Yuzhiran Dev
1855f190f5
- 新增 PromptConfig 模型 + API，支持提示词在线编辑（16条默认） - 调度器动态读取 TaskConfig.schedule，admin 可调执行时间 - 新增 KeywordDomainMap、SensitiveWord、ContentCleanRule、TrendFieldMapping 表 - DOMAINS、TREND_DOMAIN_MAP、PLATFORM_TAGS、china_pains、RSS关键词、priority_weights 全部迁移到 DB - tasks.html 重构：卡片网格+配置/产出/历史/提示词四个Tab，折叠显示 - 清理冗余代码：DEFAULT_PROMPTS死代码、collector.py unreachable代码、compliance_checker bug - strip_thinking_html 改用 DB 规则优先
130 lines
4.6 KiB
Python
130 lines
4.6 KiB
Python
#!/usr/bin/env python3
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"""
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大纲阶段：基于选题和研究笔记，用 LLM 动态生成结构化大纲
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"""
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import json, datetime, logging, sys
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from pathlib import Path
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from typing import Dict
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PROJECT_ROOT = Path(__file__).parent.parent
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sys.path.insert(0, str(PROJECT_ROOT))
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sys.path.insert(0, str(PROJECT_ROOT / "platform" / "backend"))
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from db_helper import get_topic_by_id
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from prompt_loader import get_prompt, get_prompt_params
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try:
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from app.core.nvidia_client import call_llm
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HAVE_LLM = True
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except ImportError:
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HAVE_LLM = False
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DATA_DIR = PROJECT_ROOT / "automation" / "data"
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RESEARCH_DIR = DATA_DIR / "research"
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OUTPUT_DIR = DATA_DIR / "outlines"
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LOGS_DIR = PROJECT_ROOT / "automation" / "logs"
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TODAY = datetime.datetime.now().strftime("%Y-%m-%d")
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s',
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handlers=[logging.FileHandler(LOGS_DIR / f"outline_{TODAY}.log"), logging.StreamHandler()])
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logger = logging.getLogger(__name__)
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class Outliner:
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def __init__(self, topic_id: str):
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self.topic_id = topic_id
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self.topic = self._load_topic()
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research_file = RESEARCH_DIR / TODAY / f"{topic_id}_research.md"
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self.research_notes = research_file.read_text(encoding='utf-8') if research_file.exists() else ""
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self.output_dir = OUTPUT_DIR / TODAY
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self.output_dir.mkdir(parents=True, exist_ok=True)
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def _load_topic(self) -> Dict:
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topic = get_topic_by_id(self.topic_id)
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if not topic: raise ValueError(f"Topic {self.topic_id} not found")
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return topic
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def generate_outline(self) -> str:
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title = self.topic['title']
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field = self.topic.get('field', '')
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core = self.topic.get('core_concept', '')
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pain = self.topic.get('audience_pain', '')
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angle = self.topic.get('unique_angle', '')
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cases_summary = self.research_notes[:2000] if self.research_notes else "暂无研究笔记"
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if HAVE_LLM:
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_now = datetime.datetime.now()
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prompt = get_prompt("outline_generation",
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date=_now.strftime('%Y年%m月%d日'),
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year=_now.year,
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title=title,
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field=field,
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core=core,
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pain=pain,
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angle=angle,
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cases_summary=cases_summary,
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)
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try:
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params = get_prompt_params("outline_generation")
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outline = call_llm(prompt, temperature=params.get("temperature", 0.7), max_tokens=params.get("max_tokens", 4000), system_prompt="你是一个有经验的内容编辑，擅长为不同选题设计差异化的文章结构。")
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logger.info(f"LLM 大纲生成成功，长度：{len(outline)}")
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return f"# 文章大纲：{title}\n\n{outline}\n\n---\n*大纲生成时间：{TODAY}*"
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except Exception as e:
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logger.warning(f"LLM 大纲生成失败: {e}，使用模板")
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return self._template_outline(title, field, core, pain, angle)
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def _template_outline(self, title, field, core, pain, angle) -> str:
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case_count = self.research_notes.count('### 案例') if self.research_notes else 0
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case_section = f"""## 四、全球/行业趋势与案例
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- 引用研究笔记中的 {case_count} 个案例，精选 2-3 个详述
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- 数据支撑：提取研究笔记中的关键数据
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- 趋势分析""" if case_count > 0 else ""
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return f"""# 文章大纲：{title}
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## 一、引言
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- 场景切入：{title}
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- 点明文章价值
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## 二、核心观点
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{core}
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## 三、受众痛点分析
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{pain}
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{case_section}
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## {"五" if case_section else "四"}、本土落地建议
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- 结合{field}领域特点
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- 提供可执行的步骤
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- 注意事项
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## {"六" if case_section else "五"}、独特视角：{angle}
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## {"七" if case_section else "六"}、行动指南
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1. 了解现状 2. 制定方案 3. 小范围验证 4. 持续优化
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## {"八" if case_section else "七"}、总结与鼓励
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---
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*大纲生成时间：{TODAY}*"""
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def save(self):
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outline_text = self.generate_outline()
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out_path = self.output_dir / f"{self.topic_id}_outline.md"
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out_path.write_text(outline_text, encoding='utf-8')
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logger.info(f"大纲已保存: {out_path}")
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return out_path
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def main():
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('--topic-id', required=True)
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args = parser.parse_args()
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o = Outliner(args.topic_id)
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o.save()
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print(f"SUCCESS: Outline created for {args.topic_id}")
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sys.exit(0)
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if __name__ == "__main__":
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main()
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