dev：修复记忆构建文本名称问题

src/heart_flow/sub_mind.py

@@ -8,7 +8,7 @@ from src.individuality.individuality import Individuality
 import random
 from ..plugins.utils.prompt_builder import Prompt, global_prompt_manager
 from src.do_tool.tool_use import ToolUser
-from src.plugins.utils.json_utils import safe_json_dumps, normalize_llm_response, process_llm_tool_calls
+from src.plugins.utils.json_utils import safe_json_dumps, process_llm_tool_calls
 from src.heart_flow.chat_state_info import ChatStateInfo
 from src.plugins.chat.chat_stream import chat_manager
 from src.plugins.heartFC_chat.heartFC_Cycleinfo import CycleInfo
@@ -20,14 +20,12 @@ logger = get_logger("sub_heartflow")
 def init_prompt():
     prompt = ""
     prompt += "{extra_info}\n"
-    prompt += "{prompt_personality}\n"
+    prompt += "你的名字是{bot_name},{prompt_personality}\n"
     prompt += "{last_loop_prompt}\n"
     prompt += "{cycle_info_block}\n"
     prompt += "现在是{time_now}，你正在上网，和qq群里的网友们聊天，以下是正在进行的聊天内容：\n{chat_observe_info}\n"
     prompt += "\n你现在{mood_info}\n"
-    prompt += (
-        "请仔细阅读当前群聊内容，分析讨论话题和群成员关系，分析你刚刚发言和别人对你的发言的反应，思考你要不要回复。"
-    )
+    prompt += "请仔细阅读当前群聊内容，分析讨论话题和群成员关系，分析你刚刚发言和别人对你的发言的反应，思考你要不要回复。然后思考你是否需要使用函数工具。"
     prompt += "思考并输出你的内心想法\n"
     prompt += "输出要求：\n"
     prompt += "1. 根据聊天内容生成你的想法，{hf_do_next}\n"
@@ -66,7 +64,7 @@ class SubMind:
         self.current_mind = ""
         self.past_mind = []
         self.structured_info = {}
-        
+
         name = chat_manager.get_stream_name(self.subheartflow_id)
         self.log_prefix = f"[{name}] "
 
@@ -80,8 +78,6 @@ class SubMind:
         # 更新活跃时间
         self.last_active_time = time.time()
 
-        
-        
         # ---------- 1. 准备基础数据 ----------
         # 获取现有想法和情绪状态
         current_thinking_info = self.current_mind
@@ -106,18 +102,7 @@ class SubMind:
         individuality = Individuality.get_instance()
 
         # 构建个性部分
-        prompt_personality = f"你正在扮演名为{individuality.personality.bot_nickname}的人类，你"
-        prompt_personality += individuality.personality.personality_core
-
-        # 随机添加个性侧面
-        if individuality.personality.personality_sides:
-            random_side = random.choice(individuality.personality.personality_sides)
-            prompt_personality += f"，{random_side}"
-
-        # 随机添加身份细节
-        if individuality.identity.identity_detail:
-            random_detail = random.choice(individuality.identity.identity_detail)
-            prompt_personality += f"，{random_detail}"
+        prompt_personality = individuality.get_prompt(x_person=2, level=2)
 
         # 获取当前时间
         time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
@@ -211,7 +196,7 @@ class SubMind:
         prompt = (await global_prompt_manager.get_prompt_async("sub_heartflow_prompt_before")).format(
             extra_info="",  # 可以在这里添加额外信息
             prompt_personality=prompt_personality,
-            bot_name=individuality.personality.bot_nickname,
+            bot_name=individuality.name,
             time_now=time_now,
             chat_observe_info=chat_observe_info,
             mood_info=mood_info,
@@ -228,34 +213,34 @@ class SubMind:
 
         try:
             # 调用LLM生成响应
-            response, _reasoning_content, tool_calls = await self.llm_model.generate_response_tool_async(prompt=prompt, tools=tools)
+            response, _reasoning_content, tool_calls = await self.llm_model.generate_response_tool_async(
+                prompt=prompt, tools=tools
+            )
 
             logger.debug(f"{self.log_prefix} 子心流输出的原始LLM响应: {response}")
-            
+
             # 直接使用LLM返回的文本响应作为 content
             content = response if response else ""
 
             if tool_calls:
                 # 直接将 tool_calls 传递给处理函数
                 success, valid_tool_calls, error_msg = process_llm_tool_calls(
-                        tool_calls, log_prefix=f"{self.log_prefix} "
+                    tool_calls, log_prefix=f"{self.log_prefix} "
                 )
-                
+
                 if success and valid_tool_calls:
                     # 记录工具调用信息
                     tool_calls_str = ", ".join(
                         [call.get("function", {}).get("name", "未知工具") for call in valid_tool_calls]
                     )
-                    logger.info(
-                        f"{self.log_prefix} 模型请求调用{len(valid_tool_calls)}个工具: {tool_calls_str}"
-                    )
+                    logger.info(f"{self.log_prefix} 模型请求调用{len(valid_tool_calls)}个工具: {tool_calls_str}")
 
                     # 收集工具执行结果
                     await self._execute_tool_calls(valid_tool_calls, tool_instance)
                 elif not success:
                     logger.warning(f"{self.log_prefix} 处理工具调用时出错: {error_msg}")
             else:
-                logger.info(f"{self.log_prefix} 心流未使用工具") # 修改日志信息，明确是未使用工具而不是未处理
+                logger.info(f"{self.log_prefix} 心流未使用工具")  # 修改日志信息，明确是未使用工具而不是未处理
 
         except Exception as e:
             # 处理总体异常