from typing import AsyncGenerator from src.llm_models.utils_model import LLMRequest, RequestType from src.llm_models.payload_content.message import MessageBuilder from src.config.config import model_config from src.chat.message_receive.message import MessageRecvS4U from src.mais4u.mais4u_chat.s4u_prompt import prompt_builder from src.common.logger import get_logger import re logger = get_logger("s4u_stream_generator") class S4UStreamGenerator: def __init__(self): # 使用LLMRequest替代AsyncOpenAIClient self.llm_request = LLMRequest( model_set=model_config.model_task_config.replyer, request_type="s4u_replyer" ) self.current_model_name = "unknown model" self.partial_response = "" # 正则表达式用于按句子切分，同时处理各种标点和边缘情况 # 匹配常见的句子结束符，但会忽略引号内和数字中的标点 self.sentence_split_pattern = re.compile( r'([^\s\w"\'([{]*["\'([{].*?["\'}\])][^\s\w"\'([{]*|' # 匹配被引号/括号包裹的内容 r'[^.。!?？！\n\r]+(?:[.。!?？！\n\r](?![\'"])|$))', # 匹配直到句子结束符 re.UNICODE | re.DOTALL, ) self.chat_stream = None async def build_last_internal_message(self, message: MessageRecvS4U, previous_reply_context: str = ""): # person_id = PersonInfoManager.get_person_id( # message.chat_stream.user_info.platform, message.chat_stream.user_info.user_id # ) # person_info_manager = get_person_info_manager() # person_name = await person_info_manager.get_value(person_id, "person_name") # if message.chat_stream.user_info.user_nickname: # if person_name: # sender_name = f"[{message.chat_stream.user_info.user_nickname}]（你叫ta{person_name}）" # else: # sender_name = f"[{message.chat_stream.user_info.user_nickname}]" # else: # sender_name = f"用户({message.chat_stream.user_info.user_id})" # 构建prompt if previous_reply_context: message_txt = f""" 你正在回复用户的消息，但中途被打断了。这是已有的对话上下文: [你已经对上一条消息说的话]: {previous_reply_context} --- [这是用户发来的新消息, 你需要结合上下文，对此进行回复]: {message.processed_plain_text} """ return True, message_txt else: message_txt = message.processed_plain_text return False, message_txt async def generate_response( self, message: MessageRecvS4U, previous_reply_context: str = "" ) -> AsyncGenerator[str, None]: """根据当前模型类型选择对应的生成函数""" # 从global_config中获取模型概率值并选择模型 self.partial_response = "" message_txt = message.processed_plain_text if not message.is_internal: interupted, message_txt_added = await self.build_last_internal_message(message, previous_reply_context) if interupted: message_txt = message_txt_added message.chat_stream = self.chat_stream prompt = await prompt_builder.build_prompt_normal( message=message, message_txt=message_txt, ) logger.info( f"{self.current_model_name}思考:{message_txt[:30] + '...' if len(message_txt) > 30 else message_txt}" ) # noqa: E501 # 使用LLMRequest进行流式生成 async for chunk in self._generate_response_with_llm_request(prompt): yield chunk async def _generate_response_with_llm_request(self, prompt: str) -> AsyncGenerator[str, None]: """使用LLMRequest进行流式响应生成""" # 构建消息 message_builder = MessageBuilder() message_builder.add_text_content(prompt) messages = [message_builder.build()] # 选择模型 model_info, api_provider, client = self.llm_request._select_model() self.current_model_name = model_info.name # 如果模型支持强制流式模式，使用真正的流式处理 if model_info.force_stream_mode: # 简化流式处理：直接使用LLMRequest的流式功能 try: # 直接调用LLMRequest的流式处理 response = await self.llm_request._execute_request( api_provider=api_provider, client=client, request_type=RequestType.RESPONSE, model_info=model_info, message_list=messages, ) # 处理响应内容 content = response.content or "" if content: # 将内容按句子分割并输出 async for chunk in self._process_content_streaming(content): yield chunk except Exception as e: logger.error(f"流式请求执行失败: {e}") # 如果流式请求失败，回退到普通模式 response = await self.llm_request._execute_request( api_provider=api_provider, client=client, request_type=RequestType.RESPONSE, model_info=model_info, message_list=messages, ) content = response.content or "" async for chunk in self._process_content_streaming(content): yield chunk else: # 如果不支持流式，使用普通方式然后模拟流式输出 response = await self.llm_request._execute_request( api_provider=api_provider, client=client, request_type=RequestType.RESPONSE, model_info=model_info, message_list=messages, ) content = response.content or "" async for chunk in self._process_content_streaming(content): yield chunk async def _process_buffer_streaming(self, buffer: str) -> AsyncGenerator[str, None]: """实时处理缓冲区内容，输出完整句子""" # 使用正则表达式匹配完整句子 for match in self.sentence_split_pattern.finditer(buffer): sentence = match.group(0).strip() if sentence and match.end(0) <= len(buffer): # 检查句子是否完整（以标点符号结尾） if sentence.endswith(("。", "！", "？", ".", "!", "?")): if sentence not in [",", "，", ".", "。", "!", "！", "?", "？"]: self.partial_response += sentence yield sentence async def _process_content_streaming(self, content: str) -> AsyncGenerator[str, None]: """处理内容进行流式输出（用于非流式模型的模拟流式输出）""" buffer = content punctuation_buffer = "" # 使用正则表达式匹配句子 last_match_end = 0 for match in self.sentence_split_pattern.finditer(buffer): sentence = match.group(0).strip() if sentence: # 检查是否只是一个标点符号 if sentence in [",", "，", ".", "。", "!", "！", "?", "？"]: punctuation_buffer += sentence else: # 发送之前累积的标点和当前句子 to_yield = punctuation_buffer + sentence if to_yield.endswith((",", "，")): to_yield = to_yield.rstrip(",，") self.partial_response += to_yield yield to_yield punctuation_buffer = "" # 清空标点符号缓冲区 last_match_end = match.end(0) # 发送缓冲区中剩余的任何内容 remaining = buffer[last_match_end:].strip() to_yield = (punctuation_buffer + remaining).strip() if to_yield: if to_yield.endswith(("，", ",")): to_yield = to_yield.rstrip("，,") if to_yield: self.partial_response += to_yield yield to_yield async def _generate_response_with_model( self, prompt: str, client, model_name: str, **kwargs, ) -> AsyncGenerator[str, None]: """保留原有方法签名以保持兼容性，但重定向到新的实现""" async for chunk in self._generate_response_with_llm_request(prompt): yield chunk