feat: 完全分离回复 兴趣和 消息阅读；添加概率回复机制，优化兴趣监控逻辑，重构相关功能以支持更灵活的回复触发条件
This commit is contained in:
SengokuCola
@@ -18,6 +18,8 @@ from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
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from ...utils.timer_calculater import Timer
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from src.do_tool.tool_use import ToolUser
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from .interest import InterestManager, InterestChatting
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from src.plugins.chat.chat_stream import chat_manager
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from src.plugins.chat.message import MessageInfo
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# 定义日志配置
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chat_config = LogConfig(
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@@ -28,7 +30,6 @@ chat_config = LogConfig(
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logger = get_module_logger("heartFC_chat", config=chat_config)
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# 新增常量
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INTEREST_LEVEL_REPLY_THRESHOLD = 4.0
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INTEREST_MONITOR_INTERVAL_SECONDS = 1
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class HeartFC_Chat:
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@@ -41,87 +42,105 @@ class HeartFC_Chat:
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self._interest_monitor_task: Optional[asyncio.Task] = None
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async def start(self):
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"""Starts asynchronous tasks like the interest monitor."""
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logger.info("HeartFC_Chat starting asynchronous tasks...")
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"""启动异步任务,如兴趣监控器"""
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logger.info("HeartFC_Chat 正在启动异步任务...")
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await self.interest_manager.start_background_tasks()
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self._initialize_monitor_task()
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logger.info("HeartFC_Chat asynchronous tasks started.")
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logger.info("HeartFC_Chat 异步任务启动完成")
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def _initialize_monitor_task(self):
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"""启动后台兴趣监控任务"""
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"""启动后台兴趣监控任务，可以检查兴趣是否足以开启心流对话"""
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if self._interest_monitor_task is None or self._interest_monitor_task.done():
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try:
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loop = asyncio.get_running_loop()
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self._interest_monitor_task = loop.create_task(self._interest_monitor_loop())
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logger.info(f"Interest monitor task created. Interval: {INTEREST_MONITOR_INTERVAL_SECONDS}s, Level Threshold: {INTEREST_LEVEL_REPLY_THRESHOLD}")
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logger.info(f"兴趣监控任务已创建。监控间隔: {INTEREST_MONITOR_INTERVAL_SECONDS}秒。")
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except RuntimeError:
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logger.error("Failed to create interest monitor task: No running event loop.")
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logger.error("创建兴趣监控任务失败：没有运行中的事件循环。")
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raise
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else:
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logger.warning("Interest monitor task creation skipped: already running or exists.")
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logger.warning("跳过兴趣监控任务创建：任务已存在或正在运行。")
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async def _interest_monitor_loop(self):
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"""后台任务，定期检查兴趣度变化并触发回复"""
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logger.info("Interest monitor loop starting...")
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await asyncio.sleep(0.3)
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logger.info("兴趣监控循环开始...")
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while True:
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await asyncio.sleep(INTEREST_MONITOR_INTERVAL_SECONDS)
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try:
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interest_items_snapshot: List[tuple[str, InterestChatting]] = []
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stream_ids = list(self.interest_manager.interest_dict.keys())
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for stream_id in stream_ids:
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chatting_instance = self.interest_manager.get_interest_chatting(stream_id)
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if chatting_instance:
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interest_items_snapshot.append((stream_id, chatting_instance))
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# --- 修改：遍历 SubHeartflow 并检查触发器 ---
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active_stream_ids = list(heartflow.get_all_subheartflows_streams_ids()) # 需要 heartflow 提供此方法
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logger.trace(f"检查 {len(active_stream_ids)} 个活跃流是否足以开启心流对话...")
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for stream_id, chatting_instance in interest_items_snapshot:
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triggering_message = chatting_instance.last_triggering_message
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current_interest = chatting_instance.get_interest()
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for stream_id in active_stream_ids:
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sub_hf = heartflow.get_subheartflow(stream_id)
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if not sub_hf:
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logger.warning(f"监控循环: 无法获取活跃流 {stream_id} 的 sub_hf")
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continue
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# 添加调试日志，检查触发条件
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# logger.debug(f"[兴趣监控][{stream_id}] 当前兴趣: {current_interest:.2f}, 阈值: {INTEREST_LEVEL_REPLY_THRESHOLD}, 触发消息存在: {triggering_message is not None}")
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# --- 获取 Observation 和消息列表 --- #
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observation = sub_hf._get_primary_observation()
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if not observation:
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logger.warning(f"[{stream_id}] SubHeartflow 没有在观察，无法检查触发器。")
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continue
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observed_messages = observation.talking_message # 获取消息字典列表
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# --- 结束获取 --- #
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if current_interest > INTEREST_LEVEL_REPLY_THRESHOLD and triggering_message is not None:
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logger.info(f"[{stream_id}] 检测到高兴趣度 ({current_interest:.2f} > {INTEREST_LEVEL_REPLY_THRESHOLD}). 基于消息 ID: {triggering_message.message_info.message_id} 的上下文触发回复") # 更新日志信息使其更清晰
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should_trigger = False
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try:
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# check_reply_trigger 可以选择性地接收 observed_messages 作为参数
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should_trigger = await sub_hf.check_reply_trigger() # 目前 check_reply_trigger 还不处理这个
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except Exception as e:
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logger.error(f"错误调用 check_reply_trigger 流 {stream_id}: {e}")
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logger.error(traceback.format_exc())
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chatting_instance.reset_trigger_info()
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logger.debug(f"[{stream_id}] Trigger info reset before starting reply task.")
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if should_trigger:
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logger.info(f"[{stream_id}] SubHeartflow 决定开启心流对话。")
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# 调用修改后的处理函数，传递 stream_id 和 observed_messages
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asyncio.create_task(self._process_triggered_reply(stream_id, observed_messages))
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asyncio.create_task(self._process_triggered_reply(stream_id, triggering_message))
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except asyncio.CancelledError:
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logger.info("Interest monitor loop cancelled.")
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logger.info("兴趣监控循环已取消。")
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break
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except Exception as e:
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logger.error(f"Error in interest monitor loop: {e}")
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logger.error(f"兴趣监控循环错误: {e}")
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logger.error(traceback.format_exc())
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await asyncio.sleep(5)
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await asyncio.sleep(5) # 发生错误时等待
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async def _process_triggered_reply(self, stream_id: str, triggering_message: MessageRecv):
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"""Helper coroutine to handle the processing of a triggered reply based on interest level."""
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async def _process_triggered_reply(self, stream_id: str, observed_messages: List[dict]):
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"""Helper coroutine to handle the processing of a triggered reply based on SubHeartflow trigger."""
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try:
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logger.info(f"[{stream_id}] Starting level-triggered reply generation for message ID: {triggering_message.message_info.message_id}...")
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await self.trigger_reply_generation(triggering_message)
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logger.info(f"[{stream_id}] SubHeartflow 触发回复...")
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# 调用修改后的 trigger_reply_generation
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await self.trigger_reply_generation(stream_id, observed_messages)
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# 在回复处理后降低兴趣度，降低固定值：新阈值的一半
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decrease_value = INTEREST_LEVEL_REPLY_THRESHOLD / 2
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self.interest_manager.decrease_interest(stream_id, value=decrease_value)
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post_trigger_interest = self.interest_manager.get_interest(stream_id)
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# 更新日志以反映降低的是基于新阈值的固定值
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logger.info(f"[{stream_id}] Interest decreased by fixed value {decrease_value:.2f} (LevelThreshold/2) after processing level-triggered reply. Current interest: {post_trigger_interest:.2f}")
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# --- 调整兴趣降低逻辑 ---
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# 这里的兴趣降低可能不再适用，或者需要基于不同的逻辑
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# 例如，回复后可以将 SubHeartflow 的某种"回复意愿"状态重置
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# 暂时注释掉，或根据需要调整
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# chatting_instance = self.interest_manager.get_interest_chatting(stream_id)
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# if chatting_instance:
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# decrease_value = chatting_instance.trigger_threshold / 2 # 使用实例的阈值
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# self.interest_manager.decrease_interest(stream_id, value=decrease_value)
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# post_trigger_interest = self.interest_manager.get_interest(stream_id) # 获取更新后的兴趣
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# logger.info(f"[{stream_id}] Interest decreased by {decrease_value:.2f} (InstanceThreshold/2) after processing triggered reply. Current interest: {post_trigger_interest:.2f}")
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# else:
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# logger.warning(f"[{stream_id}] Could not find InterestChatting instance after reply processing to decrease interest.")
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logger.debug(f"[{stream_id}] Reply processing finished. (Interest decrease logic needs review).")
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except Exception as e:
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logger.error(f"Error processing level-triggered reply for stream_id {stream_id}, context message_id {triggering_message.message_info.message_id}: {e}")
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logger.error(f"Error processing SubHeartflow-triggered reply for stream_id {stream_id}: {e}") # 更新日志信息
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logger.error(traceback.format_exc())
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# --- 结束修改 ---
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async def _create_thinking_message(self, message: MessageRecv):
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"""创建思考消息 (从 message 获取信息)"""
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chat = message.chat_stream
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if not chat:
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logger.error(f"Cannot create thinking message, chat_stream is None for message ID: {message.message_info.message_id}")
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return None
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userinfo = message.message_info.user_info # 发起思考的用户（即原始消息发送者）
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messageinfo = message.message_info # 原始消息信息
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async def _create_thinking_message(self, anchor_message: Optional[MessageRecv]):
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"""创建思考消息 (尝试锚定到 anchor_message)"""
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if not anchor_message or not anchor_message.chat_stream:
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logger.error("无法创建思考消息，缺少有效的锚点消息或聊天流。")
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return None
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chat = anchor_message.chat_stream
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messageinfo = anchor_message.message_info
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bot_user_info = UserInfo(
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user_id=global_config.BOT_QQ,
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user_nickname=global_config.BOT_NICKNAME,
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@@ -133,17 +152,21 @@ class HeartFC_Chat:
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thinking_message = MessageThinking(
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message_id=thinking_id,
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chat_stream=chat,
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bot_user_info=bot_user_info, # 思考消息的发出者是 bot
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reply=message, # 回复的是原始消息
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bot_user_info=bot_user_info,
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reply=anchor_message, # 回复的是锚点消息
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thinking_start_time=thinking_time_point,
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)
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MessageManager().add_message(thinking_message)
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return thinking_id
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async def _send_response_messages(self, message: MessageRecv, response_set: List[str], thinking_id) -> MessageSending:
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chat = message.chat_stream
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async def _send_response_messages(self, anchor_message: Optional[MessageRecv], response_set: List[str], thinking_id) -> Optional[MessageSending]:
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"""发送回复消息 (尝试锚定到 anchor_message)"""
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if not anchor_message or not anchor_message.chat_stream:
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logger.error("无法发送回复，缺少有效的锚点消息或聊天流。")
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return None
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chat = anchor_message.chat_stream
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container = MessageManager().get_container(chat.stream_id)
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thinking_message = None
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for msg in container.messages:
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@@ -152,26 +175,26 @@ class HeartFC_Chat:
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container.messages.remove(msg)
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break
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if not thinking_message:
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logger.warning("未找到对应的思考消息，可能已超时被移除")
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logger.warning(f"[{chat.stream_id}] 未找到对应的思考消息 {thinking_id}，可能已超时被移除")
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return None
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thinking_start_time = thinking_message.thinking_start_time
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message_set = MessageSet(chat, thinking_id)
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mark_head = False
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first_bot_msg = None
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for msg in response_set:
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message_segment = Seg(type="text", data=msg)
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for msg_text in response_set:
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message_segment = Seg(type="text", data=msg_text)
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bot_message = MessageSending(
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message_id=thinking_id,
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message_id=thinking_id, # 使用 thinking_id 作为批次标识
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chat_stream=chat,
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bot_user_info=UserInfo(
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user_id=global_config.BOT_QQ,
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user_nickname=global_config.BOT_NICKNAME,
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platform=message.message_info.platform, # 从传入的 message 获取 platform
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platform=anchor_message.message_info.platform,
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),
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sender_info=message.message_info.user_info, # 发送给谁
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sender_info=anchor_message.message_info.user_info, # 发送给锚点消息的用户
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message_segment=message_segment,
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reply=message, # 回复原始消息
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reply=anchor_message, # 回复锚点消息
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is_head=not mark_head,
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is_emoji=False,
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thinking_start_time=thinking_start_time,
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@@ -180,185 +203,277 @@ class HeartFC_Chat:
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mark_head = True
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first_bot_msg = bot_message
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message_set.add_message(bot_message)
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MessageManager().add_message(message_set)
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return first_bot_msg
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async def _handle_emoji(self, message: MessageRecv, response_set, send_emoji=""):
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"""处理表情包 (从 message 获取信息)"""
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chat = message.chat_stream
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if message_set.messages: # 确保有消息才添加
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MessageManager().add_message(message_set)
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return first_bot_msg
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else:
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logger.warning(f"[{chat.stream_id}] 没有生成有效的回复消息集，无法发送。")
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return None
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async def _handle_emoji(self, anchor_message: Optional[MessageRecv], response_set, send_emoji=""):
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"""处理表情包 (尝试锚定到 anchor_message)"""
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if not anchor_message or not anchor_message.chat_stream:
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logger.error("无法处理表情包，缺少有效的锚点消息或聊天流。")
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return
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chat = anchor_message.chat_stream
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if send_emoji:
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emoji_raw = await emoji_manager.get_emoji_for_text(send_emoji)
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else:
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emoji_text_source = "".join(response_set) if response_set else ""
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emoji_raw = await emoji_manager.get_emoji_for_text(emoji_text_source)
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if emoji_raw:
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emoji_path, description = emoji_raw
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emoji_cq = image_path_to_base64(emoji_path)
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thinking_time_point = round(message.message_info.time, 2)
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# 使用当前时间戳，因为没有原始消息的时间戳
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thinking_time_point = round(time.time(), 2)
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message_segment = Seg(type="emoji", data=emoji_cq)
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bot_message = MessageSending(
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message_id="mt" + str(thinking_time_point),
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message_id="me" + str(thinking_time_point), # 使用不同的 ID 前缀?
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chat_stream=chat,
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bot_user_info=UserInfo(
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user_id=global_config.BOT_QQ,
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user_nickname=global_config.BOT_NICKNAME,
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platform=message.message_info.platform,
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platform=anchor_message.message_info.platform,
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),
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sender_info=message.message_info.user_info, # 发送给谁
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sender_info=anchor_message.message_info.user_info,
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message_segment=message_segment,
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reply=message, # 回复原始消息
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reply=anchor_message, # 回复锚点消息
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is_head=False,
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is_emoji=True,
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)
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MessageManager().add_message(bot_message)
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async def _update_relationship(self, message: MessageRecv, response_set):
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"""更新关系情绪"""
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async def _update_relationship(self, anchor_message: Optional[MessageRecv], response_set):
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"""更新关系情绪 (尝试基于 anchor_message)"""
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if not anchor_message or not anchor_message.chat_stream:
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logger.error("无法更新关系情绪，缺少有效的锚点消息或聊天流。")
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return
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# 关系更新依赖于理解回复是针对谁的，以及原始消息的上下文
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# 这里的实现可能需要调整，取决于关系管理器如何工作
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ori_response = ",".join(response_set)
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stance, emotion = await self.gpt._get_emotion_tags(ori_response, message.processed_plain_text)
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# 注意：anchor_message.processed_plain_text 是锚点消息的文本，不一定是思考的全部上下文
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stance, emotion = await self.gpt._get_emotion_tags(ori_response, anchor_message.processed_plain_text)
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await relationship_manager.calculate_update_relationship_value(
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chat_stream=message.chat_stream, label=emotion, stance=stance
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chat_stream=anchor_message.chat_stream, # 使用锚点消息的流
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label=emotion,
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stance=stance
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)
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self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor)
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async def trigger_reply_generation(self, message: MessageRecv):
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"""根据意愿阈值触发的实际回复生成和发送逻辑 (V3 - 简化参数)"""
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chat = message.chat_stream
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userinfo = message.message_info.user_info
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messageinfo = message.message_info
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async def trigger_reply_generation(self, stream_id: str, observed_messages: List[dict]):
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"""根据 SubHeartflow 的触发信号生成回复 (基于观察)"""
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chat = None
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sub_hf = None
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anchor_message: Optional[MessageRecv] = None # <--- 重命名，用于锚定回复的消息对象
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userinfo: Optional[UserInfo] = None
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messageinfo: Optional[MessageInfo] = None
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timing_results = {}
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current_mind = None
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response_set = None
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thinking_id = None
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info_catcher = None
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try:
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# --- 1. 获取核心对象：ChatStream 和 SubHeartflow ---
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try:
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with Timer("观察", timing_results):
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sub_hf = heartflow.get_subheartflow(chat.stream_id)
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if not sub_hf:
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logger.warning(f"尝试观察时未找到 stream_id {chat.stream_id} 的 subheartflow")
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with Timer("获取聊天流和子心流", timing_results):
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||||
chat = chat_manager.get_stream(stream_id)
|
||||
if not chat:
|
||||
logger.error(f"[{stream_id}] 无法找到聊天流对象，无法生成回复。")
|
||||
return
|
||||
sub_hf = heartflow.get_subheartflow(stream_id)
|
||||
if not sub_hf:
|
||||
logger.error(f"[{stream_id}] 无法找到子心流对象，无法生成回复。")
|
||||
return
|
||||
await sub_hf.do_observe()
|
||||
except Exception as e:
|
||||
logger.error(f"心流观察失败: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
logger.error(f"[{stream_id}] 获取 ChatStream 或 SubHeartflow 时出错: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
return
|
||||
|
||||
container = MessageManager().get_container(chat.stream_id)
|
||||
thinking_count = container.count_thinking_messages()
|
||||
max_thinking_messages = getattr(global_config, 'max_concurrent_thinking_messages', 3)
|
||||
if thinking_count >= max_thinking_messages:
|
||||
logger.warning(f"聊天流 {chat.stream_id} 已有 {thinking_count} 条思考消息，取消回复。触发消息: {message.processed_plain_text[:30]}...")
|
||||
# --- 2. 尝试从 observed_messages 重建最后一条消息作为锚点 --- #
|
||||
try:
|
||||
with Timer("获取最后消息锚点", timing_results):
|
||||
if observed_messages:
|
||||
last_msg_dict = observed_messages[-1] # 直接从传入列表获取最后一条
|
||||
# 尝试从字典重建 MessageRecv 对象（可能需要调整 MessageRecv 的构造方式或创建一个辅助函数）
|
||||
# 这是一个简化示例，假设 MessageRecv 可以从字典初始化
|
||||
# 你可能需要根据 MessageRecv 的实际 __init__ 来调整
|
||||
try:
|
||||
anchor_message = MessageRecv(last_msg_dict) # 假设 MessageRecv 支持从字典创建
|
||||
userinfo = anchor_message.message_info.user_info
|
||||
messageinfo = anchor_message.message_info
|
||||
logger.debug(f"[{stream_id}] 获取到最后消息作为锚点: ID={messageinfo.message_id}, Sender={userinfo.user_nickname}")
|
||||
except Exception as e_msg:
|
||||
logger.error(f"[{stream_id}] 从字典重建最后消息 MessageRecv 失败: {e_msg}. 字典: {last_msg_dict}")
|
||||
anchor_message = None # 重置以表示失败
|
||||
else:
|
||||
logger.warning(f"[{stream_id}] 无法从 Observation 获取最后消息锚点。")
|
||||
except Exception as e:
|
||||
logger.error(f"[{stream_id}] 获取最后消息锚点时出错: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
# 即使没有锚点，也可能继续尝试生成非回复性消息，取决于后续逻辑
|
||||
|
||||
# --- 3. 检查是否能继续 (需要思考消息锚点) ---
|
||||
if not anchor_message:
|
||||
logger.warning(f"[{stream_id}] 没有有效的消息锚点，无法创建思考消息和发送回复。取消回复生成。")
|
||||
return
|
||||
|
||||
# --- 4. 检查并发思考限制 (使用 anchor_message 简化获取) ---
|
||||
try:
|
||||
container = MessageManager().get_container(chat.stream_id)
|
||||
thinking_count = container.count_thinking_messages()
|
||||
max_thinking_messages = getattr(global_config, 'max_concurrent_thinking_messages', 3)
|
||||
if thinking_count >= max_thinking_messages:
|
||||
logger.warning(f"聊天流 {chat.stream_id} 已有 {thinking_count} 条思考消息，取消回复。")
|
||||
return
|
||||
except Exception as e:
|
||||
logger.error(f"[{stream_id}] 检查并发思考限制时出错: {e}")
|
||||
return
|
||||
|
||||
# --- 5. 创建思考消息 (使用 anchor_message) ---
|
||||
try:
|
||||
with Timer("创建思考消息", timing_results):
|
||||
thinking_id = await self._create_thinking_message(message)
|
||||
# 注意：这里传递 anchor_message 给 _create_thinking_message
|
||||
thinking_id = await self._create_thinking_message(anchor_message)
|
||||
except Exception as e:
|
||||
logger.error(f"心流创建思考消息失败: {e}")
|
||||
logger.error(f"[{stream_id}] 创建思考消息失败: {e}")
|
||||
return
|
||||
if not thinking_id:
|
||||
logger.error("未能成功创建思考消息 ID，无法继续回复流程。")
|
||||
logger.error(f"[{stream_id}] 未能成功创建思考消息 ID，无法继续回复流程。")
|
||||
return
|
||||
|
||||
logger.trace(f"创建捕捉器，thinking_id:{thinking_id}")
|
||||
# --- 6. 信息捕捉器 (使用 anchor_message) ---
|
||||
logger.trace(f"[{stream_id}] 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)
|
||||
info_catcher.catch_decide_to_response(anchor_message)
|
||||
|
||||
# --- 7. 思考前使用工具 --- #
|
||||
get_mid_memory_id = []
|
||||
tool_result_info = {}
|
||||
send_emoji = ""
|
||||
observation_context_text = "" # 从 observation 获取上下文文本
|
||||
try:
|
||||
with Timer("思考前使用工具", timing_results):
|
||||
tool_result = await self.tool_user.use_tool(
|
||||
message.processed_plain_text,
|
||||
userinfo.user_nickname,
|
||||
chat,
|
||||
heartflow.get_subheartflow(chat.stream_id),
|
||||
)
|
||||
if tool_result.get("used_tools", False):
|
||||
if "structured_info" in tool_result:
|
||||
tool_result_info = tool_result["structured_info"]
|
||||
get_mid_memory_id = []
|
||||
for tool_name, tool_data in tool_result_info.items():
|
||||
if tool_name == "mid_chat_mem":
|
||||
for mid_memory in tool_data:
|
||||
get_mid_memory_id.append(mid_memory["content"])
|
||||
if tool_name == "send_emoji":
|
||||
send_emoji = tool_data[0]["content"]
|
||||
except Exception as e:
|
||||
logger.error(f"思考前工具调用失败: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
# --- 使用传入的 observed_messages 构建上下文文本 --- #
|
||||
if observed_messages:
|
||||
# 可以选择转换全部消息，或只转换最后几条
|
||||
# 这里示例转换全部消息
|
||||
context_texts = []
|
||||
for msg_dict in observed_messages:
|
||||
# 假设 detailed_plain_text 字段包含所需文本
|
||||
# 你可能需要更复杂的逻辑来格式化，例如添加发送者和时间
|
||||
text = msg_dict.get('detailed_plain_text', '')
|
||||
if text: context_texts.append(text)
|
||||
observation_context_text = "\n".join(context_texts)
|
||||
logger.debug(f"[{stream_id}] Context for tools:\n{observation_context_text[-200:]}...") # 打印部分上下文
|
||||
else:
|
||||
logger.warning(f"[{stream_id}] observed_messages 列表为空，无法为工具提供上下文。")
|
||||
|
||||
current_mind, past_mind = "", ""
|
||||
try:
|
||||
with Timer("思考前脑内状态", timing_results):
|
||||
sub_hf = heartflow.get_subheartflow(chat.stream_id)
|
||||
if sub_hf:
|
||||
current_mind, past_mind = await sub_hf.do_thinking_before_reply(
|
||||
message_txt=message.processed_plain_text,
|
||||
sender_info=userinfo,
|
||||
if observation_context_text:
|
||||
with Timer("思考前使用工具", timing_results):
|
||||
tool_result = await self.tool_user.use_tool(
|
||||
message_txt=observation_context_text, # <--- 使用观察上下文
|
||||
chat_stream=chat,
|
||||
obs_id=get_mid_memory_id,
|
||||
extra_info=tool_result_info,
|
||||
sub_heartflow=sub_hf
|
||||
)
|
||||
else:
|
||||
logger.warning(f"尝试思考前状态时未找到 stream_id {chat.stream_id} 的 subheartflow")
|
||||
if tool_result.get("used_tools", False):
|
||||
if "structured_info" in tool_result:
|
||||
tool_result_info = tool_result["structured_info"]
|
||||
get_mid_memory_id = []
|
||||
for tool_name, tool_data in tool_result_info.items():
|
||||
if tool_name == "mid_chat_mem":
|
||||
for mid_memory in tool_data:
|
||||
get_mid_memory_id.append(mid_memory["content"])
|
||||
if tool_name == "send_emoji":
|
||||
send_emoji = tool_data[0]["content"]
|
||||
except Exception as e:
|
||||
logger.error(f"心流思考前脑内状态失败: {e}")
|
||||
logger.error(f"[{stream_id}] 思考前工具调用失败: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
if info_catcher:
|
||||
info_catcher.catch_afer_shf_step(timing_results.get("思考前脑内状态"), past_mind, current_mind)
|
||||
|
||||
# --- 8. 调用 SubHeartflow 进行思考 (不传递具体消息文本和发送者) ---
|
||||
try:
|
||||
with Timer("生成回复", timing_results):
|
||||
response_set = await self.gpt.generate_response(message, thinking_id)
|
||||
with Timer("生成内心想法(SubHF)", timing_results):
|
||||
# 不再传递 message_txt 和 sender_info, SubHeartflow 应基于其内部观察
|
||||
current_mind, past_mind = await sub_hf.do_thinking_before_reply(
|
||||
chat_stream=chat,
|
||||
extra_info=tool_result_info,
|
||||
obs_id=get_mid_memory_id,
|
||||
)
|
||||
logger.info(f"[{stream_id}] SubHeartflow 思考完成: {current_mind}")
|
||||
except Exception as e:
|
||||
logger.error(f"GPT 生成回复失败: {e}")
|
||||
logger.error(f"[{stream_id}] SubHeartflow 思考失败: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
if info_catcher: info_catcher.done_catch()
|
||||
return # 思考失败则不继续
|
||||
if info_catcher:
|
||||
info_catcher.catch_afer_shf_step(timing_results.get("生成内心想法(SubHF)"), past_mind, current_mind)
|
||||
|
||||
# --- 9. 调用 ResponseGenerator 生成回复 (使用 anchor_message 和 current_mind) ---
|
||||
try:
|
||||
with Timer("生成最终回复(GPT)", timing_results):
|
||||
response_set = await self.gpt.generate_response(anchor_message, thinking_id, current_mind=current_mind)
|
||||
except Exception as e:
|
||||
logger.error(f"[{stream_id}] GPT 生成回复失败: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
if info_catcher: info_catcher.done_catch()
|
||||
return
|
||||
if info_catcher:
|
||||
info_catcher.catch_after_generate_response(timing_results.get("生成回复"))
|
||||
info_catcher.catch_after_generate_response(timing_results.get("生成最终回复(GPT)"))
|
||||
if not response_set:
|
||||
logger.info("回复生成失败，返回为空")
|
||||
logger.info(f"[{stream_id}] 回复生成失败或为空。")
|
||||
if info_catcher: info_catcher.done_catch()
|
||||
return
|
||||
|
||||
# --- 10. 发送消息 (使用 anchor_message) ---
|
||||
first_bot_msg = None
|
||||
try:
|
||||
with Timer("发送消息", timing_results):
|
||||
first_bot_msg = await self._send_response_messages(message, response_set, thinking_id)
|
||||
first_bot_msg = await self._send_response_messages(anchor_message, response_set, thinking_id)
|
||||
except Exception as e:
|
||||
logger.error(f"心流发送消息失败: {e}")
|
||||
logger.error(f"[{stream_id}] 发送消息失败: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
if info_catcher:
|
||||
info_catcher.catch_after_response(timing_results.get("发送消息"), response_set, first_bot_msg)
|
||||
info_catcher.done_catch()
|
||||
info_catcher.done_catch() # 完成捕捉
|
||||
|
||||
# --- 11. 处理表情包 (使用 anchor_message) ---
|
||||
try:
|
||||
with Timer("处理表情包", timing_results):
|
||||
if send_emoji:
|
||||
logger.info(f"麦麦决定发送表情包{send_emoji}")
|
||||
await self._handle_emoji(message, response_set, send_emoji)
|
||||
logger.info(f"[{stream_id}] 决定发送表情包 {send_emoji}")
|
||||
await self._handle_emoji(anchor_message, response_set, send_emoji)
|
||||
except Exception as e:
|
||||
logger.error(f"心流处理表情包失败: {e}")
|
||||
logger.error(f"[{stream_id}] 处理表情包失败: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
|
||||
# --- 12. 记录性能日志 --- #
|
||||
timing_str = " | ".join([f"{step}: {duration:.2f}秒" for step, duration in timing_results.items()])
|
||||
trigger_msg = message.processed_plain_text
|
||||
response_msg = " ".join(response_set) if response_set else "无回复"
|
||||
logger.info(f"回复任务完成: 触发消息: {trigger_msg[:20]}... | 思维消息: {response_msg[:20]}... | 性能计时: {timing_str}")
|
||||
logger.info(f"[{stream_id}] 回复任务完成 (Observation Triggered): | 思维消息: {response_msg[:30]}... | 性能计时: {timing_str}")
|
||||
|
||||
if first_bot_msg:
|
||||
# --- 13. 更新关系情绪 (使用 anchor_message) ---
|
||||
if first_bot_msg: # 仅在成功发送消息后
|
||||
try:
|
||||
with Timer("更新关系情绪", timing_results):
|
||||
await self._update_relationship(message, response_set)
|
||||
await self._update_relationship(anchor_message, response_set)
|
||||
except Exception as e:
|
||||
logger.error(f"更新关系情绪失败: {e}")
|
||||
logger.error(f"[{stream_id}] 更新关系情绪失败: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"回复生成任务失败 (trigger_reply_generation V3): {e}")
|
||||
logger.error(f"回复生成任务失败 (trigger_reply_generation V4 - Observation Triggered): {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
|
||||
finally:
|
||||
pass
|
||||
# 可以在这里添加清理逻辑，如果有的话
|
||||
pass
|
||||
# --- 结束重构 ---
|
||||
|
||||
# _create_thinking_message, _send_response_messages, _handle_emoji, _update_relationship
|
||||
# 这几个辅助方法目前仍然依赖 MessageRecv 对象。
|
||||
# 如果无法可靠地从 Observation 获取并重建最后一条消息的 MessageRecv，
|
||||
# 或者希望回复不锚定具体消息，那么这些方法也需要进一步重构。
|
||||
|
||||
Reference in New Issue
Block a user