import time import traceback from typing import List, Optional, Dict import asyncio from asyncio import Lock from ...moods.moods import MoodManager from ....config.config import global_config from ...chat.emoji_manager import emoji_manager from .heartFC__generator import ResponseGenerator from ...chat.message import MessageSending, MessageRecv, MessageThinking, MessageSet from .messagesender import MessageManager from ...chat.utils_image import image_path_to_base64 from ...message import UserInfo, Seg from src.heart_flow.heartflow import heartflow from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig from ...person_info.relationship_manager import relationship_manager from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager from ...utils.timer_calculater import Timer from src.do_tool.tool_use import ToolUser from .interest import InterestManager from src.plugins.chat.chat_stream import chat_manager from src.plugins.chat.message import BaseMessageInfo from .pf_chatting import PFChatting # 定义日志配置 chat_config = LogConfig( console_format=CHAT_STYLE_CONFIG["console_format"], file_format=CHAT_STYLE_CONFIG["file_format"], ) logger = get_module_logger("heartFC_chat", config=chat_config) # 新增常量 INTEREST_MONITOR_INTERVAL_SECONDS = 1 class HeartFC_Chat: _instance = None # For potential singleton access if needed by MessageManager def __init__(self): # --- Updated Init --- if HeartFC_Chat._instance is not None: # Prevent re-initialization if used as a singleton return self.logger = logger # Make logger accessible via self self.gpt = ResponseGenerator() self.mood_manager = MoodManager.get_instance() self.mood_manager.start_mood_update() self.tool_user = ToolUser() self.interest_manager = InterestManager() self._interest_monitor_task: Optional[asyncio.Task] = None # --- New PFChatting Management --- self.pf_chatting_instances: Dict[str, PFChatting] = {} self._pf_chatting_lock = Lock() # --- End New PFChatting Management --- HeartFC_Chat._instance = self # Register instance # --- End Updated Init --- # --- Added Class Method for Singleton Access --- @classmethod def get_instance(cls): return cls._instance # --- End Added Class Method --- async def start(self): """启动异步任务,如兴趣监控器""" logger.info("HeartFC_Chat 正在启动异步任务...") await self.interest_manager.start_background_tasks() self._initialize_monitor_task() logger.info("HeartFC_Chat 异步任务启动完成") def _initialize_monitor_task(self): """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""" if self._interest_monitor_task is None or self._interest_monitor_task.done(): try: loop = asyncio.get_running_loop() self._interest_monitor_task = loop.create_task(self._interest_monitor_loop()) logger.info(f"兴趣监控任务已创建。监控间隔: {INTEREST_MONITOR_INTERVAL_SECONDS}秒。") except RuntimeError: logger.error("创建兴趣监控任务失败：没有运行中的事件循环。") raise else: logger.warning("跳过兴趣监控任务创建：任务已存在或正在运行。") # --- Added PFChatting Instance Manager --- async def _get_or_create_pf_chatting(self, stream_id: str) -> Optional[PFChatting]: """获取现有PFChatting实例或创建新实例。""" async with self._pf_chatting_lock: if stream_id not in self.pf_chatting_instances: self.logger.info(f"为流 {stream_id} 创建新的PFChatting实例") # 传递 self (HeartFC_Chat 实例) 进行依赖注入 instance = PFChatting(stream_id, self) # 执行异步初始化 if not await instance._initialize(): self.logger.error(f"为流 {stream_id} 初始化PFChatting失败") return None self.pf_chatting_instances[stream_id] = instance return self.pf_chatting_instances[stream_id] # --- End Added PFChatting Instance Manager --- async def _interest_monitor_loop(self): """后台任务，定期检查兴趣度变化并触发回复""" logger.info("兴趣监控循环开始...") while True: await asyncio.sleep(INTEREST_MONITOR_INTERVAL_SECONDS) try: active_stream_ids = list(heartflow.get_all_subheartflows_streams_ids()) # logger.trace(f"检查 {len(active_stream_ids)} 个活跃流是否足以开启心流对话...") # 调试日志 for stream_id in active_stream_ids: stream_name = chat_manager.get_stream_name(stream_id) or stream_id # 获取流名称 sub_hf = heartflow.get_subheartflow(stream_id) if not sub_hf: logger.warning(f"监控循环: 无法获取活跃流 {stream_name} 的 sub_hf") continue should_trigger = False try: interest_chatting = self.interest_manager.get_interest_chatting(stream_id) if interest_chatting: should_trigger = interest_chatting.should_evaluate_reply() # if should_trigger: # logger.info(f"[{stream_name}] 基于兴趣概率决定启动交流模式 (概率: {interest_chatting.current_reply_probability:.4f})。") else: logger.trace(f"[{stream_name}] 没有找到对应的 InterestChatting 实例，跳过基于兴趣的触发检查。") except Exception as e: logger.error(f"检查兴趣触发器时出错 流 {stream_name}: {e}") logger.error(traceback.format_exc()) if should_trigger: pf_instance = await self._get_or_create_pf_chatting(stream_id) if pf_instance: # logger.info(f"[{stream_name}] 触发条件满足, 委托给PFChatting.") asyncio.create_task(pf_instance.add_time()) else: logger.error(f"[{stream_name}] 无法获取或创建PFChatting实例。跳过触发。") except asyncio.CancelledError: logger.info("兴趣监控循环已取消。") break except Exception as e: logger.error(f"兴趣监控循环错误: {e}") logger.error(traceback.format_exc()) await asyncio.sleep(5) # 发生错误时等待 async def _create_thinking_message(self, anchor_message: Optional[MessageRecv]): """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("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, ) MessageManager().add_message(thinking_message) return thinking_id async def _send_response_messages(self, anchor_message: Optional[MessageRecv], response_set: List[str], thinking_id) -> 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("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 container = MessageManager().get_container(chat.stream_id) thinking_message = None for msg in container.messages: if isinstance(msg, MessageThinking) and msg.message_info.message_id == thinking_id: thinking_message = msg container.messages.remove(msg) 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}] 未找到对应的思考消息 {thinking_id}，可能已超时被移除") return None thinking_start_time = thinking_message.thinking_start_time message_set = MessageSet(chat, thinking_id) mark_head = False first_bot_msg = None 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=UserInfo( user_id=global_config.BOT_QQ, user_nickname=global_config.BOT_NICKNAME, platform=anchor_message.message_info.platform, ), 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) if message_set.messages: # 确保有消息才添加 MessageManager().add_message(message_set) return first_bot_msg else: stream_name = chat_manager.get_stream_name(chat.stream_id) or chat.stream_id # 获取流名称 logger.warning(f"[{stream_name}] 没有生成有效的回复消息集，无法发送。") return None async def _handle_emoji(self, anchor_message: Optional[MessageRecv], response_set, send_emoji=""): """处理表情包 (尝试锚定到 anchor_message)""" if not anchor_message or not anchor_message.chat_stream: logger.error("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) # 使用当前时间戳，因为没有原始消息的时间戳 thinking_time_point = round(time.time(), 2) message_segment = Seg(type="emoji", data=emoji_cq) bot_message = MessageSending( message_id="me" + str(thinking_time_point), # 使用不同的 ID 前缀? chat_stream=chat, bot_user_info=UserInfo( user_id=global_config.BOT_QQ, user_nickname=global_config.BOT_NICKNAME, platform=anchor_message.message_info.platform, ), 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, ) MessageManager().add_message(bot_message) async def _update_relationship(self, anchor_message: Optional[MessageRecv], response_set): """更新关系情绪 (尝试基于 anchor_message)""" if not anchor_message or not anchor_message.chat_stream: logger.error("无法更新关系情绪，缺少有效的锚点消息或聊天流。") return # 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 # 这里的实现可能需要调整，取决于关系管理器如何工作 ori_response = ",".join(response_set) # 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 stance, emotion = await self.gpt._get_emotion_tags(ori_response, anchor_message.processed_plain_text) await relationship_manager.calculate_update_relationship_value( chat_stream=anchor_message.chat_stream, # 使用锚点消息的流 label=emotion, stance=stance ) self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor) async def trigger_reply_generation(self, stream_id: str, observed_messages: List[dict]): """根据 SubHeartflow 的触发信号生成回复 (基于观察)""" stream_name = chat_manager.get_stream_name(stream_id) or stream_id # <--- 在开始时获取名称 chat = None sub_hf = None anchor_message: Optional[MessageRecv] = None # <--- 重命名，用于锚定回复的消息对象 userinfo: Optional[UserInfo] = None messageinfo: Optional[BaseMessageInfo] = None timing_results = {} current_mind = None response_set = None thinking_id = None info_catcher = None try: # --- 1. 获取核心对象：ChatStream 和 SubHeartflow --- try: with Timer("获取聊天流和子心流", timing_results): chat = chat_manager.get_stream(stream_id) if not chat: logger.error(f"[{stream_name}] 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_name}] 无法找到子心流对象，无法生成回复。") return except Exception as e: logger.error(f"[{stream_name}] 获取 ChatStream 或 SubHeartflow 时出错: {e}") logger.error(traceback.format_exc()) return # --- 2. 尝试从 observed_messages 重建最后一条消息作为锚点, 失败则创建占位符 --- # try: with Timer("获取或创建锚点消息", timing_results): reconstruction_failed = False if observed_messages: try: last_msg_dict = observed_messages[-1] logger.debug(f"[{stream_name}] Attempting to reconstruct MessageRecv from last observed message.") anchor_message = MessageRecv(last_msg_dict, chat_stream=chat) if not (anchor_message and anchor_message.message_info and anchor_message.message_info.message_id and anchor_message.message_info.user_info): raise ValueError("Reconstructed MessageRecv missing essential info.") userinfo = anchor_message.message_info.user_info messageinfo = anchor_message.message_info logger.debug(f"[{stream_name}] Successfully reconstructed anchor message: ID={messageinfo.message_id}, Sender={userinfo.user_nickname}") except Exception as e_reconstruct: logger.warning(f"[{stream_name}] Reconstructing MessageRecv from observed message failed: {e_reconstruct}. Will create placeholder.") reconstruction_failed = True else: logger.warning(f"[{stream_name}] observed_messages is empty. Will create placeholder anchor message.") reconstruction_failed = True # Treat empty observed_messages as a failure to reconstruct # 如果重建失败或 observed_messages 为空，创建占位符 if reconstruction_failed: placeholder_id = f"mid_{int(time.time() * 1000)}" # 使用毫秒时间戳增加唯一性 placeholder_user = UserInfo(user_id="system_trigger", user_nickname="系统触发") placeholder_msg_info = BaseMessageInfo( message_id=placeholder_id, platform=chat.platform, group_info=chat.group_info, user_info=placeholder_user, time=time.time() # 其他 BaseMessageInfo 可能需要的字段设为默认值或 None ) # 创建 MessageRecv 实例，注意它需要消息字典结构，我们创建一个最小化的 placeholder_msg_dict = { "message_info": placeholder_msg_info.to_dict(), "processed_plain_text": "", # 提供空文本 "raw_message": "", "time": placeholder_msg_info.time, } # 先只用字典创建实例 anchor_message = MessageRecv(placeholder_msg_dict) # 然后调用方法更新 chat_stream anchor_message.update_chat_stream(chat) userinfo = anchor_message.message_info.user_info messageinfo = anchor_message.message_info logger.info(f"[{stream_name}] Created placeholder anchor message: ID={messageinfo.message_id}, Sender={userinfo.user_nickname}") except Exception as e: logger.error(f"[{stream_name}] 获取或创建锚点消息时出错: {e}") logger.error(traceback.format_exc()) anchor_message = None # 确保出错时 anchor_message 为 None # --- 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 {stream_name} 已有 {thinking_count} 条思考消息，取消回复。") return except Exception as e: logger.error(f"[{stream_name}] 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): # 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"[{stream_name}] 创建思考消息失败: {e}") return if not thinking_id: logger.error(f"[{stream_name}] 未能成功创建思考消息 ID，无法继续回复流程。") return # --- 6. 信息捕捉器 (使用 anchor_message) --- logger.trace(f"[{stream_name}] 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(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: # --- 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_name}] Context for tools:\n{observation_context_text[-200:]}...") # 打印部分上下文 else: logger.warning(f"[{stream_name}] observed_messages 列表为空，无法为工具提供上下文。") 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, sub_heartflow=sub_hf ) 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"[{stream_name}] 思考前工具调用失败: {e}") logger.error(traceback.format_exc()) # --- 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( # sender_info=userinfo, chat_stream=chat, extra_info=tool_result_info, obs_id=get_mid_memory_id, ) logger.info(f"[{stream_name}] SubHeartflow 思考完成: {current_mind}") except Exception as e: logger.error(f"[{stream_name}] 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) response_set = await self.gpt.generate_response(anchor_message, thinking_id) except Exception as e: logger.error(f"[{stream_name}] 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("生成最终回复(GPT)")) if not response_set: logger.info(f"[{stream_name}] 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(anchor_message, response_set, thinking_id) except Exception as e: logger.error(f"[{stream_name}] 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() # 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"[{stream_name}] 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"[{stream_name}] 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()]) response_msg = " ".join(response_set) if response_set else "无回复" logger.info(f"[{stream_name}] 回复任务完成 (Observation Triggered): | 思维消息: {response_msg[:30]}... | 性能计时: {timing_str}") # --- 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(anchor_message, response_set) except Exception as e: logger.error(f"[{stream_name}] 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 V4 - Observation Triggered): {e}") logger.error(traceback.format_exc()) finally: # 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。