feat: implement obsidian-kb skill with long video chunking
- Multi-source content aggregation (Bilibili/YouTube/Zhihu) - Smart inbox management with AI quality scoring - Long video chunking analysis (30min+ auto-segmentation) - Topic-based organization - Git auto-sync to Obsidian - Template system with Jinja2 and fallback rendering - CLI commands: subscribe, inbox, process, skip, status, search - Comprehensive test suite with unit and integration tests - Documentation: README.md and SKILL.md Checkpoint 7-8 completed by Agent E
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OpenClaw Agent
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"""Obsidian KB - 知识库内容管理模型"""
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from enum import Enum
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from dataclasses import dataclass, field, asdict
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from typing import Optional, List, Dict, Any
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from datetime import datetime
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import uuid
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import json
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class ContentType(Enum):
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"""内容类型"""
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VIDEO = "video"
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ARTICLE = "article"
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class ContentStatus(Enum):
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"""内容处理状态"""
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NEW = "new" # 新发现
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SCANNED = "scanned" # 已扫描元数据
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CONFIRMED = "confirmed" # 已确认处理
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ANALYZED = "analyzed" # 已AI分析
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GENERATED = "generated" # 已生成笔记
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SKIPPED = "skipped" # 已跳过
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@dataclass
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class ContentChunk:
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"""内容块 - 用于分段处理"""
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id: str = field(default_factory=lambda: str(uuid.uuid4())[:8])
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content: str = "" # 内容文本
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start_time: Optional[int] = None # 开始时间（秒）
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end_time: Optional[int] = None # 结束时间（秒）
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def to_dict(self) -> Dict[str, Any]:
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return asdict(self)
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@classmethod
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def from_dict(cls, data: Dict[str, Any]) -> "ContentChunk":
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return cls(**data)
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@dataclass
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class ContentAnalysis:
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"""内容分析结果 - 用于模板渲染"""
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one_liner: str = "" # 一句话摘要
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summary: str = "" # 详细摘要
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key_points: List[str] = field(default_factory=list) # 关键要点
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tags: List[str] = field(default_factory=list) # 标签
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quality_score: float = 0.0 # 质量评分 0-100
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# 教程视频专用字段
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prerequisites: List[str] = field(default_factory=list) # 前置知识
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outline: List[Dict[str, Any]] = field(default_factory=list) # 大纲分段
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pitfalls: List[str] = field(default_factory=list) # 常见坑点
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practice_advice: List[str] = field(default_factory=list) # 实践建议
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def to_dict(self) -> Dict[str, Any]:
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return asdict(self)
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@classmethod
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def from_dict(cls, data: Dict[str, Any]) -> "ContentAnalysis":
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return cls(**data)
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def is_high_quality(self, threshold: float = 60.0) -> bool:
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"""判断是否高质量内容"""
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return self.quality_score >= threshold
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@dataclass
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class QualityMetrics:
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"""内容质量指标"""
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overall_score: float = 0.0 # 综合评分 0-1
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information_density: float = 0.0 # 信息密度
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originality: float = 0.0 # 原创性
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practicality: float = 0.0 # 实用性
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depth: float = 0.0 # 深度
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def to_dict(self) -> Dict[str, Any]:
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return asdict(self)
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@classmethod
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def from_dict(cls, data: Dict[str, Any]) -> "QualityMetrics":
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return cls(**data)
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@dataclass
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class AnalysisResult:
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"""AI深度分析结果"""
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main_topics: List[str] = field(default_factory=list) # 主要主题
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key_insights: List[str] = field(default_factory=list) # 关键洞察
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timeline_summary: List[Dict[str, Any]] = field(default_factory=list) # 时间线摘要
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content_structure: str = "" # 内容结构描述
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recommended_tags: List[str] = field(default_factory=list) # 推荐标签
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target_audience: str = "" # 目标受众
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difficulty_level: str = "intermediate" # 难度级别
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def to_dict(self) -> Dict[str, Any]:
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return asdict(self)
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@classmethod
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def from_dict(cls, data: Dict[str, Any]) -> "AnalysisResult":
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return cls(**data)
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@dataclass
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class TimelineEntry:
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"""时间线条目"""
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timestamp: str
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title: str
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description: str
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def to_dict(self) -> Dict[str, str]:
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return asdict(self)
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@classmethod
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def from_dict(cls, data: Dict[str, str]) -> "TimelineEntry":
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return cls(**data)
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@dataclass
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class Content:
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"""内容数据模型"""
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# 基本信息
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id: str = field(default_factory=lambda: str(uuid.uuid4())[:8])
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source: str = "" # 来源: bilibili, youtube, zhihu, local
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source_id: str = "" # 来源唯一ID
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url: str = ""
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title: str = ""
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author: str = ""
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description: str = ""
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duration: Optional[int] = None # 视频时长(秒)
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# 内容类型与状态
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content_type: ContentType = ContentType.VIDEO
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status: ContentStatus = ContentStatus.NEW
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# 元数据
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created_at: str = field(default_factory=lambda: datetime.now().isoformat())
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processed_at: Optional[str] = None
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# 原始内容
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transcript: str = "" # 字幕/原文
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# AI分析结果
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summary: str = "" # 摘要
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one_liner: str = "" # 一句话摘要
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key_points: List[str] = field(default_factory=list) # 关键要点
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tags: List[str] = field(default_factory=list) # 标签
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quality_score: float = 0.0 # 质量评分
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analysis: Optional[ContentAnalysis] = None # 详细分析结果
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outline: List[str] = field(default_factory=list) # 大纲
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timeline: List[TimelineEntry] = field(default_factory=list) # 时间线
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quality_metrics: QualityMetrics = field(default_factory=QualityMetrics)
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# 分类与标签
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category: str = "" # 主题分类
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# 输出
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output_path: Optional[str] = None # 生成的笔记路径
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note_path: Optional[str] = None # 别名，用于兼容
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def to_dict(self) -> Dict[str, Any]:
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"""序列化为字典"""
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data = asdict(self)
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data['content_type'] = self.content_type.value
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data['status'] = self.status.value
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data['quality_metrics'] = self.quality_metrics.to_dict()
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data['timeline'] = [e.to_dict() for e in self.timeline]
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return data
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@classmethod
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def from_dict(cls, data: Dict[str, Any]) -> "Content":
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"""从字典反序列化"""
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# 处理枚举
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data['content_type'] = ContentType(data.get('content_type', 'video'))
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data['status'] = ContentStatus(data.get('status', 'new'))
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# 处理嵌套对象
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if 'quality_metrics' in data and isinstance(data['quality_metrics'], dict):
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data['quality_metrics'] = QualityMetrics.from_dict(data['quality_metrics'])
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else:
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data['quality_metrics'] = QualityMetrics()
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if 'timeline' in data and isinstance(data['timeline'], list):
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data['timeline'] = [TimelineEntry.from_dict(e) for e in data['timeline']]
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else:
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data['timeline'] = []
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return cls(**{k: v for k, v in data.items() if k in [f.name for f in cls.__dataclass_fields__.values()]})
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def to_json(self) -> str:
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"""序列化为JSON字符串"""
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return json.dumps(self.to_dict(), ensure_ascii=False, indent=2)
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@classmethod
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def from_json(cls, json_str: str) -> "Content":
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"""从JSON字符串反序列化"""
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return cls.from_dict(json.loads(json_str))
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def is_high_quality(self, threshold: float = 0.7) -> bool:
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"""判断是否高质量内容"""
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return self.quality_metrics.overall_score >= threshold
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def update_status(self, status: ContentStatus):
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"""更新状态"""
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self.status = status
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if status == ContentStatus.GENERATED:
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self.processed_at = datetime.now().isoformat()
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