#!/usr/bin/env python3 """ 大纲阶段:基于选题和研究笔记,用 LLM 动态生成结构化大纲 """ import json, datetime, logging, sys from pathlib import Path from typing import Dict PROJECT_ROOT = Path(__file__).parent.parent sys.path.insert(0, str(PROJECT_ROOT)) sys.path.insert(0, str(PROJECT_ROOT / "platform" / "backend")) from db_helper import get_topic_by_id from prompt_loader import get_prompt, get_prompt_params try: from app.core.nvidia_client import call_llm HAVE_LLM = True except ImportError: HAVE_LLM = False DATA_DIR = PROJECT_ROOT / "automation" / "data" RESEARCH_DIR = DATA_DIR / "research" OUTPUT_DIR = DATA_DIR / "outlines" LOGS_DIR = PROJECT_ROOT / "automation" / "logs" TODAY = datetime.datetime.now().strftime("%Y-%m-%d") logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', handlers=[logging.FileHandler(LOGS_DIR / f"outline_{TODAY}.log"), logging.StreamHandler()]) logger = logging.getLogger(__name__) class Outliner: def __init__(self, topic_id: str): self.topic_id = topic_id self.topic = self._load_topic() research_file = RESEARCH_DIR / TODAY / f"{topic_id}_research.md" self.research_notes = research_file.read_text(encoding='utf-8') if research_file.exists() else "" self.output_dir = OUTPUT_DIR / TODAY self.output_dir.mkdir(parents=True, exist_ok=True) def _load_topic(self) -> Dict: topic = get_topic_by_id(self.topic_id) if not topic: raise ValueError(f"Topic {self.topic_id} not found") return topic def generate_outline(self) -> str: title = self.topic['title'] field = self.topic.get('field', '') core = self.topic.get('core_concept', '') pain = self.topic.get('audience_pain', '') angle = self.topic.get('unique_angle', '') cases_summary = self.research_notes[:2000] if self.research_notes else "暂无研究笔记" if HAVE_LLM: _now = datetime.datetime.now() prompt = get_prompt("outline_generation", date=_now.strftime('%Y年%m月%d日'), year=_now.year, title=title, field=field, core=core, pain=pain, angle=angle, cases_summary=cases_summary, ) try: params = get_prompt_params("outline_generation") 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。") logger.info(f"LLM 大纲生成成功,长度:{len(outline)}") return f"# 文章大纲:{title}\n\n{outline}\n\n---\n*大纲生成时间:{TODAY}*" except Exception as e: logger.warning(f"LLM 大纲生成失败: {e},使用模板") return self._template_outline(title, field, core, pain, angle) def _template_outline(self, title, field, core, pain, angle) -> str: case_count = self.research_notes.count('### 案例') if self.research_notes else 0 case_section = f"""## 四、全球/行业趋势与案例 - 引用研究笔记中的 {case_count} 个案例,精选 2-3 个详述 - 数据支撑:提取研究笔记中的关键数据 - 趋势分析""" if case_count > 0 else "" return f"""# 文章大纲:{title} ## 一、引言 - 场景切入:{title} - 点明文章价值 ## 二、核心观点 {core} ## 三、受众痛点分析 {pain} {case_section} ## {"五" if case_section else "四"}、本土落地建议 - 结合{field}领域特点 - 提供可执行的步骤 - 注意事项 ## {"六" if case_section else "五"}、独特视角:{angle} ## {"七" if case_section else "六"}、行动指南 1. 了解现状 2. 制定方案 3. 小范围验证 4. 持续优化 ## {"八" if case_section else "七"}、总结与鼓励 --- *大纲生成时间:{TODAY}*""" def save(self): outline_text = self.generate_outline() out_path = self.output_dir / f"{self.topic_id}_outline.md" out_path.write_text(outline_text, encoding='utf-8') logger.info(f"大纲已保存: {out_path}") return out_path def main(): import argparse parser = argparse.ArgumentParser() parser.add_argument('--topic-id', required=True) args = parser.parse_args() o = Outliner(args.topic_id) o.save() print(f"SUCCESS: Outline created for {args.topic_id}") sys.exit(0) if __name__ == "__main__": main()