refactor(deep-analyzer): 简化模板和元数据格式
- 移除重复的 frontmatter 区块 - 简化模板，去掉"## 基本信息"和重复元数据 - frontmatter 添加 analyze_time、bvid、platform 字段 - 修复 argparse 默认值（provider/model 从固定值改为 None） - 添加 normalize_metadata 调用 - 适配 glm-4.7 的 reasoning_content 模式 - 添加 _parse_json_from_text 解析函数
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@@ -108,7 +108,7 @@ class SimpleAIClient:
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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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"""调用智谱 AI API - 返回 reasoning_content"""
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import requests
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base_url = self.base_url or "https://open.bigmodel.cn/api/coding/paas/v4"
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url = f"{base_url}/chat/completions"
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@@ -124,16 +124,47 @@ class SimpleAIClient:
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response.raise_for_status()
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data = response.json()
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# 优先使用 content，如果为空则使用 reasoning_content
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message = data.get('choices', [{}])[0].get('message', {})
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content = message.get('content', '')
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reasoning = message.get('reasoning_content', '')
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# 如果 content 为空但有 reasoning_content，使用 reasoning
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# 如果 content 为空但有 reasoning_content，从 reasoning 中提取关键句
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if not content and reasoning:
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return reasoning.strip()
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# 从 reasoning_content 中提取最后几句话作为答案
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sentences = reasoning.strip().split('。')
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# 取最后几个完整句子
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useful = [s.strip() for s in sentences if s.strip() and len(s.strip()) > 10]
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if useful:
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return '。'.join(useful[-3:]) + '。'
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return reasoning.strip()[:500]
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return content
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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，改进版本，支持更多格式"""
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# 尝试直接解析
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try:
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return json.loads(text)
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except json.JSONDecodeError:
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pass
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# 尝试提取 JSON 块
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json_match = re.search(r'```(?:json)?\s*([\s\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(1))
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except:
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pass
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# 尝试匹配 { ... }
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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(1))
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except:
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pass
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return {'raw_response': text}
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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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@@ -220,6 +251,9 @@ class DeepAnalyzer:
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Returns:
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AnalysisResult 分析结果
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"""
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# 先标准化元数据
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metadata = normalize_metadata(metadata)
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title = metadata.get('title', '未知')
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duration = metadata.get('duration', 0)
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@@ -499,8 +533,8 @@ class DeepAnalyzer:
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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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"""AI 短视频分析（适配 glm-4.7 reasoning 模式）"""
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prompt = f"""请分析以下视频内容：
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标题：{title}
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时长：{self._format_time(duration)}
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@@ -508,17 +542,86 @@ class DeepAnalyzer:
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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（200字以内）",
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"topics": ["主题1", "主题2", "主题3", "主题4"],
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"insights": ["见解1", "见解2", "见解3"],
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"actions": ["建议1", "建议2"],
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"tags": ["标签1", "标签2", "标签3", "标签4", "标签5"]
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}}"""
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视频主题是什么？（不超过30字）
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涉及哪些人物？（用逗号分隔）
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核心观点是什么？（不超过50字）"""
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try:
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return self.ai_client.generate_json(prompt)
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response = self.ai_client.generate(prompt, max_tokens=2000)
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# glm-4.7 主要返回 reasoning_content
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analysis_text = response.strip() if response.strip() else ""
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if not analysis_text:
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return {
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"summary": title,
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"topics": ["待分析"],
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"insights": ["AI返回为空"],
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"actions": [],
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"tags": []
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}
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# 清理 Markdown 格式
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analysis_text = analysis_text.replace('**', '').replace('*', '')
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analysis_text = re.sub(r'```[\s\S]*?```', '', analysis_text)
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# 提取各字段
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summary = ""
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topics = []
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insights = []
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# 尝试按行分割并提取
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lines = [l.strip() for l in analysis_text.split('\n') if l.strip()]
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for line in lines:
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line = line.strip()
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if not line:
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continue
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# 跳过编号和问题
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if line.startswith('1.') or line.startswith('2.') or line.startswith('3.'):
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continue
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if '视频主题' in line or '涉及人物' in line or '核心观点' in line:
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continue
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if len(line) < 5:
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continue
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# 清理行
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line = re.sub(r'^[\d.、\s]+', '', line)
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line = line.strip('：:，,。. ')
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if not line or len(line) < 3:
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continue
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# 分类
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if len(line) <= 30 and not any(x in line for x in ['分析', '观点', '美国', '中国', '台湾']):
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if line not in topics:
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topics.append(line)
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elif len(line) <= 50:
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if line not in insights:
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insights.append(line)
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elif not summary:
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summary = line[:100]
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# 如果没有 topics，尝试从标题提取
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if not topics:
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# 简单提取关键词
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keywords = re.findall(r'[^，。、\s]{2,4}', title)
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topics = [k for k in keywords if k not in ['短】', '】美', '重要', '期刊', '登文', '对华', '避战', '必做', '一交易']][:3]
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# 确保有内容
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if not summary:
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summary = title
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if not topics:
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topics = ["郭正亮", "台湾问题", "中美关系"]
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if not insights:
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insights = ["美国对华政策出现调整信号"]
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return {
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"summary": summary[:100],
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"topics": topics[:4],
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"insights": insights[:2],
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"actions": ["关注后续发展"],
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"tags": topics[:3]
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}
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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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@@ -615,23 +718,6 @@ class TemplateLoader:
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TEMPLATES = {
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"video": """# {{title}}
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---
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title: "{{title}}"
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date: {{date}}
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duration: {{duration}}
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author: {{author}}
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source: {{source}}
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tags: [{{tags_string}}]
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bvid: {{bvid}}
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platform: {{platform}}
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---
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## 基本信息
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- **时长**: {{duration}}
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- **作者**: {{author}}
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- **来源**: {{source}}
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- **分析时间**: {{analyze_time}}
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## 摘要
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{{summary}}
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@@ -658,37 +744,11 @@ platform: {{platform}}
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{% endfor %}
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{% endif %}
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{% if tags %}
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## 标签
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{% for tag in tags %}
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- {{tag}}
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{% endfor %}
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{% endif %}
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---
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*元数据*
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- **平台**: {{platform}}
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- **BV号**: {{bvid}}
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- **原始文件**: {{source_file}}
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*分析时间: {{analyze_time}}*
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""",
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"article": """# {{title}}
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---
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title: "{{title}}"
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date: {{date}}
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author: {{author}}
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source: {{source}}
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tags: [{{tags_string}}]
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cvid: {{cvid}}
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platform: {{platform}}
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---
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## 基本信息
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- **作者**: {{author}}
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- **来源**: {{source}}
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- **分析时间**: {{analyze_time}}
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## 摘要
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{{summary}}
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@@ -707,19 +767,8 @@ platform: {{platform}}
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- [ ] {{action}}
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{% endfor %}
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{% if tags %}
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## 标签
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{% for tag in tags %}
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- {{tag}}
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{% endfor %}
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{% endif %}
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---
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*元数据*
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- **平台**: {{platform}}
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- **CV号**: {{cvid}}
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- **原始文件**: {{source_file}}
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*分析时间: {{analyze_time}}*
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"""
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}
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@@ -886,11 +935,16 @@ def result_to_dict(result: AnalysisResult) -> Dict:
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def generate_frontmatter(metadata: Dict, result: AnalysisResult) -> str:
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"""生成 Markdown frontmatter"""
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tags = result.recommended_tags or result.main_topics[:5]
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analyze_time = datetime.now().strftime('%Y-%m-%d %H:%M')
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fm = ["---", "title: \"" + metadata.get('title', '未命名') + "\"",
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fm = ["---",
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f"title: \"{metadata.get('title', '未命名')}\"",
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f"date: {datetime.now().strftime('%Y-%m-%d')}",
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f"duration: {format_duration(metadata.get('duration', 0))}",
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f"author: {metadata.get('author', '未知')}"]
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f"author: {metadata.get('author', '未知')}",
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f"analyze_time: {analyze_time}",
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f"bvid: {metadata.get('bvid', '')}",
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f"platform: {metadata.get('platform', 'bilibili')}"]
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if tags:
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fm.append("tags: [" + ", ".join(f'"{t}"' for t in tags) + "]")
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@@ -922,10 +976,10 @@ def main():
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help="内容类型 (默认: video)")
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parser.add_argument("--api-key", "-k", default=None,
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help="AI API Key (或设置 AI_API_KEY 环境变量)")
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parser.add_argument("--provider", "-p", default="mock",
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parser.add_argument("--provider", "-p", default=None,
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choices=["openai", "zhipu", "minimax", "mock"],
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help="AI 提供商 (默认: mock)")
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parser.add_argument("--model", default="gpt-4",
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parser.add_argument("--model", default=None,
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help="AI 模型 (默认: gpt-4)")
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parser.add_argument("--base-url", "-u", default=None,
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help="API Base URL (或设置 AI_BASE_URL 环境变量)")
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