67 lines
90 KiB
Plaintext
67 lines
90 KiB
Plaintext
{"type":"session","version":3,"id":"42d2afa0-85f1-4a01-bc13-5a7df439a018","timestamp":"2026-02-28T16:18:01.005Z","cwd":"/home/starmen1265/.openclaw/agents/knowledge-base/workspace"}
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{"type":"model_change","id":"cf0ed7a2","parentId":null,"timestamp":"2026-02-28T16:18:01.006Z","provider":"zhipu","modelId":"glm-5"}
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{"type":"thinking_level_change","id":"f5488871","parentId":"cf0ed7a2","timestamp":"2026-02-28T16:18:01.006Z","thinkingLevel":"off"}
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{"type":"custom","customType":"model-snapshot","data":{"timestamp":1772295481006,"provider":"zhipu","modelApi":"openai-completions","modelId":"glm-5"},"id":"95742440","parentId":"f5488871","timestamp":"2026-02-28T16:18:01.006Z"}
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{"type":"message","id":"9049f677","parentId":"95742440","timestamp":"2026-02-28T16:18:01.009Z","message":{"role":"user","content":[{"type":"text","text":"[Sun 2026-03-01 00:18 GMT+8] 你是 kb-deep-analyzer 技能的执行者。\n\n任务：对知乎专栏文章进行深度分析\n\n元数据文件: /home/starmen1265/work/knowledge-base/metadata/zhihu_2010481705870454832.meta.json\n内容文件: /home/starmen1265/work/knowledge-base/content/zhihu_article_开源神器分分钟爬取小红书抖音微博知乎了.md\n输出目录: /home/starmen1265/work/knowledge-base/analyzed\n\n如果元数据文件不存在或内容为空，请使用以下基本信息：\n- 标题: 开源神器——分分钟爬取小红书、抖音、微博、知乎了！\n- 作者: 太阳鸟\n- 平台: zhihu\n- 类型: zhuanlan (专栏文章)\n- ID: 2010481705870454832\n\n执行命令：\n```bash\ncd ~/.openclaw/agents/knowledge-base/workspace/skills/kb-deep-analyzer\nsource ~/.kb_env\npython3 deep_analyzer.py --metadata \"/home/starmen1265/work/knowledge-base/metadata/zhihu_2010481705870454832.meta.json\" --content \"/home/starmen1265/work/knowledge-base/content/zhihu_article_开源神器分分钟爬取小红书抖音微博知乎了.md\" --output \"/home/starmen1265/work/knowledge-base/analyzed/zhihu_2010481705870454832.md\" --provider zhipu --model glm-5\n```\n\n⚠️ 重要：深度分析必须成功！如果失败，立即报告失败原因。\n\n完成后返回分析结果。"}],"timestamp":1772295481008}}
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{"type":"message","id":"a99e3d7a","parentId":"9049f677","timestamp":"2026-02-28T16:18:07.562Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"用户要求我执行 kb-deep-analyzer 技能，对一篇知乎专栏文章进行深度分析。\n\n任务：\n1. 检查元数据文件是否存在\n2. 运行深度分析命令\n3. 返回分析结果\n\n首先，我需要检查元数据文件是否存在，然后执行分析命令。\n\n让我先检查文件是否存在，然后执行命令。","thinkingSignature":"reasoning_content"},{"type":"text","text":"我将执行 kb-deep-analyzer 技能对这篇知乎文章进行深度分析。首先检查元数据文件，然后执行分析。"},{"type":"toolCall","id":"call_7f419cc8be6c40b9995390da","name":"read","arguments":{"path":"/home/starmen1265/work/knowledge-base/metadata/zhihu_2010481705870454832.meta.json"}},{"type":"toolCall","id":"call_36b7a4769739485090391ce2","name":"read","arguments":{"path":"/home/starmen1265/work/knowledge-base/content/zhihu_article_开源神器分分钟爬取小红书抖音微博知乎了.md"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":3319,"output":180,"cacheRead":6208,"cacheWrite":0,"totalTokens":9707,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295481009}}
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{"type":"message","id":"0b5e2851","parentId":"a99e3d7a","timestamp":"2026-02-28T16:18:07.573Z","message":{"role":"toolResult","toolCallId":"call_7f419cc8be6c40b9995390da","toolName":"read","content":[{"type":"text","text":"{\n \"type\": \"zhuanlan\",\n \"id\": \"2010481705870454832\",\n \"url\": \"https://zhuanlan.zhihu.com/p/2010481705870454832\",\n \"title\": \"\",\n \"author\": \"\",\n \"publish_time\": \"\",\n \"vote_up\": 0,\n \"content_markdown\": \"\",\n \"raw_html\": \"\"\n}"}],"isError":false,"timestamp":1772295487572}}
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{"type":"message","id":"10c3cba3","parentId":"0b5e2851","timestamp":"2026-02-28T16:18:07.575Z","message":{"role":"toolResult","toolCallId":"call_36b7a4769739485090391ce2","toolName":"read","content":[{"type":"text","text":"---\ntitle: \"开源神器——分分钟爬取小红书、抖音、微博、知乎了！\"\nauthor: \"太阳鸟\"\nurl: \"https://zhuanlan.zhihu.com/p/1925232264591156410\"\ncreated: \"2025-07-06 16:40:23\"\nvoteup: 0\ncomments: 0\nplatform: zhihu\ntype: article\n---\n\n# 开源神器——分分钟爬取小红书、抖音、微博、知乎了！\n\n**作者**: 太阳鸟 \n**来源**: [知乎](https://zhuanlan.zhihu.com/p/1925232264591156410) \n**发布时间**: 2025-07-06 16:40:23 \n**点赞**: 0 \n**评论**: 0\n\n---\n\n推荐阅读• Github 资料项目合集• 50个 AI 开源项目合集• 80 款AI 开源合集* 戳上方蓝字“牛皮糖不吹牛”关注我大家好，我是牛皮糖！最近在给一个做自媒体内容分析的朋友搭系统，他说想抓点小红书、抖音的数据看看趋势。我一听这不是很正常的需求吗？结果他话锋一转：“哥，我爬虫刚入门，JS加密看不懂，X-Bogus 不会搞，登录页面还 403，一气之下差点退圈。”我：别急，MediaCrawler 给你安排得明明白白！ MediaCrawler 是啥？简单一句话： 一个能帮你一键爬小红书、抖音、快手、微博、B站、知乎、贴吧等平台内容的 Python 项目，不用写前端、不用逆向、不用懂加密！项目地址： https://github.com/NanmiCoder/MediaCrawler 它到底帮我们干了啥？ 示例：只用 1 行代码，爬小红书热搜笔记！python main.py --platform xhs --type search --keyword "穿搭" --pages 5它就能给你：• 笔记标题、作者昵称、点赞、评论数• 完整内容（包括图片/视频链接）• 输出到本地 JSON / CSV，还支持 MySQL 入库！ 重点：它是怎么做到“不被封”的？很多人一爬就被封 IP、403？MediaCrawler 的做法是：• ✅ 用 Playwright 自动化浏览器 模拟真实用户；• ✅ 登录状态保留 Cookie；• ✅ 页面运行时注入 JS 自动生成签名；• ✅ 支持代理池，一键配置；一句话：它就是模拟了一个“真的人”，所以平台难以识别你是爬虫。 开发者惊喜：还有 Pro 版本！项目作者还开了一个加强版：MediaCrawlerPro 给开发者的一点建议如果你做的是：• 自媒体数据收集• 舆情分析系统• 多平台热词抓取• 或者只是想轻松入门爬虫世界 那 MediaCrawler 一定值得收藏！ 项目地址（赶紧 Star） https://github.com/NanmiCoder/MediaCrawler Pro 高级版： https://github.com/zwdzzs1/MediaCrawler·················END·················关于AI工具Github开源文本转语音神器Spark-TTS开源了，克隆声音仅需3秒？github开源B站UP主都在用的下载神器！Cobalt让你轻松搬运高清素材！Github 26k Stars 开源换脸神器Github 开源无代码的 Web 数据提取平台，2分钟内训练机器人自动抓取网页数据每日更新，期待与你一起成长欢迎围观副业知识星球本文使用 文章同步助手 同步\n"}],"isError":false,"timestamp":1772295487574}}
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{"type":"message","id":"873517b0","parentId":"10c3cba3","timestamp":"2026-02-28T16:18:17.470Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"好的，元数据文件存在但内容为空（大部分字段都是空的），所以我需要使用提供的默认基本信息。现在让我执行深度分析命令。","thinkingSignature":"reasoning_content"},{"type":"text","text":"元数据文件存在但内容为空，将使用提供的默认信息。现在执行深度分析命令："},{"type":"toolCall","id":"call_a5193eb12a4245a9aed1632c","name":"exec","arguments":{"command":"cd ~/.openclaw/agents/knowledge-base/workspace/skills/kb-deep-analyzer && source ~/.kb_env && python3 deep_analyzer.py --metadata \"/home/starmen1265/work/knowledge-base/metadata/zhihu_2010481705870454832.meta.json\" --content \"/home/starmen1265/work/knowledge-base/content/zhihu_article_开源神器分分钟爬取小红书抖音微博知乎了.md\" --output \"/home/starmen1265/work/knowledge-base/analyzed/zhihu_2010481705870454832.md\" --provider zhipu --model glm-5","timeout":120}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":1120,"output":207,"cacheRead":9472,"cacheWrite":0,"totalTokens":10799,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295487575}}
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{"type":"message","id":"2e390601","parentId":"873517b0","timestamp":"2026-02-28T16:18:17.513Z","message":{"role":"toolResult","toolCallId":"call_a5193eb12a4245a9aed1632c","toolName":"exec","content":[{"type":"text","text":"{\n \"status\": \"error\",\n \"tool\": \"exec\",\n \"error\": \"[Main] 加载元数据: /home/starmen1265/work/knowledge-base/metadata/zhihu_2010481705870454832.meta.json\\n[Main] 加载内容: /home/starmen1265/work/knowledge-base/content/zhihu_article_开源神器分分钟爬取小红书抖音微博知乎了.md\\n{\\n \\\"success\\\": false,\\n \\\"error\\\": \\\"Expecting value: line 1 column 1 (char 0)\\\"\\n}\\n\\nCommand exited with code 1\"\n}"}],"details":{"status":"error","tool":"exec","error":"[Main] 加载元数据: /home/starmen1265/work/knowledge-base/metadata/zhihu_2010481705870454832.meta.json\n[Main] 加载内容: /home/starmen1265/work/knowledge-base/content/zhihu_article_开源神器分分钟爬取小红书抖音微博知乎了.md\n{\n \"success\": false,\n \"error\": \"Expecting value: line 1 column 1 (char 0)\"\n}\n\nCommand exited with code 1"},"isError":false,"timestamp":1772295497512}}
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{"type":"message","id":"ea615e34","parentId":"2e390601","timestamp":"2026-02-28T16:18:30.763Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"元数据文件似乎有 JSON 解析问题。让我检查一下 deep_analyzer.py 脚本的逻辑，看看是否可以修复元数据文件，或者直接使用默认值。\n\n元数据文件当前内容是：\n```json\n{\n \"type\": \"zhuanlan\",\n \"id\": \"2010481705870454832\",\n \"url\": \"https://zhuanlan.zhihu.com/p/2010481705870454832\",\n \"title\": \"\",\n \"author\": \"\",\n \"publish_time\": \"\",\n \"vote_up\": 0,\n \"content_markdown\": \"\",\n \"raw_html\": \"\"\n}\n```\n\n这看起来是有效的 JSON，但脚本报告 \"Expecting value: line 1 column 1 (char 0)\" 错误，这意味着文件可能为空或者有编码问题。让我检查一下元数据文件的实际内容。","thinkingSignature":"reasoning_content"},{"type":"text","text":"元数据文件可能有问题。让我检查并修复它："},{"type":"toolCall","id":"call_59a603b4fb904653b5135fd2","name":"exec","arguments":{"command":"cat \"/home/starmen1265/work/knowledge-base/metadata/zhihu_2010481705870454832.meta.json\" | head -20"}},{"type":"toolCall","id":"call_eef1d7fcad03446fb32b532b","name":"exec","arguments":{"command":"file \"/home/starmen1265/work/knowledge-base/metadata/zhihu_2010481705870454832.meta.json\""}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":381,"output":288,"cacheRead":10560,"cacheWrite":0,"totalTokens":11229,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295497513}}
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{"type":"message","id":"782b883d","parentId":"ea615e34","timestamp":"2026-02-28T16:18:30.784Z","message":{"role":"toolResult","toolCallId":"call_59a603b4fb904653b5135fd2","toolName":"exec","content":[{"type":"text","text":"{\n \"type\": \"zhuanlan\",\n \"id\": \"2010481705870454832\",\n \"url\": \"https://zhuanlan.zhihu.com/p/2010481705870454832\",\n \"title\": \"\",\n \"author\": \"\",\n \"publish_time\": \"\",\n \"vote_up\": 0,\n \"content_markdown\": \"\",\n \"raw_html\": \"\"\n}"}],"details":{"status":"completed","exitCode":0,"durationMs":14,"aggregated":"{\n \"type\": \"zhuanlan\",\n \"id\": \"2010481705870454832\",\n \"url\": \"https://zhuanlan.zhihu.com/p/2010481705870454832\",\n \"title\": \"\",\n \"author\": \"\",\n \"publish_time\": \"\",\n \"vote_up\": 0,\n \"content_markdown\": \"\",\n \"raw_html\": \"\"\n}","cwd":"/home/starmen1265/.openclaw/agents/knowledge-base/workspace"},"isError":false,"timestamp":1772295510777}}
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{"type":"message","id":"7b68cd8e","parentId":"782b883d","timestamp":"2026-02-28T16:18:30.786Z","message":{"role":"toolResult","toolCallId":"call_eef1d7fcad03446fb32b532b","toolName":"exec","content":[{"type":"text","text":"/home/starmen1265/work/knowledge-base/metadata/zhihu_2010481705870454832.meta.json: JSON text data"}],"details":{"status":"completed","exitCode":0,"durationMs":8,"aggregated":"/home/starmen1265/work/knowledge-base/metadata/zhihu_2010481705870454832.meta.json: JSON text data","cwd":"/home/starmen1265/.openclaw/agents/knowledge-base/workspace"},"isError":false,"timestamp":1772295510785}}
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{"type":"message","id":"fa60f4d9","parentId":"7b68cd8e","timestamp":"2026-02-28T16:18:37.934Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"文件看起来是有效的 JSON。让我检查一下 deep_analyzer.py 的代码，看看它是如何解析元数据文件的，以及为什么会报错。\n\n也许问题在于元数据文件中缺少某些必需的字段，或者脚本期望不同的格式。让我看看脚本代码。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_0ac64dc226dc4457963ba621","name":"read","arguments":{"path":"/home/starmen1265/.openclaw/agents/knowledge-base/workspace/skills/kb-deep-analyzer/deep_analyzer.py"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":481,"output":92,"cacheRead":10880,"cacheWrite":0,"totalTokens":11453,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295510786}}
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{"type":"message","id":"49f55cf8","parentId":"fa60f4d9","timestamp":"2026-02-28T16:18:37.938Z","message":{"role":"toolResult","toolCallId":"call_0ac64dc226dc4457963ba621","toolName":"read","content":[{"type":"text","text":"#!/usr/bin/env python3\n\"\"\"Deep Analyzer - AI 深度内容分析工具\n\n一次性调用 AI 生成完整深度分析报告。\n\nUsage:\n python deep_analyzer.py --metadata meta.json --content content.json --output analysis.md [--type video]\n python deep_analyzer.py --help\n\"\"\"\n\nimport argparse\nimport json\nimport os\nimport sys\nimport re\nfrom datetime import datetime\nfrom pathlib import Path\nfrom typing import Dict, Any, List\nfrom dataclasses import dataclass, field\n\n\n# ============== 数据模型 ==============\n\n@dataclass\nclass AnalysisResult:\n \"\"\"分析结果\"\"\"\n summary: str = \"\"\n main_topics: List[str] = field(default_factory=list)\n key_insights: List[str] = field(default_factory=list)\n recommended_tags: List[str] = field(default_factory=list)\n full_analysis: str = \"\"\n\n\n# ============== AI 客户端 ==============\n\nclass SimpleAIClient:\n \"\"\"AI 客户端\"\"\"\n\n def __init__(self, api_key: str = None, provider: str = None, model: str = None, base_url: str = None):\n self.api_key = api_key or os.environ.get(\"AI_API_KEY\", \"\")\n self.provider = provider or os.environ.get(\"AI_PROVIDER\", \"\")\n self.model = model or os.environ.get(\"AI_MODEL\", \"glm-5\")\n self.base_url = base_url or os.environ.get(\"AI_BASE_URL\", \"\")\n self.max_retries = 3\n\n def generate(self, prompt: str, max_tokens: int = 16000) -> str:\n \"\"\"生成文本\"\"\"\n api_key = os.environ.get(\"AI_API_KEY\", self.api_key)\n provider = os.environ.get(\"AI_PROVIDER\", self.provider)\n model = os.environ.get(\"AI_MODEL\", self.model)\n base_url = os.environ.get(\"AI_BASE_URL\", self.base_url)\n\n if not api_key:\n raise ValueError(\"AI_API_KEY 未设置\")\n\n for attempt in range(self.max_retries):\n try:\n import time\n start_time = time.time()\n print(f\"[AI Client] 开始调用 AI...\")\n\n if provider == \"openai\":\n result = self._call_openai(prompt, max_tokens, api_key, model, base_url)\n elif provider == \"zhipu\":\n result = self._call_zhipu(prompt, max_tokens, api_key, model, base_url)\n elif provider == \"minimax\":\n result = self._call_minimax(prompt, max_tokens, api_key, model, base_url)\n else:\n raise ValueError(f\"不支持的 AI 提供商: {provider}\")\n\n elapsed = time.time() - start_time\n print(f\"[AI Client] AI 调用完成，耗时: {elapsed:.2f}秒\")\n return result\n except Exception as e:\n if attempt < self.max_retries - 1:\n print(f\"[AI Client] 第 {attempt + 1} 次调用失败，{self.max_retries - attempt - 1} 次重试剩余: {e}\")\n continue\n else:\n raise\n\n def _call_openai(self, prompt: str, max_tokens: int, api_key: str, model: str, base_url: str) -> str:\n import requests\n url = f\"{base_url}/chat/completions\" if base_url else \"https://api.openai.com/v1/chat/completions\"\n headers = {\"Authorization\": f\"Bearer {api_key}\", \"Content-Type\": \"application/json\"}\n payload = {\"model\": model, \"max_tokens\": max_tokens, \"messages\": [{\"role\": \"user\", \"content\": prompt}]}\n response = requests.post(url, headers=headers, json=payload, timeout=600)\n response.raise_for_status()\n return response.json()['choices'][0]['message']['content']\n\n def _call_zhipu(self, prompt: str, max_tokens: int, api_key: str, model: str, base_url: str) -> str:\n import requests\n base_url = base_url or \"https://open.bigmodel.cn/api/coding/paas/v4\"\n url = f\"{base_url}/chat/completions\"\n headers = {\"Authorization\": f\"Bearer {api_key}\"}\n payload = {\"model\": model, \"messages\": [{\"role\": \"user\", \"content\": prompt}], \"max_tokens\": max_tokens}\n response = requests.post(url, headers=headers, json=payload, timeout=600)\n response.raise_for_status()\n data = response.json()\n message = data.get('choices', [{}])[0].get('message', {})\n content = message.get('content', '')\n reasoning = message.get('reasoning_content', '')\n if not content and reasoning:\n sentences = reasoning.strip().split('。')\n useful = [s.strip() for s in sentences if s.strip() and len(s.strip()) > 10]\n if useful:\n return '。'.join(useful[-3:]) + '。'\n return reasoning.strip()[:500]\n return content\n\n def _call_minimax(self, prompt: str, max_tokens: int, api_key: str, model: str, base_url: str) -> str:\n import requests\n url = base_url or \"https://api.minimaxi.com/anthropic/v1/messages\"\n headers = {\"Authorization\": f\"Bearer {api_key}\", \"Content-Type\": \"application/json\"}\n payload = {\"model\": model, \"max_tokens\": max_tokens, \"messages\": [{\"role\": \"user\", \"content\": prompt}]}\n response = requests.post(url, headers=headers, json=payload, timeout=600)\n response.raise_for_status()\n data = response.json()\n for item in data.get('content', []):\n if item.get('type') == 'text':\n return item.get('text', '')\n return str(data)\n\n\n# ============== 分析器 ==============\n\nclass DeepAnalyzer:\n \"\"\"深度内容分析器\"\"\"\n\n def __init__(self, ai_client: SimpleAIClient = None):\n self.ai_client = ai_client or SimpleAIClient()\n\n def analyze(self, metadata: Dict[str, Any], content: Dict[str, Any]) -> AnalysisResult:\n \"\"\"一次性调用 AI 生成完整分析报告\"\"\"\n \n # 标准化元数据\n metadata = self._normalize_metadata(metadata)\n \n title = metadata.get('title', '未知')\n duration = metadata.get('duration', 0)\n author = metadata.get('author', '')\n pubtime = metadata.get('pubtime', '')\n url = metadata.get('url', '')\n bvid = metadata.get('bvid', '')\n \n # 获取内容 - 支持多种字段名\n text = content.get('text', '') or content.get('subtitle', '') or content.get('subtitles', '') or content.get('description', '')\n \n print(f\"[DeepAnalyzer] 开始分析: {title}\")\n print(f\"[DeepAnalyzer] 时长: {duration}秒, 内容长度: {len(text)} 字符\")\n \n # 计算分片数（按10分钟分片）\n num_chunks = max(1, (duration + 599) // 600) # 向上取整到10分钟\n print(f\"[DeepAnalyzer] 将分 {num_chunks} 段分析\")\n \n # 构建 prompt\n prompt = self._build_prompt(\n title=title,\n duration=duration,\n duration_str=self._format_duration(duration),\n author=author,\n pubtime=pubtime,\n url=url,\n bvid=bvid,\n text=text,\n num_chunks=num_chunks\n )\n \n # 调用 AI\n try:\n full_analysis = self.ai_client.generate(prompt, max_tokens=16000)\n except Exception as e:\n print(f\"[DeepAnalyzer] AI 调用失败: {e}\")\n raise\n \n # 解析结果\n result = self._parse_analysis(full_analysis, text, duration)\n \n return result\n \n def _build_prompt(self, title: str, duration: int, duration_str: str, author: str, \n pubtime: str, url: str, bvid: str, text: str, num_chunks: int) -> str:\n \"\"\"构建分析 prompt\"\"\"\n \n # 截取适当长度的文本（20-30K 字符）\n max_chars = 30000\n if len(text) > max_chars:\n text_sample = text[:max_chars] + \"\\n\\n[...]\"\n else:\n text_sample = text\n \n prompt = f\"\"\"你是一个专业的视频内容分析师。请根据以下信息生成深度分析报告：\n\n【视频信息】\n标题：{title}\n作者：{author}\n时长：{duration_str}（{duration}秒）\n发布日期：{pubtime}\n平台：bilibili\nURL：{url}\n\n【内容】（完整字幕）\n{text_sample}\n\n【输出要求】必须使用以下格式：\n\n## 标题\n[自己总结的简洁标题，禁止使用原标题]\n\n## 摘要\n[200-400字]\n\n## 主要主题\n[从内容中提取不超过10个主题，每个不超过10字]\n- 主题1\n- 主题2\n...\n\n## 关键见解\n[若干条深刻见解]\n- 见解1\n- 见解2\n...\n\n## 标签\n[从内容中提取3-5个精炼标签，每个不超过10字]\n- 标签1\n- 标签2\n...\n\n## 时间轴\n### 第 1 部分 (00:00 - 10:00)\n[详细分析，至少500字]\n\n### 第 2 部分 (10:00 - 20:00)\n[详细分析，至少500字]\n\n...（以此类推）\n\n【重要】分析应足够详尽，读者阅读后可了解视频核心内容，无需再看原视频。\"\"\"\n\n return prompt\n \n def _parse_analysis(self, analysis_text: str, original_text: str, duration: int) -> AnalysisResult:\n \"\"\"解析 AI 返回的分析结果\"\"\"\n result = AnalysisResult()\n result.full_analysis = analysis_text\n \n # 提取各部分（使用正则）\n lines = analysis_text.split('\\n')\n \n # 简单解析：按 ## 标题分割\n current_section = \"\"\n in_section = None\n \n for line in lines:\n if line.startswith('##'):\n # 保存上一个 section\n if in_section:\n self._store_section(result, in_section, current_section)\n # 开始新 section\n section_name = line.lstrip('#').strip()\n in_section = section_name\n current_section = \"\"\n elif in_section:\n current_section += line + \"\\n\"\n \n # 存储最后一个 section\n if in_section:\n self._store_section(result, in_section, current_section)\n \n # 如果解析失败，使用原始文本\n if not result.summary:\n result.summary = analysis_text[:500]\n \n return result\n \n def _store_section(self, result: AnalysisResult, section_name: str, content: str):\n \"\"\"存储解析到的 section\"\"\"\n content = content.strip()\n if not content:\n return\n \n section_lower = section_name.lower()\n \n if '摘要' in section_lower:\n result.summary = content[:500]\n elif '主题' in section_lower:\n # 提取主题\n topics = []\n for line in content.split('\\n'):\n line = line.strip().lstrip('-').strip()\n if line and len(line) <= 15:\n topics.append(line)\n result.main_topics = topics[:10]\n elif '见解' in section_lower or '观点' in section_lower:\n # 提取见解\n insights = []\n for line in content.split('\\n'):\n line = line.strip().lstrip('-').strip()\n if line and len(line) > 5:\n insights.append(line)\n result.key_insights = insights\n elif '标签' in section_lower:\n # 提取标签\n tags = []\n for line in content.split('\\n'):\n line = line.strip().lstrip('-').strip()\n if line and len(line) <= 10:\n tags.append(line)\n result.recommended_tags = tags[:5]\n \n def _normalize_metadata(self, metadata: Dict) -> Dict:\n \"\"\"标准化元数据\"\"\"\n if not metadata:\n return {}\n \n # 如果已经扁平\n if 'title' in metadata:\n duration = metadata.get('duration', 0)\n if isinstance(duration, str) and ':' in duration:\n parts = duration.split(':')\n try:\n metadata['duration'] = int(parts[0]) * 60 + int(parts[1])\n except:\n pass\n return metadata\n \n # 嵌套结构\n if 'metadata' in metadata:\n meta = metadata['metadata']\n normalized = {}\n for k, v in meta.items():\n normalized[k] = v\n # 处理 duration\n duration = normalized.get('duration', 0)\n if isinstance(duration, str) and ':' in duration:\n parts = duration.split(':')\n try:\n normalized['duration'] = int(parts[0]) * 60 + int(parts[1])\n except:\n pass\n return normalized\n \n return metadata\n \n def _format_duration(self, seconds: int) -> str:\n \"\"\"格式化时长\"\"\"\n h = seconds // 3600\n m = (seconds % 3600) // 60\n s = seconds % 60\n if h > 0:\n return f\"{h}:{m:02d}:{s:02d}\"\n return f\"{m}:{s:02d}\"\n\n\n# ============== 工具函数 ==============\n\ndef load_json(file_path: str) -> Dict:\n \"\"\"加载 JSON 文件\"\"\"\n if not file_path or not os.path.exists(file_path):\n return {}\n with open(file_path, 'r', encoding='utf-8') as f:\n return json.load(f)\n\n\ndef generate_frontmatter(metadata: Dict, result: AnalysisResult) -> str:\n \"\"\"生成 Markdown frontmatter\"\"\"\n # 提取标签\n tags = result.recommended_tags or []\n \n # 格式化日期\n pubtime = metadata.get('pubtime', '')\n if pubtime:\n date_str = str(pubtime)[:10]\n else:\n date_str = datetime.now().strftime('%Y-%m-%d')\n \n fm = [\"---\", \n f\"title: \\\"{metadata.get('title', '未命名')}\\\"\",\n f\"date: {date_str}\",\n f\"duration: {metadata.get('duration_str', metadata.get('duration', 0))}\",\n f\"author: {metadata.get('author', '未知')}\",\n f\"bvid: {metadata.get('bvid', '')}\",\n f\"platform: {metadata.get('platform', 'bilibili')}\"]\n \n # 添加 URL\n url = metadata.get('url', '')\n if url:\n fm.append(f\"url: {url}\")\n \n if tags:\n fm.append(\"tags: [\" + \", \".join(f'\"{t}\"' for t in tags) + \"]\")\n \n fm.append(\"---\")\n return '\\n'.join(fm)\n\n\n# ============== 主程序 ==============\n\ndef main():\n parser = argparse.ArgumentParser(description=\"Deep Analyzer - AI 深度内容分析工具\")\n parser.add_argument(\"--metadata\", \"-m\", required=True, help=\"元数据 JSON 文件路径\")\n parser.add_argument(\"--content\", \"-c\", required=True, help=\"内容 JSON 文件路径\")\n parser.add_argument(\"--output\", \"-o\", required=True, help=\"输出 Markdown 文件路径\")\n parser.add_argument(\"--type\", \"-t\", default=\"video\", choices=[\"video\", \"article\"], help=\"内容类型\")\n parser.add_argument(\"--api-key\", \"-k\", default=None, help=\"AI API Key\")\n parser.add_argument(\"--provider\", \"-p\", default=None, choices=[\"openai\", \"zhipu\", \"minimax\"], help=\"AI 提供商\")\n parser.add_argument(\"--model\", default=None, help=\"AI 模型\")\n parser.add_argument(\"--base-url\", \"-u\", default=None, help=\"API Base URL\")\n parser.add_argument(\"--no-frontmatter\", action=\"store_true\", help=\"不生成 frontmatter\")\n\n args = parser.parse_args()\n\n # 加载数据\n print(f\"[Main] 加载元数据: {args.metadata}\")\n raw_metadata = load_json(args.metadata)\n\n print(f\"[Main] 加载内容: {args.content}\")\n content = load_json(args.content)\n\n # 标准化元数据\n metadata = {}\n if 'title' in raw_metadata:\n metadata = raw_metadata.copy()\n # 处理 duration\n duration = metadata.get('duration', 0)\n if isinstance(duration, str) and ':' in duration:\n parts = duration.split(':')\n try:\n metadata['duration'] = int(parts[0]) * 60 + int(parts[1])\n except:\n pass\n metadata['duration_str'] = format_duration(metadata.get('duration', 0))\n elif 'metadata' in raw_metadata:\n meta = raw_metadata['metadata']\n metadata = meta.copy()\n duration = metadata.get('duration', 0)\n if isinstance(duration, str) and ':' in duration:\n parts = duration.split(':')\n try:\n metadata['duration'] = int(parts[0]) * 60 + int(parts[1])\n except:\n pass\n metadata['duration_str'] = format_duration(metadata.get('duration', 0))\n \n # 初始化\n ai_client = SimpleAIClient(\n api_key=args.api_key or os.environ.get(\"AI_API_KEY\", \"\"),\n provider=args.provider or os.environ.get(\"AI_PROVIDER\", \"\"),\n model=args.model or os.environ.get(\"AI_MODEL\", \"glm-5\"),\n base_url=args.base_url or os.environ.get(\"AI_BASE_URL\", \"\")\n )\n analyzer = DeepAnalyzer(ai_client)\n\n # 执行分析\n print(f\"[Main] 开始分析...\")\n result = analyzer.analyze(metadata, content)\n\n # 使用 AI 生成的原始内容（保留 Markdown 格式）\n full_analysis = result.full_analysis\n \n # 删除内容中的\"标签\" section（只保留 frontmatter 中的 tags）\n # 匹配 ## 标签 或 标签（无##）到下一个 ## 或文件结尾之间的内容\n full_analysis = re.sub(r'(## ?)?标签\\n[\\s\\S]*?(?=\\n## ?|\\n时间轴|---|\\Z)', '', full_analysis)\n # 清理多余的空行\n full_analysis = re.sub(r'\\n{3,}', '\\n\\n', full_analysis)\n\n # 写入输出\n os.makedirs(os.path.dirname(os.path.abspath(args.output)), exist_ok=True)\n \n with open(args.output, 'w', encoding='utf-8') as f:\n # 添加 frontmatter\n if not args.no_frontmatter:\n fm = generate_frontmatter(metadata, result)\n f.write(fm + \"\\n\\n\")\n \n # 写入分析内容\n f.write(full_analysis)\n\n # 输出结果\n output = {\n \"success\": True,\n \"filename\": Path(args.output).name,\n \"filepath\": str(Path(args.output).resolve()),\n \"message\": \"深度分析完成\",\n \"stats\": {\n \"summary_length\": len(result.summary) if result.summary else 0,\n \"topics_count\": len(result.main_topics),\n \"insights_count\": len(result.key_insights),\n \"tags_count\": len(result.recommended_tags)\n }\n }\n print(json.dumps(output, ensure_ascii=False, indent=2))\n\n\ndef format_duration(seconds: int) -> str:\n \"\"\"格式化时长\"\"\"\n h = seconds // 3600\n m = (seconds % 3600) // 60\n s = seconds % 60\n if h > 0:\n return f\"{h}:{m:02d}:{s:02d}\"\n return f\"{m}:{s:02d}\"\n\n\nif __name__ == \"__main__\":\n try:\n main()\n except Exception as e:\n print(json.dumps({\"success\": False, \"error\": str(e)}, ensure_ascii=False, indent=2))\n sys.exit(1)\n"}],"isError":false,"timestamp":1772295517937}}
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{"type":"message","id":"008424c9","parentId":"49f55cf8","timestamp":"2026-02-28T16:19:24.112Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"我发现问题了。脚本期望内容文件是 JSON 格式，但实际的内容文件是 Markdown 格式（以 `---` 开头的 YAML frontmatter）。\n\n错误消息 \"Expecting value: line 1 column 1 (char 0)\" 是因为 `json.load()` 无法解析 Markdown 文件。\n\n我需要：\n1. 要么修改脚本以支持 Markdown 格式的内容文件\n2. 要么将内容文件转换为 JSON 格式\n3. 要么检查是否有其他参数或方法来处理 Markdown 文件\n\n看代码，`load_json` 函数是用于加载内容文件的，它期望的是 JSON 格式。但用户提供的内容文件是 Markdown 格式。\n\n让我检查一下是否有其他方式来处理这个问题，或者我需要修改脚本来支持 Markdown 输入。\n\n实际上，我需要修改脚本来支持 Markdown 格式的内容文件。让我创建一个修复版本，或者直接运行修改后的命令。\n\n但考虑到这是一个技能执行任务，我应该：\n1. 先检查脚本的版本和预期行为\n2. 如果需要，修复脚本以支持 Markdown 输入\n3. 或者将 Markdown 内容转换为 JSON 格式\n\n让我先尝试将 Markdown 内容转换为 JSON 格式，然后再运行分析。\n\n实际上，看代码的 `_build_prompt` 方法，它从内容中提取 `text`、`subtitle`、`subtitles` 或 `description` 字段。\n\n我需要创建一个包含 `text` 字段的 JSON 文件，将 Markdown 内容作为 `text` 的值。这样可以确保脚本能够正确解析和处理内容。","thinkingSignature":"reasoning_content"},{"type":"text","text":"我发现问题了！脚本期望内容文件是 JSON 格式，但实际文件是 Markdown。让我将内容转换为 JSON 格式："},{"type":"toolCall","id":"call_5c3f7f1a0f5e4b2596985bd3","name":"exec","arguments":{"command":"cat > /tmp/zhihu_content.json << 'JSONEOF'\n{\n \"text\": \"# 开源神器——分分钟爬取小红书、抖音、微博、知乎了！\\n\\n**作者**: 太阳鸟 \\n**来源**: [知乎](https://zhuanlan.zhihu.com/p/1925232264591156410) \\n**发布时间**: 2025-07-06 16:40:23 \\n**点赞**: 0 \\n**评论**: 0\\n\\n---\\n\\n推荐阅读• Github 资料项目合集• 50个 AI 开源项目合集• 80 款AI 开源合集* 戳上方蓝字\\\"牛皮糖不吹牛\\\"关注我大家好，我是牛皮糖！最近在给一个做自媒体内容分析的朋友搭系统，他说想抓点小红书、抖音的数据看看趋势。我一听这不是很正常的需求吗？结果他话锋一转：\\\"哥，我爬虫刚入门，JS加密看不懂，X-Bogus 不会搞，登录页面还 403，一气之下差点退圈。\\\"我：别急，MediaCrawler 给你安排得明明白白！ MediaCrawler 是啥？简单一句话： 一个能帮你一键爬小红书、抖音、快手、微博、B站、知乎、贴吧等平台内容的 Python 项目，不用写前端、不用逆向、不用懂加密！项目地址： https://github.com/NanmiCoder/MediaCrawler 它到底帮我们干了啥？ 示例：只用 1 行代码，爬小红书热搜笔记！python main.py --platform xhs --type search --keyword "穿搭" --pages 5它就能给你：• 笔记标题、作者昵称、点赞、评论数• 完整内容（包括图片/视频链接）• 输出到本地 JSON / CSV，还支持 MySQL 入库！ 重点：它是怎么做到\\\"不被封\\\"的？很多人一爬就被封 IP、403？MediaCrawler 的做法是：• ✅ 用 Playwright 自动化浏览器 模拟真实用户；• ✅ 登录状态保留 Cookie；• ✅ 页面运行时注入 JS 自动生成签名；• ✅ 支持代理池，一键配置；一句话：它就是模拟了一个\\\"真的人\\\"，所以平台难以识别你是爬虫。 开发者惊喜：还有 Pro 版本！项目作者还开了一个加强版：MediaCrawlerPro 给开发者的一点建议如果你做的是：• 自媒体数据收集• 舆情分析系统• 多平台热词抓取• 或者只是想轻松入门爬虫世界 那 MediaCrawler 一定值得收藏！ 项目地址（赶紧 Star） https://github.com/NanmiCoder/MediaCrawler Pro 高级版： https://github.com/zwdzzs1/MediaCrawler·················END·················关于AI工具Github开源文本转语音神器Spark-TTS开源了，克隆声音仅需3秒？github开源B站UP主都在用的下载神器！Cobalt让你轻松搬运高清素材！Github 26k Stars 开源换脸神器Github 开源无代码的 Web 数据提取平台，2分钟内训练机器人自动抓取网页数据每日更新，期待与你一起成长欢迎围观副业知识星球本文使用 文章同步助手 同步\"\n}\nJSONEOF\ncat /tmp/zhihu_content.json"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":4476,"output":1094,"cacheRead":11328,"cacheWrite":0,"totalTokens":16898,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295517938}}
|
||
{"type":"message","id":"ef68e611","parentId":"008424c9","timestamp":"2026-02-28T16:19:24.127Z","message":{"role":"toolResult","toolCallId":"call_5c3f7f1a0f5e4b2596985bd3","toolName":"exec","content":[{"type":"text","text":"{\n \"text\": \"# 开源神器——分分钟爬取小红书、抖音、微博、知乎了！\\n\\n**作者**: 太阳鸟 \\n**来源**: [知乎](https://zhuanlan.zhihu.com/p/1925232264591156410) \\n**发布时间**: 2025-07-06 16:40:23 \\n**点赞**: 0 \\n**评论**: 0\\n\\n---\\n\\n推荐阅读• Github 资料项目合集• 50个 AI 开源项目合集• 80 款AI 开源合集* 戳上方蓝字\\\"牛皮糖不吹牛\\\"关注我大家好，我是牛皮糖！最近在给一个做自媒体内容分析的朋友搭系统，他说想抓点小红书、抖音的数据看看趋势。我一听这不是很正常的需求吗？结果他话锋一转：\\\"哥，我爬虫刚入门，JS加密看不懂，X-Bogus 不会搞，登录页面还 403，一气之下差点退圈。\\\"我：别急，MediaCrawler 给你安排得明明白白！ MediaCrawler 是啥？简单一句话： 一个能帮你一键爬小红书、抖音、快手、微博、B站、知乎、贴吧等平台内容的 Python 项目，不用写前端、不用逆向、不用懂加密！项目地址： https://github.com/NanmiCoder/MediaCrawler 它到底帮我们干了啥？ 示例：只用 1 行代码，爬小红书热搜笔记！python main.py --platform xhs --type search --keyword "穿搭" --pages 5它就能给你：• 笔记标题、作者昵称、点赞、评论数• 完整内容（包括图片/视频链接）• 输出到本地 JSON / CSV，还支持 MySQL 入库！ 重点：它是怎么做到\\\"不被封\\\"的？很多人一爬就被封 IP、403？MediaCrawler 的做法是：• ✅ 用 Playwright 自动化浏览器 模拟真实用户；• ✅ 登录状态保留 Cookie；• ✅ 页面运行时注入 JS 自动生成签名；• ✅ 支持代理池，一键配置；一句话：它就是模拟了一个\\\"真的人\\\"，所以平台难以识别你是爬虫。 开发者惊喜：还有 Pro 版本！项目作者还开了一个加强版：MediaCrawlerPro 给开发者的一点建议如果你做的是：• 自媒体数据收集• 舆情分析系统• 多平台热词抓取• 或者只是想轻松入门爬虫世界 那 MediaCrawler 一定值得收藏！ 项目地址（赶紧 Star） https://github.com/NanmiCoder/MediaCrawler Pro 高级版： https://github.com/zwdzzs1/MediaCrawler·················END·················关于AI工具Github开源文本转语音神器Spark-TTS开源了，克隆声音仅需3秒？github开源B站UP主都在用的下载神器！Cobalt让你轻松搬运高清素材！Github 26k Stars 开源换脸神器Github 开源无代码的 Web 数据提取平台，2分钟内训练机器人自动抓取网页数据每日更新，期待与你一起成长欢迎围观副业知识星球本文使用 文章同步助手 同步\"\n}"}],"details":{"status":"completed","exitCode":0,"durationMs":14,"aggregated":"{\n \"text\": \"# 开源神器——分分钟爬取小红书、抖音、微博、知乎了！\\n\\n**作者**: 太阳鸟 \\n**来源**: [知乎](https://zhuanlan.zhihu.com/p/1925232264591156410) \\n**发布时间**: 2025-07-06 16:40:23 \\n**点赞**: 0 \\n**评论**: 0\\n\\n---\\n\\n推荐阅读• Github 资料项目合集• 50个 AI 开源项目合集• 80 款AI 开源合集* 戳上方蓝字\\\"牛皮糖不吹牛\\\"关注我大家好，我是牛皮糖！最近在给一个做自媒体内容分析的朋友搭系统，他说想抓点小红书、抖音的数据看看趋势。我一听这不是很正常的需求吗？结果他话锋一转：\\\"哥，我爬虫刚入门，JS加密看不懂，X-Bogus 不会搞，登录页面还 403，一气之下差点退圈。\\\"我：别急，MediaCrawler 给你安排得明明白白！ MediaCrawler 是啥？简单一句话： 一个能帮你一键爬小红书、抖音、快手、微博、B站、知乎、贴吧等平台内容的 Python 项目，不用写前端、不用逆向、不用懂加密！项目地址： https://github.com/NanmiCoder/MediaCrawler 它到底帮我们干了啥？ 示例：只用 1 行代码，爬小红书热搜笔记！python main.py --platform xhs --type search --keyword "穿搭" --pages 5它就能给你：• 笔记标题、作者昵称、点赞、评论数• 完整内容（包括图片/视频链接）• 输出到本地 JSON / CSV，还支持 MySQL 入库！ 重点：它是怎么做到\\\"不被封\\\"的？很多人一爬就被封 IP、403？MediaCrawler 的做法是：• ✅ 用 Playwright 自动化浏览器 模拟真实用户；• ✅ 登录状态保留 Cookie；• ✅ 页面运行时注入 JS 自动生成签名；• ✅ 支持代理池，一键配置；一句话：它就是模拟了一个\\\"真的人\\\"，所以平台难以识别你是爬虫。 开发者惊喜：还有 Pro 版本！项目作者还开了一个加强版：MediaCrawlerPro 给开发者的一点建议如果你做的是：• 自媒体数据收集• 舆情分析系统• 多平台热词抓取• 或者只是想轻松入门爬虫世界 那 MediaCrawler 一定值得收藏！ 项目地址（赶紧 Star） https://github.com/NanmiCoder/MediaCrawler Pro 高级版： https://github.com/zwdzzs1/MediaCrawler·················END·················关于AI工具Github开源文本转语音神器Spark-TTS开源了，克隆声音仅需3秒？github开源B站UP主都在用的下载神器！Cobalt让你轻松搬运高清素材！Github 26k Stars 开源换脸神器Github 开源无代码的 Web 数据提取平台，2分钟内训练机器人自动抓取网页数据每日更新，期待与你一起成长欢迎围观副业知识星球本文使用 文章同步助手 同步\"\n}","cwd":"/home/starmen1265/.openclaw/agents/knowledge-base/workspace"},"isError":false,"timestamp":1772295564126}}
|
||
{"type":"message","id":"bd701136","parentId":"ef68e611","timestamp":"2026-02-28T16:19:33.608Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"现在我还需要创建一个正确的元数据 JSON 文件，因为原来的文件大部分字段是空的。让我创建一个包含正确信息的元数据文件。","thinkingSignature":"reasoning_content"},{"type":"text","text":"现在创建正确的元数据 JSON 文件："},{"type":"toolCall","id":"call_ed00aa9427f0472591398a29","name":"exec","arguments":{"command":"cat > /tmp/zhihu_meta.json << 'JSONEOF'\n{\n \"type\": \"zhuanlan\",\n \"id\": \"2010481705870454832\",\n \"url\": \"https://zhuanlan.zhihu.com/p/2010481705870454832\",\n \"title\": \"开源神器——分分钟爬取小红书、抖音、微博、知乎了！\",\n \"author\": \"太阳鸟\",\n \"pubtime\": \"2025-07-06 16:40:23\",\n \"platform\": \"zhihu\",\n \"duration\": 0,\n \"duration_str\": \"0:00\"\n}\nJSONEOF\ncat /tmp/zhihu_meta.json"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":1873,"output":204,"cacheRead":15744,"cacheWrite":0,"totalTokens":17821,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295564127}}
|
||
{"type":"message","id":"277a69a4","parentId":"bd701136","timestamp":"2026-02-28T16:19:33.622Z","message":{"role":"toolResult","toolCallId":"call_ed00aa9427f0472591398a29","toolName":"exec","content":[{"type":"text","text":"{\n \"type\": \"zhuanlan\",\n \"id\": \"2010481705870454832\",\n \"url\": \"https://zhuanlan.zhihu.com/p/2010481705870454832\",\n \"title\": \"开源神器——分分钟爬取小红书、抖音、微博、知乎了！\",\n \"author\": \"太阳鸟\",\n \"pubtime\": \"2025-07-06 16:40:23\",\n \"platform\": \"zhihu\",\n \"duration\": 0,\n \"duration_str\": \"0:00\"\n}"}],"details":{"status":"completed","exitCode":0,"durationMs":13,"aggregated":"{\n \"type\": \"zhuanlan\",\n \"id\": \"2010481705870454832\",\n \"url\": \"https://zhuanlan.zhihu.com/p/2010481705870454832\",\n \"title\": \"开源神器——分分钟爬取小红书、抖音、微博、知乎了！\",\n \"author\": \"太阳鸟\",\n \"pubtime\": \"2025-07-06 16:40:23\",\n \"platform\": \"zhihu\",\n \"duration\": 0,\n \"duration_str\": \"0:00\"\n}","cwd":"/home/starmen1265/.openclaw/agents/knowledge-base/workspace"},"isError":false,"timestamp":1772295573621}}
|
||
{"type":"message","id":"4852da39","parentId":"277a69a4","timestamp":"2026-02-28T16:19:45.284Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"好的，现在我可以使用这些临时的 JSON 文件来运行分析命令了。但是，我注意到这个脚本是为视频内容设计的（它提到 `duration` 和时间轴分析）。对于文章，我需要修改脚本或者看看它是否可以处理文章类型。\n\n让我看看脚本是否有 `--type` 参数来处理文章。是的，有 `--type article` 参数。让我使用这个参数重新运行。\n\n但是，我注意到脚本的 `_build_prompt` 方法仍然是为视频设计的（它提到\"视频信息\"、\"时长\"、\"时间轴\"等）。对于文章分析，这个脚本可能需要修改。\n\n不过，让我先尝试运行它，看看会发生什么。如果失败，我会修改脚本来支持文章分析。","thinkingSignature":"reasoning_content"},{"type":"text","text":"现在使用临时 JSON 文件重新运行分析："},{"type":"toolCall","id":"call_1902b11c7a3246c5ab595120","name":"exec","arguments":{"command":"cd ~/.openclaw/agents/knowledge-base/workspace/skills/kb-deep-analyzer && source ~/.kb_env && python3 deep_analyzer.py --metadata /tmp/zhihu_meta.json --content /tmp/zhihu_content.json --output \"/home/starmen1265/work/knowledge-base/analyzed/zhihu_2010481705870454832.md\" --provider zhipu --model glm-5 --type article","timeout":120}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":356,"output":267,"cacheRead":17600,"cacheWrite":0,"totalTokens":18223,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295573622}}
|
||
{"type":"message","id":"a1ea1c43","parentId":"4852da39","timestamp":"2026-02-28T16:19:55.308Z","message":{"role":"toolResult","toolCallId":"call_1902b11c7a3246c5ab595120","toolName":"exec","content":[{"type":"text","text":"Command still running (session nimble-cove, pid 1350097). Use process (list/poll/log/write/kill/clear/remove) for follow-up."}],"details":{"status":"running","sessionId":"nimble-cove","pid":1350097,"startedAt":1772295585285,"cwd":"/home/starmen1265/.openclaw/agents/knowledge-base/workspace","tail":""},"isError":false,"timestamp":1772295595298}}
|
||
{"type":"message","id":"31c9ae39","parentId":"a1ea1c43","timestamp":"2026-02-28T16:20:00.340Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"命令正在后台运行。让我检查它的输出。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_d58d136990954a1db6decc27","name":"process","arguments":{"action":"log","sessionId":"nimble-cove"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":342,"output":30,"cacheRead":17920,"cacheWrite":0,"totalTokens":18292,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295595308}}
|
||
{"type":"message","id":"6814e7d2","parentId":"31c9ae39","timestamp":"2026-02-28T16:20:00.350Z","message":{"role":"toolResult","toolCallId":"call_d58d136990954a1db6decc27","toolName":"process","content":[{"type":"text","text":"(no output yet)"}],"details":{"status":"running","sessionId":"nimble-cove","total":0,"totalLines":0,"totalChars":0,"truncated":false,"name":"cd ~/.openclaw/agents/kno...kills/kb-deep-analyzer"},"isError":false,"timestamp":1772295600349}}
|
||
{"type":"message","id":"577fe15c","parentId":"6814e7d2","timestamp":"2026-02-28T16:20:03.268Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"让我等一会儿再检查，AI 调用可能需要一些时间。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_286e1a445f254857b5d7c1ae","name":"process","arguments":{"action":"poll","sessionId":"nimble-cove"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":60,"output":36,"cacheRead":18240,"cacheWrite":0,"totalTokens":18336,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295600350}}
|
||
{"type":"message","id":"30362b7f","parentId":"577fe15c","timestamp":"2026-02-28T16:20:03.280Z","message":{"role":"toolResult","toolCallId":"call_286e1a445f254857b5d7c1ae","toolName":"process","content":[{"type":"text","text":"(no new output)\n\nProcess still running."}],"details":{"status":"running","sessionId":"nimble-cove","aggregated":"","name":"cd ~/.openclaw/agents/kno...kills/kb-deep-analyzer"},"isError":false,"timestamp":1772295603279}}
|
||
{"type":"message","id":"65dbddb4","parentId":"30362b7f","timestamp":"2026-02-28T16:20:05.967Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"AI 调用需要时间。让我等待更长时间。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_941e2c3f6ef54b9cadb0a239","name":"process","arguments":{"action":"poll","sessionId":"nimble-cove"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":108,"output":33,"cacheRead":18240,"cacheWrite":0,"totalTokens":18381,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295603280}}
|
||
{"type":"message","id":"1d5c21e7","parentId":"65dbddb4","timestamp":"2026-02-28T16:20:05.978Z","message":{"role":"toolResult","toolCallId":"call_941e2c3f6ef54b9cadb0a239","toolName":"process","content":[{"type":"text","text":"(no new output)\n\nProcess still running."}],"details":{"status":"running","sessionId":"nimble-cove","aggregated":"","name":"cd ~/.openclaw/agents/kno...kills/kb-deep-analyzer"},"isError":false,"timestamp":1772295605977}}
|
||
{"type":"message","id":"b89619aa","parentId":"1d5c21e7","timestamp":"2026-02-28T16:20:11.566Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"让我再等一会儿，AI API 调用可能需要 30-60 秒。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_4b7b9d7d77844267a3605e24","name":"process","arguments":{"action":"poll","sessionId":"nimble-cove"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":89,"output":40,"cacheRead":18304,"cacheWrite":0,"totalTokens":18433,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295605978}}
|
||
{"type":"message","id":"544a3b0a","parentId":"b89619aa","timestamp":"2026-02-28T16:20:11.577Z","message":{"role":"toolResult","toolCallId":"call_4b7b9d7d77844267a3605e24","toolName":"process","content":[{"type":"text","text":"(no new output)\n\nProcess still running."}],"details":{"status":"running","sessionId":"nimble-cove","aggregated":"","name":"cd ~/.openclaw/agents/kno...kills/kb-deep-analyzer"},"isError":false,"timestamp":1772295611576}}
|
||
{"type":"message","id":"2c0fc0cb","parentId":"544a3b0a","timestamp":"2026-02-28T16:20:15.396Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"让我再等待更长时间。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_96dcfe08ac9948ebaf81e9bf","name":"process","arguments":{"action":"poll","sessionId":"nimble-cove"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":77,"output":26,"cacheRead":18368,"cacheWrite":0,"totalTokens":18471,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295611577}}
|
||
{"type":"message","id":"e20f042e","parentId":"2c0fc0cb","timestamp":"2026-02-28T16:20:15.405Z","message":{"role":"toolResult","toolCallId":"call_96dcfe08ac9948ebaf81e9bf","toolName":"process","content":[{"type":"text","text":"(no new output)\n\nProcess still running."}],"details":{"status":"running","sessionId":"nimble-cove","aggregated":"","name":"cd ~/.openclaw/agents/kno...kills/kb-deep-analyzer"},"isError":false,"timestamp":1772295615403}}
|
||
{"type":"message","id":"db31b0ee","parentId":"e20f042e","timestamp":"2026-02-28T16:20:20.098Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"让我再等一会儿。如果它持续运行很长时间，可能有问题。让我再等几秒钟。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_4d17d4989e074d87a7f76105","name":"process","arguments":{"action":"poll","sessionId":"nimble-cove"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":51,"output":40,"cacheRead":18432,"cacheWrite":0,"totalTokens":18523,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295615405}}
|
||
{"type":"message","id":"dc4fbfcf","parentId":"db31b0ee","timestamp":"2026-02-28T16:20:20.109Z","message":{"role":"toolResult","toolCallId":"call_4d17d4989e074d87a7f76105","toolName":"process","content":[{"type":"text","text":"(no new output)\n\nProcess still running."}],"details":{"status":"running","sessionId":"nimble-cove","aggregated":"","name":"cd ~/.openclaw/agents/kno...kills/kb-deep-analyzer"},"isError":false,"timestamp":1772295620108}}
|
||
{"type":"message","id":"022379e5","parentId":"dc4fbfcf","timestamp":"2026-02-28T16:20:22.986Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"让我再等一会儿。如果还是没有输出，我可能需要检查是否有错误或者手动终止进程。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_d4c7d4e198ae43b2b107aa41","name":"process","arguments":{"action":"poll","sessionId":"nimble-cove"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":103,"output":40,"cacheRead":18432,"cacheWrite":0,"totalTokens":18575,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295620109}}
|
||
{"type":"message","id":"f39c7f97","parentId":"022379e5","timestamp":"2026-02-28T16:20:22.996Z","message":{"role":"toolResult","toolCallId":"call_d4c7d4e198ae43b2b107aa41","toolName":"process","content":[{"type":"text","text":"(no new output)\n\nProcess still running."}],"details":{"status":"running","sessionId":"nimble-cove","aggregated":"","name":"cd ~/.openclaw/agents/kno...kills/kb-deep-analyzer"},"isError":false,"timestamp":1772295622994}}
|
||
{"type":"message","id":"5fe1f3c7","parentId":"f39c7f97","timestamp":"2026-02-28T16:20:29.417Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"进程已经运行了一段时间但没有任何输出。这可能意味着：\n1. AI 调用正在进行中，需要更长时间\n2. 脚本卡住了\n3. 有某些错误但没有输出\n\n让我再等一会儿，如果还是没有输出，我会检查进程状态或尝试其他方法。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_c86c9ca17a834173b5694c4c","name":"process","arguments":{"action":"poll","sessionId":"nimble-cove"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":91,"output":81,"cacheRead":18496,"cacheWrite":0,"totalTokens":18668,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295622996}}
|
||
{"type":"message","id":"f71376df","parentId":"5fe1f3c7","timestamp":"2026-02-28T16:20:29.428Z","message":{"role":"toolResult","toolCallId":"call_c86c9ca17a834173b5694c4c","toolName":"process","content":[{"type":"text","text":"(no new output)\n\nProcess still running."}],"details":{"status":"running","sessionId":"nimble-cove","aggregated":"","name":"cd ~/.openclaw/agents/kno...kills/kb-deep-analyzer"},"isError":false,"timestamp":1772295629426}}
|
||
{"type":"message","id":"55af6782","parentId":"f71376df","timestamp":"2026-02-28T16:20:35.306Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"进程已经运行超过 60 秒了，仍然没有输出。这可能意味着脚本在等待 AI 响应，或者有问题。让我再等一会儿，如果还是没有输出，我会检查日志或尝试其他方法。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_11bb961571f94ebcbf0d2931","name":"process","arguments":{"action":"poll","sessionId":"nimble-cove"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":120,"output":64,"cacheRead":18560,"cacheWrite":0,"totalTokens":18744,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295629428}}
|
||
{"type":"message","id":"7d7e74d9","parentId":"55af6782","timestamp":"2026-02-28T16:20:35.318Z","message":{"role":"toolResult","toolCallId":"call_11bb961571f94ebcbf0d2931","toolName":"process","content":[{"type":"text","text":"(no new output)\n\nProcess still running."}],"details":{"status":"running","sessionId":"nimble-cove","aggregated":"","name":"cd ~/.openclaw/agents/kno...kills/kb-deep-analyzer"},"isError":false,"timestamp":1772295635316}}
|
||
{"type":"message","id":"1617a3e2","parentId":"7d7e74d9","timestamp":"2026-02-28T16:20:39.331Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"让我检查一下输出文件是否已经创建，即使进程还在运行。这可以帮助我确定脚本是否正在工作。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_fa273dae8ce6478c9cbc2225","name":"exec","arguments":{"command":"ls -la /home/starmen1265/work/knowledge-base/analyzed/ 2>&1 || echo \"Directory does not exist\""}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":132,"output":63,"cacheRead":18624,"cacheWrite":0,"totalTokens":18819,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295635318}}
|
||
{"type":"message","id":"9818ad13","parentId":"1617a3e2","timestamp":"2026-02-28T16:20:39.349Z","message":{"role":"toolResult","toolCallId":"call_fa273dae8ce6478c9cbc2225","toolName":"exec","content":[{"type":"text","text":"总计 20\ndrwxrwxr-x 2 starmen1265 starmen1265 4096 2月 27 18:31 .\ndrwxrwxr-x 9 starmen1265 starmen1265 4096 2月 27 20:45 ..\n-rw-rw-r-- 1 starmen1265 starmen1265 8846 2月 27 18:31 zhihu_1894054073625461813.md"}],"details":{"status":"completed","exitCode":0,"durationMs":15,"aggregated":"总计 20\ndrwxrwxr-x 2 starmen1265 starmen1265 4096 2月 27 18:31 .\ndrwxrwxr-x 9 starmen1265 starmen1265 4096 2月 27 20:45 ..\n-rw-rw-r-- 1 starmen1265 starmen1265 8846 2月 27 18:31 zhihu_1894054073625461813.md","cwd":"/home/starmen1265/.openclaw/agents/knowledge-base/workspace"},"isError":false,"timestamp":1772295639347}}
|
||
{"type":"message","id":"651ed544","parentId":"9818ad13","timestamp":"2026-02-28T16:20:44.175Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"目录存在，但输出文件还没有创建。让我再等一会儿，看看进程是否完成。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_73fed7f4ba3747ddb7ada950","name":"process","arguments":{"action":"poll","sessionId":"nimble-cove"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":189,"output":39,"cacheRead":18752,"cacheWrite":0,"totalTokens":18980,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295639349}}
|
||
{"type":"message","id":"a21fe629","parentId":"651ed544","timestamp":"2026-02-28T16:20:44.186Z","message":{"role":"toolResult","toolCallId":"call_73fed7f4ba3747ddb7ada950","toolName":"process","content":[{"type":"text","text":"(no new output)\n\nProcess still running."}],"details":{"status":"running","sessionId":"nimble-cove","aggregated":"","name":"cd ~/.openclaw/agents/kno...kills/kb-deep-analyzer"},"isError":false,"timestamp":1772295644184}}
|
||
{"type":"message","id":"a105c9e9","parentId":"a21fe629","timestamp":"2026-02-28T16:20:48.003Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"进程还在运行但没有输出。让我再等一会儿，然后如果还是没有输出，我会检查是否有问题。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_014b8cc26906427cba40319f","name":"process","arguments":{"action":"poll","sessionId":"nimble-cove"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":112,"output":41,"cacheRead":18880,"cacheWrite":0,"totalTokens":19033,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295644186}}
|
||
{"type":"message","id":"5e78de11","parentId":"a105c9e9","timestamp":"2026-02-28T16:20:48.013Z","message":{"role":"toolResult","toolCallId":"call_014b8cc26906427cba40319f","toolName":"process","content":[{"type":"text","text":"(no new output)\n\nProcess still running."}],"details":{"status":"running","sessionId":"nimble-cove","aggregated":"","name":"cd ~/.openclaw/agents/kno...kills/kb-deep-analyzer"},"isError":false,"timestamp":1772295648011}}
|
||
{"type":"message","id":"7e0fbd5c","parentId":"5e78de11","timestamp":"2026-02-28T16:20:52.766Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"让我再等待一次，然后检查进程的详细状态。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_b2e3d5b369384f028c234dbd","name":"process","arguments":{"action":"poll","sessionId":"nimble-cove"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":101,"output":32,"cacheRead":18944,"cacheWrite":0,"totalTokens":19077,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295648013}}
|
||
{"type":"message","id":"e0d46f73","parentId":"7e0fbd5c","timestamp":"2026-02-28T16:20:52.777Z","message":{"role":"toolResult","toolCallId":"call_b2e3d5b369384f028c234dbd","toolName":"process","content":[{"type":"text","text":"(no new output)\n\nProcess still running."}],"details":{"status":"running","sessionId":"nimble-cove","aggregated":"","name":"cd ~/.openclaw/agents/kno...kills/kb-deep-analyzer"},"isError":false,"timestamp":1772295652776}}
|
||
{"type":"message","id":"be4ecfad","parentId":"e0d46f73","timestamp":"2026-02-28T16:20:56.469Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"让我检查进程的系统资源使用情况，看看它是否真的在运行。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_e4ddfdf8ec7e4bb2b45e672d","name":"exec","arguments":{"command":"ps aux | grep -E \"1350097|deep_analyzer\" | grep -v grep"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":81,"output":46,"cacheRead":19008,"cacheWrite":0,"totalTokens":19135,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295652777}}
|
||
{"type":"message","id":"f0fa3218","parentId":"be4ecfad","timestamp":"2026-02-28T16:20:56.497Z","message":{"role":"toolResult","toolCallId":"call_e4ddfdf8ec7e4bb2b45e672d","toolName":"exec","content":[{"type":"text","text":"starmen+ 1350097 0.0 0.1 43868 32504 ? Ss 00:19 0:00 python3 deep_analyzer.py --metadata /tmp/zhihu_meta.json --content /tmp/zhihu_content.json --output /home/starmen1265/work/knowledge-base/analyzed/zhihu_2010481705870454832.md --provider zhipu --model glm-5 --type article\nstarmen+ 1350119 2.0 0.1 43860 32508 ? Ss 00:20 0:00 python3 deep_analyzer.py --metadata /home/starmen1265/work/knowledge-base/metadata/zhihu_2010481705870454832.meta.json --content /home/starmen1265/work/knowledge-base/content/zhihu_2010481705870454832.json --output /home/starmen1265/work/knowledge-base/analyzed/zhihu_2010481705870454832.md --provider zhipu --model glm-5"}],"details":{"status":"completed","exitCode":0,"durationMs":26,"aggregated":"starmen+ 1350097 0.0 0.1 43868 32504 ? Ss 00:19 0:00 python3 deep_analyzer.py --metadata /tmp/zhihu_meta.json --content /tmp/zhihu_content.json --output /home/starmen1265/work/knowledge-base/analyzed/zhihu_2010481705870454832.md --provider zhipu --model glm-5 --type article\nstarmen+ 1350119 2.0 0.1 43860 32508 ? Ss 00:20 0:00 python3 deep_analyzer.py --metadata /home/starmen1265/work/knowledge-base/metadata/zhihu_2010481705870454832.meta.json --content /home/starmen1265/work/knowledge-base/content/zhihu_2010481705870454832.json --output /home/starmen1265/work/knowledge-base/analyzed/zhihu_2010481705870454832.md --provider zhipu --model glm-5","cwd":"/home/starmen1265/.openclaw/agents/knowledge-base/workspace"},"isError":false,"timestamp":1772295656496}}
|
||
{"type":"message","id":"b45acc9c","parentId":"f0fa3218","timestamp":"2026-02-28T16:21:03.327Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"有两个进程在运行！一个是我启动的（PID 1350097），另一个似乎也是类似的命令。让我检查一下是否有其他进程在干扰。\n\n让我等待我的进程完成，或者检查它的输出。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_e00a35e9d6894e22a72255fd","name":"process","arguments":{"action":"poll","sessionId":"nimble-cove"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":331,"output":63,"cacheRead":19072,"cacheWrite":0,"totalTokens":19466,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295656497}}
|
||
{"type":"message","id":"b1f23e8f","parentId":"b45acc9c","timestamp":"2026-02-28T16:21:03.338Z","message":{"role":"toolResult","toolCallId":"call_e00a35e9d6894e22a72255fd","toolName":"process","content":[{"type":"text","text":"(no new output)\n\nProcess still running."}],"details":{"status":"running","sessionId":"nimble-cove","aggregated":"","name":"cd ~/.openclaw/agents/kno...kills/kb-deep-analyzer"},"isError":false,"timestamp":1772295663337}}
|
||
{"type":"message","id":"a862751e","parentId":"b1f23e8f","timestamp":"2026-02-28T16:21:08.980Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"让我再等一会儿。AI API 调用可能需要较长时间，特别是对于长文本分析。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_e91312c315ae48a0992bbf58","name":"process","arguments":{"action":"poll","sessionId":"nimble-cove"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":86,"output":42,"cacheRead":19392,"cacheWrite":0,"totalTokens":19520,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295663338}}
|
||
{"type":"message","id":"faba6791","parentId":"a862751e","timestamp":"2026-02-28T16:21:08.991Z","message":{"role":"toolResult","toolCallId":"call_e91312c315ae48a0992bbf58","toolName":"process","content":[{"type":"text","text":"(no new output)\n\nProcess still running."}],"details":{"status":"running","sessionId":"nimble-cove","aggregated":"","name":"cd ~/.openclaw/agents/kno...kills/kb-deep-analyzer"},"isError":false,"timestamp":1772295668990}}
|
||
{"type":"message","id":"39a94470","parentId":"faba6791","timestamp":"2026-02-28T16:21:17.213Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"让我检查一下环境变量是否正确设置，以及 API 配置是否正确。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_a38df1f434a34ba999775439","name":"exec","arguments":{"command":"source ~/.kb_env && echo \"AI_PROVIDER: $AI_PROVIDER\" && echo \"AI_MODEL: $AI_MODEL\" && echo \"AI_API_KEY set: $(if [ -n \\\"$AI_API_KEY\\\" ]; then echo 'yes'; else echo 'no'; fi)\" && echo \"AI_BASE_URL: $AI_BASE_URL\""}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":76,"output":93,"cacheRead":19456,"cacheWrite":0,"totalTokens":19625,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295668991}}
|
||
{"type":"message","id":"20c006d9","parentId":"39a94470","timestamp":"2026-02-28T16:21:17.231Z","message":{"role":"toolResult","toolCallId":"call_a38df1f434a34ba999775439","toolName":"exec","content":[{"type":"text","text":"AI_PROVIDER: zhipu\nAI_MODEL: glm-5\nAI_API_KEY set: yes\nAI_BASE_URL: https://open.bigmodel.cn/api/coding/paas/v4"}],"details":{"status":"completed","exitCode":0,"durationMs":16,"aggregated":"AI_PROVIDER: zhipu\nAI_MODEL: glm-5\nAI_API_KEY set: yes\nAI_BASE_URL: https://open.bigmodel.cn/api/coding/paas/v4","cwd":"/home/starmen1265/.openclaw/agents/knowledge-base/workspace"},"isError":false,"timestamp":1772295677229}}
|
||
{"type":"message","id":"f38f666a","parentId":"20c006d9","timestamp":"2026-02-28T16:21:24.202Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"环境变量看起来是正确的。让我再等一会儿，看看进程是否完成。如果还是没有输出，我可能需要手动测试 API 调用是否工作。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_b408bf7c75fe46df8a735c53","name":"process","arguments":{"action":"poll","sessionId":"nimble-cove"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":148,"output":53,"cacheRead":19520,"cacheWrite":0,"totalTokens":19721,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295677231}}
|
||
{"type":"message","id":"3e09b240","parentId":"f38f666a","timestamp":"2026-02-28T16:21:24.213Z","message":{"role":"toolResult","toolCallId":"call_b408bf7c75fe46df8a735c53","toolName":"process","content":[{"type":"text","text":"(no new output)\n\nProcess still running."}],"details":{"status":"running","sessionId":"nimble-cove","aggregated":"","name":"cd ~/.openclaw/agents/kno...kills/kb-deep-analyzer"},"isError":false,"timestamp":1772295684211}}
|
||
{"type":"message","id":"52ff0246","parentId":"3e09b240","timestamp":"2026-02-28T16:21:40.384Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"进程已经运行了超过 2 分钟，仍然没有输出。这不正常。让我尝试手动测试 API 调用，看看是否有问题。","thinkingSignature":"reasoning_content"},{"type":"text","text":"让我手动测试 API 连接："},{"type":"toolCall","id":"call_d9f494f11af84d5d98e94d32","name":"exec","arguments":{"command":"source ~/.kb_env && python3 << 'PYEOF'\nimport os\nimport requests\nimport json\n\napi_key = os.environ.get(\"AI_API_KEY\", \"\")\nbase_url = os.environ.get(\"AI_BASE_URL\", \"https://open.bigmodel.cn/api/coding/paas/v4\")\nmodel = os.environ.get(\"AI_MODEL\", \"glm-5\")\n\nprint(f\"Testing API connection...\")\nprint(f\"Base URL: {base_url}\")\nprint(f\"Model: {model}\")\nprint(f\"API Key (first 10 chars): {api_key[:10]}...\")\n\nurl = f\"{base_url}/chat/completions\"\nheaders = {\"Authorization\": f\"Bearer {api_key}\"}\npayload = {\n \"model\": model,\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello, this is a test. Please respond with 'OK'.\"}],\n \"max_tokens\": 50\n}\n\ntry:\n response = requests.post(url, headers=headers, json=payload, timeout=30)\n print(f\"Status Code: {response.status_code}\")\n print(f\"Response: {response.text[:500]}\")\nexcept Exception as e:\n print(f\"Error: {e}\")\nPYEOF\n","timeout":40}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":85,"output":305,"cacheRead":19648,"cacheWrite":0,"totalTokens":20038,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295684213}}
|
||
{"type":"message","id":"55bed34d","parentId":"52ff0246","timestamp":"2026-02-28T16:21:42.753Z","message":{"role":"toolResult","toolCallId":"call_d9f494f11af84d5d98e94d32","toolName":"exec","content":[{"type":"text","text":"Testing API connection...\nBase URL: https://open.bigmodel.cn/api/coding/paas/v4\nModel: glm-5\nAPI Key (first 10 chars): 80ec82f2b1...\nStatus Code: 200\nResponse: {\"choices\":[{\"finish_reason\":\"length\",\"index\":0,\"message\":{\"content\":\"\",\"reasoning_content\":\"The user has sent a simple message: \\\"Hello, this is a test. Please respond with 'OK'.\\\"\\n\\n1. **Analyze the Request:** The user is asking for a specific confirmation response ('OK') to verify functionality.\\n2.\",\"role\":\"assistant\"}}],\"created\":1772295702,\"id\":\"2026030100214086f2bf1373224f05\",\"model\":\"glm-5\",\"object\":\"chat.completion\",\"request_id\":\"2026030100214086f2bf1373224f05\",\"usage\":{\"completion_to"}],"details":{"status":"completed","exitCode":0,"durationMs":2368,"aggregated":"Testing API connection...\nBase URL: https://open.bigmodel.cn/api/coding/paas/v4\nModel: glm-5\nAPI Key (first 10 chars): 80ec82f2b1...\nStatus Code: 200\nResponse: {\"choices\":[{\"finish_reason\":\"length\",\"index\":0,\"message\":{\"content\":\"\",\"reasoning_content\":\"The user has sent a simple message: \\\"Hello, this is a test. Please respond with 'OK'.\\\"\\n\\n1. **Analyze the Request:** The user is asking for a specific confirmation response ('OK') to verify functionality.\\n2.\",\"role\":\"assistant\"}}],\"created\":1772295702,\"id\":\"2026030100214086f2bf1373224f05\",\"model\":\"glm-5\",\"object\":\"chat.completion\",\"request_id\":\"2026030100214086f2bf1373224f05\",\"usage\":{\"completion_to","cwd":"/home/starmen1265/.openclaw/agents/knowledge-base/workspace"},"isError":false,"timestamp":1772295702752}}
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{"type":"message","id":"1397d800","parentId":"55bed34d","timestamp":"2026-02-28T16:21:49.998Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"API 连接正常，但返回的 `content` 是空的，只有 `reasoning_content`。这是 GLM-5 的一个特性 - 它先进行推理，然后生成内容。但是在这个测试中，由于 `max_tokens` 太小（50），它只返回了推理部分。\n\n让我检查一下我的进程是否还在运行，以及是否有任何输出。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_da474fd9a6cf4cfa95dce40e","name":"process","arguments":{"action":"poll","sessionId":"nimble-cove"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":529,"output":95,"cacheRead":19712,"cacheWrite":0,"totalTokens":20336,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295702753}}
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{"type":"message","id":"63f656aa","parentId":"1397d800","timestamp":"2026-02-28T16:21:50.007Z","message":{"role":"toolResult","toolCallId":"call_da474fd9a6cf4cfa95dce40e","toolName":"process","content":[{"type":"text","text":"[Main] 加载元数据: /tmp/zhihu_meta.json\n[Main] 加载内容: /tmp/zhihu_content.json\n[Main] 开始分析...\n[DeepAnalyzer] 开始分析: 开源神器——分分钟爬取小红书、抖音、微博、知乎了！\n[DeepAnalyzer] 时长: 0秒, 内容长度: 1337 字符\n[DeepAnalyzer] 将分 1 段分析\n[AI Client] 开始调用 AI...\n[AI Client] AI 调用完成，耗时: 116.99秒\n{\n \"success\": true,\n \"filename\": \"zhihu_2010481705870454832.md\",\n \"filepath\": \"/home/starmen1265/work/knowledge-base/analyzed/zhihu_2010481705870454832.md\",\n \"message\": \"深度分析完成\",\n \"stats\": {\n \"summary_length\": 380,\n \"topics_count\": 7,\n \"insights_count\": 4,\n \"tags_count\": 5\n }\n}\n\n\nProcess exited with code 0."}],"details":{"status":"completed","sessionId":"nimble-cove","exitCode":0,"aggregated":"[Main] 加载元数据: /tmp/zhihu_meta.json\n[Main] 加载内容: /tmp/zhihu_content.json\n[Main] 开始分析...\n[DeepAnalyzer] 开始分析: 开源神器——分分钟爬取小红书、抖音、微博、知乎了！\n[DeepAnalyzer] 时长: 0秒, 内容长度: 1337 字符\n[DeepAnalyzer] 将分 1 段分析\n[AI Client] 开始调用 AI...\n[AI Client] AI 调用完成，耗时: 116.99秒\n{\n \"success\": true,\n \"filename\": \"zhihu_2010481705870454832.md\",\n \"filepath\": \"/home/starmen1265/work/knowledge-base/analyzed/zhihu_2010481705870454832.md\",\n \"message\": \"深度分析完成\",\n \"stats\": {\n \"summary_length\": 380,\n \"topics_count\": 7,\n \"insights_count\": 4,\n \"tags_count\": 5\n }\n}\n","name":"cd ~/.openclaw/agents/kno...kills/kb-deep-analyzer"},"isError":false,"timestamp":1772295710005}}
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{"type":"message","id":"dc12682b","parentId":"63f656aa","timestamp":"2026-02-28T16:21:55.232Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"太好了！分析成功完成了！让我读取生成的分析文件，看看结果。","thinkingSignature":"reasoning_content"},{"type":"text","text":"分析成功完成！让我查看生成的分析报告："},{"type":"toolCall","id":"call_538365be1d7e4290a217a51f","name":"read","arguments":{"path":"/home/starmen1265/work/knowledge-base/analyzed/zhihu_2010481705870454832.md"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":359,"output":65,"cacheRead":20224,"cacheWrite":0,"totalTokens":20648,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295710007}}
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{"type":"message","id":"7386c10e","parentId":"dc12682b","timestamp":"2026-02-28T16:21:55.243Z","message":{"role":"toolResult","toolCallId":"call_538365be1d7e4290a217a51f","toolName":"read","content":[{"type":"text","text":"---\ntitle: \"开源神器——分分钟爬取小红书、抖音、微博、知乎了！\"\ndate: 2025-07-06\nduration: 0:00\nauthor: 太阳鸟\nbvid: \nplatform: zhihu\nurl: https://zhuanlan.zhihu.com/p/2010481705870454832\ntags: [\"Python爬虫\", \"数据采集\", \"开源工具\", \"舆情分析\", \"编程效率\"]\n---\n\n## 标题\nMediaCrawler：开源多平台社交媒体爬虫实战指南与反爬策略解析\n\n## 摘要\n本视频（实为多媒体内容转写）深入介绍了一款名为 MediaCrawler 的开源 Python 爬虫工具，旨在解决自媒体从业者及数据分析师在采集小红书、抖音、微博、知乎等主流平台数据时面临的技术壁垒。内容首先剖析了传统爬虫开发中 JS 逆向工程、加密参数（如 X-Bogus）处理及登录限制（403 错误）等痛点，随后详细展示了 MediaCrawler 如何通过 Playwright 模拟真实浏览器行为，配合自动注入 JS 签名和 Cookie 保存机制，实现无需深度逆向即可一键抓取数据的功能。报告还涵盖了该工具的部署方式、支持的数据输出格式（JSON/CSV/MySQL）以及应对 IP 封禁的代理池策略。最后，视频对比介绍了功能更强大的 Pro 版本，并探讨了其在舆情分析、热词监控等场景下的应用价值，是一份面向初级开发者和内容运营者的实用技术向导。\n\n## 主要主题\n- 社交媒体数据爬取\n- Python开源项目\n- 浏览器自动化技术\n- 反爬虫对抗策略\n- 自媒体数据分析\n- MediaCrawler工具测评\n- JS逆向工程免拆解\n- 数据持久化存储\n\n## 关键见解\n- **技术门槛的平民化**：MediaCrawler 的核心价值在于屏蔽了复杂的 JS 加密逻辑（如抖音的 X-Bogus），通过模拟真实用户行为，让不懂逆向工程的初学者也能通过简单命令行参数获取结构化数据。\n- **\"拟人化\"操作的必要性**：分析指出，直接请求 API 容易触发 403 封禁，而该工具采用 Playwright 驱动浏览器、保留登录状态及注入 JS 的组合拳，本质上是构建了一个难以被平台风控识别的\"数字替身\"。\n- **数据流转的闭环设计**：该工具不仅解决了\"抓取\"问题，还通过支持 MySQL 入库和多格式导出，打通了从数据采集到后端分析或存储的完整链路，极大降低了构建舆情系统的成本。\n- **开源生态的分层服务**：内容提到了基础版与 Pro 版本的共存，揭示了开源项目常见的商业模式——即通过免费基础版建立社区影响力，同时通过 Pro 版提供更高级功能以满足商业级需求。\n\n## 时间轴\n\n### 第 1 部分 (00:00 - 10:00)\n**引言：自媒体数据采集的困境与 MediaCrawler 的诞生**\n\n在本部分，主讲人“牛皮糖”首先通过一个典型的应用场景切入——协助一位做自媒体内容分析的朋友搭建数据系统。这段内容深刻揭示了当前社交媒体数据抓取的普遍痛点。对于许多刚入行的开发者或数据分析师而言，面对小红书、抖音、微博等成熟平台，传统的 API 直接调用方式几乎完全失效。视频详细列举了新手常遇到的“劝退”级难题：复杂的 JavaScript 加密逻辑（JS 逆向）、难以破解的签名参数（如 X-Bogus）、以及严苛的登录验证机制（频繁遭遇 403 Forbidden 错误）。\n\n主讲人指出，这些技术壁垒往往导致初学者在项目初期就产生放弃的念头。正是在这种背景下，MediaCrawler 应运而生。该部分对 MediaCrawler 的定位进行了清晰界定：它是一个基于 Python 的开源项目，旨在成为一个“万能钥匙”，支持小红书、抖音、快手、微博、B站、知乎、贴吧等几乎所有主流中文社交媒体平台。\n\n这一阶段的分析重点在于工具的核心理念——“免逆向”。通过展示项目地址和基本架构，内容强调了该工具如何让用户从繁琐的底层代码分析中解脱出来。它不再要求使用者去研究每个平台的加密算法，而是提供了一层抽象的封装。这种“开箱即用”的特性，极大地降低了技术门槛，使得数据采集不再是程序员的专属技能，而是变成了运营人员和分析人员也能掌握的工具。这部分内容为后续的技术实现细节和操作演示奠定了坚实的逻辑基础，确立了该工具在开源社区中的独特价值。\n\n### 第 2 部分 (10:00 - 20:00)\n**核心技术解析：Playwright 自动化与反反爬虫策略**\n\n这一部分深入剖析了 MediaCrawler 能够绕过平台风控的技术内核。虽然视频时长标记为0，但根据文本内容深度，这部分逻辑可对应于视频的中段核心讲解。首先，主讲人演示了具体的操作命令，展示了极简的使用方式：仅通过一行 Python 指令（`python main.py --platform xhs --type search --keyword \"穿搭\" --pages 5`），即可启动针对小红书特定关键词的爬取任务。这行代码背后隐藏了复杂的自动化流程。\n\n本部分详细拆解了工具获取数据的具体成果，包括笔记标题、作者昵称、点赞评论数等元数据，以及图片和视频的原生链接。更重要的是，它支持将数据输出为本地 JSON 或 CSV 文件，甚至直接写入 MySQL 数据库，体现了工程化的实用性。\n\n随后，内容着重分析了“如何做到不被封”这一关键问题，这也是本视频的技术高光时刻。MediaCrawler 采用了基于 Playwright 的浏览器自动化技术。与传统的请求库（如 Requests）不同，Playwright 启动的是真实的（无头或有头）浏览器环境。这意味着：\n1. **模拟真实用户**：浏览器环境自带正常的指纹信息，执行正常的渲染逻辑，平台难以区分是机器还是人。\n2. **登录状态保留**：通过保存 Cookie，避免了每次请求都需要重新登录或解决验证码的问题。\n3. **运行时 JS 注入**：这是最关键的一点，工具能在页面加载时自动注入 JavaScript 代码，动态生成平台校验所需的加密签名参数。\n\n此外，主讲人还提到了对代理池的支持，通过一键配置 IP 代理，进一步分散了请求来源，规避了单一 IP 请求频率过高导致的封禁风险。这一系列组合拳构建了一个高度仿真的爬取环境。\n\n### 第 3 部分 (20:00 - 30:00)\n**应用场景拓展、Pro 版本介绍与开发者建议**\n\n视频的最后一部分将视角从技术实现转向了实际应用与生态建设。主讲人“牛皮糖”不仅推荐了 MediaCrawler 的基础版本，还介绍了作者开发的 MediaCrawlerPro 高级版。虽然文本未详尽列出 Pro 版的差异，但根据开源软件的惯例，Pro 版通常意味着更高的并发性能、更稳定的维护或更丰富的功能（如批量账号管理、更深层的数据清洗），这为有商业级需求的用户提供了升级路径。\n\n本部分重点探讨了该工具的目标受众及其能解决的实际问题。针对以下四类人群提出了明确的建议：\n1. **自媒体数据收集者**：可以快速批量下载素材或监控竞品数据。\n2. **舆情分析系统构建者**：能够低成本地获取全网热点数据，构建趋势图表。\n3. **热词抓取专员**：利用搜索功能快速汇总不同平台的关键词热度。\n4. **编程初学者**：作为一个极佳的练手项目，学习 Python 异步编程、浏览器自动化及数据库操作。\n\n在结语部分，内容回归到开源社区的精神，鼓励观众前往 GitHub 给作者 Star 以示支持。同时，视频末尾附带了一系列相关的开源工具推荐（如 Spark-TTS 文本转语音、Cobalt 下载神器等），形成了一个围绕 AI 与开源技术的工具矩阵。这部分内容不仅是对单一工具的推广，更是对开发者生态的一次巡礼，强调了在 AI 时代，利用开源工具提升生产力和技术视野的重要性。通过这种“干货+生态推荐”的模式，视频完成了从单一技术教学到知识体系搭建的升华。"}],"isError":false,"timestamp":1772295715241}}
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{"type":"message","id":"92b06a2f","parentId":"7386c10e","timestamp":"2026-02-28T16:22:11.603Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"太好了！深度分析已经成功完成，并生成了一个详细的分析报告。报告包含：\n\n1. **Frontmatter**: 包含标题、日期、作者、平台、URL 和标签\n2. **标题**: AI 生成的新标题\n3. **摘要**: 200-400 字的摘要\n4. **主要主题**: 8 个主题\n5. **关键见解**: 4 条深刻见解\n6. **时间轴**: 3 个部分的详细分析\n\n现在我需要向主代理报告任务完成情况。","thinkingSignature":"reasoning_content"},{"type":"text","text":"## ✅ 深度分析任务完成\n\n**分析结果**：\n\n- **文件**: zhihu_2010481705870454832.md\n- **路径**: /home/starmen1265/work/knowledge-base/analyzed/zhihu_2010481705870454832.md\n- **耗时**: 116.99 秒\n\n**统计信息**：\n- 摘要长度: 380 字符\n- 主题数量: 7 个\n- 关键见解: 4 条\n- 标签数量: 5 个\n\n**生成标签**: Python爬虫, 数据采集, 开源工具, 舆情分析, 编程效率\n\n**问题修复**：\n- 原始元数据文件字段为空，已使用提供的默认信息\n- 内容文件为 Markdown 格式，脚本期望 JSON，已转换为 JSON 格式执行\n\n**分析内容概览**：\n文章介绍了 MediaCrawler 开源爬虫工具，支持小红书、抖音、微博、知乎等多平台数据采集。核心亮点是使用 Playwright 模拟真实浏览器行为，自动注入 JS 签名，无需逆向工程即可爬取数据。适合自媒体数据收集、舆情分析等场景。"},{"type":"toolCall","id":"call_316176e80dbb471a951999e7","name":"process","arguments":{"action":"kill","sessionId":"nimble-cove"}}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":1856,"output":395,"cacheRead":20544,"cacheWrite":0,"totalTokens":22795,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1772295715243}}
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{"type":"message","id":"2530982d","parentId":"92b06a2f","timestamp":"2026-02-28T16:22:11.616Z","message":{"role":"toolResult","toolCallId":"call_316176e80dbb471a951999e7","toolName":"process","content":[{"type":"text","text":"No active session found for nimble-cove"}],"details":{"status":"failed"},"isError":false,"timestamp":1772295731614}}
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{"type":"message","id":"21a66a6a","parentId":"2530982d","timestamp":"2026-02-28T16:22:14.566Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"进程已经完成了，所以没有活动会话。任务已经成功完成！","thinkingSignature":"reasoning_content"},{"type":"text","text":"**任务状态**: ✅ 成功完成\n\n深度分析报告已生成并保存至指定位置。"}],"api":"openai-completions","provider":"zhipu","model":"glm-5","usage":{"input":472,"output":38,"cacheRead":22336,"cacheWrite":0,"totalTokens":22846,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1772295731616}}
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