223 lines
8.0 KiB
Markdown
223 lines
8.0 KiB
Markdown
---
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phase: 13
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plan: 07
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type: execute
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wave: 2
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depends_on:
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- 13-01
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- 13-02
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- 13-03
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- 13-04
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- 13-05
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- 13-06
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files_modified:
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- kb_workflow/pipeline/processors/srt.py
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- tests/test_srt_processor_integration.py
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autonomous: true
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requirements:
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- CONFIG-03
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must_haves:
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truths:
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- "SRTProcessor 支持启用/禁用 LLM 语义分段"
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- "启用时使用 SemanticSegmentationEngine，禁用时使用规则分段"
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- "知乎/文章平台继续使用 AnalyzeProcessor，不受影响"
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artifacts:
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- path: "kb_workflow/pipeline/processors/srt.py"
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provides: "SRTProcessor 集成 LLM 分段"
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min_lines: 350
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exports: ["SRTProcessor.__init__", "SRTProcessor.process"]
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- path: "tests/test_srt_processor_integration.py"
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provides: "SRTProcessor 集成测试"
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min_lines: 100
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key_links:
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- from: "SRTProcessor.__init__"
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to: "SemanticSegmentationEngine"
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via: "enable_semantic_segmentation 参数"
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pattern: "enable_semantic.*segmentation"
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- from: "SRTProcessor.process"
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to: "SemanticSegmentationEngine.segment"
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via: "条件分支选择分段方式"
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pattern: "if.*semantic.*else.*rule"
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- from: "Pipeline.run"
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to: "AnalyzeProcessor/SRTProcessor"
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via: "平台路由逻辑（保持不变）"
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pattern: "platform.*bilibili.*srt"
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---
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<objective>
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集成 SemanticSegmentationEngine 到 SRTProcessor
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目的：让 SRTProcessor 支持 LLM 语义分段（可选），保持向后兼容
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输出：更新的 SRTProcessor，集成测试
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</objective>
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<execution_context>
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@/home/starmen1265/.claude/get-shit-done/workflows/execute-plan.md
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@/home/starmen1265/.claude/get-shit-done/templates/summary.md
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</execution_context>
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<context>
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@.planning/phases/13-llm/13-CONTEXT.md
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@.planning/phases/13-llm/13-RESEARCH.md
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# User Decision (Locked)
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- **SRTProcessor 集成**: 配置开关控制 — 新增 `enable_semantic_segmentation` 参数，True 时用 LLM 分段，False 时保留规则分段作为后备选项
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- **现有 SemanticSegmenter**: 完全替代 — 新代码完全替代旧代码，SemanticSegmenter 被 SemanticSegmentationEngine 取代
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# Existing Code (from srt.py)
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```python
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class SRTProcessor(BaseProcessor):
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def __init__(
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self,
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state_manager: Optional[WorkflowStateManager] = None,
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enable_correction: bool = True
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):
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...
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self.segmenter = SemanticSegmenter() # 规则分段
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```
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</context>
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<tasks>
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<task type="auto">
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<name>Task 1: 集成 SemanticSegmentationEngine 到 SRTProcessor</name>
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<files>kb_workflow/pipeline/processors/srt.py, tests/test_srt_processor_integration.py</files>
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<action>
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更新 SRTProcessor，集成 LLM 语义分段：
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1. 在 __init__ 中添加 `enable_semantic_segmentation` 参数：
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```python
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def __init__(
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self,
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state_manager: Optional[WorkflowStateManager] = None,
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enable_correction: bool = True,
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enable_semantic_segmentation: bool = False # 新增参数
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):
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super().__init__(state_manager)
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self.enable_correction = enable_correction
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self.enable_semantic_segmentation = enable_semantic_segmentation
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# 初始化分段器
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if enable_semantic_segmentation:
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try:
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from kb_workflow.srt.semantic_engine import SemanticSegmentationEngine
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from kb_workflow.deep_analyzer import SimpleAIClient
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ai_client = SimpleAIClient()
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if ai_client.api_key:
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self.segmenter = SemanticSegmentationEngine(ai_client=ai_client)
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logger.info("Semantic segmentation enabled")
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else:
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logger.info("AI API key not configured, using rule-based segmenter")
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self.segmenter = SemanticSegmenter()
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except Exception as e:
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logger.warning(f"Failed to initialize semantic engine: {e}, using rule-based segmenter")
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self.segmenter = SemanticSegmenter()
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else:
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self.segmenter = SemanticSegmenter()
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# 初始化纠错器（保持不变）
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...
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```
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2. 更新 process() 方法中的分段调用：
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```python
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# Step 2: 语义分段
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logger.info("Segmenting subtitles into paragraphs")
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if self.enable_semantic_segmentation and isinstance(self.segmenter, SemanticSegmentationEngine):
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# LLM 语义分段
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paragraphs = self.segmenter.segment(segments, metadata=metadata)
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else:
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# 规则分段
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paragraphs = self.segmenter.segment(segments)
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logger.info(f"Generated {len(paragraphs)} paragraphs")
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```
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3. 更新 _generate_markdown() 以支持带标题的分段：
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```python
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def _generate_markdown(
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self,
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output_file: Path,
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metadata: Dict[str, Any],
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paragraphs: List[str], # 或 List[VideoSegment]
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tags: List[str]
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) -> None:
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...
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# 如果 paragraphs 包含 VideoSegment（LLM 分段结果）
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if paragraphs and hasattr(paragraphs[0], 'title'):
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for seg in paragraphs:
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if seg.title:
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content_parts.append(f"## {seg.title}")
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content_parts.append(seg.content)
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content_parts.append("")
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else:
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# 规则分段（纯文本）
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content_parts.append("## 内容")
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content_parts.append("")
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for para in paragraphs:
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content_parts.append(para)
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content_parts.append("")
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...
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```
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4. 更新 SRTInput/Output Schema（如果需要）：
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- 考虑是否需要添加 semantic_segmentation_enabled 字段
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5. 创建集成测试：
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```python
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# tests/test_srt_processor_integration.py
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def test_srt_processor_semantic_mode():
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"""测试 SRTProcessor 语义分段模式"""
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# mock AI client
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# enable_semantic_segmentation=True
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# 验证调用 SemanticSegmentationEngine
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def test_srt_processor_rule_based_mode():
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"""测试 SRTProcessor 规则分段模式"""
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# enable_semantic_segmentation=False
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# 验证使用 SemanticSegmenter
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def test_srt_processor_semantic_fallback():
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"""测试 AI 不可用时降级到规则分段"""
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# AI client 无效
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# 验证降级到 SemanticSegmenter
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```
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6. 确保 Pipeline 平台路由逻辑不受影响（Phase 12 已实现）：
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- B站视频使用 SRTProcessor
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- 知乎/文章继续使用 AnalyzeProcessor
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- 不修改 Pipeline.run() 中的路由逻辑
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</action>
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<verify>
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<automated>pytest tests/test_srt_processor_integration.py::test_srt_processor_semantic_mode -x</automated>
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</verify>
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<done>
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- SRTProcessor 支持 enable_semantic_segmentation 参数
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- 启用时使用 SemanticSegmentationEngine
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- 禁用时使用 SemanticSegmenter（向后兼容）
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- AI 不可用时自动降级到规则分段
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- _generate_markdown() 支持带标题的分段
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- 集成测试验证所有场景
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- 平台路由逻辑不受影响（知乎/文章继续使用 AnalyzeProcessor）
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</done>
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</task>
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</tasks>
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<verification>
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- pytest tests/test_srt_processor_integration.py -v 全部通过
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- pytest tests/test_srt_processor.py -v 现有测试不回归
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- 平台路由逻辑不受影响
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- 向后兼容（enable_semantic_segmentation=False 时行为不变）
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</verification>
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<success_criteria>
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1. SRTProcessor 支持 enable_semantic_segmentation 参数
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2. 启用时使用 LLM 分段，禁用时使用规则分段
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3. AI 不可用时自动降级
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4. 集成测试验证所有场景
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5. 平台路由逻辑不受影响
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</success_criteria>
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<output>
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After completion, create `.planning/phases/13-llm/13-07-SUMMARY.md`
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</output>
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