🤖 自动格式化代码 [skip ci]

src/common/logger.py

@@ -283,17 +283,13 @@ WILLING_STYLE_CONFIG = {
         "file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 意愿 | {message}"),
     },
     "simple": {
-        "console_format": (
+        "console_format": ("<green>{time:MM-DD HH:mm}</green> | <light-blue>意愿</light-blue> | {message}"),  # noqa: E501
-            "<green>{time:MM-DD HH:mm}</green> | <light-blue>意愿</light-blue> | {message}"
-        ),  # noqa: E501
         "file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 意愿 | {message}"),
     },
 }
 
 CONFIRM_STYLE_CONFIG = {
-    "console_format": (
+    "console_format": ("<RED>{message}</RED>"),  # noqa: E501
-        "<RED>{message}</RED>"
-    ),  # noqa: E501
     "file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | EULA与PRIVACY确认 | {message}"),
 }
 

src/do_tool/tool_can_use/__init__.py

@@ -4,16 +4,16 @@ from src.do_tool.tool_can_use.base_tool import (
     discover_tools,
     get_all_tool_definitions,
     get_tool_instance,
-    TOOL_REGISTRY
+    TOOL_REGISTRY,
 )
 
 __all__ = [
-    'BaseTool',
+    "BaseTool",
-    'register_tool',
+    "register_tool",
-    'discover_tools',
+    "discover_tools",
-    'get_all_tool_definitions',
+    "get_all_tool_definitions",
-    'get_tool_instance',
+    "get_tool_instance",
-    'TOOL_REGISTRY'
+    "TOOL_REGISTRY",
 ]
 
 # 自动发现并注册工具

src/do_tool/tool_can_use/base_tool.py

@@ -10,8 +10,10 @@ logger = get_module_logger("base_tool")
 # 工具注册表
 TOOL_REGISTRY = {}
 
+
 class BaseTool:
     """所有工具的基类"""
+
     # 工具名称，子类必须重写
     name = None
     # 工具描述，子类必须重写
@@ -31,11 +33,7 @@ class BaseTool:
 
         return {
             "type": "function",
-            "function": {
+            "function": {"name": cls.name, "description": cls.description, "parameters": cls.parameters},
-                "name": cls.name,
-                "description": cls.description,
-                "parameters": cls.parameters
-            }
         }
 
     async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:

src/do_tool/tool_can_use/fibonacci_sequence_tool.py

@@ -4,20 +4,16 @@ from typing import Dict, Any
 
 logger = get_module_logger("fibonacci_sequence_tool")
 
+
 class FibonacciSequenceTool(BaseTool):
     """生成斐波那契数列的工具"""
+
     name = "fibonacci_sequence"
     description = "生成指定长度的斐波那契数列"
     parameters = {
         "type": "object",
-        "properties": {
+        "properties": {"n": {"type": "integer", "description": "斐波那契数列的长度", "minimum": 1}},
-            "n": {
+        "required": ["n"],
-                "type": "integer",
-                "description": "斐波那契数列的长度",
-                "minimum": 1
-            }
-        },
-        "required": ["n"]
     }
 
     async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
@@ -41,16 +37,11 @@ class FibonacciSequenceTool(BaseTool):
                 sequence.append(a)
                 a, b = b, a + b
 
-            return {
+            return {"name": self.name, "content": sequence}
-                "name": self.name,
-                "content": sequence
-            }
         except Exception as e:
             logger.error(f"fibonacci_sequence工具执行失败: {str(e)}")
-            return {
+            return {"name": self.name, "content": f"执行失败: {str(e)}"}
-                "name": self.name,
+
-                "content": f"执行失败: {str(e)}"
-            }
 
 # 注册工具
 register_tool(FibonacciSequenceTool)

src/do_tool/tool_can_use/generate_buddha_emoji_tool.py

@@ -4,8 +4,10 @@ from typing import Dict, Any
 
 logger = get_module_logger("generate_buddha_emoji_tool")
 
+
 class GenerateBuddhaEmojiTool(BaseTool):
     """生成佛祖颜文字的工具类"""
+
     name = "generate_buddha_emoji"
     description = "生成一个佛祖的颜文字表情"
     parameters = {
@@ -13,7 +15,7 @@ class GenerateBuddhaEmojiTool(BaseTool):
         "properties": {
             # 无参数
         },
-        "required": []
+        "required": [],
     }
 
     async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
@@ -29,16 +31,11 @@ class GenerateBuddhaEmojiTool(BaseTool):
         try:
             buddha_emoji = "这是一个佛祖emoji：༼ つ ◕_◕ ༽つ"
 
-            return {
+            return {"name": self.name, "content": buddha_emoji}
-                "name": self.name,
-                "content": buddha_emoji
-            }
         except Exception as e:
             logger.error(f"generate_buddha_emoji工具执行失败: {str(e)}")
-            return {
+            return {"name": self.name, "content": f"执行失败: {str(e)}"}
-                "name": self.name,
+
-                "content": f"执行失败: {str(e)}"
-            }
 
 # 注册工具
 register_tool(GenerateBuddhaEmojiTool)

src/do_tool/tool_can_use/generate_cmd_tutorial_tool.py

@@ -4,15 +4,13 @@ from typing import Dict, Any
 
 logger = get_module_logger("generate_cmd_tutorial_tool")
 
+
 class GenerateCmdTutorialTool(BaseTool):
     """生成Windows CMD基本操作教程的工具"""
+
     name = "generate_cmd_tutorial"
     description = "生成关于Windows命令提示符(CMD)的基本操作教程，包括常用命令和使用方法"
-    parameters = {
+    parameters = {"type": "object", "properties": {}, "required": []}
-        "type": "object",
-        "properties": {},
-        "required": []
-    }
 
     async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
         """执行工具功能
@@ -58,16 +56,11 @@ class GenerateCmdTutorialTool(BaseTool):
 注意：使用命令时要小心，特别是删除操作。
 """
 
-            return {
+            return {"name": self.name, "content": tutorial_content}
-                "name": self.name,
-                "content": tutorial_content
-            }
         except Exception as e:
             logger.error(f"generate_cmd_tutorial工具执行失败: {str(e)}")
-            return {
+            return {"name": self.name, "content": f"执行失败: {str(e)}"}
-                "name": self.name,
+
-                "content": f"执行失败: {str(e)}"
-            }
 
 # 注册工具
 register_tool(GenerateCmdTutorialTool)

src/do_tool/tool_can_use/get_current_task.py

@@ -5,23 +5,19 @@ from typing import Dict, Any
 
 logger = get_module_logger("get_current_task_tool")
 
+
 class GetCurrentTaskTool(BaseTool):
     """获取当前正在做的事情/最近的任务工具"""
+
     name = "get_current_task"
     description = "获取当前正在做的事情/最近的任务"
     parameters = {
         "type": "object",
         "properties": {
-            "num": {
+            "num": {"type": "integer", "description": "要获取的任务数量"},
-                "type": "integer",
+            "time_info": {"type": "boolean", "description": "是否包含时间信息"},
-                "description": "要获取的任务数量"
-            },
-            "time_info": {
-                "type": "boolean",
-                "description": "是否包含时间信息"
-            }
         },
-        "required": []
+        "required": [],
     }
 
     async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
@@ -48,16 +44,11 @@ class GetCurrentTaskTool(BaseTool):
             else:
                 task_info = "当前没有正在进行的任务"
 
-            return {
+            return {"name": "get_current_task", "content": f"当前任务信息: {task_info}"}
-                "name": "get_current_task",
-                "content": f"当前任务信息: {task_info}"
-            }
         except Exception as e:
             logger.error(f"获取当前任务工具执行失败: {str(e)}")
-            return {
+            return {"name": "get_current_task", "content": f"获取当前任务失败: {str(e)}"}
-                "name": "get_current_task",
+
-                "content": f"获取当前任务失败: {str(e)}"
-            }
 
 # 注册工具
 register_tool(GetCurrentTaskTool)

src/do_tool/tool_can_use/get_knowledge.py

@@ -6,23 +6,19 @@ from typing import Dict, Any, Union
 
 logger = get_module_logger("get_knowledge_tool")
 
+
 class SearchKnowledgeTool(BaseTool):
     """从知识库中搜索相关信息的工具"""
+
     name = "search_knowledge"
     description = "从知识库中搜索相关信息"
     parameters = {
         "type": "object",
         "properties": {
-            "query": {
+            "query": {"type": "string", "description": "搜索查询关键词"},
-                "type": "string",
+            "threshold": {"type": "number", "description": "相似度阈值，0.0到1.0之间"},
-                "description": "搜索查询关键词"
-            },
-            "threshold": {
-                "type": "number",
-                "description": "相似度阈值，0.0到1.0之间"
-            }
         },
-        "required": ["query"]
+        "required": ["query"],
     }
 
     async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
@@ -47,20 +43,11 @@ class SearchKnowledgeTool(BaseTool):
                     content = f"你知道这些知识: {knowledge_info}"
                 else:
                     content = f"你不太了解有关{query}的知识"
-                return {
+                return {"name": "search_knowledge", "content": content}
-                    "name": "search_knowledge",
+            return {"name": "search_knowledge", "content": f"无法获取关于'{query}'的嵌入向量"}
-                    "content": content
-                }
-            return {
-                "name": "search_knowledge",
-                "content": f"无法获取关于'{query}'的嵌入向量"
-            }
         except Exception as e:
             logger.error(f"知识库搜索工具执行失败: {str(e)}")
-            return {
+            return {"name": "search_knowledge", "content": f"知识库搜索失败: {str(e)}"}
-                "name": "search_knowledge",
-                "content": f"知识库搜索失败: {str(e)}"
-            }
 
     def get_info_from_db(
         self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
@@ -143,5 +130,6 @@ class SearchKnowledgeTool(BaseTool):
             # 返回所有找到的内容，用换行分隔
             return "\n".join(str(result["content"]) for result in results)
 
+
 # 注册工具
 register_tool(SearchKnowledgeTool)

src/do_tool/tool_can_use/get_memory.py

@@ -5,23 +5,19 @@ from typing import Dict, Any
 
 logger = get_module_logger("get_memory_tool")
 
+
 class GetMemoryTool(BaseTool):
     """从记忆系统中获取相关记忆的工具"""
+
     name = "get_memory"
     description = "从记忆系统中获取相关记忆"
     parameters = {
         "type": "object",
         "properties": {
-            "text": {
+            "text": {"type": "string", "description": "要查询的相关文本"},
-                "type": "string",
+            "max_memory_num": {"type": "integer", "description": "最大返回记忆数量"},
-                "description": "要查询的相关文本"
-            },
-            "max_memory_num": {
-                "type": "integer",
-                "description": "最大返回记忆数量"
-            }
         },
-        "required": ["text"]
+        "required": ["text"],
     }
 
     async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
@@ -40,11 +36,7 @@ class GetMemoryTool(BaseTool):
 
             # 调用记忆系统
             related_memory = await HippocampusManager.get_instance().get_memory_from_text(
-                text=text, 
+                text=text, max_memory_num=max_memory_num, max_memory_length=2, max_depth=3, fast_retrieval=False
-                max_memory_num=max_memory_num, 
-                max_memory_length=2, 
-                max_depth=3, 
-                fast_retrieval=False
             )
 
             memory_info = ""
@@ -57,16 +49,11 @@ class GetMemoryTool(BaseTool):
             else:
                 content = f"你不太记得有关{text}的记忆，你对此不太了解"
 
-            return {
+            return {"name": "get_memory", "content": content}
-                "name": "get_memory",
-                "content": content
-            }
         except Exception as e:
             logger.error(f"记忆获取工具执行失败: {str(e)}")
-            return {
+            return {"name": "get_memory", "content": f"记忆获取失败: {str(e)}"}
-                "name": "get_memory",
+
-                "content": f"记忆获取失败: {str(e)}"
-            }
 
 # 注册工具
 register_tool(GetMemoryTool)

src/do_tool/tool_use.py

@@ -16,7 +16,7 @@ class ToolUser:
             model=global_config.llm_heartflow, temperature=0.2, max_tokens=1000, request_type="tool_use"
         )
 
-    async def _build_tool_prompt(self, message_txt:str, sender_name:str, chat_stream:ChatStream):
+    async def _build_tool_prompt(self, message_txt: str, sender_name: str, chat_stream: ChatStream):
         """构建工具使用的提示词
 
         Args:
@@ -28,9 +28,7 @@ class ToolUser:
             str: 构建好的提示词
         """
         new_messages = list(
-            db.messages.find({"chat_id": chat_stream.stream_id, "time": {"$gt": time.time()}})
+            db.messages.find({"chat_id": chat_stream.stream_id, "time": {"$gt": time.time()}}).sort("time", 1).limit(15)
-            .sort("time", 1)
-            .limit(15)
         )
         new_messages_str = ""
         for msg in new_messages:
@@ -55,7 +53,7 @@ class ToolUser:
         """
         return get_all_tool_definitions()
 
-    async def _execute_tool_call(self, tool_call, message_txt:str):
+    async def _execute_tool_call(self, tool_call, message_txt: str):
         """执行特定的工具调用
 
         Args:
@@ -82,14 +80,14 @@ class ToolUser:
                     "tool_call_id": tool_call["id"],
                     "role": "tool",
                     "name": function_name,
-                    "content": result["content"]
+                    "content": result["content"],
                 }
             return None
         except Exception as e:
             logger.error(f"执行工具调用时发生错误: {str(e)}")
             return None
 
-    async def use_tool(self, message_txt:str, sender_name:str, chat_stream:ChatStream):
+    async def use_tool(self, message_txt: str, sender_name: str, chat_stream: ChatStream):
         """使用工具辅助思考，判断是否需要额外信息
 
         Args:
@@ -113,15 +111,13 @@ class ToolUser:
                 "messages": [{"role": "user", "content": prompt}],
                 "max_tokens": global_config.max_response_length,
                 "tools": tools,
-                "temperature": 0.2
+                "temperature": 0.2,
             }
 
             logger.debug(f"发送工具调用请求，模型: {self.llm_model_tool.model_name}")
             # 发送请求获取模型是否需要调用工具
             response = await self.llm_model_tool._execute_request(
-                endpoint="/chat/completions", 
+                endpoint="/chat/completions", payload=payload, prompt=prompt
-                payload=payload,
-                prompt=prompt
             )
 
             # 根据返回值数量判断是否有工具调用

src/heart_flow/heartflow.py

@@ -43,7 +43,6 @@ def init_prompt():
 
 class CurrentState:
     def __init__(self):
-
         self.current_state_info = ""
 
         self.mood_manager = MoodManager()
@@ -67,9 +66,6 @@ class Heartflow:
 
         self._subheartflows: Dict[Any, SubHeartflow] = {}
 
-        
-        
-
     async def _cleanup_inactive_subheartflows(self):
         """定期清理不活跃的子心流"""
         while True:
@@ -103,7 +99,6 @@ class Heartflow:
             await asyncio.sleep(global_config.heart_flow_update_interval)  # 5分钟思考一次
 
     async def heartflow_start_working(self):
-        
         # 启动清理任务
         asyncio.create_task(self._cleanup_inactive_subheartflows())
 

src/heart_flow/sub_heartflow.py

@@ -5,9 +5,11 @@ from src.plugins.models.utils_model import LLM_request
 from src.plugins.config.config import global_config
 import re
 import time
+
 # from src.plugins.schedule.schedule_generator import bot_schedule
 # from src.plugins.memory_system.Hippocampus import HippocampusManager
 from src.common.logger import get_module_logger, LogConfig, SUB_HEARTFLOW_STYLE_CONFIG  # noqa: E402
+
 # from src.plugins.chat.utils import get_embedding
 # from src.common.database import db
 # from typing import Union
@@ -17,7 +19,7 @@ from src.plugins.chat.chat_stream import ChatStream
 from src.plugins.person_info.relationship_manager import relationship_manager
 from src.plugins.chat.utils import get_recent_group_speaker
 from src.do_tool.tool_use import ToolUser
-from ..plugins.utils.prompt_builder import Prompt,global_prompt_manager   
+from ..plugins.utils.prompt_builder import Prompt, global_prompt_manager
 
 subheartflow_config = LogConfig(
     # 使用海马体专用样式
@@ -26,6 +28,7 @@ subheartflow_config = LogConfig(
 )
 logger = get_module_logger("subheartflow", config=subheartflow_config)
 
+
 def init_prompt():
     prompt = ""
     # prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
@@ -41,7 +44,7 @@ def init_prompt():
     prompt += "思考时可以想想如何对群聊内容进行回复。回复的要求是：平淡一些，简短一些，说中文，尽量不要说你说过的话\n"
     prompt += "请注意不要输出多余内容(包括前后缀，冒号和引号，括号， 表情，等)，不要带有括号和动作描写"
     prompt += "记得结合上述的消息，生成内心想法，文字不要浮夸，注意你就是{bot_name}，{bot_name}指的就是你。"
-    Prompt(prompt,"sub_heartflow_prompt_before")
+    Prompt(prompt, "sub_heartflow_prompt_before")
     prompt = ""
     # prompt += f"你现在正在做的事情是：{schedule_info}\n"
     prompt += "{prompt_personality}\n"
@@ -52,8 +55,7 @@ def init_prompt():
     prompt += "你现在{mood_info}"
     prompt += "现在你接下去继续思考，产生新的想法，记得保留你刚刚的想法，不要分点输出，输出连贯的内心独白"
     prompt += "不要太长，但是记得结合上述的消息，要记得你的人设，关注聊天和新内容，关注你回复的内容，不要思考太多:"
-    Prompt(prompt,'sub_heartflow_prompt_after')
+    Prompt(prompt, "sub_heartflow_prompt_after")
-    
 
 
 class CurrentState:
@@ -79,7 +81,6 @@ class SubHeartflow:
             model=global_config.llm_sub_heartflow, temperature=0.2, max_tokens=600, request_type="sub_heart_flow"
         )
 
-
         self.main_heartflow_info = ""
 
         self.last_reply_time = time.time()
@@ -145,12 +146,12 @@ class SubHeartflow:
             ):  # 5分钟无回复/不在场，销毁
                 logger.info(f"子心流 {self.subheartflow_id} 已经5分钟没有激活，正在销毁...")
                 break  # 退出循环，销毁自己
+
     async def do_observe(self):
         observation = self.observations[0]
         await observation.observe()
 
-
+    async def do_thinking_before_reply(self, message_txt: str, sender_name: str, chat_stream: ChatStream):
-    async def do_thinking_before_reply(self, message_txt:str, sender_name:str, chat_stream:ChatStream):
         current_thinking_info = self.current_mind
         mood_info = self.current_state.mood
         # mood_info = "你很生气，很愤怒"
@@ -204,9 +205,9 @@ class SubHeartflow:
         #     f"{relation_prompt}关系等级越大，关系越好，请分析聊天记录，"
         #     f"根据你和说话者{sender_name}的关系和态度进行回复，明确你的立场和情感。"
         # )
-        relation_prompt_all = (await global_prompt_manager.get_prompt_async('relationship_prompt')).format(
+        relation_prompt_all = (await global_prompt_manager.get_prompt_async("relationship_prompt")).format(
-            relation_prompt,sender_name
+            relation_prompt, sender_name
-            )
+        )
 
         # prompt = ""
         # # prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
@@ -224,9 +225,16 @@ class SubHeartflow:
         # prompt += "请注意不要输出多余内容(包括前后缀，冒号和引号，括号， 表情，等)，不要带有括号和动作描写"
         # prompt += f"记得结合上述的消息，生成内心想法，文字不要浮夸，注意你就是{self.bot_name}，{self.bot_name}指的就是你。"
 
-        prompt= (await global_prompt_manager.get_prompt_async("sub_heartflow_prompt_before")).format(
+        prompt = (await global_prompt_manager.get_prompt_async("sub_heartflow_prompt_before")).format(
-            collected_info,relation_prompt_all,prompt_personality,current_thinking_info,chat_observe_info,mood_info,sender_name,
+            collected_info,
-            message_txt,self.bot_name
+            relation_prompt_all,
+            prompt_personality,
+            current_thinking_info,
+            chat_observe_info,
+            mood_info,
+            sender_name,
+            message_txt,
+            self.bot_name,
         )
 
         try:
@@ -281,8 +289,8 @@ class SubHeartflow:
         # prompt += f"你现在{mood_info}"
         # prompt += "现在你接下去继续思考，产生新的想法，记得保留你刚刚的想法，不要分点输出，输出连贯的内心独白"
         # prompt += "不要太长，但是记得结合上述的消息，要记得你的人设，关注聊天和新内容，关注你回复的内容，不要思考太多:"
-        prompt=(await global_prompt_manager.get_prompt_async('sub_heartflow_prompt_after')).format(
+        prompt = (await global_prompt_manager.get_prompt_async("sub_heartflow_prompt_after")).format(
-            prompt_personality,chat_observe_info,current_thinking_info,message_new_info,reply_info,mood_info
+            prompt_personality, chat_observe_info, current_thinking_info, message_new_info, reply_info, mood_info
         )
 
         try:
@@ -343,5 +351,6 @@ class SubHeartflow:
         self.past_mind.append(self.current_mind)
         self.current_mind = response
 
+
 init_prompt()
 # subheartflow = SubHeartflow()

src/plugins/PFC/action_planner.py

@@ -53,8 +53,8 @@ class ActionPlanner:
                     goal = goal_reason[0]
                     reasoning = goal_reason[1] if len(goal_reason) > 1 else "没有明确原因"
                 elif isinstance(goal_reason, dict):
-                    goal = goal_reason.get('goal')
+                    goal = goal_reason.get("goal")
-                    reasoning = goal_reason.get('reasoning', "没有明确原因")
+                    reasoning = goal_reason.get("reasoning", "没有明确原因")
                 else:
                     # 如果是其他类型，尝试转为字符串
                     goal = str(goal_reason)
@@ -68,7 +68,11 @@ class ActionPlanner:
             goals_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n"
 
         # 获取聊天历史记录
-        chat_history_list = observation_info.chat_history[-20:] if len(observation_info.chat_history) >= 20 else observation_info.chat_history
+        chat_history_list = (
+            observation_info.chat_history[-20:]
+            if len(observation_info.chat_history) >= 20
+            else observation_info.chat_history
+        )
         chat_history_text = ""
         for msg in chat_history_list:
             chat_history_text += f"{msg.get('detailed_plain_text', '')}\n"
@@ -85,15 +89,21 @@ class ActionPlanner:
         personality_text = f"你的名字是{self.name}，{self.personality_info}"
 
         # 构建action历史文本
-        action_history_list = conversation_info.done_action[-10:] if len(conversation_info.done_action) >= 10 else conversation_info.done_action
+        action_history_list = (
+            conversation_info.done_action[-10:]
+            if len(conversation_info.done_action) >= 10
+            else conversation_info.done_action
+        )
         action_history_text = "你之前做的事情是："
         for action in action_history_list:
             if isinstance(action, dict):
-                action_type = action.get('action')
+                action_type = action.get("action")
-                action_reason = action.get('reason')
+                action_reason = action.get("reason")
-                action_status = action.get('status')
+                action_status = action.get("status")
                 if action_status == "recall":
-                    action_history_text += f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
+                    action_history_text += (
+                        f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
+                    )
                 elif action_status == "done":
                     action_history_text += f"你之前做了：{action_type}，原因：{action_reason}\n"
             elif isinstance(action, tuple):
@@ -102,7 +112,9 @@ class ActionPlanner:
                 action_reason = action[1] if len(action) > 1 else "未知原因"
                 action_status = action[2] if len(action) > 2 else "done"
                 if action_status == "recall":
-                    action_history_text += f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
+                    action_history_text += (
+                        f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
+                    )
                 elif action_status == "done":
                     action_history_text += f"你之前做了：{action_type}，原因：{action_reason}\n"
 
@@ -147,7 +159,14 @@ end_conversation: 结束对话，长时间没回复或者当你觉得谈话暂
             reason = result["reason"]
 
             # 验证action类型
-            if action not in ["direct_reply", "fetch_knowledge", "wait", "listening", "rethink_goal", "end_conversation"]:
+            if action not in [
+                "direct_reply",
+                "fetch_knowledge",
+                "wait",
+                "listening",
+                "rethink_goal",
+                "end_conversation",
+            ]:
                 logger.warning(f"未知的行动类型: {action}，默认使用listening")
                 action = "listening"
 

src/plugins/PFC/chat_observer.py

@@ -6,7 +6,7 @@ from src.common.logger import get_module_logger
 from ..message.message_base import UserInfo
 from ..config.config import global_config
 from .chat_states import NotificationManager, create_new_message_notification, create_cold_chat_notification
-from .message_storage import  MongoDBMessageStorage
+from .message_storage import MongoDBMessageStorage
 
 logger = get_module_logger("chat_observer")
 
@@ -51,7 +51,6 @@ class ChatObserver:
 
         self.waiting_start_time: float = time.time()  # 等待开始时间，初始化为当前时间
 
-
         # 运行状态
         self._running: bool = False
         self._task: Optional[asyncio.Task] = None
@@ -94,10 +93,11 @@ class ChatObserver:
             message: 消息数据
         """
         try:
-
             # 发送新消息通知
             # logger.info(f"发送新ccchandleer消息通知: {message}")
-            notification = create_new_message_notification(sender="chat_observer", target="observation_info", message=message)
+            notification = create_new_message_notification(
+                sender="chat_observer", target="observation_info", message=message
+            )
             # logger.info(f"发送新消ddddd息通知: {notification}")
             # print(self.notification_manager)
             await self.notification_manager.send_notification(notification)
@@ -131,7 +131,6 @@ class ChatObserver:
             notification = create_cold_chat_notification(sender="chat_observer", target="pfc", is_cold=is_cold)
             await self.notification_manager.send_notification(notification)
 
-
     def new_message_after(self, time_point: float) -> bool:
         """判断是否在指定时间点后有新消息
 
@@ -347,7 +346,6 @@ class ChatObserver:
 
         return time_info
 
-
     def get_cached_messages(self, limit: int = 50) -> List[Dict[str, Any]]:
         """获取缓存的消息历史
 

src/plugins/PFC/chat_states.py

@@ -141,7 +141,6 @@ class NotificationManager:
             else:
                 self._active_states.discard(notification.type)
 
-
         # 调用目标接收者的处理器
         target = notification.target
         if target in self._handlers:
@@ -295,5 +294,3 @@ class ChatStateManager:
 
         current_time = datetime.now().timestamp()
         return (current_time - self.state_info.last_message_time) <= threshold
-    
-    

src/plugins/PFC/conversation.py

@@ -65,7 +65,6 @@ class Conversation:
             self.observation_info.bind_to_chat_observer(self.chat_observer)
             # print(self.chat_observer.get_cached_messages(limit=)
 
-            
             self.conversation_info = ConversationInfo()
         except Exception as e:
             logger.error(f"初始化对话实例：注册信息组件失败: {e}")
@@ -174,7 +173,6 @@ class Conversation:
 
             await self._send_reply()
 
-            
             conversation_info.done_action[-1].update(
                 {
                     "status": "done",
@@ -208,7 +206,6 @@ class Conversation:
             logger.info("倾听对方发言...")
             await self.waiter.wait_listening(conversation_info)
 
-
         elif action == "end_conversation":
             self.should_continue = False
             logger.info("决定结束对话...")
@@ -239,9 +236,7 @@ class Conversation:
             return
 
         try:
-            await self.direct_sender.send_message(
+            await self.direct_sender.send_message(chat_stream=self.chat_stream, content=self.generated_reply)
-                chat_stream=self.chat_stream, content=self.generated_reply
-            )
             self.chat_observer.trigger_update()  # 触发立即更新
             if not await self.chat_observer.wait_for_update():
                 logger.warning("等待消息更新超时")

src/plugins/PFC/message_storage.py

@@ -2,6 +2,7 @@ from abc import ABC, abstractmethod
 from typing import List, Dict, Any
 from src.common.database import db
 
+
 class MessageStorage(ABC):
     """消息存储接口"""
 

src/plugins/PFC/observation_info.py

@@ -41,7 +41,7 @@ class ObservationInfoHandler(NotificationHandler):
                 "processed_plain_text": processed_plain_text,
                 "detailed_plain_text": detailed_plain_text,
                 "user_info": user_info,
-                "time": time_value
+                "time": time_value,
             }
 
             self.observation_info.update_from_message(message)
@@ -233,4 +233,3 @@ class ObservationInfo:
         self.unprocessed_messages.clear()
         self.chat_history_count = len(self.chat_history)
         self.new_messages_count = 0
-

src/plugins/PFC/pfc.py

@@ -1,6 +1,7 @@
 # Programmable Friendly Conversationalist
 # Prefrontal cortex
 import datetime
+
 # import asyncio
 from typing import List, Optional, Tuple, TYPE_CHECKING
 from src.common.logger import get_module_logger
@@ -63,8 +64,8 @@ class GoalAnalyzer:
                     goal = goal_reason[0]
                     reasoning = goal_reason[1] if len(goal_reason) > 1 else "没有明确原因"
                 elif isinstance(goal_reason, dict):
-                    goal = goal_reason.get('goal')
+                    goal = goal_reason.get("goal")
-                    reasoning = goal_reason.get('reasoning', "没有明确原因")
+                    reasoning = goal_reason.get("reasoning", "没有明确原因")
                 else:
                     # 如果是其他类型，尝试转为字符串
                     goal = str(goal_reason)
@@ -143,9 +144,7 @@ class GoalAnalyzer:
 
         # 使用改进后的get_items_from_json函数处理JSON数组
         success, result = get_items_from_json(
-            content, "goal", "reasoning", 
+            content, "goal", "reasoning", required_types={"goal": str, "reasoning": str}, allow_array=True
-            required_types={"goal": str, "reasoning": str},
-            allow_array=True
         )
 
         if success:
@@ -293,7 +292,6 @@ class GoalAnalyzer:
             return False, False, f"分析出错: {str(e)}"
 
 
-
 class DirectMessageSender:
     """直接发送消息到平台的发送器"""
 

src/plugins/PFC/reply_generator.py

@@ -49,8 +49,8 @@ class ReplyGenerator:
                     goal = goal_reason[0]
                     reasoning = goal_reason[1] if len(goal_reason) > 1 else "没有明确原因"
                 elif isinstance(goal_reason, dict):
-                    goal = goal_reason.get('goal')
+                    goal = goal_reason.get("goal")
-                    reasoning = goal_reason.get('reasoning', "没有明确原因")
+                    reasoning = goal_reason.get("reasoning", "没有明确原因")
                 else:
                     # 如果是其他类型，尝试转为字符串
                     goal = str(goal_reason)
@@ -64,7 +64,11 @@ class ReplyGenerator:
             goals_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n"
 
         # 获取聊天历史记录
-        chat_history_list = observation_info.chat_history[-20:] if len(observation_info.chat_history) >= 20 else observation_info.chat_history
+        chat_history_list = (
+            observation_info.chat_history[-20:]
+            if len(observation_info.chat_history) >= 20
+            else observation_info.chat_history
+        )
         chat_history_text = ""
         for msg in chat_history_list:
             chat_history_text += f"{msg.get('detailed_plain_text', '')}\n"
@@ -81,15 +85,21 @@ class ReplyGenerator:
         personality_text = f"你的名字是{self.name}，{self.personality_info}"
 
         # 构建action历史文本
-        action_history_list = conversation_info.done_action[-10:] if len(conversation_info.done_action) >= 10 else conversation_info.done_action
+        action_history_list = (
+            conversation_info.done_action[-10:]
+            if len(conversation_info.done_action) >= 10
+            else conversation_info.done_action
+        )
         action_history_text = "你之前做的事情是："
         for action in action_history_list:
             if isinstance(action, dict):
-                action_type = action.get('action')
+                action_type = action.get("action")
-                action_reason = action.get('reason')
+                action_reason = action.get("reason")
-                action_status = action.get('status')
+                action_status = action.get("status")
                 if action_status == "recall":
-                    action_history_text += f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
+                    action_history_text += (
+                        f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
+                    )
                 elif action_status == "done":
                     action_history_text += f"你之前做了：{action_type}，原因：{action_reason}\n"
             elif isinstance(action, tuple):
@@ -98,7 +108,9 @@ class ReplyGenerator:
                 action_reason = action[1] if len(action) > 1 else "未知原因"
                 action_status = action[2] if len(action) > 2 else "done"
                 if action_status == "recall":
-                    action_history_text += f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
+                    action_history_text += (
+                        f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
+                    )
                 elif action_status == "done":
                     action_history_text += f"你之前做了：{action_type}，原因：{action_reason}\n"
 

src/plugins/PFC/waiter.py

@@ -41,8 +41,8 @@ class Waiter:
 
                 logger.info("等待超过300秒，结束对话")
                 wait_goal = {
-                    "goal": f"你等待了{self.wait_accumulated_time/60}分钟，思考接下来要做什么",
+                    "goal": f"你等待了{self.wait_accumulated_time / 60}分钟，思考接下来要做什么",
-                    "reason": "对方很久没有回复你的消息了"
+                    "reason": "对方很久没有回复你的消息了",
                 }
                 conversation_info.goal_list.append(wait_goal)
                 print(f"添加目标: {wait_goal}")
@@ -73,8 +73,8 @@ class Waiter:
                 self.wait_accumulated_time += 300
                 logger.info("等待超过300秒，结束对话")
                 wait_goal = {
-                    "goal": f"你等待了{self.wait_accumulated_time/60}分钟，思考接下来要做什么",
+                    "goal": f"你等待了{self.wait_accumulated_time / 60}分钟，思考接下来要做什么",
-                    "reason": "对方话说一半消失了，很久没有回复"
+                    "reason": "对方话说一半消失了，很久没有回复",
                 }
                 conversation_info.goal_list.append(wait_goal)
                 print(f"添加目标: {wait_goal}")
@@ -83,4 +83,3 @@ class Waiter:
 
             await asyncio.sleep(1)
             logger.info("等待中...")
-

src/plugins/chat/bot.py

@@ -8,7 +8,7 @@ from ..chat_module.only_process.only_message_process import MessageProcessor
 from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
 from ..chat_module.think_flow_chat.think_flow_chat import ThinkFlowChat
 from ..chat_module.reasoning_chat.reasoning_chat import ReasoningChat
-from ..utils.prompt_builder import Prompt,global_prompt_manager
+from ..utils.prompt_builder import Prompt, global_prompt_manager
 import traceback
 
 # 定义日志配置
@@ -91,15 +91,15 @@ class ChatBot:
                 return
 
             if message.message_info.template_info and not message.message_info.template_info.template_default:
-                template_group_name=message.message_info.template_info.template_name
+                template_group_name = message.message_info.template_info.template_name
-                template_items=message.message_info.template_info.template_items
+                template_items = message.message_info.template_info.template_items
                 async with global_prompt_manager.async_message_scope(template_group_name):
-                    if isinstance(template_items,dict):
+                    if isinstance(template_items, dict):
                         for k in template_items.keys():
-                            await Prompt.create_async(template_items[k],k)
+                            await Prompt.create_async(template_items[k], k)
                             print(f"注册{template_items[k]},{k}")
             else:
-                template_group_name=None
+                template_group_name = None
 
             async def preprocess():
                 if global_config.enable_pfc_chatting:

src/plugins/chat/utils.py

@@ -87,7 +87,6 @@ async def get_embedding(text, request_type="embedding"):
     return embedding
 
 
-
 async def get_recent_group_messages(chat_id: str, limit: int = 12) -> list:
     """从数据库获取群组最近的消息记录
 

src/plugins/chat_module/reasoning_chat/reasoning_generator.py

@@ -38,7 +38,7 @@ class ResponseGenerator:
         self.current_model_type = "r1"  # 默认使用 R1
         self.current_model_name = "unknown model"
 
-    async def generate_response(self, message: MessageThinking,thinking_id:str) -> Optional[Union[str, List[str]]]:
+    async def generate_response(self, message: MessageThinking, thinking_id: str) -> Optional[Union[str, List[str]]]:
         """根据当前模型类型选择对应的生成函数"""
         # 从global_config中获取模型概率值并选择模型
         if random.random() < global_config.MODEL_R1_PROBABILITY:
@@ -52,7 +52,7 @@ class ResponseGenerator:
             f"{self.current_model_type}思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}"
         )  # noqa: E501
 
-        model_response = await self._generate_response_with_model(message, current_model,thinking_id)
+        model_response = await self._generate_response_with_model(message, current_model, thinking_id)
 
         # print(f"raw_content: {model_response}")
 
@@ -65,7 +65,7 @@ class ResponseGenerator:
             logger.info(f"{self.current_model_type}思考，失败")
             return None
 
-    async def _generate_response_with_model(self, message: MessageThinking, model: LLM_request,thinking_id:str):
+    async def _generate_response_with_model(self, message: MessageThinking, model: LLM_request, thinking_id: str):
         sender_name = ""
 
         info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
@@ -96,11 +96,8 @@ class ResponseGenerator:
             content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
 
             info_catcher.catch_after_llm_generated(
-                prompt=prompt,
+                prompt=prompt, response=content, reasoning_content=reasoning_content, model_name=self.current_model_name
-                response=content,
+            )
-                reasoning_content=reasoning_content,
-                model_name=self.current_model_name)
-        
 
         except Exception:
             logger.exception("生成回复时出错")
@@ -118,7 +115,6 @@ class ResponseGenerator:
 
         return content
 
-
     # def _save_to_db(
     #     self,
     #     message: MessageRecv,

src/plugins/chat_module/reasoning_chat/reasoning_prompt_builder.py

@@ -144,12 +144,10 @@ class PromptBuilder:
                     for pattern in rule.get("regex", []):
                         result = pattern.search(message_txt)
                         if result:
-                            reaction = rule.get('reaction', '')
+                            reaction = rule.get("reaction", "")
                             for name, content in result.groupdict().items():
-                                reaction = reaction.replace(f'[{name}]', content)
+                                reaction = reaction.replace(f"[{name}]", content)
-                            logger.info(
+                            logger.info(f"匹配到以下正则表达式：{pattern}，触发反应：{reaction}")
-                                f"匹配到以下正则表达式：{pattern}，触发反应：{reaction}"
-                            )
                             keywords_reaction_prompt += reaction + "，"
                             break
 

src/plugins/chat_module/think_flow_chat/think_flow_chat.py

@@ -59,11 +59,7 @@ class ThinkFlowChat:
 
         return thinking_id
 
-    async def _send_response_messages(self, 
+    async def _send_response_messages(self, message, chat, response_set: List[str], thinking_id) -> MessageSending:
-                                      message, 
-                                      chat, 
-                                      response_set:List[str], 
-                                      thinking_id) -> MessageSending:
         """发送回复消息"""
         container = message_manager.get_container(chat.stream_id)
         thinking_message = None
@@ -261,8 +257,6 @@ class ThinkFlowChat:
             try:
                 do_reply = True
 
-                
-
                 # 回复前处理
                 await willing_manager.before_generate_reply_handle(message.message_info.message_id)
 
@@ -294,21 +288,21 @@ class ThinkFlowChat:
                 # 思考前脑内状态
                 try:
                     timer1 = time.time()
-                    current_mind,past_mind = await heartflow.get_subheartflow(chat.stream_id).do_thinking_before_reply(
+                    current_mind, past_mind = await heartflow.get_subheartflow(chat.stream_id).do_thinking_before_reply(
-                        message_txt = message.processed_plain_text,
+                        message_txt=message.processed_plain_text,
-                        sender_name = message.message_info.user_info.user_nickname,
+                        sender_name=message.message_info.user_info.user_nickname,
-                        chat_stream = chat
+                        chat_stream=chat,
                     )
                     timer2 = time.time()
                     timing_results["思考前脑内状态"] = timer2 - timer1
                 except Exception as e:
                     logger.error(f"心流思考前脑内状态失败: {e}")
 
-                info_catcher.catch_afer_shf_step(timing_results["思考前脑内状态"],past_mind,current_mind)
+                info_catcher.catch_afer_shf_step(timing_results["思考前脑内状态"], past_mind, current_mind)
 
                 # 生成回复
                 timer1 = time.time()
-                response_set = await self.gpt.generate_response(message,thinking_id)
+                response_set = await self.gpt.generate_response(message, thinking_id)
                 timer2 = time.time()
                 timing_results["生成回复"] = timer2 - timer1
 
@@ -327,9 +321,7 @@ class ThinkFlowChat:
                 except Exception as e:
                     logger.error(f"心流发送消息失败: {e}")
 
-                
+                info_catcher.catch_after_response(timing_results["发送消息"], response_set, first_bot_msg)
-                info_catcher.catch_after_response(timing_results["发送消息"],response_set,first_bot_msg)
-                
 
                 info_catcher.done_catch()
 

src/plugins/chat_module/think_flow_chat/think_flow_generator.py

@@ -35,10 +35,9 @@ class ResponseGenerator:
         self.current_model_type = "r1"  # 默认使用 R1
         self.current_model_name = "unknown model"
 
-    async def generate_response(self, message: MessageRecv,thinking_id:str) -> Optional[List[str]]:
+    async def generate_response(self, message: MessageRecv, thinking_id: str) -> Optional[List[str]]:
         """根据当前模型类型选择对应的生成函数"""
 
-
         logger.info(
             f"思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}"
         )
@@ -51,25 +50,33 @@ class ResponseGenerator:
         if random.random() > 0:
             checked = False
             current_model = self.model_normal
-            current_model.temperature = 0.3 * arousal_multiplier #激活度越高，温度越高
+            current_model.temperature = 0.3 * arousal_multiplier  # 激活度越高，温度越高
-            model_response = await self._generate_response_with_model(message, current_model,thinking_id,mode="normal")
+            model_response = await self._generate_response_with_model(
+                message, current_model, thinking_id, mode="normal"
+            )
 
             model_checked_response = model_response
         else:
             checked = True
             current_model = self.model_normal
-            current_model.temperature = 0.3 * arousal_multiplier #激活度越高，温度越高
+            current_model.temperature = 0.3 * arousal_multiplier  # 激活度越高，温度越高
             print(f"生成{message.processed_plain_text}回复温度是：{current_model.temperature}")
-            model_response = await self._generate_response_with_model(message, current_model,thinking_id,mode="simple")
+            model_response = await self._generate_response_with_model(
+                message, current_model, thinking_id, mode="simple"
+            )
 
             current_model.temperature = 0.3
-            model_checked_response = await self._check_response_with_model(message, model_response, current_model,thinking_id)
+            model_checked_response = await self._check_response_with_model(
+                message, model_response, current_model, thinking_id
+            )
 
         time2 = time.time()
 
         if model_response:
             if checked:
-                logger.info(f"{global_config.BOT_NICKNAME}的回复是：{model_response}，思忖后，回复是：{model_checked_response},生成回复时间: {time2 - time1}秒")
+                logger.info(
+                    f"{global_config.BOT_NICKNAME}的回复是：{model_response}，思忖后，回复是：{model_checked_response},生成回复时间: {time2 - time1}秒"
+                )
             else:
                 logger.info(f"{global_config.BOT_NICKNAME}的回复是：{model_response},生成回复时间: {time2 - time1}秒")
 
@@ -80,7 +87,9 @@ class ResponseGenerator:
             logger.info(f"{self.current_model_type}思考，失败")
             return None
 
-    async def _generate_response_with_model(self, message: MessageRecv, model: LLM_request,thinking_id:str,mode:str = "normal") -> str:
+    async def _generate_response_with_model(
+        self, message: MessageRecv, model: LLM_request, thinking_id: str, mode: str = "normal"
+    ) -> str:
         sender_name = ""
 
         info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
@@ -117,22 +126,19 @@ class ResponseGenerator:
         try:
             content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
 
-            
             info_catcher.catch_after_llm_generated(
-                prompt=prompt,
+                prompt=prompt, response=content, reasoning_content=reasoning_content, model_name=self.current_model_name
-                response=content,
+            )
-                reasoning_content=reasoning_content,
-                model_name=self.current_model_name)
 
         except Exception:
             logger.exception("生成回复时出错")
             return None
 
-
         return content
 
-    async def _check_response_with_model(self, message: MessageRecv, content:str, model: LLM_request,thinking_id:str) -> str:
+    async def _check_response_with_model(
-        
+        self, message: MessageRecv, content: str, model: LLM_request, thinking_id: str
+    ) -> str:
         _info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
 
         sender_name = ""
@@ -146,7 +152,6 @@ class ResponseGenerator:
         else:
             sender_name = f"用户({message.chat_stream.user_info.user_id})"
 
-            
         # 构建prompt
         timer1 = time.time()
         prompt = await prompt_builder._build_prompt_check_response(
@@ -154,7 +159,7 @@ class ResponseGenerator:
             message_txt=message.processed_plain_text,
             sender_name=sender_name,
             stream_id=message.chat_stream.stream_id,
-            content=content
+            content=content,
         )
         timer2 = time.time()
         logger.info(f"构建check_prompt: {prompt}")
@@ -163,7 +168,6 @@ class ResponseGenerator:
         try:
             checked_content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
 
-            
             # info_catcher.catch_after_llm_generated(
             #     prompt=prompt,
             #     response=content,
@@ -174,7 +178,6 @@ class ResponseGenerator:
             logger.exception("检查回复时出错")
             return None
 
-
         return checked_content
 
     async def _get_emotion_tags(self, content: str, processed_plain_text: str):

src/plugins/chat_module/think_flow_chat/think_flow_prompt_builder.py

@@ -110,12 +110,10 @@ class PromptBuilder:
                     for pattern in rule.get("regex", []):
                         result = pattern.search(message_txt)
                         if result:
-                            reaction = rule.get('reaction', '')
+                            reaction = rule.get("reaction", "")
                             for name, content in result.groupdict().items():
-                                reaction = reaction.replace(f'[{name}]', content)
+                                reaction = reaction.replace(f"[{name}]", content)
-                            logger.info(
+                            logger.info(f"匹配到以下正则表达式：{pattern}，触发反应：{reaction}")
-                                f"匹配到以下正则表达式：{pattern}，触发反应：{reaction}"
-                            )
                             keywords_reaction_prompt += reaction + "，"
                             break
 

src/plugins/memory_system/Hippocampus.py

@@ -225,6 +225,7 @@ class Memory_graph:
 
         return None
 
+
 # 海马体
 class Hippocampus:
     def __init__(self):
@@ -653,7 +654,6 @@ class Hippocampus:
         return activation_ratio
 
 
-
 # 负责海马体与其他部分的交互
 class EntorhinalCortex:
     def __init__(self, hippocampus: Hippocampus):

src/plugins/memory_system/debug_memory.py

@@ -27,7 +27,6 @@ async def test_memory_system():
         # 测试记忆检索
         test_text = "千石可乐在群里聊天"
 
-
         # test_text = '''千石可乐：分不清AI的陪伴和人类的陪伴,是这样吗？'''
         print(f"开始测试记忆检索，测试文本: {test_text}\n")
         memories = await hippocampus_manager.get_memory_from_text(

src/plugins/models/utils_model.py

@@ -657,9 +657,7 @@ class LLM_request:
             **kwargs,
         }
 
-        response = await self._execute_request(
+        response = await self._execute_request(endpoint="/chat/completions", payload=data, prompt=prompt)
-            endpoint="/chat/completions", payload=data, prompt=prompt
-        )
         # 原样返回响应，不做处理
         return response
 

src/plugins/moods/moods.py

@@ -242,10 +242,10 @@ class MoodManager:
     def get_arousal_multiplier(self) -> float:
         """根据当前情绪状态返回唤醒度乘数"""
         if self.current_mood.arousal > 0.4:
-            multiplier = 1 + min(0.15,(self.current_mood.arousal - 0.4)/3)
+            multiplier = 1 + min(0.15, (self.current_mood.arousal - 0.4) / 3)
             return multiplier
         elif self.current_mood.arousal < -0.4:
-            multiplier = 1 - min(0.15,((0 - self.current_mood.arousal) - 0.4)/3)
+            multiplier = 1 - min(0.15, ((0 - self.current_mood.arousal) - 0.4) / 3)
             return multiplier
         return 1.0
 

src/plugins/respon_info_catcher/info_catcher.py

@@ -1,16 +1,17 @@
 from src.plugins.config.config import global_config
-from src.plugins.chat.message import MessageRecv,MessageSending,Message
+from src.plugins.chat.message import MessageRecv, MessageSending, Message
 from src.common.database import db
 import time
 import traceback
 from typing import List
 
+
 class InfoCatcher:
     def __init__(self):
-        self.chat_history = [] # 聊天历史，长度为三倍使用的上下文
+        self.chat_history = []  # 聊天历史，长度为三倍使用的上下文
         self.context_length = global_config.MAX_CONTEXT_SIZE
-        self.chat_history_in_thinking = [] # 思考期间的聊天内容
+        self.chat_history_in_thinking = []  # 思考期间的聊天内容
-        self.chat_history_after_response = [] # 回复后的聊天内容，长度为一倍上下文
+        self.chat_history_after_response = []  # 回复后的聊天内容，长度为一倍上下文
 
         self.chat_id = ""
         self.response_mode = global_config.response_mode
@@ -32,16 +33,11 @@ class InfoCatcher:
             "sub_heartflow_model": "",
             "prompt": "",
             "response": "",
-            "model": ""
+            "model": "",
         }
 
         # 使用字典来存储 reasoning 模式的数据
-        self.reasoning_data = {
+        self.reasoning_data = {"thinking_log": "", "prompt": "", "response": "", "model": ""}
-            "thinking_log": "",
-            "prompt": "",
-            "response": "",
-            "model": ""
-        }
 
         # 耗时
         self.timing_results = {
@@ -51,7 +47,7 @@ class InfoCatcher:
             "make_response_time": 0,
         }
 
-    def catch_decide_to_response(self,message:MessageRecv):
+    def catch_decide_to_response(self, message: MessageRecv):
         # 搜集决定回复时的信息
         self.trigger_response_message = message
         self.trigger_response_text = message.detailed_plain_text
@@ -62,12 +58,12 @@ class InfoCatcher:
 
         self.chat_history = self.get_message_from_db_before_msg(message)
 
-    def catch_after_observe(self,obs_duration:float):#这里可以有更多信息
+    def catch_after_observe(self, obs_duration: float):  # 这里可以有更多信息
         self.timing_results["sub_heartflow_observe_time"] = obs_duration
 
     # def catch_shf
 
-    def catch_afer_shf_step(self,step_duration:float,past_mind:str,current_mind:str):
+    def catch_afer_shf_step(self, step_duration: float, past_mind: str, current_mind: str):
         self.timing_results["sub_heartflow_step_time"] = step_duration
         if len(past_mind) > 1:
             self.heartflow_data["sub_heartflow_before"] = past_mind[-1]
@@ -76,10 +72,7 @@ class InfoCatcher:
             self.heartflow_data["sub_heartflow_before"] = past_mind[-1]
             self.heartflow_data["sub_heartflow_now"] = current_mind
 
-    def catch_after_llm_generated(self,prompt:str,
+    def catch_after_llm_generated(self, prompt: str, response: str, reasoning_content: str = "", model_name: str = ""):
-                                  response:str,
-                                  reasoning_content:str = "",
-                                  model_name:str = ""):
         if self.response_mode == "heart_flow":
             self.heartflow_data["prompt"] = prompt
             self.heartflow_data["response"] = response
@@ -92,20 +85,20 @@ class InfoCatcher:
 
         self.response_text = response
 
-    def catch_after_generate_response(self,response_duration:float):
+    def catch_after_generate_response(self, response_duration: float):
         self.timing_results["make_response_time"] = response_duration
 
-        
+    def catch_after_response(
-        
+        self, response_duration: float, response_message: List[str], first_bot_msg: MessageSending
-    def catch_after_response(self,response_duration:float,
+    ):
-                             response_message:List[str],
-                             first_bot_msg:MessageSending):
         self.timing_results["make_response_time"] = response_duration
         self.response_time = time.time()
         for msg in response_message:
             self.response_messages.append(msg)
 
-        self.chat_history_in_thinking = self.get_message_from_db_between_msgs(self.trigger_response_message,first_bot_msg)
+        self.chat_history_in_thinking = self.get_message_from_db_between_msgs(
+            self.trigger_response_message, first_bot_msg
+        )
 
     def get_message_from_db_between_msgs(self, message_start: Message, message_end: Message):
         try:
@@ -118,10 +111,7 @@ class InfoCatcher:
 
             # 查询数据库，获取 chat_id 相同且时间在 start 和 end 之间的数据
             messages_between = db.messages.find(
-                {
+                {"chat_id": chat_id, "time": {"$gt": time_start, "$lt": time_end}}
-                    "chat_id": chat_id,
-                    "time": {"$gt": time_start, "$lt": time_end}
-                }
             ).sort("time", -1)
 
             result = list(messages_between)
@@ -140,14 +130,16 @@ class InfoCatcher:
         chat_id = message.chat_stream.stream_id
 
         # 查询数据库，获取 chat_id 相同且 message_id 小于当前消息的 30 条数据
-        messages_before = db.messages.find(
+        messages_before = (
-            {"chat_id": chat_id, "message_id": {"$lt": message_id}}
+            db.messages.find({"chat_id": chat_id, "message_id": {"$lt": message_id}})
-        ).sort("time", -1).limit(self.context_length*3) #获取更多历史信息
+            .sort("time", -1)
+            .limit(self.context_length * 3)
+        )  # 获取更多历史信息
 
         return list(messages_before)
 
     def message_list_to_dict(self, message_list):
-        #存储简化的聊天记录
+        # 存储简化的聊天记录
         result = []
         for message in message_list:
             if not isinstance(message, dict):
@@ -198,7 +190,7 @@ class InfoCatcher:
                 "timing_results": self.timing_results,
                 "chat_history": self.message_list_to_dict(self.chat_history),
                 "chat_history_in_thinking": self.message_list_to_dict(self.chat_history_in_thinking),
-                "chat_history_after_response": self.message_list_to_dict(self.chat_history_after_response)
+                "chat_history_after_response": self.message_list_to_dict(self.chat_history_after_response),
             }
 
             # 根据不同的响应模式添加相应的数据
@@ -216,13 +208,15 @@ class InfoCatcher:
             print(traceback.format_exc())
             return False
 
+
 class InfoCatcherManager:
     def __init__(self):
         self.info_catchers = {}
 
-    def get_info_catcher(self,thinking_id:str) -> InfoCatcher:
+    def get_info_catcher(self, thinking_id: str) -> InfoCatcher:
         if thinking_id not in self.info_catchers:
             self.info_catchers[thinking_id] = InfoCatcher()
         return self.info_catchers[thinking_id]
 
+
 info_catcher_manager = InfoCatcherManager()

src/plugins/schedule/schedule_generator.py

@@ -32,7 +32,7 @@ class ScheduleGenerator:
         # 使用离线LLM模型
         self.llm_scheduler_all = LLM_request(
             model=global_config.llm_reasoning,
-            temperature=global_config.SCHEDULE_TEMPERATURE+0.3,
+            temperature=global_config.SCHEDULE_TEMPERATURE + 0.3,
             max_tokens=7000,
             request_type="schedule",
         )

src/plugins/storage/storage.py

@@ -8,6 +8,7 @@ from src.common.logger import get_module_logger
 
 logger = get_module_logger("message_storage")
 
+
 class MessageStorage:
     async def store_message(self, message: Union[MessageSending, MessageRecv], chat_stream: ChatStream) -> None:
         """存储消息到数据库"""

src/plugins/utils/prompt_builder.py

@@ -5,6 +5,7 @@ from typing import Dict, Any, Optional, List, Union
 from contextlib import asynccontextmanager
 import asyncio
 
+
 class PromptContext:
     def __init__(self):
         self._context_prompts: Dict[str, Dict[str, "Prompt"]] = {}
@@ -129,7 +130,9 @@ class Prompt(str):
         return obj
 
     @classmethod
-    async def create_async(cls, fstr: str, name: Optional[str] = None, args: Union[List[Any], tuple[Any, ...]] = None, **kwargs):
+    async def create_async(
+        cls, fstr: str, name: Optional[str] = None, args: Union[List[Any], tuple[Any, ...]] = None, **kwargs
+    ):
         """异步创建Prompt实例"""
         prompt = cls(fstr, name, args, **kwargs)
         if global_prompt_manager._context._current_context:

src/plugins/willing/mode_classical.py

@@ -1,6 +1,7 @@
 import asyncio
 from .willing_manager import BaseWillingManager
 
+
 class ClassicalWillingManager(BaseWillingManager):
     def __init__(self):
         super().__init__()
@@ -41,10 +42,15 @@ class ClassicalWillingManager(BaseWillingManager):
 
         self.chat_reply_willing[chat_id] = min(current_willing, 3.0)
 
-        reply_probability = min(max((current_willing - 0.5), 0.01) * self.global_config.response_willing_amplifier * 2, 1)
+        reply_probability = min(
+            max((current_willing - 0.5), 0.01) * self.global_config.response_willing_amplifier * 2, 1
+        )
 
         # 检查群组权限（如果是群聊）
-        if willing_info.group_info and willing_info.group_info.group_id in self.global_config.talk_frequency_down_groups:
+        if (
+            willing_info.group_info
+            and willing_info.group_info.group_id in self.global_config.talk_frequency_down_groups
+        ):
             reply_probability = reply_probability / self.global_config.down_frequency_rate
 
         if is_emoji_not_reply:
@@ -74,5 +80,3 @@ class ClassicalWillingManager(BaseWillingManager):
 
     async def set_variable_parameters(self, parameters):
         return await super().set_variable_parameters(parameters)
-            
-            

src/plugins/willing/mode_custom.py

@@ -4,4 +4,3 @@ from .willing_manager import BaseWillingManager
 class CustomWillingManager(BaseWillingManager):
     def __init__(self):
         super().__init__()
-

src/plugins/willing/mode_dynamic.py

@@ -20,7 +20,6 @@ class DynamicWillingManager(BaseWillingManager):
         self._decay_task = None
         self._mode_switch_task = None
 
-
     async def async_task_starter(self):
         if self._decay_task is None:
             self._decay_task = asyncio.create_task(self._decay_reply_willing())
@@ -84,7 +83,9 @@ class DynamicWillingManager(BaseWillingManager):
             self.chat_high_willing_mode[chat_id] = True
             self.chat_reply_willing[chat_id] = 1.0  # 设置为较高回复意愿
             self.chat_high_willing_duration[chat_id] = random.randint(180, 240)  # 3-4分钟
-            self.logger.debug(f"聊天流 {chat_id} 切换到高回复意愿期，持续 {self.chat_high_willing_duration[chat_id]} 秒")
+            self.logger.debug(
+                f"聊天流 {chat_id} 切换到高回复意愿期，持续 {self.chat_high_willing_duration[chat_id]} 秒"
+            )
 
         self.chat_last_mode_change[chat_id] = time.time()
         self.chat_msg_count[chat_id] = 0  # 重置消息计数
@@ -148,7 +149,9 @@ class DynamicWillingManager(BaseWillingManager):
 
         # 根据话题兴趣度适当调整
         if willing_info.interested_rate > 0.5:
-            current_willing += (willing_info.interested_rate - 0.5) * 0.5 * self.global_config.response_interested_rate_amplifier
+            current_willing += (
+                (willing_info.interested_rate - 0.5) * 0.5 * self.global_config.response_interested_rate_amplifier
+            )
 
         # 根据当前模式计算回复概率
         base_probability = 0.0

src/plugins/willing/mode_mxp.py

@@ -17,19 +17,22 @@ Mxp 模式：梦溪畔独家赞助
 中策是发issue
 下下策是询问一个菜鸟（@梦溪畔）
 """
+
 from .willing_manager import BaseWillingManager
 from typing import Dict
 import asyncio
 import time
 import math
 
+
 class MxpWillingManager(BaseWillingManager):
     """Mxp意愿管理器"""
+
     def __init__(self):
         super().__init__()
         self.chat_person_reply_willing: Dict[str, Dict[str, float]] = {}  # chat_id: {person_id: 意愿值}
         self.chat_new_message_time: Dict[str, list[float]] = {}  # 聊天流ID: 消息时间
-        self.last_response_person: Dict[str, tuple[str, int]] = {} # 上次回复的用户信息
+        self.last_response_person: Dict[str, tuple[str, int]] = {}  # 上次回复的用户信息
         self.temporary_willing: float = 0  # 临时意愿值
 
         # 可变参数
@@ -39,8 +42,8 @@ class MxpWillingManager(BaseWillingManager):
         self.basic_maximum_willing = 0.5  # 基础最大意愿值
         self.mention_willing_gain = 0.6  # 提及意愿增益
         self.interest_willing_gain = 0.3  # 兴趣意愿增益
-        self.emoji_response_penalty = self.global_config.emoji_response_penalty # 表情包回复惩罚
+        self.emoji_response_penalty = self.global_config.emoji_response_penalty  # 表情包回复惩罚
-        self.down_frequency_rate = self.global_config.down_frequency_rate # 降低回复频率的群组惩罚系数
+        self.down_frequency_rate = self.global_config.down_frequency_rate  # 降低回复频率的群组惩罚系数
         self.single_chat_gain = 0.12  # 单聊增益
 
     async def async_task_starter(self) -> None:
@@ -73,9 +76,13 @@ class MxpWillingManager(BaseWillingManager):
             w_info = self.ongoing_messages[message_id]
             if w_info.is_mentioned_bot:
                 self.chat_person_reply_willing[w_info.chat_id][w_info.person_id] += 0.2
-            if w_info.chat_id in self.last_response_person and self.last_response_person[w_info.chat_id][0] == w_info.person_id:
+            if (
-                self.chat_person_reply_willing[w_info.chat_id][w_info.person_id] +=\
+                w_info.chat_id in self.last_response_person
-                self.single_chat_gain * (2 * self.last_response_person[w_info.chat_id][1] + 1)
+                and self.last_response_person[w_info.chat_id][0] == w_info.person_id
+            ):
+                self.chat_person_reply_willing[w_info.chat_id][w_info.person_id] += self.single_chat_gain * (
+                    2 * self.last_response_person[w_info.chat_id][1] + 1
+                )
             now_chat_new_person = self.last_response_person.get(w_info.chat_id, ["", 0])
             if now_chat_new_person[0] != w_info.person_id:
                 self.last_response_person[w_info.chat_id] = [w_info.person_id, 0]
@@ -98,7 +105,10 @@ class MxpWillingManager(BaseWillingManager):
             rel_level = self._get_relationship_level_num(rel_value)
             current_willing += rel_level * 0.1
 
-            if w_info.chat_id in self.last_response_person and self.last_response_person[w_info.chat_id][0] == w_info.person_id:
+            if (
+                w_info.chat_id in self.last_response_person
+                and self.last_response_person[w_info.chat_id][0] == w_info.person_id
+            ):
                 current_willing += self.single_chat_gain * (2 * self.last_response_person[w_info.chat_id][1] + 1)
 
             chat_ongoing_messages = [msg for msg in self.ongoing_messages.values() if msg.chat_id == w_info.chat_id]
@@ -141,16 +151,22 @@ class MxpWillingManager(BaseWillingManager):
                             self.logger.debug(f"聊天流{chat_id}不存在，错误")
                             continue
                         basic_willing = self.chat_reply_willing[chat_id]
-                        person_willing[person_id] = basic_willing + (willing - basic_willing) * self.intention_decay_rate
+                        person_willing[person_id] = (
+                            basic_willing + (willing - basic_willing) * self.intention_decay_rate
+                        )
 
     def setup(self, message, chat, is_mentioned_bot, interested_rate):
         super().setup(message, chat, is_mentioned_bot, interested_rate)
 
-        self.chat_reply_willing[chat.stream_id] = self.chat_reply_willing.get(chat.stream_id, self.basic_maximum_willing)
+        self.chat_reply_willing[chat.stream_id] = self.chat_reply_willing.get(
+            chat.stream_id, self.basic_maximum_willing
+        )
         self.chat_person_reply_willing[chat.stream_id] = self.chat_person_reply_willing.get(chat.stream_id, {})
-        self.chat_person_reply_willing[chat.stream_id][self.ongoing_messages[message.message_info.message_id].person_id] = \
+        self.chat_person_reply_willing[chat.stream_id][
-        self.chat_person_reply_willing[chat.stream_id].get(self.ongoing_messages[message.message_info.message_id].person_id, 
+            self.ongoing_messages[message.message_info.message_id].person_id
-                                                           self.chat_reply_willing[chat.stream_id])
+        ] = self.chat_person_reply_willing[chat.stream_id].get(
+            self.ongoing_messages[message.message_info.message_id].person_id, self.chat_reply_willing[chat.stream_id]
+        )
 
         if chat.stream_id not in self.chat_new_message_time:
             self.chat_new_message_time[chat.stream_id] = []
@@ -174,10 +190,11 @@ class MxpWillingManager(BaseWillingManager):
             await asyncio.sleep(update_time)
             async with self.lock:
                 for chat_id, message_times in self.chat_new_message_time.items():
-
                     # 清理过期消息
                     current_time = time.time()
-                    message_times = [msg_time for msg_time in message_times if current_time - msg_time < self.message_expiration_time]
+                    message_times = [
+                        msg_time for msg_time in message_times if current_time - msg_time < self.message_expiration_time
+                    ]
                     self.chat_new_message_time[chat_id] = message_times
 
                     if len(message_times) < self.number_of_message_storage:
@@ -185,7 +202,9 @@ class MxpWillingManager(BaseWillingManager):
                         update_time = 20
                     elif len(message_times) == self.number_of_message_storage:
                         time_interval = current_time - message_times[0]
-                        basic_willing = self.basic_maximum_willing * math.sqrt(time_interval / self.message_expiration_time)
+                        basic_willing = self.basic_maximum_willing * math.sqrt(
+                            time_interval / self.message_expiration_time
+                        )
                         self.chat_reply_willing[chat_id] = basic_willing
                         update_time = 17 * math.sqrt(time_interval / self.message_expiration_time) + 3
                     else:
@@ -203,7 +222,7 @@ class MxpWillingManager(BaseWillingManager):
             "interest_willing_gain": "兴趣意愿增益",
             "emoji_response_penalty": "表情包回复惩罚",
             "down_frequency_rate": "降低回复频率的群组惩罚系数",
-            "single_chat_gain": "单聊增益（不仅是私聊）"
+            "single_chat_gain": "单聊增益（不仅是私聊）",
         }
 
     async def set_variable_parameters(self, parameters: Dict[str, any]):

src/plugins/willing/willing_manager.py

@@ -1,4 +1,3 @@
-
 from src.common.logger import LogConfig, WILLING_STYLE_CONFIG, LoguruLogger, get_module_logger
 from dataclasses import dataclass
 from ..config.config import global_config, BotConfig
@@ -38,6 +37,7 @@ willing_config = LogConfig(
 )
 logger = get_module_logger("willing", config=willing_config)
 
+
 @dataclass
 class WillingInfo:
     """此类保存意愿模块常用的参数
@@ -53,6 +53,7 @@ class WillingInfo:
         is_emoji (bool): 是否为表情包
         interested_rate (float): 兴趣度
     """
+
     message: MessageRecv
     chat: ChatStream
     person_info_manager: PersonInfoManager
@@ -64,18 +65,17 @@ class WillingInfo:
     interested_rate: float
     # current_mood: float  当前心情？
 
+
 class BaseWillingManager(ABC):
     """回复意愿管理基类"""
 
     @classmethod
-    def create(cls, manager_type: str) -> 'BaseWillingManager':
+    def create(cls, manager_type: str) -> "BaseWillingManager":
         try:
             module = importlib.import_module(f".mode_{manager_type}", __package__)
             manager_class = getattr(module, f"{manager_type.capitalize()}WillingManager")
             if not issubclass(manager_class, cls):
-                raise TypeError(
+                raise TypeError(f"Manager class {manager_class.__name__} is not a subclass of {cls.__name__}")
-                    f"Manager class {manager_class.__name__} is not a subclass of {cls.__name__}"
-                )
             else:
                 logger.info(f"成功载入willing模式：{manager_type}")
             return manager_class()
@@ -173,5 +173,6 @@ def init_willing_manager() -> BaseWillingManager:
     mode = global_config.willing_mode.lower()
     return BaseWillingManager.create(mode)
 
+
 # 全局willing_manager对象
 willing_manager = init_willing_manager()