == 3
|
||
assert "你好，世界！" in segments[0].text
|
||
assert "<>&\"'" in segments[1].text
|
||
# 多行字幕应该合并
|
||
assert "多行" in segments[2].text
|
||
|
||
def test_parse_invalid_srt_raises_error(self, invalid_srt_content: str):
|
||
"""Test 3: 空文件或格式错误的文件抛出 ValueError"""
|
||
from kb_workflow.srt.parser import parse_srt
|
||
|
||
with pytest.raises(ValueError, match="Invalid SRT format"):
|
||
parse_srt(invalid_srt_content)
|
||
|
||
def test_parse_empty_content_raises_error(self, empty_content: str):
|
||
"""Test 3b: 空内容抛出 ValueError"""
|
||
from kb_workflow.srt.parser import parse_srt
|
||
|
||
with pytest.raises(ValueError, match="Empty SRT content"):
|
||
parse_srt(empty_content)
|
||
|
||
|
||
# =============================================================================
|
||
# Test 1b: SRT Parser - parse_srt_file
|
||
# =============================================================================
|
||
|
||
class TestParseSrtFile:
|
||
"""测试从文件解析 SRT"""
|
||
|
||
def test_parse_srt_file(self, sample_srt_file: Path):
|
||
"""从文件解析 SRT"""
|
||
from kb_workflow.srt.parser import parse_srt_file
|
||
|
||
segments = parse_srt_file(sample_srt_file)
|
||
|
||
assert len(segments) == 3
|
||
assert segments[0].text == "第一句字幕内容"
|
||
|
||
def test_parse_nonexistent_file_raises_error(self):
|
||
"""不存在的文件抛出 FileNotFoundError"""
|
||
from kb_workflow.srt.parser import parse_srt_file
|
||
|
||
with pytest.raises(FileNotFoundError):
|
||
parse_srt_file(Path("/nonexistent/file.srt"))
|
||
|
||
|
||
# =============================================================================
|
||
# Test 2: Semantic Segmenter
|
||
# =============================================================================
|
||
|
||
@pytest.fixture
|
||
def short_segments():
|
||
"""连续短句字幕分段"""
|
||
from kb_workflow.srt.parser import Segment
|
||
return [
|
||
Segment(id=1, start_time=0.0, end_time=2.0, text="这是第一句短句"),
|
||
Segment(id=2, start_time=2.0, end_time=4.0, text="这是第二句短句"),
|
||
Segment(id=3, start_time=4.0, end_time=6.0, text="这是第三句短句"),
|
||
]
|
||
|
||
|
||
@pytest.fixture
|
||
def segments_with_gap():
|
||
"""带有长停顿的字幕分段"""
|
||
from kb_workflow.srt.parser import Segment
|
||
return [
|
||
Segment(id=1, start_time=0.0, end_time=5.0, text="第一段内容在这里"),
|
||
Segment(id=2, start_time=5.0, end_time=10.0, text="继续第一段"),
|
||
# 5秒停顿
|
||
Segment(id=3, start_time=15.0, end_time=20.0, text="第二段从这里开始"),
|
||
Segment(id=4, start_time=20.0, end_time=25.0, text="继续第二段"),
|
||
]
|
||
|
||
|
||
@pytest.fixture
|
||
def long_text_segment():
|
||
"""长文本分段（超过500字符）"""
|
||
from kb_workflow.srt.parser import Segment
|
||
long_text = "这是一段很长的文本，" * 50 # 约600字符
|
||
return [
|
||
Segment(id=1, start_time=0.0, end_time=60.0, text=long_text),
|
||
]
|
||
|
||
|
||
class TestSemanticSegmenter:
|
||
"""测试语义分段器"""
|
||
|
||
def test_merge_short_segments(self, short_segments):
|
||
"""Test 1: 连续短句被合并为段落"""
|
||
from kb_workflow.srt.segmenter import SemanticSegmenter
|
||
|
||
segmenter = SemanticSegmenter()
|
||
paragraphs = segmenter.segment(short_segments)
|
||
|
||
# 所有短句应该被合并
|
||
assert len(paragraphs) >= 1
|
||
# 验证所有内容都被保留
|
||
merged_text = ' '.join(paragraphs)
|
||
assert "第一句短句" in merged_text
|
||
assert "第二句短句" in merged_text
|
||
assert "第三句短句" in merged_text
|
||
|
||
def test_segment_on_topic_change(self, segments_with_gap):
|
||
"""Test 2: 主题变化时自动分段（基于时间间隔）"""
|
||
from kb_workflow.srt.segmenter import SemanticSegmenter
|
||
|
||
segmenter = SemanticSegmenter(gap_threshold=3.0)
|
||
paragraphs = segmenter.segment(segments_with_gap)
|
||
|
||
# 应该分成两段（因为中间有5秒停顿）
|
||
assert len(paragraphs) == 2
|
||
assert "第一段" in paragraphs[0]
|
||
assert "第二段" in paragraphs[1]
|
||
|
||
def test_max_paragraph_length(self, long_text_segment):
|
||
"""Test 3: 单个段落不超过 500 字符"""
|
||
from kb_workflow.srt.segmenter import SemanticSegmenter
|
||
|
||
segmenter = SemanticSegmenter(max_paragraph_chars=500)
|
||
paragraphs = segmenter.segment(long_text_segment)
|
||
|
||
# 每个段落不应该超过限制
|
||
for para in paragraphs:
|
||
assert len(para) <= 520 # 允许小范围超出（最后一块）
|
||
|
||
def test_empty_segments(self):
|
||
"""空分段列表返回空列表"""
|
||
from kb_workflow.srt.segmenter import SemanticSegmenter
|
||
|
||
segmenter = SemanticSegmenter()
|
||
paragraphs = segmenter.segment([])
|
||
|
||
assert paragraphs == []
|
||
|
||
|
||
# =============================================================================
|
||
# Test 3: ASR Corrector
|
||
# =============================================================================
|
||
|
||
class TestASRCorrector:
|
||
"""测试 ASR 纠错器"""
|
||
|
||
def test_correct_obvious_errors(self):
|
||
"""Test 1: 明显的同音字错误被纠正"""
|
||
from kb_workflow.srt.corrector import ASRCorrector
|
||
|
||
# 创建一个 mock AI 客户端
|
||
class MockAIClient:
|
||
def generate(self, prompt: str, max_tokens: int = 16000) -> str:
|
||
# 模拟纠错结果
|
||
if "什么" in prompt:
|
||
return "什么是机器学习"
|
||
return prompt
|
||
|
||
corrector = ASRCorrector(ai_client=MockAIClient())
|
||
result = corrector.correct("什么是机器学习")
|
||
|
||
# 纠错后的文本应该被返回
|
||
assert result is not None
|
||
assert len(result) > 0
|
||
|
||
def test_correct_contextual_errors(self):
|
||
"""Test 2: 上下文相关的错误被纠正"""
|
||
from kb_workflow.srt.corrector import ASRCorrector
|
||
|
||
class MockAIClient:
|
||
def generate(self, prompt: str, max_tokens: int = 16000) -> str:
|
||
# 模拟纠错结果
|
||
return "这是一个测试文本，已经被纠正了。"
|
||
|
||
corrector = ASRCorrector(ai_client=MockAIClient())
|
||
result = corrector.correct("这是一个测试文本，已经被纠正了。")
|
||
|
||
assert "测试" in result
|
||
|
||
def test_no_overcorrection(self):
|
||
"""Test 3: 正确文本不被过度修改"""
|
||
from kb_workflow.srt.corrector import ASRCorrector
|
||
|
||
original_text = "这是一段完全正确的中文文本，不需要修改。"
|
||
|
||
class MockAIClient:
|
||
def generate(self, prompt: str, max_tokens: int = 16000) -> str:
|
||
# 返回原文，表示没有错误
|
||
return original_text
|
||
|
||
corrector = ASRCorrector(ai_client=MockAIClient())
|
||
result = corrector.correct(original_text)
|
||
|
||
# 正确文本应该基本保持不变
|
||
assert result == original_text
|
||
|
||
def test_ai_unavailable_returns_original(self):
|
||
"""AI 不可用时返回原文"""
|
||
from kb_workflow.srt.corrector import ASRCorrector
|
||
|
||
# 没有提供 AI 客户端
|
||
corrector = ASRCorrector(ai_client=None)
|
||
text = "这是原始文本"
|
||
result = corrector.correct(text)
|
||
|
||
# 应该返回原文
|
||
assert result == text
|
||
|
||
def test_batch_processing(self):
|
||
"""长文本被分批处理"""
|
||
from kb_workflow.srt.corrector import ASRCorrector
|
||
|
||
# 创建超长文本
|
||
long_text = "这是一段很长的文本。" * 1000 # 约 10,000 字符
|
||
|
||
call_count = 0
|
||
|
||
class MockAIClient:
|
||
def generate(self, prompt: str, max_tokens: int = 16000, disable_reasoning: bool = False) -> str:
|
||
nonlocal call_count
|
||
call_count += 1
|
||
# 返回提示词中的文本部分
|
||
return "处理后的文本"
|
||
|
||
corrector = ASRCorrector(ai_client=MockAIClient(), batch_size=5000)
|
||
result = corrector.correct(long_text)
|
||
|
||
# 应该被分批处理（至少调用2次）
|
||
assert call_count >= 2
|
||
assert result is not None
|
||
|
||
def test_empty_text(self):
|
||
"""空文本返回空字符串"""
|
||
from kb_workflow.srt.corrector import ASRCorrector
|
||
|
||
corrector = ASRCorrector(ai_client=None)
|
||
result = corrector.correct("")
|
||
|
||
assert result == ""
|
||
|
||
|
||
# =============================================================================
|
||
# Test 4: SRTProcessor
|
||
# =============================================================================
|
||
|
||
|
||
@pytest.fixture
|
||
def srt_test_files(tmp_path: Path):
|
||
"""创建测试用的 SRT 和元数据文件"""
|
||
# 创建 SRT 文件
|
||
srt_content = """1
|
||
00:00:00,000 --> 00:00:05,000
|
||
第一句字幕内容
|
||
|
||
2
|
||
00:00:05,000 --> 00:00:10,000
|
||
第二句字幕内容
|
||
|
||
3
|
||
00:00:10,000 --> 00:00:15,000
|
||
第三句字幕内容
|
||
"""
|
||
srt_file = tmp_path / "test.srt"
|
||
srt_file.write_text(srt_content, encoding="utf-8")
|
||
|
||
# 创建元数据文件
|
||
metadata = {
|
||
"title": "测试视频标题",
|
||
"author": "测试作者",
|
||
"platform": "bilibili",
|
||
"bvid": "BV1test123",
|
||
"duration": 60,
|
||
"url": "https://www.bilibili.com/video/BV1test123",
|
||
"pubtime": "2026-03-19"
|
||
}
|
||
metadata_file = tmp_path / "metadata.json"
|
||
metadata_file.write_text(json.dumps(metadata, ensure_ascii=False), encoding="utf-8")
|
||
|
||
# 输出文件路径
|
||
output_file = tmp_path / "output.md"
|
||
|
||
return {
|
||
"srt_file": str(srt_file),
|
||
"metadata_file": str(metadata_file),
|
||
"output_file": str(output_file)
|
||
}
|
||
|
||
|
||
class TestSRTProcessor:
|
||
"""测试 SRTProcessor 处理器"""
|
||
|
||
def test_srt_processor_generates_markdown(self, srt_test_files):
|
||
"""SRTProcessor 能处理 SRT 文件生成 Markdown"""
|
||
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
||
from kb_workflow.pipeline.schemas import SRTInput
|
||
|
||
# 明确禁用语义分段以使用规则分段器（测试规则分段输出）
|
||
processor = SRTProcessor(
|
||
enable_correction=False,
|
||
enable_semantic_segmentation=False
|
||
)
|
||
|
||
input_data = SRTInput(
|
||
content_id="BV1test123",
|
||
platform="bilibili",
|
||
srt_file=srt_test_files["srt_file"],
|
||
metadata_file=srt_test_files["metadata_file"],
|
||
output_file=srt_test_files["output_file"]
|
||
)
|
||
|
||
# 运行处理
|
||
result = asyncio.run(processor.process(input_data))
|
||
|
||
assert result.success
|
||
assert Path(result.output_file).exists()
|
||
|
||
# 验证 Markdown 内容
|
||
content = Path(result.output_file).read_text(encoding="utf-8")
|
||
assert "---" in content # frontmatter
|
||
assert "title:" in content
|
||
assert "测试视频标题" in content
|
||
assert "## 内容" in content
|
||
assert "第一句字幕内容" in content
|
||
|
||
def test_srt_processor_with_yaml_frontmatter(self, srt_test_files):
|
||
"""输出包含 YAML frontmatter（元数据和标签）"""
|
||
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
||
from kb_workflow.pipeline.schemas import SRTInput
|
||
|
||
processor = SRTProcessor(enable_correction=False)
|
||
|
||
input_data = SRTInput(
|
||
content_id="BV1test123",
|
||
platform="bilibili",
|
||
srt_file=srt_test_files["srt_file"],
|
||
metadata_file=srt_test_files["metadata_file"],
|
||
output_file=srt_test_files["output_file"]
|
||
)
|
||
|
||
result = asyncio.run(processor.process(input_data))
|
||
|
||
content = Path(result.output_file).read_text(encoding="utf-8")
|
||
|
||
# 验证 frontmatter 字段
|
||
assert "title:" in content
|
||
assert "date:" in content
|
||
assert "author:" in content
|
||
assert "platform:" in content
|
||
assert "bvid:" in content
|
||
assert "url:" in content
|
||
|
||
def test_srt_processor_follows_base_processor_pattern(self):
|
||
"""处理器遵循 BaseProcessor 模式"""
|
||
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
||
from kb_workflow.pipeline.processors.base import BaseProcessor
|
||
|
||
# SRTProcessor 应该继承 BaseProcessor
|
||
assert issubclass(SRTProcessor, BaseProcessor)
|
||
|
||
# 应该有 STEP_NAME
|
||
assert hasattr(SRTProcessor, 'STEP_NAME')
|
||
assert SRTProcessor.STEP_NAME == "srt"
|
||
|
||
def test_srt_processor_handles_missing_metadata(self, tmp_path: Path):
|
||
"""元数据文件缺失时能正常处理"""
|
||
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
||
from kb_workflow.pipeline.schemas import SRTInput
|
||
|
||
# 创建 SRT 文件
|
||
srt_content = "1\n00:00:00,000 --> 00:00:05,000\n测试内容\n"
|
||
srt_file = tmp_path / "test.srt"
|
||
srt_file.write_text(srt_content, encoding="utf-8")
|
||
|
||
# 不存在的元数据文件
|
||
output_file = tmp_path / "output.md"
|
||
|
||
processor = SRTProcessor(enable_correction=False)
|
||
|
||
input_data = SRTInput(
|
||
content_id="test123",
|
||
platform="bilibili",
|
||
srt_file=str(srt_file),
|
||
metadata_file=str(tmp_path / "nonexistent.json"),
|
||
output_file=str(output_file)
|
||
)
|
||
|
||
result = asyncio.run(processor.process(input_data))
|
||
|
||
assert result.success
|
||
assert Path(result.output_file).exists()
|
||
|
||
|
||
# =============================================================================
|
||
# Test 5: Semantic Segmentation Toggle (COST-03)
|
||
# =============================================================================
|
||
|
||
class TestSemanticSegmentationToggle:
|
||
"""测试语义分段配置开关 (COST-03)"""
|
||
|
||
def test_semantic_segmentation_enabled(self, sample_srt_file_for_toggle):
|
||
"""测试启用语义分段"""
|
||
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
||
from unittest.mock import patch
|
||
|
||
with patch('kb_workflow.deep_analyzer.SimpleAIClient') as mock_client:
|
||
mock_client.return_value = Mock(api_key="test_key")
|
||
|
||
processor = SRTProcessor(
|
||
enable_semantic_segmentation=True
|
||
)
|
||
|
||
# 验证创建了 SemanticSegmentationEngine
|
||
from kb_workflow.srt.semantic_engine import SemanticSegmentationEngine
|
||
assert isinstance(processor.segmenter, SemanticSegmentationEngine)
|
||
# 验证缓存已启用（默认）
|
||
assert processor.segmenter.strategy.cache is not None
|
||
|
||
def test_semantic_segmentation_disabled(self, sample_srt_file_for_toggle):
|
||
"""测试禁用语义分段（默认）"""
|
||
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
||
from kb_workflow.srt.segmenter import SemanticSegmenter
|
||
|
||
processor = SRTProcessor(
|
||
enable_semantic_segmentation=False
|
||
)
|
||
|
||
# 验证使用规则分段器
|
||
assert isinstance(processor.segmenter, SemanticSegmenter)
|
||
|
||
def test_semantic_segmentation_with_cache(self, sample_srt_file_for_toggle):
|
||
"""测试启用语义分段和缓存"""
|
||
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
||
from unittest.mock import patch
|
||
|
||
with patch('kb_workflow.deep_analyzer.SimpleAIClient') as mock_client:
|
||
mock_client.return_value = Mock(api_key="test_key")
|
||
|
||
processor = SRTProcessor(
|
||
enable_semantic_segmentation=True,
|
||
enable_cache=True
|
||
)
|
||
|
||
# 验证创建了 SemanticSegmentationEngine 且缓存已启用
|
||
from kb_workflow.srt.semantic_engine import SemanticSegmentationEngine
|
||
assert isinstance(processor.segmenter, SemanticSegmentationEngine)
|
||
assert processor.segmenter.strategy.cache is not None
|
||
|
||
def test_semantic_segmentation_without_cache(self, sample_srt_file_for_toggle):
|
||
"""测试启用语义分段但禁用缓存"""
|
||
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
||
from unittest.mock import patch
|
||
|
||
with patch('kb_workflow.deep_analyzer.SimpleAIClient') as mock_client:
|
||
mock_client.return_value = Mock(api_key="test_key")
|
||
|
||
processor = SRTProcessor(
|
||
enable_semantic_segmentation=True,
|
||
enable_cache=False
|
||
)
|
||
|
||
# 验证创建了 SemanticSegmentationEngine 但缓存未启用
|
||
from kb_workflow.srt.semantic_engine import SemanticSegmentationEngine
|
||
assert isinstance(processor.segmenter, SemanticSegmentationEngine)
|
||
assert processor.segmenter.strategy.cache is None
|
||
|
||
@pytest.fixture
|
||
def sample_srt_file_for_toggle(self, tmp_path):
|
||
"""创建示例 SRT 文件"""
|
||
srt_content = """1
|
||
00:00:00,000 --> 00:00:01,000
|
||
第一句字幕
|
||
|
||
2
|
||
00:00:01,000 --> 00:00:02,000
|
||
第二句字幕
|
||
"""
|
||
srt_file = tmp_path / "test.srt"
|
||
srt_file.write_text(srt_content, encoding='utf-8')
|
||
return srt_file
|
||
|
||
|
||
# =============================================================================
|
||
# Test 6: Configuration Integration (CONFIG-01, CONFIG-02) - Plan 15-02
|
||
# =============================================================================
|
||
|
||
class TestConfigIntegration:
|
||
"""测试配置集成 (CONFIG-01, CONFIG-02, OUTPUT-01, OUTPUT-02, OUTPUT-03)"""
|
||
|
||
@pytest.fixture
|
||
def mock_env_base(self):
|
||
"""基础环境变量"""
|
||
return {
|
||
"KB_BASE_PATH": "/tmp/kb",
|
||
"KB_ARCHIVE_PATH": "/tmp/archive"
|
||
}
|
||
|
||
def test_srt_processor_reads_from_kbsettings(self, mock_env_base):
|
||
"""Test 1: enable_semantic_segmentation=None 时从 KBSettings 读取配置"""
|
||
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
||
from kb_workflow.pipeline.config import get_settings
|
||
|
||
# 清除缓存以确保使用新的环境变量
|
||
get_settings.cache_clear()
|
||
|
||
with patch.dict(os.environ, {
|
||
**mock_env_base,
|
||
"KB_ENABLE_SEMANTIC_SEGMENTATION": "true"
|
||
}, clear=False):
|
||
get_settings.cache_clear()
|
||
processor = SRTProcessor()
|
||
|
||
# 验证从配置读取
|
||
assert processor.enable_semantic_segmentation is True
|
||
|
||
def test_srt_processor_constructor_overrides_config(self, mock_env_base):
|
||
"""Test 2: 显式传入参数时覆盖配置值（参数优先级高于配置）"""
|
||
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
||
from kb_workflow.pipeline.config import get_settings
|
||
|
||
get_settings.cache_clear()
|
||
|
||
with patch.dict(os.environ, {
|
||
**mock_env_base,
|
||
"KB_ENABLE_SEMANTIC_SEGMENTATION": "true"
|
||
}, clear=False):
|
||
get_settings.cache_clear()
|
||
# 显式传入 False 应该覆盖配置的 True
|
||
processor = SRTProcessor(enable_semantic_segmentation=False)
|
||
|
||
assert processor.enable_semantic_segmentation is False
|
||
|
||
def test_config_disabled_uses_rule_based(self, mock_env_base):
|
||
"""Test 3: 配置为 False 且无参数时使用规则分段"""
|
||
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
||
from kb_workflow.srt.segmenter import SemanticSegmenter
|
||
from kb_workflow.pipeline.config import get_settings
|
||
|
||
get_settings.cache_clear()
|
||
|
||
with patch.dict(os.environ, {
|
||
**mock_env_base,
|
||
"KB_ENABLE_SEMANTIC_SEGMENTATION": "false"
|
||
}, clear=False):
|
||
get_settings.cache_clear()
|
||
processor = SRTProcessor()
|
||
|
||
# 验证使用规则分段器
|
||
assert isinstance(processor.segmenter, SemanticSegmenter)
|
||
assert processor.enable_semantic_segmentation is False
|
||
|
||
def test_config_enabled_uses_llm_segmentation(self, mock_env_base):
|
||
"""Test 4: 配置为 True 且无参数时使用 LLM 分段"""
|
||
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
||
from kb_workflow.srt.semantic_engine import SemanticSegmentationEngine
|
||
from kb_workflow.pipeline.config import get_settings
|
||
|
||
get_settings.cache_clear()
|
||
|
||
with patch.dict(os.environ, {
|
||
**mock_env_base,
|
||
"KB_ENABLE_SEMANTIC_SEGMENTATION": "true"
|
||
}, clear=False):
|
||
get_settings.cache_clear()
|
||
|
||
with patch('kb_workflow.deep_analyzer.SimpleAIClient') as mock_client:
|
||
mock_client.return_value = Mock(api_key="test_key")
|
||
|
||
processor = SRTProcessor()
|
||
|
||
# 验证使用 LLM 分段引擎
|
||
assert isinstance(processor.segmenter, SemanticSegmentationEngine)
|
||
assert processor.enable_semantic_segmentation is True
|
||
|
||
def test_initialization_logs_config_status(self, mock_env_base, caplog):
|
||
"""Test 5: 初始化日志记录配置状态（INFO 级别）"""
|
||
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
||
from kb_workflow.pipeline.config import get_settings
|
||
import logging
|
||
|
||
get_settings.cache_clear()
|
||
|
||
with patch.dict(os.environ, {
|
||
**mock_env_base,
|
||
"KB_ENABLE_SEMANTIC_SEGMENTATION": "false"
|
||
}, clear=False):
|
||
get_settings.cache_clear()
|
||
|
||
with caplog.at_level(logging.INFO):
|
||
processor = SRTProcessor()
|
||
|
||
# 验证日志记录了配置状态
|
||
assert any("semantic_segmentation=" in record.message for record in caplog.records)
|
||
assert any("disabled" in record.message for record in caplog.records)
|
||
|
||
|
||
# =============================================================================
|
||
# Test 7: Output Format Compatibility (OUTPUT-01, OUTPUT-02, OUTPUT-03)
|
||
# =============================================================================
|
||
|
||
class TestOutputFormatCompatibility:
|
||
"""测试输出格式兼容性"""
|
||
|
||
@pytest.fixture
|
||
def srt_files_for_output(self, tmp_path):
|
||
"""创建输出格式测试用的文件"""
|
||
# 创建 SRT 文件
|
||
srt_content = """1
|
||
00:00:00,000 --> 00:00:05,000
|
||
这是第一段内容
|
||
|
||
2
|
||
00:00:05,000 --> 00:00:10,000
|
||
这是第二段内容
|
||
"""
|
||
srt_file = tmp_path / "test.srt"
|
||
srt_file.write_text(srt_content, encoding="utf-8")
|
||
|
||
# 创建元数据文件
|
||
metadata = {
|
||
"title": "输出格式测试",
|
||
"author": "测试作者",
|
||
"platform": "bilibili"
|
||
}
|
||
metadata_file = tmp_path / "metadata.json"
|
||
metadata_file.write_text(json.dumps(metadata, ensure_ascii=False), encoding="utf-8")
|
||
|
||
output_file = tmp_path / "output.md"
|
||
|
||
return {
|
||
"srt_file": str(srt_file),
|
||
"metadata_file": str(metadata_file),
|
||
"output_file": str(output_file)
|
||
}
|
||
|
||
def test_output_format_with_semantic_disabled(self, srt_files_for_output):
|
||
"""Test 3: 规则分段输出 "## 内容" 标题（OUTPUT-03）"""
|
||
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
||
from kb_workflow.pipeline.schemas import SRTInput
|
||
|
||
processor = SRTProcessor(
|
||
enable_semantic_segmentation=False,
|
||
enable_correction=False
|
||
)
|
||
|
||
input_data = SRTInput(
|
||
content_id="test123",
|
||
platform="bilibili",
|
||
srt_file=srt_files_for_output["srt_file"],
|
||
metadata_file=srt_files_for_output["metadata_file"],
|
||
output_file=srt_files_for_output["output_file"]
|
||
)
|
||
|
||
result = asyncio.run(processor.process(input_data))
|
||
|
||
# 验证输出文件
|
||
content = Path(result.output_file).read_text(encoding="utf-8")
|
||
|
||
# 验证 "## 内容" 标题（OUTPUT-03）
|
||
assert "## 内容" in content
|
||
# 验证段落内容直接输出
|
||
assert "这是第一段内容" in content
|
||
assert "这是第二段内容" in content
|
||
|
||
def test_generate_markdown_with_video_segments(self):
|
||
"""Test 1: LLM 分段输出带 H2 标题的 Markdown（OUTPUT-01）"""
|
||
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
||
from kb_workflow.srt.semantic_engine import VideoSegment
|
||
|
||
processor = SRTProcessor(enable_correction=False)
|
||
|
||
# 创建 VideoSegment 对象模拟 LLM 分段结果
|
||
mock_segments = [
|
||
VideoSegment(title="第一段", content="这是第一段内容", start_index=0, end_index=10),
|
||
VideoSegment(title=None, content="这是第二段内容", start_index=10, end_index=20),
|
||
VideoSegment(title="第三段", content="这是第三段内容", start_index=20, end_index=30)
|
||
]
|
||
|
||
# 创建临时输出文件
|
||
import tempfile
|
||
with tempfile.NamedTemporaryFile(mode='w', suffix='.md', delete=False, encoding='utf-8') as f:
|
||
output_file = Path(f.name)
|
||
|
||
try:
|
||
# 调用 _generate_markdown 方法
|
||
processor._generate_markdown(
|
||
output_file=output_file,
|
||
metadata={"title": "测试"},
|
||
paragraphs=mock_segments,
|
||
tags=[]
|
||
)
|
||
|
||
# 验证输出文件
|
||
content = output_file.read_text(encoding="utf-8")
|
||
|
||
# 验证 H2 标题（OUTPUT-01）
|
||
assert "## 第一段" in content
|
||
assert "## 第三段" in content
|
||
# 验证 title=None 时不输出标题（OUTPUT-02）
|
||
assert "## 第二段" not in content
|
||
assert "这是第二段内容" in content
|
||
# 验证内容正确输出
|
||
assert "这是第一段内容" in content
|
||
assert "这是第三段内容" in content
|
||
finally:
|
||
# 清理临时文件
|
||
if output_file.exists():
|
||
output_file.unlink()
|
||
|
||
def test_generate_markdown_with_plain_text(self):
|
||
"""Test 3b: 规则分段输出纯文本（带 "## 内容" 标题）"""
|
||
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
||
|
||
processor = SRTProcessor(enable_correction=False)
|
||
|
||
# 创建纯文本列表模拟规则分段结果
|
||
mock_paragraphs = [
|
||
"这是第一段内容",
|
||
"这是第二段内容"
|
||
]
|
||
|
||
# 创建临时输出文件
|
||
import tempfile
|
||
with tempfile.NamedTemporaryFile(mode='w', suffix='.md', delete=False, encoding='utf-8') as f:
|
||
output_file = Path(f.name)
|
||
|
||
try:
|
||
# 调用 _generate_markdown 方法
|
||
processor._generate_markdown(
|
||
output_file=output_file,
|
||
metadata={"title": "测试"},
|
||
paragraphs=mock_paragraphs,
|
||
tags=[]
|
||
)
|
||
|
||
# 验证输出文件
|
||
content = output_file.read_text(encoding="utf-8")
|
||
|
||
# 验证 "## 内容" 标题（OUTPUT-03）
|
||
assert "## 内容" in content
|
||
# 验证段落内容直接输出
|
||
assert "这是第一段内容" in content
|
||
assert "这是第二段内容" in content
|
||
finally:
|
||
# 清理临时文件
|
||
if output_file.exists():
|
||
output_file.unlink()
|
||
|
||
def test_generate_markdown_with_null_titles(self):
|
||
"""Test 2: 所有 title=None 时不输出分段标题（OUTPUT-02）"""
|
||
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
||
from kb_workflow.srt.semantic_engine import VideoSegment
|
||
|
||
processor = SRTProcessor(enable_correction=False)
|
||
|
||
# 创建全部 title=None 的 VideoSegment
|
||
mock_segments = [
|
||
VideoSegment(title=None, content="第一段内容", start_index=0, end_index=10),
|
||
VideoSegment(title=None, content="第二段内容", start_index=10, end_index=20)
|
||
]
|
||
|
||
# 创建临时输出文件
|
||
import tempfile
|
||
with tempfile.NamedTemporaryFile(mode='w', suffix='.md', delete=False, encoding='utf-8') as f:
|
||
output_file = Path(f.name)
|
||
|
||
try:
|
||
# 调用 _generate_markdown 方法
|
||
processor._generate_markdown(
|
||
output_file=output_file,
|
||
metadata={"title": "测试"},
|
||
paragraphs=mock_segments,
|
||
tags=[]
|
||
)
|
||
|
||
# 验证输出文件
|
||
content = output_file.read_text(encoding="utf-8")
|
||
|
||
# 验证没有分段标题（只有 frontmatter 和内容）
|
||
lines = content.split('\n')
|
||
# 找到 frontmatter 结束后的内容
|
||
in_content = False
|
||
for line in lines:
|
||
if line == "---":
|
||
if in_content:
|
||
break
|
||
in_content = True
|
||
continue
|
||
if in_content and line.startswith("## "):
|
||
# 只能有 "## 内容" 标题，不能有其他分段标题
|
||
assert line == "## 内容"
|
||
|
||
# 验证内容仍然存在
|
||
assert "第一段内容" in content
|
||
assert "第二段内容" in content
|
||
finally:
|
||
# 清理临时文件
|
||
if output_file.exists():
|
||
output_file.unlink()
|
||
|
||
def test_both_modes_valid_output(self, srt_files_for_output):
|
||
"""Test 4: 两种模式输出结构不同但都有效"""
|
||
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
||
from kb_workflow.pipeline.schemas import SRTInput
|
||
|
||
# 测试规则分段模式
|
||
processor = SRTProcessor(
|
||
enable_semantic_segmentation=False,
|
||
enable_correction=False
|
||
)
|
||
|
||
input_data = SRTInput(
|
||
content_id="test123",
|
||
platform="bilibili",
|
||
srt_file=srt_files_for_output["srt_file"],
|
||
metadata_file=srt_files_for_output["metadata_file"],
|
||
output_file=srt_files_for_output["output_file"]
|
||
)
|
||
|
||
result = asyncio.run(processor.process(input_data))
|
||
assert result.success
|
||
assert Path(result.output_file).exists()
|
||
|
||
# 验证输出文件存在且非空
|
||
content = Path(result.output_file).read_text(encoding="utf-8")
|
||
assert len(content) > 0
|
||
assert "---" in content # frontmatter
|
||
assert "## 内容" in content
|