docs(14-03): complete semantic cache implementation plan
- Created SUMMARY.md with implementation details - Updated STATE.md with new decisions and session info - Updated ROADMAP.md progress (1/6 plans complete) - Marked COST-02 requirement complete Duration: 1 min (72s) Tasks: 1 (TDD: RED → GREEN) Tests: 11 passed Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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### 成本控制 (COST)
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- [ ] **COST-01**: 长视频字幕进行分块处理，避免上下文窗口溢出
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- [ ] **COST-02**: 实现智能缓存，相同内容不重复调用 LLM
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- [x] **COST-02**: 实现智能缓存，相同内容不重复调用 LLM
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- [ ] **COST-03**: 提供配置开关，用户可选择是否启用 LLM 分段
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### 输出格式 (OUTPUT)
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@@ -120,7 +120,7 @@
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| ROBUST-03 | Phase 14 | Pending |
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| ROBUST-04 | Phase 14 | Pending |
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| COST-01 | Phase 14 | Pending |
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| COST-02 | Phase 14 | Pending |
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| COST-02 | Phase 14 | Complete |
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| COST-03 | Phase 14 | Pending |
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| OUTPUT-01 | Phase 15 | Pending |
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| OUTPUT-02 | Phase 15 | Pending |
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@@ -233,7 +233,7 @@ Phases execute in numeric order: 1 → 2 → ... → 12 → 13 → 14 → 15
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| 11. Stealth Fingerprinting | v1.4 | 2/2 | Complete | 2026-03-18 |
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| 12. SRT 简化处理器 | v1.5 | 2/2 | Complete | 2026-03-21 |
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| 13. 核心 LLM 分段功能 | 6/7 | Complete | 2026-03-21 | - |
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| 14. 鲁棒性与成本控制 | v1.6 | 0/6 | Draft | 2026-03-23 |
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| 14. 鲁棒性与成本控制 | 1/6 | In Progress| | 2026-03-23 |
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| 15. 输出优化与兼容性 | v1.6 | 0/0 | Not started | - |
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---
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+11
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@@ -3,14 +3,14 @@ gsd_state_version: 1.0
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milestone: v1.4
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milestone_name: Zhihu Anti-Crawler
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status: executing
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stopped_at: Phase 14 context gathered
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last_updated: "2026-03-23T12:51:30.858Z"
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stopped_at: Completed 14-03-PLAN.md
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last_updated: "2026-03-23T14:36:46.811Z"
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last_activity: 2026-03-22 — Phase 13 Plan 07 completed (SRTProcessor integration)
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progress:
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total_phases: 15
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completed_phases: 11
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total_plans: 38
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completed_plans: 37
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total_plans: 44
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completed_plans: 38
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percent: 31
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---
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@@ -84,6 +84,9 @@ v1.6 Phase 13 decisions (from 13-CONTEXT.md):
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- [Phase 13]: Strategy Pattern with Protocol interface for extensibility (STRAT-01)
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- [Phase 13]: Unified segmentation strategy for all video types (per user decision, STRAT-02)
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- [Phase 13]: Strategy injection via constructor for easy testing and extension (STRAT-03)
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- [Phase 14-robustness-cost]: 缓存键使用 SHA256 内容哈希，确保相同内容精确匹配
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- [Phase 14-robustness-cost]: 缓存文件包含 version 字段（1.0），支持未来格式变更
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- [Phase 14-robustness-cost]: 缓存失败时返回 None，不中断主流程（错误容错）
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### Technical Context
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@@ -132,12 +135,13 @@ None.
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| 13 | 05 | 2min | 1 | 2 | 2026-03-22 |
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| 13 | 06 | 12min | 1 | 2 | 2026-03-22 |
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| 13 | 07 | 4min | 1 | 4 | 2026-03-22 |
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| Phase 14-robustness-cost P03 | 72 | 1 tasks | 3 files |
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## Session Continuity
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Last session: 2026-03-23T12:51:30.856Z
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Stopped at: Phase 14 context gathered
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Resume file: .planning/phases/14-robustness-cost/14-CONTEXT.md
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Last session: 2026-03-23T14:36:46.809Z
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Stopped at: Completed 14-03-PLAN.md
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Resume file: None
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**Next step:** Phase 13 complete, continue to Phase 14 (鲁棒性) or Phase 15 (优化)
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---
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phase: 14-robustness-cost
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plan: 03
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subsystem: caching
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tags: [cache, sha256, content-hashing, llm-optimization]
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# Dependency graph
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requires:
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- phase: 13-llm
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provides: SemanticSegmentationEngine, SegmentationResult, VideoSegment
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provides:
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- SemanticCache 缓存管理器
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- compute_content_hash() 内容哈希函数
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- 缓存目录结构 (.planning/cache/semantic/)
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affects: [14-04, 14-05, semantic-segmentation]
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# Tech tracking
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tech-stack:
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added: [hashlib, json, pathlib]
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patterns: [content-based-caching, versioned-cache-format, error-tolerant-cache]
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key-files:
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created:
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- kb_workflow/srt/cache/semantic_cache.py
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- kb_workflow/srt/cache/__init__.py
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- tests/test_semantic_robustness.py
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modified: []
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key-decisions:
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- "缓存键使用 SHA256 内容哈希，确保相同内容精确匹配"
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- "缓存文件包含 version 字段（1.0），支持未来格式变更"
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- "缓存失败时返回 None，不中断主流程（错误容错）"
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patterns-established:
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- "Pattern: Content-based caching - 使用内容哈希而非 ID 作为缓存键"
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- "Pattern: Versioned cache format - 缓存数据包含版本号便于迁移"
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- "Pattern: Error-tolerant cache - 缓存读写失败时降级，不影响核心功能"
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requirements-completed: [COST-02]
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# Metrics
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duration: 1min
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completed: 2026-03-23
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---
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# Phase 14: 鲁棒性与成本控制 - Plan 03 Summary
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**基于 SHA256 内容哈希的智能缓存系统，避免重复 LLM 调用，降低 API 成本和延迟**
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## Performance
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- **Duration:** 1 min (72s)
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- **Started:** 2026-03-23T14:34:58Z
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- **Completed:** 2026-03-23T14:36:09Z
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- **Tasks:** 1 (TDD cycle: RED → GREEN)
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- **Files modified:** 3
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## Accomplishments
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- **SemanticCache 缓存管理器**: 实现完整的读写功能，支持基于内容哈希的缓存
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- **SHA256 哈希函数**: `compute_content_hash()` 生成 64 字符十六进制缓存键
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- **版本控制机制**: 缓存文件包含 `version` 字段，支持未来格式变更
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- **错误容错设计**: 缓存读写失败时返回 None，不中断主流程
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- **全面测试覆盖**: 11 个测试用例，覆盖缓存命中/未命中、版本控制、错误处理
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## Task Commits
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Each task was committed atomically:
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1. **Task 1: 创建 SemanticCache 缓存管理器** - TDD Cycle
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- `b6e923c` (test) - 添加失败的测试用例（RED）
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- `baf7c0f` (feat) - 实现 SemanticCache 使测试通过（GREEN）
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**Plan metadata:** TBD (docs: complete plan)
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_Note: TDD tasks followed RED → GREEN flow. No refactoring needed._
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## Files Created/Modified
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### Created
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- `kb_workflow/srt/cache/semantic_cache.py` - 缓存管理器核心实现
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- `compute_content_hash()`: SHA256 哈希计算
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- `SemanticCache.get()`: 从缓存读取 SegmentationResult
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- `SemanticCache.set()`: 保存 SegmentationResult 到缓存
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- 支持版本控制和错误容错
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- `kb_workflow/srt/cache/__init__.py` - 缓存模块导出
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- 导出 `SemanticCache` 和 `compute_content_hash`
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- `tests/test_semantic_robustness.py` - 缓存功能测试套件
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- `TestComputeContentHash`: 测试哈希计算（3 个测试）
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- `TestSemanticCache`: 测试缓存功能（8 个测试）
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- 覆盖：缓存命中/未命中、版本控制、错误处理、多缓存项
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### Modified
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None
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## Decisions Made
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所有决策遵守 14-CONTEXT.md 中的用户决策：
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1. **缓存键使用 SHA256 内容哈希**
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- 理由: 精确匹配，相同内容一定命中缓存
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- 实现: `hashlib.sha256(content.encode('utf-8')).hexdigest()`
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2. **缓存存储在本地文件系统**
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- 理由: 简单持久化，无需外部依赖
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- 路径: `.planning/cache/semantic/`
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3. **缓存 TTL 永久有效**
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- 理由: 字幕内容不变，分段结果应该一致
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- 清理: 手动删除缓存文件
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4. **缓存包含完整的 SegmentationResult**
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- 理由: 包含 video_type 和 segments，完整保存 LLM 输出
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- 格式: JSON + version 字段
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5. **错误容错设计**
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- 理由: 缓存失败不应中断主流程
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- 实现: 读写失败时返回 None，记录 warning 日志
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## Deviations from Plan
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None - plan executed exactly as written.
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## Issues Encountered
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None - implementation proceeded smoothly with TDD approach.
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## User Setup Required
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None - no external service configuration required.
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缓存目录会在首次使用时自动创建：`.planning/cache/semantic/`
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## Next Phase Readiness
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### Ready for Integration
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- ✅ SemanticCache 类已实现并测试
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- ✅ 缓存模块可以导出到其他组件
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- ✅ 错误容错机制已就位
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### Integration Points
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计划 14-04 和 14-05 可以将 SemanticCache 集成到 UnifiedSegmentationStrategy：
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```python
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from kb_workflow.srt.cache import SemanticCache
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class UnifiedSegmentationStrategy:
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def __init__(self, ...):
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self.cache = SemanticCache() # 添加缓存层
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def segment(self, segments, metadata):
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content = " ".join(seg.text for seg in segments)
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# 尝试从缓存读取
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cached_result = self.cache.get(content)
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if cached_result:
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return cached_result.segments
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# 调用 LLM
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result = self._call_llm(content, metadata)
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# 保存到缓存
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self.cache.set(content, result)
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return result.segments
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```
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### Blockers/Concerns
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None.
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## Verification
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所有测试通过：
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```bash
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pytest tests/test_semantic_robustness.py::TestComputeContentHash \
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tests/test_semantic_robustness.py::TestSemanticCache -v
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```
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结果：11 passed in 0.08s
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### Success Criteria Met
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- ✅ 相同内容第二次调用时从缓存读取
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- ✅ 缓存文件存储在 `.planning/cache/semantic/` 目录
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- ✅ 缓存文件命名格式: `{sha256_hash}.json`
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- ✅ 缓存内容包含完整的 SegmentationResult (JSON 格式)
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- ✅ 缓存命中时记录 debug 级别日志
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- ✅ 所有测试通过 (11/11)
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---
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*Phase: 14-robustness-cost*
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*Completed: 2026-03-23*
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