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kb-skills/.planning/phases/14-robustness-cost/14-03-SUMMARY.md
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starmen1265 bf359628fc 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>
2026-03-23 22:36:50 +08:00

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phase, plan, subsystem, tags, requires, provides, affects, tech-stack, key-files, key-decisions, patterns-established, requirements-completed, duration, completed
phase plan subsystem tags requires provides affects tech-stack key-files key-decisions patterns-established requirements-completed duration completed
14-robustness-cost 03 caching
cache
sha256
content-hashing
llm-optimization
phase provides
13-llm SemanticSegmentationEngine, SegmentationResult, VideoSegment
SemanticCache 缓存管理器
compute_content_hash() 内容哈希函数
缓存目录结构 (.planning/cache/semantic/)
14-04
14-05
semantic-segmentation
added patterns
hashlib
json
pathlib
content-based-caching
versioned-cache-format
error-tolerant-cache
created modified
kb_workflow/srt/cache/semantic_cache.py
kb_workflow/srt/cache/__init__.py
tests/test_semantic_robustness.py
缓存键使用 SHA256 内容哈希,确保相同内容精确匹配
缓存文件包含 version 字段(1.0),支持未来格式变更
缓存失败时返回 None,不中断主流程(错误容错)
Pattern: Content-based caching - 使用内容哈希而非 ID 作为缓存键
Pattern: Versioned cache format - 缓存数据包含版本号便于迁移
Pattern: Error-tolerant cache - 缓存读写失败时降级,不影响核心功能
COST-02
1min 2026-03-23

Phase 14: 鲁棒性与成本控制 - Plan 03 Summary

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

Performance

  • Duration: 1 min (72s)
  • Started: 2026-03-23T14:34:58Z
  • Completed: 2026-03-23T14:36:09Z
  • Tasks: 1 (TDD cycle: RED → GREEN)
  • Files modified: 3

Accomplishments

  • 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
  • SHA256 哈希函数: compute_content_hash() 生成 64 字符十六进制缓存键
  • 版本控制机制: 缓存文件包含 version 字段,支持未来格式变更
  • 错误容错设计: 缓存读写失败时返回 None,不中断主流程
  • 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

Task Commits

Each task was committed atomically:

  1. Task 1: 创建 SemanticCache 缓存管理器 - TDD Cycle
    • b6e923c (test) - 添加失败的测试用例(RED)
    • baf7c0f (feat) - 实现 SemanticCache 使测试通过(GREEN)

Plan metadata: TBD (docs: complete plan)

Note: TDD tasks followed RED → GREEN flow. No refactoring needed.

Files Created/Modified

Created

  • kb_workflow/srt/cache/semantic_cache.py - 缓存管理器核心实现

    • compute_content_hash(): SHA256 哈希计算
    • SemanticCache.get(): 从缓存读取 SegmentationResult
    • SemanticCache.set(): 保存 SegmentationResult 到缓存
    • 支持版本控制和错误容错
  • kb_workflow/srt/cache/__init__.py - 缓存模块导出

    • 导出 SemanticCache 和 compute_content_hash
  • tests/test_semantic_robustness.py - 缓存功能测试套件

    • TestComputeContentHash: 测试哈希计算(3 个测试)
    • TestSemanticCache: 测试缓存功能(8 个测试)
    • 覆盖:缓存命中/未命中、版本控制、错误处理、多缓存项

Modified

None

Decisions Made

所有决策遵守 14-CONTEXT.md 中的用户决策:

  1. 缓存键使用 SHA256 内容哈希

    • 理由: 精确匹配,相同内容一定命中缓存
    • 实现: hashlib.sha256(content.encode('utf-8')).hexdigest()
  2. 缓存存储在本地文件系统

    • 理由: 简单持久化,无需外部依赖
    • 路径: .planning/cache/semantic/
  3. 缓存 TTL 永久有效

    • 理由: 字幕内容不变,分段结果应该一致
    • 清理: 手动删除缓存文件
  4. 缓存包含完整的 SegmentationResult

    • 理由: 包含 video_type 和 segments,完整保存 LLM 输出
    • 格式: JSON + version 字段
  5. 错误容错设计

    • 理由: 缓存失败不应中断主流程
    • 实现: 读写失败时返回 None,记录 warning 日志

Deviations from Plan

None - plan executed exactly as written.

Issues Encountered

None - implementation proceeded smoothly with TDD approach.

User Setup Required

None - no external service configuration required.

缓存目录会在首次使用时自动创建:.planning/cache/semantic/

Next Phase Readiness

Ready for Integration

  • ✅ SemanticCache 类已实现并测试
  • ✅ 缓存模块可以导出到其他组件
  • ✅ 错误容错机制已就位

Integration Points

计划 14-04 和 14-05 可以将 SemanticCache 集成到 UnifiedSegmentationStrategy:

from kb_workflow.srt.cache import SemanticCache

class UnifiedSegmentationStrategy:
    def __init__(self, ...):
        self.cache = SemanticCache()  # 添加缓存层

    def segment(self, segments, metadata):
        content = " ".join(seg.text for seg in segments)

        # 尝试从缓存读取
        cached_result = self.cache.get(content)
        if cached_result:
            return cached_result.segments

        # 调用 LLM
        result = self._call_llm(content, metadata)

        # 保存到缓存
        self.cache.set(content, result)

        return result.segments

Blockers/Concerns

None.

Verification

所有测试通过:

pytest tests/test_semantic_robustness.py::TestComputeContentHash \
       tests/test_semantic_robustness.py::TestSemanticCache -v

结果:11 passed in 0.08s

Success Criteria Met

  • ✅ 相同内容第二次调用时从缓存读取
  • ✅ 缓存文件存储在 .planning/cache/semantic/ 目录
  • ✅ 缓存文件命名格式: {sha256_hash}.json
  • ✅ 缓存内容包含完整的 SegmentationResult (JSON 格式)
  • ✅ 缓存命中时记录 debug 级别日志
  • ✅ 所有测试通过 (11/11)

Phase: 14-robustness-cost Completed: 2026-03-23