拆分0.1.3
1
This commit is contained in:
SengokuCola
@@ -1,108 +1,367 @@
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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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import requests
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from functools import partial
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from .message import Message
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from .config import BotConfig
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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 subprocess
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import os
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import aiohttp
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import sys
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import threading
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import queue
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import numpy as np
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from dotenv import load_dotenv
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from .relationship_manager import relationship_manager
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from ..schedule.schedule_generator import bot_schedule
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from .prompt_builder import prompt_builder
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from .config import llm_config
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from .willing_manager import willing_manager
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from .utils import get_embedding, split_into_sentences, process_text_with_typos
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import aiohttp
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# 获取当前文件的绝对路径
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current_dir = os.path.dirname(os.path.abspath(__file__))
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root_dir = os.path.abspath(os.path.join(current_dir, '..', '..', '..'))
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load_dotenv(os.path.join(root_dir, '.env'))
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class ReasoningWindow:
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def __init__(self):
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self.process = None
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self.message_queue = queue.Queue()
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self.is_running = False
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self.content_file = "reasoning_content.txt"
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def start(self):
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if self.process is None:
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# 创建用于显示的批处理文件
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with open("reasoning_window.bat", "w", encoding="utf-8") as f:
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f.write('@echo off\n')
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f.write('chcp 65001\n') # 设置UTF-8编码
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f.write('title Magellan Reasoning Process\n')
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f.write('echo Waiting for reasoning content...\n')
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f.write(':loop\n')
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f.write('if exist "reasoning_update.txt" (\n')
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f.write(' type "reasoning_update.txt" >> "reasoning_content.txt"\n')
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f.write(' del "reasoning_update.txt"\n')
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f.write(' cls\n')
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f.write(' type "reasoning_content.txt"\n')
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f.write(')\n')
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f.write('timeout /t 1 /nobreak >nul\n')
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f.write('goto loop\n')
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# 清空内容文件
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with open(self.content_file, "w", encoding="utf-8") as f:
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f.write("")
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# 启动新窗口
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startupinfo = subprocess.STARTUPINFO()
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startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW
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self.process = subprocess.Popen(['cmd', '/c', 'start', 'reasoning_window.bat'],
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shell=True,
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startupinfo=startupinfo)
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self.is_running = True
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# 启动处理线程
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threading.Thread(target=self._process_messages, daemon=True).start()
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def _process_messages(self):
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while self.is_running:
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try:
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# 获取新消息
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text = self.message_queue.get(timeout=1)
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# 写入更新文件
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with open("reasoning_update.txt", "w", encoding="utf-8") as f:
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f.write(text)
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except queue.Empty:
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continue
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except Exception as e:
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print(f"处理推理内容时出错: {e}")
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def update_content(self, text: str):
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if self.is_running:
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self.message_queue.put(text)
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def stop(self):
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self.is_running = False
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if self.process:
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self.process.terminate()
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self.process = None
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# 清理文件
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for file in ["reasoning_window.bat", "reasoning_content.txt", "reasoning_update.txt"]:
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if os.path.exists(file):
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os.remove(file)
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# 创建全局单例
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reasoning_window = ReasoningWindow()
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class LLMResponseGenerator:
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def __init__(self, config: BotConfig):
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self.config = config
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self.API_KEY = os.getenv('SILICONFLOW_KEY')
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self.BASE_URL =os.getenv('SILICONFLOW_BASE_URL')
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self.client = OpenAI(
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api_key=self.API_KEY,
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base_url=self.BASE_URL
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api_key=llm_config.SILICONFLOW_API_KEY,
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base_url=llm_config.SILICONFLOW_BASE_URL
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)
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self.db = Database.get_instance()
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reasoning_window.start()
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# 当前使用的模型类型
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self.current_model_type = 'r1' # 默认使用 R1
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async def generate_response(self, text: str) -> Optional[str]:
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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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if random.random() < self.config.MODEL_R1_PROBABILITY:
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# 使用随机数选择模型
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rand = random.random()
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if rand < 0.6: # 60%概率使用 R1
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self.current_model_type = "r1"
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else:
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elif rand < 0.5: # 20%概率使用 V3
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self.current_model_type = "v3"
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else: # 20%概率使用 R1-Distill
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self.current_model_type = "r1_distill"
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print(f"+++++++++++++++++麦麦{self.current_model_type}思考中+++++++++++++++++")
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if self.current_model_type == 'r1':
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model_response = await self._generate_v3_response(text)
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model_response = await self._generate_r1_response(message)
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elif self.current_model_type == 'v3':
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model_response = await self._generate_v3_response(message)
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else:
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model_response = await self._generate_v3_response(text)
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model_response = await self._generate_r1_distill_response(message)
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# 打印情感标签
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print(f'麦麦的回复------------------------------是：{model_response}')
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print(f'麦麦的回复是：{model_response}')
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model_response, emotion = await self._process_response(model_response)
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return 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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return model_response, emotion
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async def _generate_r1_response(self, text: str) -> Optional[str]:
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async def _generate_r1_response(self, message: Message) -> Optional[str]:
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"""使用 DeepSeek-R1 模型生成回复"""
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messages = [{"role": "user", "content": text}]
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async with aiohttp.ClientSession() as session:
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {self.API_KEY}"
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}
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payload = {
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"model": "Pro/deepseek-ai/DeepSeek-R1",
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"messages": messages,
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"stream": False,
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"max_tokens": 1024,
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"temperature": 0.8
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}
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async with session.post(f"{self.BASE_URL}/chat/completions",
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headers=headers,
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json=payload) as response:
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result = await response.json()
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if "choices" in result and len(result["choices"]) > 0:
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content = result["choices"][0]["message"]["content"]
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reasoning_content = result["choices"][0]["message"].get("reasoning_content", "")
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print(f"Content: {content}")
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print(f"Reasoning: {reasoning_content}")
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return content
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return None
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# 获取群聊上下文
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group_chat = await self._get_group_chat_context(message)
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sender_name = message.user_nickname or f"用户{message.user_id}"
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if relationship_manager.get_relationship(message.user_id):
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relationship_value = relationship_manager.get_relationship(message.user_id).relationship_value
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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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else:
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relationship_value = 0.0
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# 构建 prompt
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prompt = 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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def create_completion():
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return self.client.chat.completions.create(
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model="Pro/deepseek-ai/DeepSeek-R1",
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messages=[{"role": "user", "content": prompt}],
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stream=False,
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max_tokens=1024
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)
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loop = asyncio.get_event_loop()
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response = await loop.run_in_executor(None, create_completion)
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if response.choices[0].message.content:
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content = response.choices[0].message.content
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reasoning_content = response.choices[0].message.reasoning_content
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else:
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return None
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# 更新推理窗口
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self._update_reasoning_window(message, prompt, reasoning_content, content, sender_name)
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return content
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async def _generate_v3_response(self, text: str) -> Optional[str]:
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async def _generate_v3_response(self, message: Message) -> Optional[str]:
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"""使用 DeepSeek-V3 模型生成回复"""
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {self.API_KEY}"
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}
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# 获取群聊上下文
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group_chat = await self._get_group_chat_context(message)
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sender_name = message.user_nickname or f"用户{message.user_id}"
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payload = {
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"model": "Pro/deepseek-ai/DeepSeek-V3",
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"messages": [{"role": "user", "content": text}],
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"max_tokens": 1024,
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"temperature": 0.8
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}
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if relationship_manager.get_relationship(message.user_id):
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relationship_value = relationship_manager.get_relationship(message.user_id).relationship_value
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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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else:
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relationship_value = 0.0
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async with aiohttp.ClientSession() as session:
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async with session.post(f"{self.BASE_URL}/chat/completions",
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headers=headers,
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json=payload) as response:
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result = await response.json()
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if "choices" in result and len(result["choices"]) > 0:
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content = result["choices"][0]["message"]["content"]
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return content
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else:
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print(f"[ERROR] V3 回复发送生成失败: {result}")
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prompt = prompt_builder._build_prompt(message.processed_plain_text, sender_name, relationship_value, group_id=message.group_id)
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messages = [{"role": "user", "content": prompt}]
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loop = asyncio.get_event_loop()
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create_completion = partial(
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self.client.chat.completions.create,
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model="Pro/deepseek-ai/DeepSeek-V3",
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messages=messages,
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stream=False,
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max_tokens=1024,
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temperature=0.8
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)
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response = await loop.run_in_executor(None, create_completion)
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if response.choices[0].message.content:
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content = response.choices[0].message.content
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# V3 模型没有 reasoning_content
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self._update_reasoning_window(message, prompt, "V3模型无推理过程", content, sender_name)
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return content
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else:
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print(f"[ERROR] V3 回复发送生成失败: {response}")
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return None
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async def _generate_r1_distill_response(self, message: Message) -> Optional[str]:
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"""使用 DeepSeek-R1-Distill-Qwen-32B 模型生成回复"""
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# 获取群聊上下文
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group_chat = await self._get_group_chat_context(message)
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sender_name = message.user_nickname or f"用户{message.user_id}"
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if relationship_manager.get_relationship(message.user_id):
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relationship_value = relationship_manager.get_relationship(message.user_id).relationship_value
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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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else:
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relationship_value = 0.0
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# 构建 prompt
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prompt = 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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def create_completion():
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return self.client.chat.completions.create(
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model="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
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messages=[{"role": "user", "content": prompt}],
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stream=False,
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max_tokens=1024
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)
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loop = asyncio.get_event_loop()
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response = await loop.run_in_executor(None, create_completion)
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if response.choices[0].message.content:
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content = response.choices[0].message.content
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reasoning_content = response.choices[0].message.reasoning_content
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else:
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return None
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# 更新推理窗口
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self._update_reasoning_window(message, prompt, reasoning_content, content, sender_name)
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return content
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async def _get_group_chat_context(self, message: Message) -> str:
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"""获取群聊上下文"""
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recent_messages = self.db.db.messages.find(
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{"group_id": message.group_id}
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).sort("time", -1).limit(15)
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messages_list = list(recent_messages)[::-1]
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group_chat = ""
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for msg_dict in messages_list:
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time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(msg_dict['time']))
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display_name = msg_dict.get('user_nickname', f"用户{msg_dict['user_id']}")
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content = msg_dict.get('processed_plain_text', msg_dict['plain_text'])
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group_chat += f"[{time_str}] {display_name}: {content}\n"
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return group_chat
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def _update_reasoning_window(self, message, prompt, reasoning_content, content, sender_name):
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"""更新推理窗口内容"""
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current_time = time.strftime("%Y-%m-%d %H:%M:%S")
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# 获取当前使用的模型名称
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model_name = {
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'r1': 'DeepSeek-R1',
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'v3': 'DeepSeek-V3',
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'r1_distill': 'DeepSeek-R1-Distill-Qwen-32B'
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}.get(self.current_model_type, '未知模型')
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display_text = (
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f"Time: {current_time}\n"
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f"Group: {message.group_name}\n"
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f"User: {sender_name}\n"
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f"Model: {model_name}\n"
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f"\033[1;32mMessage:\033[0m {message.processed_plain_text}\n\n"
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f"\033[1;32mPrompt:\033[0m \n{prompt}\n"
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f"\n-------------------------------------------------------"
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f"\n\033[1;32mReasoning Process:\033[0m\n{reasoning_content}\n"
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f"\n\033[1;32mResponse Content:\033[0m\n{content}\n"
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f"\n{'='*50}\n"
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)
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reasoning_window.update_content(display_text)
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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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messages = [{"role": "user", "content": prompt}]
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loop = asyncio.get_event_loop()
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create_completion = partial(
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self.client.chat.completions.create,
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model="Pro/deepseek-ai/DeepSeek-V3",
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messages=messages,
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stream=False,
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max_tokens=30,
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temperature=0.6
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)
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response = await loop.run_in_executor(None, create_completion)
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if response.choices[0].message.content:
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# 确保返回的是列表格式
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emotion_tag = response.choices[0].message.content.strip()
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return [emotion_tag] # 将单个标签包装成列表返回
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return ["neutral"] # 如果无法获取情感标签，返回默认值
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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"] # 发生错误时返回默认值
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async def _process_response(self, content: str) -> Tuple[Union[str, 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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# 添加错别字和处理标点符号
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processed_response = process_text_with_typos(content)
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# 处理长消息
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if len(processed_response) > 5:
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sentences = split_into_sentences(processed_response)
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print(f"分割后的句子: {sentences}")
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messages = []
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current_message = ""
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for sentence in sentences:
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if len(current_message) + len(sentence) <= 5:
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current_message += ' '
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current_message += sentence
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else:
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if current_message:
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messages.append(current_message.strip())
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current_message = sentence
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if current_message:
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messages.append(current_message.strip())
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return messages, emotion_tags
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return processed_response, emotion_tags
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# 创建全局实例
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llm_response = LLMResponseGenerator(config=BotConfig())
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