108 lines
3.2 KiB
Markdown
108 lines
3.2 KiB
Markdown
---
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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: planning
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stopped_at: Phase 13 context gathered
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last_updated: "2026-03-21T16:14:07.594Z"
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last_activity: 2026-03-21 — Roadmap created for v1.6
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progress:
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total_phases: 15
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completed_phases: 10
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total_plans: 31
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completed_plans: 30
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percent: 0
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---
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# Project State
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## Project Reference
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See: .planning/PROJECT.md (updated 2026-03-21)
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**Core value:** 可靠、可维护的处理管道 - 每个步骤独立、可验证、可恢复
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**Current focus:** 语义分段改进 — 使用 LLM 实现智能分段
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## Current Position
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Phase: Phase 13 - 核心 LLM 分段功能
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Plan: TBD
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Status: Planning
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Last activity: 2026-03-21 — Roadmap created for v1.6
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Progress: [░░░░░░░░░░] 0% (Phase 13/15)
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### Phase 13 Goal
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实现基于 LLM 的智能语义分段，识别视频类型并生成主题标题
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### Phase 13 Requirements (18 total)
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- VIDEO-01, VIDEO-02, VIDEO-03 (视频类型识别)
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- BOUND-01, BOUND-02, BOUND-03, BOUND-04 (语义边界检测)
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- COMP-01, COMP-02, COMP-03 (内容完整性保护)
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- TITLE-01, TITLE-02, TITLE-03 (主题标题生成)
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- SENT-01, SENT-02, SENT-03 (中文句边界检测)
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- STRAT-01, STRAT-02, STRAT-03 (分段策略)
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## Accumulated Context
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### Decisions
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Key decisions from v1.0-v1.5 (preserved):
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- 管道模型 — BaseProcessor + Pipeline 架构
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- 步骤级状态 — WorkflowStateManager
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- Schema 验证 — Pydantic v2
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- 顺序执行 — 简单性和可调试性
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- CLI subparsers — 可扩展命令结构
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- SRT 独立模块 — kb_workflow/srt/ 职责分离
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- ASR 纠错可选 — AI 不可用时返回原文
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v1.6 scope (待验证):
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- LLM 语义分析 — 替代简单规则分段
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- 通用自适应 — 自动识别视频类型
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- 主题识别与标题 — 主题章节生成标题
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- 内容完整性 — 避免错误截断话题
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### Technical Context
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**架构集成:**
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- 扩展现有 `SemanticSegmenter`，保持向后兼容
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- 新增 `SemanticSegmentationEngine` 模块（`kb_workflow/srt/semantic_engine.py`）
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- 在 `SRTProcessor` 中添加 LLM 分支（`enable_semantic_segmentation` 参数）
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**技术栈:**
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- SimpleAIClient（已集成 zhipu/openai/minimax）
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- Pydantic v2（LLM 响应验证）
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- 现有 SemanticSegmenter（规则分段保留）
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**数据流:**
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```
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SRT Input → Parse Subtitles → Video Type Detection →
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Semantic Segmentation → Extract Paragraphs → ASR Correction →
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Deep Analysis
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```
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### Known Risks
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**高优先级风险:**
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1. **上下文窗口溢出** — 长视频（>1小时）字幕超10万字符，需分块处理
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2. **LLM 输出格式不稳定** — 需多层 fallback 策略
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3. **分段边界破坏完整性** — 需句边界保护机制
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4. **分段不一致性** — LLM 随机性导致多次运行结果不同
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5. **成本失控** — 高频 LLM 调用可能导致费用激增
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**缓解策略:**
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- Phase 13: 实现 Pydantic 验证 + 句边界保护
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- Phase 14: 实现分块处理 + 缓存 + 温度控制
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### Blockers/Concerns
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None.
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## Session Continuity
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Last session: 2026-03-21T16:14:07.592Z
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Stopped at: Phase 13 context gathered
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Resume file: .planning/phases/13-llm/13-CONTEXT.md
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**Next step:** `/gsd:plan-phase 13` - 开始 Phase 13 的详细规划
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