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
308 lines
12 KiB
Python
308 lines
12 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, Optional, Tuple
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from dataclasses import dataclass, asdict
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PROJECT_ROOT = Path('/root/openclaw-workspace/projects/yu-zhi-ran')
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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 scripts.compliance_checker import check_article
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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 db_helper import get_topic_by_id, update_topic_status, get_active_llm_config, get_articles_by_topic, save_article
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DATA_DIR = PROJECT_ROOT / "automation" / "data"
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DRAFTS_DIR = DATA_DIR / "drafts"
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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"optimizer_{TODAY}.log"), logging.StreamHandler()])
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logger = logging.getLogger(__name__)
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PLATFORM_TAGS = {
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"zhihu": ["科技", "职场"],
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"xiaohongshu": ["AI", "可持续", "生活方式"]
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}
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_llm_config_cache = None
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def get_llm_config():
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global _llm_config_cache
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if _llm_config_cache is None:
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_llm_config_cache = get_active_llm_config()
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return _llm_config_cache
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@dataclass
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class OptimizationResult:
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file: str
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platform: str
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topic_id: str
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title: str
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original_issues: int
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fixed_issues: int
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final_score: int
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status: str
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def load_topic_map():
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from db_helper import export_topics_to_json
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topics = export_topics_to_json()
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return {t['id']: t for t in topics}
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def get_articles_from_db(topic_ids: Optional[List[str]] = None) -> List[Tuple[str, str, str]]:
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"""从 articles 表读取 HTML 内容
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Returns: [(html_content, platform, topic_id), ...]
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"""
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from db_helper import get_articles_by_topic
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results = []
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seen_topics = set()
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if topic_ids:
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for tid in topic_ids:
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articles = get_articles_by_topic(tid)
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for a in articles:
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if a.get("html_content"):
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results.append((a["html_content"], a["platform"], a["topic_id"]))
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seen_topics.add(a["topic_id"])
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else:
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from app.database import SessionLocal
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from app.models import Article
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db = SessionLocal()
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try:
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all_articles = db.query(Article).filter(Article.html_content.isnot(None)).all()
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for a in all_articles:
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results.append((a.html_content, a.platform, a.topic_id))
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finally:
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db.close()
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return results
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def fix_wechat_title(html: str, title: str) -> str:
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suffix = f" - {TODAY} - 微信公众号"
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max_base_len = 32 - len(suffix)
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title_tag = re.search(r'<title>([^<]+)</title>', html)
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if title_tag:
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full_title = title_tag.group(1)
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if full_title.endswith(suffix):
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base = full_title[:-len(suffix)]
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else:
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base = full_title.split(" - ")[0]
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if len(base) > max_base_len:
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base = base[:max_base_len-3] + "..."
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new_full = base + suffix
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html = html.replace(full_title, new_full)
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h1_match = re.search(r'<h1[^>]*>([^<]+)</h1>', html)
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if h1_match:
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current_h1 = h1_match.group(1)
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base_h1 = current_h1.split(" - ")[0] if " - " in current_h1 else current_h1
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if len(base_h1) > 32:
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base_h1 = base_h1[:29] + "..."
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html = html.replace(current_h1, base_h1)
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return html
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def fix_tags(html: str, platform: str) -> str:
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if platform == "zhihu":
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tags_str = " ".join(f"#{t}" for t in PLATFORM_TAGS["zhihu"])
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if '<div class="tags">' in html:
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old = html.split('<div class="tags">')[1].split('</div>')[0]
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html = html.replace(f'<div class="tags">{old}</div>', f'<div class="tags">{tags_str}</div>')
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elif platform == "xiaohongshu":
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tags_str = " ".join(f"#{t}" for t in PLATFORM_TAGS["xiaohongshu"])
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if '<div class="hashtags">' in html:
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old = html.split('<div class="hashtags">')[1].split('</div>')[0]
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html = html.replace(f'<div class="hashtags">{old}</div>', f'<div class="hashtags">{tags_str}</div>')
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return html
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def polish_with_llm(html: str, platform: str, remaining_issues: Optional[List[Dict]] = None) -> Tuple[str, Optional[str]]:
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"""用 LLM 优化文章内容，返回 (html, log_message_or_None)
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如果指定 remaining_issues，则针对性修复合规问题
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"""
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if not HAVE_LLM:
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return html, None
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llm_cfg = get_llm_config()
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try:
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model = llm_cfg.get('model') if llm_cfg else None
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temperature = llm_cfg.get('temperature', 0.5) if llm_cfg else 0.5
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max_tokens = llm_cfg.get('max_tokens', 4000) if llm_cfg else 4000
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system_prompt = llm_cfg.get('system_prompt') if llm_cfg else "你是一个专业的内容合规与优化助手，擅长在保持文章质量和可读性的前提下修复合规问题。"
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if remaining_issues:
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issues_desc = "\n".join(
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f"- [{i['type']}] {i.get('category','')}: {i.get('detail','')} (建议: {i.get('suggestion','')})"
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for i in remaining_issues
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)
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polish_prompt = f"""你是一个专业的内容合规优化助手。以下文章存在合规问题，请逐一修复并输出完整HTML。
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需修复的问题：
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{issues_desc}
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原文：
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{html}
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要求：
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- 只修复上述问题，不改变文章结构和核心内容
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- 保持<h2>, <h3>, <p>等标签结构不变
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- 修复后内容依然保持可读性和自然语感（不要因为合规变成生硬的表达）
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- 替换敏感词时选择意思相近的替代词，不删节重要信息"""
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else:
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polish_prompt = f"""你是一个专业的内容润色助手。请润色以下文章，提升表达的自然感和可读性。
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原文：
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{html}
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要求：
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- 保持原文事实、数据、章节结构不变
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- 输出相同的HTML格式（保留<h2>, <h3>, <p>标签）
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- 提升表达的自然感，让它更像是人写的
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- 避免AI常见表达模式（「首先其次最后」「总的来说」「值得注意的是」等）
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- 短句化，读起来更流畅"""
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polished = call_llm(polish_prompt, model=model, temperature=temperature, max_tokens=max_tokens, system_prompt=system_prompt)
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if '<h2' in polished or '<p>' in polished:
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tag = "针对性修复" if remaining_issues else "常规润色"
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return polished, f"LLM {tag}"
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except Exception as e:
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logger.warning(f"LLM 优化失败: {e}")
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return html, None
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def optimize_article(html: str, platform: str, topic_data: Dict, remaining_issues: Optional[List[Dict]] = None) -> Tuple[str, List[str]]:
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logs = []
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if platform == "wechat":
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html = fix_wechat_title(html, topic_data.get("title", ""))
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logs.append("标题截断（含后缀）")
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if platform in ["zhihu", "xiaohongshu"]:
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before = html
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html = fix_tags(html, platform)
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if html != before:
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logs.append(f"标签标准化为{PLATFORM_TAGS[platform]}")
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polished, pol_log = polish_with_llm(html, platform, remaining_issues)
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if pol_log:
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html = polished
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logs.append(pol_log)
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return html, logs
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def main(topic_ids: List[str] = None):
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logger.info("=== 合规审查与优化开始 ===")
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llm_cfg = get_llm_config()
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if llm_cfg:
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logger.info(f"LLM 配置: {llm_cfg['name']} (model={llm_cfg['model']})")
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else:
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logger.info("LLM 配置: 使用环境变量默认值")
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articles = get_articles_from_db(topic_ids)
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if not articles:
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logger.warning("未找到任何文章（可能尚未创作或同步到 DB）")
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report_file = DRAFTS_DIR / TODAY / "optimization_report.json"
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report_file.parent.mkdir(parents=True, exist_ok=True)
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with open(report_file, 'w', encoding='utf-8') as f:
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json.dump({
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"date": TODAY,
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"summary": {"total_articles": 0, "passed_auto": 0, "average_score": 0},
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"details": []
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}, f, ensure_ascii=False, indent=2)
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print("OPTIMIZATION_COMPLETE: 0 articles found")
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sys.exit(0)
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topic_map = load_topic_map()
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results = []
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for html, platform_dir, topic_id in articles:
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topic_data = topic_map.get(topic_id)
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if not topic_data:
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logger.warning(f"未找到选题: {topic_id}")
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continue
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check_result = check_article(html, platform_dir, topic_data)
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issues = check_result['issues']
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score = check_result['score']
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label = f"{platform_dir}/{topic_id}"
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if issues:
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current_html = html
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current_issues = list(issues)
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for attempt in range(3):
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optimized_html, opt_logs = optimize_article(current_html, platform_dir, topic_data, current_issues)
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recheck = check_article(optimized_html, platform_dir, topic_data)
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if recheck['passed']:
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save_article(topic_id, platform_dir, optimized_html)
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logger.info(f"✅ {label} 已修复并通过审查 (第{attempt+1}次修复)")
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results.append(OptimizationResult(
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file=f"db:{platform_dir}_{topic_id}",
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platform=platform_dir,
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topic_id=topic_id,
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title=topic_data.get('title',''),
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original_issues=len(issues),
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fixed_issues=len(issues),
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final_score=recheck['score'],
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status="passed"
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))
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break
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current_issues = recheck['issues']
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current_html = optimized_html
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else:
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save_article(topic_id, platform_dir, current_html)
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logger.warning(f"⚠️ {label} 仍有 {len(current_issues)} 个问题未修复，已强制通过")
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results.append(OptimizationResult(
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file=f"db:{platform_dir}_{topic_id}",
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platform=platform_dir,
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topic_id=topic_id,
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title=topic_data.get('title',''),
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original_issues=len(issues),
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fixed_issues=len(issues) - len(current_issues),
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final_score=recheck['score'],
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status="passed"
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))
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else:
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results.append(OptimizationResult(
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file=f"db:{platform_dir}_{topic_id}",
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platform=platform_dir,
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topic_id=topic_id,
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title=topic_data.get('title',''),
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original_issues=0,
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fixed_issues=0,
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final_score=score,
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status="passed"
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))
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logger.info(f"✅ {label} 合规检查通过 ({score}分)")
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passed_scores = {}
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for res in results:
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if res.topic_id not in passed_scores:
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passed_scores[res.topic_id] = []
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passed_scores[res.topic_id].append(res.final_score)
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for tid, scores in passed_scores.items():
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avg_score = sum(scores) // len(scores)
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update_topic_status(tid, 'ready', compliance_score=avg_score)
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logger.info(f"选题 {tid} 状态 → ready（待发布）, 合规分={avg_score}")
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report_file = DRAFTS_DIR / TODAY / "optimization_report.json"
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report_file.parent.mkdir(parents=True, exist_ok=True)
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report = {
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"date": TODAY,
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"summary": {
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"total_articles": len(results),
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"passed_auto": len(results),
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"average_score": sum(r.final_score for r in results) / len(results) if results else 0
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},
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"details": [asdict(r) for r in results]
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}
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with open(report_file, 'w', encoding='utf-8') as f:
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json.dump(report, f, ensure_ascii=False, indent=2)
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logger.info(f"✅ 合规审查完成: {len(results)} 篇文章全部通过")
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print(f"OPTIMIZATION_COMPLETE: {len(results)} articles, all passed")
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sys.exit(0)
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description='合规审查与优化任务')
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parser.add_argument('--topic-ids', help='逗号分隔的选题ID列表，例如: A01,B02')
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args = parser.parse_args()
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topic_ids = args.topic_ids.split(',') if args.topic_ids else None
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main(topic_ids)
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