更好的计时器小工具，感谢D指导

src/plugins/chat_module/reasoning_chat/reasoning_chat.py

@@ -19,6 +19,7 @@ from ...chat.chat_stream import chat_manager
 from ...person_info.relationship_manager import relationship_manager
 from ...chat.message_buffer import message_buffer
 from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
+from ...utils.timer_calculater import Timer
 
 # 定义日志配置
 chat_config = LogConfig(
@@ -173,12 +174,10 @@ class ReasoningChat:
         await self.storage.store_message(message, chat)
 
         # 记忆激活
-        timer1 = time.time()
+        with Timer("记忆激活", timing_results):
-        interested_rate = await HippocampusManager.get_instance().get_activate_from_text(
+            interested_rate = await HippocampusManager.get_instance().get_activate_from_text(
-            message.processed_plain_text, fast_retrieval=True
+                message.processed_plain_text, fast_retrieval=True
-        )
+            )
-        timer2 = time.time()
-        timing_results["记忆激活"] = timer2 - timer1
 
         # 查询缓冲器结果，会整合前面跳过的消息，改变processed_plain_text
         buffer_result = await message_buffer.query_buffer_result(message)
@@ -228,10 +227,8 @@ class ReasoningChat:
             await willing_manager.before_generate_reply_handle(message.message_info.message_id)
 
             # 创建思考消息
-            timer1 = time.time()
+            with Timer("创建思考消息", timing_results):
-            thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo)
+                thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo)
-            timer2 = time.time()
-            timing_results["创建思考消息"] = timer2 - timer1
 
             logger.debug(f"创建捕捉器，thinking_id:{thinking_id}")
 
@@ -239,11 +236,9 @@ class ReasoningChat:
             info_catcher.catch_decide_to_response(message)
 
             # 生成回复
-            timer1 = time.time()
             try:
-                response_set = await self.gpt.generate_response(message, thinking_id)
+                with Timer("生成回复", timing_results):
-                timer2 = time.time()
+                    response_set = await self.gpt.generate_response(message, thinking_id)
-                timing_results["生成回复"] = timer2 - timer1
 
                 info_catcher.catch_after_generate_response(timing_results["生成回复"])
             except Exception as e:
@@ -255,26 +250,20 @@ class ReasoningChat:
                 return
 
             # 发送消息
-            timer1 = time.time()
+            with Timer("发送消息", timing_results):
-            first_bot_msg = await self._send_response_messages(message, chat, response_set, thinking_id)
+                first_bot_msg = await self._send_response_messages(message, chat, response_set, thinking_id)
-            timer2 = time.time()
-            timing_results["发送消息"] = timer2 - timer1
 
             info_catcher.catch_after_response(timing_results["发送消息"], response_set, first_bot_msg)
 
             info_catcher.done_catch()
 
             # 处理表情包
-            timer1 = time.time()
+            with Timer("处理表情包", timing_results):
-            await self._handle_emoji(message, chat, response_set)
+                await self._handle_emoji(message, chat, response_set)
-            timer2 = time.time()
-            timing_results["处理表情包"] = timer2 - timer1
 
             # 更新关系情绪
-            timer1 = time.time()
+            with Timer("更新关系情绪", timing_results):
-            await self._update_relationship(message, response_set)
+                await self._update_relationship(message, response_set)
-            timer2 = time.time()
-            timing_results["更新关系情绪"] = timer2 - timer1
 
             # 回复后处理
             await willing_manager.after_generate_reply_handle(message.message_info.message_id)

src/plugins/chat_module/reasoning_chat/reasoning_generator.py

@@ -7,6 +7,7 @@ from ...config.config import global_config
 from ...chat.message import MessageThinking
 from .reasoning_prompt_builder import prompt_builder
 from ...chat.utils import process_llm_response
+from ...utils.timer_calculater import Timer
 from src.common.logger import get_module_logger, LogConfig, LLM_STYLE_CONFIG
 from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
 
@@ -38,7 +39,7 @@ 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 +53,7 @@ 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 +66,7 @@ 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)
@@ -82,25 +83,21 @@ class ResponseGenerator:
 
         logger.debug("开始使用生成回复-2")
         # 构建prompt
-        timer1 = time.time()
+        with Timer() as t_build_prompt:
-        prompt = await prompt_builder._build_prompt(
+            prompt = await prompt_builder._build_prompt(
-            message.chat_stream,
+                message.chat_stream,
-            message_txt=message.processed_plain_text,
+                message_txt=message.processed_plain_text,
-            sender_name=sender_name,
+                sender_name=sender_name,
-            stream_id=message.chat_stream.stream_id,
+                stream_id=message.chat_stream.stream_id,
-        )
+            )
-        timer2 = time.time()
+        logger.info(f"构建prompt时间: {t_build_prompt.human_readable()}")
-        logger.info(f"构建prompt时间: {timer2 - timer1}秒")
 
         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")
@@ -118,7 +115,6 @@ class ResponseGenerator:
 
         return content
 
-
     # def _save_to_db(
     #     self,
     #     message: MessageRecv,

src/plugins/chat_module/think_flow_chat/think_flow_chat.py

@@ -20,6 +20,7 @@ from ...chat.chat_stream import chat_manager
 from ...person_info.relationship_manager import relationship_manager
 from ...chat.message_buffer import message_buffer
 from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
+from ...utils.timer_calculater import Timer
 
 # 定义日志配置
 chat_config = LogConfig(
@@ -59,11 +60,7 @@ 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
@@ -200,12 +197,10 @@ class ThinkFlowChat:
         logger.debug(f"存储成功{message.processed_plain_text}")
 
         # 记忆激活
-        timer1 = time.time()
+        with Timer("记忆激活", timing_results):
-        interested_rate = await HippocampusManager.get_instance().get_activate_from_text(
+            interested_rate = await HippocampusManager.get_instance().get_activate_from_text(
-            message.processed_plain_text, fast_retrieval=True
+                message.processed_plain_text, fast_retrieval=True
-        )
+            )
-        timer2 = time.time()
-        timing_results["记忆激活"] = timer2 - timer1
         logger.debug(f"记忆激活: {interested_rate}")
 
         # 查询缓冲器结果，会整合前面跳过的消息，改变processed_plain_text
@@ -261,17 +256,13 @@ class ThinkFlowChat:
             try:
                 do_reply = True
 
-                
-
                 # 回复前处理
                 await willing_manager.before_generate_reply_handle(message.message_info.message_id)
 
                 # 创建思考消息
                 try:
-                    timer1 = time.time()
+                    with Timer("创建思考消息", timing_results):
-                    thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo)
+                        thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo)
-                    timer2 = time.time()
-                    timing_results["创建思考消息"] = timer2 - timer1
                 except Exception as e:
                     logger.error(f"心流创建思考消息失败: {e}")
 
@@ -282,10 +273,8 @@ class ThinkFlowChat:
 
                 try:
                     # 观察
-                    timer1 = time.time()
+                    with Timer("观察", timing_results):
-                    await heartflow.get_subheartflow(chat.stream_id).do_observe()
+                        await heartflow.get_subheartflow(chat.stream_id).do_observe()
-                    timer2 = time.time()
-                    timing_results["观察"] = timer2 - timer1
                 except Exception as e:
                     logger.error(f"心流观察失败: {e}")
 
@@ -293,24 +282,22 @@ class ThinkFlowChat:
 
                 # 思考前脑内状态
                 try:
-                    timer1 = time.time()
+                    with Timer("思考前脑内状态", timing_results):
-                    current_mind,past_mind = await heartflow.get_subheartflow(chat.stream_id).do_thinking_before_reply(
+                        current_mind, past_mind = await heartflow.get_subheartflow(
-                        message_txt = message.processed_plain_text,
+                            chat.stream_id
-                        sender_name = message.message_info.user_info.user_nickname,
+                        ).do_thinking_before_reply(
-                        chat_stream = chat
+                            message_txt=message.processed_plain_text,
-                    )
+                            sender_name=message.message_info.user_info.user_nickname,
-                    timer2 = time.time()
+                            chat_stream=chat,
-                    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()
+                with Timer("生成回复", timing_results):
-                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
 
                 info_catcher.catch_after_generate_response(timing_results["生成回复"])
 
@@ -320,43 +307,33 @@ class ThinkFlowChat:
 
                 # 发送消息
                 try:
-                    timer1 = time.time()
+                    with Timer("发送消息", timing_results):
-                    first_bot_msg = await self._send_response_messages(message, chat, response_set, thinking_id)
+                        first_bot_msg = await self._send_response_messages(message, chat, response_set, thinking_id)
-                    timer2 = time.time()
-                    timing_results["发送消息"] = timer2 - timer1
                 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()
 
                 # 处理表情包
                 try:
-                    timer1 = time.time()
+                    with Timer("处理表情包", timing_results):
-                    await self._handle_emoji(message, chat, response_set)
+                        await self._handle_emoji(message, chat, response_set)
-                    timer2 = time.time()
-                    timing_results["处理表情包"] = timer2 - timer1
                 except Exception as e:
                     logger.error(f"心流处理表情包失败: {e}")
 
                 # 更新心流
                 try:
-                    timer1 = time.time()
+                    with Timer("更新心流", timing_results):
-                    await self._update_using_response(message, response_set)
+                        await self._update_using_response(message, response_set)
-                    timer2 = time.time()
-                    timing_results["更新心流"] = timer2 - timer1
                 except Exception as e:
                     logger.error(f"心流更新失败: {e}")
 
                 # 更新关系情绪
                 try:
-                    timer1 = time.time()
+                    with Timer("更新关系情绪", timing_results):
-                    await self._update_relationship(message, response_set)
+                        await self._update_relationship(message, response_set)
-                    timer2 = time.time()
-                    timing_results["更新关系情绪"] = timer2 - timer1
                 except Exception as e:
                     logger.error(f"心流更新关系情绪失败: {e}")
 

src/plugins/chat_module/think_flow_chat/think_flow_generator.py

@@ -1,4 +1,3 @@
-import time
 from typing import List, Optional
 import random
 
@@ -10,6 +9,7 @@ from .think_flow_prompt_builder import prompt_builder
 from ...chat.utils import process_llm_response
 from src.common.logger import get_module_logger, LogConfig, LLM_STYLE_CONFIG
 from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
+from ...utils.timer_calculater import Timer
 
 from src.plugins.moods.moods import MoodManager
 
@@ -35,43 +35,49 @@ 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}"
         )
 
         arousal_multiplier = MoodManager.get_instance().get_arousal_multiplier()
 
-        time1 = time.time()
+        with Timer() as t_generate_response:
-        
-        checked = False
-        if random.random() > 0:
             checked = False
-            current_model = self.model_normal
+            if random.random() > 0:
-            current_model.temperature = 0.3 * arousal_multiplier #激活度越高，温度越高
+                checked = False
-            model_response = await self._generate_response_with_model(message, current_model,thinking_id,mode="normal")
+                current_model = self.model_normal
+                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_checked_response = model_response
+                model_checked_response = model_response
-        else:
+            else:
-            checked = True
+                checked = True
-            current_model = self.model_normal
+                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}")
+                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
+                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: {t_generate_response.human_readable()}"
+                )
             else:
-                logger.info(f"{global_config.BOT_NICKNAME}的回复是：{model_response},生成回复时间: {time2 - time1}秒")
+                logger.info(
+                    f"{global_config.BOT_NICKNAME}的回复是：{model_response},生成回复时间: {t_generate_response.human_readable()}"
+                )
 
             model_processed_response = await self._process_response(model_checked_response)
 
@@ -80,7 +86,9 @@ 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)
@@ -96,43 +104,39 @@ class ResponseGenerator:
             sender_name = f"用户({message.chat_stream.user_info.user_id})"
 
         # 构建prompt
-        timer1 = time.time()
+        with Timer() as t_build_prompt:
-        if mode == "normal":
+            if mode == "normal":
-            prompt = await prompt_builder._build_prompt(
+                prompt = await prompt_builder._build_prompt(
-                message.chat_stream,
+                    message.chat_stream,
-                message_txt=message.processed_plain_text,
+                    message_txt=message.processed_plain_text,
-                sender_name=sender_name,
+                    sender_name=sender_name,
-                stream_id=message.chat_stream.stream_id,
+                    stream_id=message.chat_stream.stream_id,
-            )
+                )
-        elif mode == "simple":
+            elif mode == "simple":
-            prompt = await prompt_builder._build_prompt_simple(
+                prompt = await prompt_builder._build_prompt_simple(
-                message.chat_stream,
+                    message.chat_stream,
-                message_txt=message.processed_plain_text,
+                    message_txt=message.processed_plain_text,
-                sender_name=sender_name,
+                    sender_name=sender_name,
-                stream_id=message.chat_stream.stream_id,
+                    stream_id=message.chat_stream.stream_id,
-            )
+                )
-        timer2 = time.time()
+        logger.info(f"构建{mode}prompt时间: {t_build_prompt.human_readable()}")
-        logger.info(f"构建{mode}prompt时间: {timer2 - timer1}秒")
 
         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 = ""
@@ -146,24 +150,21 @@ class ResponseGenerator:
         else:
             sender_name = f"用户({message.chat_stream.user_info.user_id})"
 
-            
         # 构建prompt
-        timer1 = time.time()
+        with Timer() as t_build_prompt_check:
-        prompt = await prompt_builder._build_prompt_check_response(
+            prompt = await prompt_builder._build_prompt_check_response(
-            message.chat_stream,
+                message.chat_stream,
-            message_txt=message.processed_plain_text,
+                message_txt=message.processed_plain_text,
-            sender_name=sender_name,
+                sender_name=sender_name,
-            stream_id=message.chat_stream.stream_id,
+                stream_id=message.chat_stream.stream_id,
-            content=content
+                content=content,
-        )
+            )
-        timer2 = time.time()
         logger.info(f"构建check_prompt: {prompt}")
-        logger.info(f"构建check_prompt时间: {timer2 - timer1}秒")
+        logger.info(f"构建check_prompt时间: {t_build_prompt_check.human_readable()}")
 
         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 +175,6 @@ 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/utils/timer_calculater.py

@@ -0,0 +1,58 @@
+from time import perf_counter
+from typing import Dict, Optional
+
+"""
+计时器：用于性能计时
+
+感谢D指导
+"""
+class TimerTypeError(TypeError):
+    """自定义类型错误异常"""
+    def __init__(self, param_name, expected_type, actual_type):
+        super().__init__(
+            f"Invalid type for '{param_name}'. "
+            f"Expected {expected_type}, got {actual_type.__name__}"
+        )
+
+class Timer:
+    def __init__(self, name: Optional[str] = None, storage: Optional[Dict[str, float]] = None):
+        self.name = name  # 计时器名称
+        self.storage = storage  # 计时结果存储
+        self.elapsed = None  # 计时结果
+
+    def _validate_types(self, name, storage):
+        """类型验证核心方法"""
+        # 验证 name 类型
+        if name is not None and not isinstance(name, str):
+            raise TimerTypeError(
+                param_name="name",
+                expected_type="Optional[str]",
+                actual_type=type(name)
+            )
+            
+        # 验证 storage 类型
+        if storage is not None and not isinstance(storage, dict):
+            raise TimerTypeError(
+                param_name="storage",
+                expected_type="Optional[Dict[str, float]]",
+                actual_type=type(storage)
+            )
+    def __enter__(self):
+        self.start = perf_counter()
+        return self
+
+    def __exit__(self, *args):
+        self.end = perf_counter()
+        self.elapsed = self.end - self.start
+        if isinstance(self.storage, dict) and self.name:
+            self.storage[self.name] = self.elapsed
+            
+    def get_result(self) -> float:
+        """安全获取计时结果"""
+        return self.elapsed or 0.0
+
+    def human_readable(self) -> str:
+        """返回人类可读时间格式"""
+        if self.elapsed >= 1:
+            return f"{self.elapsed:.2f}秒"
+        return f"{self.elapsed*1000:.2f}毫秒"