diff --git a/src/chat/brain_chat/PFC/action_planner.py b/src/chat/brain_chat/PFC/action_planner.py new file mode 100644 index 00000000..4770c6ce --- /dev/null +++ b/src/chat/brain_chat/PFC/action_planner.py @@ -0,0 +1,491 @@ +import time +from typing import Tuple, Optional # 增加了 Optional +from src.common.logger_manager import get_logger +from ..models.utils_model import LLMRequest +from ...config.config import global_config +from .chat_observer import ChatObserver +from .pfc_utils import get_items_from_json +from src.individuality.individuality import Individuality +from .observation_info import ObservationInfo +from .conversation_info import ConversationInfo +from src.plugins.utils.chat_message_builder import build_readable_messages + + +logger = get_logger("pfc_action_planner") + + +# --- 定义 Prompt 模板 --- + +# Prompt(1): 首次回复或非连续回复时的决策 Prompt +PROMPT_INITIAL_REPLY = """{persona_text}。现在你在参与一场QQ私聊，请根据以下【所有信息】审慎且灵活的决策下一步行动，可以回复，可以倾听，可以调取知识，甚至可以屏蔽对方： + +【当前对话目标】 +{goals_str} +{knowledge_info_str} + +【最近行动历史概要】 +{action_history_summary} +【上一次行动的详细情况和结果】 +{last_action_context} +【时间和超时提示】 +{time_since_last_bot_message_info}{timeout_context} +【最近的对话记录】(包括你已成功发送的消息 和 新收到的消息) +{chat_history_text} + +------ +可选行动类型以及解释： +fetch_knowledge: 需要调取知识或记忆，当需要专业知识或特定信息时选择，对方若提到你不太认识的人名或实体也可以尝试选择 +listening: 倾听对方发言，当你认为对方话才说到一半，发言明显未结束时选择 +direct_reply: 直接回复对方 +rethink_goal: 思考一个对话目标，当你觉得目前对话需要目标，或当前目标不再适用，或话题卡住时选择。注意私聊的环境是灵活的，有可能需要经常选择 +end_conversation: 结束对话，对方长时间没回复或者当你觉得对话告一段落时可以选择 +block_and_ignore: 更加极端的结束对话方式，直接结束对话并在一段时间内无视对方所有发言（屏蔽），当对话让你感到十分不适，或你遭到各类骚扰时选择 + +请以JSON格式输出你的决策： +{{ + "action": "选择的行动类型 (必须是上面列表中的一个)", + "reason": "选择该行动的详细原因 (必须有解释你是如何根据“上一次行动结果”、“对话记录”和自身设定人设做出合理判断的)" +}} + +注意：请严格按照JSON格式输出，不要包含任何其他内容。""" + +# Prompt(2): 上一次成功回复后，决定继续发言时的决策 Prompt +PROMPT_FOLLOW_UP = """{persona_text}。现在你在参与一场QQ私聊，刚刚你已经回复了对方，请根据以下【所有信息】审慎且灵活的决策下一步行动，可以继续发送新消息，可以等待，可以倾听，可以调取知识，甚至可以屏蔽对方： + +【当前对话目标】 +{goals_str} +{knowledge_info_str} + +【最近行动历史概要】 +{action_history_summary} +【上一次行动的详细情况和结果】 +{last_action_context} +【时间和超时提示】 +{time_since_last_bot_message_info}{timeout_context} +【最近的对话记录】(包括你已成功发送的消息 和 新收到的消息) +{chat_history_text} + +------ +可选行动类型以及解释： +fetch_knowledge: 需要调取知识，当需要专业知识或特定信息时选择，对方若提到你不太认识的人名或实体也可以尝试选择 +wait: 暂时不说话，留给对方交互空间，等待对方回复（尤其是在你刚发言后、或上次发言因重复、发言过多被拒时、或不确定做什么时，这是不错的选择） +listening: 倾听对方发言（虽然你刚发过言，但如果对方立刻回复且明显话没说完，可以选择这个） +send_new_message: 发送一条新消息继续对话，允许适当的追问、补充、深入话题，或开启相关新话题。**但是避免在因重复被拒后立即使用，也不要在对方没有回复的情况下过多的“消息轰炸”或重复发言** +rethink_goal: 思考一个对话目标，当你觉得目前对话需要目标，或当前目标不再适用，或话题卡住时选择。注意私聊的环境是灵活的，有可能需要经常选择 +end_conversation: 结束对话，对方长时间没回复或者当你觉得对话告一段落时可以选择 +block_and_ignore: 更加极端的结束对话方式，直接结束对话并在一段时间内无视对方所有发言（屏蔽），当对话让你感到十分不适，或你遭到各类骚扰时选择 + +请以JSON格式输出你的决策： +{{ + "action": "选择的行动类型 (必须是上面列表中的一个)", + "reason": "选择该行动的详细原因 (必须有解释你是如何根据“上一次行动结果”、“对话记录”和自身设定人设做出合理判断的。请说明你为什么选择继续发言而不是等待，以及打算发送什么类型的新消息连续发言，必须记录已经发言了几次)" +}} + +注意：请严格按照JSON格式输出，不要包含任何其他内容。""" + +# 新增：Prompt(3): 决定是否在结束对话前发送告别语 +PROMPT_END_DECISION = """{persona_text}。刚刚你决定结束一场 QQ 私聊。 + +【你们之前的聊天记录】 +{chat_history_text} + +你觉得你们的对话已经完整结束了吗？有时候，在对话自然结束后再说点什么可能会有点奇怪，但有时也可能需要一条简短的消息来圆满结束。 +如果觉得确实有必要再发一条简短、自然、符合你人设的告别消息（比如 "好，下次再聊~" 或 "嗯，先这样吧"），就输出 "yes"。 +如果觉得当前状态下直接结束对话更好，没有必要再发消息，就输出 "no"。 + +请以 JSON 格式输出你的选择： +{{ + "say_bye": "yes/no", + "reason": "选择 yes 或 no 的原因和内心想法 (简要说明)" +}} + +注意：请严格按照 JSON 格式输出，不要包含任何其他内容。""" + + +# ActionPlanner 类定义，顶格 +class ActionPlanner: + """行动规划器""" + + def __init__(self, stream_id: str, private_name: str): + self.llm = LLMRequest( + model=global_config.llm_PFC_action_planner, + temperature=global_config.llm_PFC_action_planner["temp"], + max_tokens=1500, + request_type="action_planning", + ) + self.personality_info = Individuality.get_instance().get_prompt(x_person=2, level=3) + self.name = global_config.BOT_NICKNAME + self.private_name = private_name + self.chat_observer = ChatObserver.get_instance(stream_id, private_name) + # self.action_planner_info = ActionPlannerInfo() # 移除未使用的变量 + + # 修改 plan 方法签名，增加 last_successful_reply_action 参数 + async def plan( + self, + observation_info: ObservationInfo, + conversation_info: ConversationInfo, + last_successful_reply_action: Optional[str], + ) -> Tuple[str, str]: + """规划下一步行动 + + Args: + observation_info: 决策信息 + conversation_info: 对话信息 + last_successful_reply_action: 上一次成功的回复动作类型 ('direct_reply' 或 'send_new_message' 或 None) + + Returns: + Tuple[str, str]: (行动类型, 行动原因) + """ + # --- 获取 Bot 上次发言时间信息 --- + # (这部分逻辑不变) + time_since_last_bot_message_info = "" + try: + bot_id = str(global_config.BOT_QQ) + if hasattr(observation_info, "chat_history") and observation_info.chat_history: + for i in range(len(observation_info.chat_history) - 1, -1, -1): + msg = observation_info.chat_history[i] + if not isinstance(msg, dict): + continue + sender_info = msg.get("user_info", {}) + sender_id = str(sender_info.get("user_id")) if isinstance(sender_info, dict) else None + msg_time = msg.get("time") + if sender_id == bot_id and msg_time: + time_diff = time.time() - msg_time + if time_diff < 60.0: + time_since_last_bot_message_info = ( + f"提示：你上一条成功发送的消息是在 {time_diff:.1f} 秒前。\n" + ) + break + else: + logger.debug( + f"[私聊][{self.private_name}]Observation info chat history is empty or not available for bot time check." + ) + except AttributeError: + logger.warning( + f"[私聊][{self.private_name}]ObservationInfo object might not have chat_history attribute yet for bot time check." + ) + except Exception as e: + logger.warning(f"[私聊][{self.private_name}]获取 Bot 上次发言时间时出错: {e}") + + # --- 获取超时提示信息 --- + # (这部分逻辑不变) + timeout_context = "" + try: + if hasattr(conversation_info, "goal_list") and conversation_info.goal_list: + last_goal_dict = conversation_info.goal_list[-1] + if isinstance(last_goal_dict, dict) and "goal" in last_goal_dict: + last_goal_text = last_goal_dict["goal"] + if isinstance(last_goal_text, str) and "分钟，思考接下来要做什么" in last_goal_text: + try: + timeout_minutes_text = last_goal_text.split("，")[0].replace("你等待了", "") + timeout_context = f"重要提示：对方已经长时间（{timeout_minutes_text}）没有回复你的消息了（这可能代表对方繁忙/不想回复/没注意到你的消息等情况，或在对方看来本次聊天已告一段落），请基于此情况规划下一步。\n" + except Exception: + timeout_context = "重要提示：对方已经长时间没有回复你的消息了（这可能代表对方繁忙/不想回复/没注意到你的消息等情况，或在对方看来本次聊天已告一段落），请基于此情况规划下一步。\n" + else: + logger.debug( + f"[私聊][{self.private_name}]Conversation info goal_list is empty or not available for timeout check." + ) + except AttributeError: + logger.warning( + f"[私聊][{self.private_name}]ConversationInfo object might not have goal_list attribute yet for timeout check." + ) + except Exception as e: + logger.warning(f"[私聊][{self.private_name}]检查超时目标时出错: {e}") + + # --- 构建通用 Prompt 参数 --- + logger.debug( + f"[私聊][{self.private_name}]开始规划行动：当前目标: {getattr(conversation_info, 'goal_list', '不可用')}" + ) + + # 构建对话目标 (goals_str) + goals_str = "" + try: + if hasattr(conversation_info, "goal_list") and conversation_info.goal_list: + for goal_reason in conversation_info.goal_list: + if isinstance(goal_reason, dict): + goal = goal_reason.get("goal", "目标内容缺失") + reasoning = goal_reason.get("reasoning", "没有明确原因") + else: + goal = str(goal_reason) + reasoning = "没有明确原因" + + goal = str(goal) if goal is not None else "目标内容缺失" + reasoning = str(reasoning) if reasoning is not None else "没有明确原因" + goals_str += f"- 目标：{goal}\n 原因：{reasoning}\n" + + if not goals_str: + goals_str = "- 目前没有明确对话目标，请考虑设定一个。\n" + else: + goals_str = "- 目前没有明确对话目标，请考虑设定一个。\n" + except AttributeError: + logger.warning( + f"[私聊][{self.private_name}]ConversationInfo object might not have goal_list attribute yet." + ) + goals_str = "- 获取对话目标时出错。\n" + except Exception as e: + logger.error(f"[私聊][{self.private_name}]构建对话目标字符串时出错: {e}") + goals_str = "- 构建对话目标时出错。\n" + + # --- 知识信息字符串构建开始 --- + knowledge_info_str = "【已获取的相关知识和记忆】\n" + try: + # 检查 conversation_info 是否有 knowledge_list 并且不为空 + if hasattr(conversation_info, "knowledge_list") and conversation_info.knowledge_list: + # 最多只显示最近的 5 条知识，防止 Prompt 过长 + recent_knowledge = conversation_info.knowledge_list[-5:] + for i, knowledge_item in enumerate(recent_knowledge): + if isinstance(knowledge_item, dict): + query = knowledge_item.get("query", "未知查询") + knowledge = knowledge_item.get("knowledge", "无知识内容") + source = knowledge_item.get("source", "未知来源") + # 只取知识内容的前 2000 个字，避免太长 + knowledge_snippet = knowledge[:2000] + "..." if len(knowledge) > 2000 else knowledge + knowledge_info_str += ( + f"{i + 1}. 关于 '{query}' 的知识 (来源: {source}):\n {knowledge_snippet}\n" + ) + else: + # 处理列表里不是字典的异常情况 + knowledge_info_str += f"{i + 1}. 发现一条格式不正确的知识记录。\n" + + if not recent_knowledge: # 如果 knowledge_list 存在但为空 + knowledge_info_str += "- 暂无相关知识和记忆。\n" + + else: + # 如果 conversation_info 没有 knowledge_list 属性，或者列表为空 + knowledge_info_str += "- 暂无相关知识记忆。\n" + except AttributeError: + logger.warning(f"[私聊][{self.private_name}]ConversationInfo 对象可能缺少 knowledge_list 属性。") + knowledge_info_str += "- 获取知识列表时出错。\n" + except Exception as e: + logger.error(f"[私聊][{self.private_name}]构建知识信息字符串时出错: {e}") + knowledge_info_str += "- 处理知识列表时出错。\n" + # --- 知识信息字符串构建结束 --- + + # 获取聊天历史记录 (chat_history_text) + try: + if hasattr(observation_info, "chat_history") and observation_info.chat_history: + chat_history_text = observation_info.chat_history_str + if not chat_history_text: + chat_history_text = "还没有聊天记录。\n" + else: + chat_history_text = "还没有聊天记录。\n" + + if hasattr(observation_info, "new_messages_count") and observation_info.new_messages_count > 0: + if hasattr(observation_info, "unprocessed_messages") and observation_info.unprocessed_messages: + new_messages_list = observation_info.unprocessed_messages + new_messages_str = await build_readable_messages( + new_messages_list, + replace_bot_name=True, + merge_messages=False, + timestamp_mode="relative", + read_mark=0.0, + ) + chat_history_text += ( + f"\n--- 以下是 {observation_info.new_messages_count} 条新消息 ---\n{new_messages_str}" + ) + else: + logger.warning( + f"[私聊][{self.private_name}]ObservationInfo has new_messages_count > 0 but unprocessed_messages is empty or missing." + ) + except AttributeError: + logger.warning( + f"[私聊][{self.private_name}]ObservationInfo object might be missing expected attributes for chat history." + ) + chat_history_text = "获取聊天记录时出错。\n" + except Exception as e: + logger.error(f"[私聊][{self.private_name}]处理聊天记录时发生未知错误: {e}") + chat_history_text = "处理聊天记录时出错。\n" + + # 构建 Persona 文本 (persona_text) + persona_text = f"你的名字是{self.name}，{self.personality_info}。" + + # 构建行动历史和上一次行动结果 (action_history_summary, last_action_context) + # (这部分逻辑不变) + action_history_summary = "你最近执行的行动历史：\n" + last_action_context = "关于你【上一次尝试】的行动：\n" + action_history_list = [] + try: + if hasattr(conversation_info, "done_action") and conversation_info.done_action: + action_history_list = conversation_info.done_action[-5:] + else: + logger.debug(f"[私聊][{self.private_name}]Conversation info done_action is empty or not available.") + except AttributeError: + logger.warning( + f"[私聊][{self.private_name}]ConversationInfo object might not have done_action attribute yet." + ) + except Exception as e: + logger.error(f"[私聊][{self.private_name}]访问行动历史时出错: {e}") + + if not action_history_list: + action_history_summary += "- 还没有执行过行动。\n" + last_action_context += "- 这是你规划的第一个行动。\n" + else: + for i, action_data in enumerate(action_history_list): + action_type = "未知" + plan_reason = "未知" + status = "未知" + final_reason = "" + action_time = "" + + if isinstance(action_data, dict): + action_type = action_data.get("action", "未知") + plan_reason = action_data.get("plan_reason", "未知规划原因") + status = action_data.get("status", "未知") + final_reason = action_data.get("final_reason", "") + action_time = action_data.get("time", "") + elif isinstance(action_data, tuple): + # 假设旧格式兼容 + if len(action_data) > 0: + action_type = action_data[0] + if len(action_data) > 1: + plan_reason = action_data[1] # 可能是规划原因或最终原因 + if len(action_data) > 2: + status = action_data[2] + if status == "recall" and len(action_data) > 3: + final_reason = action_data[3] + elif status == "done" and action_type in ["direct_reply", "send_new_message"]: + plan_reason = "成功发送" # 简化显示 + + reason_text = f", 失败/取消原因: {final_reason}" if final_reason else "" + summary_line = f"- 时间:{action_time}, 尝试行动:'{action_type}', 状态:{status}{reason_text}" + action_history_summary += summary_line + "\n" + + if i == len(action_history_list) - 1: + last_action_context += f"- 上次【规划】的行动是: '{action_type}'\n" + last_action_context += f"- 当时规划的【原因】是: {plan_reason}\n" + if status == "done": + last_action_context += "- 该行动已【成功执行】。\n" + # 记录这次成功的行动类型，供下次决策 + # self.last_successful_action_type = action_type # 不在这里记录，由 conversation 控制 + elif status == "recall": + last_action_context += "- 但该行动最终【未能执行/被取消】。\n" + if final_reason: + last_action_context += f"- 【重要】失败/取消的具体原因是: “{final_reason}”\n" + else: + last_action_context += "- 【重要】失败/取消原因未明确记录。\n" + # self.last_successful_action_type = None # 行动失败，清除记录 + else: + last_action_context += f"- 该行动当前状态: {status}\n" + # self.last_successful_action_type = None # 非完成状态，清除记录 + + # --- 选择 Prompt --- + if last_successful_reply_action in ["direct_reply", "send_new_message"]: + prompt_template = PROMPT_FOLLOW_UP + logger.debug(f"[私聊][{self.private_name}]使用 PROMPT_FOLLOW_UP (追问决策)") + else: + prompt_template = PROMPT_INITIAL_REPLY + logger.debug(f"[私聊][{self.private_name}]使用 PROMPT_INITIAL_REPLY (首次/非连续回复决策)") + + # --- 格式化最终的 Prompt --- + prompt = prompt_template.format( + persona_text=persona_text, + goals_str=goals_str if goals_str.strip() else "- 目前没有明确对话目标，请考虑设定一个。", + action_history_summary=action_history_summary, + last_action_context=last_action_context, + time_since_last_bot_message_info=time_since_last_bot_message_info, + timeout_context=timeout_context, + chat_history_text=chat_history_text if chat_history_text.strip() else "还没有聊天记录。", + knowledge_info_str=knowledge_info_str, + ) + + logger.debug(f"[私聊][{self.private_name}]发送到LLM的最终提示词:\n------\n{prompt}\n------") + try: + content, _ = await self.llm.generate_response_async(prompt) + logger.debug(f"[私聊][{self.private_name}]LLM (行动规划) 原始返回内容: {content}") + + # --- 初始行动规划解析 --- + success, initial_result = get_items_from_json( + content, + self.private_name, + "action", + "reason", + default_values={"action": "wait", "reason": "LLM返回格式错误或未提供原因，默认等待"}, + ) + + initial_action = initial_result.get("action", "wait") + initial_reason = initial_result.get("reason", "LLM未提供原因，默认等待") + + # 检查是否需要进行结束对话决策 --- + if initial_action == "end_conversation": + logger.info(f"[私聊][{self.private_name}]初步规划结束对话，进入告别决策...") + + # 使用新的 PROMPT_END_DECISION + end_decision_prompt = PROMPT_END_DECISION.format( + persona_text=persona_text, # 复用之前的 persona_text + chat_history_text=chat_history_text, # 复用之前的 chat_history_text + ) + + logger.debug( + f"[私聊][{self.private_name}]发送到LLM的结束决策提示词:\n------\n{end_decision_prompt}\n------" + ) + try: + end_content, _ = await self.llm.generate_response_async(end_decision_prompt) # 再次调用LLM + logger.debug(f"[私聊][{self.private_name}]LLM (结束决策) 原始返回内容: {end_content}") + + # 解析结束决策的JSON + end_success, end_result = get_items_from_json( + end_content, + self.private_name, + "say_bye", + "reason", + default_values={"say_bye": "no", "reason": "结束决策LLM返回格式错误，默认不告别"}, + required_types={"say_bye": str, "reason": str}, # 明确类型 + ) + + say_bye_decision = end_result.get("say_bye", "no").lower() # 转小写方便比较 + end_decision_reason = end_result.get("reason", "未提供原因") + + if end_success and say_bye_decision == "yes": + # 决定要告别，返回新的 'say_goodbye' 动作 + logger.info( + f"[私聊][{self.private_name}]结束决策: yes, 准备生成告别语. 原因: {end_decision_reason}" + ) + # 注意：这里的 reason 可以考虑拼接初始原因和结束决策原因，或者只用结束决策原因 + final_action = "say_goodbye" + final_reason = f"决定发送告别语。决策原因: {end_decision_reason} (原结束理由: {initial_reason})" + return final_action, final_reason + else: + # 决定不告别 (包括解析失败或明确说no) + logger.info( + f"[私聊][{self.private_name}]结束决策: no, 直接结束对话. 原因: {end_decision_reason}" + ) + # 返回原始的 'end_conversation' 动作 + final_action = "end_conversation" + final_reason = initial_reason # 保持原始的结束理由 + return final_action, final_reason + + except Exception as end_e: + logger.error(f"[私聊][{self.private_name}]调用结束决策LLM或处理结果时出错: {str(end_e)}") + # 出错时，默认执行原始的结束对话 + logger.warning(f"[私聊][{self.private_name}]结束决策出错，将按原计划执行 end_conversation") + return "end_conversation", initial_reason # 返回原始动作和原因 + + else: + action = initial_action + reason = initial_reason + + # 验证action类型 (保持不变) + valid_actions = [ + "direct_reply", + "send_new_message", + "fetch_knowledge", + "wait", + "listening", + "rethink_goal", + "end_conversation", # 仍然需要验证，因为可能从上面决策后返回 + "block_and_ignore", + "say_goodbye", # 也要验证这个新动作 + ] + if action not in valid_actions: + logger.warning(f"[私聊][{self.private_name}]LLM返回了未知的行动类型: '{action}'，强制改为 wait") + reason = f"(原始行动'{action}'无效，已强制改为wait) {reason}" + action = "wait" + + logger.info(f"[私聊][{self.private_name}]规划的行动: {action}") + logger.info(f"[私聊][{self.private_name}]行动原因: {reason}") + return action, reason + + except Exception as e: + # 外层异常处理保持不变 + logger.error(f"[私聊][{self.private_name}]规划行动时调用 LLM 或处理结果出错: {str(e)}") + return "wait", f"行动规划处理中发生错误，暂时等待: {str(e)}" diff --git a/src/chat/brain_chat/PFC/chat_observer.py b/src/chat/brain_chat/PFC/chat_observer.py new file mode 100644 index 00000000..22cbf27d --- /dev/null +++ b/src/chat/brain_chat/PFC/chat_observer.py @@ -0,0 +1,379 @@ +import time +import asyncio +import traceback +from typing import Optional, Dict, Any, List +from src.common.logger import get_module_logger +from maim_message import UserInfo +from ...config.config import global_config +from .chat_states import NotificationManager, create_new_message_notification, create_cold_chat_notification +from .message_storage import MongoDBMessageStorage +from rich.traceback import install + +install(extra_lines=3) + +logger = get_module_logger("chat_observer") + + +class ChatObserver: + """聊天状态观察器""" + + # 类级别的实例管理 + _instances: Dict[str, "ChatObserver"] = {} + + @classmethod + def get_instance(cls, stream_id: str, private_name: str) -> "ChatObserver": + """获取或创建观察器实例 + + Args: + stream_id: 聊天流ID + private_name: 私聊名称 + + Returns: + ChatObserver: 观察器实例 + """ + if stream_id not in cls._instances: + cls._instances[stream_id] = cls(stream_id, private_name) + return cls._instances[stream_id] + + def __init__(self, stream_id: str, private_name: str): + """初始化观察器 + + Args: + stream_id: 聊天流ID + """ + self.last_check_time = None + self.last_bot_speak_time = None + self.last_user_speak_time = None + if stream_id in self._instances: + raise RuntimeError(f"ChatObserver for {stream_id} already exists. Use get_instance() instead.") + + self.stream_id = stream_id + self.private_name = private_name + self.message_storage = MongoDBMessageStorage() + + # self.last_user_speak_time: Optional[float] = None # 对方上次发言时间 + # self.last_bot_speak_time: Optional[float] = None # 机器人上次发言时间 + # self.last_check_time: float = time.time() # 上次查看聊天记录时间 + self.last_message_read: Optional[Dict[str, Any]] = None # 最后读取的消息ID + self.last_message_time: float = time.time() + + self.waiting_start_time: float = time.time() # 等待开始时间，初始化为当前时间 + + # 运行状态 + self._running: bool = False + self._task: Optional[asyncio.Task] = None + self._update_event = asyncio.Event() # 触发更新的事件 + self._update_complete = asyncio.Event() # 更新完成的事件 + + # 通知管理器 + self.notification_manager = NotificationManager() + + # 冷场检查配置 + self.cold_chat_threshold: float = 60.0 # 60秒无消息判定为冷场 + self.last_cold_chat_check: float = time.time() + self.is_cold_chat_state: bool = False + + self.update_event = asyncio.Event() + self.update_interval = 2 # 更新间隔（秒） + self.message_cache = [] + self.update_running = False + + async def check(self) -> bool: + """检查距离上一次观察之后是否有了新消息 + + Returns: + bool: 是否有新消息 + """ + logger.debug(f"[私聊][{self.private_name}]检查距离上一次观察之后是否有了新消息: {self.last_check_time}") + + new_message_exists = await self.message_storage.has_new_messages(self.stream_id, self.last_check_time) + + if new_message_exists: + logger.debug(f"[私聊][{self.private_name}]发现新消息") + self.last_check_time = time.time() + + return new_message_exists + + async def _add_message_to_history(self, message: Dict[str, Any]): + """添加消息到历史记录并发送通知 + + Args: + message: 消息数据 + """ + try: + # 发送新消息通知 + notification = create_new_message_notification( + sender="chat_observer", target="observation_info", message=message + ) + # print(self.notification_manager) + await self.notification_manager.send_notification(notification) + except Exception as e: + logger.error(f"[私聊][{self.private_name}]添加消息到历史记录时出错: {e}") + print(traceback.format_exc()) + + # 检查并更新冷场状态 + await self._check_cold_chat() + + async def _check_cold_chat(self): + """检查是否处于冷场状态并发送通知""" + current_time = time.time() + + # 每10秒检查一次冷场状态 + if current_time - self.last_cold_chat_check < 10: + return + + self.last_cold_chat_check = current_time + + # 判断是否冷场 + is_cold = ( + True + if self.last_message_time is None + else (current_time - self.last_message_time) > self.cold_chat_threshold + ) + + # 如果冷场状态发生变化，发送通知 + if is_cold != self.is_cold_chat_state: + self.is_cold_chat_state = is_cold + notification = create_cold_chat_notification(sender="chat_observer", target="pfc", is_cold=is_cold) + await self.notification_manager.send_notification(notification) + + def new_message_after(self, time_point: float) -> bool: + """判断是否在指定时间点后有新消息 + + Args: + time_point: 时间戳 + + Returns: + bool: 是否有新消息 + """ + + if self.last_message_time is None: + logger.debug(f"[私聊][{self.private_name}]没有最后消息时间，返回 False") + return False + + has_new = self.last_message_time > time_point + logger.debug( + f"[私聊][{self.private_name}]判断是否在指定时间点后有新消息: {self.last_message_time} > {time_point} = {has_new}" + ) + return has_new + + def get_message_history( + self, + start_time: Optional[float] = None, + end_time: Optional[float] = None, + limit: Optional[int] = None, + user_id: Optional[str] = None, + ) -> List[Dict[str, Any]]: + """获取消息历史 + + Args: + start_time: 开始时间戳 + end_time: 结束时间戳 + limit: 限制返回消息数量 + user_id: 指定用户ID + + Returns: + List[Dict[str, Any]]: 消息列表 + """ + filtered_messages = self.message_history + + if start_time is not None: + filtered_messages = [m for m in filtered_messages if m["time"] >= start_time] + + if end_time is not None: + filtered_messages = [m for m in filtered_messages if m["time"] <= end_time] + + if user_id is not None: + filtered_messages = [ + m for m in filtered_messages if UserInfo.from_dict(m.get("user_info", {})).user_id == user_id + ] + + if limit is not None: + filtered_messages = filtered_messages[-limit:] + + return filtered_messages + + async def _fetch_new_messages(self) -> List[Dict[str, Any]]: + """获取新消息 + + Returns: + List[Dict[str, Any]]: 新消息列表 + """ + new_messages = await self.message_storage.get_messages_after(self.stream_id, self.last_message_time) + + if new_messages: + self.last_message_read = new_messages[-1] + self.last_message_time = new_messages[-1]["time"] + + # print(f"获取数据库中找到的新消息: {new_messages}") + + return new_messages + + async def _fetch_new_messages_before(self, time_point: float) -> List[Dict[str, Any]]: + """获取指定时间点之前的消息 + + Args: + time_point: 时间戳 + + Returns: + List[Dict[str, Any]]: 最多5条消息 + """ + new_messages = await self.message_storage.get_messages_before(self.stream_id, time_point) + + if new_messages: + self.last_message_read = new_messages[-1]["message_id"] + + logger.debug(f"[私聊][{self.private_name}]获取指定时间点111之前的消息: {new_messages}") + + return new_messages + + """主要观察循环""" + + async def _update_loop(self): + """更新循环""" + # try: + # start_time = time.time() + # messages = await self._fetch_new_messages_before(start_time) + # for message in messages: + # await self._add_message_to_history(message) + # logger.debug(f"[私聊][{self.private_name}]缓冲消息: {messages}") + # except Exception as e: + # logger.error(f"[私聊][{self.private_name}]缓冲消息出错: {e}") + + while self._running: + try: + # 等待事件或超时（1秒） + try: + # print("等待事件") + await asyncio.wait_for(self._update_event.wait(), timeout=1) + + except asyncio.TimeoutError: + # print("超时") + pass # 超时后也执行一次检查 + + self._update_event.clear() # 重置触发事件 + self._update_complete.clear() # 重置完成事件 + + # 获取新消息 + new_messages = await self._fetch_new_messages() + + if new_messages: + # 处理新消息 + for message in new_messages: + await self._add_message_to_history(message) + + # 设置完成事件 + self._update_complete.set() + + except Exception as e: + logger.error(f"[私聊][{self.private_name}]更新循环出错: {e}") + logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}") + self._update_complete.set() # 即使出错也要设置完成事件 + + def trigger_update(self): + """触发一次立即更新""" + self._update_event.set() + + async def wait_for_update(self, timeout: float = 5.0) -> bool: + """等待更新完成 + + Args: + timeout: 超时时间（秒） + + Returns: + bool: 是否成功完成更新（False表示超时） + """ + try: + await asyncio.wait_for(self._update_complete.wait(), timeout=timeout) + return True + except asyncio.TimeoutError: + logger.warning(f"[私聊][{self.private_name}]等待更新完成超时（{timeout}秒）") + return False + + def start(self): + """启动观察器""" + if self._running: + return + + self._running = True + self._task = asyncio.create_task(self._update_loop()) + logger.debug(f"[私聊][{self.private_name}]ChatObserver for {self.stream_id} started") + + def stop(self): + """停止观察器""" + self._running = False + self._update_event.set() # 设置事件以解除等待 + self._update_complete.set() # 设置完成事件以解除等待 + if self._task: + self._task.cancel() + logger.debug(f"[私聊][{self.private_name}]ChatObserver for {self.stream_id} stopped") + + async def process_chat_history(self, messages: list): + """处理聊天历史 + + Args: + messages: 消息列表 + """ + self.update_check_time() + + for msg in messages: + try: + user_info = UserInfo.from_dict(msg.get("user_info", {})) + if user_info.user_id == global_config.BOT_QQ: + self.update_bot_speak_time(msg["time"]) + else: + self.update_user_speak_time(msg["time"]) + except Exception as e: + logger.warning(f"[私聊][{self.private_name}]处理消息时间时出错: {e}") + continue + + def update_check_time(self): + """更新查看时间""" + self.last_check_time = time.time() + + def update_bot_speak_time(self, speak_time: Optional[float] = None): + """更新机器人说话时间""" + self.last_bot_speak_time = speak_time or time.time() + + def update_user_speak_time(self, speak_time: Optional[float] = None): + """更新用户说话时间""" + self.last_user_speak_time = speak_time or time.time() + + def get_time_info(self) -> str: + """获取时间信息文本""" + current_time = time.time() + time_info = "" + + if self.last_bot_speak_time: + bot_speak_ago = current_time - self.last_bot_speak_time + time_info += f"\n距离你上次发言已经过去了{int(bot_speak_ago)}秒" + + if self.last_user_speak_time: + user_speak_ago = current_time - self.last_user_speak_time + time_info += f"\n距离对方上次发言已经过去了{int(user_speak_ago)}秒" + + return time_info + + def get_cached_messages(self, limit: int = 50) -> List[Dict[str, Any]]: + """获取缓存的消息历史 + + Args: + limit: 获取的最大消息数量，默认50 + + Returns: + List[Dict[str, Any]]: 缓存的消息历史列表 + """ + return self.message_cache[-limit:] + + def get_last_message(self) -> Optional[Dict[str, Any]]: + """获取最后一条消息 + + Returns: + Optional[Dict[str, Any]]: 最后一条消息，如果没有则返回None + """ + if not self.message_cache: + return None + return self.message_cache[-1] + + def __str__(self): + return f"ChatObserver for {self.stream_id}" diff --git a/src/chat/brain_chat/PFC/chat_states.py b/src/chat/brain_chat/PFC/chat_states.py new file mode 100644 index 00000000..4b839b7b --- /dev/null +++ b/src/chat/brain_chat/PFC/chat_states.py @@ -0,0 +1,290 @@ +from enum import Enum, auto +from typing import Optional, Dict, Any, List, Set +from dataclasses import dataclass +from datetime import datetime +from abc import ABC, abstractmethod + + +class ChatState(Enum): + """聊天状态枚举""" + + NORMAL = auto() # 正常状态 + NEW_MESSAGE = auto() # 有新消息 + COLD_CHAT = auto() # 冷场状态 + ACTIVE_CHAT = auto() # 活跃状态 + BOT_SPEAKING = auto() # 机器人正在说话 + USER_SPEAKING = auto() # 用户正在说话 + SILENT = auto() # 沉默状态 + ERROR = auto() # 错误状态 + + +class NotificationType(Enum): + """通知类型枚举""" + + NEW_MESSAGE = auto() # 新消息通知 + COLD_CHAT = auto() # 冷场通知 + ACTIVE_CHAT = auto() # 活跃通知 + BOT_SPEAKING = auto() # 机器人说话通知 + USER_SPEAKING = auto() # 用户说话通知 + MESSAGE_DELETED = auto() # 消息删除通知 + USER_JOINED = auto() # 用户加入通知 + USER_LEFT = auto() # 用户离开通知 + ERROR = auto() # 错误通知 + + +@dataclass +class ChatStateInfo: + """聊天状态信息""" + + state: ChatState + last_message_time: Optional[float] = None + last_message_content: Optional[str] = None + last_speaker: Optional[str] = None + message_count: int = 0 + cold_duration: float = 0.0 # 冷场持续时间（秒） + active_duration: float = 0.0 # 活跃持续时间（秒） + + +@dataclass +class Notification: + """通知基类""" + + type: NotificationType + timestamp: float + sender: str # 发送者标识 + target: str # 接收者标识 + data: Dict[str, Any] + + def to_dict(self) -> Dict[str, Any]: + """转换为字典格式""" + return {"type": self.type.name, "timestamp": self.timestamp, "data": self.data} + + +@dataclass +class StateNotification(Notification): + """持续状态通知""" + + is_active: bool = True + + def to_dict(self) -> Dict[str, Any]: + base_dict = super().to_dict() + base_dict["is_active"] = self.is_active + return base_dict + + +class NotificationHandler(ABC): + """通知处理器接口""" + + @abstractmethod + async def handle_notification(self, notification: Notification): + """处理通知""" + pass + + +class NotificationManager: + """通知管理器""" + + def __init__(self): + # 按接收者和通知类型存储处理器 + self._handlers: Dict[str, Dict[NotificationType, List[NotificationHandler]]] = {} + self._active_states: Set[NotificationType] = set() + self._notification_history: List[Notification] = [] + + def register_handler(self, target: str, notification_type: NotificationType, handler: NotificationHandler): + """注册通知处理器 + + Args: + target: 接收者标识（例如："pfc"） + notification_type: 要处理的通知类型 + handler: 处理器实例 + """ + if target not in self._handlers: + self._handlers[target] = {} + if notification_type not in self._handlers[target]: + self._handlers[target][notification_type] = [] + # print(self._handlers[target][notification_type]) + self._handlers[target][notification_type].append(handler) + # print(self._handlers[target][notification_type]) + + def unregister_handler(self, target: str, notification_type: NotificationType, handler: NotificationHandler): + """注销通知处理器 + + Args: + target: 接收者标识 + notification_type: 通知类型 + handler: 要注销的处理器实例 + """ + if target in self._handlers and notification_type in self._handlers[target]: + handlers = self._handlers[target][notification_type] + if handler in handlers: + handlers.remove(handler) + # 如果该类型的处理器列表为空，删除该类型 + if not handlers: + del self._handlers[target][notification_type] + # 如果该目标没有任何处理器，删除该目标 + if not self._handlers[target]: + del self._handlers[target] + + async def send_notification(self, notification: Notification): + """发送通知""" + self._notification_history.append(notification) + + # 如果是状态通知，更新活跃状态 + if isinstance(notification, StateNotification): + if notification.is_active: + self._active_states.add(notification.type) + else: + self._active_states.discard(notification.type) + + # 调用目标接收者的处理器 + target = notification.target + if target in self._handlers: + handlers = self._handlers[target].get(notification.type, []) + # print(handlers) + for handler in handlers: + # print(f"调用处理器: {handler}") + await handler.handle_notification(notification) + + def get_active_states(self) -> Set[NotificationType]: + """获取当前活跃的状态""" + return self._active_states.copy() + + def is_state_active(self, state_type: NotificationType) -> bool: + """检查特定状态是否活跃""" + return state_type in self._active_states + + def get_notification_history( + self, sender: Optional[str] = None, target: Optional[str] = None, limit: Optional[int] = None + ) -> List[Notification]: + """获取通知历史 + + Args: + sender: 过滤特定发送者的通知 + target: 过滤特定接收者的通知 + limit: 限制返回数量 + """ + history = self._notification_history + + if sender: + history = [n for n in history if n.sender == sender] + if target: + history = [n for n in history if n.target == target] + + if limit is not None: + history = history[-limit:] + + return history + + def __str__(self): + str = "" + for target, handlers in self._handlers.items(): + for notification_type, handler_list in handlers.items(): + str += f"NotificationManager for {target} {notification_type} {handler_list}" + return str + + +# 一些常用的通知创建函数 +def create_new_message_notification(sender: str, target: str, message: Dict[str, Any]) -> Notification: + """创建新消息通知""" + return Notification( + type=NotificationType.NEW_MESSAGE, + timestamp=datetime.now().timestamp(), + sender=sender, + target=target, + data={ + "message_id": message.get("message_id"), + "processed_plain_text": message.get("processed_plain_text"), + "detailed_plain_text": message.get("detailed_plain_text"), + "user_info": message.get("user_info"), + "time": message.get("time"), + }, + ) + + +def create_cold_chat_notification(sender: str, target: str, is_cold: bool) -> StateNotification: + """创建冷场状态通知""" + return StateNotification( + type=NotificationType.COLD_CHAT, + timestamp=datetime.now().timestamp(), + sender=sender, + target=target, + data={"is_cold": is_cold}, + is_active=is_cold, + ) + + +def create_active_chat_notification(sender: str, target: str, is_active: bool) -> StateNotification: + """创建活跃状态通知""" + return StateNotification( + type=NotificationType.ACTIVE_CHAT, + timestamp=datetime.now().timestamp(), + sender=sender, + target=target, + data={"is_active": is_active}, + is_active=is_active, + ) + + +class ChatStateManager: + """聊天状态管理器""" + + def __init__(self): + self.current_state = ChatState.NORMAL + self.state_info = ChatStateInfo(state=ChatState.NORMAL) + self.state_history: list[ChatStateInfo] = [] + + def update_state(self, new_state: ChatState, **kwargs): + """更新聊天状态 + + Args: + new_state: 新的状态 + **kwargs: 其他状态信息 + """ + self.current_state = new_state + self.state_info.state = new_state + + # 更新其他状态信息 + for key, value in kwargs.items(): + if hasattr(self.state_info, key): + setattr(self.state_info, key, value) + + # 记录状态历史 + self.state_history.append(self.state_info) + + def get_current_state_info(self) -> ChatStateInfo: + """获取当前状态信息""" + return self.state_info + + def get_state_history(self) -> list[ChatStateInfo]: + """获取状态历史""" + return self.state_history + + def is_cold_chat(self, threshold: float = 60.0) -> bool: + """判断是否处于冷场状态 + + Args: + threshold: 冷场阈值（秒） + + Returns: + bool: 是否冷场 + """ + if not self.state_info.last_message_time: + return True + + current_time = datetime.now().timestamp() + return (current_time - self.state_info.last_message_time) > threshold + + def is_active_chat(self, threshold: float = 5.0) -> bool: + """判断是否处于活跃状态 + + Args: + threshold: 活跃阈值（秒） + + Returns: + bool: 是否活跃 + """ + if not self.state_info.last_message_time: + return False + + current_time = datetime.now().timestamp() + return (current_time - self.state_info.last_message_time) <= threshold diff --git a/src/chat/brain_chat/PFC/conversation.py b/src/chat/brain_chat/PFC/conversation.py new file mode 100644 index 00000000..0bc4cae8 --- /dev/null +++ b/src/chat/brain_chat/PFC/conversation.py @@ -0,0 +1,701 @@ +import time +import asyncio +import datetime + +# from .message_storage import MongoDBMessageStorage +from src.plugins.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat + +# from ...config.config import global_config +from typing import Dict, Any, Optional +from ..chat.message import Message +from .pfc_types import ConversationState +from .pfc import ChatObserver, GoalAnalyzer +from .message_sender import DirectMessageSender +from src.common.logger_manager import get_logger +from .action_planner import ActionPlanner +from .observation_info import ObservationInfo +from .conversation_info import ConversationInfo # 确保导入 ConversationInfo +from .reply_generator import ReplyGenerator +from ..chat.chat_stream import ChatStream +from maim_message import UserInfo +from src.plugins.chat.chat_stream import chat_manager +from .pfc_KnowledgeFetcher import KnowledgeFetcher +from .waiter import Waiter + +import traceback +from rich.traceback import install + +install(extra_lines=3) + +logger = get_logger("pfc") + + +class Conversation: + """对话类，负责管理单个对话的状态和行为""" + + def __init__(self, stream_id: str, private_name: str): + """初始化对话实例 + + Args: + stream_id: 聊天流ID + """ + self.stream_id = stream_id + self.private_name = private_name + self.state = ConversationState.INIT + self.should_continue = False + self.ignore_until_timestamp: Optional[float] = None + + # 回复相关 + self.generated_reply = "" + + async def _initialize(self): + """初始化实例，注册所有组件""" + + try: + self.action_planner = ActionPlanner(self.stream_id, self.private_name) + self.goal_analyzer = GoalAnalyzer(self.stream_id, self.private_name) + self.reply_generator = ReplyGenerator(self.stream_id, self.private_name) + self.knowledge_fetcher = KnowledgeFetcher(self.private_name) + self.waiter = Waiter(self.stream_id, self.private_name) + self.direct_sender = DirectMessageSender(self.private_name) + + # 获取聊天流信息 + self.chat_stream = chat_manager.get_stream(self.stream_id) + + self.stop_action_planner = False + except Exception as e: + logger.error(f"[私聊][{self.private_name}]初始化对话实例：注册运行组件失败: {e}") + logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}") + raise + + try: + # 决策所需要的信息，包括自身自信和观察信息两部分 + # 注册观察器和观测信息 + self.chat_observer = ChatObserver.get_instance(self.stream_id, self.private_name) + self.chat_observer.start() + self.observation_info = ObservationInfo(self.private_name) + self.observation_info.bind_to_chat_observer(self.chat_observer) + # print(self.chat_observer.get_cached_messages(limit=) + + self.conversation_info = ConversationInfo() + except Exception as e: + logger.error(f"[私聊][{self.private_name}]初始化对话实例：注册信息组件失败: {e}") + logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}") + raise + try: + logger.info(f"[私聊][{self.private_name}]为 {self.stream_id} 加载初始聊天记录...") + initial_messages = get_raw_msg_before_timestamp_with_chat( # + chat_id=self.stream_id, + timestamp=time.time(), + limit=30, # 加载最近30条作为初始上下文，可以调整 + ) + chat_talking_prompt = await build_readable_messages( + initial_messages, + replace_bot_name=True, + merge_messages=False, + timestamp_mode="relative", + read_mark=0.0, + ) + if initial_messages: + # 将加载的消息填充到 ObservationInfo 的 chat_history + self.observation_info.chat_history = initial_messages + self.observation_info.chat_history_str = chat_talking_prompt + "\n" + self.observation_info.chat_history_count = len(initial_messages) + + # 更新 ObservationInfo 中的时间戳等信息 + last_msg = initial_messages[-1] + self.observation_info.last_message_time = last_msg.get("time") + last_user_info = UserInfo.from_dict(last_msg.get("user_info", {})) + self.observation_info.last_message_sender = last_user_info.user_id + self.observation_info.last_message_content = last_msg.get("processed_plain_text", "") + + logger.info( + f"[私聊][{self.private_name}]成功加载 {len(initial_messages)} 条初始聊天记录。最后一条消息时间: {self.observation_info.last_message_time}" + ) + + # 让 ChatObserver 从加载的最后一条消息之后开始同步 + self.chat_observer.last_message_time = self.observation_info.last_message_time + self.chat_observer.last_message_read = last_msg # 更新 observer 的最后读取记录 + else: + logger.info(f"[私聊][{self.private_name}]没有找到初始聊天记录。") + + except Exception as load_err: + logger.error(f"[私聊][{self.private_name}]加载初始聊天记录时出错: {load_err}") + # 出错也要继续，只是没有历史记录而已 + # 组件准备完成，启动该论对话 + self.should_continue = True + asyncio.create_task(self.start()) + + async def start(self): + """开始对话流程""" + try: + logger.info(f"[私聊][{self.private_name}]对话系统启动中...") + asyncio.create_task(self._plan_and_action_loop()) + except Exception as e: + logger.error(f"[私聊][{self.private_name}]启动对话系统失败: {e}") + raise + + async def _plan_and_action_loop(self): + """思考步，PFC核心循环模块""" + while self.should_continue: + # 忽略逻辑 + if self.ignore_until_timestamp and time.time() < self.ignore_until_timestamp: + await asyncio.sleep(30) + continue + elif self.ignore_until_timestamp and time.time() >= self.ignore_until_timestamp: + logger.info(f"[私聊][{self.private_name}]忽略时间已到 {self.stream_id}，准备结束对话。") + self.ignore_until_timestamp = None + self.should_continue = False + continue + try: + # --- 在规划前记录当前新消息数量 --- + initial_new_message_count = 0 + if hasattr(self.observation_info, "new_messages_count"): + initial_new_message_count = self.observation_info.new_messages_count + 1 # 算上麦麦自己发的那一条 + else: + logger.warning( + f"[私聊][{self.private_name}]ObservationInfo missing 'new_messages_count' before planning." + ) + + # --- 调用 Action Planner --- + # 传递 self.conversation_info.last_successful_reply_action + action, reason = await self.action_planner.plan( + self.observation_info, self.conversation_info, self.conversation_info.last_successful_reply_action + ) + + # --- 规划后检查是否有 *更多* 新消息到达 --- + current_new_message_count = 0 + if hasattr(self.observation_info, "new_messages_count"): + current_new_message_count = self.observation_info.new_messages_count + else: + logger.warning( + f"[私聊][{self.private_name}]ObservationInfo missing 'new_messages_count' after planning." + ) + + if current_new_message_count > initial_new_message_count + 2: + logger.info( + f"[私聊][{self.private_name}]规划期间发现新增消息 ({initial_new_message_count} -> {current_new_message_count})，跳过本次行动，重新规划" + ) + # 如果规划期间有新消息，也应该重置上次回复状态，因为现在要响应新消息了 + self.conversation_info.last_successful_reply_action = None + await asyncio.sleep(0.1) + continue + + # 包含 send_new_message + if initial_new_message_count > 0 and action in ["direct_reply", "send_new_message"]: + if hasattr(self.observation_info, "clear_unprocessed_messages"): + logger.debug( + f"[私聊][{self.private_name}]准备执行 {action}，清理 {initial_new_message_count} 条规划时已知的新消息。" + ) + await self.observation_info.clear_unprocessed_messages() + if hasattr(self.observation_info, "new_messages_count"): + self.observation_info.new_messages_count = 0 + else: + logger.error( + f"[私聊][{self.private_name}]无法清理未处理消息: ObservationInfo 缺少 clear_unprocessed_messages 方法！" + ) + + await self._handle_action(action, reason, self.observation_info, self.conversation_info) + + # 检查是否需要结束对话 (逻辑不变) + goal_ended = False + if hasattr(self.conversation_info, "goal_list") and self.conversation_info.goal_list: + for goal_item in self.conversation_info.goal_list: + if isinstance(goal_item, dict): + current_goal = goal_item.get("goal") + + if current_goal == "结束对话": + goal_ended = True + break + + if goal_ended: + self.should_continue = False + logger.info(f"[私聊][{self.private_name}]检测到'结束对话'目标，停止循环。") + + except Exception as loop_err: + logger.error(f"[私聊][{self.private_name}]PFC主循环出错: {loop_err}") + logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}") + await asyncio.sleep(1) + + if self.should_continue: + await asyncio.sleep(0.1) + + logger.info(f"[私聊][{self.private_name}]PFC 循环结束 for stream_id: {self.stream_id}") + + def _check_new_messages_after_planning(self): + """检查在规划后是否有新消息""" + # 检查 ObservationInfo 是否已初始化并且有 new_messages_count 属性 + if not hasattr(self, "observation_info") or not hasattr(self.observation_info, "new_messages_count"): + logger.warning( + f"[私聊][{self.private_name}]ObservationInfo 未初始化或缺少 'new_messages_count' 属性，无法检查新消息。" + ) + return False # 或者根据需要抛出错误 + + if self.observation_info.new_messages_count > 2: + logger.info( + f"[私聊][{self.private_name}]生成/执行动作期间收到 {self.observation_info.new_messages_count} 条新消息，取消当前动作并重新规划" + ) + # 如果有新消息，也应该重置上次回复状态 + if hasattr(self, "conversation_info"): # 确保 conversation_info 已初始化 + self.conversation_info.last_successful_reply_action = None + else: + logger.warning( + f"[私聊][{self.private_name}]ConversationInfo 未初始化，无法重置 last_successful_reply_action。" + ) + return True + return False + + def _convert_to_message(self, msg_dict: Dict[str, Any]) -> Message: + """将消息字典转换为Message对象""" + try: + # 尝试从 msg_dict 直接获取 chat_stream，如果失败则从全局 chat_manager 获取 + chat_info = msg_dict.get("chat_info") + if chat_info and isinstance(chat_info, dict): + chat_stream = ChatStream.from_dict(chat_info) + elif self.chat_stream: # 使用实例变量中的 chat_stream + chat_stream = self.chat_stream + else: # Fallback: 尝试从 manager 获取 (可能需要 stream_id) + chat_stream = chat_manager.get_stream(self.stream_id) + if not chat_stream: + raise ValueError(f"无法确定 ChatStream for stream_id {self.stream_id}") + + user_info = UserInfo.from_dict(msg_dict.get("user_info", {})) + + return Message( + message_id=msg_dict.get("message_id", f"gen_{time.time()}"), # 提供默认 ID + chat_stream=chat_stream, # 使用确定的 chat_stream + time=msg_dict.get("time", time.time()), # 提供默认时间 + user_info=user_info, + processed_plain_text=msg_dict.get("processed_plain_text", ""), + detailed_plain_text=msg_dict.get("detailed_plain_text", ""), + ) + except Exception as e: + logger.warning(f"[私聊][{self.private_name}]转换消息时出错: {e}") + # 可以选择返回 None 或重新抛出异常，这里选择重新抛出以指示问题 + raise ValueError(f"无法将字典转换为 Message 对象: {e}") from e + + async def _handle_action( + self, action: str, reason: str, observation_info: ObservationInfo, conversation_info: ConversationInfo + ): + """处理规划的行动""" + + logger.debug(f"[私聊][{self.private_name}]执行行动: {action}, 原因: {reason}") + + # 记录action历史 (逻辑不变) + current_action_record = { + "action": action, + "plan_reason": reason, + "status": "start", + "time": datetime.datetime.now().strftime("%H:%M:%S"), + "final_reason": None, + } + # 确保 done_action 列表存在 + if not hasattr(conversation_info, "done_action"): + conversation_info.done_action = [] + conversation_info.done_action.append(current_action_record) + action_index = len(conversation_info.done_action) - 1 + + action_successful = False # 用于标记动作是否成功完成 + + # --- 根据不同的 action 执行 --- + + # send_new_message 失败后执行 wait + if action == "send_new_message": + max_reply_attempts = 3 + reply_attempt_count = 0 + is_suitable = False + need_replan = False + check_reason = "未进行尝试" + final_reply_to_send = "" + + while reply_attempt_count < max_reply_attempts and not is_suitable: + reply_attempt_count += 1 + logger.info( + f"[私聊][{self.private_name}]尝试生成追问回复 (第 {reply_attempt_count}/{max_reply_attempts} 次)..." + ) + self.state = ConversationState.GENERATING + + # 1. 生成回复 (调用 generate 时传入 action_type) + self.generated_reply = await self.reply_generator.generate( + observation_info, conversation_info, action_type="send_new_message" + ) + logger.info( + f"[私聊][{self.private_name}]第 {reply_attempt_count} 次生成的追问回复: {self.generated_reply}" + ) + + # 2. 检查回复 (逻辑不变) + self.state = ConversationState.CHECKING + try: + current_goal_str = conversation_info.goal_list[0]["goal"] if conversation_info.goal_list else "" + is_suitable, check_reason, need_replan = await self.reply_generator.check_reply( + reply=self.generated_reply, + goal=current_goal_str, + chat_history=observation_info.chat_history, + chat_history_str=observation_info.chat_history_str, + retry_count=reply_attempt_count - 1, + ) + logger.info( + f"[私聊][{self.private_name}]第 {reply_attempt_count} 次追问检查结果: 合适={is_suitable}, 原因='{check_reason}', 需重新规划={need_replan}" + ) + if is_suitable: + final_reply_to_send = self.generated_reply + break + elif need_replan: + logger.warning( + f"[私聊][{self.private_name}]第 {reply_attempt_count} 次追问检查建议重新规划，停止尝试。原因: {check_reason}" + ) + break + except Exception as check_err: + logger.error( + f"[私聊][{self.private_name}]第 {reply_attempt_count} 次调用 ReplyChecker (追问) 时出错: {check_err}" + ) + check_reason = f"第 {reply_attempt_count} 次检查过程出错: {check_err}" + break + + # 循环结束，处理最终结果 + if is_suitable: + # 检查是否有新消息 + if self._check_new_messages_after_planning(): + logger.info(f"[私聊][{self.private_name}]生成追问回复期间收到新消息，取消发送，重新规划行动") + conversation_info.done_action[action_index].update( + {"status": "recall", "final_reason": f"有新消息，取消发送追问: {final_reply_to_send}"} + ) + return # 直接返回，重新规划 + + # 发送合适的回复 + self.generated_reply = final_reply_to_send + # --- 在这里调用 _send_reply --- + await self._send_reply() # <--- 调用恢复后的函数 + + # 更新状态: 标记上次成功是 send_new_message + self.conversation_info.last_successful_reply_action = "send_new_message" + action_successful = True # 标记动作成功 + + elif need_replan: + # 打回动作决策 + logger.warning( + f"[私聊][{self.private_name}]经过 {reply_attempt_count} 次尝试，追问回复决定打回动作决策。打回原因: {check_reason}" + ) + conversation_info.done_action[action_index].update( + {"status": "recall", "final_reason": f"追问尝试{reply_attempt_count}次后打回: {check_reason}"} + ) + + else: + # 追问失败 + logger.warning( + f"[私聊][{self.private_name}]经过 {reply_attempt_count} 次尝试，未能生成合适的追问回复。最终原因: {check_reason}" + ) + conversation_info.done_action[action_index].update( + {"status": "recall", "final_reason": f"追问尝试{reply_attempt_count}次后失败: {check_reason}"} + ) + # 重置状态: 追问失败，下次用初始 prompt + self.conversation_info.last_successful_reply_action = None + + # 执行 Wait 操作 + logger.info(f"[私聊][{self.private_name}]由于无法生成合适追问回复，执行 'wait' 操作...") + self.state = ConversationState.WAITING + await self.waiter.wait(self.conversation_info) + wait_action_record = { + "action": "wait", + "plan_reason": "因 send_new_message 多次尝试失败而执行的后备等待", + "status": "done", + "time": datetime.datetime.now().strftime("%H:%M:%S"), + "final_reason": None, + } + conversation_info.done_action.append(wait_action_record) + + elif action == "direct_reply": + max_reply_attempts = 3 + reply_attempt_count = 0 + is_suitable = False + need_replan = False + check_reason = "未进行尝试" + final_reply_to_send = "" + + while reply_attempt_count < max_reply_attempts and not is_suitable: + reply_attempt_count += 1 + logger.info( + f"[私聊][{self.private_name}]尝试生成首次回复 (第 {reply_attempt_count}/{max_reply_attempts} 次)..." + ) + self.state = ConversationState.GENERATING + + # 1. 生成回复 + self.generated_reply = await self.reply_generator.generate( + observation_info, conversation_info, action_type="direct_reply" + ) + logger.info( + f"[私聊][{self.private_name}]第 {reply_attempt_count} 次生成的首次回复: {self.generated_reply}" + ) + + # 2. 检查回复 + self.state = ConversationState.CHECKING + try: + current_goal_str = conversation_info.goal_list[0]["goal"] if conversation_info.goal_list else "" + is_suitable, check_reason, need_replan = await self.reply_generator.check_reply( + reply=self.generated_reply, + goal=current_goal_str, + chat_history=observation_info.chat_history, + chat_history_str=observation_info.chat_history_str, + retry_count=reply_attempt_count - 1, + ) + logger.info( + f"[私聊][{self.private_name}]第 {reply_attempt_count} 次首次回复检查结果: 合适={is_suitable}, 原因='{check_reason}', 需重新规划={need_replan}" + ) + if is_suitable: + final_reply_to_send = self.generated_reply + break + elif need_replan: + logger.warning( + f"[私聊][{self.private_name}]第 {reply_attempt_count} 次首次回复检查建议重新规划，停止尝试。原因: {check_reason}" + ) + break + except Exception as check_err: + logger.error( + f"[私聊][{self.private_name}]第 {reply_attempt_count} 次调用 ReplyChecker (首次回复) 时出错: {check_err}" + ) + check_reason = f"第 {reply_attempt_count} 次检查过程出错: {check_err}" + break + + # 循环结束，处理最终结果 + if is_suitable: + # 检查是否有新消息 + if self._check_new_messages_after_planning(): + logger.info(f"[私聊][{self.private_name}]生成首次回复期间收到新消息，取消发送，重新规划行动") + conversation_info.done_action[action_index].update( + {"status": "recall", "final_reason": f"有新消息，取消发送首次回复: {final_reply_to_send}"} + ) + return # 直接返回，重新规划 + + # 发送合适的回复 + self.generated_reply = final_reply_to_send + # --- 在这里调用 _send_reply --- + await self._send_reply() # <--- 调用恢复后的函数 + + # 更新状态: 标记上次成功是 direct_reply + self.conversation_info.last_successful_reply_action = "direct_reply" + action_successful = True # 标记动作成功 + + elif need_replan: + # 打回动作决策 + logger.warning( + f"[私聊][{self.private_name}]经过 {reply_attempt_count} 次尝试，首次回复决定打回动作决策。打回原因: {check_reason}" + ) + conversation_info.done_action[action_index].update( + {"status": "recall", "final_reason": f"首次回复尝试{reply_attempt_count}次后打回: {check_reason}"} + ) + + else: + # 首次回复失败 + logger.warning( + f"[私聊][{self.private_name}]经过 {reply_attempt_count} 次尝试，未能生成合适的首次回复。最终原因: {check_reason}" + ) + conversation_info.done_action[action_index].update( + {"status": "recall", "final_reason": f"首次回复尝试{reply_attempt_count}次后失败: {check_reason}"} + ) + # 重置状态: 首次回复失败，下次还是用初始 prompt + self.conversation_info.last_successful_reply_action = None + + # 执行 Wait 操作 (保持原有逻辑) + logger.info(f"[私聊][{self.private_name}]由于无法生成合适首次回复，执行 'wait' 操作...") + self.state = ConversationState.WAITING + await self.waiter.wait(self.conversation_info) + wait_action_record = { + "action": "wait", + "plan_reason": "因 direct_reply 多次尝试失败而执行的后备等待", + "status": "done", + "time": datetime.datetime.now().strftime("%H:%M:%S"), + "final_reason": None, + } + conversation_info.done_action.append(wait_action_record) + + elif action == "fetch_knowledge": + self.state = ConversationState.FETCHING + knowledge_query = reason + try: + # 检查 knowledge_fetcher 是否存在 + if not hasattr(self, "knowledge_fetcher"): + logger.error(f"[私聊][{self.private_name}]KnowledgeFetcher 未初始化，无法获取知识。") + raise AttributeError("KnowledgeFetcher not initialized") + + knowledge, source = await self.knowledge_fetcher.fetch(knowledge_query, observation_info.chat_history) + logger.info(f"[私聊][{self.private_name}]获取到知识: {knowledge[:100]}..., 来源: {source}") + if knowledge: + # 确保 knowledge_list 存在 + if not hasattr(conversation_info, "knowledge_list"): + conversation_info.knowledge_list = [] + conversation_info.knowledge_list.append( + {"query": knowledge_query, "knowledge": knowledge, "source": source} + ) + action_successful = True + except Exception as fetch_err: + logger.error(f"[私聊][{self.private_name}]获取知识时出错: {str(fetch_err)}") + conversation_info.done_action[action_index].update( + {"status": "recall", "final_reason": f"获取知识失败: {str(fetch_err)}"} + ) + self.conversation_info.last_successful_reply_action = None # 重置状态 + + elif action == "rethink_goal": + self.state = ConversationState.RETHINKING + try: + # 检查 goal_analyzer 是否存在 + if not hasattr(self, "goal_analyzer"): + logger.error(f"[私聊][{self.private_name}]GoalAnalyzer 未初始化，无法重新思考目标。") + raise AttributeError("GoalAnalyzer not initialized") + await self.goal_analyzer.analyze_goal(conversation_info, observation_info) + action_successful = True + except Exception as rethink_err: + logger.error(f"[私聊][{self.private_name}]重新思考目标时出错: {rethink_err}") + conversation_info.done_action[action_index].update( + {"status": "recall", "final_reason": f"重新思考目标失败: {rethink_err}"} + ) + self.conversation_info.last_successful_reply_action = None # 重置状态 + + elif action == "listening": + self.state = ConversationState.LISTENING + logger.info(f"[私聊][{self.private_name}]倾听对方发言...") + try: + # 检查 waiter 是否存在 + if not hasattr(self, "waiter"): + logger.error(f"[私聊][{self.private_name}]Waiter 未初始化，无法倾听。") + raise AttributeError("Waiter not initialized") + await self.waiter.wait_listening(conversation_info) + action_successful = True # Listening 完成就算成功 + except Exception as listen_err: + logger.error(f"[私聊][{self.private_name}]倾听时出错: {listen_err}") + conversation_info.done_action[action_index].update( + {"status": "recall", "final_reason": f"倾听失败: {listen_err}"} + ) + self.conversation_info.last_successful_reply_action = None # 重置状态 + + elif action == "say_goodbye": + self.state = ConversationState.GENERATING # 也可以定义一个新的状态，如 ENDING + logger.info(f"[私聊][{self.private_name}]执行行动: 生成并发送告别语...") + try: + # 1. 生成告别语 (使用 'say_goodbye' action_type) + self.generated_reply = await self.reply_generator.generate( + observation_info, conversation_info, action_type="say_goodbye" + ) + logger.info(f"[私聊][{self.private_name}]生成的告别语: {self.generated_reply}") + + # 2. 直接发送告别语 (不经过检查) + if self.generated_reply: # 确保生成了内容 + await self._send_reply() # 调用发送方法 + # 发送成功后，标记动作成功 + action_successful = True + logger.info(f"[私聊][{self.private_name}]告别语已发送。") + else: + logger.warning(f"[私聊][{self.private_name}]未能生成告别语内容，无法发送。") + action_successful = False # 标记动作失败 + conversation_info.done_action[action_index].update( + {"status": "recall", "final_reason": "未能生成告别语内容"} + ) + + # 3. 无论是否发送成功，都准备结束对话 + self.should_continue = False + logger.info(f"[私聊][{self.private_name}]发送告别语流程结束，即将停止对话实例。") + + except Exception as goodbye_err: + logger.error(f"[私聊][{self.private_name}]生成或发送告别语时出错: {goodbye_err}") + logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}") + # 即使出错，也结束对话 + self.should_continue = False + action_successful = False # 标记动作失败 + conversation_info.done_action[action_index].update( + {"status": "recall", "final_reason": f"生成或发送告别语时出错: {goodbye_err}"} + ) + + elif action == "end_conversation": + # 这个分支现在只会在 action_planner 最终决定不告别时被调用 + self.should_continue = False + logger.info(f"[私聊][{self.private_name}]收到最终结束指令，停止对话...") + action_successful = True # 标记这个指令本身是成功的 + + elif action == "block_and_ignore": + logger.info(f"[私聊][{self.private_name}]不想再理你了...") + ignore_duration_seconds = 10 * 60 + self.ignore_until_timestamp = time.time() + ignore_duration_seconds + logger.info( + f"[私聊][{self.private_name}]将忽略此对话直到: {datetime.datetime.fromtimestamp(self.ignore_until_timestamp)}" + ) + self.state = ConversationState.IGNORED + action_successful = True # 标记动作成功 + + else: # 对应 'wait' 动作 + self.state = ConversationState.WAITING + logger.info(f"[私聊][{self.private_name}]等待更多信息...") + try: + # 检查 waiter 是否存在 + if not hasattr(self, "waiter"): + logger.error(f"[私聊][{self.private_name}]Waiter 未初始化，无法等待。") + raise AttributeError("Waiter not initialized") + _timeout_occurred = await self.waiter.wait(self.conversation_info) + action_successful = True # Wait 完成就算成功 + except Exception as wait_err: + logger.error(f"[私聊][{self.private_name}]等待时出错: {wait_err}") + conversation_info.done_action[action_index].update( + {"status": "recall", "final_reason": f"等待失败: {wait_err}"} + ) + self.conversation_info.last_successful_reply_action = None # 重置状态 + + # --- 更新 Action History 状态 --- + # 只有当动作本身成功时，才更新状态为 done + if action_successful: + conversation_info.done_action[action_index].update( + { + "status": "done", + "time": datetime.datetime.now().strftime("%H:%M:%S"), + } + ) + # 重置状态: 对于非回复类动作的成功，清除上次回复状态 + if action not in ["direct_reply", "send_new_message"]: + self.conversation_info.last_successful_reply_action = None + logger.debug(f"[私聊][{self.private_name}]动作 {action} 成功完成，重置 last_successful_reply_action") + # 如果动作是 recall 状态，在各自的处理逻辑中已经更新了 done_action + + async def _send_reply(self): + """发送回复""" + if not self.generated_reply: + logger.warning(f"[私聊][{self.private_name}]没有生成回复内容，无法发送。") + return + + try: + _current_time = time.time() + reply_content = self.generated_reply + + # 发送消息 (确保 direct_sender 和 chat_stream 有效) + if not hasattr(self, "direct_sender") or not self.direct_sender: + logger.error(f"[私聊][{self.private_name}]DirectMessageSender 未初始化，无法发送回复。") + return + if not self.chat_stream: + logger.error(f"[私聊][{self.private_name}]ChatStream 未初始化，无法发送回复。") + return + + await self.direct_sender.send_message(chat_stream=self.chat_stream, content=reply_content) + + # 发送成功后，手动触发 observer 更新可能导致重复处理自己发送的消息 + # 更好的做法是依赖 observer 的自动轮询或数据库触发器（如果支持） + # 暂时注释掉，观察是否影响 ObservationInfo 的更新 + # self.chat_observer.trigger_update() + # if not await self.chat_observer.wait_for_update(): + # logger.warning(f"[私聊][{self.private_name}]等待 ChatObserver 更新完成超时") + + self.state = ConversationState.ANALYZING # 更新状态 + + except Exception as e: + logger.error(f"[私聊][{self.private_name}]发送消息或更新状态时失败: {str(e)}") + logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}") + self.state = ConversationState.ANALYZING + + async def _send_timeout_message(self): + """发送超时结束消息""" + try: + messages = self.chat_observer.get_cached_messages(limit=1) + if not messages: + return + + latest_message = self._convert_to_message(messages[0]) + await self.direct_sender.send_message( + chat_stream=self.chat_stream, content="TODO:超时消息", reply_to_message=latest_message + ) + except Exception as e: + logger.error(f"[私聊][{self.private_name}]发送超时消息失败: {str(e)}") diff --git a/src/chat/brain_chat/PFC/conversation_info.py b/src/chat/brain_chat/PFC/conversation_info.py new file mode 100644 index 00000000..04524b69 --- /dev/null +++ b/src/chat/brain_chat/PFC/conversation_info.py @@ -0,0 +1,10 @@ +from typing import Optional + + +class ConversationInfo: + def __init__(self): + self.done_action = [] + self.goal_list = [] + self.knowledge_list = [] + self.memory_list = [] + self.last_successful_reply_action: Optional[str] = None diff --git a/src/chat/brain_chat/PFC/message_sender.py b/src/chat/brain_chat/PFC/message_sender.py new file mode 100644 index 00000000..12c2143e --- /dev/null +++ b/src/chat/brain_chat/PFC/message_sender.py @@ -0,0 +1,81 @@ +import time +from typing import Optional +from src.common.logger import get_module_logger +from ..chat.chat_stream import ChatStream +from ..chat.message import Message +from maim_message import UserInfo, Seg +from src.plugins.chat.message import MessageSending, MessageSet +from src.plugins.chat.message_sender import message_manager +from ..storage.storage import MessageStorage +from ...config.config import global_config +from rich.traceback import install + +install(extra_lines=3) + + +logger = get_module_logger("message_sender") + + +class DirectMessageSender: + """直接消息发送器""" + + def __init__(self, private_name: str): + self.private_name = private_name + self.storage = MessageStorage() + + async def send_message( + self, + chat_stream: ChatStream, + content: str, + reply_to_message: Optional[Message] = None, + ) -> None: + """发送消息到聊天流 + + Args: + chat_stream: 聊天流 + content: 消息内容 + reply_to_message: 要回复的消息（可选） + """ + try: + # 创建消息内容 + segments = Seg(type="seglist", data=[Seg(type="text", data=content)]) + + # 获取麦麦的信息 + bot_user_info = UserInfo( + user_id=global_config.BOT_QQ, + user_nickname=global_config.BOT_NICKNAME, + platform=chat_stream.platform, + ) + + # 用当前时间作为message_id，和之前那套sender一样 + message_id = f"dm{round(time.time(), 2)}" + + # 构建消息对象 + message = MessageSending( + message_id=message_id, + chat_stream=chat_stream, + bot_user_info=bot_user_info, + sender_info=reply_to_message.message_info.user_info if reply_to_message else None, + message_segment=segments, + reply=reply_to_message, + is_head=True, + is_emoji=False, + thinking_start_time=time.time(), + ) + + # 处理消息 + await message.process() + + # 不知道有什么用，先留下来了，和之前那套sender一样 + _message_json = message.to_dict() + + # 发送消息 + message_set = MessageSet(chat_stream, message_id) + message_set.add_message(message) + await message_manager.add_message(message_set) + await self.storage.store_message(message, chat_stream) + logger.info(f"[私聊][{self.private_name}]PFC消息已发送: {content}") + + except Exception as e: + logger.error(f"[私聊][{self.private_name}]PFC消息发送失败: {str(e)}") + raise diff --git a/src/chat/brain_chat/PFC/message_storage.py b/src/chat/brain_chat/PFC/message_storage.py new file mode 100644 index 00000000..cd6a01e3 --- /dev/null +++ b/src/chat/brain_chat/PFC/message_storage.py @@ -0,0 +1,119 @@ +from abc import ABC, abstractmethod +from typing import List, Dict, Any +from src.common.database import db + + +class MessageStorage(ABC): + """消息存储接口""" + + @abstractmethod + async def get_messages_after(self, chat_id: str, message: Dict[str, Any]) -> List[Dict[str, Any]]: + """获取指定消息ID之后的所有消息 + + Args: + chat_id: 聊天ID + message: 消息 + + Returns: + List[Dict[str, Any]]: 消息列表 + """ + pass + + @abstractmethod + async def get_messages_before(self, chat_id: str, time_point: float, limit: int = 5) -> List[Dict[str, Any]]: + """获取指定时间点之前的消息 + + Args: + chat_id: 聊天ID + time_point: 时间戳 + limit: 最大消息数量 + + Returns: + List[Dict[str, Any]]: 消息列表 + """ + pass + + @abstractmethod + async def has_new_messages(self, chat_id: str, after_time: float) -> bool: + """检查是否有新消息 + + Args: + chat_id: 聊天ID + after_time: 时间戳 + + Returns: + bool: 是否有新消息 + """ + pass + + +class MongoDBMessageStorage(MessageStorage): + """MongoDB消息存储实现""" + + async def get_messages_after(self, chat_id: str, message_time: float) -> List[Dict[str, Any]]: + query = {"chat_id": chat_id, "time": {"$gt": message_time}} + # print(f"storage_check_message: {message_time}") + + return list(db.messages.find(query).sort("time", 1)) + + async def get_messages_before(self, chat_id: str, time_point: float, limit: int = 5) -> List[Dict[str, Any]]: + query = {"chat_id": chat_id, "time": {"$lt": time_point}} + + messages = list(db.messages.find(query).sort("time", -1).limit(limit)) + + # 将消息按时间正序排列 + messages.reverse() + return messages + + async def has_new_messages(self, chat_id: str, after_time: float) -> bool: + query = {"chat_id": chat_id, "time": {"$gt": after_time}} + + return db.messages.find_one(query) is not None + + +# # 创建一个内存消息存储实现，用于测试 +# class InMemoryMessageStorage(MessageStorage): +# """内存消息存储实现，主要用于测试""" + +# def __init__(self): +# self.messages: Dict[str, List[Dict[str, Any]]] = {} + +# async def get_messages_after(self, chat_id: str, message_id: Optional[str] = None) -> List[Dict[str, Any]]: +# if chat_id not in self.messages: +# return [] + +# messages = self.messages[chat_id] +# if not message_id: +# return messages + +# # 找到message_id的索引 +# try: +# index = next(i for i, m in enumerate(messages) if m["message_id"] == message_id) +# return messages[index + 1:] +# except StopIteration: +# return [] + +# async def get_messages_before(self, chat_id: str, time_point: float, limit: int = 5) -> List[Dict[str, Any]]: +# if chat_id not in self.messages: +# return [] + +# messages = [ +# m for m in self.messages[chat_id] +# if m["time"] < time_point +# ] + +# return messages[-limit:] + +# async def has_new_messages(self, chat_id: str, after_time: float) -> bool: +# if chat_id not in self.messages: +# return False + +# return any(m["time"] > after_time for m in self.messages[chat_id]) + +# # 测试辅助方法 +# def add_message(self, chat_id: str, message: Dict[str, Any]): +# """添加测试消息""" +# if chat_id not in self.messages: +# self.messages[chat_id] = [] +# self.messages[chat_id].append(message) +# self.messages[chat_id].sort(key=lambda m: m["time"]) diff --git a/src/chat/brain_chat/PFC/observation_info.py b/src/chat/brain_chat/PFC/observation_info.py new file mode 100644 index 00000000..c7572955 --- /dev/null +++ b/src/chat/brain_chat/PFC/observation_info.py @@ -0,0 +1,389 @@ +from typing import List, Optional, Dict, Any, Set +from maim_message import UserInfo +import time +from src.common.logger import get_module_logger +from .chat_observer import ChatObserver +from .chat_states import NotificationHandler, NotificationType, Notification +from src.plugins.utils.chat_message_builder import build_readable_messages +import traceback # 导入 traceback 用于调试 + +logger = get_module_logger("observation_info") + + +class ObservationInfoHandler(NotificationHandler): + """ObservationInfo的通知处理器""" + + def __init__(self, observation_info: "ObservationInfo", private_name: str): + """初始化处理器 + + Args: + observation_info: 要更新的ObservationInfo实例 + private_name: 私聊对象的名称，用于日志记录 + """ + self.observation_info = observation_info + # 将 private_name 存储在 handler 实例中 + self.private_name = private_name + + async def handle_notification(self, notification: Notification): # 添加类型提示 + # 获取通知类型和数据 + notification_type = notification.type + data = notification.data + + try: # 添加错误处理块 + if notification_type == NotificationType.NEW_MESSAGE: + # 处理新消息通知 + # logger.debug(f"[私聊][{self.private_name}]收到新消息通知data: {data}") # 可以在需要时取消注释 + message_id = data.get("message_id") + processed_plain_text = data.get("processed_plain_text") + detailed_plain_text = data.get("detailed_plain_text") + user_info_dict = data.get("user_info") # 先获取字典 + time_value = data.get("time") + + # 确保 user_info 是字典类型再创建 UserInfo 对象 + user_info = None + if isinstance(user_info_dict, dict): + try: + user_info = UserInfo.from_dict(user_info_dict) + except Exception as e: + logger.error( + f"[私聊][{self.private_name}]从字典创建 UserInfo 时出错: {e}, 字典内容: {user_info_dict}" + ) + # 可以选择在这里返回或记录错误，避免后续代码出错 + return + elif user_info_dict is not None: + logger.warning( + f"[私聊][{self.private_name}]收到的 user_info 不是预期的字典类型: {type(user_info_dict)}" + ) + # 根据需要处理非字典情况，这里暂时返回 + return + + message = { + "message_id": message_id, + "processed_plain_text": processed_plain_text, + "detailed_plain_text": detailed_plain_text, + "user_info": user_info_dict, # 存储原始字典或 UserInfo 对象，取决于你的 update_from_message 如何处理 + "time": time_value, + } + # 传递 UserInfo 对象（如果成功创建）或原始字典 + await self.observation_info.update_from_message(message, user_info) # 修改：传递 user_info 对象 + + elif notification_type == NotificationType.COLD_CHAT: + # 处理冷场通知 + is_cold = data.get("is_cold", False) + await self.observation_info.update_cold_chat_status(is_cold, time.time()) # 修改：改为 await 调用 + + elif notification_type == NotificationType.ACTIVE_CHAT: + # 处理活跃通知 (通常由 COLD_CHAT 的反向状态处理) + is_active = data.get("is_active", False) + self.observation_info.is_cold = not is_active + + elif notification_type == NotificationType.BOT_SPEAKING: + # 处理机器人说话通知 (按需实现) + self.observation_info.is_typing = False + self.observation_info.last_bot_speak_time = time.time() + + elif notification_type == NotificationType.USER_SPEAKING: + # 处理用户说话通知 + self.observation_info.is_typing = False + self.observation_info.last_user_speak_time = time.time() + + elif notification_type == NotificationType.MESSAGE_DELETED: + # 处理消息删除通知 + message_id = data.get("message_id") + # 从 unprocessed_messages 中移除被删除的消息 + original_count = len(self.observation_info.unprocessed_messages) + self.observation_info.unprocessed_messages = [ + msg for msg in self.observation_info.unprocessed_messages if msg.get("message_id") != message_id + ] + if len(self.observation_info.unprocessed_messages) < original_count: + logger.info(f"[私聊][{self.private_name}]移除了未处理的消息 (ID: {message_id})") + + elif notification_type == NotificationType.USER_JOINED: + # 处理用户加入通知 (如果适用私聊场景) + user_id = data.get("user_id") + if user_id: + self.observation_info.active_users.add(str(user_id)) # 确保是字符串 + + elif notification_type == NotificationType.USER_LEFT: + # 处理用户离开通知 (如果适用私聊场景) + user_id = data.get("user_id") + if user_id: + self.observation_info.active_users.discard(str(user_id)) # 确保是字符串 + + elif notification_type == NotificationType.ERROR: + # 处理错误通知 + error_msg = data.get("error", "未提供错误信息") + logger.error(f"[私聊][{self.private_name}]收到错误通知: {error_msg}") + + except Exception as e: + logger.error(f"[私聊][{self.private_name}]处理通知时发生错误: {e}") + logger.error(traceback.format_exc()) # 打印详细堆栈信息 + + +# @dataclass <-- 这个，不需要了（递黄瓜） +class ObservationInfo: + """决策信息类，用于收集和管理来自chat_observer的通知信息 (手动实现 __init__)""" + + # 类型提示保留，可用于文档和静态分析 + private_name: str + chat_history: List[Dict[str, Any]] + chat_history_str: str + unprocessed_messages: List[Dict[str, Any]] + active_users: Set[str] + last_bot_speak_time: Optional[float] + last_user_speak_time: Optional[float] + last_message_time: Optional[float] + last_message_id: Optional[str] + last_message_content: str + last_message_sender: Optional[str] + bot_id: Optional[str] + chat_history_count: int + new_messages_count: int + cold_chat_start_time: Optional[float] + cold_chat_duration: float + is_typing: bool + is_cold_chat: bool + changed: bool + chat_observer: Optional[ChatObserver] + handler: Optional[ObservationInfoHandler] + + def __init__(self, private_name: str): + """ + 手动初始化 ObservationInfo 的所有实例变量。 + """ + + # 接收的参数 + self.private_name: str = private_name + + # data_list + self.chat_history: List[Dict[str, Any]] = [] + self.chat_history_str: str = "" + self.unprocessed_messages: List[Dict[str, Any]] = [] + self.active_users: Set[str] = set() + + # data + self.last_bot_speak_time: Optional[float] = None + self.last_user_speak_time: Optional[float] = None + self.last_message_time: Optional[float] = None + self.last_message_id: Optional[str] = None + self.last_message_content: str = "" + self.last_message_sender: Optional[str] = None + self.bot_id: Optional[str] = None + self.chat_history_count: int = 0 + self.new_messages_count: int = 0 + self.cold_chat_start_time: Optional[float] = None + self.cold_chat_duration: float = 0.0 + + # state + self.is_typing: bool = False + self.is_cold_chat: bool = False + self.changed: bool = False + + # 关联对象 + self.chat_observer: Optional[ChatObserver] = None + + self.handler: ObservationInfoHandler = ObservationInfoHandler(self, self.private_name) + + def bind_to_chat_observer(self, chat_observer: ChatObserver): + """绑定到指定的chat_observer + + Args: + chat_observer: 要绑定的 ChatObserver 实例 + """ + if self.chat_observer: + logger.warning(f"[私聊][{self.private_name}]尝试重复绑定 ChatObserver") + return + + self.chat_observer = chat_observer + try: + if not self.handler: # 确保 handler 已经被创建 + logger.error(f"[私聊][{self.private_name}] 尝试绑定时 handler 未初始化！") + self.chat_observer = None # 重置，防止后续错误 + return + + # 注册关心的通知类型 + self.chat_observer.notification_manager.register_handler( + target="observation_info", notification_type=NotificationType.NEW_MESSAGE, handler=self.handler + ) + self.chat_observer.notification_manager.register_handler( + target="observation_info", notification_type=NotificationType.COLD_CHAT, handler=self.handler + ) + # 可以根据需要注册更多通知类型 + # self.chat_observer.notification_manager.register_handler( + # target="observation_info", notification_type=NotificationType.MESSAGE_DELETED, handler=self.handler + # ) + logger.info(f"[私聊][{self.private_name}]成功绑定到 ChatObserver") + except Exception as e: + logger.error(f"[私聊][{self.private_name}]绑定到 ChatObserver 时出错: {e}") + self.chat_observer = None # 绑定失败，重置 + + def unbind_from_chat_observer(self): + """解除与chat_observer的绑定""" + if ( + self.chat_observer and hasattr(self.chat_observer, "notification_manager") and self.handler + ): # 增加 handler 检查 + try: + self.chat_observer.notification_manager.unregister_handler( + target="observation_info", notification_type=NotificationType.NEW_MESSAGE, handler=self.handler + ) + self.chat_observer.notification_manager.unregister_handler( + target="observation_info", notification_type=NotificationType.COLD_CHAT, handler=self.handler + ) + # 如果注册了其他类型，也要在这里注销 + # self.chat_observer.notification_manager.unregister_handler( + # target="observation_info", notification_type=NotificationType.MESSAGE_DELETED, handler=self.handler + # ) + logger.info(f"[私聊][{self.private_name}]成功从 ChatObserver 解绑") + except Exception as e: + logger.error(f"[私聊][{self.private_name}]从 ChatObserver 解绑时出错: {e}") + finally: # 确保 chat_observer 被重置 + self.chat_observer = None + else: + logger.warning(f"[私聊][{self.private_name}]尝试解绑时 ChatObserver 不存在、无效或 handler 未设置") + + # 修改：update_from_message 接收 UserInfo 对象 + async def update_from_message(self, message: Dict[str, Any], user_info: Optional[UserInfo]): + """从消息更新信息 + + Args: + message: 消息数据字典 + user_info: 解析后的 UserInfo 对象 (可能为 None) + """ + message_time = message.get("time") + message_id = message.get("message_id") + processed_text = message.get("processed_plain_text", "") + + # 只有在新消息到达时才更新 last_message 相关信息 + if message_time and message_time > (self.last_message_time or 0): + self.last_message_time = message_time + self.last_message_id = message_id + self.last_message_content = processed_text + # 重置冷场计时器 + self.is_cold_chat = False + self.cold_chat_start_time = None + self.cold_chat_duration = 0.0 + + if user_info: + sender_id = str(user_info.user_id) # 确保是字符串 + self.last_message_sender = sender_id + # 更新发言时间 + if sender_id == self.bot_id: + self.last_bot_speak_time = message_time + else: + self.last_user_speak_time = message_time + self.active_users.add(sender_id) # 用户发言则认为其活跃 + else: + logger.warning( + f"[私聊][{self.private_name}]处理消息更新时缺少有效的 UserInfo 对象, message_id: {message_id}" + ) + self.last_message_sender = None # 发送者未知 + + # 将原始消息字典添加到未处理列表 + self.unprocessed_messages.append(message) + self.new_messages_count = len(self.unprocessed_messages) # 直接用列表长度 + + # logger.debug(f"[私聊][{self.private_name}]消息更新: last_time={self.last_message_time}, new_count={self.new_messages_count}") + self.update_changed() # 标记状态已改变 + else: + # 如果消息时间戳不是最新的，可能不需要处理，或者记录一个警告 + pass + # logger.warning(f"[私聊][{self.private_name}]收到过时或无效时间戳的消息: ID={message_id}, time={message_time}") + + def update_changed(self): + """标记状态已改变，并重置标记""" + # logger.debug(f"[私聊][{self.private_name}]状态标记为已改变 (changed=True)") + self.changed = True + + async def update_cold_chat_status(self, is_cold: bool, current_time: float): + """更新冷场状态 + + Args: + is_cold: 是否处于冷场状态 + current_time: 当前时间戳 + """ + if is_cold != self.is_cold_chat: # 仅在状态变化时更新 + self.is_cold_chat = is_cold + if is_cold: + # 进入冷场状态 + self.cold_chat_start_time = ( + self.last_message_time or current_time + ) # 从最后消息时间开始算，或从当前时间开始 + logger.info(f"[私聊][{self.private_name}]进入冷场状态，开始时间: {self.cold_chat_start_time}") + else: + # 结束冷场状态 + if self.cold_chat_start_time: + self.cold_chat_duration = current_time - self.cold_chat_start_time + logger.info(f"[私聊][{self.private_name}]结束冷场状态，持续时间: {self.cold_chat_duration:.2f} 秒") + self.cold_chat_start_time = None # 重置开始时间 + self.update_changed() # 状态变化，标记改变 + + # 即使状态没变，如果是冷场状态，也更新持续时间 + if self.is_cold_chat and self.cold_chat_start_time: + self.cold_chat_duration = current_time - self.cold_chat_start_time + + def get_active_duration(self) -> float: + """获取当前活跃时长 (距离最后一条消息的时间) + + Returns: + float: 最后一条消息到现在的时长（秒） + """ + if not self.last_message_time: + return 0.0 + return time.time() - self.last_message_time + + def get_user_response_time(self) -> Optional[float]: + """获取用户最后响应时间 (距离用户最后发言的时间) + + Returns: + Optional[float]: 用户最后发言到现在的时长（秒），如果没有用户发言则返回None + """ + if not self.last_user_speak_time: + return None + return time.time() - self.last_user_speak_time + + def get_bot_response_time(self) -> Optional[float]: + """获取机器人最后响应时间 (距离机器人最后发言的时间) + + Returns: + Optional[float]: 机器人最后发言到现在的时长（秒），如果没有机器人发言则返回None + """ + if not self.last_bot_speak_time: + return None + return time.time() - self.last_bot_speak_time + + async def clear_unprocessed_messages(self): + """将未处理消息移入历史记录，并更新相关状态""" + if not self.unprocessed_messages: + return # 没有未处理消息，直接返回 + + # logger.debug(f"[私聊][{self.private_name}]处理 {len(self.unprocessed_messages)} 条未处理消息...") + # 将未处理消息添加到历史记录中 (确保历史记录有长度限制，避免无限增长) + max_history_len = 100 # 示例：最多保留100条历史记录 + self.chat_history.extend(self.unprocessed_messages) + if len(self.chat_history) > max_history_len: + self.chat_history = self.chat_history[-max_history_len:] + + # 更新历史记录字符串 (只使用最近一部分生成，例如20条) + history_slice_for_str = self.chat_history[-20:] + try: + self.chat_history_str = await build_readable_messages( + history_slice_for_str, + replace_bot_name=True, + merge_messages=False, + timestamp_mode="relative", + read_mark=0.0, # read_mark 可能需要根据逻辑调整 + ) + except Exception as e: + logger.error(f"[私聊][{self.private_name}]构建聊天记录字符串时出错: {e}") + self.chat_history_str = "[构建聊天记录出错]" # 提供错误提示 + + # 清空未处理消息列表和计数 + # cleared_count = len(self.unprocessed_messages) + self.unprocessed_messages.clear() + self.new_messages_count = 0 + # self.has_unread_messages = False # 这个状态可以通过 new_messages_count 判断 + + self.chat_history_count = len(self.chat_history) # 更新历史记录总数 + # logger.debug(f"[私聊][{self.private_name}]已处理 {cleared_count} 条消息，当前历史记录 {self.chat_history_count} 条。") + + self.update_changed() # 状态改变 diff --git a/src/chat/brain_chat/PFC/pfc.py b/src/chat/brain_chat/PFC/pfc.py new file mode 100644 index 00000000..b17ee21d --- /dev/null +++ b/src/chat/brain_chat/PFC/pfc.py @@ -0,0 +1,345 @@ +from typing import List, Tuple, TYPE_CHECKING +from src.common.logger import get_module_logger +from ..models.utils_model import LLMRequest +from ...config.config import global_config +from .chat_observer import ChatObserver +from .pfc_utils import get_items_from_json +from src.individuality.individuality import Individuality +from .conversation_info import ConversationInfo +from .observation_info import ObservationInfo +from src.plugins.utils.chat_message_builder import build_readable_messages +from rich.traceback import install + +install(extra_lines=3) + +if TYPE_CHECKING: + pass + +logger = get_module_logger("pfc") + + +def _calculate_similarity(goal1: str, goal2: str) -> float: + """简单计算两个目标之间的相似度 + + 这里使用一个简单的实现，实际可以使用更复杂的文本相似度算法 + + Args: + goal1: 第一个目标 + goal2: 第二个目标 + + Returns: + float: 相似度得分 (0-1) + """ + # 简单实现：检查重叠字数比例 + words1 = set(goal1) + words2 = set(goal2) + overlap = len(words1.intersection(words2)) + total = len(words1.union(words2)) + return overlap / total if total > 0 else 0 + + +class GoalAnalyzer: + """对话目标分析器""" + + def __init__(self, stream_id: str, private_name: str): + self.llm = LLMRequest( + model=global_config.llm_normal, temperature=0.7, max_tokens=1000, request_type="conversation_goal" + ) + + self.personality_info = Individuality.get_instance().get_prompt(x_person=2, level=3) + self.name = global_config.BOT_NICKNAME + self.nick_name = global_config.BOT_ALIAS_NAMES + self.private_name = private_name + self.chat_observer = ChatObserver.get_instance(stream_id, private_name) + + # 多目标存储结构 + self.goals = [] # 存储多个目标 + self.max_goals = 3 # 同时保持的最大目标数量 + self.current_goal_and_reason = None + + async def analyze_goal(self, conversation_info: ConversationInfo, observation_info: ObservationInfo): + """分析对话历史并设定目标 + + Args: + conversation_info: 对话信息 + observation_info: 观察信息 + + Returns: + Tuple[str, str, str]: (目标, 方法, 原因) + """ + # 构建对话目标 + goals_str = "" + if conversation_info.goal_list: + for goal_reason in conversation_info.goal_list: + if isinstance(goal_reason, dict): + goal = goal_reason.get("goal", "目标内容缺失") + reasoning = goal_reason.get("reasoning", "没有明确原因") + else: + goal = str(goal_reason) + reasoning = "没有明确原因" + + goal_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n" + goals_str += goal_str + else: + goal = "目前没有明确对话目标" + reasoning = "目前没有明确对话目标，最好思考一个对话目标" + goals_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n" + + # 获取聊天历史记录 + chat_history_text = observation_info.chat_history_str + + if observation_info.new_messages_count > 0: + new_messages_list = observation_info.unprocessed_messages + new_messages_str = await build_readable_messages( + new_messages_list, + replace_bot_name=True, + merge_messages=False, + timestamp_mode="relative", + read_mark=0.0, + ) + chat_history_text += f"\n--- 以下是 {observation_info.new_messages_count} 条新消息 ---\n{new_messages_str}" + + # await observation_info.clear_unprocessed_messages() + + persona_text = f"你的名字是{self.name}，{self.personality_info}。" + # 构建action历史文本 + action_history_list = conversation_info.done_action + action_history_text = "你之前做的事情是：" + for action in action_history_list: + action_history_text += f"{action}\n" + + prompt = f"""{persona_text}。现在你在参与一场QQ聊天，请分析以下聊天记录，并根据你的性格特征确定多个明确的对话目标。 +这些目标应该反映出对话的不同方面和意图。 + +{action_history_text} +当前对话目标： +{goals_str} + +聊天记录： +{chat_history_text} + +请分析当前对话并确定最适合的对话目标。你可以： +1. 保持现有目标不变 +2. 修改现有目标 +3. 添加新目标 +4. 删除不再相关的目标 +5. 如果你想结束对话，请设置一个目标，目标goal为"结束对话"，原因reasoning为你希望结束对话 + +请以JSON数组格式输出当前的所有对话目标，每个目标包含以下字段： +1. goal: 对话目标（简短的一句话） +2. reasoning: 对话原因，为什么设定这个目标（简要解释） + +输出格式示例： +[ +{{ + "goal": "回答用户关于Python编程的具体问题", + "reasoning": "用户提出了关于Python的技术问题，需要专业且准确的解答" +}}, +{{ + "goal": "回答用户关于python安装的具体问题", + "reasoning": "用户提出了关于Python的技术问题，需要专业且准确的解答" +}} +]""" + + logger.debug(f"[私聊][{self.private_name}]发送到LLM的提示词: {prompt}") + try: + content, _ = await self.llm.generate_response_async(prompt) + logger.debug(f"[私聊][{self.private_name}]LLM原始返回内容: {content}") + except Exception as e: + logger.error(f"[私聊][{self.private_name}]分析对话目标时出错: {str(e)}") + content = "" + + # 使用改进后的get_items_from_json函数处理JSON数组 + success, result = get_items_from_json( + content, + self.private_name, + "goal", + "reasoning", + required_types={"goal": str, "reasoning": str}, + allow_array=True, + ) + + if success: + # 判断结果是单个字典还是字典列表 + if isinstance(result, list): + # 清空现有目标列表并添加新目标 + conversation_info.goal_list = [] + for item in result: + conversation_info.goal_list.append(item) + + # 返回第一个目标作为当前主要目标（如果有） + if result: + first_goal = result[0] + return first_goal.get("goal", ""), "", first_goal.get("reasoning", "") + else: + # 单个目标的情况 + conversation_info.goal_list.append(result) + return goal, "", reasoning + + # 如果解析失败，返回默认值 + return "", "", "" + + async def _update_goals(self, new_goal: str, method: str, reasoning: str): + """更新目标列表 + + Args: + new_goal: 新的目标 + method: 实现目标的方法 + reasoning: 目标的原因 + """ + # 检查新目标是否与现有目标相似 + for i, (existing_goal, _, _) in enumerate(self.goals): + if _calculate_similarity(new_goal, existing_goal) > 0.7: # 相似度阈值 + # 更新现有目标 + self.goals[i] = (new_goal, method, reasoning) + # 将此目标移到列表前面（最主要的位置） + self.goals.insert(0, self.goals.pop(i)) + return + + # 添加新目标到列表前面 + self.goals.insert(0, (new_goal, method, reasoning)) + + # 限制目标数量 + if len(self.goals) > self.max_goals: + self.goals.pop() # 移除最老的目标 + + async def get_all_goals(self) -> List[Tuple[str, str, str]]: + """获取所有当前目标 + + Returns: + List[Tuple[str, str, str]]: 目标列表，每项为(目标, 方法, 原因) + """ + return self.goals.copy() + + async def get_alternative_goals(self) -> List[Tuple[str, str, str]]: + """获取除了当前主要目标外的其他备选目标 + + Returns: + List[Tuple[str, str, str]]: 备选目标列表 + """ + if len(self.goals) <= 1: + return [] + return self.goals[1:].copy() + + async def analyze_conversation(self, goal, reasoning): + messages = self.chat_observer.get_cached_messages() + chat_history_text = await build_readable_messages( + messages, + replace_bot_name=True, + merge_messages=False, + timestamp_mode="relative", + read_mark=0.0, + ) + + persona_text = f"你的名字是{self.name}，{self.personality_info}。" + # ===> Persona 文本构建结束 <=== + + # --- 修改 Prompt 字符串，使用 persona_text --- + prompt = f"""{persona_text}。现在你在参与一场QQ聊天， + 当前对话目标：{goal} + 产生该对话目标的原因：{reasoning} + + 请分析以下聊天记录，并根据你的性格特征评估该目标是否已经达到，或者你是否希望停止该次对话。 + 聊天记录： + {chat_history_text} + 请以JSON格式输出，包含以下字段： + 1. goal_achieved: 对话目标是否已经达到（true/false） + 2. stop_conversation: 是否希望停止该次对话（true/false） + 3. reason: 为什么希望停止该次对话（简要解释） + +输出格式示例： +{{ + "goal_achieved": true, + "stop_conversation": false, + "reason": "虽然目标已达成，但对话仍然有继续的价值" +}}""" + + try: + content, _ = await self.llm.generate_response_async(prompt) + logger.debug(f"[私聊][{self.private_name}]LLM原始返回内容: {content}") + + # 尝试解析JSON + success, result = get_items_from_json( + content, + self.private_name, + "goal_achieved", + "stop_conversation", + "reason", + required_types={"goal_achieved": bool, "stop_conversation": bool, "reason": str}, + ) + + if not success: + logger.error(f"[私聊][{self.private_name}]无法解析对话分析结果JSON") + return False, False, "解析结果失败" + + goal_achieved = result["goal_achieved"] + stop_conversation = result["stop_conversation"] + reason = result["reason"] + + return goal_achieved, stop_conversation, reason + + except Exception as e: + logger.error(f"[私聊][{self.private_name}]分析对话状态时出错: {str(e)}") + return False, False, f"分析出错: {str(e)}" + + +# 先注释掉，万一以后出问题了还能开回来（（（ +# class DirectMessageSender: +# """直接发送消息到平台的发送器""" + +# def __init__(self, private_name: str): +# self.logger = get_module_logger("direct_sender") +# self.storage = MessageStorage() +# self.private_name = private_name + +# async def send_via_ws(self, message: MessageSending) -> None: +# try: +# await global_api.send_message(message) +# except Exception as e: +# raise ValueError(f"未找到平台：{message.message_info.platform} 的url配置，请检查配置文件") from e + +# async def send_message( +# self, +# chat_stream: ChatStream, +# content: str, +# reply_to_message: Optional[Message] = None, +# ) -> None: +# """直接发送消息到平台 + +# Args: +# chat_stream: 聊天流 +# content: 消息内容 +# reply_to_message: 要回复的消息 +# """ +# # 构建消息对象 +# message_segment = Seg(type="text", data=content) +# bot_user_info = UserInfo( +# user_id=global_config.BOT_QQ, +# user_nickname=global_config.BOT_NICKNAME, +# platform=chat_stream.platform, +# ) + +# message = MessageSending( +# message_id=f"dm{round(time.time(), 2)}", +# chat_stream=chat_stream, +# bot_user_info=bot_user_info, +# sender_info=reply_to_message.message_info.user_info if reply_to_message else None, +# message_segment=message_segment, +# reply=reply_to_message, +# is_head=True, +# is_emoji=False, +# thinking_start_time=time.time(), +# ) + +# # 处理消息 +# await message.process() + +# _message_json = message.to_dict() + +# # 发送消息 +# try: +# await self.send_via_ws(message) +# await self.storage.store_message(message, chat_stream) +# logger.success(f"[私聊][{self.private_name}]PFC消息已发送: {content}") +# except Exception as e: +# logger.error(f"[私聊][{self.private_name}]PFC消息发送失败: {str(e)}") diff --git a/src/chat/brain_chat/PFC/pfc_KnowledgeFetcher.py b/src/chat/brain_chat/PFC/pfc_KnowledgeFetcher.py new file mode 100644 index 00000000..0989339d --- /dev/null +++ b/src/chat/brain_chat/PFC/pfc_KnowledgeFetcher.py @@ -0,0 +1,85 @@ +from typing import List, Tuple +from src.common.logger import get_module_logger +from src.plugins.memory_system.Hippocampus import HippocampusManager +from ..models.utils_model import LLMRequest +from ...config.config import global_config +from ..chat.message import Message +from ..knowledge.knowledge_lib import qa_manager +from ..utils.chat_message_builder import build_readable_messages + +logger = get_module_logger("knowledge_fetcher") + + +class KnowledgeFetcher: + """知识调取器""" + + def __init__(self, private_name: str): + self.llm = LLMRequest( + model=global_config.llm_normal, + temperature=global_config.llm_normal["temp"], + max_tokens=1000, + request_type="knowledge_fetch", + ) + self.private_name = private_name + + def _lpmm_get_knowledge(self, query: str) -> str: + """获取相关知识 + + Args: + query: 查询内容 + + Returns: + str: 构造好的,带相关度的知识 + """ + + logger.debug(f"[私聊][{self.private_name}]正在从LPMM知识库中获取知识") + try: + knowledge_info = qa_manager.get_knowledge(query) + logger.debug(f"[私聊][{self.private_name}]LPMM知识库查询结果: {knowledge_info:150}") + return knowledge_info + except Exception as e: + logger.error(f"[私聊][{self.private_name}]LPMM知识库搜索工具执行失败: {str(e)}") + return "未找到匹配的知识" + + async def fetch(self, query: str, chat_history: List[Message]) -> Tuple[str, str]: + """获取相关知识 + + Args: + query: 查询内容 + chat_history: 聊天历史 + + Returns: + Tuple[str, str]: (获取的知识, 知识来源) + """ + # 构建查询上下文 + chat_history_text = await build_readable_messages( + chat_history, + replace_bot_name=True, + merge_messages=False, + timestamp_mode="relative", + read_mark=0.0, + ) + + # 从记忆中获取相关知识 + related_memory = await HippocampusManager.get_instance().get_memory_from_text( + text=f"{query}\n{chat_history_text}", + max_memory_num=3, + max_memory_length=2, + max_depth=3, + fast_retrieval=False, + ) + knowledge_text = "" + sources_text = "无记忆匹配" # 默认值 + if related_memory: + sources = [] + for memory in related_memory: + knowledge_text += memory[1] + "\n" + sources.append(f"记忆片段{memory[0]}") + knowledge_text = knowledge_text.strip() + sources_text = "，".join(sources) + + knowledge_text += "\n现在有以下**知识**可供参考：\n " + knowledge_text += self._lpmm_get_knowledge(query) + knowledge_text += "\n请记住这些**知识**，并根据**知识**回答问题。\n" + + return knowledge_text or "未找到相关知识", sources_text or "无记忆匹配" diff --git a/src/chat/brain_chat/PFC/pfc_manager.py b/src/chat/brain_chat/PFC/pfc_manager.py new file mode 100644 index 00000000..7837606c --- /dev/null +++ b/src/chat/brain_chat/PFC/pfc_manager.py @@ -0,0 +1,115 @@ +import time +from typing import Dict, Optional +from src.common.logger import get_module_logger +from .conversation import Conversation +import traceback + +logger = get_module_logger("pfc_manager") + + +class PFCManager: + """PFC对话管理器，负责管理所有对话实例""" + + # 单例模式 + _instance = None + + # 会话实例管理 + _instances: Dict[str, Conversation] = {} + _initializing: Dict[str, bool] = {} + + @classmethod + def get_instance(cls) -> "PFCManager": + """获取管理器单例 + + Returns: + PFCManager: 管理器实例 + """ + if cls._instance is None: + cls._instance = PFCManager() + return cls._instance + + async def get_or_create_conversation(self, stream_id: str, private_name: str) -> Optional[Conversation]: + """获取或创建对话实例 + + Args: + stream_id: 聊天流ID + private_name: 私聊名称 + + Returns: + Optional[Conversation]: 对话实例，创建失败则返回None + """ + # 检查是否已经有实例 + if stream_id in self._initializing and self._initializing[stream_id]: + logger.debug(f"[私聊][{private_name}]会话实例正在初始化中: {stream_id}") + return None + + if stream_id in self._instances and self._instances[stream_id].should_continue: + logger.debug(f"[私聊][{private_name}]使用现有会话实例: {stream_id}") + return self._instances[stream_id] + if stream_id in self._instances: + instance = self._instances[stream_id] + if ( + hasattr(instance, "ignore_until_timestamp") + and instance.ignore_until_timestamp + and time.time() < instance.ignore_until_timestamp + ): + logger.debug(f"[私聊][{private_name}]会话实例当前处于忽略状态: {stream_id}") + # 返回 None 阻止交互。或者可以返回实例但标记它被忽略了喵？ + # 还是返回 None 吧喵。 + return None + + # 检查 should_continue 状态 + if instance.should_continue: + logger.debug(f"[私聊][{private_name}]使用现有会话实例: {stream_id}") + return instance + # else: 实例存在但不应继续 + try: + # 创建新实例 + logger.info(f"[私聊][{private_name}]创建新的对话实例: {stream_id}") + self._initializing[stream_id] = True + # 创建实例 + conversation_instance = Conversation(stream_id, private_name) + self._instances[stream_id] = conversation_instance + + # 启动实例初始化 + await self._initialize_conversation(conversation_instance) + except Exception as e: + logger.error(f"[私聊][{private_name}]创建会话实例失败: {stream_id}, 错误: {e}") + return None + + return conversation_instance + + async def _initialize_conversation(self, conversation: Conversation): + """初始化会话实例 + + Args: + conversation: 要初始化的会话实例 + """ + stream_id = conversation.stream_id + private_name = conversation.private_name + + try: + logger.info(f"[私聊][{private_name}]开始初始化会话实例: {stream_id}") + # 启动初始化流程 + await conversation._initialize() + + # 标记初始化完成 + self._initializing[stream_id] = False + + logger.info(f"[私聊][{private_name}]会话实例 {stream_id} 初始化完成") + + except Exception as e: + logger.error(f"[私聊][{private_name}]管理器初始化会话实例失败: {stream_id}, 错误: {e}") + logger.error(f"[私聊][{private_name}]{traceback.format_exc()}") + # 清理失败的初始化 + + async def get_conversation(self, stream_id: str) -> Optional[Conversation]: + """获取已存在的会话实例 + + Args: + stream_id: 聊天流ID + + Returns: + Optional[Conversation]: 会话实例，不存在则返回None + """ + return self._instances.get(stream_id) diff --git a/src/chat/brain_chat/PFC/pfc_types.py b/src/chat/brain_chat/PFC/pfc_types.py new file mode 100644 index 00000000..0ea5eda6 --- /dev/null +++ b/src/chat/brain_chat/PFC/pfc_types.py @@ -0,0 +1,23 @@ +from enum import Enum +from typing import Literal + + +class ConversationState(Enum): + """对话状态""" + + INIT = "初始化" + RETHINKING = "重新思考" + ANALYZING = "分析历史" + PLANNING = "规划目标" + GENERATING = "生成回复" + CHECKING = "检查回复" + SENDING = "发送消息" + FETCHING = "获取知识" + WAITING = "等待" + LISTENING = "倾听" + ENDED = "结束" + JUDGING = "判断" + IGNORED = "屏蔽" + + +ActionType = Literal["direct_reply", "fetch_knowledge", "wait"] diff --git a/src/chat/brain_chat/PFC/pfc_utils.py b/src/chat/brain_chat/PFC/pfc_utils.py new file mode 100644 index 00000000..2f7bd5e0 --- /dev/null +++ b/src/chat/brain_chat/PFC/pfc_utils.py @@ -0,0 +1,127 @@ +import json +import re +from typing import Dict, Any, Optional, Tuple, List, Union +from src.common.logger import get_module_logger + +logger = get_module_logger("pfc_utils") + + +def get_items_from_json( + content: str, + private_name: str, + *items: str, + default_values: Optional[Dict[str, Any]] = None, + required_types: Optional[Dict[str, type]] = None, + allow_array: bool = True, +) -> Tuple[bool, Union[Dict[str, Any], List[Dict[str, Any]]]]: + """从文本中提取JSON内容并获取指定字段 + + Args: + content: 包含JSON的文本 + private_name: 私聊名称 + *items: 要提取的字段名 + default_values: 字段的默认值，格式为 {字段名: 默认值} + required_types: 字段的必需类型，格式为 {字段名: 类型} + allow_array: 是否允许解析JSON数组 + + Returns: + Tuple[bool, Union[Dict[str, Any], List[Dict[str, Any]]]]: (是否成功, 提取的字段字典或字典列表) + """ + content = content.strip() + result = {} + + # 设置默认值 + if default_values: + result.update(default_values) + + # 首先尝试解析为JSON数组 + if allow_array: + try: + # 尝试找到文本中的JSON数组 + array_pattern = r"\[[\s\S]*\]" + array_match = re.search(array_pattern, content) + if array_match: + array_content = array_match.group() + json_array = json.loads(array_content) + + # 确认是数组类型 + if isinstance(json_array, list): + # 验证数组中的每个项目是否包含所有必需字段 + valid_items = [] + for item in json_array: + if not isinstance(item, dict): + continue + + # 检查是否有所有必需字段 + if all(field in item for field in items): + # 验证字段类型 + if required_types: + type_valid = True + for field, expected_type in required_types.items(): + if field in item and not isinstance(item[field], expected_type): + type_valid = False + break + + if not type_valid: + continue + + # 验证字符串字段不为空 + string_valid = True + for field in items: + if isinstance(item[field], str) and not item[field].strip(): + string_valid = False + break + + if not string_valid: + continue + + valid_items.append(item) + + if valid_items: + return True, valid_items + except json.JSONDecodeError: + logger.debug(f"[私聊][{private_name}]JSON数组解析失败，尝试解析单个JSON对象") + except Exception as e: + logger.debug(f"[私聊][{private_name}]尝试解析JSON数组时出错: {str(e)}") + + # 尝试解析JSON对象 + try: + json_data = json.loads(content) + except json.JSONDecodeError: + # 如果直接解析失败，尝试查找和提取JSON部分 + json_pattern = r"\{[^{}]*\}" + json_match = re.search(json_pattern, content) + if json_match: + try: + json_data = json.loads(json_match.group()) + except json.JSONDecodeError: + logger.error(f"[私聊][{private_name}]提取的JSON内容解析失败") + return False, result + else: + logger.error(f"[私聊][{private_name}]无法在返回内容中找到有效的JSON") + return False, result + + # 提取字段 + for item in items: + if item in json_data: + result[item] = json_data[item] + + # 验证必需字段 + if not all(item in result for item in items): + logger.error(f"[私聊][{private_name}]JSON缺少必要字段，实际内容: {json_data}") + return False, result + + # 验证字段类型 + if required_types: + for field, expected_type in required_types.items(): + if field in result and not isinstance(result[field], expected_type): + logger.error(f"[私聊][{private_name}]{field} 必须是 {expected_type.__name__} 类型") + return False, result + + # 验证字符串字段不为空 + for field in items: + if isinstance(result[field], str) and not result[field].strip(): + logger.error(f"[私聊][{private_name}]{field} 不能为空") + return False, result + + return True, result diff --git a/src/chat/brain_chat/PFC/reply_checker.py b/src/chat/brain_chat/PFC/reply_checker.py new file mode 100644 index 00000000..35e9af50 --- /dev/null +++ b/src/chat/brain_chat/PFC/reply_checker.py @@ -0,0 +1,183 @@ +import json +from typing import Tuple, List, Dict, Any +from src.common.logger import get_module_logger +from ..models.utils_model import LLMRequest +from ...config.config import global_config +from .chat_observer import ChatObserver +from maim_message import UserInfo + +logger = get_module_logger("reply_checker") + + +class ReplyChecker: + """回复检查器""" + + def __init__(self, stream_id: str, private_name: str): + self.llm = LLMRequest( + model=global_config.llm_PFC_reply_checker, temperature=0.50, max_tokens=1000, request_type="reply_check" + ) + self.name = global_config.BOT_NICKNAME + self.private_name = private_name + self.chat_observer = ChatObserver.get_instance(stream_id, private_name) + self.max_retries = 3 # 最大重试次数 + + async def check( + self, reply: str, goal: str, chat_history: List[Dict[str, Any]], chat_history_text: str, retry_count: int = 0 + ) -> Tuple[bool, str, bool]: + """检查生成的回复是否合适 + + Args: + reply: 生成的回复 + goal: 对话目标 + chat_history: 对话历史记录 + chat_history_text: 对话历史记录文本 + retry_count: 当前重试次数 + + Returns: + Tuple[bool, str, bool]: (是否合适, 原因, 是否需要重新规划) + """ + # 不再从 observer 获取，直接使用传入的 chat_history + # messages = self.chat_observer.get_cached_messages(limit=20) + try: + # 筛选出最近由 Bot 自己发送的消息 + bot_messages = [] + for msg in reversed(chat_history): + user_info = UserInfo.from_dict(msg.get("user_info", {})) + if str(user_info.user_id) == str(global_config.BOT_QQ): # 确保比较的是字符串 + bot_messages.append(msg.get("processed_plain_text", "")) + if len(bot_messages) >= 2: # 只和最近的两条比较 + break + # 进行比较 + if bot_messages: + # 可以用简单比较，或者更复杂的相似度库 (如 difflib) + # 简单比较：是否完全相同 + if reply == bot_messages[0]: # 和最近一条完全一样 + logger.warning( + f"[私聊][{self.private_name}]ReplyChecker 检测到回复与上一条 Bot 消息完全相同: '{reply}'" + ) + return ( + False, + "被逻辑检查拒绝：回复内容与你上一条发言完全相同，可以选择深入话题或寻找其它话题或等待", + True, + ) # 不合适，需要返回至决策层 + # 2. 相似度检查 (如果精确匹配未通过) + import difflib # 导入 difflib 库 + + # 计算编辑距离相似度，ratio() 返回 0 到 1 之间的浮点数 + similarity_ratio = difflib.SequenceMatcher(None, reply, bot_messages[0]).ratio() + logger.debug(f"[私聊][{self.private_name}]ReplyChecker - 相似度: {similarity_ratio:.2f}") + + # 设置一个相似度阈值 + similarity_threshold = 0.9 + if similarity_ratio > similarity_threshold: + logger.warning( + f"[私聊][{self.private_name}]ReplyChecker 检测到回复与上一条 Bot 消息高度相似 (相似度 {similarity_ratio:.2f}): '{reply}'" + ) + return ( + False, + f"被逻辑检查拒绝：回复内容与你上一条发言高度相似 (相似度 {similarity_ratio:.2f})，可以选择深入话题或寻找其它话题或等待。", + True, + ) + + except Exception as e: + import traceback + + logger.error(f"[私聊][{self.private_name}]检查回复时出错: 类型={type(e)}, 值={e}") + logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}") # 打印详细的回溯信息 + + prompt = f"""你是一个聊天逻辑检查器，请检查以下回复或消息是否合适： + +当前对话目标：{goal} +最新的对话记录： +{chat_history_text} + +待检查的消息： +{reply} + +请结合聊天记录检查以下几点： +1. 这条消息是否依然符合当前对话目标和实现方式 +2. 这条消息是否与最新的对话记录保持一致性 +3. 是否存在重复发言，或重复表达同质内容（尤其是只是换一种方式表达了相同的含义） +4. 这条消息是否包含违规内容（例如血腥暴力，政治敏感等） +5. 这条消息是否以发送者的角度发言（不要让发送者自己回复自己的消息） +6. 这条消息是否通俗易懂 +7. 这条消息是否有些多余，例如在对方没有回复的情况下，依然连续多次“消息轰炸”（尤其是已经连续发送3条信息的情况，这很可能不合理，需要着重判断） +8. 这条消息是否使用了完全没必要的修辞 +9. 这条消息是否逻辑通顺 +10. 这条消息是否太过冗长了（通常私聊的每条消息长度在20字以内，除非特殊情况） +11. 在连续多次发送消息的情况下，这条消息是否衔接自然，会不会显得奇怪（例如连续两条消息中部分内容重叠） + +请以JSON格式输出，包含以下字段： +1. suitable: 是否合适 (true/false) +2. reason: 原因说明 +3. need_replan: 是否需要重新决策 (true/false)，当你认为此时已经不适合发消息，需要规划其它行动时，设为true + +输出格式示例： +{{ + "suitable": true, + "reason": "回复符合要求，虽然有可能略微偏离目标，但是整体内容流畅得体", + "need_replan": false +}} + +注意：请严格按照JSON格式输出，不要包含任何其他内容。""" + + try: + content, _ = await self.llm.generate_response_async(prompt) + logger.debug(f"[私聊][{self.private_name}]检查回复的原始返回: {content}") + + # 清理内容，尝试提取JSON部分 + content = content.strip() + try: + # 尝试直接解析 + result = json.loads(content) + except json.JSONDecodeError: + # 如果直接解析失败，尝试查找和提取JSON部分 + import re + + json_pattern = r"\{[^{}]*\}" + json_match = re.search(json_pattern, content) + if json_match: + try: + result = json.loads(json_match.group()) + except json.JSONDecodeError: + # 如果JSON解析失败，尝试从文本中提取结果 + is_suitable = "不合适" not in content.lower() and "违规" not in content.lower() + reason = content[:100] if content else "无法解析响应" + need_replan = "重新规划" in content.lower() or "目标不适合" in content.lower() + return is_suitable, reason, need_replan + else: + # 如果找不到JSON，从文本中判断 + is_suitable = "不合适" not in content.lower() and "违规" not in content.lower() + reason = content[:100] if content else "无法解析响应" + need_replan = "重新规划" in content.lower() or "目标不适合" in content.lower() + return is_suitable, reason, need_replan + + # 验证JSON字段 + suitable = result.get("suitable", None) + reason = result.get("reason", "未提供原因") + need_replan = result.get("need_replan", False) + + # 如果suitable字段是字符串，转换为布尔值 + if isinstance(suitable, str): + suitable = suitable.lower() == "true" + + # 如果suitable字段不存在或不是布尔值，从reason中判断 + if suitable is None: + suitable = "不合适" not in reason.lower() and "违规" not in reason.lower() + + # 如果不合适且未达到最大重试次数，返回需要重试 + if not suitable and retry_count < self.max_retries: + return False, reason, False + + # 如果不合适且已达到最大重试次数，返回需要重新规划 + if not suitable and retry_count >= self.max_retries: + return False, f"多次重试后仍不合适: {reason}", True + + return suitable, reason, need_replan + + except Exception as e: + logger.error(f"[私聊][{self.private_name}]检查回复时出错: {e}") + # 如果出错且已达到最大重试次数，建议重新规划 + if retry_count >= self.max_retries: + return False, "多次检查失败，建议重新规划", True + return False, f"检查过程出错，建议重试: {str(e)}", False diff --git a/src/chat/brain_chat/PFC/reply_generator.py b/src/chat/brain_chat/PFC/reply_generator.py new file mode 100644 index 00000000..890f807c --- /dev/null +++ b/src/chat/brain_chat/PFC/reply_generator.py @@ -0,0 +1,228 @@ +from typing import Tuple, List, Dict, Any +from src.common.logger import get_module_logger +from ..models.utils_model import LLMRequest +from ...config.config import global_config +from .chat_observer import ChatObserver +from .reply_checker import ReplyChecker +from src.individuality.individuality import Individuality +from .observation_info import ObservationInfo +from .conversation_info import ConversationInfo +from src.plugins.utils.chat_message_builder import build_readable_messages + +logger = get_module_logger("reply_generator") + +# --- 定义 Prompt 模板 --- + +# Prompt for direct_reply (首次回复) +PROMPT_DIRECT_REPLY = """{persona_text}。现在你在参与一场QQ私聊，请根据以下信息生成一条回复： + +当前对话目标：{goals_str} + +{knowledge_info_str} + +最近的聊天记录： +{chat_history_text} + + +请根据上述信息，结合聊天记录，回复对方。该回复应该： +1. 符合对话目标，以"你"的角度发言（不要自己与自己对话！） +2. 符合你的性格特征和身份细节 +3. 通俗易懂，自然流畅，像正常聊天一样，简短（通常20字以内，除非特殊情况） +4. 可以适当利用相关知识，但不要生硬引用 +5. 自然、得体，结合聊天记录逻辑合理，且没有重复表达同质内容 + +请注意把握聊天内容，不要回复的太有条理，可以有个性。请分清"你"和对方说的话，不要把"你"说的话当做对方说的话，这是你自己说的话。 +可以回复得自然随意自然一些，就像真人一样，注意把握聊天内容，整体风格可以平和、简短，不要刻意突出自身学科背景，不要说你说过的话，可以简短，多简短都可以，但是避免冗长。 +请你注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出回复内容。 +不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。 + +请直接输出回复内容，不需要任何额外格式。""" + +# Prompt for send_new_message (追问/补充) +PROMPT_SEND_NEW_MESSAGE = """{persona_text}。现在你在参与一场QQ私聊，**刚刚你已经发送了一条或多条消息**，现在请根据以下信息再发一条新消息： + +当前对话目标：{goals_str} + +{knowledge_info_str} + +最近的聊天记录： +{chat_history_text} + + +请根据上述信息，结合聊天记录，继续发一条新消息（例如对之前消息的补充，深入话题，或追问等等）。该消息应该： +1. 符合对话目标，以"你"的角度发言（不要自己与自己对话！） +2. 符合你的性格特征和身份细节 +3. 通俗易懂，自然流畅，像正常聊天一样，简短（通常20字以内，除非特殊情况） +4. 可以适当利用相关知识，但不要生硬引用 +5. 跟之前你发的消息自然的衔接，逻辑合理，且没有重复表达同质内容或部分重叠内容 + +请注意把握聊天内容，不用太有条理，可以有个性。请分清"你"和对方说的话，不要把"你"说的话当做对方说的话，这是你自己说的话。 +这条消息可以自然随意自然一些，就像真人一样，注意把握聊天内容，整体风格可以平和、简短，不要刻意突出自身学科背景，不要说你说过的话，可以简短，多简短都可以，但是避免冗长。 +请你注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出消息内容。 +不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。 + +请直接输出回复内容，不需要任何额外格式。""" + +# Prompt for say_goodbye (告别语生成) +PROMPT_FAREWELL = """{persona_text}。你在参与一场 QQ 私聊，现在对话似乎已经结束，你决定再发一条最后的消息来圆满结束。 + +最近的聊天记录： +{chat_history_text} + +请根据上述信息，结合聊天记录，构思一条**简短、自然、符合你人设**的最后的消息。 +这条消息应该： +1. 从你自己的角度发言。 +2. 符合你的性格特征和身份细节。 +3. 通俗易懂，自然流畅，通常很简短。 +4. 自然地为这场对话画上句号，避免开启新话题或显得冗长、刻意。 + +请像真人一样随意自然，**简洁是关键**。 +不要输出多余内容（包括前后缀、冒号、引号、括号、表情包、at或@等）。 + +请直接输出最终的告别消息内容，不需要任何额外格式。""" + + +class ReplyGenerator: + """回复生成器""" + + def __init__(self, stream_id: str, private_name: str): + self.llm = LLMRequest( + model=global_config.llm_PFC_chat, + temperature=global_config.llm_PFC_chat["temp"], + max_tokens=300, + request_type="reply_generation", + ) + self.personality_info = Individuality.get_instance().get_prompt(x_person=2, level=3) + self.name = global_config.BOT_NICKNAME + self.private_name = private_name + self.chat_observer = ChatObserver.get_instance(stream_id, private_name) + self.reply_checker = ReplyChecker(stream_id, private_name) + + # 修改 generate 方法签名，增加 action_type 参数 + async def generate( + self, observation_info: ObservationInfo, conversation_info: ConversationInfo, action_type: str + ) -> str: + """生成回复 + + Args: + observation_info: 观察信息 + conversation_info: 对话信息 + action_type: 当前执行的动作类型 ('direct_reply' 或 'send_new_message') + + Returns: + str: 生成的回复 + """ + # 构建提示词 + logger.debug( + f"[私聊][{self.private_name}]开始生成回复 (动作类型: {action_type})：当前目标: {conversation_info.goal_list}" + ) + + # --- 构建通用 Prompt 参数 --- + # (这部分逻辑基本不变) + + # 构建对话目标 (goals_str) + goals_str = "" + if conversation_info.goal_list: + for goal_reason in conversation_info.goal_list: + if isinstance(goal_reason, dict): + goal = goal_reason.get("goal", "目标内容缺失") + reasoning = goal_reason.get("reasoning", "没有明确原因") + else: + goal = str(goal_reason) + reasoning = "没有明确原因" + + goal = str(goal) if goal is not None else "目标内容缺失" + reasoning = str(reasoning) if reasoning is not None else "没有明确原因" + goals_str += f"- 目标：{goal}\n 原因：{reasoning}\n" + else: + goals_str = "- 目前没有明确对话目标\n" # 简化无目标情况 + + # --- 新增：构建知识信息字符串 --- + knowledge_info_str = "【供参考的相关知识和记忆】\n" # 稍微改下标题，表明是供参考 + try: + # 检查 conversation_info 是否有 knowledge_list 并且不为空 + if hasattr(conversation_info, "knowledge_list") and conversation_info.knowledge_list: + # 最多只显示最近的 5 条知识 + recent_knowledge = conversation_info.knowledge_list[-5:] + for i, knowledge_item in enumerate(recent_knowledge): + if isinstance(knowledge_item, dict): + query = knowledge_item.get("query", "未知查询") + knowledge = knowledge_item.get("knowledge", "无知识内容") + source = knowledge_item.get("source", "未知来源") + # 只取知识内容的前 2000 个字 + knowledge_snippet = knowledge[:2000] + "..." if len(knowledge) > 2000 else knowledge + knowledge_info_str += ( + f"{i + 1}. 关于 '{query}' (来源: {source}): {knowledge_snippet}\n" # 格式微调，更简洁 + ) + else: + knowledge_info_str += f"{i + 1}. 发现一条格式不正确的知识记录。\n" + + if not recent_knowledge: + knowledge_info_str += "- 暂无。\n" # 更简洁的提示 + + else: + knowledge_info_str += "- 暂无。\n" + except AttributeError: + logger.warning(f"[私聊][{self.private_name}]ConversationInfo 对象可能缺少 knowledge_list 属性。") + knowledge_info_str += "- 获取知识列表时出错。\n" + except Exception as e: + logger.error(f"[私聊][{self.private_name}]构建知识信息字符串时出错: {e}") + knowledge_info_str += "- 处理知识列表时出错。\n" + + # 获取聊天历史记录 (chat_history_text) + chat_history_text = observation_info.chat_history_str + if observation_info.new_messages_count > 0 and observation_info.unprocessed_messages: + new_messages_list = observation_info.unprocessed_messages + new_messages_str = await build_readable_messages( + new_messages_list, + replace_bot_name=True, + merge_messages=False, + timestamp_mode="relative", + read_mark=0.0, + ) + chat_history_text += f"\n--- 以下是 {observation_info.new_messages_count} 条新消息 ---\n{new_messages_str}" + elif not chat_history_text: + chat_history_text = "还没有聊天记录。" + + # 构建 Persona 文本 (persona_text) + persona_text = f"你的名字是{self.name}，{self.personality_info}。" + + # --- 选择 Prompt --- + if action_type == "send_new_message": + prompt_template = PROMPT_SEND_NEW_MESSAGE + logger.info(f"[私聊][{self.private_name}]使用 PROMPT_SEND_NEW_MESSAGE (追问生成)") + elif action_type == "say_goodbye": # 处理告别动作 + prompt_template = PROMPT_FAREWELL + logger.info(f"[私聊][{self.private_name}]使用 PROMPT_FAREWELL (告别语生成)") + else: # 默认使用 direct_reply 的 prompt (包括 'direct_reply' 或其他未明确处理的类型) + prompt_template = PROMPT_DIRECT_REPLY + logger.info(f"[私聊][{self.private_name}]使用 PROMPT_DIRECT_REPLY (首次/非连续回复生成)") + + # --- 格式化最终的 Prompt --- + prompt = prompt_template.format( + persona_text=persona_text, + goals_str=goals_str, + chat_history_text=chat_history_text, + knowledge_info_str=knowledge_info_str, + ) + + # --- 调用 LLM 生成 --- + logger.debug(f"[私聊][{self.private_name}]发送到LLM的生成提示词:\n------\n{prompt}\n------") + try: + content, _ = await self.llm.generate_response_async(prompt) + logger.debug(f"[私聊][{self.private_name}]生成的回复: {content}") + # 移除旧的检查新消息逻辑，这应该由 conversation 控制流处理 + return content + + except Exception as e: + logger.error(f"[私聊][{self.private_name}]生成回复时出错: {e}") + return "抱歉，我现在有点混乱，让我重新思考一下..." + + # check_reply 方法保持不变 + async def check_reply( + self, reply: str, goal: str, chat_history: List[Dict[str, Any]], chat_history_str: str, retry_count: int = 0 + ) -> Tuple[bool, str, bool]: + """检查回复是否合适 + (此方法逻辑保持不变) + """ + return await self.reply_checker.check(reply, goal, chat_history, chat_history_str, retry_count) diff --git a/src/chat/brain_chat/PFC/waiter.py b/src/chat/brain_chat/PFC/waiter.py new file mode 100644 index 00000000..0f5881fc --- /dev/null +++ b/src/chat/brain_chat/PFC/waiter.py @@ -0,0 +1,79 @@ +from src.common.logger import get_module_logger +from .chat_observer import ChatObserver +from .conversation_info import ConversationInfo + +# from src.individuality.individuality import Individuality # 不再需要 +from ...config.config import global_config +import time +import asyncio + +logger = get_module_logger("waiter") + +# --- 在这里设定你想要的超时时间（秒） --- +# 例如： 120 秒 = 2 分钟 +DESIRED_TIMEOUT_SECONDS = 300 + + +class Waiter: + """等待处理类""" + + def __init__(self, stream_id: str, private_name: str): + self.chat_observer = ChatObserver.get_instance(stream_id, private_name) + self.name = global_config.BOT_NICKNAME + self.private_name = private_name + # self.wait_accumulated_time = 0 # 不再需要累加计时 + + async def wait(self, conversation_info: ConversationInfo) -> bool: + """等待用户新消息或超时""" + wait_start_time = time.time() + logger.info(f"[私聊][{self.private_name}]进入常规等待状态 (超时: {DESIRED_TIMEOUT_SECONDS} 秒)...") + + while True: + # 检查是否有新消息 + if self.chat_observer.new_message_after(wait_start_time): + logger.info(f"[私聊][{self.private_name}]等待结束，收到新消息") + return False # 返回 False 表示不是超时 + + # 检查是否超时 + elapsed_time = time.time() - wait_start_time + if elapsed_time > DESIRED_TIMEOUT_SECONDS: + logger.info(f"[私聊][{self.private_name}]等待超过 {DESIRED_TIMEOUT_SECONDS} 秒...添加思考目标。") + wait_goal = { + "goal": f"你等待了{elapsed_time / 60:.1f}分钟，注意可能在对方看来聊天已经结束，思考接下来要做什么", + "reasoning": "对方很久没有回复你的消息了", + } + conversation_info.goal_list.append(wait_goal) + logger.info(f"[私聊][{self.private_name}]添加目标: {wait_goal}") + return True # 返回 True 表示超时 + + await asyncio.sleep(5) # 每 5 秒检查一次 + logger.debug( + f"[私聊][{self.private_name}]等待中..." + ) # 可以考虑把这个频繁日志注释掉，只在超时或收到消息时输出 + + async def wait_listening(self, conversation_info: ConversationInfo) -> bool: + """倾听用户发言或超时""" + wait_start_time = time.time() + logger.info(f"[私聊][{self.private_name}]进入倾听等待状态 (超时: {DESIRED_TIMEOUT_SECONDS} 秒)...") + + while True: + # 检查是否有新消息 + if self.chat_observer.new_message_after(wait_start_time): + logger.info(f"[私聊][{self.private_name}]倾听等待结束，收到新消息") + return False # 返回 False 表示不是超时 + + # 检查是否超时 + elapsed_time = time.time() - wait_start_time + if elapsed_time > DESIRED_TIMEOUT_SECONDS: + logger.info(f"[私聊][{self.private_name}]倾听等待超过 {DESIRED_TIMEOUT_SECONDS} 秒...添加思考目标。") + wait_goal = { + # 保持 goal 文本一致 + "goal": f"你等待了{elapsed_time / 60:.1f}分钟，对方似乎话说一半突然消失了，可能忙去了？也可能忘记了回复？要问问吗？还是结束对话？或继续等待？思考接下来要做什么", + "reasoning": "对方话说一半消失了，很久没有回复", + } + conversation_info.goal_list.append(wait_goal) + logger.info(f"[私聊][{self.private_name}]添加目标: {wait_goal}") + return True # 返回 True 表示超时 + + await asyncio.sleep(5) # 每 5 秒检查一次 + logger.debug(f"[私聊][{self.private_name}]倾听等待中...") # 同上，可以考虑注释掉 diff --git a/src/chat/brain_chat/brain_chat.py b/src/chat/brain_chat/brain_chat.py index 61f7ca9f..51607547 100644 --- a/src/chat/brain_chat/brain_chat.py +++ b/src/chat/brain_chat/brain_chat.py @@ -25,6 +25,7 @@ from src.chat.utils.chat_message_builder import ( build_readable_messages_with_id, get_raw_msg_before_timestamp_with_chat, ) +from src.chat.brain_chat.brain_reply_checker import BrainReplyChecker, BrainLLMReplyChecker if TYPE_CHECKING: from src.common.data_models.database_data_model import DatabaseMessages @@ -87,6 +88,11 @@ class BrainChatting: self.running: bool = False self._loop_task: Optional[asyncio.Task] = None # 主循环任务 + # 轻量级回复检查器（比 PFC 更宽松） + self.reply_checker = BrainReplyChecker(chat_id=self.stream_id) + # 使用 planner 模型的一次性 LLM 检查器 + self.llm_reply_checker = BrainLLMReplyChecker(chat_id=self.stream_id, max_retries=1) + # 添加循环信息管理相关的属性 self.history_loop: List[CycleDetail] = [] self._cycle_counter = 0 @@ -96,6 +102,12 @@ class BrainChatting: self.more_plan = False + # 最近一次是否成功进行了 reply，用于选择 BrainPlanner 的 Prompt + self._last_successful_reply: bool = False + + # 类似 PFC 的 block_and_ignore：在该时间点之前不主动参与该聊天 + self._ignore_until_timestamp: Optional[float] = None + async def start(self): """检查是否需要启动主循环，如果未激活则启动。""" @@ -157,6 +169,14 @@ class BrainChatting: ) async def _loopbody(self): # sourcery skip: hoist-if-from-if + # 如果当前处于 block_and_ignore 冷却期，直接跳过本轮思考 + if self._ignore_until_timestamp and time.time() < self._ignore_until_timestamp: + await asyncio.sleep(0.5) + return True + elif self._ignore_until_timestamp and time.time() >= self._ignore_until_timestamp: + logger.info(f"{self.log_prefix} block_and_ignore 冷却结束，恢复该聊天的正常思考") + self._ignore_until_timestamp = None + recent_messages_list = message_api.get_messages_by_time_in_chat( chat_id=self.stream_id, start_time=self.last_read_time, @@ -296,6 +316,10 @@ class BrainChatting: chat_content_block=chat_content_block, message_id_list=message_id_list, interest=global_config.personality.interest, + prompt_key=( + "brain_planner_prompt_follow_up" if self._last_successful_reply else "brain_planner_prompt_initial" + ), + log_prompt=True, ) continue_flag, modified_message = await events_manager.handle_mai_events( EventType.ON_PLAN, None, prompt_info[0], None, self.chat_stream.stream_id @@ -309,6 +333,7 @@ class BrainChatting: action_to_use_info = await self.action_planner.plan( loop_start_time=self.last_read_time, available_actions=available_actions, + last_successful_reply=self._last_successful_reply, ) # 3. 并行执行所有动作 @@ -524,50 +549,131 @@ class BrainChatting: return {"action_type": "no_reply", "success": True, "reply_text": "", "command": ""} elif action_planner_info.action_type == "reply": - try: - success, llm_response = await generator_api.generate_reply( - chat_stream=self.chat_stream, - reply_message=action_planner_info.action_message, - available_actions=available_actions, - chosen_actions=chosen_action_plan_infos, - reply_reason=action_planner_info.reasoning or "", - enable_tool=global_config.tool.enable_tool, - request_type="replyer", - from_plugin=False, - ) + # 使用规则 + 一次 LLM ReplyChecker 包一层重试逻辑 + retry_count = 0 - if not success or not llm_response or not llm_response.reply_set: - if action_planner_info.action_message: - logger.info( - f"对 {action_planner_info.action_message.processed_plain_text} 的回复生成失败" - ) - else: - logger.info("回复生成失败") + while True: + try: + success, llm_response = await generator_api.generate_reply( + chat_stream=self.chat_stream, + reply_message=action_planner_info.action_message, + available_actions=available_actions, + chosen_actions=chosen_action_plan_infos, + reply_reason=action_planner_info.reasoning or "", + enable_tool=global_config.tool.enable_tool, + request_type="replyer", + from_plugin=False, + ) + + if not success or not llm_response or not llm_response.reply_set: + if action_planner_info.action_message: + logger.info( + f"对 {action_planner_info.action_message.processed_plain_text} 的回复生成失败" + ) + else: + logger.info("回复生成失败") + return { + "action_type": "reply", + "success": False, + "reply_text": "", + "loop_info": None, + } + + except asyncio.CancelledError: + logger.debug(f"{self.log_prefix} 并行执行：回复生成任务已被取消") return {"action_type": "reply", "success": False, "reply_text": "", "loop_info": None} - except asyncio.CancelledError: - logger.debug(f"{self.log_prefix} 并行执行：回复生成任务已被取消") - return {"action_type": "reply", "success": False, "reply_text": "", "loop_info": None} - response_set = llm_response.reply_set - selected_expressions = llm_response.selected_expressions - loop_info, reply_text, _ = await self._send_and_store_reply( - response_set=response_set, - action_message=action_planner_info.action_message, # type: ignore - cycle_timers=cycle_timers, - thinking_id=thinking_id, - actions=chosen_action_plan_infos, - selected_expressions=selected_expressions, - ) - return { - "action_type": "reply", - "success": True, - "reply_text": reply_text, - "loop_info": loop_info, - } + response_set = llm_response.reply_set + + # 预先拼接一次纯文本，供检查使用（与发送逻辑解耦） + preview_text = "" + for reply_content in response_set.reply_data: + if reply_content.content_type != ReplyContentType.TEXT: + continue + data: str = reply_content.content # type: ignore + preview_text += data + + # 规则检查（不调用 LLM） + rule_suitable, rule_reason, rule_need_retry = self.reply_checker.check( + reply_text=preview_text, retry_count=retry_count + ) + + # LLM 检查（使用 planner 模型，一次机会） + llm_suitable, llm_reason, llm_need_retry = await self.llm_reply_checker.check( + reply_text=preview_text, retry_count=retry_count + ) + + # 是否需要重生成：只要有一方建议重试，且还在重试次数之内 + if (rule_need_retry or llm_need_retry) and retry_count < max( + self.reply_checker.max_retries, self.llm_reply_checker.max_retries + ): + retry_count += 1 + logger.info( + f"{self.log_prefix} ReplyChecker 建议重试（第 {retry_count} 次），" + f"rule: {rule_reason}; llm: {llm_reason}" + ) + continue + + # 到这里为止，不再重试：即使有一方认为“不太理想”，也只记录原因并放行 + if not rule_suitable or not llm_suitable: + logger.info( + f"{self.log_prefix} ReplyChecker 判断回复可能不太理想，" + f"rule: {rule_reason}; llm: {llm_reason}，本次仍将发送。" + ) + + selected_expressions = llm_response.selected_expressions + loop_info, reply_text, _ = await self._send_and_store_reply( + response_set=response_set, + action_message=action_planner_info.action_message, # type: ignore + cycle_timers=cycle_timers, + thinking_id=thinking_id, + actions=chosen_action_plan_infos, + selected_expressions=selected_expressions, + ) + # 标记这次循环已经成功进行了回复，下一轮 Planner 使用 follow_up Prompt + self._last_successful_reply = True + return { + "action_type": "reply", + "success": True, + "reply_text": reply_text, + "loop_info": loop_info, + } # 其他动作 else: - # 执行普通动作 + # 内建 wait / listening / block_and_ignore：不通过插件系统，直接在这里处理 + if action_planner_info.action_type in ["wait", "listening", "block_and_ignore"]: + reason = action_planner_info.reasoning or "" + + if action_planner_info.action_type == "block_and_ignore": + # 设置一段时间的忽略窗口，例如 10 分钟 + ignore_minutes = 10 + self._ignore_until_timestamp = time.time() + ignore_minutes * 60 + logger.info( + f"{self.log_prefix} 收到 block_and_ignore 动作，将在接下来 {ignore_minutes} 分钟内不再主动参与该聊天" + ) + + # 统一将这三种策略动作记录到数据库，便于后续分析 + await database_api.store_action_info( + chat_stream=self.chat_stream, + action_build_into_prompt=False, + action_prompt_display=reason or f"执行动作: {action_planner_info.action_type}", + action_done=True, + thinking_id=thinking_id, + action_data={"reason": reason}, + action_name=action_planner_info.action_type, + ) + + # 这些动作本身不产生文本回复 + self._last_successful_reply = False + return { + "action_type": action_planner_info.action_type, + "success": True, + "reply_text": "", + "command": "", + } + + # 其余动作：走原有插件 Action 体系 with Timer("动作执行", cycle_timers): success, reply_text, command = await self._handle_action( action_planner_info.action_type, @@ -577,6 +683,10 @@ class BrainChatting: thinking_id, action_planner_info.action_message, ) + # 非 reply 类动作执行成功时，清空最近成功回复标记，让下一轮回到 initial Prompt + if success and action_planner_info.action_type != "reply": + self._last_successful_reply = False + return { "action_type": action_planner_info.action_type, "success": success, diff --git a/src/chat/brain_chat/brain_planner.py b/src/chat/brain_chat/brain_planner.py index ac68dca3..aa3841a3 100644 --- a/src/chat/brain_chat/brain_planner.py +++ b/src/chat/brain_chat/brain_planner.py @@ -35,6 +35,7 @@ install(extra_lines=3) def init_prompt(): + # 初次 / 非连续回复时使用的 Planner Prompt Prompt( """ {time_block} @@ -64,6 +65,33 @@ no_reply "action": "no_reply", }} +wait +动作描述： +在当前轮次暂时不再发言，等待对方进一步发言或后续更合适的时机再回复。这通常用于你已经表达清楚一轮，想给对方留出空间。 +{{ + "action": "wait", + "target_message_id":"想要作为这次等待依据的消息id（通常是对方的最新消息）", + "reason":"选择等待的原因" +}} + +listening +动作描述： +倾听对方继续说话，你感觉对方的话还没说完，或者刚刚发了好几条连续消息，这时你可以选择保持安静，专注“听”而不是马上回复。 +{{ + "action": "listening", + "target_message_id":"你正在倾听的那条消息id（通常是对方的最新消息）", + "reason":"选择倾听的原因" +}} + +block_and_ignore +动作描述： +当你觉得当前对话让你非常不适、存在明显骚扰或恶意时，可以选择在一段时间内不再主动回应该对话（对方再发消息你也先不理）。 +{{ + "action": "block_and_ignore", + "target_message_id":"触发你做出这一决定的消息id", + "reason":"为什么你认为需要暂时屏蔽这段对话" +}} + {action_options_text} 请选择合适的action，并说明触发action的消息id和选择该action的原因。消息id格式:m+数字 @@ -92,7 +120,97 @@ no_reply ``` """, - "brain_planner_prompt", + "brain_planner_prompt_initial", + ) + + # 刚刚已经回复过，对“要不要继续说 / 追问”更敏感的 Planner Prompt + Prompt( + """ +{time_block} +{name_block} +你的兴趣是：{interest} +{chat_context_description}，以下是具体的聊天内容 +**聊天内容** +{chat_content_block} + +**动作记录** +{actions_before_now_block} + +**可用的action** +reply +动作描述： +在你刚刚已经进行过一次或多次回复的前提下，你可以选择： +- 继续顺着正在进行的聊天内容进行补充或追问 +- 也可以选择暂时不再回复，给对方留出回复空间 +{{ + "action": "reply", + "target_message_id":"想要回复的消息id", + "reason":"继续回复的原因（或者解释为什么当前仍然适合连续发言）" +}} + +no_reply +动作描述： +保持沉默，等待对方发言，特别是在你已经连续发言或对方长时间未回复的情况下可以更多考虑这一选项 +{{ + "action": "no_reply", +}} + +wait +动作描述： +你刚刚已经发过一轮，现在选择暂时不再继续追问或补充，给对方更多时间和空间来回应。 +{{ + "action": "wait", + "target_message_id":"想要作为这次等待依据的消息id（通常是你刚刚回复的那条或对方的最新消息）", + "reason":"为什么此时更适合等待而不是继续连续发言" +}} + +listening +动作描述： +你感觉对方还有话要说，或者刚刚连续发送了多条消息，这时你可以选择继续“听”而不是马上再插话。 +{{ + "action": "listening", + "target_message_id":"你正在倾听的那条消息id（通常是对方的最新消息）", + "reason":"你为什么认为对方还需要继续表达" +}} + +block_and_ignore +动作描述： +如果你在连续若干轮对话后，明确感到这是不友善的骚扰或让你极度不适的对话，可以选择在一段时间内不再回应这条对话。 +{{ + "action": "block_and_ignore", + "target_message_id":"触发你做出这一决定的消息id", + "reason":"为什么你认为需要暂时屏蔽这段对话" +}} + +{action_options_text} + +请选择合适的action，并说明触发action的消息id和选择该action的原因。消息id格式:m+数字 +先输出你的选择思考理由，再输出你选择的action，理由是一段平文本，不要分点，精简。 +**动作选择要求** +请你根据聊天内容,用户的最新消息和以下标准选择合适的动作: +{plan_style} +{moderation_prompt} + +请选择所有符合使用要求的action，动作用json格式输出，如果输出多个json，每个json都要单独用```json包裹，你可以重复使用同一个动作或不同动作: +**示例** +// 理由文本 +```json +{{ + "action":"动作名", + "target_message_id":"触发动作的消息id", + //对应参数 +}} +``` +```json +{{ + "action":"动作名", + "target_message_id":"触发动作的消息id", + //对应参数 +}} +``` + +""", + "brain_planner_prompt_follow_up", ) Prompt( @@ -171,7 +289,8 @@ class BrainPlanner: # 验证action是否可用 available_action_names = [action_name for action_name, _ in current_available_actions] - internal_action_names = ["no_reply", "reply", "wait_time"] + # 内部保留动作（不依赖插件系统） + internal_action_names = ["no_reply", "reply", "wait_time", "wait", "listening", "block_and_ignore"] if action not in internal_action_names and action not in available_action_names: logger.warning( @@ -215,6 +334,7 @@ class BrainPlanner: self, available_actions: Dict[str, ActionInfo], loop_start_time: float = 0.0, + last_successful_reply: bool = False, ) -> List[ActionPlannerInfo]: # sourcery skip: use-named-expression """ @@ -257,7 +377,11 @@ class BrainPlanner: logger.debug(f"{self.log_prefix}过滤后有{len(filtered_actions)}个可用动作") - # 构建包含所有动作的提示词 + # 构建包含所有动作的提示词：根据是否刚刚成功回复来选择不同的 Prompt + prompt_key = ( + "brain_planner_prompt_follow_up" if last_successful_reply else "brain_planner_prompt_initial" + ) + # 这里不记录日志，避免重复打印，由调用方按需控制 log_prompt prompt, message_id_list = await self.build_planner_prompt( is_group_chat=is_group_chat, chat_target_info=chat_target_info, @@ -265,6 +389,8 @@ class BrainPlanner: chat_content_block=chat_content_block, message_id_list=message_id_list, interest=global_config.personality.interest, + prompt_key=prompt_key, + log_prompt=False, ) # 调用LLM获取决策 @@ -286,6 +412,8 @@ class BrainPlanner: message_id_list: List[Tuple[str, "DatabaseMessages"]], chat_content_block: str = "", interest: str = "", + prompt_key: str = "brain_planner_prompt_initial", + log_prompt: bool = False, ) -> tuple[str, List[Tuple[str, "DatabaseMessages"]]]: """构建 Planner LLM 的提示词 (获取模板并填充数据)""" try: @@ -321,7 +449,7 @@ class BrainPlanner: name_block = f"你的名字是{bot_name}{bot_nickname}，请注意哪些是你自己的发言。" # 获取主规划器模板并填充 - planner_prompt_template = await global_prompt_manager.get_prompt_async("brain_planner_prompt") + planner_prompt_template = await global_prompt_manager.get_prompt_async(prompt_key) prompt = planner_prompt_template.format( time_block=time_block, chat_context_description=chat_context_description, @@ -334,6 +462,10 @@ class BrainPlanner: plan_style=global_config.personality.private_plan_style, ) + # 调试：按需展示本次 Planner 使用的 Prompt + if log_prompt: + logger.info(f"{self.log_prefix} BrainPlanner Prompt [{prompt_key}]:\n{prompt}") + return prompt, message_id_list except Exception as e: logger.error(f"构建 Planner 提示词时出错: {e}") diff --git a/src/chat/brain_chat/brain_reply_checker.py b/src/chat/brain_chat/brain_reply_checker.py new file mode 100644 index 00000000..9c2c8602 --- /dev/null +++ b/src/chat/brain_chat/brain_reply_checker.py @@ -0,0 +1,217 @@ +from __future__ import annotations + +import traceback +from typing import Tuple, Optional +import time + +from src.common.logger import get_logger +from src.config.config import global_config, model_config +from src.plugin_system.apis import message_api +from src.llm_models.utils_model import LLMRequest + + +logger = get_logger("bc_reply_checker") + + +class BrainReplyChecker: + """ + BrainChat 的轻量级回复检查器 + + 设计目标： + - 与 BrainChat 主循环低耦合：只依赖 chat_id 和 message_api + - 更宽松：只做少量简单检查，尽量不阻塞发送 + - 非 LLM：避免额外的模型调用开销 + """ + + def __init__(self, chat_id: str, max_retries: int = 1) -> None: + self.chat_id = chat_id + # 比 PFC 更宽松：默认只允许 1 次重试 + self.max_retries = max_retries + + def _get_last_bot_text(self) -> Optional[str]: + """ + 获取当前会话中 Bot 最近一次发送的文本内容（如果有）。 + """ + try: + # end_time 必须是数字，这里使用当前时间戳 + recent_messages = message_api.get_messages_by_time_in_chat( + chat_id=self.chat_id, + start_time=0, + end_time=time.time(), + limit=20, + limit_mode="latest", + filter_mai=False, + filter_command=False, + filter_intercept_message_level=1, + ) + + # 使用新配置中的 QQ 账号字段 + bot_id = str(global_config.bot.qq_account) + for msg in reversed(recent_messages): + try: + if str(getattr(msg.user_info, "user_id", "")) == bot_id: + text = getattr(msg, "processed_plain_text", None) + if text: + return str(text) + except Exception: + # 单条消息解析失败不影响整体 + continue + except Exception as e: + logger.warning(f"[{self.chat_id}] 获取最近 Bot 消息失败: {e}") + + return None + + def check( + self, + reply_text: str, + retry_count: int = 0, + ) -> Tuple[bool, str, bool]: + """ + 检查生成的回复是否合适（宽松版本）。 + + 返回: + (suitable, reason, need_retry) + """ + reply_text = reply_text or "" + reply_text = reply_text.strip() + + if not reply_text: + return False, "回复内容为空", retry_count < self.max_retries + + # 1. 与最近一条 Bot 消息做重复/高度相似检查 + last_bot_text = self._get_last_bot_text() + if last_bot_text: + last_bot_text = last_bot_text.strip() + if reply_text == last_bot_text: + logger.info(f"[{self.chat_id}] ReplyChecker: 与上一条 Bot 消息完全相同，尝试重试生成。") + need_retry = retry_count < self.max_retries + return ( + not need_retry, # 如果已经没有重试机会，就放行 + "回复内容与上一条完全相同", + need_retry, + ) + + # 2. 粗略长度限制（过长时给一次重试机会，但整体仍偏宽松） + max_len = 300 + if len(reply_text) > max_len: + logger.info(f"[{self.chat_id}] ReplyChecker: 回复长度为 {len(reply_text)}，超过 {max_len} 字。") + need_retry = retry_count < self.max_retries + return ( + not need_retry, # 超过长度但重试耗尽时也允许发送 + f"回复内容偏长（{len(reply_text)} 字）", + need_retry, + ) + + # 其他情况全部放行 + return True, "通过检查", False + + +class BrainLLMReplyChecker: + """ + 使用 planner 模型做一次轻量 LLM 逻辑检查。 + + - 不参与主决策，只作为“这句话现在说合适吗”的顾问 + - 至多触发一次重生成机会 + """ + + def __init__(self, chat_id: str, max_retries: int = 1) -> None: + self.chat_id = chat_id + self.max_retries = max_retries + # 复用 planner 模型配置 + self.llm = LLMRequest(model_set=model_config.model_task_config.planner, request_type="brain_reply_check") + + def _build_chat_history_text(self, limit: int = 15) -> str: + """构造一段简短的聊天文本上下文，供 LLM 参考。""" + try: + recent_messages = message_api.get_messages_by_time_in_chat( + chat_id=self.chat_id, + start_time=0, + end_time=time.time(), # end_time 也必须是数字 + limit=limit, + limit_mode="latest", + filter_mai=False, + filter_command=False, + filter_intercept_message_level=1, + ) + + lines = [] + for msg in recent_messages: + try: + user = getattr(msg.user_info, "user_nickname", None) or getattr( + msg.user_info, "user_id", "unknown" + ) + text = getattr(msg, "processed_plain_text", "") or "" + if text: + lines.append(f"{user}: {text}") + except Exception: + continue + + return "\n".join(lines) if lines else "（当前几乎没有聊天记录）" + except Exception as e: + logger.warning(f"[{self.chat_id}] 构造聊天上下文文本失败: {e}") + return "（构造聊天上下文时出错）" + + async def check(self, reply_text: str, retry_count: int = 0) -> Tuple[bool, str, bool]: + """ + 使用 planner 模型检查一次回复是否合适。 + + 返回: + (suitable, reason, need_retry) + """ + reply_text = (reply_text or "").strip() + if not reply_text: + return False, "回复内容为空", retry_count < self.max_retries + + chat_history_text = self._build_chat_history_text() + + prompt = f"""你是一个聊天逻辑检查器，使用 JSON 评估下面这条回复是否适合当前上下文。 + +最近的聊天记录（按时间从旧到新）： +{chat_history_text} + +候选回复： +{reply_text} + +请综合考虑： +1. 是否和最近的聊天内容衔接自然 +2. 是否明显重复、啰嗦或完全没必要 +3. 是否有可能被认为不礼貌或不合时宜 +4. 是否在当前时机继续说话会打扰对方（如果对方已经长时间没回，可以宽松一点，只要内容自然即可） + +请只用 JSON 格式回答，不要输出多余文字，例如： +{{ + "suitable": true, + "reason": "整体自然得体" +}} + +其中： +- suitable: 是否建议发送 (true/false) +- reason: 你的简短理由 +""" + + # 调试：展示用于 LLM 检查的 Prompt + logger.info(f"[{self.chat_id}] BrainLLMReplyChecker Prompt:\n{prompt}") + + try: + content, _ = await self.llm.generate_response_async(prompt=prompt) + content = (content or "").strip() + + import json + + result = json.loads(content) + suitable = bool(result.get("suitable", True)) + reason = str(result.get("reason", "未提供原因")).strip() or "未提供原因" + except Exception as e: + logger.warning(f"[{self.chat_id}] LLM 回复检查失败，将默认放行: {e}") + logger.debug(f"[{self.chat_id}] LLM 返回内容: {content[:200] if content else '(空)'}") + logger.debug(traceback.format_exc()) + return True, "LLM 检查失败，默认放行", False + + if not suitable and retry_count < self.max_retries: + # 给一次重新生成机会 + return False, reason, True + + # 不适合但已经没有重试机会时，只记录原因但不强制拦截 + return True, reason, False + +