import random import time from typing import List, Optional, Tuple, Union from nonebot import get_driver from loguru import logger from ...common.database import Database from ..models.utils_model import LLM_request from .config import global_config from .message import MessageRecv, MessageThinking, Message from .prompt_builder import prompt_builder from .relationship_manager import relationship_manager from .utils import process_llm_response driver = get_driver() config = driver.config class ResponseGenerator: def __init__(self): self.model_r1 = LLM_request( model=global_config.llm_reasoning, temperature=0.7, max_tokens=1000, stream=True, ) self.model_v3 = LLM_request( model=global_config.llm_normal, temperature=0.7, max_tokens=1000 ) self.model_r1_distill = LLM_request( model=global_config.llm_reasoning_minor, temperature=0.7, max_tokens=1000 ) self.model_v25 = LLM_request( model=global_config.llm_normal_minor, temperature=0.7, max_tokens=1000 ) self.db = Database.get_instance() self.current_model_type = "r1" # 默认使用 R1 async def generate_response( self, message: MessageThinking ) -> Optional[Union[str, List[str]]]: """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 rand = random.random() if rand < global_config.MODEL_R1_PROBABILITY: self.current_model_type = "r1" current_model = self.model_r1 elif ( rand < global_config.MODEL_R1_PROBABILITY + global_config.MODEL_V3_PROBABILITY ): self.current_model_type = "v3" current_model = self.model_v3 else: self.current_model_type = "r1_distill" current_model = self.model_r1_distill logger.info(f"{global_config.BOT_NICKNAME}{self.current_model_type}思考中") model_response = await self._generate_response_with_model( message, current_model ) raw_content = model_response # print(f"raw_content: {raw_content}") # print(f"model_response: {model_response}") if model_response: logger.info(f'{global_config.BOT_NICKNAME}的回复是：{model_response}') model_response = await self._process_response(model_response) if model_response: return model_response, raw_content return None, raw_content async def _generate_response_with_model( self, message: MessageThinking, model: LLM_request ) -> Optional[str]: """使用指定的模型生成回复""" sender_name = ( message.chat_stream.user_info.user_nickname or f"用户{message.chat_stream.user_info.user_id}" ) if message.chat_stream.user_info.user_cardname: sender_name = f"[({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}]{message.chat_stream.user_info.user_cardname}" # 获取关系值 relationship_value = ( relationship_manager.get_relationship( message.chat_stream ).relationship_value if relationship_manager.get_relationship(message.chat_stream) else 0.0 ) if relationship_value != 0.0: # print(f"\033[1;32m[关系管理]\033[0m 回复中_当前关系值: {relationship_value}") pass # 构建prompt prompt, prompt_check = await prompt_builder._build_prompt( message_txt=message.processed_plain_text, sender_name=sender_name, relationship_value=relationship_value, stream_id=message.chat_stream.stream_id, ) # 读空气模块 简化逻辑，先停用 # if global_config.enable_kuuki_read: # content_check, reasoning_content_check = await self.model_v3.generate_response(prompt_check) # print(f"\033[1;32m[读空气]\033[0m 读空气结果为{content_check}") # if 'yes' not in content_check.lower() and random.random() < 0.3: # self._save_to_db( # message=message, # sender_name=sender_name, # prompt=prompt, # prompt_check=prompt_check, # content="", # content_check=content_check, # reasoning_content="", # reasoning_content_check=reasoning_content_check # ) # return None # 生成回复 try: content, reasoning_content = await model.generate_response(prompt) except Exception: logger.exception("生成回复时出错") return None # 保存到数据库 self._save_to_db( message=message, sender_name=sender_name, prompt=prompt, prompt_check=prompt_check, content=content, # content_check=content_check if global_config.enable_kuuki_read else "", reasoning_content=reasoning_content, # reasoning_content_check=reasoning_content_check if global_config.enable_kuuki_read else "" ) return content # def _save_to_db(self, message: Message, sender_name: str, prompt: str, prompt_check: str, # content: str, content_check: str, reasoning_content: str, reasoning_content_check: str): def _save_to_db( self, message: MessageRecv, sender_name: str, prompt: str, prompt_check: str, content: str, reasoning_content: str, ): """保存对话记录到数据库""" self.db.db.reasoning_logs.insert_one( { "time": time.time(), "chat_id": message.chat_stream.stream_id, "user": sender_name, "message": message.processed_plain_text, "model": self.current_model_type, # 'reasoning_check': reasoning_content_check, # 'response_check': content_check, "reasoning": reasoning_content, "response": content, "prompt": prompt, "prompt_check": prompt_check, } ) async def _get_emotion_tags(self, content: str) -> List[str]: """提取情感标签""" try: prompt = f"""请从以下内容中，从"happy,angry,sad,surprised,disgusted,fearful,neutral"中选出最匹配的1个情感标签并输出 只输出标签就好，不要输出其他内容: 内容：{content} 输出： """ content, _ = await self.model_v25.generate_response(prompt) content = content.strip() if content in [ "happy", "angry", "sad", "surprised", "disgusted", "fearful", "neutral", ]: return [content] else: return ["neutral"] except Exception as e: print(f"获取情感标签时出错: {e}") return ["neutral"] async def _process_response(self, content: str) -> Tuple[List[str], List[str]]: """处理响应内容，返回处理后的内容和情感标签""" if not content: return None, [] processed_response = process_llm_response(content) # print(f"得到了处理后的llm返回{processed_response}") return processed_response class InitiativeMessageGenerate: def __init__(self): self.db = Database.get_instance() self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7) self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7) self.model_r1_distill = LLM_request( model=global_config.llm_reasoning_minor, temperature=0.7 ) def gen_response(self, message: Message): topic_select_prompt, dots_for_select, prompt_template = ( prompt_builder._build_initiative_prompt_select(message.group_id) ) content_select, reasoning = self.model_v3.generate_response(topic_select_prompt) logger.debug(f"{content_select} {reasoning}") topics_list = [dot[0] for dot in dots_for_select] if content_select: if content_select in topics_list: select_dot = dots_for_select[topics_list.index(content_select)] else: return None else: return None prompt_check, memory = prompt_builder._build_initiative_prompt_check( select_dot[1], prompt_template ) content_check, reasoning_check = self.model_v3.generate_response(prompt_check) logger.info(f"{content_check} {reasoning_check}") if "yes" not in content_check.lower(): return None prompt = prompt_builder._build_initiative_prompt( select_dot, prompt_template, memory ) content, reasoning = self.model_r1.generate_response_async(prompt) logger.debug(f"[DEBUG] {content} {reasoning}") return content