import asyncio import time import traceback from typing import List, Optional, Dict, Any from src.plugins.chat.message import MessageRecv, BaseMessageInfo, MessageThinking, MessageSending from src.plugins.chat.message import MessageSet, Seg # Local import needed after move from src.plugins.chat.chat_stream import ChatStream from src.plugins.chat.message import UserInfo from src.plugins.chat.chat_stream import chat_manager from src.common.logger import get_module_logger, LogConfig, PFC_STYLE_CONFIG # 引入 DEFAULT_CONFIG from src.plugins.models.utils_model import LLMRequest from src.config.config import global_config from src.plugins.chat.utils_image import image_path_to_base64 # Local import needed after move from src.plugins.utils.timer_calculater import Timer # <--- Import Timer from src.plugins.heartFC_chat.heartFC_generator import HeartFCGenerator from src.do_tool.tool_use import ToolUser from ..chat.message_sender import message_manager # <-- Import the global manager from src.plugins.chat.emoji_manager import emoji_manager from src.plugins.utils.json_utils import process_llm_tool_response # 导入新的JSON工具 from src.heart_flow.sub_mind import SubMind from src.heart_flow.observation import Observation # --- End import --- INITIAL_DURATION = 60.0 # 定义日志配置 (使用 loguru 格式) interest_log_config = LogConfig( console_format=PFC_STYLE_CONFIG["console_format"], # 使用默认控制台格式 file_format=PFC_STYLE_CONFIG["file_format"], # 使用默认文件格式 ) logger = get_module_logger("HeartFCLoop", config=interest_log_config) # Logger Name Changed PLANNER_TOOL_DEFINITION = [ { "type": "function", "function": { "name": "decide_reply_action", "description": "根据当前聊天内容和上下文，决定机器人是否应该回复以及如何回复。", "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": ["no_reply", "text_reply", "emoji_reply"], "description": "决定采取的行动：'no_reply'(不回复), 'text_reply'(文本回复, 可选附带表情) 或 'emoji_reply'(仅表情回复)。", }, "reasoning": {"type": "string", "description": "做出此决定的简要理由。"}, "emoji_query": { "type": "string", "description": "如果行动是'emoji_reply'，指定表情的主题或概念。如果行动是'text_reply'且希望在文本后追加表情，也在此指定表情主题。", }, }, "required": ["action", "reasoning"], }, }, } ] class HeartFChatting: """ 管理一个连续的Plan-Replier-Sender循环 用于在特定聊天流中生成回复。 其生命周期现在由其关联的 SubHeartflow 的 FOCUSED 状态控制。 """ def __init__( self, chat_id: str, sub_mind: SubMind, observations: Observation ): """ HeartFChatting 初始化函数 参数: chat_id: 聊天流唯一标识符(如stream_id) """ # 基础属性 self.stream_id: str = chat_id # 聊天流ID self.chat_stream: Optional[ChatStream] = None # 关联的聊天流 self.sub_mind: SubMind = sub_mind # 关联的子思维 self.observations: Observation = observations # 关联的观察 # 初始化状态控制 self._initialized = False # 是否已初始化标志 self._processing_lock = asyncio.Lock() # 处理锁(确保单次Plan-Replier-Sender周期) # 依赖注入存储 self.gpt_instance = HeartFCGenerator() # 文本回复生成器 self.tool_user = ToolUser() # 工具使用实例 # LLM规划器配置 self.planner_llm = LLMRequest( model=global_config.llm_normal, temperature=global_config.llm_normal["temp"], max_tokens=1000, request_type="action_planning", # 用于动作规划 ) # 循环控制内部状态 self._loop_active: bool = False # 循环是否正在运行 self._loop_task: Optional[asyncio.Task] = None # 主循环任务 def _get_log_prefix(self) -> str: """获取日志前缀，包含可读的流名称""" stream_name = chat_manager.get_stream_name(self.stream_id) or self.stream_id return f"[{stream_name}]" async def _initialize(self) -> bool: """ 懒初始化以使用提供的标识符解析chat_stream和sub_hf。 确保实例已准备好处理触发器。 """ if self._initialized: return True log_prefix = self._get_log_prefix() # 获取前缀 try: self.chat_stream = chat_manager.get_stream(self.stream_id) if not self.chat_stream: logger.error(f"{log_prefix} 获取ChatStream失败。") return False self._initialized = True logger.info(f"麦麦感觉到了，激发了HeartFChatting{log_prefix} 初始化成功。") return True except Exception as e: logger.error(f"{log_prefix} 初始化失败: {e}") logger.error(traceback.format_exc()) return False async def start(self): """ 显式尝试启动 HeartFChatting 的主循环。 如果循环未激活，则启动循环。 """ log_prefix = self._get_log_prefix() if not self._initialized: if not await self._initialize(): logger.error(f"{log_prefix} 无法启动循环: 初始化失败。") return logger.info(f"{log_prefix} 尝试显式启动循环...") await self._start_loop_if_needed() async def _start_loop_if_needed(self): """检查是否需要启动主循环，如果未激活则启动。""" log_prefix = self._get_log_prefix() should_start_loop = False # 直接检查是否激活，无需检查计时器 if not self._loop_active: should_start_loop = True self._loop_active = True # 标记为活动，防止重复启动 if should_start_loop: # 检查是否已有任务在运行（理论上不应该，因为 _loop_active=False） if self._loop_task and not self._loop_task.done(): logger.warning(f"{log_prefix} 发现之前的循环任务仍在运行（不符合预期）。取消旧任务。") self._loop_task.cancel() try: # 等待旧任务确实被取消 await asyncio.wait_for(self._loop_task, timeout=0.5) except (asyncio.CancelledError, asyncio.TimeoutError): pass # 忽略取消或超时错误 self._loop_task = None # 清理旧任务引用 logger.info(f"{log_prefix} 循环未激活，启动主循环...") # 创建新的循环任务 self._loop_task = asyncio.create_task(self._run_pf_loop()) # 添加完成回调 self._loop_task.add_done_callback(self._handle_loop_completion) # else: # logger.trace(f"{log_prefix} 不需要启动循环（已激活）") # 可以取消注释以进行调试 def _handle_loop_completion(self, task: asyncio.Task): """当 _run_pf_loop 任务完成时执行的回调。""" log_prefix = self._get_log_prefix() try: exception = task.exception() if exception: logger.error(f"{log_prefix} HeartFChatting: 麦麦脱离了聊天(异常): {exception}") logger.error(traceback.format_exc()) # Log full traceback for exceptions else: # Loop completing normally now means it was cancelled/shutdown externally logger.info(f"{log_prefix} HeartFChatting: 麦麦脱离了聊天 (外部停止)") except asyncio.CancelledError: logger.info(f"{log_prefix} HeartFChatting: 麦麦脱离了聊天(任务取消)") finally: self._loop_active = False self._loop_task = None if self._processing_lock.locked(): logger.warning(f"{log_prefix} HeartFChatting: 处理锁在循环结束时仍被锁定，强制释放。") self._processing_lock.release() async def _run_pf_loop(self): """ 主循环，持续进行计划并可能回复消息，直到被外部取消。 管理每个循环周期的处理锁。 """ log_prefix = self._get_log_prefix() logger.info(f"{log_prefix} HeartFChatting: 麦麦打算好好聊聊 (进入专注模式)") try: thinking_id = "" while True: # Loop indefinitely until cancelled cycle_timers = {} # <--- Initialize timers dict for this cycle # Access MessageManager directly if message_manager.check_if_sending_message_exist(self.stream_id, thinking_id): # logger.info(f"{log_prefix} HeartFChatting: 麦麦还在发消息，等会再规划") await asyncio.sleep(1) continue else: # logger.info(f"{log_prefix} HeartFChatting: 麦麦不发消息了，开始规划") pass # 记录循环周期开始时间，用于计时和休眠计算 loop_cycle_start_time = time.monotonic() action_taken_this_cycle = False acquired_lock = False planner_start_db_time = 0.0 # 初始化 try: with Timer("Total Cycle", cycle_timers) as _total_timer: # <--- Start total cycle timer # Use try_acquire pattern or timeout? await self._processing_lock.acquire() acquired_lock = True # logger.debug(f"{log_prefix} HeartFChatting: 循环获取到处理锁") # 在规划前记录数据库时间戳 planner_start_db_time = time.time() # --- Planner --- # planner_result = {} with Timer("Planner", cycle_timers): # <--- Start Planner timer planner_result = await self._planner() action = planner_result.get("action", "error") reasoning = planner_result.get("reasoning", "Planner did not provide reasoning.") emoji_query = planner_result.get("emoji_query", "") llm_error = planner_result.get("llm_error", False) if llm_error: logger.error(f"{log_prefix} Planner LLM 失败，跳过本周期回复尝试。理由: {reasoning}") # Optionally add a longer sleep? action_taken_this_cycle = False # Ensure no action is counted # Continue to sleep logic elif action == "text_reply": logger.debug(f"{log_prefix} HeartFChatting: 麦麦决定回复文本. 理由: {reasoning}") action_taken_this_cycle = True anchor_message = await self._get_anchor_message() if not anchor_message: logger.error(f"{log_prefix} 循环: 无法获取锚点消息用于回复. 跳过周期.") else: # --- Create Thinking Message (Moved) --- thinking_id = await self._create_thinking_message(anchor_message) if not thinking_id: logger.error(f"{log_prefix} 循环: 无法创建思考ID. 跳过周期.") else: replier_result = None try: # --- Replier Work --- # with Timer("Replier", cycle_timers): # <--- Start Replier timer replier_result = await self._replier_work( anchor_message=anchor_message, thinking_id=thinking_id, reason=reasoning, ) except Exception as e_replier: logger.error(f"{log_prefix} 循环: 回复器工作失败: {e_replier}") # self._cleanup_thinking_message(thinking_id) <-- Remove cleanup call if replier_result: # --- Sender Work --- # try: with Timer("Sender", cycle_timers): # <--- Start Sender timer await self._sender( thinking_id=thinking_id, anchor_message=anchor_message, response_set=replier_result, send_emoji=emoji_query, ) # logger.info(f"{log_prefix} 循环: 发送器完成成功.") except Exception as e_sender: logger.error(f"{log_prefix} 循环: 发送器失败: {e_sender}") # _sender should handle cleanup, but double check # self._cleanup_thinking_message(thinking_id) <-- Remove cleanup call else: logger.warning(f"{log_prefix} 循环: 回复器未产生结果. 跳过发送.") # self._cleanup_thinking_message(thinking_id) <-- Remove cleanup call elif action == "emoji_reply": logger.info( f"{log_prefix} HeartFChatting: 麦麦决定回复表情 ('{emoji_query}'). 理由: {reasoning}" ) action_taken_this_cycle = True anchor = await self._get_anchor_message() if anchor: try: # --- Handle Emoji (Moved) --- # with Timer("Emoji Handler", cycle_timers): # <--- Start Emoji timer await self._handle_emoji(anchor, [], emoji_query) except Exception as e_emoji: logger.error(f"{log_prefix} 循环: 发送表情失败: {e_emoji}") else: logger.warning(f"{log_prefix} 循环: 无法发送表情, 无法获取锚点.") action_taken_this_cycle = True # 即使发送失败，Planner 也决策了动作 elif action == "no_reply": logger.info(f"{log_prefix} HeartFChatting: 麦麦决定不回复. 原因: {reasoning}") action_taken_this_cycle = False # 标记为未执行动作 # --- 新增：等待新消息 --- logger.debug(f"{log_prefix} HeartFChatting: 开始等待新消息 (自 {planner_start_db_time})...") observation = None observation = self.observations[0] if observation: with Timer("Wait New Msg", cycle_timers): # <--- Start Wait timer wait_start_time = time.monotonic() while True: # 检查是否有新消息 has_new = await observation.has_new_messages_since(planner_start_db_time) if has_new: logger.info(f"{log_prefix} HeartFChatting: 检测到新消息，结束等待。") break # 收到新消息，退出等待 # 检查等待是否超时（例如，防止无限等待） if time.monotonic() - wait_start_time > 60: # 等待60秒示例 logger.warning(f"{log_prefix} HeartFChatting: 等待新消息超时（60秒）。") break # 超时退出 # 等待一段时间再检查 try: await asyncio.sleep(1.5) # 检查间隔 except asyncio.CancelledError: logger.info(f"{log_prefix} 等待新消息的 sleep 被中断。") raise # 重新抛出取消错误，以便外层循环处理 else: logger.warning( f"{log_prefix} HeartFChatting: 无法获取 Observation 实例，无法等待新消息。" ) # --- 等待结束 --- elif action == "error": # Action specifically set to error by planner logger.error(f"{log_prefix} HeartFChatting: Planner返回错误状态. 原因: {reasoning}") action_taken_this_cycle = False else: # Unknown action from planner logger.warning( f"{log_prefix} HeartFChatting: Planner返回未知动作 '{action}'. 原因: {reasoning}" ) action_taken_this_cycle = False # --- Print Timer Results --- # if cycle_timers: # 先检查cycle_timers是否非空 timer_strings = [] for name, elapsed in cycle_timers.items(): # 直接格式化存储在字典中的浮点数 elapsed formatted_time = f"{elapsed * 1000:.2f}毫秒" if elapsed < 1 else f"{elapsed:.2f}秒" timer_strings.append(f"{name}: {formatted_time}") if timer_strings: # 如果有有效计时器数据才打印 logger.debug(f"{log_prefix} 该次决策耗时: {'; '.join(timer_strings)}") # --- Timer Decrement Removed --- # cycle_duration = time.monotonic() - loop_cycle_start_time except Exception as e_cycle: logger.error(f"{log_prefix} 循环周期执行时发生错误: {e_cycle}") logger.error(traceback.format_exc()) if acquired_lock and self._processing_lock.locked(): self._processing_lock.release() acquired_lock = False logger.warning(f"{log_prefix} 由于循环周期中的错误释放了处理锁.") finally: if acquired_lock: self._processing_lock.release() # logger.trace(f"{log_prefix} 循环释放了处理锁.") # Reduce noise if cycle_duration > 0.1: logger.debug(f"{log_prefix} HeartFChatting: 周期耗时 {cycle_duration:.2f}s.") # --- Delay --- # try: sleep_duration = 0.0 if not action_taken_this_cycle and cycle_duration < 1.5: sleep_duration = 1.5 - cycle_duration elif cycle_duration < 0.2: # Keep minimal sleep even after action sleep_duration = 0.2 if sleep_duration > 0: # logger.debug(f"{log_prefix} Sleeping for {sleep_duration:.2f}s") await asyncio.sleep(sleep_duration) except asyncio.CancelledError: logger.info(f"{log_prefix} Sleep interrupted, loop likely cancelling.") break # Exit loop immediately on cancellation except asyncio.CancelledError: logger.info(f"{log_prefix} HeartFChatting: 麦麦的聊天主循环被取消了") except Exception as e_loop_outer: logger.error(f"{log_prefix} HeartFChatting: 麦麦的聊天主循环意外出错: {e_loop_outer}") logger.error(traceback.format_exc()) finally: # State reset is primarily handled by _handle_loop_completion callback logger.info(f"{log_prefix} HeartFChatting: 麦麦的聊天主循环结束。") async def _planner(self) -> Dict[str, Any]: """ 规划器 (Planner): 使用LLM根据上下文决定是否和如何回复。 """ log_prefix = self._get_log_prefix() observed_messages: List[dict] = [] current_mind: Optional[str] = None llm_error = False try: observation = self.observations[0] await observation.observe() observed_messages = observation.talking_message observed_messages_str = observation.talking_message_str except Exception as e: logger.error(f"{log_prefix}[Planner] 获取观察信息时出错: {e}") try: current_mind, _past_mind = await self.sub_mind.do_thinking_before_reply() except Exception as e_subhf: logger.error(f"{log_prefix}[Planner] SubHeartflow 思考失败: {e_subhf}") current_mind = "[思考时出错]" # --- 使用 LLM 进行决策 --- # action = "no_reply" # 默认动作 emoji_query = "" # 默认表情查询 reasoning = "默认决策或获取决策失败" llm_error = False # LLM错误标志 try: prompt = await self._build_planner_prompt(observed_messages_str, current_mind, self.sub_mind.structured_info) payload = { "model": self.planner_llm.model_name, "messages": [{"role": "user", "content": prompt}], "tools": PLANNER_TOOL_DEFINITION, "tool_choice": {"type": "function", "function": {"name": "decide_reply_action"}}, } # 执行LLM请求 try: response = await self.planner_llm._execute_request( endpoint="/chat/completions", payload=payload, prompt=prompt ) except Exception as req_e: logger.error(f"{log_prefix}[Planner] LLM请求执行失败: {req_e}") return { "action": "error", "reasoning": f"LLM请求执行失败: {req_e}", "emoji_query": "", "current_mind": current_mind, "observed_messages": observed_messages, "llm_error": True, } # 使用辅助函数处理工具调用响应 success, arguments, error_msg = process_llm_tool_response( response, expected_tool_name="decide_reply_action", log_prefix=f"{log_prefix}[Planner] " ) if success: # 提取决策参数 action = arguments.get("action", "no_reply") reasoning = arguments.get("reasoning", "未提供理由") emoji_query = arguments.get("emoji_query", "") # 记录决策结果 logger.debug(f"{log_prefix}[Planner] 决策结果: {action}, 理由: {reasoning}, 表情查询: '{emoji_query}'") else: # 处理工具调用失败 logger.warning(f"{log_prefix}[Planner] {error_msg}") action = "error" reasoning = error_msg llm_error = True except Exception as llm_e: logger.error(f"{log_prefix}[Planner] Planner LLM处理过程中出错: {llm_e}") logger.error(traceback.format_exc()) # 记录完整堆栈以便调试 action = "error" reasoning = f"LLM处理失败: {llm_e}" llm_error = True # --- 结束 LLM 决策 --- # return { "action": action, "reasoning": reasoning, "emoji_query": emoji_query, "current_mind": current_mind, "observed_messages": observed_messages, "llm_error": llm_error, } async def _get_anchor_message(self) -> Optional[MessageRecv]: """ 重构观察到的最后一条消息作为回复的锚点， 如果重构失败或观察为空，则创建一个占位符。 """ try: placeholder_id = f"mid_pf_{int(time.time() * 1000)}" placeholder_user = UserInfo( user_id="system_trigger", user_nickname="System Trigger", platform=self.chat_stream.platform ) placeholder_msg_info = BaseMessageInfo( message_id=placeholder_id, platform=self.chat_stream.platform, group_info=self.chat_stream.group_info, user_info=placeholder_user, time=time.time(), ) placeholder_msg_dict = { "message_info": placeholder_msg_info.to_dict(), "processed_plain_text": "[System Trigger Context]", "raw_message": "", "time": placeholder_msg_info.time, } anchor_message = MessageRecv(placeholder_msg_dict) anchor_message.update_chat_stream(self.chat_stream) logger.info( f"{self._get_log_prefix()} Created placeholder anchor message: ID={anchor_message.message_info.message_id}" ) return anchor_message except Exception as e: logger.error(f"{self._get_log_prefix()} Error getting/creating anchor message: {e}") logger.error(traceback.format_exc()) return None # --- 发送器 (Sender) --- # async def _sender( self, thinking_id: str, anchor_message: MessageRecv, response_set: List[str], send_emoji: str, # Emoji query decided by planner or tools ): """ 发送器 (Sender): 使用本类的方法发送生成的回复。 处理相关的操作，如发送表情和更新关系。 """ log_prefix = self._get_log_prefix() first_bot_msg: Optional[MessageSending] = None # 尝试发送回复消息 first_bot_msg = await self._send_response_messages(anchor_message, response_set, thinking_id) if first_bot_msg: # --- 处理关联表情(如果指定) --- # if send_emoji: logger.info(f"{log_prefix}[Sender-{thinking_id}] 正在发送关联表情: '{send_emoji}'") # 优先使用first_bot_msg作为锚点，否则回退到原始锚点 emoji_anchor = first_bot_msg if first_bot_msg else anchor_message await self._handle_emoji(emoji_anchor, response_set, send_emoji) else: # logger.warning(f"{log_prefix}[Sender-{thinking_id}] 发送回复失败(_send_response_messages返回None)。思考消息{thinking_id}可能已被移除。") # 无需清理，因为_send_response_messages返回None意味着已处理/已删除 raise RuntimeError("发送回复失败，_send_response_messages返回None") async def shutdown(self): """优雅关闭HeartFChatting实例，取消活动循环任务""" log_prefix = self._get_log_prefix() logger.info(f"{log_prefix} 正在关闭HeartFChatting...") # 取消循环任务 if self._loop_task and not self._loop_task.done(): logger.info(f"{log_prefix} 正在取消HeartFChatting循环任务") self._loop_task.cancel() try: await asyncio.wait_for(self._loop_task, timeout=1.0) logger.info(f"{log_prefix} HeartFChatting循环任务已取消") except (asyncio.CancelledError, asyncio.TimeoutError): pass except Exception as e: logger.error(f"{log_prefix} 取消循环任务出错: {e}") else: logger.info(f"{log_prefix} 没有活动的HeartFChatting循环任务") # 清理状态 self._loop_active = False self._loop_task = None if self._processing_lock.locked(): self._processing_lock.release() logger.warning(f"{log_prefix} 已释放处理锁") logger.info(f"{log_prefix} HeartFChatting关闭完成") async def _build_planner_prompt( self, observed_messages_str: str, current_mind: Optional[str], structured_info: Dict[str, Any] ) -> str: """构建 Planner LLM 的提示词""" prompt = f"你的名字是 {global_config.BOT_NICKNAME}。是一个女大二学生，正在QQ聊天，正在决定是否以及如何回应当前的聊天。\n" if structured_info: prompt += f"以下是一些额外的信息：\n{structured_info}\n" if observed_messages_str: prompt += "观察到的最新聊天内容如下 (最近的消息在最后)：\n---\n" prompt += observed_messages_str prompt += "\n---" else: prompt += "当前没有观察到新的聊天内容。\n" prompt += "\n看了以上内容，你产生的内心想法是：" if current_mind: prompt += f"\n---\n{current_mind}\n---\n\n" else: prompt += " [没有特别的想法] \n\n" prompt += ( "请结合你的内心想法和观察到的聊天内容，分析情况并使用 'decide_reply_action' 工具来决定你的最终行动。\n" "决策依据：\n" "1. 如果聊天内容无聊、与你无关、或者你的内心想法认为不适合回复（例如在讨论你不懂或不感兴趣的话题），选择 'no_reply'。\n" "2. 如果聊天内容值得回应，且适合用文字表达（参考你的内心想法），选择 'text_reply'。如果你有情绪想表达，想在文字后追加一个表达情绪的表情，请同时提供 'emoji_query' (例如：'开心的'、'惊讶的')。\n" "3. 如果聊天内容或你的内心想法适合用一个表情来回应（例如表示赞同、惊讶、无语等），选择 'emoji_reply' 并提供表情主题 'emoji_query'。\n" "4. 如果最后一条消息是你自己发的，并且之后没有人回复你，通常选择 'no_reply'，除非有特殊原因需要追问。\n" "5. 除非大家都在这么做，或者有特殊理由，否则不要重复别人刚刚说过的话或简单附和。\n" "6. 表情包是用来表达情绪的，不要直接回复或评价别人的表情包，而是根据对话内容和情绪选择是否用表情回应。\n" "7. 如果观察到的内容只有你自己的发言，选择 'no_reply'。\n" "8. 不要回复你自己的话，不要把自己的话当做别人说的。\n" "必须调用 'decide_reply_action' 工具并提供 'action' 和 'reasoning'。如果选择了 'emoji_reply' 或者选择了 'text_reply' 并想追加表情，则必须提供 'emoji_query'。" ) return prompt # --- 回复器 (Replier) 的定义 --- # async def _replier_work( self, reason: str, anchor_message: MessageRecv, thinking_id: str, ) -> Optional[List[str]]: """ 回复器 (Replier): 核心逻辑用于生成回复。 """ log_prefix = self._get_log_prefix() response_set: Optional[List[str]] = None try: response_set = await self.gpt_instance.generate_response( structured_info=self.sub_mind.structured_info, current_mind_info=self.sub_mind.current_mind, reason=reason, message=anchor_message, # Pass anchor_message positionally (matches 'message' parameter) thinking_id=thinking_id, # Pass thinking_id positionally ) if not response_set: logger.warning(f"{log_prefix}[Replier-{thinking_id}] LLM生成了一个空回复集。") return None # --- 准备并返回结果 --- # # logger.info(f"{log_prefix}[Replier-{thinking_id}] 成功生成了回复集: {' '.join(response_set)[:50]}...") return response_set except Exception as e: logger.error(f"{log_prefix}[Replier-{thinking_id}] Unexpected error in replier_work: {e}") logger.error(traceback.format_exc()) return None # --- Methods moved from HeartFCController start --- async def _create_thinking_message(self, anchor_message: Optional[MessageRecv]) -> Optional[str]: """创建思考消息 (尝试锚定到 anchor_message)""" if not anchor_message or not anchor_message.chat_stream: logger.error(f"{self._get_log_prefix()} 无法创建思考消息，缺少有效的锚点消息或聊天流。") return None chat = anchor_message.chat_stream messageinfo = anchor_message.message_info bot_user_info = UserInfo( user_id=global_config.BOT_QQ, user_nickname=global_config.BOT_NICKNAME, platform=messageinfo.platform, ) thinking_time_point = round(time.time(), 2) thinking_id = "mt" + str(thinking_time_point) thinking_message = MessageThinking( message_id=thinking_id, chat_stream=chat, bot_user_info=bot_user_info, reply=anchor_message, # 回复的是锚点消息 thinking_start_time=thinking_time_point, ) # Access MessageManager directly await message_manager.add_message(thinking_message) return thinking_id async def _send_response_messages( self, anchor_message: Optional[MessageRecv], response_set: List[str], thinking_id: str ) -> Optional[MessageSending]: """发送回复消息 (尝试锚定到 anchor_message)""" if not anchor_message or not anchor_message.chat_stream: logger.error(f"{self._get_log_prefix()} 无法发送回复，缺少有效的锚点消息或聊天流。") return None chat = anchor_message.chat_stream # Access MessageManager directly container = await message_manager.get_container(chat.stream_id) thinking_message = None # 移除思考消息 for msg in container.messages[:]: # Iterate over a copy if isinstance(msg, MessageThinking) and msg.message_info.message_id == thinking_id: thinking_message = msg container.messages.remove(msg) # Remove the message directly here logger.debug(f"{self._get_log_prefix()} Removed thinking message {thinking_id} via iteration.") break if not thinking_message: stream_name = chat_manager.get_stream_name(chat.stream_id) or chat.stream_id # 获取流名称 logger.warning(f"[{stream_name}] {thinking_id}，思考太久了，超时被移除") return None thinking_start_time = thinking_message.thinking_start_time message_set = MessageSet(chat, thinking_id) mark_head = False first_bot_msg = None bot_user_info = UserInfo( user_id=global_config.BOT_QQ, user_nickname=global_config.BOT_NICKNAME, platform=anchor_message.message_info.platform, ) for msg_text in response_set: message_segment = Seg(type="text", data=msg_text) bot_message = MessageSending( message_id=thinking_id, # 使用 thinking_id 作为批次标识 chat_stream=chat, bot_user_info=bot_user_info, sender_info=anchor_message.message_info.user_info, # 发送给锚点消息的用户 message_segment=message_segment, reply=anchor_message, # 回复锚点消息 is_head=not mark_head, is_emoji=False, thinking_start_time=thinking_start_time, ) if not mark_head: mark_head = True first_bot_msg = bot_message message_set.add_message(bot_message) # Access MessageManager directly await message_manager.add_message(message_set) return first_bot_msg async def _handle_emoji(self, anchor_message: Optional[MessageRecv], response_set: List[str], send_emoji: str = ""): """处理表情包 (尝试锚定到 anchor_message)""" if not anchor_message or not anchor_message.chat_stream: logger.error(f"{self._get_log_prefix()} 无法处理表情包，缺少有效的锚点消息或聊天流。") return chat = anchor_message.chat_stream if send_emoji: emoji_raw = await emoji_manager.get_emoji_for_text(send_emoji) else: emoji_text_source = "".join(response_set) if response_set else "" emoji_raw = await emoji_manager.get_emoji_for_text(emoji_text_source) if emoji_raw: emoji_path, _description = emoji_raw emoji_cq = image_path_to_base64(emoji_path) thinking_time_point = round(time.time(), 2) message_segment = Seg(type="emoji", data=emoji_cq) bot_user_info = UserInfo( user_id=global_config.BOT_QQ, user_nickname=global_config.BOT_NICKNAME, platform=anchor_message.message_info.platform, ) bot_message = MessageSending( message_id="me" + str(thinking_time_point), # 使用不同的 ID 前缀? chat_stream=chat, bot_user_info=bot_user_info, sender_info=anchor_message.message_info.user_info, message_segment=message_segment, reply=anchor_message, # 回复锚点消息 is_head=False, is_emoji=True, ) # Access MessageManager directly await message_manager.add_message(bot_message)