kb-skills/.planning/phases/13-llm/13-07-PLAN.md

phase, plan, type, wave, depends_on, files_modified, autonomous, requirements, must_haves

phase	plan	type	wave	depends_on	files_modified	autonomous	requirements	must_haves
13	07	execute	2	13-01 13-02 13-03 13-04 13-05 13-06	kb_workflow/pipeline/processors/srt.py tests/test_srt_processor_integration.py	true	CONFIG-03	truths artifacts key_links SRTProcessor 支持启用/禁用 LLM 语义分段 启用时使用 SemanticSegmentationEngine，禁用时使用规则分段 知乎/文章平台继续使用 AnalyzeProcessor，不受影响 path provides min_lines exports kb_workflow/pipeline/processors/srt.py SRTProcessor 集成 LLM 分段 350 SRTProcessor.__init__ SRTProcessor.process path provides min_lines tests/test_srt_processor_integration.py SRTProcessor 集成测试 100 from to via pattern SRTProcessor.__init__ SemanticSegmentationEngine enable_semantic_segmentation 参数 enable_semantic.*segmentation from to via pattern SRTProcessor.process SemanticSegmentationEngine.segment 条件分支选择分段方式 if.*semantic.*else.*rule from to via pattern Pipeline.run AnalyzeProcessor/SRTProcessor 平台路由逻辑（保持不变） platform.*bilibili.*srt

集成 SemanticSegmentationEngine 到 SRTProcessor

目的：让 SRTProcessor 支持 LLM 语义分段（可选），保持向后兼容 输出：更新的 SRTProcessor，集成测试

<execution_context> @/home/starmen1265/.claude/get-shit-done/workflows/execute-plan.md @/home/starmen1265/.claude/get-shit-done/templates/summary.md </execution_context>

@.planning/phases/13-llm/13-CONTEXT.md @.planning/phases/13-llm/13-RESEARCH.md

User Decision (Locked)

• 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
• 现有 SemanticSegmenter: 完全替代 — 新代码完全替代旧代码，SemanticSegmenter 被 SemanticSegmentationEngine 取代

Existing Code (from srt.py)

class SRTProcessor(BaseProcessor):
    def __init__(
        self,
        state_manager: Optional[WorkflowStateManager] = None,
        enable_correction: bool = True
    ):
        ...
        self.segmenter = SemanticSegmenter()  # 规则分段

Task 1: 集成 SemanticSegmentationEngine 到 SRTProcessor kb_workflow/pipeline/processors/srt.py, tests/test_srt_processor_integration.py 更新 SRTProcessor，集成 LLM 语义分段：

  1. 在 __init__ 中添加 `enable_semantic_segmentation` 参数：
     ```python
     def __init__(
         self,
         state_manager: Optional[WorkflowStateManager] = None,
         enable_correction: bool = True,
         enable_semantic_segmentation: bool = False  # 新增参数
     ):
         super().__init__(state_manager)
         self.enable_correction = enable_correction
         self.enable_semantic_segmentation = enable_semantic_segmentation

         # 初始化分段器
         if enable_semantic_segmentation:
             try:
                 from kb_workflow.srt.semantic_engine import SemanticSegmentationEngine
                 from kb_workflow.deep_analyzer import SimpleAIClient

                 ai_client = SimpleAIClient()
                 if ai_client.api_key:
                     self.segmenter = SemanticSegmentationEngine(ai_client=ai_client)
                     logger.info("Semantic segmentation enabled")
                 else:
                     logger.info("AI API key not configured, using rule-based segmenter")
                     self.segmenter = SemanticSegmenter()
             except Exception as e:
                 logger.warning(f"Failed to initialize semantic engine: {e}, using rule-based segmenter")
                 self.segmenter = SemanticSegmenter()
         else:
             self.segmenter = SemanticSegmenter()

         # 初始化纠错器（保持不变）
         ...
     ```

  2. 更新 process() 方法中的分段调用：
     ```python
     # Step 2: 语义分段
     logger.info("Segmenting subtitles into paragraphs")
     if self.enable_semantic_segmentation and isinstance(self.segmenter, SemanticSegmentationEngine):
         # LLM 语义分段
         paragraphs = self.segmenter.segment(segments, metadata=metadata)
     else:
         # 规则分段
         paragraphs = self.segmenter.segment(segments)
     logger.info(f"Generated {len(paragraphs)} paragraphs")
     ```

  3. 更新 _generate_markdown() 以支持带标题的分段：
     ```python
     def _generate_markdown(
         self,
         output_file: Path,
         metadata: Dict[str, Any],
         paragraphs: List[str],  # 或 List[VideoSegment]
         tags: List[str]
     ) -> None:
         ...
         # 如果 paragraphs 包含 VideoSegment（LLM 分段结果）
         if paragraphs and hasattr(paragraphs[0], 'title'):
             for seg in paragraphs:
                 if seg.title:
                     content_parts.append(f"## {seg.title}")
                 content_parts.append(seg.content)
                 content_parts.append("")
         else:
             # 规则分段（纯文本）
             content_parts.append("## 内容")
             content_parts.append("")
             for para in paragraphs:
                 content_parts.append(para)
                 content_parts.append("")
         ...
     ```

  4. 更新 SRTInput/Output Schema（如果需要）：
     - 考虑是否需要添加 semantic_segmentation_enabled 字段

  5. 创建集成测试：
     ```python
     # tests/test_srt_processor_integration.py
     def test_srt_processor_semantic_mode():
         """测试 SRTProcessor 语义分段模式"""
         # mock AI client
         # enable_semantic_segmentation=True
         # 验证调用 SemanticSegmentationEngine

     def test_srt_processor_rule_based_mode():
         """测试 SRTProcessor 规则分段模式"""
         # enable_semantic_segmentation=False
         # 验证使用 SemanticSegmenter

     def test_srt_processor_semantic_fallback():
         """测试 AI 不可用时降级到规则分段"""
         # AI client 无效
         # 验证降级到 SemanticSegmenter
     ```

  6. 确保 Pipeline 平台路由逻辑不受影响（Phase 12 已实现）：
     - B站视频使用 SRTProcessor
     - 知乎/文章继续使用 AnalyzeProcessor
     - 不修改 Pipeline.run() 中的路由逻辑
</action>
<verify>
  <automated>pytest tests/test_srt_processor_integration.py::test_srt_processor_semantic_mode -x</automated>
</verify>
<done>
  - SRTProcessor 支持 enable_semantic_segmentation 参数
  - 启用时使用 SemanticSegmentationEngine
  - 禁用时使用 SemanticSegmenter（向后兼容）
  - AI 不可用时自动降级到规则分段
  - _generate_markdown() 支持带标题的分段
  - 集成测试验证所有场景
  - 平台路由逻辑不受影响（知乎/文章继续使用 AnalyzeProcessor）
</done>

- pytest tests/test_srt_processor_integration.py -v 全部通过 - pytest tests/test_srt_processor.py -v 现有测试不回归 - 平台路由逻辑不受影响 - 向后兼容（enable_semantic_segmentation=False 时行为不变）

<success_criteria>

1. SRTProcessor 支持 enable_semantic_segmentation 参数
2. 启用时使用 LLM 分段，禁用时使用规则分段
3. AI 不可用时自动降级
4. 集成测试验证所有场景
5. 平台路由逻辑不受影响 </success_criteria>

After completion, create `.planning/phases/13-llm/13-07-SUMMARY.md`