fix：实例化 normal_chat，支持不同子聊天拥有不同参数，修复各种问题

src/heart_flow/mind.py

@@ -0,0 +1,141 @@
+import traceback
+from typing import TYPE_CHECKING
+
+from src.common.logger import get_module_logger
+from src.plugins.models.utils_model import LLMRequest
+from src.individuality.individuality import Individuality
+from src.plugins.utils.prompt_builder import global_prompt_manager
+from src.config.config import global_config
+
+# Need access to SubHeartflowManager to get minds and update them
+if TYPE_CHECKING:
+    from src.heart_flow.subheartflow_manager import SubHeartflowManager
+    from src.heart_flow.mai_state_manager import MaiStateInfo
+
+logger = get_module_logger("mind")
+
+
+class Mind:
+    """封装 Mai 的思考过程，包括生成内心独白和汇总想法。"""
+
+    def __init__(self, subheartflow_manager: "SubHeartflowManager", llm_model: LLMRequest):
+        self.subheartflow_manager = subheartflow_manager
+        self.llm_model = llm_model
+        self.individuality = Individuality.get_instance()
+        # Main mind state is still managed by Heartflow for now
+        # self.current_mind = "你什么也没想"
+        # self.past_mind = []
+
+    async def do_a_thinking(self, current_main_mind: str, mai_state_info: "MaiStateInfo", schedule_info: str):
+        """
+        执行一次主心流思考过程，生成新的内心独白。
+
+        Args:
+            current_main_mind: 当前的主心流想法。
+            mai_state_info: 当前的 Mai 状态信息 (用于获取 mood)。
+            schedule_info: 当前的日程信息。
+
+        Returns:
+            str: 生成的新的内心独白，如果出错则返回提示信息。
+        """
+        logger.debug("Mind: 执行思考...")
+
+        # --- 构建 Prompt --- #
+        personality_info = (
+            self.individuality.get_prompt_snippet()
+            if hasattr(self.individuality, "get_prompt_snippet")
+            else self.individuality.personality.personality_core
+        )
+        mood_info = mai_state_info.get_mood_prompt()
+        related_memory_info = "memory"  # TODO: Implement memory retrieval
+
+        # Get subflow minds summary via internal method
+        try:
+            sub_flows_info = await self._get_subflows_summary(current_main_mind, mai_state_info)
+        except Exception as e:
+            logger.error(f"[Mind Thinking] 获取子心流想法汇总失败: {e}")
+            logger.error(traceback.format_exc())
+            sub_flows_info = "(获取子心流想法时出错)"
+
+        # Format prompt
+        try:
+            prompt = (await global_prompt_manager.get_prompt_async("thinking_prompt")).format(
+                schedule_info=schedule_info,
+                personality_info=personality_info,
+                related_memory_info=related_memory_info,
+                current_thinking_info=current_main_mind,  # Use passed current mind
+                sub_flows_info=sub_flows_info,
+                mood_info=mood_info,
+            )
+        except Exception as e:
+            logger.error(f"[Mind Thinking] 格式化 thinking_prompt 失败: {e}")
+            return "(思考时格式化Prompt出错...)"
+
+        # --- 调用 LLM --- #
+        try:
+            response, reasoning_content = await self.llm_model.generate_response_async(prompt)
+            if not response:
+                logger.warning("[Mind Thinking] 内心独白 LLM 返回空结果。")
+                response = "(暂时没什么想法...)"
+            logger.info(f"Mind: 新想法生成: {response[:100]}...")  # Log truncated response
+            return response
+        except Exception as e:
+            logger.error(f"[Mind Thinking] 内心独白 LLM 调用失败: {e}")
+            logger.error(traceback.format_exc())
+            return "(思考时调用LLM出错...)"
+
+    async def _get_subflows_summary(self, current_main_mind: str, mai_state_info: "MaiStateInfo") -> str:
+        """获取所有活跃子心流的想法，并使用 LLM 进行汇总。"""
+        # 1. Get active minds from SubHeartflowManager
+        sub_minds_list = self.subheartflow_manager.get_active_subflow_minds()
+
+        if not sub_minds_list:
+            return "(当前没有活跃的子心流想法)"
+
+        minds_str = "\n".join([f"- {mind}" for mind in sub_minds_list])
+        logger.debug(f"Mind: 获取到 {len(sub_minds_list)} 个子心流想法进行汇总。")
+
+        # 2. Call LLM for summary
+        # --- 构建 Prompt --- #
+        personality_info = (
+            self.individuality.get_prompt_snippet()
+            if hasattr(self.individuality, "get_prompt_snippet")
+            else self.individuality.personality.personality_core
+        )
+        mood_info = mai_state_info.get_mood_prompt()
+        bot_name = global_config.BOT_NICKNAME
+
+        try:
+            prompt = (await global_prompt_manager.get_prompt_async("mind_summary_prompt")).format(
+                personality_info=personality_info,
+                bot_name=bot_name,
+                current_mind=current_main_mind,  # Use main mind passed for context
+                minds_str=minds_str,
+                mood_info=mood_info,
+            )
+        except Exception as e:
+            logger.error(f"[Mind Summary] 格式化 mind_summary_prompt 失败: {e}")
+            return "(汇总想法时格式化Prompt出错...)"
+
+        # --- 调用 LLM --- #
+        try:
+            response, reasoning_content = await self.llm_model.generate_response_async(prompt)
+            if not response:
+                logger.warning("[Mind Summary] 想法汇总 LLM 返回空结果。")
+                return "(想法汇总失败...)"
+            logger.debug(f"Mind: 子想法汇总完成: {response[:100]}...")
+            return response
+        except Exception as e:
+            logger.error(f"[Mind Summary] 想法汇总 LLM 调用失败: {e}")
+            logger.error(traceback.format_exc())
+            return "(想法汇总时调用LLM出错...)"
+
+    def update_subflows_with_main_mind(self, main_mind: str):
+        """触发 SubHeartflowManager 更新所有子心流的主心流信息。"""
+        logger.debug("Mind: 请求更新子心流的主想法信息。")
+        self.subheartflow_manager.update_main_mind_in_subflows(main_mind)
+
+
+# Note: update_current_mind (managing self.current_mind and self.past_mind)
+# remains in Heartflow for now, as Heartflow is the central coordinator holding the main state.
+# Mind class focuses solely on the *process* of thinking and summarizing.