199 lines
8.6 KiB
Python
199 lines
8.6 KiB
Python
from typing import Dict, Any, List, Optional, Union, Tuple
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from openai import OpenAI
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import asyncio
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from functools import partial
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from .message import Message
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from .config import global_config
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from ...common.database import Database
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import random
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import time
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import numpy as np
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from .relationship_manager import relationship_manager
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from .prompt_builder import prompt_builder
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from .config import global_config
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from .utils import process_llm_response
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from nonebot import get_driver
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from ..models.utils_model import LLM_request
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driver = get_driver()
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config = driver.config
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class ResponseGenerator:
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def __init__(self):
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self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7,max_tokens=1000)
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self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7,max_tokens=1000)
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self.model_r1_distill = LLM_request(model=global_config.llm_reasoning_minor, temperature=0.7,max_tokens=1000)
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self.db = Database.get_instance()
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self.current_model_type = 'r1' # 默认使用 R1
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async def generate_response(self, message: Message) -> Optional[Union[str, List[str]]]:
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"""根据当前模型类型选择对应的生成函数"""
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# 从global_config中获取模型概率值并选择模型
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rand = random.random()
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if rand < global_config.MODEL_R1_PROBABILITY:
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self.current_model_type = 'r1'
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current_model = self.model_r1
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elif rand < global_config.MODEL_R1_PROBABILITY + global_config.MODEL_V3_PROBABILITY:
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self.current_model_type = 'v3'
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current_model = self.model_v3
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else:
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self.current_model_type = 'r1_distill'
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current_model = self.model_r1_distill
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print(f"+++++++++++++++++{global_config.BOT_NICKNAME}{self.current_model_type}思考中+++++++++++++++++")
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model_response = await self._generate_response_with_model(message, current_model)
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if model_response:
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print(f'{global_config.BOT_NICKNAME}的回复是：{model_response}')
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model_response, emotion = await self._process_response(model_response)
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if model_response:
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print(f"为 '{model_response}' 获取到的情感标签为：{emotion}")
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valuedict={
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'happy':0.5,'angry':-1,'sad':-0.5,'surprised':0.5,'disgusted':-1.5,'fearful':-0.25,'neutral':0.25
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}
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await relationship_manager.update_relationship_value(message.user_id, relationship_value=valuedict[emotion[0]])
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return model_response, emotion
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return None, []
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async def _generate_response_with_model(self, message: Message, model: LLM_request) -> Optional[str]:
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"""使用指定的模型生成回复"""
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sender_name = message.user_nickname or f"用户{message.user_id}"
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if message.user_cardname:
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sender_name=f"[({message.user_id}){message.user_nickname}]{message.user_cardname}"
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# 获取关系值
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relationship_value = relationship_manager.get_relationship(message.user_id).relationship_value if relationship_manager.get_relationship(message.user_id) else 0.0
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if relationship_value != 0.0:
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print(f"\033[1;32m[关系管理]\033[0m 回复中_当前关系值: {relationship_value}")
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# 构建prompt
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prompt, prompt_check = prompt_builder._build_prompt(
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message_txt=message.processed_plain_text,
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sender_name=sender_name,
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relationship_value=relationship_value,
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group_id=message.group_id
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)
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# 读空气模块 简化逻辑，先停用
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# if global_config.enable_kuuki_read:
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# content_check, reasoning_content_check = await self.model_v3.generate_response(prompt_check)
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# print(f"\033[1;32m[读空气]\033[0m 读空气结果为{content_check}")
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# if 'yes' not in content_check.lower() and random.random() < 0.3:
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# self._save_to_db(
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# message=message,
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# sender_name=sender_name,
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# prompt=prompt,
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# prompt_check=prompt_check,
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# content="",
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# content_check=content_check,
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# reasoning_content="",
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# reasoning_content_check=reasoning_content_check
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# )
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# return None
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# 生成回复
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content, reasoning_content = await model.generate_response(prompt)
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# 保存到数据库
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self._save_to_db(
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message=message,
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sender_name=sender_name,
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prompt=prompt,
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prompt_check=prompt_check,
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content=content,
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# content_check=content_check if global_config.enable_kuuki_read else "",
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reasoning_content=reasoning_content,
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# reasoning_content_check=reasoning_content_check if global_config.enable_kuuki_read else ""
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)
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return content
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# def _save_to_db(self, message: Message, sender_name: str, prompt: str, prompt_check: str,
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# content: str, content_check: str, reasoning_content: str, reasoning_content_check: str):
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def _save_to_db(self, message: Message, sender_name: str, prompt: str, prompt_check: str,
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content: str, reasoning_content: str,):
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"""保存对话记录到数据库"""
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self.db.db.reasoning_logs.insert_one({
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'time': time.time(),
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'group_id': message.group_id,
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'user': sender_name,
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'message': message.processed_plain_text,
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'model': self.current_model_type,
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# 'reasoning_check': reasoning_content_check,
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# 'response_check': content_check,
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'reasoning': reasoning_content,
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'response': content,
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'prompt': prompt,
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'prompt_check': prompt_check
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})
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async def _get_emotion_tags(self, content: str) -> List[str]:
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"""提取情感标签"""
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try:
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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
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只输出标签就好，不要输出其他内容:
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内容：{content}
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输出：
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'''
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content, _ = await self.model_v3.generate_response(prompt)
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content=content.strip()
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if content in ['happy','angry','sad','surprised','disgusted','fearful','neutral']:
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return [content]
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else:
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return ["neutral"]
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except Exception as e:
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print(f"获取情感标签时出错: {e}")
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return ["neutral"]
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async def _process_response(self, content: str) -> Tuple[List[str], List[str]]:
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"""处理响应内容，返回处理后的内容和情感标签"""
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if not content:
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return None, []
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emotion_tags = await self._get_emotion_tags(content)
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processed_response = process_llm_response(content)
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return processed_response, emotion_tags
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class InitiativeMessageGenerate:
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def __init__(self):
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self.db = Database.get_instance()
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self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7)
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self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7)
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self.model_r1_distill = LLM_request(
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model=global_config.llm_reasoning_minor, temperature=0.7
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)
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def gen_response(self, message: Message):
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topic_select_prompt, dots_for_select, prompt_template = (
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prompt_builder._build_initiative_prompt_select(message.group_id)
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)
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content_select, reasoning = self.model_v3.generate_response(topic_select_prompt)
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print(f"[DEBUG] {content_select} {reasoning}")
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topics_list = [dot[0] for dot in dots_for_select]
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if content_select:
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if content_select in topics_list:
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select_dot = dots_for_select[topics_list.index(content_select)]
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else:
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return None
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else:
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return None
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prompt_check, memory = prompt_builder._build_initiative_prompt_check(
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select_dot[1], prompt_template
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)
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content_check, reasoning_check = self.model_v3.generate_response(prompt_check)
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print(f"[DEBUG] {content_check} {reasoning_check}")
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if "yes" not in content_check.lower():
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return None
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prompt = prompt_builder._build_initiative_prompt(
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select_dot, prompt_template, memory
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)
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content, reasoning = self.model_r1.generate_response(prompt)
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print(f"[DEBUG] {content} {reasoning}")
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return content
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