318 lines
13 KiB
Python
318 lines
13 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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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 os
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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 .utils import get_embedding, split_into_sentences, process_text_with_typos
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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 LLMResponseGenerator:
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def __init__(self, config: BotConfig):
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self.config = config
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self.client = OpenAI(
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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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# 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, 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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# 使用随机数选择模型
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rand = random.random()
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if rand < 0.8: # 60%概率使用 R1
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self.current_model_type = "r1"
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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_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_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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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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return model_response, emotion
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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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# 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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# 获取推理内容
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reasoning_content = "模型思考过程：\n" + prompt
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if hasattr(response.choices[0].message, "reasoning"):
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reasoning_content = response.choices[0].message.reasoning or reasoning_content
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elif hasattr(response.choices[0].message, "reasoning_content"):
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reasoning_content = response.choices[0].message.reasoning_content or reasoning_content
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# 保存推理结果到数据库
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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': "DeepSeek-R1",
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'reasoning': reasoning_content,
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'response': content,
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'prompt': prompt
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})
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else:
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return None
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return content
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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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# 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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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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# 保存推理结果到数据库
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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': "DeepSeek-V3",
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'reasoning': "V3模型无推理过程",
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'response': content,
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'prompt': prompt
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})
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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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# 获取推理内容
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reasoning_content = "模型思考过程：\n" + prompt
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if hasattr(response.choices[0].message, "reasoning"):
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reasoning_content = response.choices[0].message.reasoning or reasoning_content
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elif hasattr(response.choices[0].message, "reasoning_content"):
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reasoning_content = response.choices[0].message.reasoning_content or reasoning_content
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# 保存推理结果到数据库
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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': "DeepSeek-R1-Distill",
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'reasoning': reasoning_content,
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'response': content,
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'prompt': prompt
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})
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else:
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return None
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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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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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# 检查回复是否过长（超过200个字符）
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if len(content) > 200:
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print(f"回复过长 ({len(content)} 字符)，返回默认回复")
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return "麦麦不知道哦", ["angry"]
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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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# 检查分割后的消息数量是否过多（超过3条）
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if len(messages) > 3:
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print(f"分割后消息数量过多 ({len(messages)} 条)，返回默认回复")
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return "麦麦不知道哦", ["angry"]
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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()) |