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 - AI客户端
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C5.3: 实现长视频分段分析逻辑
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"""
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import os
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import json
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from typing import Dict, Any, List, Optional
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from src.models import ContentType
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from src.config import get_config
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from src.content_chunker import ContentChunker, get_chunking_strategy, chunk_content
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class AIClient:
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"""
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AI客户端 - 支持分段分析长视频内容
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"""
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def __init__(self):
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self.config = get_config()
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self.chunker = ContentChunker()
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def quick_generate(self, prompt: str, max_tokens: int = 100) -> str:
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"""快速生成（用于轻量扫描）"""
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# 模拟AI响应（实际应调用API）
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return self._mock_generate(prompt, max_tokens)
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def generate_json(self, prompt: str) -> Dict[str, Any]:
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"""生成JSON格式响应"""
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response = self.quick_generate(prompt, max_tokens=1000)
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try:
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return json.loads(response)
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except:
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return self._parse_json_from_text(response)
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def _deep_analyze(self, content_text: str, content_type: str, duration: Optional[int] = None) -> Dict[str, Any]:
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"""
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C5.3: 深度分析入口，根据内容时长决定是否分段分析
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Args:
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content_text: 内容文本
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content_type: 内容类型
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duration: 视频时长（秒）
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Returns:
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分析结果字典
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"""
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duration = duration or 0
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print(f"[AIClient] 深度分析: type={content_type}, duration={duration}s")
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# 长视频（>=30分钟）且是视频类型，使用分段分析
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if duration >= 1800 and content_type == "video":
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print(f"[AIClient] 使用分段分析策略")
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return self._analyze_in_chunks(content_text, duration)
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# 短视频直接分析
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print(f"[AIClient] 使用直接分析策略")
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return self._analyze_direct(content_text)
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def _analyze_in_chunks(self, content_text: str, duration: int) -> Dict[str, Any]:
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"""
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C5.3: 分段分析长视频
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Args:
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content_text: SRT字幕文本
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duration: 视频时长（秒）
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Returns:
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合并后的分析结果
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"""
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# 1. 获取分段策略
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strategy = get_chunking_strategy(duration)
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print(f"[AIClient] 分段策略: {strategy['description']}")
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# 2. 执行分片
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chunks = chunk_content(content_text, duration)
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print(f"[AIClient] 内容已分为 {len(chunks)} 段")
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# 3. 逐段分析
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chunk_summaries = []
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for i, chunk in enumerate(chunks):
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print(f"[AIClient] 分析第 {i+1}/{len(chunks)} 段...")
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summary = self._analyze_chunk(chunk.text, chunk.index)
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# 处理时间格式（可能是float秒数或字符串）
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start_time = chunk.start_time
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end_time = chunk.end_time
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if isinstance(start_time, (int, float)):
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start_time = self._format_time(start_time)
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if isinstance(end_time, (int, float)):
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end_time = self._format_time(end_time)
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chunk_summaries.append({
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'index': chunk.index,
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'start_time': start_time,
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'end_time': end_time,
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'summary': summary
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})
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# 4. 合并所有段摘要
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print(f"[AIClient] 合并 {len(chunk_summaries)} 段分析结果")
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final_result = self._merge_summaries(chunk_summaries, "video")
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# 5. 构建完整时间轴
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final_result['timeline'] = self._build_timeline_from_chunks(chunk_summaries)
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return final_result
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def _analyze_chunk(self, chunk_text: str, chunk_index: int) -> str:
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"""
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C5.3: 分析单个内容分片
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Args:
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chunk_text: 分片文本
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chunk_index: 分片索引
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Returns:
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分片摘要（限制500字以内）
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"""
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# 截断文本避免过长
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text_sample = chunk_text[:3000] if len(chunk_text) > 3000 else chunk_text
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prompt = f"""请对以下视频片段进行简要分析（限制在300字以内）：
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片段内容：
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{text_sample}
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请提供：
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1. 这段的核心主题（50字以内）
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2. 关键要点（3-5条）
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3. 重要细节或引用
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请用中文回答，控制在300字以内。"""
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try:
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summary = self.quick_generate(prompt, max_tokens=500)
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# 确保不超过500字
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if len(summary) > 500:
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summary = summary[:497] + "..."
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return summary
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except Exception as e:
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print(f"[AIClient] 分片 {chunk_index} 分析失败: {e}")
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return f"第{chunk_index+1}段分析失败"
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def _merge_summaries(self, chunk_summaries: List[Dict], content_type: str) -> Dict[str, Any]:
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"""
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C5.3: 合并所有段摘要，生成完整分析报告
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Args:
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chunk_summaries: 各段摘要列表
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content_type: 内容类型
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Returns:
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完整分析结果
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"""
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# 构建合并提示
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summaries_text = "\n\n".join([
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f"=== 第{s['index']+1}段 ({s['start_time']}-{s['end_time']}) ===\n{s['summary']}"
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for s in chunk_summaries
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])
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prompt = f"""基于以下各段分析，生成完整的视频分析报告：
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{summaries_text}
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请提供以下格式的JSON：
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{{
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"summary": "整体摘要（200字以内）",
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"topics": ["主题1", "主题2", "主题3", "主题4"],
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"insights": ["深入见解1", "深入见解2", "深入见解3"],
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"actions": ["建议行动1", "建议行动2"],
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"full_analysis": "详细分析（500字以内）"
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}}"""
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try:
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result = self.generate_json(prompt)
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# 确保必要字段存在
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if 'topics' not in result:
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result['topics'] = ["主题A", "主题B", "主题C"]
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if 'insights' not in result:
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result['insights'] = ["重要见解1", "重要见解2"]
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if 'actions' not in result:
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result['actions'] = []
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if 'summary' not in result:
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result['summary'] = "长视频分析完成"
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if 'full_analysis' not in result:
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result['full_analysis'] = summaries_text[:500]
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return result
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except Exception as e:
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print(f"[AIClient] 合并摘要失败: {e}")
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# 返回基础合并结果
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return {
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'summary': f'视频共分为{len(chunk_summaries)}个部分进行分析',
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'topics': [f"主题{i+1}" for i in range(min(4, len(chunk_summaries)))],
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'insights': ['从分段分析中综合得出'],
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'actions': [],
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'full_analysis': summaries_text[:800]
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}
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def _analyze_direct(self, content_text: str) -> Dict[str, Any]:
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"""直接分析（不分段）"""
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# 截断文本
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text_sample = content_text[:5000] if len(content_text) > 5000 else content_text
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prompt = f"""请分析以下内容，提供JSON格式结果：
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{text_sample}
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返回格式：
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{{
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"summary": "整体摘要（150字以内）",
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"topics": ["主题1", "主题2", "主题3"],
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"insights": ["见解1", "见解2"],
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"actions": ["行动建议1"],
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"timeline": [
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{{"start_time": "00:00", "end_time": "05:00", "title": "第一部分", "summary": "..."}}
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]
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}}"""
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try:
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return self.generate_json(prompt)
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except Exception as e:
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print(f"[AIClient] 直接分析失败: {e}")
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return {
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'summary': '分析失败',
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'topics': ['无法解析'],
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'insights': ['请检查内容格式'],
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'actions': [],
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'timeline': []
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}
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def _format_time(self, seconds: float) -> str:
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"""格式化秒数为时间字符串"""
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hours = int(seconds // 3600)
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minutes = int((seconds % 3600) // 60)
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secs = int(seconds % 60)
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if hours > 0:
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return f"{hours:02d}:{minutes:02d}:{secs:02d}"
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return f"{minutes:02d}:{secs:02d}"
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def _build_timeline_from_chunks(self, chunk_summaries: List[Dict]) -> List[Dict[str, Any]]:
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"""从分片摘要构建时间轴"""
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timeline = []
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for summary in chunk_summaries:
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timeline.append({
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'index': summary['index'],
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'start_time': summary['start_time'],
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'end_time': summary['end_time'],
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'title': f"第{summary['index']+1}部分",
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'summary': summary['summary'][:200] + "..." if len(summary['summary']) > 200 else summary['summary']
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})
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return timeline
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def _mock_generate(self, prompt: str, max_tokens: int) -> str:
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"""模拟AI生成（实际实现应调用真实API）"""
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# 根据prompt内容返回模拟响应
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prompt_lower = prompt.lower()
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if 'json' in prompt_lower or 'json格式' in prompt:
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if '主题' in prompt or 'topic' in prompt_lower:
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return json.dumps({
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'summary': '这是一个关于人工智能和技术发展的内容，涵盖了多个重要主题。',
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'topics': ['人工智能', '机器学习', '未来发展', '技术趋势'],
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'insights': [
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'AI正在改变我们的工作方式',
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'机器学习应用越来越广泛',
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'未来技术发展趋势值得关注'
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],
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'actions': ['深入研究相关技术', '关注行业动态'],
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'full_analysis': '详细分析内容...'
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}, ensure_ascii=False)
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return json.dumps({'result': '模拟JSON响应'})
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if '摘要' in prompt or 'summary' in prompt_lower:
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return '这是一段关于人工智能发展的重要讨论，涵盖了技术趋势和未来应用。'
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if '关键词' in prompt or 'keyword' in prompt_lower:
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return '人工智能, 机器学习, 深度学习, 神经网络, 自然语言处理'
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return '模拟AI响应内容'
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def _parse_json_from_text(self, text: str) -> Dict[str, Any]:
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"""从文本中解析JSON"""
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# 尝试提取JSON块
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import re
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json_match = re.search(r'\{[\s\S]*\}', text)
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if json_match:
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try:
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return json.loads(json_match.group())
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except:
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pass
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return {'raw_response': text}
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class MultiAIClient:
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"""
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多AI客户端 - 支持多种AI提供商的客户端
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"""
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def __init__(self):
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self.config = get_config()
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self.primary_client = AIClient()
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self.clients = {
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'openai': self.primary_client,
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'local': AIClient(), # 可以配置为本地模型
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}
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def _deep_analyze(self, content_text: str, content_type: str, duration: Optional[int] = None) -> Dict[str, Any]:
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"""使用主客户端进行深度分析"""
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return self.primary_client._deep_analyze(content_text, content_type, duration)
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def quick_generate(self, prompt: str, max_tokens: int = 100, provider: str = None) -> str:
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"""快速生成"""
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client = self.clients.get(provider, self.primary_client)
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return client.quick_generate(prompt, max_tokens)
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