from typing import List, Dict, Any, Optional from openai import OpenAI import re from urllib.parse import urlparse import time def extract_url_root_domain(url): """ 从 URL 中提取根域名 例如: - https://www.example.com/path -> example.com - sub.example.co.uk -> example.co.uk """ # 确保 URL 包含协议,如果没有则添加 if not url.startswith(('http://', 'https://')): url = 'http://' + url # 使用 urlparse 解析 URL parsed = urlparse(url).netloc if not parsed: parsed = url # 移除端口号(如果存在) parsed = parsed.split(':')[0] # 分割域名部分 parts = parsed.split('.') # 处理特殊的二级域名,如 .co.uk, .com.cn 等 if len(parts) > 2: if parts[-2] in ['co', 'com', 'org', 'gov', 'edu', 'net']: if parts[-1] in ['uk', 'cn', 'jp', 'br', 'in']: return '.'.join(parts[-3:]) # 返回主域名部分(最后两部分) return '.'.join(parts[-2:]) def get_clean_content(line): clean_line = re.sub(r'^[\*\-•#\d\.]+\s*', '', line).strip() clean_line = re.sub(r'^[\'"]|[\'"]$', '', clean_line).strip() if (clean_line.startswith('"') and clean_line.endswith('"')) or \ (clean_line.startswith("'") and clean_line.endswith("'")): clean_line = clean_line[1:-1] return clean_line def get_content_from_tag(content, tag, default_value=None): # 说明: # 1) (.*?) 懒惰匹配,尽量少匹配字符 # 2) (?=(|<\w+|$)) 使用前瞻,意味着当后面紧跟 或 <任意单词字符开头的标签> 或文本结束时,都停止匹配 # 3) re.DOTALL 使得点号 . 可以匹配换行符 pattern = rf"<{tag}>(.*?)(?=(|<\w+|$))" match = re.search(pattern, content, re.DOTALL) if match: return match.group(1).strip() return default_value def get_response_from_llm( messages: List[Dict[str, Any]], client: OpenAI, model: str, stream: Optional[bool] = False, temperature: Optional[float] = 0.6, depth: int = 0 ): try: response = client.chat.completions.create( model=model, messages=messages, temperature=temperature, stream=stream ) if hasattr(response.choices[0].message, 'content') and response.choices[0].message.content: content = response.choices[0].message.content return { "content": content.strip() } except Exception as e: print(f"LLM API error: {e}") if "Input data may contain inappropriate content" in str(e): return { "content": "" } if "Error code: 400" in str(e): return { "content": "" } if depth < 512: time.sleep(1) return get_response_from_llm(messages=messages, client=client, model=model, stream=stream, temperature=temperature, depth=depth+1) raise e