- 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>
5.8 KiB
phase, plan, subsystem, tags, requires, provides, affects, tech-stack, key-files, key-decisions, patterns-established, requirements-completed, duration, completed
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| 14-robustness-cost | 03 | caching |
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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:
- 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 SegmentationResultSemanticCache.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
- 导出
-
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:
-
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()
-
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/
-
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
-
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
-
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