Yuzhiran Dev
fef435cc78
- 审查：移除 manual_review，改为迭代LLM修复(最多3次)，合规分回写Topic - 调度：scheduler 新增话题采集定时任务 scheduled_collect (01:30) - 提示词：全链路8文件≈24个提示词升级，增强SEO/平台推荐/真人感
195 lines
7.7 KiB
Python
195 lines
7.7 KiB
Python
#!/usr/bin/env python3
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import json, datetime, logging, sys, re
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from pathlib import Path
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from typing import Dict, List
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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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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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from trends import get_trend_context
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from web_search import enrich_topic_research
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DATA_DIR = PROJECT_ROOT / "automation" / "data"
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CASES_FILE = DATA_DIR / "sustainability_cases.json"
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OUTPUT_DIR = DATA_DIR / "research"
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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"research_{TODAY}.log"), logging.StreamHandler()])
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logger = logging.getLogger(__name__)
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class Researcher:
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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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self.cases = self._load_cases()
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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:
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raise ValueError(f"Topic {self.topic_id} not found")
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return topic
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def _load_cases(self) -> List[Dict]:
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if CASES_FILE.exists():
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return json.loads(CASES_FILE.read_text(encoding='utf-8'))
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return []
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def find_relevant_cases(self, top_k: int = 5) -> List[Dict]:
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field = self.topic.get('field', '').lower()
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title = self.topic.get('title', '').lower()
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scored = []
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for case in self.cases:
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case_date = case.get('date', '')
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if case_date:
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m = re.search(r'(\d{4})', str(case_date))
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if m and int(m.group(1)) < 2025:
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continue
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score = 0
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if field and field in case.get('field', '').lower():
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score += 3
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case_title = case.get('title', '').lower()
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for word in title.split():
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if len(word) > 2 and word in case_title:
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score += 1
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if score > 0:
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scored.append((score, case))
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scored.sort(key=lambda x: x[0], reverse=True)
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return [c for _, c in scored[:top_k]]
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def _llm_summary(self, cases: List[Dict]) -> str:
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if not HAVE_LLM:
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return ""
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cases_text = json.dumps(cases, ensure_ascii=False, indent=2)
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# 获取实时搜索数据作为 LLM 参考
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search_data = enrich_topic_research(self.topic)
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search_section = f"\n## 实时搜索结果\n{search_data}\n" if search_data else ""
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prompt = f"""你是一个行业研究员+内容策略师，擅长从案例中发现真洞察，并能判断什么内容对真实读者最有价值。
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基于以下选题和相关案例，写出能支撑文章核心观点、对读者真正有用的研究发现。
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## 选题
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标题：{self.topic['title']}
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领域：{self.topic.get('field', '')}
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核心观点：{self.topic.get('core_concept', '')}
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受众痛点：{self.topic.get('audience_pain', '')}
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独特视角：{self.topic.get('unique_angle', '')}
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{search_section}
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## 相关案例（{len(cases)}个）
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{cases_text}
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## 输出要求（按顺序）：
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1. 核心发现：2-3个真正有价值的洞察（不是每个案例凑一条）。每个洞察需包含：
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- 这个发现对读者意味着什么（不要只说事实，要说意义）
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- 可以用什么数据或案例支撑
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2. SEO关键词建议：这篇文章应该重点布局哪些搜索词（3-5个，包含1-2个长尾词）
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3. 讨论点：哪个观点最有争议或最可能引发讨论？这能帮助文章获得平台推荐
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4. 待验证：指出1-2个不确定的方向，作者需进一步核实
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风格：说人话，每条洞察2-3句话直击要点。避免「首先其次最后」「综上所述」。"""
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try:
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return call_llm(prompt, temperature=0.5, max_tokens=1200, system_prompt="你是一个行业研究员，擅长从案例中发现真洞察。")
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except Exception as e:
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logger.warning(f"LLM 研究发现摘要生成失败: {e}")
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return ""
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def generate_notes(self) -> str:
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cases = self.find_relevant_cases()
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trend_context = get_trend_context(self.topic.get('field'))
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search_data = enrich_topic_research(self.topic)
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lines = [
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f"# 研究笔记：{self.topic['title']}",
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f"\n## 选题信息",
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f"- **ID**: {self.topic['id']}",
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f"- **领域**: {self.topic.get('field')}",
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f"- **核心观点**: {self.topic.get('core_concept', '待补充')}",
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f"- **受众痛点**: {self.topic.get('audience_pain', '待补充')}",
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f"- **独特视角**: {self.topic.get('unique_angle', '待补充')}",
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f"\n{trend_context}",
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f"\n{search_data}" if search_data else "",
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f"\n## 相关案例（{len(cases)}个）\n"
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]
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for i, case in enumerate(cases, 1):
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lines.extend([
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f"### 案例 {i}: {case.get('title')}",
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f"- **来源**: {case.get('source', '未知')}",
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f"- **日期**: {case.get('date', '未知')}",
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f"- **摘要**: {case.get('summary', case.get('description', '无'))}",
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f"- **关键数据**: {case.get('key_metrics', '无')}",
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""
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])
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llm_summary = self._llm_summary(cases)
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if llm_summary:
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lines.extend([
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"## 研究发现摘要（LLM 生成）",
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llm_summary,
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""
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])
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else:
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insights = []
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for case in cases[:3]:
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summary = case.get('summary', case.get('description', ''))
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metrics = case.get('key_metrics', '')
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if summary:
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insight = f"- {case.get('title', '相关案例')}：{summary[:100]}"
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if metrics:
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insight += f"（{metrics[:80]}）"
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insights.append(insight)
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if not insights:
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insights = ["- 暂未匹配到高度相关的历史案例"]
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pain_text = self.topic.get('audience_pain', '')
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topic_insights = [f"- {pain_text[:100]}"] if pain_text else []
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lines.extend([
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"## 研究发现摘要",
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"",
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])
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lines.extend(insights)
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if topic_insights:
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lines.extend(topic_insights)
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lines.extend([
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"",
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"## 待深入研究的问题",
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])
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if cases:
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lines.append("- [ ] 验证以上案例在当前选题背景下的适用性")
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lines.extend([
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"- [ ] 收集更多本土一手数据",
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"- [ ] 确认目标受众的实际反馈",
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""
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])
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lines.append(f"*生成时间：{TODAY}*")
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return "\n".join(lines)
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def save(self):
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notes = self.generate_notes()
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out_path = self.output_dir / f"{self.topic_id}_research.md"
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out_path.write_text(notes, encoding='utf-8')
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logger.info(f"研究笔记已保存: {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, help='选题ID')
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args = parser.parse_args()
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r = Researcher(args.topic_id)
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r.save()
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print(f"SUCCESS: Research notes 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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