Files
Yuzhiran Dev 7c9a8b88b4 feat: FAQ/HowTo schema injection and SEO pipeline enhancement
- writer.py: inject_geo_metadata now detects content type (article/listicle/howto/faq/review) and injects appropriate JSON-LD (FAQPage, HowTo, ItemList)
- New helpers: _detect_content_type, _extract_faq_pairs, _extract_howto_steps
- outline.py: _extract_seo_keywords regex support for CJK
- prompt_loader.py: Minor update

Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent)

Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
2026-06-16 08:26:44 +08:00

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#!/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, re
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
@staticmethod
def _extract_seo_keywords(research_notes: str) -> str:
"""从研究笔记中提取 SEO 关键词"""
if not research_notes:
return "暂无"
# 尝试匹配 "SEO关键词建议" 块
m = re.search(r'(?:SEO关键词建议|SEO关键词|搜索词)[::]\s*(.*?)(?:\n\n|\Z)', research_notes, re.DOTALL)
if m:
return m.group(1).strip()[:300]
# 回退:取所有 # 标签或关键词模式
keywords = re.findall(r'[#]([\u4e00-\u9fff\w]{2,6})', research_notes)
if keywords:
return "".join(keywords[:5])
return "暂无"
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"
seo_keywords = self._extract_seo_keywords(self.research_notes)
if HAVE_LLM:
_now = datetime.datetime.now()
prompt = get_prompt("outline_generation",
date=_now.strftime('%Y年%m月%d'),
year=_now.year,
title=title,
field=field,
core=core,
pain=pain,
angle=angle,
cases_summary=cases_summary,
seo_keywords=seo_keywords,
)
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}
## {"" if case_section else ""}、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
## {"" if case_section else ""}、独特视角:{angle}
## {"" if case_section else ""}、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
## {"" if case_section else ""}、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()