12b03ecb8d
- 将“get_module_logger”替换为新模块“logger_manager”中的“get_logger”，以实现一致的日志设置。 - 移除了单独的日志配置设置，转而采用集中式日志管理。 - 更新了多个文件中的日志初始化方法，包括“config.py”、“change_mood.py”、“change_relationship.py”等，以简化日志记录实践。 - 引入“logger_manager.py”，用于根据模块特定的样式处理日志配置。
140 lines
6.1 KiB
Python
140 lines
6.1 KiB
Python
import traceback
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from typing import TYPE_CHECKING
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from src.common.logger_manager import get_logger
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from src.plugins.models.utils_model import LLMRequest
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from src.individuality.individuality import Individuality
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from src.plugins.utils.prompt_builder import global_prompt_manager
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from src.config.config import global_config
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# Need access to SubHeartflowManager to get minds and update them
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if TYPE_CHECKING:
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from src.heart_flow.subheartflow_manager import SubHeartflowManager
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from src.heart_flow.mai_state_manager import MaiStateInfo
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logger = get_logger("sub_heartflow_mind")
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class Mind:
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"""封装 Mai 的思考过程，包括生成内心独白和汇总想法。"""
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def __init__(self, subheartflow_manager: "SubHeartflowManager", llm_model: LLMRequest):
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self.subheartflow_manager = subheartflow_manager
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self.llm_model = llm_model
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self.individuality = Individuality.get_instance()
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async def do_a_thinking(self, current_main_mind: str, mai_state_info: "MaiStateInfo", schedule_info: str):
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"""
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执行一次主心流思考过程，生成新的内心独白。
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Args:
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current_main_mind: 当前的主心流想法。
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mai_state_info: 当前的 Mai 状态信息 (用于获取 mood)。
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schedule_info: 当前的日程信息。
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Returns:
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str: 生成的新的内心独白，如果出错则返回提示信息。
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"""
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logger.debug("Mind: 执行思考...")
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# --- 构建 Prompt --- #
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personality_info = (
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self.individuality.get_prompt_snippet()
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if hasattr(self.individuality, "get_prompt_snippet")
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else self.individuality.personality.personality_core
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)
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mood_info = mai_state_info.get_mood_prompt()
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related_memory_info = "memory" # TODO: Implement memory retrieval
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# Get subflow minds summary via internal method
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try:
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sub_flows_info = await self._get_subflows_summary(current_main_mind, mai_state_info)
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except Exception as e:
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logger.error(f"[Mind Thinking] 获取子心流想法汇总失败: {e}")
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logger.error(traceback.format_exc())
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sub_flows_info = "(获取子心流想法时出错)"
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# Format prompt
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try:
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prompt = (await global_prompt_manager.get_prompt_async("thinking_prompt")).format(
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schedule_info=schedule_info,
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personality_info=personality_info,
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related_memory_info=related_memory_info,
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current_thinking_info=current_main_mind, # Use passed current mind
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sub_flows_info=sub_flows_info,
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mood_info=mood_info,
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)
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except Exception as e:
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logger.error(f"[Mind Thinking] 格式化 thinking_prompt 失败: {e}")
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return "(思考时格式化Prompt出错...)"
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# --- 调用 LLM --- #
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try:
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response, reasoning_content = await self.llm_model.generate_response_async(prompt)
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if not response:
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logger.warning("[Mind Thinking] 内心独白 LLM 返回空结果。")
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response = "(暂时没什么想法...)"
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logger.info(f"Mind: 新想法生成: {response[:100]}...") # Log truncated response
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return response
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except Exception as e:
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logger.error(f"[Mind Thinking] 内心独白 LLM 调用失败: {e}")
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logger.error(traceback.format_exc())
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return "(思考时调用LLM出错...)"
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async def _get_subflows_summary(self, current_main_mind: str, mai_state_info: "MaiStateInfo") -> str:
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"""获取所有活跃子心流的想法，并使用 LLM 进行汇总。"""
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# 1. Get active minds from SubHeartflowManager
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sub_minds_list = self.subheartflow_manager.get_active_subflow_minds()
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if not sub_minds_list:
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return "(当前没有活跃的子心流想法)"
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minds_str = "\n".join([f"- {mind}" for mind in sub_minds_list])
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logger.debug(f"Mind: 获取到 {len(sub_minds_list)} 个子心流想法进行汇总。")
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# 2. Call LLM for summary
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# --- 构建 Prompt --- #
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personality_info = (
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self.individuality.get_prompt_snippet()
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if hasattr(self.individuality, "get_prompt_snippet")
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else self.individuality.personality.personality_core
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)
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mood_info = mai_state_info.get_mood_prompt()
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bot_name = global_config.BOT_NICKNAME
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try:
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prompt = (await global_prompt_manager.get_prompt_async("mind_summary_prompt")).format(
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personality_info=personality_info,
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bot_name=bot_name,
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current_mind=current_main_mind, # Use main mind passed for context
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minds_str=minds_str,
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mood_info=mood_info,
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)
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except Exception as e:
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logger.error(f"[Mind Summary] 格式化 mind_summary_prompt 失败: {e}")
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return "(汇总想法时格式化Prompt出错...)"
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# --- 调用 LLM --- #
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try:
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response, reasoning_content = await self.llm_model.generate_response_async(prompt)
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if not response:
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logger.warning("[Mind Summary] 想法汇总 LLM 返回空结果。")
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return "(想法汇总失败...)"
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logger.debug(f"Mind: 子想法汇总完成: {response[:100]}...")
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return response
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except Exception as e:
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logger.error(f"[Mind Summary] 想法汇总 LLM 调用失败: {e}")
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logger.error(traceback.format_exc())
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return "(想法汇总时调用LLM出错...)"
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def update_subflows_with_main_mind(self, main_mind: str):
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"""触发 SubHeartflowManager 更新所有子心流的主心流信息。"""
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logger.debug("Mind: 请求更新子心流的主想法信息。")
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self.subheartflow_manager.update_main_mind_in_subflows(main_mind)
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# Note: update_current_mind (managing self.current_mind and self.past_mind)
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# remains in Heartflow for now, as Heartflow is the central coordinator holding the main state.
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# Mind class focuses solely on the *process* of thinking and summarizing.
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