feat: Add KB knowledge base series skills
- kb-link-parser: Multi-platform URL parser (Bilibili, YouTube, Zhihu) - kb-bilibili-metadata: Bilibili video/article/up主 metadata extractor - kb-zhihu-metadata: Zhihu question/answer/user metadata extractor - kb-bilibili-content: Bilibili video download and content extraction - kb-ocr-processor: OCR text extraction from video frames - kb-deep-analyzer: AI-powered deep content analysis - kb-workflow: Workflow orchestration (inbox, archive, subscriptions, report) Also add: - ARCHITECTURE_FLOWCHART.md: System architecture diagrams - DETAILED_DESIGN.md: Complete design documentation
This commit is contained in:
OpenClaw Agent
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#!/usr/bin/env python3
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"""Deep Analyzer - AI 深度内容分析工具
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支持根据内容时长选择不同分析策略：
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- 短视频 (<10分钟): 轻量分析
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- 长视频 (>=10分钟): 详细时间轴分析
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Usage:
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python deep_analyzer.py --metadata meta.json --content content.json --output analysis.md [--type video]
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python deep_analyzer.py --help
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"""
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import argparse
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import json
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import os
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import sys
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import re
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from datetime import datetime
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from typing import Dict, Any, List, Optional, Tuple
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from dataclasses import dataclass, field, asdict
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# ============== 数据模型 ==============
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@dataclass
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class TimelineEntry:
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"""时间轴条目"""
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index: int = 0
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start_time: str = ""
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end_time: str = ""
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title: str = ""
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summary: str = ""
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@dataclass
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class AnalysisResult:
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"""分析结果"""
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summary: str = ""
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main_topics: List[str] = field(default_factory=list)
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key_insights: List[str] = field(default_factory=list)
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action_items: List[str] = field(default_factory=list)
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recommended_tags: List[str] = field(default_factory=list)
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full_analysis: str = ""
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timeline: List[Dict[str, Any]] = field(default_factory=list)
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# ============== AI 客户端 ==============
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class SimpleAIClient:
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"""简化的 AI 客户端 - 支持多提供商 fallback"""
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# 时长阈值（秒）
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SHORT_VIDEO_THRESHOLD = 600 # 10分钟
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LONG_VIDEO_THRESHOLD = 1800 # 30分钟
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def __init__(self, api_key: str = None, provider: str = "mock", model: str = "gpt-4"):
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self.api_key = api_key or os.environ.get("AI_API_KEY", "")
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self.provider = provider
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self.model = model
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self.base_url = os.environ.get("AI_BASE_URL", "")
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def generate(self, prompt: str, max_tokens: int = 1000) -> str:
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"""生成文本"""
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if not self.api_key or self.provider == "mock":
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return self._mock_generate(prompt, max_tokens)
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try:
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if self.provider == "openai":
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return self._call_openai(prompt, max_tokens)
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elif self.provider == "zhipu":
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return self._call_zhipu(prompt, max_tokens)
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elif self.provider == "minimax":
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return self._call_minimax(prompt, max_tokens)
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else:
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return self._mock_generate(prompt, max_tokens)
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except Exception as e:
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print(f"[AI Client] 调用失败: {e}")
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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.generate(prompt, max_tokens=2000)
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try:
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return json.loads(response)
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except json.JSONDecodeError:
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# 尝试从文本中提取 JSON
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return self._parse_json_from_text(response)
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def _call_openai(self, prompt: str, max_tokens: int) -> str:
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"""调用 OpenAI API"""
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import requests
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url = f"{self.base_url}/chat/completions" if self.base_url else "https://api.openai.com/v1/chat/completions"
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": self.model,
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"max_tokens": max_tokens,
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"messages": [{"role": "user", "content": prompt}]
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}
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response = requests.post(url, headers=headers, json=payload, timeout=30)
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response.raise_for_status()
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return response.json()['choices'][0]['message']['content']
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def _call_zhipu(self, prompt: str, max_tokens: int) -> str:
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"""调用智谱 AI API"""
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import requests
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base_url = self.base_url or "https://open.bigmodel.cn/api/paas/v4"
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url = f"{base_url}/chat/completions"
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headers = {"Authorization": f"Bearer {self.api_key}"}
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payload = {
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"model": self.model,
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"messages": [{"role": "user", "content": prompt}]
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}
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response = requests.post(url, headers=headers, json=payload, timeout=30)
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response.raise_for_status()
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return response.json()['choices'][0]['message']['content']
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def _call_minimax(self, prompt: str, max_tokens: int) -> str:
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"""调用 MiniMax API"""
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import requests
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url = self.base_url or "https://api.minimaxi.com/anthropic/v1/messages"
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": self.model,
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"max_tokens": max_tokens,
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"messages": [{"role": "user", "content": prompt}]
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}
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response = requests.post(url, headers=headers, json=payload, timeout=30)
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response.raise_for_status()
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data = response.json()
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for item in data.get('content', []):
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if item.get('type') == 'text':
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return item.get('text', '')
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return str(data)
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def _mock_generate(self, prompt: str, max_tokens: int) -> str:
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"""模拟 AI 生成"""
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prompt_lower = prompt.lower()
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if 'json' in prompt_lower or '请返回' in prompt:
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# 返回模拟 JSON 响应
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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': '这是一个模拟的AI分析结果，包含内容的主要主题和关键见解。',
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'topics': ['主题一', '主题二', '主题三', '主题四'],
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'insights': [
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'AI正在深刻改变内容消费方式',
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'个性化推荐提升了用户体验',
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'技术发展带来新的可能性'
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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': '详细分析内容基于模拟AI生成。'
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}, ensure_ascii=False)
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if '标签' in prompt or 'tag' in prompt_lower:
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return json.dumps({'tags': ['技术', 'AI', '视频分析', '深度学习']}, ensure_ascii=False)
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return json.dumps({'result': '模拟响应'}, ensure_ascii=False)
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if '摘要' in prompt or 'summary' in prompt_lower:
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return '这是一段关于内容深度分析的重要讨论，涵盖了多个关键主题和见解。'
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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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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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# ============== 深度分析器 ==============
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class DeepAnalyzer:
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"""深度内容分析器"""
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SHORT_VIDEO_THRESHOLD = 600 # 10分钟
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LONG_VIDEO_THRESHOLD = 1800 # 30分钟
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def __init__(self, ai_client: SimpleAIClient = None):
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self.ai_client = ai_client or SimpleAIClient()
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def analyze(self, metadata: Dict[str, Any], content: Dict[str, Any],
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content_type: str = "video") -> AnalysisResult:
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"""
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分析内容
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Args:
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metadata: 元数据字典
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content: 内容数据字典（包含 subtitle, description 等）
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content_type: 内容类型
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Returns:
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AnalysisResult 分析结果
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"""
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title = metadata.get('title', '未知')
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duration = metadata.get('duration', 0)
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subtitle = content.get('subtitle', '')
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description = content.get('description', '')
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print(f"[DeepAnalyzer] 开始分析: {title}")
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print(f"[DeepAnalyzer] 类型: {content_type}, 时长: {duration}秒")
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print(f"[DeepAnalyzer] 字幕存在: {bool(subtitle and subtitle.strip())}")
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has_subtitle = bool(subtitle and subtitle.strip())
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if has_subtitle:
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if duration < self.SHORT_VIDEO_THRESHOLD:
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print(f"[DeepAnalyzer] 使用轻量分析策略 (时长 < 10分钟)")
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result = self._analyze_short_with_subtitle(title, subtitle, duration)
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else:
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print(f"[DeepAnalyzer] 使用详细时间轴分析策略 (时长 >= 10分钟)")
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result = self._analyze_long_with_subtitle(title, subtitle, duration, content_type)
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else:
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print(f"[DeepAnalyzer] 降级处理（无字幕）")
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result = self._analyze_fallback(title, description, metadata)
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return result
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def _analyze_short_with_subtitle(self, title: str, subtitle: str, duration: int) -> AnalysisResult:
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"""有字幕的短视频轻量分析"""
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result = AnalysisResult()
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text_sample = self._get_text_sample(subtitle, max_chars=5000)
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analysis = self._analyze_with_ai_short_text(text_sample, title, duration)
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result.summary = analysis.get('summary', title)
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result.main_topics = analysis.get('topics', [])
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result.key_insights = analysis.get('insights', [])
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result.action_items = analysis.get('actions', [])
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result.timeline = self._generate_simple_timeline_from_duration(title, duration)
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return result
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def _analyze_long_with_subtitle(self, title: str, subtitle: str, duration: int,
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content_type: str) -> AnalysisResult:
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"""有字幕的长视频详细分析"""
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result = AnalysisResult()
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if duration >= self.LONG_VIDEO_THRESHOLD and content_type == "video":
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# 使用分段分析
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result = self._analyze_in_chunks(title, subtitle, duration)
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else:
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# 直接分析
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text_sample = self._get_text_sample(subtitle, max_chars=5000)
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analysis = self._analyze_with_ai_short_text(text_sample, title, duration)
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result.summary = analysis.get('summary', title)
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result.main_topics = analysis.get('topics', [])
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result.key_insights = analysis.get('insights', [])
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result.action_items = analysis.get('actions', [])
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result.full_analysis = analysis.get('full_analysis', '')
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result.timeline = self._generate_detailed_timeline(title, duration)
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return result
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def _analyze_in_chunks(self, title: str, subtitle: str, duration: int) -> AnalysisResult:
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"""分段分析长视频"""
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result = AnalysisResult()
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# 确定分段数
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if duration < 3600: # <60分钟
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num_segments = 3
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elif duration < 7200: # 60-120分钟
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num_segments = 4
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else: # >120分钟
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num_segments = 6
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# 生成分段摘要
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segment_duration = duration // num_segments
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chunk_summaries = []
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# 简单按字符数分割字幕
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subtitle_chunks = self._chunk_subtitle(subtitle, num_segments)
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for i, chunk_text in enumerate(subtitle_chunks):
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start_sec = i * segment_duration
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end_sec = min((i + 1) * segment_duration, duration)
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start_time = self._format_time(start_sec)
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end_time = self._format_time(end_sec)
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# AI 分析每段
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chunk_summary = self._analyze_chunk(chunk_text, i + 1)
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chunk_summaries.append({
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'index': i,
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'start_time': start_time,
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'end_time': end_time,
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'summary': chunk_summary
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})
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# 合并结果
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result = self._merge_summaries(chunk_summaries, title)
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result.timeline = self._build_timeline_from_chunks(chunk_summaries)
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return result
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def _analyze_chunk(self, chunk_text: str, chunk_index: int) -> str:
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"""分析单个分片"""
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text_sample = self._get_text_sample(chunk_text, max_chars=3000)
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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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请用中文简要回答，包括核心主题和关键要点。"""
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try:
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return self.ai_client.generate(prompt, max_tokens=500)
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except Exception as e:
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print(f"[DeepAnalyzer] 分片 {chunk_index} 分析失败: {e}")
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return f"第{chunk_index}段内容分析"
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def _merge_summaries(self, chunk_summaries: List[Dict], title: str) -> AnalysisResult:
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"""合并分段摘要"""
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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"""基于以下视频各段分析，生成完整的分析报告：
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标题：{title}
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各段内容：
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{summaries_text}
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请返回JSON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", "主题4"],
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"insights": ["见解1", "见解2", "见解3"],
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"actions": ["建议1", "建议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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analysis = self.ai_client.generate_json(prompt)
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result = AnalysisResult()
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result.summary = analysis.get('summary', title)
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result.main_topics = analysis.get('topics', [])
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result.key_insights = analysis.get('insights', [])
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result.action_items = analysis.get('actions', [])
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result.full_analysis = analysis.get('full_analysis', summaries_text)
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return result
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except Exception as e:
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print(f"[DeepAnalyzer] 合并摘要失败: {e}")
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return AnalysisResult(
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summary=f"共{len(chunk_summaries)}段分析完成",
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main_topics=[f"主题{i+1}" for i in range(min(4, len(chunk_summaries)))],
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key_insights=["综合各段分析得出"],
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full_analysis=summaries_text[:500]
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)
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def _analyze_fallback(self, title: str, description: str, metadata: Dict) -> AnalysisResult:
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"""无字幕降级处理"""
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result = AnalysisResult()
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if description and description.strip():
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summary = description[:200] + "..." if len(description) > 200 else description
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else:
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summary = f"无字幕内容，标题：{title}"
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# 提取标签
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tags = ["未分类", "待处理"]
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author = metadata.get('author', '')
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source = metadata.get('source', '')
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if author:
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tags.append(f"作者:{author}")
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if source:
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tags.append(f"来源:{source}")
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result.summary = summary
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result.main_topics = tags[:3]
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result.recommended_tags = tags
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result.key_insights = ["无字幕内容，仅能从描述获取基本信息"]
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result.action_items = ["建议补充字幕以便深度分析"]
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return result
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def _analyze_with_ai_short_text(self, text: str, title: str, duration: int) -> Dict[str, Any]:
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"""AI 短视频分析"""
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prompt = f"""请分析以下视频内容，以JSON格式返回：
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标题：{title}
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时长：{self._format_time(duration)}
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字幕内容：
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{text[:3000]}
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请返回以下格式JSON：
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{{
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"summary": "整体摘要（100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": ["建议1"]
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}}"""
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||||
try:
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return self.ai_client.generate_json(prompt)
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||||
except Exception as e:
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||||
print(f"[DeepAnalyzer] AI分析失败: {e}")
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return {
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"summary": title,
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"topics": ["主题1", "主题2", "主题3"],
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"insights": ["无法获取深入见解"],
|
||||
"actions": []
|
||||
}
|
||||
|
||||
def _get_text_sample(self, text: str, max_chars: int) -> str:
|
||||
"""获取文本样本（头尾采样）"""
|
||||
if len(text) <= max_chars:
|
||||
return text
|
||||
|
||||
part_size = max_chars // 3
|
||||
return (
|
||||
text[:part_size] +
|
||||
"\n\n[...省略中间部分...]\n\n" +
|
||||
text[-part_size:]
|
||||
)
|
||||
|
||||
def _chunk_subtitle(self, subtitle: str, num_chunks: int) -> List[str]:
|
||||
"""简单分割字幕"""
|
||||
if not subtitle:
|
||||
return [""] * num_chunks
|
||||
|
||||
chunk_size = len(subtitle) // num_chunks
|
||||
chunks = []
|
||||
|
||||
for i in range(num_chunks):
|
||||
start = i * chunk_size
|
||||
end = start + chunk_size if i < num_chunks - 1 else len(subtitle)
|
||||
chunks.append(subtitle[start:end])
|
||||
|
||||
return chunks
|
||||
|
||||
def _generate_simple_timeline_from_duration(self, title: str, duration: int) -> List[Dict]:
|
||||
"""生成简单时间轴"""
|
||||
segments = 2 if duration < 300 else 3
|
||||
seg_duration = duration // segments
|
||||
|
||||
timeline = []
|
||||
for i in range(segments):
|
||||
start = i * seg_duration
|
||||
end = min((i + 1) * seg_duration, duration)
|
||||
timeline.append({
|
||||
'index': i + 1,
|
||||
'start_time': self._format_time(start),
|
||||
'end_time': self._format_time(end),
|
||||
'title': f"段落 {i+1}",
|
||||
'summary': f"从{self._format_time(start)}到{self._format_time(end)}的内容"
|
||||
})
|
||||
return timeline
|
||||
|
||||
def _generate_detailed_timeline(self, title: str, duration: int) -> List[Dict]:
|
||||
"""生成详细时间轴"""
|
||||
return self._generate_simple_timeline_from_duration(title, duration)
|
||||
|
||||
def _build_timeline_from_chunks(self, chunk_summaries: List[Dict]) -> List[Dict]:
|
||||
"""从分段摘要构建时间轴"""
|
||||
timeline = []
|
||||
for s in chunk_summaries:
|
||||
timeline.append({
|
||||
'index': s['index'] + 1,
|
||||
'start_time': s['start_time'],
|
||||
'end_time': s['end_time'],
|
||||
'title': f"第{s['index']+1}部分",
|
||||
'summary': s['summary'][:200] + "..." if len(s['summary']) > 200 else s['summary']
|
||||
})
|
||||
return timeline
|
||||
|
||||
def _format_time(self, seconds: int) -> str:
|
||||
"""格式化秒数为时间字符串"""
|
||||
h = seconds // 3600
|
||||
m = (seconds % 3600) // 60
|
||||
s = seconds % 60
|
||||
if h > 0:
|
||||
return f"{h}:{m:02d}:{s:02d}"
|
||||
return f"{m}:{s:02d}"
|
||||
|
||||
|
||||
# ============== 模板系统 ==============
|
||||
|
||||
class TemplateLoader:
|
||||
"""模板加载器"""
|
||||
|
||||
TEMPLATES = {
|
||||
"video": """# {{title}}
|
||||
|
||||
## 基本信息
|
||||
- **时长**: {{duration}}
|
||||
- **作者**: {{author}}
|
||||
- **来源**: {{source}}
|
||||
- **分析时间**: {{analyze_time}}
|
||||
|
||||
## 摘要
|
||||
{{summary}}
|
||||
|
||||
## 主要主题
|
||||
{% for topic in topics %}
|
||||
- {{topic}}
|
||||
{% endfor %}
|
||||
|
||||
## 关键见解
|
||||
{% for insight in insights %}
|
||||
- {{insight}}
|
||||
{% endfor %}
|
||||
|
||||
## 建议行动
|
||||
{% for action in actions %}
|
||||
- [ ] {{action}}
|
||||
{% endfor %}
|
||||
|
||||
{% if timeline %}
|
||||
## 时间轴
|
||||
{% for entry in timeline %}
|
||||
### {{entry.index}}. {{entry.start_time}} - {{entry.end_time}}
|
||||
{{entry.title}}: {{entry.summary}}
|
||||
{% endfor %}
|
||||
{% endif %}
|
||||
|
||||
{% if tags %}
|
||||
## 标签
|
||||
{% for tag in tags %}
|
||||
- {{tag}}
|
||||
{% endfor %}
|
||||
{% endif %}
|
||||
""",
|
||||
"article": """# {{title}}
|
||||
|
||||
## 基本信息
|
||||
- **作者**: {{author}}
|
||||
- **来源**: {{source}}
|
||||
- **分析时间**: {{analyze_time}}
|
||||
|
||||
## 摘要
|
||||
{{summary}}
|
||||
|
||||
## 主要主题
|
||||
{% for topic in topics %}
|
||||
- {{topic}}
|
||||
{% endfor %}
|
||||
|
||||
## 关键见解
|
||||
{% for insight in insights %}
|
||||
- {{insight}}
|
||||
{% endfor %}
|
||||
|
||||
## 建议行动
|
||||
{% for action in actions %}
|
||||
- [ ] {{action}}
|
||||
{% endfor %}
|
||||
|
||||
{% if tags %}
|
||||
## 标签
|
||||
{% for tag in tags %}
|
||||
- {{tag}}
|
||||
{% endfor %}
|
||||
{% endif %}
|
||||
"""
|
||||
}
|
||||
|
||||
def load_template(self, template_type: str = "video") -> str:
|
||||
"""加载模板"""
|
||||
# 优先检查环境变量
|
||||
template_path = os.environ.get(f"TEMPLATE_{template_type.upper()}", "")
|
||||
if template_path and os.path.exists(template_path):
|
||||
with open(template_path, 'r', encoding='utf-8') as f:
|
||||
return f.read()
|
||||
|
||||
return self.TEMPLATES.get(template_type, self.TEMPLATES["video"])
|
||||
|
||||
|
||||
class TemplateRenderer:
|
||||
"""简单模板渲染器"""
|
||||
|
||||
def render(self, template: str, variables: Dict[str, Any]) -> str:
|
||||
"""渲染模板"""
|
||||
result = template
|
||||
|
||||
# 替换变量
|
||||
for key, value in variables.items():
|
||||
placeholder = f"{{{{{key}}}}}"
|
||||
result = result.replace(placeholder, str(value))
|
||||
|
||||
# 渲染 for 循环 (简化版本)
|
||||
# 匹配 {% for item in list %} ... {% endfor %}
|
||||
pattern = r'\{%\s*for\s+(\w+)\s+in\s+(\w+)\s*%\}(.*?)\{%\s*endfor\s*%\}'
|
||||
|
||||
def replace_loop(match):
|
||||
item_name = match.group(1)
|
||||
list_name = match.group(2)
|
||||
content = match.group(3)
|
||||
|
||||
items = variables.get(list_name, [])
|
||||
rendered = []
|
||||
for item in items:
|
||||
if isinstance(item, dict):
|
||||
# 字典项
|
||||
item_content = content
|
||||
for k, v in item.items():
|
||||
item_content = item_content.replace(f"{{{{{item_name}.{k}}}}}", str(v))
|
||||
rendered.append(item_content)
|
||||
else:
|
||||
# 简单项
|
||||
rendered.append(content.replace(f"{{{{{item_name}}}}}", str(item)))
|
||||
|
||||
return '\n'.join(rendered)
|
||||
|
||||
result = re.sub(pattern, replace_loop, result, flags=re.DOTALL)
|
||||
|
||||
# 移除空的 for 循环块
|
||||
result = re.sub(r'\{%\s*for\s+\w+\s+in\s+\w+\s*%\}\s*\{%\s*endfor\s*%\}', '', result)
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# ============== 工具函数 ==============
|
||||
|
||||
def load_json(file_path: str) -> Dict:
|
||||
"""加载 JSON 文件"""
|
||||
if not file_path or not os.path.exists(file_path):
|
||||
return {}
|
||||
|
||||
with open(file_path, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
|
||||
|
||||
def format_duration(seconds: int) -> str:
|
||||
"""格式化时长"""
|
||||
h = seconds // 3600
|
||||
m = (seconds % 3600) // 60
|
||||
s = seconds % 60
|
||||
if h > 0:
|
||||
return f"{h}:{m:02d}:{s:02d}"
|
||||
return f"{m}:{s:02d}"
|
||||
|
||||
|
||||
def result_to_dict(result: AnalysisResult) -> Dict:
|
||||
"""将分析结果转换为字典"""
|
||||
return {
|
||||
"summary": result.summary,
|
||||
"main_topics": result.main_topics,
|
||||
"key_insights": result.key_insights,
|
||||
"action_items": result.action_items,
|
||||
"recommended_tags": result.recommended_tags,
|
||||
"full_analysis": result.full_analysis,
|
||||
"timeline": result.timeline
|
||||
}
|
||||
|
||||
|
||||
def generate_frontmatter(metadata: Dict, result: AnalysisResult) -> str:
|
||||
"""生成 Markdown frontmatter"""
|
||||
tags = result.recommended_tags or result.main_topics[:5]
|
||||
|
||||
fm = ["---", "title: \"" + metadata.get('title', '未命名') + "\"",
|
||||
f"date: {datetime.now().strftime('%Y-%m-%d')}",
|
||||
f"duration: {format_duration(metadata.get('duration', 0))}",
|
||||
f"author: {metadata.get('author', '未知')}"]
|
||||
|
||||
if tags:
|
||||
fm.append("tags: [" + ", ".join(f'"{t}"' for t in tags) + "]")
|
||||
|
||||
fm.append("---")
|
||||
return '\n'.join(fm)
|
||||
|
||||
|
||||
# ============== 主程序 ==============
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Deep Analyzer - AI 深度内容分析工具",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
示例:
|
||||
python deep_analyzer.py --metadata meta.json --content content.json --output analysis.md
|
||||
python deep_analyzer.py --metadata meta.json --content content.json --type video --api-key xxx
|
||||
"""
|
||||
)
|
||||
|
||||
parser.add_argument("--metadata", "-m", required=True,
|
||||
help="元数据 JSON 文件路径")
|
||||
parser.add_argument("--content", "-c", required=True,
|
||||
help="内容 JSON 文件路径")
|
||||
parser.add_argument("--output", "-o", required=True,
|
||||
help="输出 Markdown 文件路径")
|
||||
parser.add_argument("--type", "-t", default="video", choices=["video", "article"],
|
||||
help="内容类型 (默认: video)")
|
||||
parser.add_argument("--api-key", "-k", default=None,
|
||||
help="AI API Key (或设置 AI_API_KEY 环境变量)")
|
||||
parser.add_argument("--provider", "-p", default="mock",
|
||||
choices=["openai", "zhipu", "minimax", "mock"],
|
||||
help="AI 提供商 (默认: mock)")
|
||||
parser.add_argument("--model", default="gpt-4",
|
||||
help="AI 模型 (默认: gpt-4)")
|
||||
parser.add_argument("--no-frontmatter", action="store_true",
|
||||
help="不生成 frontmatter")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# 加载数据
|
||||
print(f"[Main] 加载元数据: {args.metadata}")
|
||||
metadata = load_json(args.metadata)
|
||||
|
||||
print(f"[Main] 加载内容: {args.content}")
|
||||
content = load_json(args.content)
|
||||
|
||||
# 初始化
|
||||
ai_client = SimpleAIClient(
|
||||
api_key=args.api_key,
|
||||
provider=args.provider,
|
||||
model=args.model
|
||||
)
|
||||
analyzer = DeepAnalyzer(ai_client)
|
||||
|
||||
# 执行分析
|
||||
print(f"[Main] 开始分析...")
|
||||
result = analyzer.analyze(metadata, content, args.type)
|
||||
|
||||
# 渲染模板
|
||||
template_loader = TemplateLoader()
|
||||
renderer = TemplateRenderer()
|
||||
|
||||
template = template_loader.load_template(args.type)
|
||||
|
||||
variables = {
|
||||
"title": metadata.get('title', '未命名'),
|
||||
"duration": format_duration(metadata.get('duration', 0)),
|
||||
"author": metadata.get('author', '未知'),
|
||||
"source": metadata.get('source', ''),
|
||||
"analyze_time": datetime.now().strftime('%Y-%m-%d %H:%M'),
|
||||
"summary": result.summary,
|
||||
"topics": result.main_topics,
|
||||
"insights": result.key_insights,
|
||||
"actions": result.action_items,
|
||||
"tags": result.recommended_tags,
|
||||
"timeline": result.timeline
|
||||
}
|
||||
|
||||
markdown_content = renderer.render(template, variables)
|
||||
|
||||
# 添加 frontmatter
|
||||
if not args.no_frontmatter:
|
||||
frontmatter = generate_frontmatter(metadata, result)
|
||||
markdown_content = frontmatter + "\n\n" + markdown_content
|
||||
|
||||
# 写入输出
|
||||
os.makedirs(os.path.dirname(os.path.abspath(args.output)), exist_ok=True)
|
||||
with open(args.output, 'w', encoding='utf-8') as f:
|
||||
f.write(markdown_content)
|
||||
|
||||
print(f"[Main] 分析完成！输出: {args.output}")
|
||||
print(f"[Main] 摘要: {result.summary[:100]}...")
|
||||
print(f"[Main] 主题数: {len(result.main_topics)}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user