mirror of https://github.com/Mai-with-u/MaiBot.git
114 lines
3.8 KiB
Python
114 lines
3.8 KiB
Python
from typing import List, Optional
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from ..models.utils_model import LLMRequest
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from ...config.config import global_config
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from ..chat.message import MessageRecv
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from .heartflow_prompt_builder import prompt_builder
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from ..chat.utils import process_llm_response
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from src.common.logger import get_module_logger, LogConfig, LLM_STYLE_CONFIG
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from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
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from ..utils.timer_calculator import Timer
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from src.plugins.moods.moods import MoodManager
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# 定义日志配置
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llm_config = LogConfig(
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# 使用消息发送专用样式
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console_format=LLM_STYLE_CONFIG["console_format"],
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file_format=LLM_STYLE_CONFIG["file_format"],
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)
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logger = get_module_logger("llm_generator", config=llm_config)
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class HeartFCGenerator:
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def __init__(self):
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self.model_normal = LLMRequest(
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model=global_config.llm_normal,
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temperature=global_config.llm_normal["temp"],
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max_tokens=256,
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request_type="response_heartflow",
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)
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self.model_sum = LLMRequest(
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model=global_config.llm_summary_by_topic, temperature=0.6, max_tokens=2000, request_type="relation"
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)
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self.current_model_type = "r1" # 默认使用 R1
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self.current_model_name = "unknown model"
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async def generate_response(
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self,
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structured_info: str,
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current_mind_info: str,
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reason: str,
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message: MessageRecv,
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thinking_id: str,
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) -> Optional[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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arousal_multiplier = MoodManager.get_instance().get_arousal_multiplier()
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current_model = self.model_normal
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current_model.temperature = global_config.llm_normal["temp"] * arousal_multiplier # 激活度越高，温度越高
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model_response = await self._generate_response_with_model(
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structured_info, current_mind_info, reason, message, current_model, thinking_id
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)
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if model_response:
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model_processed_response = await self._process_response(model_response)
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return model_processed_response
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else:
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logger.info(f"{self.current_model_type}思考，失败")
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return None
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async def _generate_response_with_model(
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self,
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structured_info: str,
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current_mind_info: str,
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reason: str,
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message: MessageRecv,
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model: LLMRequest,
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thinking_id: str,
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) -> str:
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info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
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with Timer() as _build_prompt:
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prompt = await prompt_builder.build_prompt(
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build_mode="focus",
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reason=reason,
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current_mind_info=current_mind_info,
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structured_info=structured_info,
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message_txt="",
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sender_name="",
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chat_stream=message.chat_stream,
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)
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# logger.info(f"构建prompt时间: {t_build_prompt.human_readable}")
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try:
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content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
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logger.info(f"\nprompt:{prompt}\n生成回复{content}\n")
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info_catcher.catch_after_llm_generated(
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prompt=prompt, response=content, reasoning_content=reasoning_content, model_name=self.current_model_name
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)
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except Exception:
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logger.exception("生成回复时出错")
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return None
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return content
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async def _process_response(self, content: 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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processed_response = process_llm_response(content)
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# print(f"得到了处理后的llm返回{processed_response}")
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return processed_response
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