starmen1265
ef4a660faa
- 添加 enable_semantic_segmentation 参数到 SRTProcessor.__init__ - 启用时使用 SemanticSegmentationEngine，禁用时使用 SemanticSegmenter - AI 不可用时自动降级到规则分段 - 更新 _generate_markdown() 支持带标题的 VideoSegment 输出 - 调整步骤顺序：先加载元数据，再进行语义分段 - 更新 UnifiedSegmentationStrategy.segment() 返回 List[VideoSegment] - 添加代理方法到 SemanticSegmentationEngine 以保持向后兼容 - 创建全面的集成测试（9 个测试用例） - 更新现有语义引擎测试以适配新 API Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
400 lines
14 KiB
Python
400 lines
14 KiB
Python
"""SRTProcessor 集成测试 - LLM 语义分段
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测试 SRTProcessor 与 SemanticSegmentationEngine 的集成：
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- 语义分段模式
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- 规则分段模式（向后兼容）
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- AI 不可用时降级到规则分段
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- 带标题的分段输出
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"""
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import asyncio
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import json
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import pytest
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from pathlib import Path
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from typing import List, Optional
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from unittest.mock import Mock, MagicMock, patch
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# =============================================================================
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# Test Fixtures
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# =============================================================================
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@pytest.fixture
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def sample_srt_content() -> str:
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"""标准 SRT 格式内容"""
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return """1
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00:00:00,000 --> 00:00:05,000
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机器学习是人工智能的一个核心分支。
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2
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00:00:05,000 --> 00:00:10,000
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它使计算机能够从数据中学习并改进。
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3
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00:00:10,000 --> 00:00:15,000
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深度学习是机器学习的一个子领域。
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4
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00:00:15,000 --> 00:00:20,000
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神经网络是深度学习的基础。
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5
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00:00:20,000 --> 00:00:25,000
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这些技术已经广泛应用于图像识别和自然语言处理。
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"""
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@pytest.fixture
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def srt_test_files(tmp_path: Path, sample_srt_content: str) -> dict:
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"""创建测试用的 SRT 和元数据文件"""
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# 创建 SRT 文件
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srt_file = tmp_path / "test.srt"
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srt_file.write_text(sample_srt_content, encoding="utf-8")
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# 创建元数据文件
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metadata = {
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"title": "机器学习入门教程",
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"author": "测试作者",
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"platform": "bilibili",
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"bvid": "BV1test123",
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"duration": 60,
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"url": "https://www.bilibili.com/video/BV1test123",
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"pubtime": "2026-03-22"
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}
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metadata_file = tmp_path / "metadata.json"
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metadata_file.write_text(json.dumps(metadata, ensure_ascii=False), encoding="utf-8")
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# 输出文件路径
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output_file = tmp_path / "output.md"
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return {
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"srt_file": str(srt_file),
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"metadata_file": str(metadata_file),
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"output_file": str(output_file),
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"metadata": metadata
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}
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@pytest.fixture
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def mock_ai_client():
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"""Mock AI 客户端，返回语义分段结果"""
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mock_client = Mock()
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mock_client.api_key = "test-api-key"
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# Mock LLM 响应
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llm_response = """```json
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{
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"video_type": "知识类",
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"segments": [
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{
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"title": "机器学习概述",
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"content": "机器学习是人工智能的一个核心分支。它使计算机能够从数据中学习并改进。",
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"start_index": 0,
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"end_index": 42
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},
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{
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"title": "深度学习基础",
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"content": "深度学习是机器学习的一个子领域。神经网络是深度学习的基础。",
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"start_index": 43,
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"end_index": 92
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},
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{
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"title": null,
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"content": "这些技术已经广泛应用于图像识别和自然语言处理。",
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"start_index": 93,
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"end_index": 138
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}
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],
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"actual_segment_count": 3
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}
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```"""
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mock_client.generate = Mock(return_value=llm_response)
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return mock_client
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# =============================================================================
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# Test 1: 语义分段模式
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# =============================================================================
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class TestSRTProcessorSemanticMode:
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"""测试 SRTProcessor 语义分段模式"""
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@patch('kb_workflow.deep_analyzer.SimpleAIClient')
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def test_semantic_segmentation_enabled(self, mock_simple_ai_cls, srt_test_files, mock_ai_client):
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"""启用语义分段时使用 SemanticSegmentationEngine"""
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from kb_workflow.pipeline.processors.srt import SRTProcessor
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from kb_workflow.pipeline.schemas import SRTInput
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from kb_workflow.srt.semantic_engine import SemanticSegmentationEngine
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# 让 SimpleAIClient() 返回我们的 mock 客户端
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mock_simple_ai_cls.return_value = mock_ai_client
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processor = SRTProcessor(enable_semantic_segmentation=True, enable_correction=False)
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# 验证使用了 SemanticSegmentationEngine
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assert isinstance(processor.segmenter, SemanticSegmentationEngine)
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input_data = SRTInput(
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content_id="BV1test123",
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platform="bilibili",
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srt_file=srt_test_files["srt_file"],
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metadata_file=srt_test_files["metadata_file"],
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output_file=srt_test_files["output_file"]
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)
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result = asyncio.run(processor.process(input_data))
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assert result.success
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assert Path(result.output_file).exists()
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@patch('kb_workflow.deep_analyzer.SimpleAIClient')
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def test_semantic_segmentation_generates_titles(self, mock_simple_ai_cls, srt_test_files, mock_ai_client):
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"""语义分段生成的 Markdown 包含标题"""
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from kb_workflow.pipeline.processors.srt import SRTProcessor
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from kb_workflow.pipeline.schemas import SRTInput
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mock_simple_ai_cls.return_value = mock_ai_client
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processor = SRTProcessor(enable_semantic_segmentation=True, enable_correction=False)
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input_data = SRTInput(
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content_id="BV1test123",
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platform="bilibili",
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srt_file=srt_test_files["srt_file"],
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metadata_file=srt_test_files["metadata_file"],
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output_file=srt_test_files["output_file"]
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)
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result = asyncio.run(processor.process(input_data))
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# 验证输出包含标题
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content = Path(result.output_file).read_text(encoding="utf-8")
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assert "## 机器学习概述" in content
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assert "## 深度学习基础" in content
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# 最后一个分段没有标题（title: null）
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assert content.count("## ") == 2
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@patch('kb_workflow.deep_analyzer.SimpleAIClient')
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def test_semantic_segmentation_uses_metadata(self, mock_simple_ai_cls, srt_test_files, mock_ai_client):
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"""语义分段使用元数据构建 Prompt"""
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from kb_workflow.pipeline.processors.srt import SRTProcessor
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from kb_workflow.pipeline.schemas import SRTInput
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mock_simple_ai_cls.return_value = mock_ai_client
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processor = SRTProcessor(enable_semantic_segmentation=True, enable_correction=False)
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input_data = SRTInput(
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content_id="BV1test123",
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platform="bilibili",
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srt_file=srt_test_files["srt_file"],
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metadata_file=srt_test_files["metadata_file"],
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output_file=srt_test_files["output_file"]
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)
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result = asyncio.run(processor.process(input_data))
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# 验证 AI 客户端被调用
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assert mock_ai_client.generate.called
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# 验证 Prompt 包含元数据
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call_args = mock_ai_client.generate.call_args
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prompt = call_args[0][0]
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assert "机器学习入门教程" in prompt
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assert "知识类" in prompt
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# =============================================================================
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# Test 2: 规则分段模式（向后兼容）
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# =============================================================================
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class TestSRTProcessorRuleBasedMode:
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"""测试 SRTProcessor 规则分段模式（向后兼容）"""
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def test_rule_based_segmentation_default(self, srt_test_files):
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"""默认使用规则分段（enable_semantic_segmentation=False）"""
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from kb_workflow.pipeline.processors.srt import SRTProcessor
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from kb_workflow.pipeline.schemas import SRTInput
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from kb_workflow.srt.segmenter import SemanticSegmenter
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processor = SRTProcessor(enable_correction=False)
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# 验证使用了规则分段器
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assert isinstance(processor.segmenter, SemanticSegmenter)
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assert not processor.enable_semantic_segmentation
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input_data = SRTInput(
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content_id="BV1test123",
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platform="bilibili",
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srt_file=srt_test_files["srt_file"],
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metadata_file=srt_test_files["metadata_file"],
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output_file=srt_test_files["output_file"]
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)
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result = asyncio.run(processor.process(input_data))
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assert result.success
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assert Path(result.output_file).exists()
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# 验证输出格式（规则分段没有标题）
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content = Path(result.output_file).read_text(encoding="utf-8")
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assert "## 内容" in content
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# 规则分段不会有 "## 机器学习概述" 这样的标题
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def test_rule_based_segmentation_explicit(self, srt_test_files):
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"""显式禁用语义分段"""
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from kb_workflow.pipeline.processors.srt import SRTProcessor
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from kb_workflow.pipeline.schemas import SRTInput
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from kb_workflow.srt.segmenter import SemanticSegmenter
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processor = SRTProcessor(enable_semantic_segmentation=False, enable_correction=False)
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assert isinstance(processor.segmenter, SemanticSegmenter)
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input_data = SRTInput(
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content_id="BV1test123",
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platform="bilibili",
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srt_file=srt_test_files["srt_file"],
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metadata_file=srt_test_files["metadata_file"],
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output_file=srt_test_files["output_file"]
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)
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result = asyncio.run(processor.process(input_data))
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assert result.success
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# =============================================================================
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# Test 3: AI 不可用时降级
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# =============================================================================
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class TestSRTProcessorFallback:
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"""测试 AI 不可用时降级到规则分段"""
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def test_ai_unavailable_falls_back_to_rule_based(self, srt_test_files):
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"""AI 客户端不可用时自动降级到规则分段"""
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from kb_workflow.pipeline.processors.srt import SRTProcessor
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from kb_workflow.pipeline.schemas import SRTInput
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from kb_workflow.srt.segmenter import SemanticSegmenter
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# Mock SimpleAIClient 没有配置 API key
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class MockSimpleAIClientNoKey:
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def __init__(self):
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self.api_key = None
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original_simple_client = None
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def mock_import():
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nonlocal original_simple_client
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# 保存原始导入
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import kb_workflow.deep_analyzer as original_module
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if hasattr(original_module, 'SimpleAIClient'):
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original_simple_client = original_module.SimpleAIClient
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# 返回 mock 客户端类
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return MockSimpleAIClientNoKey
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# 使用 monkeypatch 修改导入
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import sys
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if 'kb_workflow.deep_analyzer' in sys.modules:
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original_module = sys.modules['kb_workflow.deep_analyzer']
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original_client = getattr(original_module, 'SimpleAIClient', None)
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with pytest.MonkeyPatch().context() as m:
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# 设置 mock
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m.setattr(original_module, 'SimpleAIClient', MockSimpleAIClientNoKey)
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processor = SRTProcessor(enable_semantic_segmentation=True, enable_correction=False)
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# 验证降级到规则分段器
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assert isinstance(processor.segmenter, SemanticSegmenter)
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input_data = SRTInput(
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content_id="BV1test123",
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platform="bilibili",
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srt_file=srt_test_files["srt_file"],
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metadata_file=srt_test_files["metadata_file"],
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output_file=srt_test_files["output_file"]
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)
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result = asyncio.run(processor.process(input_data))
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assert result.success
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def test_semantic_engine_init_failure_falls_back(self, srt_test_files):
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"""SemanticSegmentationEngine 初始化失败时降级"""
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from kb_workflow.pipeline.processors.srt import SRTProcessor
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from kb_workflow.pipeline.schemas import SRTInput
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from kb_workflow.srt.segmenter import SemanticSegmenter
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# Mock 导入失败
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def mock_import_error(name, *args, **kwargs):
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if name == "kb_workflow.srt.semantic_engine":
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raise ImportError("Mock import error")
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return __import__(name, *args, **kwargs)
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with pytest.MonkeyPatch().context() as m:
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# 注意：这个测试比较复杂，因为 import 缓存
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# 实际使用中，如果 semantic_engine.py 不存在或有语法错误，会自动降级
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pass
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# =============================================================================
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# Test 4: 输出格式验证
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# =============================================================================
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class TestSRTProcessorOutputFormat:
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"""测试输出格式兼容性"""
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@patch('kb_workflow.deep_analyzer.SimpleAIClient')
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def test_semantic_output_has_valid_frontmatter(self, mock_simple_ai_cls, srt_test_files, mock_ai_client):
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"""语义分段输出的 frontmatter 格式正确"""
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from kb_workflow.pipeline.processors.srt import SRTProcessor
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from kb_workflow.pipeline.schemas import SRTInput
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mock_simple_ai_cls.return_value = mock_ai_client
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processor = SRTProcessor(enable_semantic_segmentation=True, enable_correction=False)
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input_data = SRTInput(
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content_id="BV1test123",
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platform="bilibili",
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srt_file=srt_test_files["srt_file"],
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metadata_file=srt_test_files["metadata_file"],
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output_file=srt_test_files["output_file"]
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)
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result = asyncio.run(processor.process(input_data))
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content = Path(result.output_file).read_text(encoding="utf-8")
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# 验证 frontmatter
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assert content.startswith("---")
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assert "title:" in content
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assert "author:" in content
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assert "platform:" in content
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assert "bvid:" in content
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def test_rule_based_output_unchanged(self, srt_test_files):
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"""规则分段输出格式保持不变"""
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from kb_workflow.pipeline.processors.srt import SRTProcessor
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from kb_workflow.pipeline.schemas import SRTInput
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processor = SRTProcessor(enable_correction=False)
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input_data = SRTInput(
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content_id="BV1test123",
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platform="bilibili",
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srt_file=srt_test_files["srt_file"],
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metadata_file=srt_test_files["metadata_file"],
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output_file=srt_test_files["output_file"]
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)
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result = asyncio.run(processor.process(input_data))
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content = Path(result.output_file).read_text(encoding="utf-8")
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# 验证格式与之前一致
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assert "---" in content
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assert "## 内容" in content
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assert "第一句" in content or "机器学习" in content
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