diff --git a/src/plugins/chat_module/reasoning_chat/reasoning_chat.py b/src/plugins/chat_module/reasoning_chat/reasoning_chat.py index eea1cc8b8..2ce218a6f 100644 --- a/src/plugins/chat_module/reasoning_chat/reasoning_chat.py +++ b/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() - interested_rate = await HippocampusManager.get_instance().get_activate_from_text( - message.processed_plain_text, fast_retrieval=True - ) - timer2 = time.time() - timing_results["记忆激活"] = timer2 - timer1 + with Timer("记忆激活", timing_results): + interested_rate = await HippocampusManager.get_instance().get_activate_from_text( + message.processed_plain_text, fast_retrieval=True + ) # 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() - thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo) - timer2 = time.time() - timing_results["创建思考消息"] = timer2 - timer1 + with Timer("创建思考消息", timing_results): + thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo) 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) - timer2 = time.time() - timing_results["生成回复"] = timer2 - timer1 + with Timer("生成回复", timing_results): + response_set = await self.gpt.generate_response(message, thinking_id) 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() - 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 + with Timer("发送消息", timing_results): + first_bot_msg = await self._send_response_messages(message, chat, response_set, thinking_id) 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() - await self._handle_emoji(message, chat, response_set) - timer2 = time.time() - timing_results["处理表情包"] = timer2 - timer1 + with Timer("处理表情包", timing_results): + await self._handle_emoji(message, chat, response_set) # 更新关系情绪 - timer1 = time.time() - await self._update_relationship(message, response_set) - timer2 = time.time() - timing_results["更新关系情绪"] = timer2 - timer1 + with Timer("更新关系情绪", timing_results): + await self._update_relationship(message, response_set) # 回复后处理 await willing_manager.after_generate_reply_handle(message.message_info.message_id) diff --git a/src/plugins/chat_module/reasoning_chat/reasoning_generator.py b/src/plugins/chat_module/reasoning_chat/reasoning_generator.py index 8b81ca4b2..25416a1d5 100644 --- a/src/plugins/chat_module/reasoning_chat/reasoning_generator.py +++ b/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}") @@ -65,11 +66,11 @@ 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) - + if message.chat_stream.user_info.user_cardname and message.chat_stream.user_info.user_nickname: sender_name = ( f"[({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}]" @@ -82,26 +83,22 @@ class ResponseGenerator: logger.debug("开始使用生成回复-2") # 构建prompt - timer1 = time.time() - prompt = await prompt_builder._build_prompt( - message.chat_stream, - message_txt=message.processed_plain_text, - sender_name=sender_name, - stream_id=message.chat_stream.stream_id, - ) - timer2 = time.time() - logger.info(f"构建prompt时间: {timer2 - timer1}秒") + with Timer() as t_build_prompt: + prompt = await prompt_builder._build_prompt( + message.chat_stream, + message_txt=message.processed_plain_text, + sender_name=sender_name, + stream_id=message.chat_stream.stream_id, + ) + logger.info(f"构建prompt时间: {t_build_prompt.human_readable()}") try: content, reasoning_content, self.current_model_name = await model.generate_response(prompt) - + info_catcher.catch_after_llm_generated( - prompt=prompt, - response=content, - reasoning_content=reasoning_content, - model_name=self.current_model_name) - - + prompt=prompt, 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 @@ -118,7 +115,6 @@ class ResponseGenerator: return content - # def _save_to_db( # self, # message: MessageRecv, diff --git a/src/plugins/chat_module/think_flow_chat/think_flow_chat.py b/src/plugins/chat_module/think_flow_chat/think_flow_chat.py index 964aca55c..098ee1552 100644 --- a/src/plugins/chat_module/think_flow_chat/think_flow_chat.py +++ b/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, - message, - chat, - response_set:List[str], - thinking_id) -> MessageSending: + async def _send_response_messages(self, 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() - interested_rate = await HippocampusManager.get_instance().get_activate_from_text( - message.processed_plain_text, fast_retrieval=True - ) - timer2 = time.time() - timing_results["记忆激活"] = timer2 - timer1 + with Timer("记忆激活", timing_results): + interested_rate = await HippocampusManager.get_instance().get_activate_from_text( + message.processed_plain_text, fast_retrieval=True + ) 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 @@ -260,103 +255,85 @@ class ThinkFlowChat: if random() < reply_probability: 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() - thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo) - timer2 = time.time() - timing_results["创建思考消息"] = timer2 - timer1 + with Timer("创建思考消息", timing_results): + thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo) except Exception as e: logger.error(f"心流创建思考消息失败: {e}") - + logger.debug(f"创建捕捉器，thinking_id:{thinking_id}") - + info_catcher = info_catcher_manager.get_info_catcher(thinking_id) info_catcher.catch_decide_to_response(message) try: # 观察 - timer1 = time.time() - await heartflow.get_subheartflow(chat.stream_id).do_observe() - timer2 = time.time() - timing_results["观察"] = timer2 - timer1 + with Timer("观察", timing_results): + await heartflow.get_subheartflow(chat.stream_id).do_observe() except Exception as e: logger.error(f"心流观察失败: {e}") - + info_catcher.catch_after_observe(timing_results["观察"]) # 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( - message_txt = message.processed_plain_text, - sender_name = message.message_info.user_info.user_nickname, - chat_stream = chat - ) - timer2 = time.time() - timing_results["思考前脑内状态"] = timer2 - timer1 + 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( + message_txt=message.processed_plain_text, + sender_name=message.message_info.user_info.user_nickname, + chat_stream=chat, + ) 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) - timer2 = time.time() - timing_results["生成回复"] = timer2 - timer1 + with Timer("生成回复", timing_results): + response_set = await self.gpt.generate_response(message, thinking_id) info_catcher.catch_after_generate_response(timing_results["生成回复"]) - + if not response_set: logger.info("回复生成失败，返回为空") return # 发送消息 try: - timer1 = time.time() - 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 + with Timer("发送消息", timing_results): + first_bot_msg = await self._send_response_messages(message, chat, response_set, thinking_id) 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() - await self._handle_emoji(message, chat, response_set) - timer2 = time.time() - timing_results["处理表情包"] = timer2 - timer1 + with Timer("处理表情包", timing_results): + await self._handle_emoji(message, chat, response_set) 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() - await self._update_using_response(message, response_set) - timer2 = time.time() - timing_results["更新心流"] = timer2 - timer1 + with Timer("更新心流", timing_results): + await self._update_using_response(message, response_set) except Exception as e: logger.error(f"心流更新失败: {e}") # 更新关系情绪 try: - timer1 = time.time() - await self._update_relationship(message, response_set) - timer2 = time.time() - timing_results["更新关系情绪"] = timer2 - timer1 + with Timer("更新关系情绪", timing_results): + await self._update_relationship(message, response_set) except Exception as e: logger.error(f"心流更新关系情绪失败: {e}") diff --git a/src/plugins/chat_module/think_flow_chat/think_flow_generator.py b/src/plugins/chat_module/think_flow_chat/think_flow_generator.py index 164e8ab7c..b682a4c5b 100644 --- a/src/plugins/chat_module/think_flow_chat/think_flow_generator.py +++ b/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 @@ -35,44 +35,50 @@ 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() - - checked = False - 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 - 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 - 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") - - current_model.temperature = 0.3 - model_checked_response = await self._check_response_with_model(message, model_response, current_model,thinking_id) - time2 = time.time() + arousal_multiplier = MoodManager.get_instance().get_arousal_multiplier() + + with Timer() as t_generate_response: + checked = False + 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 + 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 + 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" + ) + + current_model.temperature = 0.3 + model_checked_response = await self._check_response_with_model( + message, model_response, current_model, thinking_id + ) 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) return model_processed_response @@ -80,11 +86,13 @@ 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) - + if message.chat_stream.user_info.user_cardname and message.chat_stream.user_info.user_nickname: sender_name = ( f"[({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}]" @@ -96,45 +104,41 @@ class ResponseGenerator: sender_name = f"用户({message.chat_stream.user_info.user_id})" # 构建prompt - timer1 = time.time() - if mode == "normal": - prompt = await prompt_builder._build_prompt( - message.chat_stream, - message_txt=message.processed_plain_text, - sender_name=sender_name, - stream_id=message.chat_stream.stream_id, - ) - elif mode == "simple": - prompt = await prompt_builder._build_prompt_simple( - message.chat_stream, - message_txt=message.processed_plain_text, - sender_name=sender_name, - stream_id=message.chat_stream.stream_id, - ) - timer2 = time.time() - logger.info(f"构建{mode}prompt时间: {timer2 - timer1}秒") + with Timer() as t_build_prompt: + if mode == "normal": + prompt = await prompt_builder._build_prompt( + message.chat_stream, + message_txt=message.processed_plain_text, + sender_name=sender_name, + stream_id=message.chat_stream.stream_id, + ) + elif mode == "simple": + prompt = await prompt_builder._build_prompt_simple( + message.chat_stream, + message_txt=message.processed_plain_text, + sender_name=sender_name, + stream_id=message.chat_stream.stream_id, + ) + logger.info(f"构建{mode}prompt时间: {t_build_prompt.human_readable()}") try: content, reasoning_content, self.current_model_name = await model.generate_response(prompt) - - + info_catcher.catch_after_llm_generated( - prompt=prompt, - response=content, - reasoning_content=reasoning_content, - model_name=self.current_model_name) - + prompt=prompt, 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 = "" if message.chat_stream.user_info.user_cardname and message.chat_stream.user_info.user_nickname: sender_name = ( @@ -145,36 +149,32 @@ class ResponseGenerator: sender_name = f"({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}" 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( - message.chat_stream, - message_txt=message.processed_plain_text, - sender_name=sender_name, - stream_id=message.chat_stream.stream_id, - content=content - ) - timer2 = time.time() + with Timer() as t_build_prompt_check: + prompt = await prompt_builder._build_prompt_check_response( + message.chat_stream, + message_txt=message.processed_plain_text, + sender_name=sender_name, + stream_id=message.chat_stream.stream_id, + content=content, + ) 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, # reasoning_content=reasoning_content, # model_name=self.current_model_name) - + except Exception: 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): diff --git a/src/plugins/utils/timer_calculater.py b/src/plugins/utils/timer_calculater.py new file mode 100644 index 000000000..97f983268 --- /dev/null +++ b/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"