Merge remote-tracking branch 'upstream/r-dev' into sync/pr-1564-upstream-20260331
# Conflicts: # src/chat/brain_chat/PFC/conversation.py # src/chat/brain_chat/PFC/pfc_KnowledgeFetcher.py # src/chat/knowledge/lpmm_ops.py
This commit is contained in:
A-Dawn
@@ -1,499 +0,0 @@
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import time
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from typing import Tuple, Optional # 增加了 Optional
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from src.common.logger import get_logger
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import global_config, model_config
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import random
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from .chat_observer import ChatObserver
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from .pfc_utils import get_items_from_json
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from src.services.message_service import build_readable_messages
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from .observation_info import ObservationInfo, dict_to_session_message
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from .conversation_info import ConversationInfo
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logger = get_logger("pfc_action_planner")
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# --- 定义 Prompt 模板 ---
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# Prompt(1): 首次回复或非连续回复时的决策 Prompt
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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：
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【当前对话目标】
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{goals_str}
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{knowledge_info_str}
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【最近行动历史概要】
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{action_history_summary}
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【上一次行动的详细情况和结果】
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{last_action_context}
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【时间和超时提示】
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{time_since_last_bot_message_info}{timeout_context}
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【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)
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{chat_history_text}
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------
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可选行动类型以及解释：
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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
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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
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direct_reply: 直接回复对方
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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
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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
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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
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请以JSON格式输出你的决策：
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{{
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"action": "选择的行动类型 (必须是上面列表中的一个)",
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"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)"
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}}
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注意：请严格按照JSON格式输出，不要包含任何其他内容。"""
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# Prompt(2): 上一次成功回复后，决定继续发言时的决策 Prompt
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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：
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【当前对话目标】
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{goals_str}
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{knowledge_info_str}
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【最近行动历史概要】
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{action_history_summary}
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【上一次行动的详细情况和结果】
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{last_action_context}
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【时间和超时提示】
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{time_since_last_bot_message_info}{timeout_context}
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【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)
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{chat_history_text}
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------
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可选行动类型以及解释：
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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
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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）
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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）
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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**
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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
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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
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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
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请以JSON格式输出你的决策：
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{{
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"action": "选择的行动类型 (必须是上面列表中的一个)",
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"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)"
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}}
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注意：请严格按照JSON格式输出，不要包含任何其他内容。"""
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# 新增：Prompt(3): 决定是否在结束对话前发送告别语
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PROMPT_END_DECISION = """{persona_text}。刚刚你决定结束一场 QQ 私聊。
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【你们之前的聊天记录】
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{chat_history_text}
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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。
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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"。
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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"。
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请以 JSON 格式输出你的选择：
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{{
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"say_bye": "yes/no",
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"reason": "选择 yes 或 no 的原因和内心想法 (简要说明)"
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}}
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注意：请严格按照 JSON 格式输出，不要包含任何其他内容。"""
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# ActionPlanner 类定义，顶格
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class ActionPlanner:
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"""行动规划器"""
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def __init__(self, stream_id: str, private_name: str):
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self.llm = LLMRequest(
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model_set=model_config.model_task_config.planner,
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request_type="action_planning",
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)
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self.personality_info = self._get_personality_prompt()
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self.name = global_config.bot.nickname
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self.private_name = private_name
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self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
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# self.action_planner_info = ActionPlannerInfo() # 移除未使用的变量
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def _get_personality_prompt(self) -> str:
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"""获取个性提示信息"""
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prompt_personality = global_config.personality.personality
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# 检查是否需要随机替换为状态
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if (
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global_config.personality.states
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and global_config.personality.state_probability > 0
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and random.random() < global_config.personality.state_probability
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):
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prompt_personality = random.choice(global_config.personality.states)
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bot_name = global_config.bot.nickname
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return f"你的名字是{bot_name},你{prompt_personality};"
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# 修改 plan 方法签名，增加 last_successful_reply_action 参数
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async def plan(
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self,
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observation_info: ObservationInfo,
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conversation_info: ConversationInfo,
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last_successful_reply_action: Optional[str],
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) -> Tuple[str, str]:
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"""规划下一步行动
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Args:
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observation_info: 决策信息
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conversation_info: 对话信息
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last_successful_reply_action: 上一次成功的回复动作类型 ('direct_reply' 或 'send_new_message' 或 None)
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Returns:
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Tuple[str, str]: (行动类型, 行动原因)
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"""
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# --- 获取 Bot 上次发言时间信息 ---
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time_since_last_bot_message_info = ""
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try:
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bot_id = str(global_config.bot.qq_account)
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chat_history = getattr(observation_info, "chat_history", None)
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if chat_history and len(chat_history) > 0:
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for i in range(len(chat_history) - 1, -1, -1):
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msg = chat_history[i]
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if not isinstance(msg, dict):
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continue
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sender_info = msg.get("user_info", {})
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sender_id = str(sender_info.get("user_id")) if isinstance(sender_info, dict) else None
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msg_time = msg.get("time")
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if sender_id == bot_id and msg_time:
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time_diff = time.time() - msg_time
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if time_diff < 60.0:
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time_since_last_bot_message_info = (
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f"提示：你上一条成功发送的消息是在 {time_diff:.1f} 秒前。\n"
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)
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break
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else:
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logger.debug(f"[私聊][{self.private_name}]聊天历史为空或尚未加载，跳过 Bot 发言时间检查。")
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except Exception as e:
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logger.debug(f"[私聊][{self.private_name}]获取 Bot 上次发言时间时出错: {e}")
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# --- 获取超时提示信息 ---
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# (这部分逻辑不变)
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timeout_context = ""
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try:
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if hasattr(conversation_info, "goal_list") and conversation_info.goal_list:
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last_goal_dict = conversation_info.goal_list[-1]
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if isinstance(last_goal_dict, dict) and "goal" in last_goal_dict:
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last_goal_text = last_goal_dict["goal"]
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if isinstance(last_goal_text, str) and "分钟，思考接下来要做什么" in last_goal_text:
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try:
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timeout_minutes_text = last_goal_text.split("，")[0].replace("你等待了", "")
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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"
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except Exception:
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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"
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else:
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logger.debug(
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f"[私聊][{self.private_name}]Conversation info goal_list is empty or not available for timeout check."
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)
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except AttributeError:
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logger.warning(
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f"[私聊][{self.private_name}]ConversationInfo object might not have goal_list attribute yet for timeout check."
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)
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except Exception as e:
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logger.warning(f"[私聊][{self.private_name}]检查超时目标时出错: {e}")
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# --- 构建通用 Prompt 参数 ---
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logger.debug(
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f"[私聊][{self.private_name}]开始规划行动：当前目标: {getattr(conversation_info, 'goal_list', '不可用')}"
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)
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# 构建对话目标 (goals_str)
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goals_str = ""
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try:
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if hasattr(conversation_info, "goal_list") and conversation_info.goal_list:
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for goal_reason in conversation_info.goal_list:
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if isinstance(goal_reason, dict):
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goal = goal_reason.get("goal", "目标内容缺失")
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reasoning = goal_reason.get("reasoning", "没有明确原因")
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else:
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goal = str(goal_reason)
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reasoning = "没有明确原因"
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goal = str(goal) if goal is not None else "目标内容缺失"
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reasoning = str(reasoning) if reasoning is not None else "没有明确原因"
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goals_str += f"- 目标：{goal}\n 原因：{reasoning}\n"
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if not goals_str:
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goals_str = "- 目前没有明确对话目标，请考虑设定一个。\n"
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else:
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goals_str = "- 目前没有明确对话目标，请考虑设定一个。\n"
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except AttributeError:
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logger.warning(
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f"[私聊][{self.private_name}]ConversationInfo object might not have goal_list attribute yet."
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)
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goals_str = "- 获取对话目标时出错。\n"
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except Exception as e:
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logger.error(f"[私聊][{self.private_name}]构建对话目标字符串时出错: {e}")
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goals_str = "- 构建对话目标时出错。\n"
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# --- 知识信息字符串构建开始 ---
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knowledge_info_str = "【已获取的相关知识和记忆】\n"
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try:
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# 检查 conversation_info 是否有 knowledge_list 并且不为空
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if hasattr(conversation_info, "knowledge_list") and conversation_info.knowledge_list:
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# 最多只显示最近的 5 条知识，防止 Prompt 过长
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recent_knowledge = conversation_info.knowledge_list[-5:]
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for i, knowledge_item in enumerate(recent_knowledge):
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if isinstance(knowledge_item, dict):
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query = knowledge_item.get("query", "未知查询")
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knowledge = knowledge_item.get("knowledge", "无知识内容")
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source = knowledge_item.get("source", "未知来源")
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# 只取知识内容的前 2000 个字，避免太长
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knowledge_snippet = knowledge[:2000] + "..." if len(knowledge) > 2000 else knowledge
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knowledge_info_str += (
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f"{i + 1}. 关于 '{query}' 的知识 (来源: {source}):\n {knowledge_snippet}\n"
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)
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else:
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# 处理列表里不是字典的异常情况
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knowledge_info_str += f"{i + 1}. 发现一条格式不正确的知识记录。\n"
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if not recent_knowledge: # 如果 knowledge_list 存在但为空
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knowledge_info_str += "- 暂无相关知识和记忆。\n"
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else:
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# 如果 conversation_info 没有 knowledge_list 属性，或者列表为空
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knowledge_info_str += "- 暂无相关知识记忆。\n"
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except AttributeError:
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logger.warning(f"[私聊][{self.private_name}]ConversationInfo 对象可能缺少 knowledge_list 属性。")
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knowledge_info_str += "- 获取知识列表时出错。\n"
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except Exception as e:
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logger.error(f"[私聊][{self.private_name}]构建知识信息字符串时出错: {e}")
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knowledge_info_str += "- 处理知识列表时出错。\n"
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# --- 知识信息字符串构建结束 ---
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# 获取聊天历史记录 (chat_history_text)
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try:
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if hasattr(observation_info, "chat_history") and observation_info.chat_history:
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chat_history_text = observation_info.chat_history_str or "还没有聊天记录。\n"
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else:
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chat_history_text = "还没有聊天记录。\n"
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if hasattr(observation_info, "new_messages_count") and observation_info.new_messages_count > 0:
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if hasattr(observation_info, "unprocessed_messages") and observation_info.unprocessed_messages:
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new_messages_list = observation_info.unprocessed_messages
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# Convert dict format to SessionMessage objects.
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session_messages = [dict_to_session_message(m) for m in new_messages_list]
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new_messages_str = build_readable_messages(
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session_messages,
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replace_bot_name=True,
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timestamp_mode="relative",
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read_mark=0.0,
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)
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chat_history_text += (
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f"\n--- 以下是 {observation_info.new_messages_count} 条新消息 ---\n{new_messages_str}"
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)
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else:
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logger.warning(
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f"[私聊][{self.private_name}]ObservationInfo has new_messages_count > 0 but unprocessed_messages is empty or missing."
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)
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except AttributeError:
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logger.warning(
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f"[私聊][{self.private_name}]ObservationInfo object might be missing expected attributes for chat history."
|
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)
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chat_history_text = "获取聊天记录时出错。\n"
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except Exception as e:
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logger.error(f"[私聊][{self.private_name}]处理聊天记录时发生未知错误: {e}")
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chat_history_text = "处理聊天记录时出错。\n"
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# 构建 Persona 文本 (persona_text)
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persona_text = f"你的名字是{self.name}，{self.personality_info}。"
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||||
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||||
# 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)
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||||
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)}"
|
||||
@@ -1,394 +0,0 @@
|
||||
import time
|
||||
import asyncio
|
||||
import traceback
|
||||
from datetime import datetime
|
||||
from typing import Optional, Dict, Any, List
|
||||
from src.common.logger import get_logger
|
||||
from sqlmodel import select, col
|
||||
from src.common.database.database import get_db_session
|
||||
from src.common.database.database_model import Messages
|
||||
from maim_message import UserInfo
|
||||
from src.config.config import global_config
|
||||
from .chat_states import NotificationManager, create_new_message_notification, create_cold_chat_notification
|
||||
from rich.traceback import install
|
||||
|
||||
install(extra_lines=3)
|
||||
|
||||
logger = get_logger("chat_observer")
|
||||
|
||||
|
||||
def _message_to_dict(message: Messages) -> Dict[str, Any]:
|
||||
"""Convert Peewee Message model to dict for PFC compatibility
|
||||
|
||||
Args:
|
||||
message: Peewee Messages model instance
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: Message dictionary
|
||||
"""
|
||||
message_timestamp = message.timestamp.timestamp() if isinstance(message.timestamp, datetime) else message.timestamp
|
||||
return {
|
||||
"message_id": message.message_id,
|
||||
"time": message_timestamp,
|
||||
"chat_id": message.session_id,
|
||||
"user_id": message.user_id,
|
||||
"user_nickname": message.user_nickname,
|
||||
"processed_plain_text": message.processed_plain_text,
|
||||
"display_message": message.display_message,
|
||||
"is_mentioned": message.is_mentioned,
|
||||
"is_command": message.is_command,
|
||||
# Add user_info dict for compatibility with existing code
|
||||
"user_info": {
|
||||
"user_id": message.user_id,
|
||||
"user_nickname": message.user_nickname,
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
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.last_message_read: Optional[Dict[str, Any]] = None
|
||||
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}")
|
||||
|
||||
last_check_time = self.last_check_time or 0.0
|
||||
last_check_dt = datetime.fromtimestamp(last_check_time)
|
||||
with get_db_session() as session:
|
||||
statement = select(Messages).where(
|
||||
(col(Messages.session_id) == self.stream_id) & (col(Messages.timestamp) > last_check_dt)
|
||||
)
|
||||
new_message_exists = session.exec(statement).first() is not None
|
||||
|
||||
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
|
||||
|
||||
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
|
||||
"""
|
||||
last_message_time = self.last_message_time or 0.0
|
||||
last_message_dt = datetime.fromtimestamp(last_message_time)
|
||||
with get_db_session() as session:
|
||||
statement = (
|
||||
select(Messages)
|
||||
.where((col(Messages.session_id) == self.stream_id) & (col(Messages.timestamp) > last_message_dt))
|
||||
.order_by(col(Messages.timestamp))
|
||||
)
|
||||
new_messages = [_message_to_dict(msg) for msg in session.exec(statement).all()]
|
||||
|
||||
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
|
||||
"""
|
||||
time_point_dt = datetime.fromtimestamp(time_point)
|
||||
with get_db_session() as session:
|
||||
statement = (
|
||||
select(Messages)
|
||||
.where((col(Messages.session_id) == self.stream_id) & (col(Messages.timestamp) < time_point_dt))
|
||||
.order_by(col(Messages.timestamp))
|
||||
.limit(5)
|
||||
)
|
||||
messages = list(session.exec(statement).all())
|
||||
messages.reverse()
|
||||
new_messages = [_message_to_dict(msg) for msg in messages]
|
||||
|
||||
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_account:
|
||||
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}"
|
||||
@@ -1,290 +0,0 @@
|
||||
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
|
||||
@@ -1,722 +0,0 @@
|
||||
import asyncio
|
||||
import datetime
|
||||
import time
|
||||
|
||||
from typing import Dict, Any, Optional
|
||||
|
||||
from src.common.data_models.mai_message_data_model import MaiMessage
|
||||
from src.services.message_service import build_readable_messages, get_messages_before_time_in_chat
|
||||
|
||||
# from .message_storage import MongoDBMessageStorage
|
||||
# from src.config.config import global_config
|
||||
from .pfc_types import ConversationState
|
||||
from .pfc import ChatObserver, GoalAnalyzer
|
||||
from .message_sender import DirectMessageSender
|
||||
from src.common.logger 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 src.chat.message_receive.chat_manager import chat_manager as _chat_manager
|
||||
from maim_message import UserInfo
|
||||
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.stream_id)
|
||||
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_session_by_session_id(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_messages_before_time_in_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 = build_readable_messages(
|
||||
initial_messages,
|
||||
replace_bot_name=True,
|
||||
timestamp_mode="relative",
|
||||
read_mark=0.0,
|
||||
)
|
||||
if initial_messages:
|
||||
# 将 SessionMessage 列表转换为 PFC 期望的 dict 格式（保持嵌套结构）
|
||||
initial_messages_dict: list[dict] = []
|
||||
for msg in initial_messages:
|
||||
user_info = msg.message_info.user_info
|
||||
msg_dict = {
|
||||
"message_id": msg.message_id,
|
||||
"time": msg.timestamp.timestamp(),
|
||||
"chat_id": msg.session_id,
|
||||
"processed_plain_text": msg.processed_plain_text,
|
||||
"display_message": msg.display_message,
|
||||
"is_mentioned": msg.is_mentioned,
|
||||
"is_command": msg.is_command,
|
||||
"user_info": {
|
||||
"user_id": user_info.user_id,
|
||||
"user_nickname": user_info.user_nickname,
|
||||
"user_cardname": user_info.user_cardname,
|
||||
"platform": msg.platform,
|
||||
},
|
||||
}
|
||||
initial_messages_dict.append(msg_dict)
|
||||
|
||||
# 将加载的消息填充到 ObservationInfo 的 chat_history
|
||||
self.observation_info.chat_history = initial_messages_dict
|
||||
self.observation_info.chat_history_str = chat_talking_prompt + "\n"
|
||||
self.observation_info.chat_history_count = len(initial_messages_dict)
|
||||
|
||||
# 更新 ObservationInfo 中的时间戳等信息
|
||||
last_msg_dict: dict = initial_messages_dict[-1]
|
||||
self.observation_info.last_message_time = last_msg_dict.get("time")
|
||||
last_user_info = UserInfo.from_dict(last_msg_dict.get("user_info", {}))
|
||||
self.observation_info.last_message_sender = last_user_info.user_id
|
||||
self.observation_info.last_message_content = last_msg_dict.get("processed_plain_text", "")
|
||||
|
||||
logger.info(
|
||||
f"[私聊][{self.private_name}]成功加载 {len(initial_messages_dict)} 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
|
||||
if self.observation_info.last_message_time:
|
||||
self.chat_observer.last_message_time = self.observation_info.last_message_time
|
||||
self.chat_observer.last_message_read = last_msg_dict # 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]) -> MaiMessage:
|
||||
"""将消息字典转换为MaiMessage对象"""
|
||||
from datetime import datetime as dt
|
||||
from src.common.data_models.mai_message_data_model import UserInfo as MaiUserInfo, MessageInfo
|
||||
from src.common.data_models.message_component_data_model import MessageSequence
|
||||
|
||||
try:
|
||||
user_info_dict = msg_dict.get("user_info", {})
|
||||
user_info = MaiUserInfo(
|
||||
user_id=user_info_dict.get("user_id", ""),
|
||||
user_nickname=user_info_dict.get("user_nickname", ""),
|
||||
user_cardname=user_info_dict.get("user_cardname"),
|
||||
)
|
||||
|
||||
msg = MaiMessage(
|
||||
message_id=msg_dict.get("message_id", f"gen_{time.time()}"),
|
||||
timestamp=dt.fromtimestamp(msg_dict.get("time", time.time())),
|
||||
)
|
||||
msg.message_info = MessageInfo(user_info=user_info)
|
||||
msg.platform = user_info_dict.get("platform", "")
|
||||
msg.session_id = self.stream_id
|
||||
msg.processed_plain_text = msg_dict.get("processed_plain_text", "")
|
||||
msg.raw_message = MessageSequence(components=[])
|
||||
msg.initialized = True
|
||||
return msg
|
||||
except Exception as e:
|
||||
logger.warning(f"[私聊][{self.private_name}]转换消息时出错: {e}")
|
||||
raise ValueError(f"无法将字典转换为 MaiMessage 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}]会话未初始化，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)}")
|
||||
@@ -1,10 +0,0 @@
|
||||
from typing import Optional
|
||||
|
||||
|
||||
class ConversationInfo:
|
||||
def __init__(self):
|
||||
self.done_action: list = []
|
||||
self.goal_list: list = []
|
||||
self.knowledge_list: list = []
|
||||
self.memory_list: list = []
|
||||
self.last_successful_reply_action: Optional[str] = None
|
||||
@@ -1,61 +0,0 @@
|
||||
"""PFC 侧消息发送封装。"""
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from rich.traceback import install
|
||||
|
||||
from src.chat.message_receive.chat_manager import BotChatSession
|
||||
from src.common.data_models.mai_message_data_model import MaiMessage
|
||||
from src.common.logger import get_logger
|
||||
from src.services import send_service as send_api
|
||||
|
||||
install(extra_lines=3)
|
||||
|
||||
logger = get_logger("message_sender")
|
||||
|
||||
|
||||
class DirectMessageSender:
|
||||
"""直接消息发送器。"""
|
||||
|
||||
def __init__(self, private_name: str) -> None:
|
||||
"""初始化直接消息发送器。
|
||||
|
||||
Args:
|
||||
private_name: 当前私聊实例的名称。
|
||||
"""
|
||||
self.private_name = private_name
|
||||
|
||||
async def send_message(
|
||||
self,
|
||||
chat_stream: BotChatSession,
|
||||
content: str,
|
||||
reply_to_message: Optional[MaiMessage] = None,
|
||||
) -> None:
|
||||
"""发送文本消息到聊天流。
|
||||
|
||||
Args:
|
||||
chat_stream: 目标聊天会话。
|
||||
content: 待发送的文本内容。
|
||||
reply_to_message: 可选的引用回复锚点消息。
|
||||
|
||||
Raises:
|
||||
RuntimeError: 当消息发送失败时抛出。
|
||||
"""
|
||||
try:
|
||||
sent = await send_api.text_to_stream(
|
||||
text=content,
|
||||
stream_id=chat_stream.session_id,
|
||||
set_reply=reply_to_message is not None,
|
||||
reply_message=reply_to_message,
|
||||
storage_message=True,
|
||||
)
|
||||
|
||||
if sent:
|
||||
logger.info(f"[私聊][{self.private_name}]PFC消息已发送: {content}")
|
||||
return
|
||||
|
||||
logger.error(f"[私聊][{self.private_name}]PFC消息发送失败")
|
||||
raise RuntimeError("消息发送失败")
|
||||
except Exception as exc:
|
||||
logger.error(f"[私聊][{self.private_name}]PFC消息发送失败: {exc}")
|
||||
raise
|
||||
@@ -1,429 +0,0 @@
|
||||
from datetime import datetime
|
||||
from typing import Any, Dict, List, Optional, Set
|
||||
|
||||
from maim_message import UserInfo
|
||||
import time
|
||||
|
||||
from src.chat.message_receive.message import SessionMessage
|
||||
from src.common.logger import get_logger
|
||||
from src.common.data_models.mai_message_data_model import MessageInfo, UserInfo as MaiUserInfo
|
||||
from src.services.message_service import build_readable_messages
|
||||
|
||||
from .chat_observer import ChatObserver
|
||||
from .chat_states import NotificationHandler, NotificationType, Notification
|
||||
import traceback # 导入 traceback 用于调试
|
||||
|
||||
logger = get_logger("observation_info")
|
||||
|
||||
|
||||
def dict_to_session_message(msg_dict: Dict[str, Any]) -> SessionMessage:
|
||||
"""Convert PFC dict format to SessionMessage object.
|
||||
|
||||
Args:
|
||||
msg_dict: Message in PFC dict format with nested user_info
|
||||
|
||||
Returns:
|
||||
SessionMessage object compatible with build_readable_messages()
|
||||
"""
|
||||
user_info_dict: Dict[str, Any] = msg_dict.get("user_info", {})
|
||||
timestamp = msg_dict.get("time", 0.0)
|
||||
platform = user_info_dict.get("platform", "")
|
||||
message = SessionMessage(
|
||||
message_id=msg_dict.get("message_id", ""),
|
||||
timestamp=datetime.fromtimestamp(timestamp),
|
||||
platform=platform,
|
||||
)
|
||||
message.message_info = MessageInfo(
|
||||
user_info=MaiUserInfo(
|
||||
user_id=user_info_dict.get("user_id", ""),
|
||||
user_nickname=user_info_dict.get("user_nickname", ""),
|
||||
user_cardname=user_info_dict.get("user_cardname"),
|
||||
)
|
||||
)
|
||||
message.session_id = msg_dict.get("chat_id", "")
|
||||
message.processed_plain_text = msg_dict.get("processed_plain_text", "")
|
||||
message.display_message = msg_dict.get("display_message", "")
|
||||
message.is_mentioned = msg_dict.get("is_mentioned", False)
|
||||
message.is_command = msg_dict.get("is_command", False)
|
||||
message.initialized = True
|
||||
return message
|
||||
|
||||
|
||||
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:
|
||||
# Convert dict format to SessionMessage objects.
|
||||
session_messages = [dict_to_session_message(m) for m in history_slice_for_str]
|
||||
self.chat_history_str = build_readable_messages(
|
||||
session_messages,
|
||||
replace_bot_name=True,
|
||||
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
|
||||
@@ -1,361 +0,0 @@
|
||||
from typing import List, Tuple, TYPE_CHECKING
|
||||
from src.common.logger import get_logger
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config, model_config
|
||||
import random
|
||||
from .chat_observer import ChatObserver
|
||||
from .pfc_utils import get_items_from_json
|
||||
from .conversation_info import ConversationInfo
|
||||
from src.services.message_service import build_readable_messages
|
||||
|
||||
from .observation_info import ObservationInfo, dict_to_session_message
|
||||
from rich.traceback import install
|
||||
|
||||
install(extra_lines=3)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
pass
|
||||
|
||||
logger = get_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_set=model_config.model_task_config.planner, request_type="conversation_goal")
|
||||
|
||||
self.personality_info = self._get_personality_prompt()
|
||||
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
|
||||
|
||||
def _get_personality_prompt(self) -> str:
|
||||
"""获取个性提示信息"""
|
||||
prompt_personality = global_config.personality.personality
|
||||
|
||||
# 检查是否需要随机替换为状态
|
||||
if (
|
||||
global_config.personality.states
|
||||
and global_config.personality.state_probability > 0
|
||||
and random.random() < global_config.personality.state_probability
|
||||
):
|
||||
prompt_personality = random.choice(global_config.personality.states)
|
||||
|
||||
bot_name = global_config.bot.nickname
|
||||
return f"你的名字是{bot_name},你{prompt_personality};"
|
||||
|
||||
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
|
||||
session_messages = [dict_to_session_message(m) for m in new_messages_list]
|
||||
new_messages_str = build_readable_messages(
|
||||
session_messages,
|
||||
replace_bot_name=True,
|
||||
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)
|
||||
goal_value = result.get("goal", "")
|
||||
reasoning_value = result.get("reasoning", "")
|
||||
return goal_value, "", reasoning_value
|
||||
|
||||
# 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()
|
||||
session_messages = [dict_to_session_message(m) for m in messages]
|
||||
chat_history_text = build_readable_messages(
|
||||
session_messages,
|
||||
replace_bot_name=True,
|
||||
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)}")
|
||||
@@ -1,115 +0,0 @@
|
||||
import time
|
||||
from typing import Dict, Optional
|
||||
from src.common.logger import get_logger
|
||||
from .conversation import Conversation
|
||||
import traceback
|
||||
|
||||
logger = get_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)
|
||||
@@ -1,23 +0,0 @@
|
||||
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"]
|
||||
@@ -1,127 +0,0 @@
|
||||
import json
|
||||
import re
|
||||
from typing import Dict, Any, Optional, Tuple, List, Union
|
||||
from src.common.logger import get_logger
|
||||
|
||||
logger = get_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
|
||||
@@ -1,198 +0,0 @@
|
||||
import json
|
||||
import random
|
||||
from typing import Tuple, List, Dict, Any
|
||||
from src.common.logger import get_logger
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config, model_config
|
||||
from .chat_observer import ChatObserver
|
||||
from maim_message import UserInfo
|
||||
|
||||
logger = get_logger("reply_checker")
|
||||
|
||||
|
||||
class ReplyChecker:
|
||||
"""回复检查器"""
|
||||
|
||||
def __init__(self, stream_id: str, private_name: str):
|
||||
self.llm = LLMRequest(model_set=model_config.model_task_config.utils, request_type="reply_check")
|
||||
self.personality_info = self._get_personality_prompt()
|
||||
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
|
||||
|
||||
def _get_personality_prompt(self) -> str:
|
||||
"""获取个性提示信息"""
|
||||
prompt_personality = global_config.personality.personality
|
||||
|
||||
# 检查是否需要随机替换为状态
|
||||
if (
|
||||
global_config.personality.states
|
||||
and global_config.personality.state_probability > 0
|
||||
and random.random() < global_config.personality.state_probability
|
||||
):
|
||||
prompt_personality = random.choice(global_config.personality.states)
|
||||
|
||||
bot_name = global_config.bot.nickname
|
||||
return f"你的名字是{bot_name},你{prompt_personality};"
|
||||
|
||||
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_account):
|
||||
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: dict = 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: dict = 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
|
||||
@@ -1,242 +0,0 @@
|
||||
from typing import Tuple, List, Dict, Any
|
||||
from src.common.logger import get_logger
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config, model_config
|
||||
import random
|
||||
from .chat_observer import ChatObserver
|
||||
from .reply_checker import ReplyChecker
|
||||
from src.services.message_service import build_readable_messages
|
||||
|
||||
from .observation_info import ObservationInfo, dict_to_session_message
|
||||
from .conversation_info import ConversationInfo
|
||||
|
||||
logger = get_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_set=model_config.model_task_config.replyer,
|
||||
request_type="reply_generation",
|
||||
)
|
||||
self.personality_info = self._get_personality_prompt()
|
||||
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)
|
||||
|
||||
def _get_personality_prompt(self) -> str:
|
||||
"""获取个性提示信息"""
|
||||
prompt_personality = global_config.personality.personality
|
||||
|
||||
# 检查是否需要随机替换为状态
|
||||
if (
|
||||
global_config.personality.states
|
||||
and global_config.personality.state_probability > 0
|
||||
and random.random() < global_config.personality.state_probability
|
||||
):
|
||||
prompt_personality = random.choice(global_config.personality.states)
|
||||
|
||||
bot_name = global_config.bot.nickname
|
||||
return f"你的名字是{bot_name},你{prompt_personality};"
|
||||
|
||||
# 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 = f"{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
|
||||
session_messages = [dict_to_session_message(m) for m in new_messages_list]
|
||||
new_messages_str = build_readable_messages(
|
||||
session_messages,
|
||||
replace_bot_name=True,
|
||||
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)
|
||||
@@ -1,79 +0,0 @@
|
||||
from src.common.logger import get_logger
|
||||
from .chat_observer import ChatObserver
|
||||
from .conversation_info import ConversationInfo
|
||||
|
||||
# from src.individuality.individuality import Individuality # 不再需要
|
||||
from src.config.config import global_config
|
||||
import time
|
||||
import asyncio
|
||||
|
||||
logger = get_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
|
||||
@@ -1,800 +0,0 @@
|
||||
import asyncio
|
||||
import random
|
||||
import time
|
||||
import traceback
|
||||
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
|
||||
from rich.traceback import install
|
||||
|
||||
from src.config.config import global_config
|
||||
from src.common.logger import get_logger
|
||||
from src.common.utils.utils_config import ExpressionConfigUtils
|
||||
from src.learners.expression_learner import ExpressionLearner
|
||||
from src.learners.jargon_miner import JargonMiner
|
||||
from src.chat.message_receive.chat_manager import BotChatSession
|
||||
from src.chat.message_receive.chat_manager import chat_manager as _chat_manager
|
||||
from src.chat.message_receive.message import SessionMessage
|
||||
from src.common.data_models.info_data_model import ActionPlannerInfo
|
||||
from src.common.data_models.message_component_data_model import MessageSequence, TextComponent
|
||||
from src.chat.utils.prompt_builder import global_prompt_manager
|
||||
from src.chat.utils.timer_calculator import Timer
|
||||
from src.chat.brain_chat.brain_planner import BrainPlanner
|
||||
from src.chat.planner_actions.action_modifier import ActionModifier
|
||||
from src.chat.planner_actions.action_manager import ActionManager
|
||||
from src.chat.heart_flow.heartFC_utils import CycleDetail
|
||||
from src.person_info.person_info import Person
|
||||
from src.core.types import ActionInfo, EventType
|
||||
from src.core.event_bus import event_bus
|
||||
from src.chat.event_helpers import build_event_message
|
||||
from src.services import (
|
||||
generator_service as generator_api,
|
||||
send_service as send_api,
|
||||
message_service as message_api,
|
||||
database_service as database_api,
|
||||
)
|
||||
from src.services.message_service import build_readable_messages_with_id, get_messages_before_time_in_chat
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from src.chat.message_receive.message import SessionMessage
|
||||
|
||||
|
||||
ERROR_LOOP_INFO = {
|
||||
"loop_plan_info": {
|
||||
"action_result": {
|
||||
"action_type": "error",
|
||||
"action_data": {},
|
||||
"reasoning": "循环处理失败",
|
||||
},
|
||||
},
|
||||
"loop_action_info": {
|
||||
"action_taken": False,
|
||||
"reply_text": "",
|
||||
"command": "",
|
||||
"taken_time": time.time(),
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
install(extra_lines=3)
|
||||
|
||||
# 注释：原来的动作修改超时常量已移除，因为改为顺序执行
|
||||
|
||||
logger = get_logger("bc") # Logger Name Changed
|
||||
|
||||
|
||||
class BrainChatting:
|
||||
"""
|
||||
管理一个连续的私聊Brain Chat循环
|
||||
用于在特定聊天流中生成回复。
|
||||
"""
|
||||
|
||||
def __init__(self, session_id: str):
|
||||
"""
|
||||
BrainChatting 初始化函数
|
||||
|
||||
参数:
|
||||
chat_id: 聊天流唯一标识符(如stream_id)
|
||||
on_stop_focus_chat: 当收到stop_focus_chat命令时调用的回调函数
|
||||
performance_version: 性能记录版本号，用于区分不同启动版本
|
||||
"""
|
||||
# 基础属性
|
||||
self.stream_id: str = session_id # 聊天流ID
|
||||
self.chat_stream: BotChatSession = _chat_manager.get_session_by_session_id(self.stream_id) # type: ignore[assignment]
|
||||
if not self.chat_stream:
|
||||
raise ValueError(f"无法找到聊天流: {self.stream_id}")
|
||||
self.log_prefix = f"[{_chat_manager.get_session_name(self.stream_id) or self.stream_id}]"
|
||||
|
||||
expr_use, jargon_learn, expr_learn = ExpressionConfigUtils.get_expression_config_for_chat(self.stream_id)
|
||||
self._enable_expression_use = expr_use
|
||||
self._enable_expression_learning = expr_learn
|
||||
self._enable_jargon_learning = jargon_learn
|
||||
self._expression_learner = ExpressionLearner(self.stream_id)
|
||||
self._jargon_miner = JargonMiner(self.stream_id, session_name=self.log_prefix.strip("[]"))
|
||||
self._min_messages_for_extraction = 30
|
||||
self._min_extraction_interval = 60
|
||||
self._last_extraction_time = 0.0
|
||||
|
||||
self.action_manager = ActionManager()
|
||||
self.action_planner = BrainPlanner(chat_id=self.stream_id, action_manager=self.action_manager)
|
||||
self.action_modifier = ActionModifier(action_manager=self.action_manager, chat_id=self.stream_id)
|
||||
|
||||
# 循环控制内部状态
|
||||
self.running: bool = False
|
||||
self._loop_task: Optional[asyncio.Task] = None # 主循环任务
|
||||
self._new_message_event = asyncio.Event() # 新消息事件，用于打断 wait
|
||||
|
||||
# 添加循环信息管理相关的属性
|
||||
self.history_loop: List[CycleDetail] = []
|
||||
self._cycle_counter = 0
|
||||
self._current_cycle_detail: CycleDetail = None # type: ignore
|
||||
|
||||
self.last_read_time = time.time() - 2
|
||||
|
||||
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
|
||||
|
||||
async def start(self):
|
||||
"""检查是否需要启动主循环，如果未激活则启动。"""
|
||||
|
||||
# 如果循环已经激活，直接返回
|
||||
if self.running:
|
||||
logger.debug(f"{self.log_prefix} BrainChatting 已激活，无需重复启动")
|
||||
return
|
||||
|
||||
try:
|
||||
# 标记为活动状态，防止重复启动
|
||||
self.running = True
|
||||
|
||||
self._loop_task = asyncio.create_task(self._main_chat_loop())
|
||||
self._loop_task.add_done_callback(self._handle_loop_completion)
|
||||
logger.info(f"{self.log_prefix} BrainChatting 启动完成")
|
||||
|
||||
except Exception as e:
|
||||
# 启动失败时重置状态
|
||||
self.running = False
|
||||
self._loop_task = None
|
||||
logger.error(f"{self.log_prefix} BrainChatting 启动失败: {e}")
|
||||
raise
|
||||
|
||||
def _handle_loop_completion(self, task: asyncio.Task):
|
||||
"""当 _hfc_loop 任务完成时执行的回调。"""
|
||||
try:
|
||||
if exception := task.exception():
|
||||
logger.error(f"{self.log_prefix} BrainChatting: 脱离了聊天(异常): {exception}")
|
||||
logger.error(traceback.format_exc()) # Log full traceback for exceptions
|
||||
else:
|
||||
logger.info(f"{self.log_prefix} BrainChatting: 脱离了聊天 (外部停止)")
|
||||
except asyncio.CancelledError:
|
||||
logger.info(f"{self.log_prefix} BrainChatting: 结束了聊天")
|
||||
|
||||
def start_cycle(self) -> Tuple[Dict[str, float], str]:
|
||||
self._cycle_counter += 1
|
||||
self._current_cycle_detail = CycleDetail(self._cycle_counter)
|
||||
self._current_cycle_detail.thinking_id = f"tid{str(round(time.time(), 2))}"
|
||||
cycle_timers = {}
|
||||
return cycle_timers, self._current_cycle_detail.thinking_id
|
||||
|
||||
def end_cycle(self, loop_info, cycle_timers):
|
||||
self._current_cycle_detail.set_loop_info(loop_info)
|
||||
self.history_loop.append(self._current_cycle_detail)
|
||||
self._current_cycle_detail.timers = cycle_timers
|
||||
self._current_cycle_detail.end_time = time.time()
|
||||
|
||||
def print_cycle_info(self, cycle_timers):
|
||||
# 记录循环信息和计时器结果
|
||||
timer_strings = []
|
||||
for name, elapsed in cycle_timers.items():
|
||||
formatted_time = f"{elapsed * 1000:.2f}毫秒" if elapsed < 1 else f"{elapsed:.2f}秒"
|
||||
timer_strings.append(f"{name}: {formatted_time}")
|
||||
|
||||
logger.info(
|
||||
f"{self.log_prefix} 第{self._current_cycle_detail.cycle_id}次思考,"
|
||||
f"耗时: {self._current_cycle_detail.end_time - self._current_cycle_detail.start_time:.1f}秒" # type: ignore
|
||||
+ (f"\n详情: {'; '.join(timer_strings)}" if timer_strings else "")
|
||||
)
|
||||
|
||||
async def _trigger_expression_learning(self, messages: List[SessionMessage]) -> None:
|
||||
if not messages:
|
||||
return
|
||||
|
||||
self._expression_learner.add_messages(messages)
|
||||
if time.time() - self._last_extraction_time < self._min_extraction_interval:
|
||||
return
|
||||
if self._expression_learner.get_cache_size() < self._min_messages_for_extraction:
|
||||
return
|
||||
if not self._enable_expression_learning:
|
||||
return
|
||||
|
||||
self._last_extraction_time = time.time()
|
||||
try:
|
||||
jargon_miner = self._jargon_miner if self._enable_jargon_learning else None
|
||||
await self._expression_learner.learn(jargon_miner)
|
||||
except Exception as exc:
|
||||
logger.error(f"{self.log_prefix} 表达学习失败: {exc}", exc_info=True)
|
||||
|
||||
async def _loopbody(self): # sourcery skip: hoist-if-from-if
|
||||
# 获取最新消息（用于上下文，但不影响是否调用 observe）
|
||||
recent_messages_list = message_api.get_messages_by_time_in_chat(
|
||||
chat_id=self.stream_id,
|
||||
start_time=self.last_read_time,
|
||||
end_time=time.time(),
|
||||
limit=20,
|
||||
limit_mode="latest",
|
||||
filter_mai=True,
|
||||
filter_command=False,
|
||||
filter_intercept_message_level=1,
|
||||
)
|
||||
|
||||
# 如果有新消息，更新 last_read_time 并触发事件以打断正在进行的 wait
|
||||
if len(recent_messages_list) >= 1:
|
||||
self.last_read_time = time.time()
|
||||
self._new_message_event.set() # 触发新消息事件，打断 wait
|
||||
|
||||
# 总是执行一次思考迭代（不管有没有新消息）
|
||||
# wait 动作会在其内部等待，不需要在这里处理
|
||||
should_continue = await self._observe(recent_messages_list=recent_messages_list)
|
||||
|
||||
if not should_continue:
|
||||
# 选择了 complete_talk，返回 False 表示需要等待新消息
|
||||
return False
|
||||
|
||||
# 继续下一次迭代（除非选择了 complete_talk）
|
||||
# 短暂等待后再继续，避免过于频繁的循环
|
||||
await asyncio.sleep(0.1)
|
||||
|
||||
return True
|
||||
|
||||
async def _send_and_store_reply(
|
||||
self,
|
||||
response_set: MessageSequence,
|
||||
action_message: SessionMessage,
|
||||
cycle_timers: Dict[str, float],
|
||||
thinking_id,
|
||||
actions,
|
||||
selected_expressions: Optional[List[int]] = None,
|
||||
) -> Tuple[Dict[str, Any], str, Dict[str, float]]:
|
||||
with Timer("回复发送", cycle_timers):
|
||||
reply_text = await self._send_response(
|
||||
reply_set=response_set,
|
||||
message_data=action_message,
|
||||
selected_expressions=selected_expressions,
|
||||
)
|
||||
|
||||
# 获取 platform，如果不存在则从 chat_stream 获取，如果还是 None 则使用默认值
|
||||
platform = action_message.platform
|
||||
if platform is None:
|
||||
platform = getattr(self.chat_stream, "platform", "unknown")
|
||||
|
||||
person = Person(platform=platform, user_id=action_message.message_info.user_info.user_id)
|
||||
person_name = person.person_name
|
||||
action_prompt_display = f"你对{person_name}进行了回复：{reply_text}"
|
||||
|
||||
await database_api.store_action_info(
|
||||
chat_stream=self.chat_stream,
|
||||
display_prompt=action_prompt_display,
|
||||
thinking_id=thinking_id,
|
||||
action_data={"reply_text": reply_text},
|
||||
action_name="reply",
|
||||
)
|
||||
|
||||
# 构建循环信息
|
||||
loop_info: Dict[str, Any] = {
|
||||
"loop_plan_info": {
|
||||
"action_result": actions,
|
||||
},
|
||||
"loop_action_info": {
|
||||
"action_taken": True,
|
||||
"reply_text": reply_text,
|
||||
"command": "",
|
||||
"taken_time": time.time(),
|
||||
},
|
||||
}
|
||||
|
||||
return loop_info, reply_text, cycle_timers
|
||||
|
||||
async def _observe(
|
||||
self, # interest_value: float = 0.0,
|
||||
recent_messages_list: Optional[List[SessionMessage]] = None,
|
||||
) -> bool: # sourcery skip: merge-else-if-into-elif, remove-redundant-if
|
||||
if recent_messages_list is None:
|
||||
recent_messages_list = []
|
||||
_reply_text = "" # 初始化reply_text变量，避免UnboundLocalError
|
||||
|
||||
async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()):
|
||||
if recent_messages_list:
|
||||
asyncio.create_task(self._trigger_expression_learning(recent_messages_list))
|
||||
|
||||
cycle_timers, thinking_id = self.start_cycle()
|
||||
logger.info(f"{self.log_prefix} 开始第{self._cycle_counter}次思考")
|
||||
|
||||
# 第一步：动作检查
|
||||
available_actions: Dict[str, ActionInfo] = {}
|
||||
try:
|
||||
await self.action_modifier.modify_actions()
|
||||
available_actions = self.action_manager.get_using_actions()
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 动作修改失败: {e}")
|
||||
|
||||
# 获取必要信息
|
||||
is_group_chat, chat_target_info, _ = self.action_planner.get_necessary_info()
|
||||
|
||||
# 一次思考迭代：Think - Act - Observe
|
||||
# 获取聊天上下文
|
||||
message_list_before_now = get_messages_before_time_in_chat(
|
||||
chat_id=self.stream_id,
|
||||
timestamp=time.time(),
|
||||
limit=int(global_config.chat.max_context_size * 0.6),
|
||||
filter_intercept_message_level=1,
|
||||
)
|
||||
chat_content_block, message_id_list = build_readable_messages_with_id(
|
||||
messages=message_list_before_now,
|
||||
timestamp_mode="normal_no_YMD",
|
||||
read_mark=self.action_planner.last_obs_time_mark,
|
||||
truncate=True,
|
||||
show_actions=True,
|
||||
)
|
||||
|
||||
prompt_info = await self.action_planner.build_planner_prompt(
|
||||
chat_target_info=chat_target_info,
|
||||
current_available_actions=available_actions,
|
||||
chat_content_block=chat_content_block,
|
||||
message_id_list=message_id_list,
|
||||
prompt_key="brain_planner",
|
||||
)
|
||||
_event_msg = build_event_message(
|
||||
EventType.ON_PLAN, llm_prompt=prompt_info[0], stream_id=self.chat_stream.session_id
|
||||
)
|
||||
continue_flag, modified_message = await event_bus.emit(EventType.ON_PLAN, _event_msg)
|
||||
if not continue_flag:
|
||||
return False
|
||||
if modified_message and modified_message._modify_flags.modify_llm_prompt:
|
||||
prompt_info = (modified_message.llm_prompt, prompt_info[1])
|
||||
|
||||
with Timer("规划器", cycle_timers):
|
||||
action_to_use_info = await self.action_planner.plan(
|
||||
loop_start_time=self.last_read_time,
|
||||
available_actions=available_actions,
|
||||
)
|
||||
|
||||
# 检查是否有 complete_talk 动作（会停止后续迭代）
|
||||
has_complete_talk = any(action.action_type == "complete_talk" for action in action_to_use_info)
|
||||
|
||||
# 并行执行所有动作
|
||||
action_tasks = [
|
||||
asyncio.create_task(
|
||||
self._execute_action(action, action_to_use_info, thinking_id, available_actions, cycle_timers)
|
||||
)
|
||||
for action in action_to_use_info
|
||||
]
|
||||
|
||||
# 并行执行所有任务
|
||||
results = await asyncio.gather(*action_tasks, return_exceptions=True)
|
||||
|
||||
# 处理执行结果
|
||||
reply_loop_info = None
|
||||
reply_text_from_reply = ""
|
||||
action_success = False
|
||||
action_reply_text = ""
|
||||
|
||||
for result in results:
|
||||
if isinstance(result, BaseException):
|
||||
logger.error(f"{self.log_prefix} 动作执行异常: {result}")
|
||||
continue
|
||||
|
||||
if result["action_type"] != "reply":
|
||||
action_success = result["success"]
|
||||
action_reply_text = result["reply_text"]
|
||||
elif result["action_type"] == "reply":
|
||||
if result["success"]:
|
||||
reply_loop_info = result["loop_info"]
|
||||
reply_text_from_reply = result["reply_text"]
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix} 回复动作执行失败")
|
||||
|
||||
# 更新观察时间标记
|
||||
self.action_planner.last_obs_time_mark = time.time()
|
||||
|
||||
# 如果选择了 complete_talk，标记为完成，不再继续迭代
|
||||
if has_complete_talk:
|
||||
logger.info(f"{self.log_prefix} 检测到 complete_talk 动作，本次思考完成")
|
||||
|
||||
# 构建循环信息
|
||||
if reply_loop_info:
|
||||
# 如果有回复信息，使用回复的loop_info作为基础
|
||||
loop_info = reply_loop_info
|
||||
# 更新动作执行信息
|
||||
loop_info["loop_action_info"].update(
|
||||
{
|
||||
"action_taken": action_success,
|
||||
"taken_time": time.time(),
|
||||
}
|
||||
)
|
||||
_reply_text = reply_text_from_reply
|
||||
else:
|
||||
# 没有回复信息，构建纯动作的loop_info
|
||||
loop_info = {
|
||||
"loop_plan_info": {
|
||||
"action_result": action_to_use_info,
|
||||
},
|
||||
"loop_action_info": {
|
||||
"action_taken": action_success,
|
||||
"reply_text": action_reply_text,
|
||||
"taken_time": time.time(),
|
||||
},
|
||||
}
|
||||
_reply_text = action_reply_text
|
||||
|
||||
# 如果选择了 complete_talk，返回 False 以停止 _loopbody 的循环
|
||||
# 否则返回 True，让 _loopbody 继续下一次迭代
|
||||
should_continue = not has_complete_talk
|
||||
|
||||
self.end_cycle(loop_info, cycle_timers)
|
||||
self.print_cycle_info(cycle_timers)
|
||||
|
||||
# 如果选择了 complete_talk，返回 False 停止循环
|
||||
# 否则返回 True，继续下一次思考迭代
|
||||
return should_continue
|
||||
|
||||
async def _main_chat_loop(self):
|
||||
"""主循环，持续进行计划并可能回复消息，直到被外部取消。"""
|
||||
try:
|
||||
while self.running:
|
||||
# 主循环
|
||||
success = await self._loopbody()
|
||||
if not success:
|
||||
# 选择了 complete，等待新消息
|
||||
logger.info(f"{self.log_prefix} 选择了 complete，等待新消息...")
|
||||
await self._wait_for_new_message()
|
||||
# 有新消息后继续循环
|
||||
continue
|
||||
await asyncio.sleep(0.1)
|
||||
except asyncio.CancelledError:
|
||||
# 设置了关闭标志位后被取消是正常流程
|
||||
logger.info(f"{self.log_prefix} 麦麦已关闭聊天")
|
||||
except Exception:
|
||||
logger.error(f"{self.log_prefix} 麦麦聊天意外错误，将于3s后尝试重新启动")
|
||||
print(traceback.format_exc())
|
||||
await asyncio.sleep(3)
|
||||
self._loop_task = asyncio.create_task(self._main_chat_loop())
|
||||
logger.error(f"{self.log_prefix} 结束了当前聊天循环")
|
||||
|
||||
async def _wait_for_new_message(self):
|
||||
"""等待新消息到达"""
|
||||
last_check_time = self.last_read_time
|
||||
check_interval = 1.0 # 每秒检查一次
|
||||
|
||||
# 清除事件状态，准备等待新消息
|
||||
self._new_message_event.clear()
|
||||
|
||||
while self.running:
|
||||
# 检查是否有新消息
|
||||
recent_messages_list = message_api.get_messages_by_time_in_chat(
|
||||
chat_id=self.stream_id,
|
||||
start_time=last_check_time,
|
||||
end_time=time.time(),
|
||||
limit=20,
|
||||
limit_mode="latest",
|
||||
filter_mai=True,
|
||||
filter_command=False,
|
||||
filter_intercept_message_level=1,
|
||||
)
|
||||
|
||||
# 如果有新消息，更新 last_read_time 并返回
|
||||
if len(recent_messages_list) >= 1:
|
||||
self.last_read_time = time.time()
|
||||
logger.info(f"{self.log_prefix} 检测到新消息，恢复循环")
|
||||
return
|
||||
|
||||
# 等待新消息事件或超时后再次检查
|
||||
try:
|
||||
await asyncio.wait_for(self._new_message_event.wait(), timeout=check_interval)
|
||||
# 事件被触发，说明有新消息
|
||||
logger.info(f"{self.log_prefix} 检测到新消息事件，恢复循环")
|
||||
return
|
||||
except asyncio.TimeoutError:
|
||||
# 超时后继续检查
|
||||
continue
|
||||
|
||||
async def _handle_action(
|
||||
self,
|
||||
action: str,
|
||||
reasoning: str,
|
||||
action_data: dict,
|
||||
cycle_timers: Dict[str, float],
|
||||
thinking_id: str,
|
||||
action_message: Optional[SessionMessage] = None,
|
||||
) -> tuple[bool, str, str]:
|
||||
"""
|
||||
处理规划动作，使用动作工厂创建相应的动作处理器
|
||||
|
||||
参数:
|
||||
action: 动作类型
|
||||
reasoning: 决策理由
|
||||
action_data: 动作数据，包含不同动作需要的参数
|
||||
cycle_timers: 计时器字典
|
||||
thinking_id: 思考ID
|
||||
|
||||
返回:
|
||||
tuple[bool, str, str]: (是否执行了动作, 思考消息ID, 命令)
|
||||
"""
|
||||
try:
|
||||
# 使用工厂创建动作处理器实例
|
||||
try:
|
||||
action_handler = self.action_manager.create_action(
|
||||
action_name=action,
|
||||
action_data=action_data,
|
||||
action_reasoning=reasoning,
|
||||
cycle_timers=cycle_timers,
|
||||
thinking_id=thinking_id,
|
||||
chat_stream=self.chat_stream,
|
||||
log_prefix=self.log_prefix,
|
||||
action_message=action_message,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 创建动作处理器时出错: {e}")
|
||||
traceback.print_exc()
|
||||
return False, "", ""
|
||||
|
||||
if not action_handler:
|
||||
logger.warning(f"{self.log_prefix} 未能创建动作处理器: {action}")
|
||||
return False, "", ""
|
||||
|
||||
# 处理动作并获取结果（固定记录一次动作信息）
|
||||
# BaseAction 定义了异步方法 execute() 作为统一执行入口
|
||||
# 这里调用 execute() 以兼容所有 Action 实现
|
||||
result = await action_handler.execute()
|
||||
success, action_text = result
|
||||
command = ""
|
||||
|
||||
return success, action_text, command
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 处理{action}时出错: {e}")
|
||||
traceback.print_exc()
|
||||
return False, "", ""
|
||||
|
||||
async def _send_response(
|
||||
self,
|
||||
reply_set: MessageSequence,
|
||||
message_data: SessionMessage,
|
||||
selected_expressions: Optional[List[int]] = None,
|
||||
) -> str:
|
||||
new_message_count = message_api.count_new_messages(
|
||||
chat_id=self.chat_stream.session_id, start_time=self.last_read_time, end_time=time.time()
|
||||
)
|
||||
|
||||
need_reply = new_message_count >= random.randint(2, 4)
|
||||
|
||||
if need_reply:
|
||||
logger.info(f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，使用引用回复")
|
||||
|
||||
reply_text = ""
|
||||
first_replied = False
|
||||
for component in reply_set.components:
|
||||
if not isinstance(component, TextComponent):
|
||||
continue
|
||||
data = component.text
|
||||
if not first_replied:
|
||||
await send_api.text_to_stream(
|
||||
text=data,
|
||||
stream_id=self.chat_stream.session_id,
|
||||
reply_message=message_data,
|
||||
set_reply=need_reply,
|
||||
typing=False,
|
||||
selected_expressions=selected_expressions,
|
||||
)
|
||||
first_replied = True
|
||||
else:
|
||||
await send_api.text_to_stream(
|
||||
text=data,
|
||||
stream_id=self.chat_stream.session_id,
|
||||
reply_message=message_data,
|
||||
set_reply=False,
|
||||
typing=True,
|
||||
selected_expressions=selected_expressions,
|
||||
)
|
||||
reply_text += data
|
||||
|
||||
return reply_text
|
||||
|
||||
async def _execute_action(
|
||||
self,
|
||||
action_planner_info: ActionPlannerInfo,
|
||||
chosen_action_plan_infos: List[ActionPlannerInfo],
|
||||
thinking_id: str,
|
||||
available_actions: Dict[str, ActionInfo],
|
||||
cycle_timers: Dict[str, float],
|
||||
):
|
||||
"""执行单个动作的通用函数"""
|
||||
try:
|
||||
with Timer(f"动作{action_planner_info.action_type}", cycle_timers):
|
||||
if action_planner_info.action_type == "complete_talk":
|
||||
# 直接处理complete_talk逻辑，不再通过动作系统
|
||||
reason = action_planner_info.reasoning or "选择完成对话"
|
||||
logger.info(f"{self.log_prefix} 选择完成对话，原因: {reason}")
|
||||
|
||||
# 存储complete_talk信息到数据库
|
||||
await database_api.store_action_info(
|
||||
chat_stream=self.chat_stream,
|
||||
display_prompt=reason,
|
||||
thinking_id=thinking_id,
|
||||
action_data={"reason": reason},
|
||||
action_name="complete_talk",
|
||||
)
|
||||
return {"action_type": "complete_talk", "success": True, "reply_text": "", "command": ""}
|
||||
|
||||
elif action_planner_info.action_type == "reply":
|
||||
try:
|
||||
# 从 Planner 的 action_data 中提取未知词语列表（仅在 reply 时使用）
|
||||
unknown_words = None
|
||||
if isinstance(action_planner_info.action_data, dict):
|
||||
uw = action_planner_info.action_data.get("unknown_words")
|
||||
if isinstance(uw, list):
|
||||
cleaned_uw: List[str] = []
|
||||
for item in uw:
|
||||
if isinstance(item, str):
|
||||
if stripped_item := item.strip():
|
||||
cleaned_uw.append(stripped_item)
|
||||
if cleaned_uw:
|
||||
unknown_words = cleaned_uw
|
||||
|
||||
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 "",
|
||||
unknown_words=unknown_words,
|
||||
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}
|
||||
|
||||
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,
|
||||
)
|
||||
# 标记这次循环已经成功进行了回复
|
||||
self._last_successful_reply = True
|
||||
return {
|
||||
"action_type": "reply",
|
||||
"success": True,
|
||||
"reply_text": reply_text,
|
||||
"loop_info": loop_info,
|
||||
}
|
||||
|
||||
# 其他动作
|
||||
else:
|
||||
# 内建 wait / listening：不通过插件系统，直接在这里处理
|
||||
if action_planner_info.action_type in ["wait", "listening"]:
|
||||
reason = action_planner_info.reasoning or ""
|
||||
action_data = action_planner_info.action_data or {}
|
||||
|
||||
if action_planner_info.action_type == "wait":
|
||||
# 获取等待时间（必填）
|
||||
wait_seconds = action_data.get("wait_seconds")
|
||||
if wait_seconds is None:
|
||||
logger.warning(f"{self.log_prefix} wait 动作缺少 wait_seconds 参数，使用默认值 5 秒")
|
||||
wait_seconds = 5
|
||||
else:
|
||||
try:
|
||||
wait_seconds = float(wait_seconds)
|
||||
if wait_seconds < 0:
|
||||
logger.warning(f"{self.log_prefix} wait_seconds 不能为负数，使用默认值 5 秒")
|
||||
wait_seconds = 5
|
||||
except (ValueError, TypeError):
|
||||
logger.warning(f"{self.log_prefix} wait_seconds 参数格式错误，使用默认值 5 秒")
|
||||
wait_seconds = 5
|
||||
|
||||
logger.info(f"{self.log_prefix} 执行 wait 动作，等待 {wait_seconds} 秒（可被新消息打断）")
|
||||
|
||||
# 清除事件状态，准备等待新消息
|
||||
self._new_message_event.clear()
|
||||
|
||||
# 记录动作信息
|
||||
await database_api.store_action_info(
|
||||
chat_stream=self.chat_stream,
|
||||
display_prompt=reason or f"等待 {wait_seconds} 秒",
|
||||
thinking_id=thinking_id,
|
||||
action_data={"reason": reason, "wait_seconds": wait_seconds},
|
||||
action_name="wait",
|
||||
)
|
||||
|
||||
# 等待指定时间，但可被新消息打断
|
||||
try:
|
||||
await asyncio.wait_for(self._new_message_event.wait(), timeout=wait_seconds)
|
||||
# 如果事件被触发，说明有新消息到达
|
||||
logger.info(f"{self.log_prefix} wait 动作被新消息打断，提前结束等待")
|
||||
except asyncio.TimeoutError:
|
||||
# 超时正常完成
|
||||
pass
|
||||
|
||||
logger.info(f"{self.log_prefix} wait 动作完成，继续下一次思考")
|
||||
|
||||
# 这些动作本身不产生文本回复
|
||||
self._last_successful_reply = False
|
||||
return {
|
||||
"action_type": "wait",
|
||||
"success": True,
|
||||
"reply_text": "",
|
||||
"command": "",
|
||||
}
|
||||
|
||||
# listening 已合并到 wait，如果遇到则转换为 wait（向后兼容）
|
||||
elif action_planner_info.action_type == "listening":
|
||||
logger.debug(f"{self.log_prefix} 检测到 listening 动作，已合并到 wait，自动转换")
|
||||
# 使用默认等待时间
|
||||
wait_seconds = 3
|
||||
|
||||
logger.info(
|
||||
f"{self.log_prefix} 执行 listening（转换为 wait）动作，等待 {wait_seconds} 秒（可被新消息打断）"
|
||||
)
|
||||
|
||||
# 清除事件状态，准备等待新消息
|
||||
self._new_message_event.clear()
|
||||
|
||||
# 记录动作信息
|
||||
await database_api.store_action_info(
|
||||
chat_stream=self.chat_stream,
|
||||
display_prompt=reason or f"倾听并等待 {wait_seconds} 秒",
|
||||
thinking_id=thinking_id,
|
||||
action_data={"reason": reason, "wait_seconds": wait_seconds},
|
||||
action_name="listening",
|
||||
)
|
||||
|
||||
# 等待指定时间，但可被新消息打断
|
||||
try:
|
||||
await asyncio.wait_for(self._new_message_event.wait(), timeout=wait_seconds)
|
||||
# 如果事件被触发，说明有新消息到达
|
||||
logger.info(f"{self.log_prefix} listening 动作被新消息打断，提前结束等待")
|
||||
except asyncio.TimeoutError:
|
||||
# 超时正常完成
|
||||
pass
|
||||
|
||||
logger.info(f"{self.log_prefix} listening 动作完成，继续下一次思考")
|
||||
|
||||
# 这些动作本身不产生文本回复
|
||||
self._last_successful_reply = False
|
||||
return {
|
||||
"action_type": "listening",
|
||||
"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,
|
||||
action_planner_info.reasoning or "",
|
||||
action_planner_info.action_data or {},
|
||||
cycle_timers,
|
||||
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,
|
||||
"reply_text": reply_text,
|
||||
"command": command,
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 执行动作时出错: {e}")
|
||||
logger.error(f"{self.log_prefix} 错误信息: {traceback.format_exc()}")
|
||||
return {
|
||||
"action_type": action_planner_info.action_type,
|
||||
"success": False,
|
||||
"reply_text": "",
|
||||
"loop_info": None,
|
||||
"error": str(e),
|
||||
}
|
||||
@@ -1,620 +0,0 @@
|
||||
from datetime import datetime
|
||||
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
|
||||
|
||||
import json
|
||||
import random
|
||||
import re
|
||||
import time
|
||||
import traceback
|
||||
|
||||
from json_repair import repair_json
|
||||
from rich.traceback import install
|
||||
|
||||
from src.chat.logger.plan_reply_logger import PlanReplyLogger
|
||||
from src.chat.message_receive.chat_manager import chat_manager as _chat_manager
|
||||
from src.chat.planner_actions.action_manager import ActionManager
|
||||
from src.chat.utils.utils import get_chat_type_and_target_info
|
||||
from src.common.data_models.info_data_model import ActionPlannerInfo
|
||||
from src.common.logger import get_logger
|
||||
from src.common.utils.utils_action import ActionUtils
|
||||
from src.config.config import global_config, model_config
|
||||
from src.core.types import ActionActivationType, ActionInfo, ComponentType
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.plugin_runtime.component_query import component_query_service
|
||||
from src.prompt.prompt_manager import prompt_manager
|
||||
from src.services.message_service import (
|
||||
build_readable_messages_with_id,
|
||||
get_actions_by_timestamp_with_chat,
|
||||
get_messages_before_time_in_chat,
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from src.common.data_models.info_data_model import TargetPersonInfo
|
||||
from src.chat.message_receive.message import SessionMessage
|
||||
|
||||
logger = get_logger("planner")
|
||||
|
||||
install(extra_lines=3)
|
||||
|
||||
|
||||
class BrainPlanner:
|
||||
def __init__(self, chat_id: str, action_manager: ActionManager):
|
||||
self.chat_id = chat_id
|
||||
self.log_prefix = f"[{_chat_manager.get_session_name(chat_id) or chat_id}]"
|
||||
self.action_manager = action_manager
|
||||
# LLM规划器配置
|
||||
self.planner_llm = LLMRequest(
|
||||
model_set=model_config.model_task_config.planner, request_type="planner"
|
||||
) # 用于动作规划
|
||||
|
||||
self.last_obs_time_mark = 0.0
|
||||
|
||||
# 计划日志记录
|
||||
self.plan_log: List[Tuple[str, float, List[ActionPlannerInfo]]] = []
|
||||
|
||||
def find_message_by_id(
|
||||
self, message_id: str, message_id_list: List[Tuple[str, "SessionMessage"]]
|
||||
) -> Optional["SessionMessage"]:
|
||||
# sourcery skip: use-next
|
||||
"""
|
||||
根据message_id从message_id_list中查找对应的原始消息
|
||||
|
||||
Args:
|
||||
message_id: 要查找的消息ID
|
||||
message_id_list: 消息ID列表，格式为[{'id': str, 'message': dict}, ...]
|
||||
|
||||
Returns:
|
||||
找到的原始消息字典，如果未找到则返回None
|
||||
"""
|
||||
for item in message_id_list:
|
||||
if item[0] == message_id:
|
||||
return item[1]
|
||||
return None
|
||||
|
||||
def _parse_single_action(
|
||||
self,
|
||||
action_json: dict,
|
||||
message_id_list: List[Tuple[str, "SessionMessage"]],
|
||||
current_available_actions: List[Tuple[str, ActionInfo]],
|
||||
) -> List[ActionPlannerInfo]:
|
||||
"""解析单个action JSON并返回ActionPlannerInfo列表"""
|
||||
action_planner_infos = []
|
||||
|
||||
try:
|
||||
action = action_json.get("action", "complete_talk")
|
||||
logger.debug(f"{self.log_prefix}解析动作JSON: action={action}, json={action_json}")
|
||||
reasoning = action_json.get("reason", "未提供原因")
|
||||
action_data = {key: value for key, value in action_json.items() if key not in ["action", "reason"]}
|
||||
# 非complete_talk动作需要target_message_id
|
||||
target_message = None
|
||||
|
||||
if target_message_id := action_json.get("target_message_id"):
|
||||
# 根据target_message_id查找原始消息
|
||||
target_message = self.find_message_by_id(target_message_id, message_id_list)
|
||||
if target_message is None:
|
||||
logger.warning(f"{self.log_prefix}无法找到target_message_id '{target_message_id}' 对应的消息")
|
||||
# 选择最新消息作为target_message
|
||||
target_message = message_id_list[-1][1]
|
||||
else:
|
||||
target_message = message_id_list[-1][1]
|
||||
logger.debug(f"{self.log_prefix}动作'{action}'缺少target_message_id，使用最新消息作为target_message")
|
||||
|
||||
# 验证action是否可用
|
||||
available_action_names = [action_name for action_name, _ in current_available_actions]
|
||||
# 内部保留动作（不依赖插件系统）
|
||||
# 注意：listening 已合并到 wait 中，如果遇到 listening 则转换为 wait
|
||||
internal_action_names = ["complete_talk", "reply", "wait_time", "wait", "listening"]
|
||||
|
||||
logger.debug(
|
||||
f"{self.log_prefix}动作验证: action={action}, internal={internal_action_names}, available={available_action_names}"
|
||||
)
|
||||
|
||||
# 将 listening 转换为 wait（向后兼容）
|
||||
if action == "listening":
|
||||
logger.debug(f"{self.log_prefix}检测到 listening 动作，已合并到 wait，自动转换")
|
||||
action = "wait"
|
||||
|
||||
if action not in internal_action_names and action not in available_action_names:
|
||||
logger.warning(
|
||||
f"{self.log_prefix}LLM 返回了当前不可用或无效的动作: '{action}' (内部动作: {internal_action_names}, 可用插件动作: {available_action_names})，将强制使用 'complete_talk'"
|
||||
)
|
||||
reasoning = (
|
||||
f"LLM 返回了当前不可用的动作 '{action}' (可用: {available_action_names})。原始理由: {reasoning}"
|
||||
)
|
||||
action = "complete_talk"
|
||||
logger.warning(f"{self.log_prefix}动作已转换为 complete_talk")
|
||||
|
||||
# 创建ActionPlannerInfo对象
|
||||
# 将列表转换为字典格式
|
||||
available_actions_dict = dict(current_available_actions)
|
||||
action_planner_infos.append(
|
||||
ActionPlannerInfo(
|
||||
action_type=action,
|
||||
reasoning=reasoning,
|
||||
action_data=action_data,
|
||||
action_message=target_message,
|
||||
available_actions=available_actions_dict,
|
||||
)
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix}解析单个action时出错: {e}")
|
||||
# 将列表转换为字典格式
|
||||
available_actions_dict = dict(current_available_actions)
|
||||
action_planner_infos.append(
|
||||
ActionPlannerInfo(
|
||||
action_type="complete_talk",
|
||||
reasoning=f"解析单个action时出错: {e}",
|
||||
action_data={},
|
||||
action_message=None,
|
||||
available_actions=available_actions_dict,
|
||||
)
|
||||
)
|
||||
|
||||
return action_planner_infos
|
||||
|
||||
async def plan(
|
||||
self,
|
||||
available_actions: Dict[str, ActionInfo],
|
||||
loop_start_time: float = 0.0,
|
||||
) -> List[ActionPlannerInfo]:
|
||||
# sourcery skip: use-named-expression
|
||||
"""
|
||||
规划器 (Planner): 使用LLM根据上下文决定做出什么动作（ReAct模式）。
|
||||
"""
|
||||
plan_start = time.perf_counter()
|
||||
|
||||
# 获取聊天上下文
|
||||
message_list_before_now = get_messages_before_time_in_chat(
|
||||
chat_id=self.chat_id,
|
||||
timestamp=time.time(),
|
||||
limit=int(global_config.chat.max_context_size * 0.6),
|
||||
filter_intercept_message_level=1,
|
||||
)
|
||||
message_id_list: list[Tuple[str, "SessionMessage"]] = []
|
||||
chat_content_block, message_id_list = build_readable_messages_with_id(
|
||||
messages=message_list_before_now,
|
||||
timestamp_mode="normal_no_YMD",
|
||||
read_mark=self.last_obs_time_mark,
|
||||
truncate=True,
|
||||
show_actions=True,
|
||||
)
|
||||
|
||||
message_list_before_now_short = message_list_before_now[-int(global_config.chat.max_context_size * 0.3) :]
|
||||
chat_content_block_short, message_id_list_short = build_readable_messages_with_id(
|
||||
messages=message_list_before_now_short,
|
||||
timestamp_mode="normal_no_YMD",
|
||||
truncate=False,
|
||||
show_actions=False,
|
||||
)
|
||||
|
||||
self.last_obs_time_mark = time.time()
|
||||
|
||||
# 获取必要信息
|
||||
is_group_chat, chat_target_info, current_available_actions = self.get_necessary_info()
|
||||
|
||||
# 提及/被@ 的处理由心流或统一判定模块驱动；Planner 不再做硬编码强制回复
|
||||
|
||||
# 应用激活类型过滤
|
||||
filtered_actions = self._filter_actions_by_activation_type(available_actions, chat_content_block_short)
|
||||
|
||||
logger.debug(f"{self.log_prefix}过滤后有{len(filtered_actions)}个可用动作")
|
||||
|
||||
prompt_build_start = time.perf_counter()
|
||||
# 构建包含所有动作的提示词：使用统一的 ReAct Prompt
|
||||
prompt_key = "brain_planner"
|
||||
# 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(
|
||||
chat_target_info=chat_target_info,
|
||||
current_available_actions=filtered_actions,
|
||||
chat_content_block=chat_content_block,
|
||||
message_id_list=message_id_list,
|
||||
prompt_key=prompt_key,
|
||||
)
|
||||
prompt_build_ms = (time.perf_counter() - prompt_build_start) * 1000
|
||||
|
||||
# 调用LLM获取决策
|
||||
reasoning, actions, llm_raw_output, llm_reasoning, llm_duration_ms = await self._execute_main_planner(
|
||||
prompt=prompt,
|
||||
message_id_list=message_id_list,
|
||||
filtered_actions=filtered_actions,
|
||||
available_actions=available_actions,
|
||||
loop_start_time=loop_start_time,
|
||||
)
|
||||
|
||||
# 记录和展示计划日志
|
||||
logger.info(
|
||||
f"{self.log_prefix}Planner: {reasoning}。选择了{len(actions)}个动作: {' '.join([a.action_type for a in actions])}"
|
||||
)
|
||||
self.add_plan_log(reasoning, actions)
|
||||
|
||||
try:
|
||||
PlanReplyLogger.log_plan(
|
||||
chat_id=self.chat_id,
|
||||
prompt=prompt,
|
||||
reasoning=reasoning,
|
||||
raw_output=llm_raw_output,
|
||||
raw_reasoning=llm_reasoning,
|
||||
actions=actions,
|
||||
timing={
|
||||
"prompt_build_ms": round(prompt_build_ms, 2),
|
||||
"llm_duration_ms": round(llm_duration_ms, 2) if llm_duration_ms is not None else None,
|
||||
"total_plan_ms": round((time.perf_counter() - plan_start) * 1000, 2),
|
||||
"loop_start_time": loop_start_time,
|
||||
},
|
||||
extra=None,
|
||||
)
|
||||
except Exception:
|
||||
logger.exception(f"{self.log_prefix}记录plan日志失败")
|
||||
|
||||
return actions
|
||||
|
||||
async def build_planner_prompt(
|
||||
self,
|
||||
chat_target_info: Optional["TargetPersonInfo"],
|
||||
current_available_actions: Dict[str, ActionInfo],
|
||||
message_id_list: List[Tuple[str, "SessionMessage"]],
|
||||
chat_content_block: str = "",
|
||||
interest: str = "",
|
||||
prompt_key: str = "brain_planner",
|
||||
) -> tuple[str, List[Tuple[str, "SessionMessage"]]]:
|
||||
"""构建 Planner LLM 的提示词 (获取模板并填充数据)"""
|
||||
try:
|
||||
# 获取最近执行过的动作
|
||||
actions_before_now = get_actions_by_timestamp_with_chat(
|
||||
chat_id=self.chat_id,
|
||||
timestamp_start=time.time() - 600,
|
||||
timestamp_end=time.time(),
|
||||
limit=6,
|
||||
)
|
||||
actions_before_now_block = ActionUtils.build_readable_action_records(actions_before_now)
|
||||
if actions_before_now_block:
|
||||
actions_before_now_block = f"你刚刚选择并执行过的action是：\n{actions_before_now_block}"
|
||||
else:
|
||||
actions_before_now_block = ""
|
||||
|
||||
chat_context_description: str = ""
|
||||
if chat_target_info:
|
||||
# 构建聊天上下文描述
|
||||
chat_context_description = (
|
||||
f"你正在和 {chat_target_info.person_name or chat_target_info.user_nickname or '对方'} 聊天中"
|
||||
)
|
||||
|
||||
# 构建动作选项块
|
||||
action_options_block = await self._build_action_options_block(current_available_actions)
|
||||
|
||||
# 其他信息
|
||||
moderation_prompt_block = "请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。"
|
||||
time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
|
||||
bot_name = global_config.bot.nickname
|
||||
bot_nickname = (
|
||||
f",也可以叫你{','.join(global_config.bot.alias_names)}" if global_config.bot.alias_names else ""
|
||||
)
|
||||
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 = prompt_manager.get_prompt(prompt_key)
|
||||
planner_prompt_template.add_context("time_block", time_block)
|
||||
planner_prompt_template.add_context("chat_context_description", chat_context_description)
|
||||
planner_prompt_template.add_context("chat_content_block", chat_content_block)
|
||||
planner_prompt_template.add_context("actions_before_now_block", actions_before_now_block)
|
||||
planner_prompt_template.add_context("action_options_text", action_options_block)
|
||||
planner_prompt_template.add_context("moderation_prompt", moderation_prompt_block)
|
||||
planner_prompt_template.add_context("name_block", name_block)
|
||||
planner_prompt_template.add_context("interest", interest)
|
||||
planner_prompt_template.add_context("plan_style", global_config.experimental.private_plan_style)
|
||||
prompt = await prompt_manager.render_prompt(planner_prompt_template)
|
||||
|
||||
return prompt, message_id_list
|
||||
except Exception as e:
|
||||
logger.error(f"构建 Planner 提示词时出错: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
return "构建 Planner Prompt 时出错", []
|
||||
|
||||
def get_necessary_info(self) -> Tuple[bool, Optional["TargetPersonInfo"], Dict[str, ActionInfo]]:
|
||||
"""
|
||||
获取 Planner 需要的必要信息
|
||||
"""
|
||||
is_group_chat = True
|
||||
is_group_chat, chat_target_info = get_chat_type_and_target_info(self.chat_id)
|
||||
logger.debug(f"{self.log_prefix}获取到聊天信息 - 群聊: {is_group_chat}, 目标信息: {chat_target_info}")
|
||||
|
||||
current_available_actions_dict = self.action_manager.get_using_actions()
|
||||
|
||||
# 获取完整的动作信息
|
||||
all_registered_actions: Dict[str, ActionInfo] = component_query_service.get_components_by_type( # type: ignore
|
||||
ComponentType.ACTION
|
||||
)
|
||||
current_available_actions = {}
|
||||
for action_name in current_available_actions_dict:
|
||||
if action_name in all_registered_actions:
|
||||
current_available_actions[action_name] = all_registered_actions[action_name]
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix}使用中的动作 {action_name} 未在已注册动作中找到")
|
||||
|
||||
return is_group_chat, chat_target_info, current_available_actions
|
||||
|
||||
def _filter_actions_by_activation_type(
|
||||
self, available_actions: Dict[str, ActionInfo], chat_content_block: str
|
||||
) -> Dict[str, ActionInfo]:
|
||||
"""根据激活类型过滤动作"""
|
||||
filtered_actions = {}
|
||||
|
||||
for action_name, action_info in available_actions.items():
|
||||
if action_info.activation_type == ActionActivationType.NEVER:
|
||||
logger.debug(f"{self.log_prefix}动作 {action_name} 设置为 NEVER 激活类型，跳过")
|
||||
continue
|
||||
elif action_info.activation_type == ActionActivationType.ALWAYS:
|
||||
filtered_actions[action_name] = action_info
|
||||
elif action_info.activation_type == ActionActivationType.RANDOM:
|
||||
if random.random() < action_info.random_activation_probability:
|
||||
filtered_actions[action_name] = action_info
|
||||
elif action_info.activation_type == ActionActivationType.KEYWORD:
|
||||
if action_info.activation_keywords:
|
||||
for keyword in action_info.activation_keywords:
|
||||
if keyword in chat_content_block:
|
||||
filtered_actions[action_name] = action_info
|
||||
break
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix}未知的激活类型: {action_info.activation_type}，跳过处理")
|
||||
|
||||
return filtered_actions
|
||||
|
||||
async def _build_action_options_block(self, current_available_actions: Dict[str, ActionInfo]) -> str:
|
||||
# sourcery skip: use-join
|
||||
"""构建动作选项块"""
|
||||
if not current_available_actions:
|
||||
return ""
|
||||
|
||||
action_options_block = ""
|
||||
for action_name, action_info in current_available_actions.items():
|
||||
# 构建参数文本
|
||||
param_text = ""
|
||||
if action_info.action_parameters:
|
||||
param_text = "\n"
|
||||
for param_name, param_description in action_info.action_parameters.items():
|
||||
param_text += f' "{param_name}":"{param_description}"\n'
|
||||
param_text = param_text.rstrip("\n")
|
||||
|
||||
# 构建要求文本
|
||||
require_text = ""
|
||||
for require_item in action_info.action_require:
|
||||
require_text += f"- {require_item}\n"
|
||||
require_text = require_text.rstrip("\n")
|
||||
|
||||
# 获取动作提示模板并填充
|
||||
using_action_prompt_template = prompt_manager.get_prompt("brain_action")
|
||||
using_action_prompt_template.add_context("action_name", action_name)
|
||||
using_action_prompt_template.add_context("action_description", action_info.description)
|
||||
using_action_prompt_template.add_context("action_parameters", param_text)
|
||||
using_action_prompt_template.add_context("action_require", require_text)
|
||||
using_action_prompt = await prompt_manager.render_prompt(using_action_prompt_template)
|
||||
|
||||
action_options_block += using_action_prompt
|
||||
|
||||
return action_options_block
|
||||
|
||||
async def _execute_main_planner(
|
||||
self,
|
||||
prompt: str,
|
||||
message_id_list: List[Tuple[str, "SessionMessage"]],
|
||||
filtered_actions: Dict[str, ActionInfo],
|
||||
available_actions: Dict[str, ActionInfo],
|
||||
loop_start_time: float,
|
||||
) -> Tuple[str, List[ActionPlannerInfo], Optional[str], Optional[str], Optional[float]]:
|
||||
"""执行主规划器"""
|
||||
llm_content = None
|
||||
actions: List[ActionPlannerInfo] = []
|
||||
extracted_reasoning = ""
|
||||
llm_reasoning = None
|
||||
llm_duration_ms = None
|
||||
|
||||
try:
|
||||
# 调用LLM
|
||||
llm_start = time.perf_counter()
|
||||
llm_content, (reasoning_content, _, _) = await self.planner_llm.generate_response_async(prompt=prompt)
|
||||
llm_duration_ms = (time.perf_counter() - llm_start) * 1000
|
||||
llm_reasoning = reasoning_content
|
||||
|
||||
logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
|
||||
logger.info(f"{self.log_prefix}规划器原始响应: {llm_content}")
|
||||
|
||||
if global_config.debug.show_planner_prompt:
|
||||
logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
|
||||
logger.info(f"{self.log_prefix}规划器原始响应: {llm_content}")
|
||||
if reasoning_content:
|
||||
logger.info(f"{self.log_prefix}规划器推理: {reasoning_content}")
|
||||
else:
|
||||
logger.debug(f"{self.log_prefix}规划器原始提示词: {prompt}")
|
||||
logger.debug(f"{self.log_prefix}规划器原始响应: {llm_content}")
|
||||
if reasoning_content:
|
||||
logger.debug(f"{self.log_prefix}规划器推理: {reasoning_content}")
|
||||
|
||||
except Exception as req_e:
|
||||
logger.error(f"{self.log_prefix}LLM 请求执行失败: {req_e}")
|
||||
extracted_reasoning = f"LLM 请求失败，模型出现问题: {req_e}"
|
||||
return (
|
||||
extracted_reasoning,
|
||||
[
|
||||
ActionPlannerInfo(
|
||||
action_type="complete_talk",
|
||||
reasoning=extracted_reasoning,
|
||||
action_data={},
|
||||
action_message=None,
|
||||
available_actions=available_actions,
|
||||
)
|
||||
],
|
||||
llm_content,
|
||||
llm_reasoning,
|
||||
llm_duration_ms,
|
||||
)
|
||||
|
||||
# 解析LLM响应
|
||||
if llm_content:
|
||||
try:
|
||||
json_objects, extracted_reasoning = self._extract_json_from_markdown(llm_content)
|
||||
if json_objects:
|
||||
logger.info(f"{self.log_prefix}从响应中提取到{len(json_objects)}个JSON对象")
|
||||
for i, json_obj in enumerate(json_objects):
|
||||
logger.info(f"{self.log_prefix}解析第{i + 1}个JSON对象: {json_obj}")
|
||||
filtered_actions_list = list(filtered_actions.items())
|
||||
for json_obj in json_objects:
|
||||
parsed_actions = self._parse_single_action(json_obj, message_id_list, filtered_actions_list)
|
||||
logger.info(f"{self.log_prefix}解析后的动作: {[a.action_type for a in parsed_actions]}")
|
||||
actions.extend(parsed_actions)
|
||||
else:
|
||||
# 尝试解析为直接的JSON
|
||||
logger.warning(f"{self.log_prefix}LLM没有返回可用动作: {llm_content}")
|
||||
extracted_reasoning = extracted_reasoning or "LLM没有返回可用动作"
|
||||
actions = self._create_complete_talk(extracted_reasoning, available_actions)
|
||||
|
||||
except Exception as json_e:
|
||||
logger.warning(f"{self.log_prefix}解析LLM响应JSON失败 {json_e}. LLM原始输出: '{llm_content}'")
|
||||
extracted_reasoning = f"解析LLM响应JSON失败: {json_e}"
|
||||
actions = self._create_complete_talk(extracted_reasoning, available_actions)
|
||||
traceback.print_exc()
|
||||
else:
|
||||
extracted_reasoning = "规划器没有获得LLM响应"
|
||||
actions = self._create_complete_talk(extracted_reasoning, available_actions)
|
||||
|
||||
# 添加循环开始时间到所有动作
|
||||
for action in actions:
|
||||
action.action_data = action.action_data or {}
|
||||
action.action_data["loop_start_time"] = loop_start_time
|
||||
|
||||
logger.debug(
|
||||
f"{self.log_prefix}规划器决定执行{len(actions)}个动作: {' '.join([a.action_type for a in actions])}"
|
||||
)
|
||||
|
||||
return extracted_reasoning, actions, llm_content, llm_reasoning, llm_duration_ms
|
||||
|
||||
def _create_complete_talk(
|
||||
self, reasoning: str, available_actions: Dict[str, ActionInfo]
|
||||
) -> List[ActionPlannerInfo]:
|
||||
"""创建complete_talk"""
|
||||
return [
|
||||
ActionPlannerInfo(
|
||||
action_type="complete_talk",
|
||||
reasoning=reasoning,
|
||||
action_data={},
|
||||
action_message=None,
|
||||
available_actions=available_actions,
|
||||
)
|
||||
]
|
||||
|
||||
def add_plan_log(self, reasoning: str, actions: List[ActionPlannerInfo]):
|
||||
"""添加计划日志"""
|
||||
self.plan_log.append((reasoning, time.time(), actions))
|
||||
if len(self.plan_log) > 20:
|
||||
self.plan_log.pop(0)
|
||||
|
||||
def _extract_json_from_markdown(self, content: str) -> Tuple[List[dict], str]:
|
||||
# sourcery skip: for-append-to-extend
|
||||
"""从Markdown格式的内容中提取JSON对象和推理内容"""
|
||||
json_objects = []
|
||||
reasoning_content = ""
|
||||
|
||||
# 使用正则表达式查找```json包裹的JSON内容
|
||||
json_pattern = r"```json\s*(.*?)\s*```"
|
||||
markdown_matches = re.findall(json_pattern, content, re.DOTALL)
|
||||
|
||||
# 提取JSON之前的内容作为推理文本
|
||||
first_json_pos = len(content)
|
||||
if markdown_matches:
|
||||
# 找到第一个```json的位置
|
||||
first_json_pos = content.find("```json")
|
||||
if first_json_pos > 0:
|
||||
reasoning_content = content[:first_json_pos].strip()
|
||||
# 清理推理内容中的注释标记
|
||||
reasoning_content = re.sub(r"^//\s*", "", reasoning_content, flags=re.MULTILINE)
|
||||
reasoning_content = reasoning_content.strip()
|
||||
|
||||
# 处理```json包裹的JSON
|
||||
for match in markdown_matches:
|
||||
try:
|
||||
# 清理可能的注释和格式问题
|
||||
json_str = re.sub(r"//.*?\n", "\n", match) # 移除单行注释
|
||||
json_str = re.sub(r"/\*.*?\*/", "", json_str, flags=re.DOTALL) # 移除多行注释
|
||||
if json_str := json_str.strip():
|
||||
# 先尝试将整个块作为一个JSON对象或数组（适用于多行JSON）
|
||||
try:
|
||||
json_obj = json.loads(repair_json(json_str))
|
||||
if isinstance(json_obj, dict):
|
||||
json_objects.append(json_obj)
|
||||
elif isinstance(json_obj, list):
|
||||
for item in json_obj:
|
||||
if isinstance(item, dict):
|
||||
json_objects.append(item)
|
||||
except json.JSONDecodeError:
|
||||
# 如果整个块解析失败，尝试按行分割（适用于多个单行JSON对象）
|
||||
lines = [line.strip() for line in json_str.split("\n") if line.strip()]
|
||||
for line in lines:
|
||||
try:
|
||||
# 尝试解析每一行作为独立的JSON对象
|
||||
json_obj = json.loads(repair_json(line))
|
||||
if isinstance(json_obj, dict):
|
||||
json_objects.append(json_obj)
|
||||
elif isinstance(json_obj, list):
|
||||
for item in json_obj:
|
||||
if isinstance(item, dict):
|
||||
json_objects.append(item)
|
||||
except json.JSONDecodeError:
|
||||
# 单行解析失败，继续下一行
|
||||
continue
|
||||
except Exception as e:
|
||||
logger.warning(f"{self.log_prefix}解析JSON块失败: {e}, 块内容: {match[:100]}...")
|
||||
continue
|
||||
|
||||
# 如果没有找到完整的```json```块，尝试查找不完整的代码块（缺少结尾```）
|
||||
if not json_objects:
|
||||
json_start_pos = content.find("```json")
|
||||
if json_start_pos != -1:
|
||||
# 找到```json之后的内容
|
||||
json_content_start = json_start_pos + 7 # ```json的长度
|
||||
# 提取从```json之后到内容结尾的所有内容
|
||||
incomplete_json_str = content[json_content_start:].strip()
|
||||
|
||||
# 提取JSON之前的内容作为推理文本
|
||||
if json_start_pos > 0:
|
||||
reasoning_content = content[:json_start_pos].strip()
|
||||
reasoning_content = re.sub(r"^//\s*", "", reasoning_content, flags=re.MULTILINE)
|
||||
reasoning_content = reasoning_content.strip()
|
||||
|
||||
if incomplete_json_str:
|
||||
try:
|
||||
# 清理可能的注释和格式问题
|
||||
json_str = re.sub(r"//.*?\n", "\n", incomplete_json_str)
|
||||
json_str = re.sub(r"/\*.*?\*/", "", json_str, flags=re.DOTALL)
|
||||
json_str = json_str.strip()
|
||||
|
||||
if json_str:
|
||||
# 尝试按行分割，每行可能是一个JSON对象
|
||||
lines = [line.strip() for line in json_str.split("\n") if line.strip()]
|
||||
for line in lines:
|
||||
try:
|
||||
json_obj = json.loads(repair_json(line))
|
||||
if isinstance(json_obj, dict):
|
||||
json_objects.append(json_obj)
|
||||
elif isinstance(json_obj, list):
|
||||
for item in json_obj:
|
||||
if isinstance(item, dict):
|
||||
json_objects.append(item)
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
# 如果按行解析没有成功，尝试将整个块作为一个JSON对象或数组
|
||||
if not json_objects:
|
||||
try:
|
||||
json_obj = json.loads(repair_json(json_str))
|
||||
if isinstance(json_obj, dict):
|
||||
json_objects.append(json_obj)
|
||||
elif isinstance(json_obj, list):
|
||||
for item in json_obj:
|
||||
if isinstance(item, dict):
|
||||
json_objects.append(item)
|
||||
except Exception as e:
|
||||
logger.debug(f"尝试解析不完整的JSON代码块失败: {e}")
|
||||
except Exception as e:
|
||||
logger.debug(f"处理不完整的JSON代码块时出错: {e}")
|
||||
|
||||
return json_objects, reasoning_content
|
||||
@@ -1,8 +1,9 @@
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
|
||||
from rich.traceback import install
|
||||
from sqlmodel import select
|
||||
from typing import Optional, Tuple, List
|
||||
|
||||
import asyncio
|
||||
import hashlib
|
||||
@@ -17,8 +18,9 @@ from src.common.database.database_model import Images, ImageType
|
||||
from src.common.database.database import get_db_session, get_db_session_manual
|
||||
from src.common.utils.utils_image import ImageUtils
|
||||
from src.prompt.prompt_manager import prompt_manager
|
||||
from src.config.config import config_manager, global_config, model_config
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import config_manager, global_config
|
||||
from src.common.data_models.llm_service_data_models import LLMGenerationOptions, LLMImageOptions
|
||||
from src.services.llm_service import LLMServiceClient
|
||||
|
||||
logger = get_logger("emoji")
|
||||
|
||||
@@ -38,8 +40,10 @@ def _ensure_directories() -> None:
|
||||
|
||||
|
||||
# TODO: 修改这个vlm为获取的vlm client，暂时使用这个VLM方法
|
||||
emoji_manager_vlm = LLMRequest(model_set=model_config.model_task_config.vlm, request_type="emoji.see")
|
||||
emoji_manager_emotion_judge_llm = LLMRequest(model_set=model_config.model_task_config.utils, request_type="emoji")
|
||||
emoji_manager_vlm = LLMServiceClient(task_name="vlm", request_type="emoji.see")
|
||||
emoji_manager_emotion_judge_llm = LLMServiceClient(
|
||||
task_name="utils", request_type="emoji"
|
||||
)
|
||||
|
||||
|
||||
class EmojiManager:
|
||||
@@ -48,11 +52,13 @@ class EmojiManager:
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
"""初始化表情包管理器。"""
|
||||
_ensure_directories()
|
||||
|
||||
self._emoji_num: int = 0
|
||||
self.emojis: list[MaiEmoji] = []
|
||||
self.emojis: List[MaiEmoji] = []
|
||||
self._maintenance_wakeup_event: asyncio.Event = asyncio.Event()
|
||||
self._pending_description_tasks: Dict[str, asyncio.Task[None]] = {}
|
||||
self._reload_callback_registered: bool = False
|
||||
|
||||
config_manager.register_reload_callback(self.reload_runtime_config)
|
||||
@@ -75,7 +81,11 @@ class EmojiManager:
|
||||
logger.info("[关闭] Emoji 模块已注销配置热重载回调")
|
||||
|
||||
async def get_emoji_description(
|
||||
self, *, emoji_bytes: Optional[bytes] = None, emoji_hash: Optional[str] = None
|
||||
self,
|
||||
*,
|
||||
emoji_bytes: Optional[bytes] = None,
|
||||
emoji_hash: Optional[str] = None,
|
||||
wait_for_build: bool = True,
|
||||
) -> Optional[Tuple[str, List[str]]]:
|
||||
"""
|
||||
根据表情包哈希获取表情包描述和情感列表的封装方法
|
||||
@@ -83,6 +93,7 @@ class EmojiManager:
|
||||
Args:
|
||||
emoji_bytes (Optional[bytes]): 表情包的字节数据，如果提供了字节数据但数据库中没有找到对应记录，则会尝试构建表情包描述
|
||||
emoji_hash (Optional[str]): 表情包的哈希值，如果提供了哈希值则优先使用哈希值查找表情包描述
|
||||
wait_for_build (bool): 未命中缓存时是否同步等待描述构建完成
|
||||
Returns:
|
||||
return (Optional[Tuple[str, List[str]]]): 如果找到对应的表情包，则返回包含描述和情感标签的元组；若没找到，则尝试构建表情包描述并返回，如果构建失败则返回 None
|
||||
Raises:
|
||||
@@ -110,27 +121,88 @@ class EmojiManager:
|
||||
# 如果提供了字节数据但数据库中没有找到，尝试构建
|
||||
if not emoji_bytes:
|
||||
return None
|
||||
if not wait_for_build:
|
||||
self._schedule_description_build(emoji_hash, emoji_bytes)
|
||||
return None
|
||||
|
||||
# 找不到尝试构建
|
||||
return await self._build_and_cache_emoji_description(emoji_hash, emoji_bytes)
|
||||
|
||||
def _schedule_description_build(self, emoji_hash: str, emoji_bytes: bytes) -> None:
|
||||
"""调度表情包描述后台构建任务。
|
||||
|
||||
Args:
|
||||
emoji_hash: 表情包哈希值。
|
||||
emoji_bytes: 表情包字节数据。
|
||||
"""
|
||||
if emoji_hash in self._pending_description_tasks:
|
||||
return
|
||||
|
||||
task = asyncio.create_task(self._build_description_in_background(emoji_hash, emoji_bytes))
|
||||
self._pending_description_tasks[emoji_hash] = task
|
||||
task.add_done_callback(lambda finished_task: self._finalize_description_build(emoji_hash, finished_task))
|
||||
|
||||
async def _build_description_in_background(self, emoji_hash: str, emoji_bytes: bytes) -> None:
|
||||
"""在后台构建并缓存表情包描述。
|
||||
|
||||
Args:
|
||||
emoji_hash: 表情包哈希值。
|
||||
emoji_bytes: 表情包字节数据。
|
||||
"""
|
||||
try:
|
||||
logger.info(f"表情包描述后台构建已开始，哈希值: {emoji_hash}")
|
||||
await self._build_and_cache_emoji_description(emoji_hash, emoji_bytes)
|
||||
logger.info(f"表情包描述后台构建完成，哈希值: {emoji_hash}")
|
||||
except Exception as exc:
|
||||
logger.warning(f"表情包描述后台构建失败，哈希值: {emoji_hash}，错误: {exc}")
|
||||
|
||||
def _finalize_description_build(self, emoji_hash: str, task: asyncio.Task[None]) -> None:
|
||||
"""回收表情包描述后台构建任务。
|
||||
|
||||
Args:
|
||||
emoji_hash: 表情包哈希值。
|
||||
task: 已完成的后台任务。
|
||||
"""
|
||||
self._pending_description_tasks.pop(emoji_hash, None)
|
||||
try:
|
||||
task.result()
|
||||
except Exception as exc:
|
||||
logger.debug(f"表情包描述后台任务结束时捕获异常，哈希值: {emoji_hash}，错误: {exc}")
|
||||
|
||||
async def _build_and_cache_emoji_description(
|
||||
self,
|
||||
emoji_hash: str,
|
||||
emoji_bytes: bytes,
|
||||
) -> Optional[Tuple[str, List[str]]]:
|
||||
"""构建并缓存表情包描述与情感标签。
|
||||
|
||||
Args:
|
||||
emoji_hash: 表情包哈希值。
|
||||
emoji_bytes: 表情包字节数据。
|
||||
|
||||
Returns:
|
||||
Optional[Tuple[str, List[str]]]: 构建成功时返回描述和情感标签，否则返回 ``None``。
|
||||
"""
|
||||
logger.info(f"未找到哈希值为 {emoji_hash} 的表情包与其描述，尝试构建描述")
|
||||
full_path = EMOJI_DIR / f"{emoji_hash}.png"
|
||||
try:
|
||||
full_path.write_bytes(emoji_bytes)
|
||||
new_emoji = MaiEmoji(full_path=full_path, image_bytes=emoji_bytes)
|
||||
await new_emoji.calculate_hash_format()
|
||||
except Exception as e:
|
||||
logger.error(f"缓存表情包文件时出错: {e}")
|
||||
raise e
|
||||
except Exception as exc:
|
||||
logger.error(f"缓存表情包文件时出错: {exc}")
|
||||
raise exc
|
||||
|
||||
success_desc, new_emoji = await self.build_emoji_description(new_emoji)
|
||||
if not success_desc:
|
||||
logger.error("构建表情包描述失败")
|
||||
return None
|
||||
|
||||
success_emotion, new_emoji = await self.build_emoji_emotion(new_emoji)
|
||||
if not success_emotion:
|
||||
logger.error("构建表情包情感标签失败")
|
||||
return None
|
||||
|
||||
# 缓存结果到数据库
|
||||
with get_db_session() as session:
|
||||
try:
|
||||
image_record = new_emoji.to_db_instance()
|
||||
@@ -139,8 +211,8 @@ class EmojiManager:
|
||||
image_record.register_time = datetime.now()
|
||||
image_record.no_file_flag = True
|
||||
session.add(image_record)
|
||||
except Exception as e:
|
||||
logger.error(f"缓存表情包描述时出错: {e}")
|
||||
except Exception as exc:
|
||||
logger.error(f"缓存表情包描述时出错: {exc}")
|
||||
return new_emoji.description, new_emoji.emotion or []
|
||||
|
||||
def load_emojis_from_db(self) -> None:
|
||||
@@ -461,9 +533,11 @@ class EmojiManager:
|
||||
emoji_replace_prompt_template.add_context("emoji_list", "\n".join(emoji_info_list))
|
||||
emoji_replace_prompt = await prompt_manager.render_prompt(emoji_replace_prompt_template)
|
||||
|
||||
decision, _ = await emoji_manager_emotion_judge_llm.generate_response_async(
|
||||
emoji_replace_prompt, temperature=0.8, max_tokens=600
|
||||
decision_result = await emoji_manager_emotion_judge_llm.generate_response(
|
||||
emoji_replace_prompt,
|
||||
options=LLMGenerationOptions(temperature=0.8, max_tokens=600),
|
||||
)
|
||||
decision = decision_result.response
|
||||
logger.info(f"[决策] 结果: {decision}")
|
||||
|
||||
# 解析决策结果
|
||||
@@ -515,33 +589,56 @@ class EmojiManager:
|
||||
image_bytes = target_emoji.image_bytes or await asyncio.to_thread(
|
||||
target_emoji.read_image_bytes, target_emoji.full_path
|
||||
)
|
||||
image_base64 = ImageUtils.image_bytes_to_base64(image_bytes)
|
||||
try:
|
||||
if image_format == "gif":
|
||||
try:
|
||||
image_bytes = await asyncio.to_thread(ImageUtils.gif_2_static_image, image_bytes)
|
||||
except Exception as e:
|
||||
logger.error(f"[构建描述] 转换 GIF 图片时出错: {e}")
|
||||
return False, target_emoji
|
||||
prompt: str = "这是一个动态图表情包，每一张图代表了动态图的某一帧，黑色背景代表透明，简短描述一下表情包表达的情感和内容，从互联网梗、meme的角度去分析，精简回答"
|
||||
image_base64 = ImageUtils.image_bytes_to_base64(image_bytes)
|
||||
description_result = await emoji_manager_vlm.generate_response_for_image(
|
||||
prompt,
|
||||
image_base64,
|
||||
"jpg",
|
||||
options=LLMImageOptions(temperature=0.5),
|
||||
)
|
||||
description = description_result.response
|
||||
else:
|
||||
prompt: str = "这是一个表情包，请详细描述一下表情包所表达的情感和内容，简短描述细节，从互联网梗、meme的角度去分析，精简回答"
|
||||
description_result = await emoji_manager_vlm.generate_response_for_image(
|
||||
prompt,
|
||||
image_base64,
|
||||
image_format,
|
||||
options=LLMImageOptions(temperature=0.5),
|
||||
)
|
||||
description = description_result.response
|
||||
except Exception as e:
|
||||
logger.error(f"[构建描述] 调用视觉模型生成表情包描述时出错: {e}")
|
||||
return False, target_emoji
|
||||
|
||||
if image_format == "gif":
|
||||
try:
|
||||
image_bytes = await asyncio.to_thread(ImageUtils.gif_2_static_image, image_bytes)
|
||||
except Exception as e:
|
||||
logger.error(f"[构建描述] 转换 GIF 图片时出错: {e}")
|
||||
return False, target_emoji
|
||||
prompt: str = "这是一个动态图表情包，每一张图代表了动态图的某一帧，黑色背景代表透明，简短描述一下表情包表达的情感和内容，从互联网梗、meme的角度去分析，精简回答"
|
||||
image_base64 = ImageUtils.image_bytes_to_base64(image_bytes)
|
||||
description, _ = await emoji_manager_vlm.generate_response_for_image(
|
||||
prompt, image_base64, "jpg", temperature=0.5
|
||||
)
|
||||
else:
|
||||
prompt: str = "这是一个表情包，请详细描述一下表情包所表达的情感和内容，简短描述细节，从互联网梗、meme的角度去分析，精简回答"
|
||||
image_base64 = ImageUtils.image_bytes_to_base64(image_bytes)
|
||||
description, _ = await emoji_manager_vlm.generate_response_for_image(
|
||||
prompt, image_base64, image_format, temperature=0.5
|
||||
)
|
||||
if not description:
|
||||
logger.warning(f"[构建描述] 视觉模型返回空描述，跳过注册: {target_emoji.file_name}")
|
||||
return False, target_emoji
|
||||
|
||||
# 表情包审查
|
||||
if global_config.emoji.content_filtration:
|
||||
filtration_prompt_template = prompt_manager.get_prompt("emoji_content_filtration")
|
||||
filtration_prompt_template.add_context("demand", global_config.emoji.filtration_prompt)
|
||||
filtration_prompt = await prompt_manager.render_prompt(filtration_prompt_template)
|
||||
llm_response, _ = await emoji_manager_vlm.generate_response_for_image(
|
||||
filtration_prompt, image_base64, image_format, temperature=0.3
|
||||
)
|
||||
try:
|
||||
filtration_prompt_template = prompt_manager.get_prompt("emoji_content_filtration")
|
||||
filtration_prompt_template.add_context("demand", global_config.emoji.filtration_prompt)
|
||||
filtration_prompt = await prompt_manager.render_prompt(filtration_prompt_template)
|
||||
filtration_result = await emoji_manager_vlm.generate_response_for_image(
|
||||
filtration_prompt,
|
||||
image_base64,
|
||||
image_format,
|
||||
options=LLMImageOptions(temperature=0.3),
|
||||
)
|
||||
llm_response = filtration_result.response
|
||||
except Exception as e:
|
||||
logger.error(f"[表情包审查] 调用视觉模型审查表情包时出错: {e}")
|
||||
return False, target_emoji
|
||||
if "否" in llm_response:
|
||||
logger.warning(f"[表情包审查] 表情包内容不符合要求，拒绝注册: {target_emoji.file_name}")
|
||||
return False, target_emoji
|
||||
@@ -567,9 +664,19 @@ class EmojiManager:
|
||||
emotion_prompt_template.add_context("description", target_emoji.description)
|
||||
emotion_prompt = await prompt_manager.render_prompt(emotion_prompt_template)
|
||||
# 调用LLM生成情感标签
|
||||
emotion_result, _ = await emoji_manager_emotion_judge_llm.generate_response_async(
|
||||
emotion_prompt, temperature=0.3, max_tokens=200
|
||||
)
|
||||
try:
|
||||
emotion_generation_result = await emoji_manager_emotion_judge_llm.generate_response(
|
||||
emotion_prompt,
|
||||
options=LLMGenerationOptions(temperature=0.3, max_tokens=200),
|
||||
)
|
||||
emotion_result = emotion_generation_result.response
|
||||
except Exception as e:
|
||||
logger.error(f"[构建情感标签] 调用模型生成情感标签时出错: {e}")
|
||||
return False, target_emoji
|
||||
|
||||
if not emotion_result:
|
||||
logger.warning(f"[构建情感标签] 情感标签结果为空，跳过注册: {target_emoji.file_name}")
|
||||
return False, target_emoji
|
||||
|
||||
# 解析情感标签结果
|
||||
emotions = [e.strip() for e in emotion_result.replace("，", ",").split(",") if e.strip()]
|
||||
@@ -651,7 +758,12 @@ class EmojiManager:
|
||||
for emoji_file in EMOJI_DIR.iterdir():
|
||||
if not emoji_file.is_file():
|
||||
continue
|
||||
if await self.register_emoji_by_filename(emoji_file):
|
||||
try:
|
||||
register_success = await self.register_emoji_by_filename(emoji_file)
|
||||
except Exception as e:
|
||||
logger.error(f"[定期维护] 注册表情包 {emoji_file.name} 时发生未处理异常: {e}")
|
||||
register_success = False
|
||||
if register_success:
|
||||
break # 每次只注册一个表情包
|
||||
try:
|
||||
emoji_file.unlink()
|
||||
|
||||
@@ -198,7 +198,6 @@ class HeartFChatting:
|
||||
"""判定和生成回复"""
|
||||
asyncio.create_task(self._trigger_expression_learning(self.message_cache))
|
||||
# TODO: 完成反思器之后的逻辑
|
||||
start_time = time.time()
|
||||
current_cycle_detail = self._start_cycle()
|
||||
logger.info(f"{self.log_prefix} 开始第{self._cycle_counter}次思考")
|
||||
|
||||
@@ -207,10 +206,7 @@ class HeartFChatting:
|
||||
# TODO: 动作执行逻辑
|
||||
|
||||
cycle_detail = self._end_cycle(current_cycle_detail)
|
||||
if wait_time := global_config.chat.planner_smooth - (time.time() - start_time) > 0:
|
||||
await asyncio.sleep(wait_time)
|
||||
else:
|
||||
await asyncio.sleep(0.1) # 最小等待时间，避免过快循环
|
||||
await asyncio.sleep(0.1) # 最小等待时间，避免过快循环
|
||||
return True
|
||||
|
||||
def _handle_loop_completion(self, task: asyncio.Task):
|
||||
|
||||
@@ -1,47 +1,50 @@
|
||||
from typing import Dict
|
||||
|
||||
import asyncio
|
||||
import traceback
|
||||
|
||||
from src.common.logger import get_logger
|
||||
from typing import Dict
|
||||
|
||||
from src.chat.message_receive.chat_manager import chat_manager
|
||||
from src.chat.heart_flow.heartFC_chat import HeartFChatting
|
||||
# from src.chat.brain_chat.brain_chat import BrainChatting
|
||||
from src.common.logger import get_logger
|
||||
from src.maisaka.runtime import MaisakaHeartFlowChatting
|
||||
|
||||
logger = get_logger("heartflow")
|
||||
|
||||
|
||||
# TODO: 恢复PFC，现在暂时禁用
|
||||
class HeartflowManager:
|
||||
"""主心流协调器，负责初始化并协调聊天，控制聊天属性"""
|
||||
"""管理 session 级别的 Maisaka 心流实例。"""
|
||||
|
||||
def __init__(self):
|
||||
# self.heartflow_chat_list: Dict[str, HeartFChatting | BrainChatting] = {}
|
||||
self.heartflow_chat_list: Dict[str, HeartFChatting] = {}
|
||||
def __init__(self) -> None:
|
||||
self.heartflow_chat_list: Dict[str, MaisakaHeartFlowChatting] = {}
|
||||
self._chat_create_locks: Dict[str, asyncio.Lock] = {}
|
||||
|
||||
async def get_or_create_heartflow_chat(self, session_id: str): # -> Optional[HeartFChatting | BrainChatting]:
|
||||
"""获取或创建一个新的HeartFChatting实例"""
|
||||
async def get_or_create_heartflow_chat(self, session_id: str) -> MaisakaHeartFlowChatting:
|
||||
"""获取或创建指定会话对应的 Maisaka runtime。"""
|
||||
try:
|
||||
if chat := self.heartflow_chat_list.get(session_id):
|
||||
return chat
|
||||
chat_session = chat_manager.get_session_by_session_id(session_id)
|
||||
if not chat_session:
|
||||
raise ValueError(f"未找到 session_id={session_id} 的聊天流")
|
||||
# new_chat = (
|
||||
# HeartFChatting(session_id=session_id) if chat_session.group_id else BrainChatting(session_id=session_id)
|
||||
# )
|
||||
new_chat = HeartFChatting(session_id=session_id)
|
||||
await new_chat.start()
|
||||
self.heartflow_chat_list[session_id] = new_chat
|
||||
return new_chat
|
||||
except Exception as e:
|
||||
logger.error(f"创建心流聊天 {session_id} 失败: {e}", exc_info=True)
|
||||
traceback.print_exc()
|
||||
raise e
|
||||
|
||||
def adjust_talk_frequency(self, session_id: str, frequency: float):
|
||||
"""调整指定聊天流的说话频率"""
|
||||
create_lock = self._chat_create_locks.setdefault(session_id, asyncio.Lock())
|
||||
async with create_lock:
|
||||
if chat := self.heartflow_chat_list.get(session_id):
|
||||
return chat
|
||||
|
||||
chat_session = chat_manager.get_session_by_session_id(session_id)
|
||||
if not chat_session:
|
||||
raise ValueError(f"未找到 session_id={session_id} 对应的聊天流")
|
||||
|
||||
new_chat = MaisakaHeartFlowChatting(session_id=session_id)
|
||||
await new_chat.start()
|
||||
self.heartflow_chat_list[session_id] = new_chat
|
||||
return new_chat
|
||||
except Exception as exc:
|
||||
logger.error(f"创建心流聊天 {session_id} 失败: {exc}", exc_info=True)
|
||||
traceback.print_exc()
|
||||
raise
|
||||
|
||||
def adjust_talk_frequency(self, session_id: str, frequency: float) -> None:
|
||||
"""调整指定聊天流的说话频率。"""
|
||||
chat = self.heartflow_chat_list.get(session_id)
|
||||
if chat and isinstance(chat, HeartFChatting):
|
||||
if chat:
|
||||
chat.adjust_talk_frequency(frequency)
|
||||
logger.info(f"已调整聊天 {session_id} 的说话频率为 {frequency}")
|
||||
else:
|
||||
|
||||
@@ -1,9 +1,11 @@
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Dict, Optional
|
||||
|
||||
from rich.traceback import install
|
||||
from sqlmodel import select
|
||||
from typing import Optional
|
||||
|
||||
import asyncio
|
||||
import base64
|
||||
import hashlib
|
||||
|
||||
@@ -11,8 +13,9 @@ from src.common.logger import get_logger
|
||||
from src.common.database.database import get_db_session
|
||||
from src.common.database.database_model import Images, ImageType
|
||||
from src.common.data_models.image_data_model import MaiImage
|
||||
from src.config.config import global_config, model_config
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
from src.common.data_models.llm_service_data_models import LLMImageOptions
|
||||
from src.services.llm_service import LLMServiceClient
|
||||
|
||||
install(extra_lines=3)
|
||||
|
||||
@@ -23,21 +26,30 @@ IMAGE_DIR = DATA_DIR / "images"
|
||||
logger = get_logger("image")
|
||||
|
||||
|
||||
def _ensure_image_dir_exists():
|
||||
def _ensure_image_dir_exists() -> None:
|
||||
"""确保图片缓存目录存在。"""
|
||||
IMAGE_DIR.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
vlm = LLMRequest(model_set=model_config.model_task_config.vlm, request_type="image")
|
||||
vlm = LLMServiceClient(task_name="vlm", request_type="image")
|
||||
|
||||
|
||||
class ImageManager:
|
||||
def __init__(self):
|
||||
"""图片描述管理器。"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
"""初始化图片管理器。"""
|
||||
_ensure_image_dir_exists()
|
||||
self._pending_description_tasks: Dict[str, asyncio.Task[None]] = {}
|
||||
|
||||
logger.info("图片管理器初始化完成")
|
||||
|
||||
async def get_image_description(
|
||||
self, *, image_hash: Optional[str] = None, image_bytes: Optional[bytes] = None
|
||||
self,
|
||||
*,
|
||||
image_hash: Optional[str] = None,
|
||||
image_bytes: Optional[bytes] = None,
|
||||
wait_for_build: bool = True,
|
||||
) -> str:
|
||||
"""
|
||||
获取图片描述的封装方法
|
||||
@@ -49,6 +61,7 @@ class ImageManager:
|
||||
Args:
|
||||
image_hash (Optional[str]): 图片的哈希值，如果提供则优先使用该
|
||||
image_bytes (Optional[bytes]): 图片的字节数据，如果提供则在数据库中找不到哈希值时使用该数据生成描述
|
||||
wait_for_build (bool): 未命中缓存时是否同步等待描述构建完成
|
||||
Returns:
|
||||
return (str): 图片描述，如果发生错误或无法生成描述则返回空字符串
|
||||
Raises:
|
||||
@@ -73,6 +86,9 @@ class ImageManager:
|
||||
if not image_bytes:
|
||||
logger.warning("图片哈希值未找到，且未提供图片字节数据，返回无描述")
|
||||
return ""
|
||||
if not wait_for_build:
|
||||
self._schedule_description_build(hash_str, image_bytes)
|
||||
return ""
|
||||
logger.info(f"图片描述未找到，哈希值: {hash_str}，准备生成新描述")
|
||||
try:
|
||||
image = await self.save_image_and_process(image_bytes)
|
||||
@@ -81,6 +97,47 @@ class ImageManager:
|
||||
logger.error(f"生成图片描述时发生错误: {e}")
|
||||
return ""
|
||||
|
||||
def _schedule_description_build(self, image_hash: str, image_bytes: bytes) -> None:
|
||||
"""调度图片描述后台构建任务。
|
||||
|
||||
Args:
|
||||
image_hash: 图片哈希值。
|
||||
image_bytes: 图片字节数据。
|
||||
"""
|
||||
if image_hash in self._pending_description_tasks:
|
||||
return
|
||||
|
||||
task = asyncio.create_task(self._build_description_in_background(image_hash, image_bytes))
|
||||
self._pending_description_tasks[image_hash] = task
|
||||
task.add_done_callback(lambda finished_task: self._finalize_description_build(image_hash, finished_task))
|
||||
|
||||
async def _build_description_in_background(self, image_hash: str, image_bytes: bytes) -> None:
|
||||
"""在后台构建并缓存图片描述。
|
||||
|
||||
Args:
|
||||
image_hash: 图片哈希值。
|
||||
image_bytes: 图片字节数据。
|
||||
"""
|
||||
try:
|
||||
logger.info(f"图片描述后台构建已开始，哈希值: {image_hash}")
|
||||
await self.save_image_and_process(image_bytes)
|
||||
logger.info(f"图片描述后台构建完成，哈希值: {image_hash}")
|
||||
except Exception as exc:
|
||||
logger.warning(f"图片描述后台构建失败，哈希值: {image_hash}，错误: {exc}")
|
||||
|
||||
def _finalize_description_build(self, image_hash: str, task: asyncio.Task[None]) -> None:
|
||||
"""回收图片描述后台构建任务。
|
||||
|
||||
Args:
|
||||
image_hash: 图片哈希值。
|
||||
task: 已完成的后台任务。
|
||||
"""
|
||||
self._pending_description_tasks.pop(image_hash, None)
|
||||
try:
|
||||
task.result()
|
||||
except Exception as exc:
|
||||
logger.debug(f"图片描述后台任务结束时捕获异常，哈希值: {image_hash}，错误: {exc}")
|
||||
|
||||
def get_image_from_db(self, image_hash: str) -> Optional[MaiImage]:
|
||||
"""
|
||||
从数据库中根据图片哈希值获取图片记录
|
||||
@@ -260,7 +317,13 @@ class ImageManager:
|
||||
prompt = global_config.personality.visual_style
|
||||
image_base64 = base64.b64encode(image_bytes).decode("utf-8")
|
||||
|
||||
description, _ = await vlm.generate_response_for_image(prompt, image_base64, image_format, 0.4)
|
||||
generation_result = await vlm.generate_response_for_image(
|
||||
prompt,
|
||||
image_base64,
|
||||
image_format,
|
||||
options=LLMImageOptions(temperature=0.4),
|
||||
)
|
||||
description = generation_result.response
|
||||
if not description:
|
||||
logger.warning("VLM未能生成图片描述")
|
||||
return description or ""
|
||||
|
||||
@@ -139,14 +139,14 @@ class EmbeddingStore:
|
||||
asyncio.set_event_loop(loop)
|
||||
|
||||
try:
|
||||
# 创建新的LLMRequest实例
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import model_config
|
||||
# 创建新的服务层实例
|
||||
from src.services.llm_service import LLMServiceClient
|
||||
|
||||
llm = LLMRequest(model_set=model_config.model_task_config.embedding, request_type="embedding")
|
||||
llm = LLMServiceClient(task_name="embedding", request_type="embedding")
|
||||
|
||||
# 使用新的事件循环运行异步方法
|
||||
embedding, _ = loop.run_until_complete(llm.get_embedding(s))
|
||||
embedding_result = loop.run_until_complete(llm.embed_text(s))
|
||||
embedding = embedding_result.embedding
|
||||
|
||||
if embedding and len(embedding) > 0:
|
||||
return embedding
|
||||
@@ -195,13 +195,12 @@ class EmbeddingStore:
|
||||
start_idx, chunk_strs = chunk_data
|
||||
chunk_results = []
|
||||
|
||||
# 为每个线程创建独立的LLMRequest实例
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import model_config
|
||||
# 为每个线程创建独立的服务层实例
|
||||
from src.services.llm_service import LLMServiceClient
|
||||
|
||||
try:
|
||||
# 创建线程专用的LLM实例
|
||||
llm = LLMRequest(model_set=model_config.model_task_config.embedding, request_type="embedding")
|
||||
# 创建线程专用的服务层实例
|
||||
llm = LLMServiceClient(task_name="embedding", request_type="embedding")
|
||||
|
||||
for i, s in enumerate(chunk_strs):
|
||||
try:
|
||||
@@ -209,7 +208,8 @@ class EmbeddingStore:
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop)
|
||||
try:
|
||||
embedding = loop.run_until_complete(llm.get_embedding(s))
|
||||
embedding_result = loop.run_until_complete(llm.embed_text(s))
|
||||
embedding = embedding_result.embedding
|
||||
finally:
|
||||
loop.close()
|
||||
|
||||
|
||||
@@ -1,18 +1,27 @@
|
||||
import asyncio
|
||||
import json
|
||||
import time
|
||||
from typing import List, Union
|
||||
from typing import Dict, List, Tuple, Union
|
||||
|
||||
from .global_logger import logger
|
||||
from . import prompt_template
|
||||
from . import INVALID_ENTITY
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from json_repair import repair_json
|
||||
|
||||
from src.services.llm_service import LLMServiceClient
|
||||
|
||||
def _extract_json_from_text(text: str):
|
||||
from . import INVALID_ENTITY
|
||||
from . import prompt_template
|
||||
from .global_logger import logger
|
||||
|
||||
|
||||
def _extract_json_from_text(text: str) -> List[str] | List[List[str]] | Dict[str, object]:
|
||||
# sourcery skip: assign-if-exp, extract-method
|
||||
"""从文本中提取JSON数据的高容错方法"""
|
||||
"""从文本中提取 JSON 数据。
|
||||
|
||||
Args:
|
||||
text: 原始模型输出文本。
|
||||
|
||||
Returns:
|
||||
List[str] | List[List[str]] | Dict[str, object]: 修复并解析后的 JSON 结果。
|
||||
"""
|
||||
if text is None:
|
||||
logger.error("输入文本为None")
|
||||
return []
|
||||
@@ -46,20 +55,30 @@ def _extract_json_from_text(text: str):
|
||||
return []
|
||||
|
||||
|
||||
def _entity_extract(llm_req: LLMRequest, paragraph: str) -> List[str]:
|
||||
def _entity_extract(llm_req: LLMServiceClient, paragraph: str) -> List[str]:
|
||||
# sourcery skip: reintroduce-else, swap-if-else-branches, use-named-expression
|
||||
"""对段落进行实体提取，返回提取出的实体列表（JSON格式）"""
|
||||
"""对单段文本执行实体提取。
|
||||
|
||||
Args:
|
||||
llm_req: LLM 服务门面实例。
|
||||
paragraph: 待提取实体的原始段落文本。
|
||||
|
||||
Returns:
|
||||
List[str]: 提取出的实体列表。
|
||||
"""
|
||||
entity_extract_context = prompt_template.build_entity_extract_context(paragraph)
|
||||
|
||||
# 使用 asyncio.run 来运行异步方法
|
||||
try:
|
||||
# 如果当前已有事件循环在运行，使用它
|
||||
loop = asyncio.get_running_loop()
|
||||
future = asyncio.run_coroutine_threadsafe(llm_req.generate_response_async(entity_extract_context), loop)
|
||||
response, _ = future.result()
|
||||
future = asyncio.run_coroutine_threadsafe(llm_req.generate_response(entity_extract_context), loop)
|
||||
generation_result = future.result()
|
||||
response = generation_result.response
|
||||
except RuntimeError:
|
||||
# 如果没有运行中的事件循环，直接使用 asyncio.run
|
||||
response, _ = asyncio.run(llm_req.generate_response_async(entity_extract_context))
|
||||
generation_result = asyncio.run(llm_req.generate_response(entity_extract_context))
|
||||
response = generation_result.response
|
||||
|
||||
# 添加调试日志
|
||||
logger.debug(f"LLM返回的原始响应: {response}")
|
||||
@@ -92,8 +111,21 @@ def _entity_extract(llm_req: LLMRequest, paragraph: str) -> List[str]:
|
||||
return entity_extract_result
|
||||
|
||||
|
||||
def _rdf_triple_extract(llm_req: LLMRequest, paragraph: str, entities: list) -> List[List[str]]:
|
||||
"""对段落进行实体提取，返回提取出的实体列表（JSON格式）"""
|
||||
def _rdf_triple_extract(
|
||||
llm_req: LLMServiceClient,
|
||||
paragraph: str,
|
||||
entities: List[str],
|
||||
) -> List[List[str]]:
|
||||
"""对单段文本执行 RDF 三元组提取。
|
||||
|
||||
Args:
|
||||
llm_req: LLM 服务门面实例。
|
||||
paragraph: 待提取的原始段落文本。
|
||||
entities: 已识别出的实体列表。
|
||||
|
||||
Returns:
|
||||
List[List[str]]: 提取出的三元组列表。
|
||||
"""
|
||||
rdf_extract_context = prompt_template.build_rdf_triple_extract_context(
|
||||
paragraph, entities=json.dumps(entities, ensure_ascii=False)
|
||||
)
|
||||
@@ -102,11 +134,13 @@ def _rdf_triple_extract(llm_req: LLMRequest, paragraph: str, entities: list) ->
|
||||
try:
|
||||
# 如果当前已有事件循环在运行，使用它
|
||||
loop = asyncio.get_running_loop()
|
||||
future = asyncio.run_coroutine_threadsafe(llm_req.generate_response_async(rdf_extract_context), loop)
|
||||
response, _ = future.result()
|
||||
future = asyncio.run_coroutine_threadsafe(llm_req.generate_response(rdf_extract_context), loop)
|
||||
generation_result = future.result()
|
||||
response = generation_result.response
|
||||
except RuntimeError:
|
||||
# 如果没有运行中的事件循环，直接使用 asyncio.run
|
||||
response, _ = asyncio.run(llm_req.generate_response_async(rdf_extract_context))
|
||||
generation_result = asyncio.run(llm_req.generate_response(rdf_extract_context))
|
||||
response = generation_result.response
|
||||
|
||||
# 添加调试日志
|
||||
logger.debug(f"RDF LLM返回的原始响应: {response}")
|
||||
@@ -140,8 +174,21 @@ def _rdf_triple_extract(llm_req: LLMRequest, paragraph: str, entities: list) ->
|
||||
|
||||
|
||||
def info_extract_from_str(
|
||||
llm_client_for_ner: LLMRequest, llm_client_for_rdf: LLMRequest, paragraph: str
|
||||
) -> Union[tuple[None, None], tuple[list[str], list[list[str]]]]:
|
||||
llm_client_for_ner: LLMServiceClient,
|
||||
llm_client_for_rdf: LLMServiceClient,
|
||||
paragraph: str,
|
||||
) -> Union[Tuple[None, None], Tuple[List[str], List[List[str]]]]:
|
||||
"""从文本中提取实体与三元组信息。
|
||||
|
||||
Args:
|
||||
llm_client_for_ner: 实体提取使用的 LLM 服务门面。
|
||||
llm_client_for_rdf: RDF 三元组提取使用的 LLM 服务门面。
|
||||
paragraph: 原始段落文本。
|
||||
|
||||
Returns:
|
||||
Union[Tuple[None, None], Tuple[List[str], List[List[str]]]]: 成功时返回
|
||||
``(实体列表, 三元组列表)``，失败时返回 ``(None, None)``。
|
||||
"""
|
||||
try_count = 0
|
||||
while True:
|
||||
try:
|
||||
@@ -176,17 +223,30 @@ def info_extract_from_str(
|
||||
|
||||
|
||||
class IEProcess:
|
||||
"""
|
||||
信息抽取处理器类，提供更方便的批次处理接口。
|
||||
"""
|
||||
"""信息抽取处理器。"""
|
||||
|
||||
def __init__(self, llm_ner: LLMRequest, llm_rdf: LLMRequest = None):
|
||||
def __init__(
|
||||
self,
|
||||
llm_ner: LLMServiceClient,
|
||||
llm_rdf: LLMServiceClient | None = None,
|
||||
) -> None:
|
||||
"""初始化信息抽取处理器。
|
||||
|
||||
Args:
|
||||
llm_ner: 实体提取使用的 LLM 服务门面。
|
||||
llm_rdf: RDF 三元组提取使用的 LLM 服务门面；为空时复用 `llm_ner`。
|
||||
"""
|
||||
self.llm_ner = llm_ner
|
||||
self.llm_rdf = llm_rdf or llm_ner
|
||||
|
||||
async def process_paragraphs(self, paragraphs: List[str]) -> List[dict]:
|
||||
"""
|
||||
异步处理多个段落。
|
||||
async def process_paragraphs(self, paragraphs: List[str]) -> List[Dict[str, object]]:
|
||||
"""异步处理多个段落。
|
||||
|
||||
Args:
|
||||
paragraphs: 待处理的段落列表。
|
||||
|
||||
Returns:
|
||||
List[Dict[str, object]]: 每个成功段落对应的抽取结果。
|
||||
"""
|
||||
from .utils.hash import get_sha256
|
||||
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
from contextlib import suppress
|
||||
from copy import deepcopy
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
import os
|
||||
@@ -10,9 +11,9 @@ from src.chat.heart_flow.heartflow_message_processor import HeartFCMessageReceiv
|
||||
from src.common.logger import get_logger
|
||||
from src.common.utils.utils_message import MessageUtils
|
||||
from src.common.utils.utils_session import SessionUtils
|
||||
from src.config.config import global_config
|
||||
from src.platform_io.route_key_factory import RouteKeyFactory
|
||||
|
||||
# from src.chat.brain_chat.PFC.pfc_manager import PFCManager
|
||||
from src.core.announcement_manager import global_announcement_manager
|
||||
from src.plugin_runtime.component_query import component_query_service
|
||||
|
||||
@@ -29,36 +30,20 @@ logger = get_logger("chat")
|
||||
|
||||
|
||||
class ChatBot:
|
||||
def __init__(self):
|
||||
def __init__(self) -> None:
|
||||
"""初始化聊天机器人入口。"""
|
||||
|
||||
self.bot = None # bot 实例引用
|
||||
self._started = False
|
||||
self.heartflow_message_receiver = HeartFCMessageReceiver() # 新增
|
||||
# self.pfc_manager = PFCManager.get_instance() # PFC管理器 # TODO: PFC恢复
|
||||
self.heartflow_message_receiver = HeartFCMessageReceiver()
|
||||
|
||||
async def _ensure_started(self):
|
||||
"""确保所有任务已启动"""
|
||||
async def _ensure_started(self) -> None:
|
||||
"""确保所有后台任务已启动。"""
|
||||
if not self._started:
|
||||
logger.debug("确保ChatBot所有任务已启动")
|
||||
|
||||
self._started = True
|
||||
|
||||
async def _create_pfc_chat(self, message: SessionMessage):
|
||||
"""创建或获取PFC对话实例
|
||||
|
||||
Args:
|
||||
message: 消息对象
|
||||
"""
|
||||
try:
|
||||
chat_id = message.session_id
|
||||
private_name = str(message.message_info.user_info.user_nickname)
|
||||
|
||||
logger.debug(f"[私聊][{private_name}]创建或获取PFC对话: {chat_id}")
|
||||
await self.pfc_manager.get_or_create_conversation(chat_id, private_name)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"创建PFC聊天失败: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
|
||||
async def _process_commands(self, message: SessionMessage) -> tuple[bool, Optional[str], bool]:
|
||||
"""使用统一组件注册表处理命令。
|
||||
|
||||
@@ -175,11 +160,12 @@ class ChatBot:
|
||||
recalled: Dict[str, Any] = {}
|
||||
recalled_id = None
|
||||
|
||||
if getattr(seg, "type", None) == "notify" and isinstance(getattr(seg, "data", None), dict):
|
||||
sub_type = seg.data.get("sub_type")
|
||||
scene = seg.data.get("scene")
|
||||
msg_id = seg.data.get("message_id")
|
||||
recalled = seg.data.get("recalled_user_info") or {}
|
||||
seg_data = getattr(seg, "data", None)
|
||||
if getattr(seg, "type", None) == "notify" and isinstance(seg_data, dict):
|
||||
sub_type = seg_data.get("sub_type")
|
||||
scene = seg_data.get("scene")
|
||||
msg_id = seg_data.get("message_id")
|
||||
recalled = seg_data.get("recalled_user_info") or {}
|
||||
if isinstance(recalled, dict):
|
||||
recalled_id = recalled.get("user_id")
|
||||
|
||||
@@ -301,7 +287,13 @@ class ChatBot:
|
||||
# pass
|
||||
|
||||
# 处理消息内容，识别表情包等二进制数据并转化为文本描述
|
||||
await message.process()
|
||||
if global_config.maisaka.direct_image_input:
|
||||
message.maisaka_original_raw_message = deepcopy(message.raw_message) # type: ignore[attr-defined]
|
||||
# 入站主链优先保证消息尽快入队，避免图片、表情包、语音分析阻塞适配器超时。
|
||||
await message.process(
|
||||
enable_heavy_media_analysis=False,
|
||||
enable_voice_transcription=False,
|
||||
)
|
||||
|
||||
# 平台层的 @ 检测由底层 is_mentioned_bot_in_message 统一处理；此处不做用户名硬编码匹配
|
||||
|
||||
@@ -335,14 +327,13 @@ class ChatBot:
|
||||
|
||||
# message.update_chat_stream(chat)
|
||||
|
||||
# 命令处理 - 使用新插件系统检查并处理命令
|
||||
# 注意：命令返回的 response 当前只用于日志记录和流程判断，
|
||||
# 不会在这里自动作为回复消息发送回会话。
|
||||
# is_command, cmd_result, continue_process = await self._process_commands(message)
|
||||
# 命令处理 - 使用新插件系统检查并处理命令。
|
||||
# 命令处理器内部自行决定是否回复消息，这里只负责流程分发与拦截。
|
||||
is_command, cmd_result, continue_process = await self._process_commands(message)
|
||||
|
||||
# # 如果是命令且不需要继续处理，则直接返回
|
||||
# if is_command and await self._handle_command_processing_result(message, cmd_result, continue_process):
|
||||
# return
|
||||
# 如果是命令且不需要继续处理，则直接返回，避免落入 HeartFlow / MaiSaka。
|
||||
if is_command and await self._handle_command_processing_result(message, cmd_result, continue_process):
|
||||
return
|
||||
|
||||
# continue_flag, modified_message = await events_manager.handle_mai_events(EventType.ON_MESSAGE, message)
|
||||
# if not continue_flag:
|
||||
@@ -362,31 +353,12 @@ class ChatBot:
|
||||
# else:
|
||||
# template_group_name = None
|
||||
|
||||
# async def preprocess():
|
||||
# # 根据聊天类型路由消息
|
||||
# if group_info is None:
|
||||
# # 私聊消息 -> PFC系统
|
||||
# logger.debug("[私聊]检测到私聊消息，路由到PFC系统")
|
||||
# await MessageStorage.store_message(message, chat)
|
||||
# await self._create_pfc_chat(message)
|
||||
# else:
|
||||
# # 群聊消息 -> HeartFlow系统
|
||||
# logger.debug("[群聊]检测到群聊消息，路由到HeartFlow系统")
|
||||
# await self.heartflow_message_receiver.process_message(message)
|
||||
|
||||
# if template_group_name:
|
||||
# async with global_prompt_manager.async_message_scope(template_group_name):
|
||||
# await preprocess()
|
||||
# else:
|
||||
# await preprocess()
|
||||
async def preprocess():
|
||||
if group_info is None:
|
||||
# logger.debug("[私聊]检测到私聊消息，路由到PFC系统")
|
||||
# MessageUtils.store_message_to_db(message) # 存储消息到数据库
|
||||
# await self._create_pfc_chat(message)
|
||||
logger.critical("暂时禁用私聊")
|
||||
logger.debug("[私聊]检测到私聊消息，路由到 Maisaka")
|
||||
await self.heartflow_message_receiver.process_message(message)
|
||||
else:
|
||||
logger.debug("[群聊]检测到群聊消息，路由到HeartFlow系统")
|
||||
logger.debug("[群聊]检测到群聊消息，路由到 Maisaka")
|
||||
await self.heartflow_message_receiver.process_message(message)
|
||||
|
||||
await preprocess()
|
||||
|
||||
@@ -1,7 +1,8 @@
|
||||
from asyncio import Task
|
||||
from typing import Dict, List, Sequence, Tuple
|
||||
|
||||
from rich.traceback import install
|
||||
from sqlmodel import select
|
||||
from typing import List, Dict, Tuple, Sequence
|
||||
|
||||
import asyncio
|
||||
|
||||
@@ -27,14 +28,36 @@ logger = get_logger("chat_message")
|
||||
|
||||
|
||||
class MsgIDMapping:
|
||||
def __init__(self):
|
||||
self.mapping: Dict[str, Tuple[str | Task, UserInfo]] = {}
|
||||
"""回复消息内容缓存。"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
"""初始化消息 ID 到内容的映射缓存。"""
|
||||
self.mapping: Dict[str, Tuple[str | Task[str], UserInfo]] = {}
|
||||
|
||||
|
||||
class SessionMessage(MaiMessage):
|
||||
async def process(self):
|
||||
"""处理消息内容，识别消息内容并转化为文本（会修改消息组件属性）"""
|
||||
tasks = [self.process_single_component(component, MsgIDMapping()) for component in self.raw_message.components]
|
||||
async def process(
|
||||
self,
|
||||
*,
|
||||
enable_heavy_media_analysis: bool = True,
|
||||
enable_voice_transcription: bool = True,
|
||||
) -> None:
|
||||
"""处理消息内容并转化为纯文本。
|
||||
|
||||
Args:
|
||||
enable_heavy_media_analysis: 是否同步执行图片与表情包描述生成。
|
||||
enable_voice_transcription: 是否同步执行语音转写。
|
||||
"""
|
||||
id_content_map = MsgIDMapping()
|
||||
tasks = [
|
||||
self.process_single_component(
|
||||
component,
|
||||
id_content_map,
|
||||
enable_heavy_media_analysis=enable_heavy_media_analysis,
|
||||
enable_voice_transcription=enable_voice_transcription,
|
||||
)
|
||||
for component in self.raw_message.components
|
||||
]
|
||||
results = await asyncio.gather(*tasks, return_exceptions=True)
|
||||
processed_texts: List[str] = []
|
||||
for result in results:
|
||||
@@ -45,50 +68,116 @@ class SessionMessage(MaiMessage):
|
||||
self.processed_plain_text = " ".join(processed_texts)
|
||||
|
||||
async def process_single_component(
|
||||
self, component: StandardMessageComponents, id_content_map: MsgIDMapping, recursion_depth: int = 0
|
||||
self,
|
||||
component: StandardMessageComponents,
|
||||
id_content_map: MsgIDMapping,
|
||||
recursion_depth: int = 0,
|
||||
*,
|
||||
enable_heavy_media_analysis: bool = True,
|
||||
enable_voice_transcription: bool = True,
|
||||
) -> str:
|
||||
"""按照类型处理单个消息组件，返回处理后的文本内容（会修改消息组件属性）"""
|
||||
"""按类型处理单个消息组件。
|
||||
|
||||
Args:
|
||||
component: 待处理的消息组件。
|
||||
id_content_map: 回复消息解析缓存。
|
||||
recursion_depth: 当前递归深度。
|
||||
enable_heavy_media_analysis: 是否同步执行图片与表情包描述生成。
|
||||
enable_voice_transcription: 是否同步执行语音转写。
|
||||
|
||||
Returns:
|
||||
str: 组件对应的文本表示。
|
||||
"""
|
||||
if isinstance(component, TextComponent):
|
||||
return component.text
|
||||
elif isinstance(component, ImageComponent):
|
||||
return await self.process_image_component(component)
|
||||
return await self.process_image_component(
|
||||
component,
|
||||
enable_heavy_media_analysis=enable_heavy_media_analysis,
|
||||
)
|
||||
elif isinstance(component, EmojiComponent):
|
||||
return await self.process_emoji_component(component)
|
||||
return await self.process_emoji_component(
|
||||
component,
|
||||
enable_heavy_media_analysis=enable_heavy_media_analysis,
|
||||
)
|
||||
elif isinstance(component, AtComponent):
|
||||
return await self.process_at_component(component)
|
||||
elif isinstance(component, VoiceComponent):
|
||||
return await self.process_voice_component(component)
|
||||
return await self.process_voice_component(
|
||||
component,
|
||||
enable_voice_transcription=enable_voice_transcription,
|
||||
)
|
||||
elif isinstance(component, ReplyComponent):
|
||||
return await self.process_reply_component(component, id_content_map)
|
||||
elif isinstance(component, ForwardNodeComponent):
|
||||
return await self.process_forward_component(component, id_content_map, recursion_depth=recursion_depth + 1)
|
||||
return await self.process_forward_component(
|
||||
component,
|
||||
id_content_map,
|
||||
recursion_depth=recursion_depth + 1,
|
||||
enable_heavy_media_analysis=enable_heavy_media_analysis,
|
||||
enable_voice_transcription=enable_voice_transcription,
|
||||
)
|
||||
else:
|
||||
raise NotImplementedError(f"暂时不支持的消息组件类型: {type(component)}")
|
||||
|
||||
async def process_image_component(self, component: ImageComponent) -> str:
|
||||
async def process_image_component(
|
||||
self,
|
||||
component: ImageComponent,
|
||||
*,
|
||||
enable_heavy_media_analysis: bool = True,
|
||||
) -> str:
|
||||
"""处理图片组件。
|
||||
|
||||
Args:
|
||||
component: 图片组件。
|
||||
enable_heavy_media_analysis: 是否同步执行图片描述生成。
|
||||
|
||||
Returns:
|
||||
str: 图片组件对应的文本表示。
|
||||
"""
|
||||
if component.content: # 先检查是否处理过
|
||||
return component.content
|
||||
from src.chat.image_system.image_manager import image_manager
|
||||
|
||||
# 获取描述
|
||||
try:
|
||||
desc = await image_manager.get_image_description(image_bytes=component.binary_data)
|
||||
desc = await image_manager.get_image_description(
|
||||
image_bytes=component.binary_data,
|
||||
wait_for_build=enable_heavy_media_analysis,
|
||||
)
|
||||
except Exception:
|
||||
desc = None # 失败置空
|
||||
|
||||
content = f"[图片：{desc}]" if desc else "[一张图片，网卡了加载不出来]"
|
||||
content = f"[图片：{desc}]" if desc else "[图片]"
|
||||
component.content = content
|
||||
component.binary_data = b"" # 处理完就丢掉二进制数据，节省内存
|
||||
return content
|
||||
|
||||
async def process_emoji_component(self, component: EmojiComponent) -> str:
|
||||
async def process_emoji_component(
|
||||
self,
|
||||
component: EmojiComponent,
|
||||
*,
|
||||
enable_heavy_media_analysis: bool = True,
|
||||
) -> str:
|
||||
"""处理表情包组件。
|
||||
|
||||
Args:
|
||||
component: 表情包组件。
|
||||
enable_heavy_media_analysis: 是否同步执行表情包描述生成。
|
||||
|
||||
Returns:
|
||||
str: 表情包组件对应的文本表示。
|
||||
"""
|
||||
if component.content: # 先检查是否处理过
|
||||
return component.content
|
||||
from src.chat.emoji_system.emoji_manager import emoji_manager
|
||||
|
||||
# 获取表情包描述
|
||||
try:
|
||||
tuple_content = await emoji_manager.get_emoji_description(emoji_bytes=component.binary_data)
|
||||
tuple_content = await emoji_manager.get_emoji_description(
|
||||
emoji_bytes=component.binary_data,
|
||||
wait_for_build=enable_heavy_media_analysis,
|
||||
)
|
||||
except Exception:
|
||||
tuple_content = None # 失败置空
|
||||
|
||||
@@ -96,7 +185,7 @@ class SessionMessage(MaiMessage):
|
||||
desc, _ = tuple_content
|
||||
content = f"[表情包: {desc}]"
|
||||
else:
|
||||
content = "[一个表情，网卡了加载不出来]"
|
||||
content = "[表情包]"
|
||||
component.content = content
|
||||
component.binary_data = b"" # 处理完就丢掉二进制数据，节省内存
|
||||
return content
|
||||
@@ -124,9 +213,26 @@ class SessionMessage(MaiMessage):
|
||||
else: # 最后使用用户ID
|
||||
return f"@{component.target_user_id}"
|
||||
|
||||
async def process_voice_component(self, component: VoiceComponent) -> str:
|
||||
async def process_voice_component(
|
||||
self,
|
||||
component: VoiceComponent,
|
||||
*,
|
||||
enable_voice_transcription: bool = True,
|
||||
) -> str:
|
||||
"""处理语音组件。
|
||||
|
||||
Args:
|
||||
component: 语音组件。
|
||||
enable_voice_transcription: 是否同步执行语音转写。
|
||||
|
||||
Returns:
|
||||
str: 语音组件对应的文本表示。
|
||||
"""
|
||||
if component.content: # 先检查是否处理过
|
||||
return component.content
|
||||
if not enable_voice_transcription:
|
||||
component.content = "[语音消息]"
|
||||
return component.content
|
||||
from src.common.utils.utils_voice import get_voice_text
|
||||
|
||||
text = await get_voice_text(component.binary_data)
|
||||
@@ -169,13 +275,37 @@ class SessionMessage(MaiMessage):
|
||||
return "[回复了一条消息，但原消息已无法访问]"
|
||||
|
||||
async def process_forward_component(
|
||||
self, component: ForwardNodeComponent, id_content_map: MsgIDMapping, recursion_depth: int = 0
|
||||
self,
|
||||
component: ForwardNodeComponent,
|
||||
id_content_map: MsgIDMapping,
|
||||
recursion_depth: int = 0,
|
||||
*,
|
||||
enable_heavy_media_analysis: bool = True,
|
||||
enable_voice_transcription: bool = True,
|
||||
) -> str:
|
||||
"""处理合并转发组件。
|
||||
|
||||
Args:
|
||||
component: 合并转发组件。
|
||||
id_content_map: 回复消息解析缓存。
|
||||
recursion_depth: 当前递归深度。
|
||||
enable_heavy_media_analysis: 是否同步执行图片与表情包描述生成。
|
||||
enable_voice_transcription: 是否同步执行语音转写。
|
||||
|
||||
Returns:
|
||||
str: 合并转发组件对应的文本表示。
|
||||
"""
|
||||
task_list: List[Task] = []
|
||||
node_user_info_list: List[UserInfo] = []
|
||||
for node in component.forward_components:
|
||||
task = asyncio.create_task(
|
||||
self._process_multiple_components(node.content, id_content_map, recursion_depth + 1)
|
||||
self._process_multiple_components(
|
||||
node.content,
|
||||
id_content_map,
|
||||
recursion_depth + 1,
|
||||
enable_heavy_media_analysis=enable_heavy_media_analysis,
|
||||
enable_voice_transcription=enable_voice_transcription,
|
||||
)
|
||||
)
|
||||
node_user_info = UserInfo(node.user_id or "未知用户", node.user_nickname, node.user_cardname)
|
||||
# 传入ID缓存映射，方便Reply组件获取并等待处理结果
|
||||
@@ -196,9 +326,36 @@ class SessionMessage(MaiMessage):
|
||||
return "【合并转发消息: \n" + "\n".join(forward_texts) + "\n】"
|
||||
|
||||
async def _process_multiple_components(
|
||||
self, components: Sequence[StandardMessageComponents], id_content_map: MsgIDMapping, recursion_depth: int = 0
|
||||
self,
|
||||
components: Sequence[StandardMessageComponents],
|
||||
id_content_map: MsgIDMapping,
|
||||
recursion_depth: int = 0,
|
||||
*,
|
||||
enable_heavy_media_analysis: bool = True,
|
||||
enable_voice_transcription: bool = True,
|
||||
) -> str:
|
||||
tasks = [self.process_single_component(component, id_content_map, recursion_depth) for component in components]
|
||||
"""并行处理多个消息组件。
|
||||
|
||||
Args:
|
||||
components: 待处理的组件序列。
|
||||
id_content_map: 回复消息解析缓存。
|
||||
recursion_depth: 当前递归深度。
|
||||
enable_heavy_media_analysis: 是否同步执行图片与表情包描述生成。
|
||||
enable_voice_transcription: 是否同步执行语音转写。
|
||||
|
||||
Returns:
|
||||
str: 多个组件拼接后的文本表示。
|
||||
"""
|
||||
tasks = [
|
||||
self.process_single_component(
|
||||
component,
|
||||
id_content_map,
|
||||
recursion_depth,
|
||||
enable_heavy_media_analysis=enable_heavy_media_analysis,
|
||||
enable_voice_transcription=enable_voice_transcription,
|
||||
)
|
||||
for component in components
|
||||
]
|
||||
results = await asyncio.gather(*tasks, return_exceptions=True) # 并行处理多个组件
|
||||
processed_texts: List[str] = []
|
||||
for result in results:
|
||||
|
||||
@@ -1,137 +0,0 @@
|
||||
from typing import Dict, Optional, Tuple
|
||||
|
||||
from src.chat.message_receive.chat_manager import BotChatSession
|
||||
from src.chat.message_receive.message import SessionMessage
|
||||
from src.common.logger import get_logger
|
||||
from src.core.types import ActionInfo
|
||||
from src.plugin_runtime.component_query import ActionExecutor, component_query_service
|
||||
|
||||
logger = get_logger("action_manager")
|
||||
|
||||
|
||||
class ActionHandle:
|
||||
"""Action 执行句柄
|
||||
|
||||
不依赖任何插件基类，内部持有 executor (async callable) 和绑定参数。
|
||||
brain_chat 调用 ``await handle.execute()`` 即可。
|
||||
"""
|
||||
|
||||
def __init__(self, executor: ActionExecutor, **kwargs):
|
||||
self._executor = executor
|
||||
self._kwargs = kwargs
|
||||
|
||||
async def execute(self) -> Tuple[bool, str]:
|
||||
return await self._executor(**self._kwargs)
|
||||
|
||||
|
||||
class ActionManager:
|
||||
"""
|
||||
动作管理器，用于管理各种类型的动作
|
||||
|
||||
使用插件运行时统一查询服务的 executor-based 模式。
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
"""初始化动作管理器"""
|
||||
|
||||
# 当前正在使用的动作集合，默认加载默认动作
|
||||
self._using_actions: Dict[str, ActionInfo] = {}
|
||||
|
||||
# 初始化时将默认动作加载到使用中的动作
|
||||
self._using_actions = component_query_service.get_default_actions()
|
||||
|
||||
# === 执行Action方法 ===
|
||||
|
||||
def create_action(
|
||||
self,
|
||||
action_name: str,
|
||||
action_data: dict,
|
||||
action_reasoning: str,
|
||||
cycle_timers: dict,
|
||||
thinking_id: str,
|
||||
chat_stream: BotChatSession,
|
||||
log_prefix: str,
|
||||
shutting_down: bool = False,
|
||||
action_message: Optional[SessionMessage] = None,
|
||||
) -> Optional[ActionHandle]:
|
||||
"""
|
||||
创建动作执行句柄
|
||||
|
||||
Args:
|
||||
action_name: 动作名称
|
||||
action_data: 动作数据
|
||||
action_reasoning: 执行理由
|
||||
cycle_timers: 计时器字典
|
||||
thinking_id: 思考ID
|
||||
chat_stream: 聊天流
|
||||
log_prefix: 日志前缀
|
||||
shutting_down: 是否正在关闭
|
||||
action_message: 动作消息记录
|
||||
|
||||
Returns:
|
||||
Optional[ActionHandle]: 执行句柄，如果动作未注册则返回 None
|
||||
"""
|
||||
try:
|
||||
executor = component_query_service.get_action_executor(action_name)
|
||||
if not executor:
|
||||
logger.warning(f"{log_prefix} 未找到Action组件: {action_name}")
|
||||
return None
|
||||
|
||||
info = component_query_service.get_action_info(action_name)
|
||||
if not info:
|
||||
logger.warning(f"{log_prefix} 未找到Action组件信息: {action_name}")
|
||||
return None
|
||||
|
||||
plugin_config = component_query_service.get_plugin_config(info.plugin_name) or {}
|
||||
|
||||
handle = ActionHandle(
|
||||
executor,
|
||||
action_data=action_data,
|
||||
action_reasoning=action_reasoning,
|
||||
cycle_timers=cycle_timers,
|
||||
thinking_id=thinking_id,
|
||||
chat_stream=chat_stream,
|
||||
log_prefix=log_prefix,
|
||||
shutting_down=shutting_down,
|
||||
plugin_config=plugin_config,
|
||||
action_message=action_message,
|
||||
)
|
||||
|
||||
logger.debug(f"创建Action执行句柄成功: {action_name}")
|
||||
return handle
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"创建Action执行句柄失败 {action_name}: {e}")
|
||||
import traceback
|
||||
|
||||
logger.error(traceback.format_exc())
|
||||
return None
|
||||
|
||||
def get_using_actions(self) -> Dict[str, ActionInfo]:
|
||||
"""获取当前正在使用的动作集合"""
|
||||
return self._using_actions.copy()
|
||||
|
||||
# === Modify相关方法 ===
|
||||
def remove_action_from_using(self, action_name: str) -> bool:
|
||||
"""
|
||||
从当前使用的动作集中移除指定动作
|
||||
|
||||
Args:
|
||||
action_name: 动作名称
|
||||
|
||||
Returns:
|
||||
bool: 移除是否成功
|
||||
"""
|
||||
if action_name not in self._using_actions:
|
||||
logger.warning(f"移除失败: 动作 {action_name} 不在当前使用的动作集中")
|
||||
return False
|
||||
|
||||
del self._using_actions[action_name]
|
||||
logger.debug(f"已从使用集中移除动作 {action_name}")
|
||||
return True
|
||||
|
||||
def restore_actions(self) -> None:
|
||||
"""恢复到默认动作集"""
|
||||
actions_to_restore = list(self._using_actions.keys())
|
||||
self._using_actions = component_query_service.get_default_actions()
|
||||
logger.debug(f"恢复动作集: 从 {actions_to_restore} 恢复到默认动作集 {list(self._using_actions.keys())}")
|
||||
@@ -1,233 +0,0 @@
|
||||
import random
|
||||
import time
|
||||
from typing import List, Dict, Tuple
|
||||
|
||||
from src.common.logger import get_logger
|
||||
from src.config.config import global_config
|
||||
from src.chat.message_receive.chat_manager import BotChatSession, chat_manager as _chat_manager
|
||||
from src.chat.planner_actions.action_manager import ActionManager
|
||||
from src.services.message_service import build_readable_messages, get_messages_before_time_in_chat
|
||||
from src.core.types import ActionActivationType, ActionInfo
|
||||
from src.core.announcement_manager import global_announcement_manager
|
||||
|
||||
logger = get_logger("action_manager")
|
||||
|
||||
|
||||
class ActionModifier:
|
||||
"""动作处理器
|
||||
|
||||
用于处理Observation对象和根据激活类型处理actions。
|
||||
集成了原有的modify_actions功能和新的激活类型处理功能。
|
||||
支持并行判定和智能缓存优化。
|
||||
"""
|
||||
|
||||
def __init__(self, action_manager: ActionManager, chat_id: str):
|
||||
"""初始化动作处理器"""
|
||||
self.chat_id = chat_id
|
||||
self.chat_stream: BotChatSession = _chat_manager.get_session_by_session_id(self.chat_id) # type: ignore
|
||||
self.log_prefix = f"[{_chat_manager.get_session_name(self.chat_id) or self.chat_id}]"
|
||||
|
||||
self.action_manager = action_manager
|
||||
|
||||
async def modify_actions(
|
||||
self,
|
||||
message_content: str = "",
|
||||
): # sourcery skip: use-named-expression
|
||||
"""
|
||||
动作修改流程，整合传统观察处理和新的激活类型判定
|
||||
|
||||
这个方法处理完整的动作管理流程：
|
||||
1. 基于观察的传统动作修改（循环历史分析、类型匹配等）
|
||||
2. 基于激活类型的智能动作判定，最终确定可用动作集
|
||||
|
||||
处理后，ActionManager 将包含最终的可用动作集，供规划器直接使用
|
||||
"""
|
||||
logger.debug(f"{self.log_prefix}开始完整动作修改流程")
|
||||
|
||||
removals_s1: List[Tuple[str, str]] = []
|
||||
removals_s2: List[Tuple[str, str]] = []
|
||||
# removals_s3: List[Tuple[str, str]] = []
|
||||
|
||||
self.action_manager.restore_actions()
|
||||
all_actions = self.action_manager.get_using_actions()
|
||||
|
||||
message_list_before_now_half = get_messages_before_time_in_chat(
|
||||
chat_id=self.chat_stream.session_id,
|
||||
timestamp=time.time(),
|
||||
limit=min(int(global_config.chat.max_context_size * 0.33), 10),
|
||||
filter_intercept_message_level=1,
|
||||
)
|
||||
|
||||
chat_content = build_readable_messages(
|
||||
message_list_before_now_half,
|
||||
replace_bot_name=True,
|
||||
timestamp_mode="relative",
|
||||
read_mark=0.0,
|
||||
show_actions=True,
|
||||
)
|
||||
|
||||
if message_content:
|
||||
chat_content = chat_content + "\n" + f"现在，最新的消息是：{message_content}"
|
||||
|
||||
# === 第一阶段：去除用户自行禁用的 ===
|
||||
disabled_actions = global_announcement_manager.get_disabled_chat_actions(self.chat_id)
|
||||
if disabled_actions:
|
||||
for disabled_action_name in disabled_actions:
|
||||
if disabled_action_name in all_actions:
|
||||
removals_s1.append((disabled_action_name, "用户自行禁用"))
|
||||
self.action_manager.remove_action_from_using(disabled_action_name)
|
||||
logger.debug(f"{self.log_prefix}阶段一移除动作: {disabled_action_name}，原因: 用户自行禁用")
|
||||
|
||||
# === 第二阶段：检查动作的关联类型 ===
|
||||
chat_context = self.chat_stream.context
|
||||
type_mismatched_actions = self._check_action_associated_types(all_actions, chat_context)
|
||||
|
||||
if type_mismatched_actions:
|
||||
removals_s2.extend(type_mismatched_actions)
|
||||
|
||||
# 应用第二阶段的移除
|
||||
for action_name, reason in removals_s2:
|
||||
self.action_manager.remove_action_from_using(action_name)
|
||||
logger.debug(f"{self.log_prefix}阶段二移除动作: {action_name}，原因: {reason}")
|
||||
|
||||
# === 第三阶段：激活类型判定 ===
|
||||
# if chat_content is not None:
|
||||
# logger.debug(f"{self.log_prefix}开始激活类型判定阶段")
|
||||
|
||||
# 获取当前使用的动作集（经过第一阶段处理）
|
||||
# current_using_actions = self.action_manager.get_using_actions()
|
||||
|
||||
# 获取因激活类型判定而需要移除的动作
|
||||
# removals_s3 = await self._get_deactivated_actions_by_type(
|
||||
# current_using_actions,
|
||||
# chat_content,
|
||||
# )
|
||||
|
||||
# 应用第三阶段的移除
|
||||
# for action_name, reason in removals_s3:
|
||||
# self.action_manager.remove_action_from_using(action_name)
|
||||
# logger.debug(f"{self.log_prefix}阶段三移除动作: {action_name}，原因: {reason}")
|
||||
|
||||
# === 统一日志记录 ===
|
||||
all_removals = removals_s1 + removals_s2
|
||||
removals_summary: str = ""
|
||||
if all_removals:
|
||||
removals_summary = " | ".join([f"{name}({reason})" for name, reason in all_removals])
|
||||
|
||||
available_actions = list(self.action_manager.get_using_actions().keys())
|
||||
available_actions_text = "、".join(available_actions) if available_actions else "无"
|
||||
logger.debug(f"{self.log_prefix} 当前可用动作: {available_actions_text}||移除: {removals_summary}")
|
||||
|
||||
def _check_action_associated_types(self, all_actions: Dict[str, ActionInfo], chat_context: BotChatSession):
|
||||
type_mismatched_actions: List[Tuple[str, str]] = []
|
||||
for action_name, action_info in all_actions.items():
|
||||
if action_info.associated_types and not chat_context.check_types(action_info.associated_types):
|
||||
associated_types_str = ", ".join(action_info.associated_types)
|
||||
reason = f"适配器不支持（需要: {associated_types_str}）"
|
||||
type_mismatched_actions.append((action_name, reason))
|
||||
logger.debug(f"{self.log_prefix}决定移除动作: {action_name}，原因: {reason}")
|
||||
return type_mismatched_actions
|
||||
|
||||
async def _get_deactivated_actions_by_type(
|
||||
self,
|
||||
actions_with_info: Dict[str, ActionInfo],
|
||||
chat_content: str = "",
|
||||
) -> List[tuple[str, str]]:
|
||||
"""
|
||||
根据激活类型过滤，返回需要停用的动作列表及原因
|
||||
|
||||
Args:
|
||||
actions_with_info: 带完整信息的动作字典
|
||||
chat_content: 聊天内容
|
||||
|
||||
Returns:
|
||||
List[Tuple[str, str]]: 需要停用的 (action_name, reason) 元组列表
|
||||
"""
|
||||
deactivated_actions = []
|
||||
|
||||
actions_to_check = list(actions_with_info.items())
|
||||
random.shuffle(actions_to_check)
|
||||
|
||||
for action_name, action_info in actions_to_check:
|
||||
activation_type = action_info.activation_type or action_info.focus_activation_type
|
||||
|
||||
if activation_type == ActionActivationType.ALWAYS:
|
||||
continue # 总是激活，无需处理
|
||||
|
||||
elif activation_type == ActionActivationType.RANDOM:
|
||||
probability = action_info.random_activation_probability
|
||||
if random.random() >= probability:
|
||||
reason = f"RANDOM类型未触发（概率{probability}）"
|
||||
deactivated_actions.append((action_name, reason))
|
||||
logger.debug(f"{self.log_prefix}未激活动作: {action_name}，原因: {reason}")
|
||||
|
||||
elif activation_type == ActionActivationType.KEYWORD:
|
||||
if not self._check_keyword_activation(action_name, action_info, chat_content):
|
||||
keywords = action_info.activation_keywords
|
||||
reason = f"关键词未匹配（关键词: {keywords}）"
|
||||
deactivated_actions.append((action_name, reason))
|
||||
logger.debug(f"{self.log_prefix}未激活动作: {action_name}，原因: {reason}")
|
||||
|
||||
elif activation_type == ActionActivationType.NEVER:
|
||||
reason = "激活类型为never"
|
||||
deactivated_actions.append((action_name, reason))
|
||||
logger.debug(f"{self.log_prefix}未激活动作: {action_name}，原因: 激活类型为never")
|
||||
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix}未知的激活类型: {activation_type}，跳过处理")
|
||||
|
||||
return deactivated_actions
|
||||
|
||||
def _check_keyword_activation(
|
||||
self,
|
||||
action_name: str,
|
||||
action_info: ActionInfo,
|
||||
chat_content: str = "",
|
||||
) -> bool:
|
||||
"""
|
||||
检查是否匹配关键词触发条件
|
||||
|
||||
Args:
|
||||
action_name: 动作名称
|
||||
action_info: 动作信息
|
||||
observed_messages_str: 观察到的聊天消息
|
||||
chat_context: 聊天上下文
|
||||
extra_context: 额外上下文
|
||||
|
||||
Returns:
|
||||
bool: 是否应该激活此action
|
||||
"""
|
||||
|
||||
activation_keywords = action_info.activation_keywords
|
||||
case_sensitive = action_info.keyword_case_sensitive
|
||||
|
||||
if not activation_keywords:
|
||||
logger.warning(f"{self.log_prefix}动作 {action_name} 设置为关键词触发但未配置关键词")
|
||||
return False
|
||||
|
||||
# 构建检索文本
|
||||
search_text = ""
|
||||
if chat_content:
|
||||
search_text += chat_content
|
||||
# if chat_context:
|
||||
# search_text += f" {chat_context}"
|
||||
# if extra_context:
|
||||
# search_text += f" {extra_context}"
|
||||
|
||||
# 如果不区分大小写，转换为小写
|
||||
if not case_sensitive:
|
||||
search_text = search_text.lower()
|
||||
|
||||
# 检查每个关键词
|
||||
matched_keywords = []
|
||||
for keyword in activation_keywords:
|
||||
check_keyword = keyword if case_sensitive else keyword.lower()
|
||||
if check_keyword in search_text:
|
||||
matched_keywords.append(keyword)
|
||||
|
||||
if matched_keywords:
|
||||
logger.debug(f"{self.log_prefix}动作 {action_name} 匹配到关键词: {matched_keywords}")
|
||||
return True
|
||||
else:
|
||||
logger.debug(f"{self.log_prefix}动作 {action_name} 未匹配到任何关键词: {activation_keywords}")
|
||||
return False
|
||||
@@ -1,933 +0,0 @@
|
||||
from collections import OrderedDict
|
||||
from datetime import datetime
|
||||
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import contextlib
|
||||
import json
|
||||
import random
|
||||
import re
|
||||
import time
|
||||
import traceback
|
||||
|
||||
from json_repair import repair_json
|
||||
from rich.traceback import install
|
||||
|
||||
from src.chat.logger.plan_reply_logger import PlanReplyLogger
|
||||
from src.chat.message_receive.chat_manager import chat_manager as _chat_manager
|
||||
from src.chat.message_receive.message import SessionMessage
|
||||
from src.chat.planner_actions.action_manager import ActionManager
|
||||
from src.chat.utils.utils import get_chat_type_and_target_info, is_bot_self
|
||||
from src.common.data_models.info_data_model import ActionPlannerInfo
|
||||
from src.common.logger import get_logger
|
||||
from src.config.config import global_config, model_config
|
||||
from src.core.types import ActionActivationType, ActionInfo, ComponentType
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.person_info.person_info import Person
|
||||
from src.plugin_runtime.component_query import component_query_service
|
||||
from src.prompt.prompt_manager import prompt_manager
|
||||
from src.services.message_service import (
|
||||
build_readable_messages_with_id,
|
||||
get_messages_before_time_in_chat,
|
||||
replace_user_references,
|
||||
translate_pid_to_description,
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from src.common.data_models.info_data_model import TargetPersonInfo
|
||||
|
||||
logger = get_logger("planner")
|
||||
|
||||
install(extra_lines=3)
|
||||
|
||||
|
||||
class ActionPlanner:
|
||||
def __init__(self, chat_id: str, action_manager: ActionManager):
|
||||
self.chat_id = chat_id
|
||||
self.log_prefix = f"[{_chat_manager.get_session_name(chat_id) or chat_id}]"
|
||||
self.action_manager = action_manager
|
||||
# LLM规划器配置
|
||||
self.planner_llm = LLMRequest(
|
||||
model_set=model_config.model_task_config.planner, request_type="planner"
|
||||
) # 用于动作规划
|
||||
|
||||
self.last_obs_time_mark = 0.0
|
||||
|
||||
self.plan_log: List[Tuple[str, float, Union[List[ActionPlannerInfo], str]]] = []
|
||||
|
||||
# 黑话缓存：使用 OrderedDict 实现 LRU，最多缓存10个
|
||||
self.unknown_words_cache: OrderedDict[str, None] = OrderedDict()
|
||||
self.unknown_words_cache_limit = 10
|
||||
|
||||
def find_message_by_id(
|
||||
self, message_id: str, message_id_list: List[Tuple[str, "SessionMessage"]]
|
||||
) -> Optional["SessionMessage"]:
|
||||
# sourcery skip: use-next
|
||||
"""
|
||||
根据message_id从message_id_list中查找对应的原始消息
|
||||
|
||||
Args:
|
||||
message_id: 要查找的消息ID
|
||||
message_id_list: 消息ID列表，格式为[{'id': str, 'message': dict}, ...]
|
||||
|
||||
Returns:
|
||||
找到的原始消息字典，如果未找到则返回None
|
||||
"""
|
||||
for item in message_id_list:
|
||||
if item[0] == message_id:
|
||||
return item[1]
|
||||
return None
|
||||
|
||||
def _replace_message_ids_with_text(
|
||||
self, text: Optional[str], message_id_list: List[Tuple[str, "SessionMessage"]]
|
||||
) -> Optional[str]:
|
||||
"""将文本中的 m+数字 消息ID替换为原消息内容，并添加双引号"""
|
||||
if not text:
|
||||
return text
|
||||
|
||||
id_to_message = dict(message_id_list)
|
||||
|
||||
# 匹配m后带2-4位数字，前后不是字母数字下划线
|
||||
pattern = r"(?<![A-Za-z0-9_])m\d{2,4}(?![A-Za-z0-9_])"
|
||||
|
||||
matches = re.findall(pattern, text)
|
||||
if matches:
|
||||
available_ids = set(id_to_message.keys())
|
||||
found_ids = set(matches)
|
||||
missing_ids = found_ids - available_ids
|
||||
if missing_ids:
|
||||
logger.info(
|
||||
f"{self.log_prefix}planner理由中引用的消息ID不在当前上下文中: {missing_ids}, 可用ID: {list(available_ids)[:10]}..."
|
||||
)
|
||||
logger.info(
|
||||
f"{self.log_prefix}planner理由替换: 找到{len(matches)}个消息ID引用，其中{len(found_ids & available_ids)}个在上下文中"
|
||||
)
|
||||
|
||||
def _replace(match: re.Match[str]) -> str:
|
||||
msg_id = match.group(0)
|
||||
message = id_to_message.get(msg_id)
|
||||
if not message:
|
||||
logger.warning(f"{self.log_prefix}planner理由引用 {msg_id} 未找到对应消息，保持原样")
|
||||
return msg_id
|
||||
|
||||
msg_text = (message.processed_plain_text or "").strip()
|
||||
if not msg_text:
|
||||
logger.warning(f"{self.log_prefix}planner理由引用 {msg_id} 的消息内容为空，保持原样")
|
||||
return msg_id
|
||||
|
||||
# 替换 [picid:xxx] 为 [图片：描述]
|
||||
pic_pattern = r"\[picid:([^\]]+)\]"
|
||||
|
||||
def replace_pic_id(pic_match: re.Match) -> str:
|
||||
pic_id = pic_match.group(1)
|
||||
description = translate_pid_to_description(pic_id)
|
||||
return f"[图片：{description}]"
|
||||
|
||||
msg_text = re.sub(pic_pattern, replace_pic_id, msg_text)
|
||||
|
||||
# 替换用户引用格式：回复<aaa:bbb> 和 @<aaa:bbb>
|
||||
platform = message.platform or ""
|
||||
if not platform:
|
||||
logger.warning(
|
||||
f"{self.log_prefix}planner: message {message.message_id} has no platform set, bot-self detection will be skipped"
|
||||
)
|
||||
msg_text = replace_user_references(msg_text, platform, replace_bot_name=True)
|
||||
|
||||
# 替换单独的 <用户名:用户ID> 格式（replace_user_references 已处理回复<和@<格式）
|
||||
# 匹配所有 <aaa:bbb> 格式，由于 replace_user_references 已经替换了回复<和@<格式，
|
||||
# 这里匹配到的应该都是单独的格式
|
||||
user_ref_pattern = r"<([^:<>]+):([^:<>]+)>"
|
||||
|
||||
def replace_user_ref(user_match: re.Match) -> str:
|
||||
user_name = user_match.group(1)
|
||||
user_id = user_match.group(2)
|
||||
try:
|
||||
# 检查是否是机器人自己
|
||||
if is_bot_self(platform, str(user_id)):
|
||||
return f"{global_config.bot.nickname}(你)"
|
||||
person = Person(platform=platform, user_id=user_id)
|
||||
return person.person_name or user_name
|
||||
except Exception:
|
||||
# 如果解析失败，使用原始昵称
|
||||
return user_name
|
||||
|
||||
msg_text = re.sub(user_ref_pattern, replace_user_ref, msg_text)
|
||||
|
||||
preview = msg_text if len(msg_text) <= 100 else f"{msg_text[:97]}..."
|
||||
logger.info(f"{self.log_prefix}planner理由引用 {msg_id} -> 消息（{preview}）")
|
||||
return f"消息（{msg_text}）"
|
||||
|
||||
return re.sub(pattern, _replace, text)
|
||||
|
||||
def _parse_single_action(
|
||||
self,
|
||||
action_json: dict,
|
||||
message_id_list: List[Tuple[str, "SessionMessage"]],
|
||||
current_available_actions: List[Tuple[str, ActionInfo]],
|
||||
extracted_reasoning: str = "",
|
||||
) -> List[ActionPlannerInfo]:
|
||||
"""解析单个action JSON并返回ActionPlannerInfo列表"""
|
||||
action_planner_infos = []
|
||||
|
||||
try:
|
||||
action = action_json.get("action", "no_reply")
|
||||
# 使用 extracted_reasoning（整体推理文本）作为 reasoning
|
||||
if extracted_reasoning:
|
||||
reasoning = self._replace_message_ids_with_text(extracted_reasoning, message_id_list)
|
||||
if reasoning is None:
|
||||
reasoning = extracted_reasoning
|
||||
else:
|
||||
reasoning = "未提供原因"
|
||||
action_data = {key: value for key, value in action_json.items() if key not in ["action"]}
|
||||
|
||||
# 非no_reply动作需要target_message_id
|
||||
target_message = None
|
||||
|
||||
target_message_id = action_json.get("target_message_id")
|
||||
if target_message_id:
|
||||
# 根据target_message_id查找原始消息
|
||||
target_message = self.find_message_by_id(target_message_id, message_id_list)
|
||||
if target_message is None:
|
||||
logger.warning(f"{self.log_prefix}无法找到target_message_id '{target_message_id}' 对应的消息")
|
||||
# 选择最新消息作为target_message
|
||||
target_message = message_id_list[-1][1]
|
||||
else:
|
||||
target_message = message_id_list[-1][1]
|
||||
logger.debug(f"{self.log_prefix}动作'{action}'缺少target_message_id，使用最新消息作为target_message")
|
||||
|
||||
if action != "no_reply" and target_message is not None and self._is_message_from_self(target_message):
|
||||
logger.info(
|
||||
f"{self.log_prefix}Planner选择了自己的消息 {target_message_id or target_message.message_id} 作为目标，强制使用 no_reply"
|
||||
)
|
||||
reasoning = f"目标消息 {target_message_id or target_message.message_id} 来自机器人自身，违反不回复自身消息规则。原始理由: {reasoning}"
|
||||
action = "no_reply"
|
||||
target_message = None
|
||||
|
||||
# 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"]
|
||||
|
||||
if action not in internal_action_names and action not in available_action_names:
|
||||
logger.warning(
|
||||
f"{self.log_prefix}LLM 返回了当前不可用或无效的动作: '{action}' (可用: {available_action_names})，将强制使用 'no_reply'"
|
||||
)
|
||||
reasoning = (
|
||||
f"LLM 返回了当前不可用的动作 '{action}' (可用: {available_action_names})。原始理由: {reasoning}"
|
||||
)
|
||||
action = "no_reply"
|
||||
|
||||
# 创建ActionPlannerInfo对象
|
||||
# 将列表转换为字典格式
|
||||
available_actions_dict = dict(current_available_actions)
|
||||
action_planner_infos.append(
|
||||
ActionPlannerInfo(
|
||||
action_type=action,
|
||||
reasoning=reasoning,
|
||||
action_data=action_data,
|
||||
action_message=target_message,
|
||||
available_actions=available_actions_dict,
|
||||
action_reasoning=extracted_reasoning or None,
|
||||
)
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix}解析单个action时出错: {e}")
|
||||
# 将列表转换为字典格式
|
||||
available_actions_dict = dict(current_available_actions)
|
||||
action_planner_infos.append(
|
||||
ActionPlannerInfo(
|
||||
action_type="no_reply",
|
||||
reasoning=f"解析单个action时出错: {e}",
|
||||
action_data={},
|
||||
action_message=None,
|
||||
available_actions=available_actions_dict,
|
||||
action_reasoning=extracted_reasoning or None,
|
||||
)
|
||||
)
|
||||
|
||||
return action_planner_infos
|
||||
|
||||
def _is_message_from_self(self, message: "SessionMessage") -> bool:
|
||||
"""判断消息是否由机器人自身发送（支持多平台，包括 WebUI）"""
|
||||
try:
|
||||
return is_bot_self(message.platform or "", str(message.message_info.user_info.user_id))
|
||||
except AttributeError:
|
||||
logger.warning(f"{self.log_prefix}检测消息发送者失败，缺少必要字段")
|
||||
return False
|
||||
|
||||
def _update_unknown_words_cache(self, new_words: List[str]) -> None:
|
||||
"""
|
||||
更新黑话缓存，将新的黑话加入缓存
|
||||
|
||||
Args:
|
||||
new_words: 新提取的黑话列表
|
||||
"""
|
||||
for word in new_words:
|
||||
if not isinstance(word, str):
|
||||
continue
|
||||
word = word.strip()
|
||||
if not word:
|
||||
continue
|
||||
|
||||
# 如果已存在，移到末尾（LRU）
|
||||
if word in self.unknown_words_cache:
|
||||
self.unknown_words_cache.move_to_end(word)
|
||||
else:
|
||||
# 添加新词
|
||||
self.unknown_words_cache[word] = None
|
||||
# 如果超过限制，移除最老的
|
||||
if len(self.unknown_words_cache) > self.unknown_words_cache_limit:
|
||||
self.unknown_words_cache.popitem(last=False)
|
||||
logger.debug(f"{self.log_prefix}黑话缓存已满，移除最老的黑话")
|
||||
|
||||
def _merge_unknown_words_with_cache(self, new_words: Optional[List[str]]) -> List[str]:
|
||||
"""
|
||||
合并新提取的黑话和缓存中的黑话
|
||||
|
||||
Args:
|
||||
new_words: 新提取的黑话列表（可能为None）
|
||||
|
||||
Returns:
|
||||
合并后的黑话列表（去重）
|
||||
"""
|
||||
# 清理新提取的黑话
|
||||
cleaned_new_words: List[str] = []
|
||||
if new_words:
|
||||
for word in new_words:
|
||||
if isinstance(word, str):
|
||||
if word := word.strip():
|
||||
cleaned_new_words.append(word)
|
||||
|
||||
# 获取缓存中的黑话列表
|
||||
cached_words = list(self.unknown_words_cache.keys())
|
||||
|
||||
# 合并并去重（保留顺序：新提取的在前，缓存的在后）
|
||||
merged_words: List[str] = []
|
||||
seen = set()
|
||||
|
||||
# 先添加新提取的
|
||||
for word in cleaned_new_words:
|
||||
if word not in seen:
|
||||
merged_words.append(word)
|
||||
seen.add(word)
|
||||
|
||||
# 再添加缓存的（如果不在新提取的列表中）
|
||||
for word in cached_words:
|
||||
if word not in seen:
|
||||
merged_words.append(word)
|
||||
seen.add(word)
|
||||
|
||||
return merged_words
|
||||
|
||||
def _process_unknown_words_cache(self, actions: List[ActionPlannerInfo]) -> None:
|
||||
"""
|
||||
处理黑话缓存逻辑：
|
||||
1. 检查是否有 reply action 提取了 unknown_words
|
||||
2. 如果没有提取，移除最老的1个
|
||||
3. 如果缓存数量大于5，移除最老的2个
|
||||
4. 对于每个 reply action，合并缓存和新提取的黑话
|
||||
5. 更新缓存
|
||||
|
||||
Args:
|
||||
actions: 解析后的动作列表
|
||||
"""
|
||||
# 先检查缓存数量，如果大于5，移除最老的2个
|
||||
if len(self.unknown_words_cache) > 5:
|
||||
# 移除最老的2个
|
||||
removed_count = 0
|
||||
for _ in range(2):
|
||||
if len(self.unknown_words_cache) > 0:
|
||||
self.unknown_words_cache.popitem(last=False)
|
||||
removed_count += 1
|
||||
if removed_count > 0:
|
||||
logger.debug(f"{self.log_prefix}缓存数量大于5，移除最老的{removed_count}个缓存")
|
||||
|
||||
# 检查是否有 reply action 提取了 unknown_words
|
||||
has_extracted_unknown_words = False
|
||||
for action in actions:
|
||||
if action.action_type == "reply":
|
||||
action_data = action.action_data or {}
|
||||
unknown_words = action_data.get("unknown_words")
|
||||
if unknown_words and isinstance(unknown_words, list) and len(unknown_words) > 0:
|
||||
has_extracted_unknown_words = True
|
||||
break
|
||||
|
||||
# 如果当前 plan 的 reply 没有提取，移除最老的1个
|
||||
if not has_extracted_unknown_words and len(self.unknown_words_cache) > 0:
|
||||
self.unknown_words_cache.popitem(last=False)
|
||||
logger.debug(f"{self.log_prefix}当前 plan 的 reply 没有提取黑话，移除最老的1个缓存")
|
||||
|
||||
# 对于每个 reply action，合并缓存和新提取的黑话
|
||||
for action in actions:
|
||||
if action.action_type == "reply":
|
||||
action_data = action.action_data or {}
|
||||
new_words = action_data.get("unknown_words")
|
||||
|
||||
# 合并新提取的和缓存的黑话列表
|
||||
if merged_words := self._merge_unknown_words_with_cache(new_words):
|
||||
action_data["unknown_words"] = merged_words
|
||||
logger.debug(
|
||||
f"{self.log_prefix}合并黑话：新提取 {len(new_words) if new_words else 0} 个，"
|
||||
f"缓存 {len(self.unknown_words_cache)} 个，合并后 {len(merged_words)} 个"
|
||||
)
|
||||
else:
|
||||
# 如果没有合并后的黑话，移除 unknown_words 字段
|
||||
action_data.pop("unknown_words", None)
|
||||
|
||||
# 更新缓存（将新提取的黑话加入缓存）
|
||||
if new_words:
|
||||
self._update_unknown_words_cache(new_words)
|
||||
|
||||
async def plan(
|
||||
self,
|
||||
available_actions: Dict[str, ActionInfo],
|
||||
loop_start_time: float = 0.0,
|
||||
force_reply_message: Optional["SessionMessage"] = None,
|
||||
) -> List[ActionPlannerInfo]:
|
||||
# sourcery skip: use-named-expression
|
||||
"""
|
||||
规划器 (Planner): 使用LLM根据上下文决定做出什么动作。
|
||||
"""
|
||||
plan_start = time.perf_counter()
|
||||
|
||||
# 获取聊天上下文
|
||||
message_list_before_now = get_messages_before_time_in_chat(
|
||||
chat_id=self.chat_id,
|
||||
timestamp=time.time(),
|
||||
limit=int(global_config.chat.max_context_size * 0.6),
|
||||
filter_intercept_message_level=1,
|
||||
)
|
||||
message_id_list: list[Tuple[str, "SessionMessage"]] = []
|
||||
chat_content_block, message_id_list = build_readable_messages_with_id(
|
||||
messages=message_list_before_now,
|
||||
timestamp_mode="normal_no_YMD",
|
||||
read_mark=self.last_obs_time_mark,
|
||||
truncate=True,
|
||||
show_actions=True,
|
||||
)
|
||||
|
||||
message_list_before_now_short = message_list_before_now[-int(global_config.chat.max_context_size * 0.3) :]
|
||||
chat_content_block_short, message_id_list_short = build_readable_messages_with_id(
|
||||
messages=message_list_before_now_short,
|
||||
timestamp_mode="normal_no_YMD",
|
||||
truncate=False,
|
||||
show_actions=False,
|
||||
)
|
||||
|
||||
self.last_obs_time_mark = time.time()
|
||||
|
||||
# 获取必要信息
|
||||
is_group_chat, chat_target_info, current_available_actions = self.get_necessary_info()
|
||||
|
||||
# 应用激活类型过滤
|
||||
filtered_actions = self._filter_actions_by_activation_type(available_actions, chat_content_block_short)
|
||||
|
||||
logger.debug(f"{self.log_prefix}过滤后有{len(filtered_actions)}个可用动作")
|
||||
|
||||
prompt_build_start = time.perf_counter()
|
||||
# 构建包含所有动作的提示词
|
||||
prompt, message_id_list = await self.build_planner_prompt(
|
||||
is_group_chat=is_group_chat,
|
||||
chat_target_info=chat_target_info,
|
||||
current_available_actions=filtered_actions,
|
||||
chat_content_block=chat_content_block,
|
||||
message_id_list=message_id_list,
|
||||
)
|
||||
prompt_build_ms = (time.perf_counter() - prompt_build_start) * 1000
|
||||
|
||||
# 调用LLM获取决策
|
||||
reasoning, actions, llm_raw_output, llm_reasoning, llm_duration_ms = await self._execute_main_planner(
|
||||
prompt=prompt,
|
||||
message_id_list=message_id_list,
|
||||
filtered_actions=filtered_actions,
|
||||
available_actions=available_actions,
|
||||
loop_start_time=loop_start_time,
|
||||
)
|
||||
|
||||
# 如果有强制回复消息，确保回复该消息
|
||||
if force_reply_message:
|
||||
# 检查是否已经有回复该消息的 action
|
||||
has_reply_to_force_message = any(
|
||||
action.action_type == "reply"
|
||||
and action.action_message
|
||||
and action.action_message.message_id == force_reply_message.message_id
|
||||
for action in actions
|
||||
)
|
||||
|
||||
# 如果没有回复该消息，强制添加回复 action
|
||||
if not has_reply_to_force_message:
|
||||
# 移除所有 no_reply action（如果有）
|
||||
actions = [a for a in actions if a.action_type != "no_reply"]
|
||||
|
||||
# 创建强制回复 action
|
||||
available_actions_dict = dict(current_available_actions)
|
||||
force_reply_action = ActionPlannerInfo(
|
||||
action_type="reply",
|
||||
reasoning="用户提及了我，必须回复该消息",
|
||||
action_data={"loop_start_time": loop_start_time},
|
||||
action_message=force_reply_message,
|
||||
available_actions=available_actions_dict,
|
||||
action_reasoning=None,
|
||||
)
|
||||
# 将强制回复 action 放在最前面
|
||||
actions.insert(0, force_reply_action)
|
||||
logger.info(f"{self.log_prefix} 检测到强制回复消息，已添加回复动作")
|
||||
|
||||
logger.info(
|
||||
f"{self.log_prefix}Planner:{reasoning}。选择了{len(actions)}个动作: {' '.join([a.action_type for a in actions])}"
|
||||
)
|
||||
|
||||
self.add_plan_log(reasoning, actions)
|
||||
|
||||
try:
|
||||
PlanReplyLogger.log_plan(
|
||||
chat_id=self.chat_id,
|
||||
prompt=prompt,
|
||||
reasoning=reasoning,
|
||||
raw_output=llm_raw_output,
|
||||
raw_reasoning=llm_reasoning,
|
||||
actions=actions,
|
||||
timing={
|
||||
"prompt_build_ms": round(prompt_build_ms, 2),
|
||||
"llm_duration_ms": round(llm_duration_ms, 2) if llm_duration_ms is not None else None,
|
||||
"total_plan_ms": round((time.perf_counter() - plan_start) * 1000, 2),
|
||||
"loop_start_time": loop_start_time,
|
||||
},
|
||||
extra=None,
|
||||
)
|
||||
except Exception:
|
||||
logger.exception(f"{self.log_prefix}记录plan日志失败")
|
||||
|
||||
return actions
|
||||
|
||||
def add_plan_log(self, reasoning: str, actions: List[ActionPlannerInfo]):
|
||||
self.plan_log.append((reasoning, time.time(), actions))
|
||||
if len(self.plan_log) > 20:
|
||||
self.plan_log.pop(0)
|
||||
|
||||
def add_plan_excute_log(self, result: str):
|
||||
self.plan_log.append(("", time.time(), result))
|
||||
if len(self.plan_log) > 20:
|
||||
self.plan_log.pop(0)
|
||||
|
||||
def get_plan_log_str(self, max_action_records: int = 2, max_execution_records: int = 5) -> str:
|
||||
"""
|
||||
获取计划日志字符串
|
||||
|
||||
Args:
|
||||
max_action_records: 显示多少条最新的action记录，默认2
|
||||
max_execution_records: 显示多少条最新执行结果记录，默认8
|
||||
|
||||
Returns:
|
||||
格式化的日志字符串
|
||||
"""
|
||||
action_records = []
|
||||
execution_records = []
|
||||
|
||||
# 从后往前遍历，收集最新的记录
|
||||
for reasoning, timestamp, content in reversed(self.plan_log):
|
||||
if isinstance(content, list) and all(isinstance(action, ActionPlannerInfo) for action in content):
|
||||
if len(action_records) < max_action_records:
|
||||
action_records.append((reasoning, timestamp, content, "action"))
|
||||
elif len(execution_records) < max_execution_records:
|
||||
execution_records.append((reasoning, timestamp, content, "execution"))
|
||||
|
||||
# 合并所有记录并按时间戳排序
|
||||
all_records = action_records + execution_records
|
||||
all_records.sort(key=lambda x: x[1]) # 按时间戳排序
|
||||
|
||||
plan_log_str = ""
|
||||
|
||||
# 按时间顺序添加所有记录
|
||||
for reasoning, timestamp, content, record_type in all_records:
|
||||
time_str = datetime.fromtimestamp(timestamp).strftime("%H:%M:%S")
|
||||
if record_type == "action":
|
||||
# plan_log_str += f"{time_str}:{reasoning}|你使用了{','.join([action.action_type for action in content])}\n"
|
||||
plan_log_str += f"{time_str}:{reasoning}\n"
|
||||
else:
|
||||
plan_log_str += f"{time_str}:你执行了action:{content}\n"
|
||||
|
||||
return plan_log_str
|
||||
|
||||
async def build_planner_prompt(
|
||||
self,
|
||||
is_group_chat: bool,
|
||||
chat_target_info: Optional["TargetPersonInfo"],
|
||||
current_available_actions: Dict[str, ActionInfo],
|
||||
message_id_list: List[Tuple[str, "SessionMessage"]],
|
||||
chat_content_block: str = "",
|
||||
interest: str = "",
|
||||
) -> tuple[str, List[Tuple[str, "SessionMessage"]]]:
|
||||
"""构建 Planner LLM 的提示词 (获取模板并填充数据)"""
|
||||
try:
|
||||
actions_before_now_block = self.get_plan_log_str()
|
||||
|
||||
# 构建聊天上下文描述
|
||||
chat_context_description = "你现在正在一个群聊中"
|
||||
|
||||
# 构建动作选项块
|
||||
action_options_block = await self._build_action_options_block(current_available_actions)
|
||||
|
||||
# 其他信息
|
||||
moderation_prompt_block = "请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。"
|
||||
time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
|
||||
bot_name = global_config.bot.nickname
|
||||
bot_nickname = (
|
||||
f",也有人叫你{','.join(global_config.bot.alias_names)}" if global_config.bot.alias_names else ""
|
||||
)
|
||||
name_block = f"你的名字是{bot_name}{bot_nickname}，请注意哪些是你自己的发言。"
|
||||
|
||||
# 根据 think_mode 配置决定 reply action 的示例 JSON
|
||||
# 在 JSON 中直接作为 action 参数携带 unknown_words
|
||||
if global_config.chat.think_mode == "classic":
|
||||
reply_action_example = ""
|
||||
if global_config.chat.llm_quote:
|
||||
reply_action_example += (
|
||||
"5.如果要明确回复消息，使用quote，如果消息不多不需要明确回复，设置quote为false\n"
|
||||
)
|
||||
reply_action_example += (
|
||||
'{{"action":"reply", "target_message_id":"消息id(m+数字)", "unknown_words":["词语1","词语2"]'
|
||||
)
|
||||
if global_config.chat.llm_quote:
|
||||
reply_action_example += ', "quote":"如果需要引用该message，设置为true"'
|
||||
reply_action_example += "}"
|
||||
else:
|
||||
reply_action_example = (
|
||||
"5.think_level表示思考深度，0表示该回复不需要思考和回忆，1表示该回复需要进行回忆和思考\n"
|
||||
)
|
||||
if global_config.chat.llm_quote:
|
||||
reply_action_example += (
|
||||
"6.如果要明确回复消息，使用quote，如果消息不多不需要明确回复，设置quote为false\n"
|
||||
)
|
||||
reply_action_example += (
|
||||
'{{"action":"reply", "think_level":数值等级(0或1), '
|
||||
'"target_message_id":"消息id(m+数字)", '
|
||||
'"unknown_words":["词语1","词语2"]'
|
||||
)
|
||||
if global_config.chat.llm_quote:
|
||||
reply_action_example += ', "quote":"如果需要引用该message，设置为true"'
|
||||
reply_action_example += "}"
|
||||
|
||||
planner_prompt_template = prompt_manager.get_prompt("planner")
|
||||
planner_prompt_template.add_context("time_block", time_block)
|
||||
planner_prompt_template.add_context("chat_context_description", chat_context_description)
|
||||
planner_prompt_template.add_context("chat_content_block", chat_content_block)
|
||||
planner_prompt_template.add_context("actions_before_now_block", actions_before_now_block)
|
||||
planner_prompt_template.add_context("action_options_text", action_options_block)
|
||||
planner_prompt_template.add_context("moderation_prompt", moderation_prompt_block)
|
||||
planner_prompt_template.add_context("name_block", name_block)
|
||||
planner_prompt_template.add_context("interest", interest)
|
||||
planner_prompt_template.add_context("plan_style", global_config.personality.plan_style)
|
||||
planner_prompt_template.add_context("reply_action_example", reply_action_example)
|
||||
prompt = await prompt_manager.render_prompt(planner_prompt_template)
|
||||
|
||||
return prompt, message_id_list
|
||||
except Exception as e:
|
||||
logger.error(f"构建 Planner 提示词时出错: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
return "构建 Planner Prompt 时出错", []
|
||||
|
||||
def get_necessary_info(self) -> Tuple[bool, Optional["TargetPersonInfo"], Dict[str, ActionInfo]]:
|
||||
"""
|
||||
获取 Planner 需要的必要信息
|
||||
"""
|
||||
is_group_chat = True
|
||||
is_group_chat, chat_target_info = get_chat_type_and_target_info(self.chat_id)
|
||||
logger.debug(f"{self.log_prefix}获取到聊天信息 - 群聊: {is_group_chat}, 目标信息: {chat_target_info}")
|
||||
|
||||
current_available_actions_dict = self.action_manager.get_using_actions()
|
||||
|
||||
# 获取完整的动作信息
|
||||
all_registered_actions: Dict[str, ActionInfo] = component_query_service.get_components_by_type( # type: ignore
|
||||
ComponentType.ACTION
|
||||
)
|
||||
current_available_actions = {}
|
||||
for action_name in current_available_actions_dict:
|
||||
if action_name in all_registered_actions:
|
||||
current_available_actions[action_name] = all_registered_actions[action_name]
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix}使用中的动作 {action_name} 未在已注册动作中找到")
|
||||
|
||||
return is_group_chat, chat_target_info, current_available_actions
|
||||
|
||||
def _filter_actions_by_activation_type(
|
||||
self, available_actions: Dict[str, ActionInfo], chat_content_block: str
|
||||
) -> Dict[str, ActionInfo]:
|
||||
"""根据激活类型过滤动作"""
|
||||
filtered_actions = {}
|
||||
|
||||
for action_name, action_info in available_actions.items():
|
||||
if action_info.activation_type == ActionActivationType.NEVER:
|
||||
logger.debug(f"{self.log_prefix}动作 {action_name} 设置为 NEVER 激活类型，跳过")
|
||||
continue
|
||||
elif action_info.activation_type == ActionActivationType.ALWAYS:
|
||||
filtered_actions[action_name] = action_info
|
||||
elif action_info.activation_type == ActionActivationType.RANDOM:
|
||||
if random.random() < action_info.random_activation_probability:
|
||||
filtered_actions[action_name] = action_info
|
||||
elif action_info.activation_type == ActionActivationType.KEYWORD:
|
||||
if action_info.activation_keywords:
|
||||
for keyword in action_info.activation_keywords:
|
||||
if keyword in chat_content_block:
|
||||
filtered_actions[action_name] = action_info
|
||||
break
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix}未知的激活类型: {action_info.activation_type}，跳过处理")
|
||||
|
||||
return filtered_actions
|
||||
|
||||
async def _build_action_options_block(self, current_available_actions: Dict[str, ActionInfo]) -> str:
|
||||
"""构建动作选项块"""
|
||||
if not current_available_actions:
|
||||
return ""
|
||||
|
||||
action_options_block = ""
|
||||
for action_name, action_info in current_available_actions.items():
|
||||
# 构建参数文本
|
||||
param_text = ""
|
||||
if action_info.action_parameters:
|
||||
param_text = "\n"
|
||||
for param_name, param_description in action_info.action_parameters.items():
|
||||
param_text += f' "{param_name}":"{param_description}"\n'
|
||||
param_text = param_text.rstrip("\n")
|
||||
|
||||
# 构建要求文本
|
||||
require_text = "\n".join(f"- {require_item}" for require_item in action_info.action_require)
|
||||
|
||||
parallel_text = "" if action_info.parallel_action else "(当选择这个动作时，请不要选择其他动作)"
|
||||
|
||||
# 获取动作提示模板并填充
|
||||
using_action_prompt = prompt_manager.get_prompt("action")
|
||||
using_action_prompt.add_context("action_name", action_name)
|
||||
using_action_prompt.add_context("action_description", action_info.description)
|
||||
using_action_prompt.add_context("action_parameters", param_text)
|
||||
using_action_prompt.add_context("action_require", require_text)
|
||||
using_action_prompt.add_context("parallel_text", parallel_text)
|
||||
using_action_rendered_prompt = await prompt_manager.render_prompt(using_action_prompt)
|
||||
|
||||
action_options_block += using_action_rendered_prompt
|
||||
|
||||
return action_options_block
|
||||
|
||||
async def _execute_main_planner(
|
||||
self,
|
||||
prompt: str,
|
||||
message_id_list: List[Tuple[str, "SessionMessage"]],
|
||||
filtered_actions: Dict[str, ActionInfo],
|
||||
available_actions: Dict[str, ActionInfo],
|
||||
loop_start_time: float,
|
||||
) -> Tuple[str, List[ActionPlannerInfo], Optional[str], Optional[str], Optional[float]]:
|
||||
"""执行主规划器"""
|
||||
llm_content = None
|
||||
actions: List[ActionPlannerInfo] = []
|
||||
llm_reasoning = None
|
||||
llm_duration_ms = None
|
||||
|
||||
try:
|
||||
# 调用LLM
|
||||
llm_start = time.perf_counter()
|
||||
llm_content, (reasoning_content, _, _) = await self.planner_llm.generate_response_async(prompt=prompt)
|
||||
llm_duration_ms = (time.perf_counter() - llm_start) * 1000
|
||||
llm_reasoning = reasoning_content
|
||||
|
||||
if global_config.debug.show_planner_prompt:
|
||||
logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
|
||||
logger.info(f"{self.log_prefix}规划器原始响应: {llm_content}")
|
||||
if reasoning_content:
|
||||
logger.info(f"{self.log_prefix}规划器推理: {reasoning_content}")
|
||||
else:
|
||||
logger.debug(f"{self.log_prefix}规划器原始提示词: {prompt}")
|
||||
logger.debug(f"{self.log_prefix}规划器原始响应: {llm_content}")
|
||||
if reasoning_content:
|
||||
logger.debug(f"{self.log_prefix}规划器推理: {reasoning_content}")
|
||||
|
||||
except Exception as req_e:
|
||||
logger.error(f"{self.log_prefix}LLM 请求执行失败: {req_e}")
|
||||
return (
|
||||
f"LLM 请求失败，模型出现问题: {req_e}",
|
||||
[
|
||||
ActionPlannerInfo(
|
||||
action_type="no_reply",
|
||||
reasoning=f"LLM 请求失败，模型出现问题: {req_e}",
|
||||
action_data={},
|
||||
action_message=None,
|
||||
available_actions=available_actions,
|
||||
)
|
||||
],
|
||||
llm_content,
|
||||
llm_reasoning,
|
||||
llm_duration_ms,
|
||||
)
|
||||
|
||||
# 解析LLM响应
|
||||
extracted_reasoning = ""
|
||||
if llm_content:
|
||||
try:
|
||||
json_objects, extracted_reasoning = self._extract_json_from_markdown(llm_content)
|
||||
extracted_reasoning = self._replace_message_ids_with_text(extracted_reasoning, message_id_list) or ""
|
||||
if json_objects:
|
||||
logger.debug(f"{self.log_prefix}从响应中提取到{len(json_objects)}个JSON对象")
|
||||
filtered_actions_list = list(filtered_actions.items())
|
||||
for json_obj in json_objects:
|
||||
actions.extend(
|
||||
self._parse_single_action(
|
||||
json_obj, message_id_list, filtered_actions_list, extracted_reasoning
|
||||
)
|
||||
)
|
||||
else:
|
||||
# 尝试解析为直接的JSON
|
||||
logger.warning(f"{self.log_prefix}LLM没有返回可用动作: {llm_content}")
|
||||
extracted_reasoning = "LLM没有返回可用动作"
|
||||
actions = self._create_no_reply("LLM没有返回可用动作", available_actions)
|
||||
|
||||
except Exception as json_e:
|
||||
logger.warning(f"{self.log_prefix}解析LLM响应JSON失败 {json_e}. LLM原始输出: '{llm_content}'")
|
||||
extracted_reasoning = f"解析LLM响应JSON失败: {json_e}"
|
||||
actions = self._create_no_reply(f"解析LLM响应JSON失败: {json_e}", available_actions)
|
||||
traceback.print_exc()
|
||||
else:
|
||||
extracted_reasoning = "规划器没有获得LLM响应"
|
||||
actions = self._create_no_reply("规划器没有获得LLM响应", available_actions)
|
||||
|
||||
# 添加循环开始时间到所有非no_reply动作
|
||||
for action in actions:
|
||||
action.action_data = action.action_data or {}
|
||||
action.action_data["loop_start_time"] = loop_start_time
|
||||
|
||||
# 去重：如果同一个动作被选择了多次，随机选择其中一个
|
||||
if actions:
|
||||
shuffled = actions.copy()
|
||||
random.shuffle(shuffled)
|
||||
actions = list({a.action_type: a for a in shuffled}.values())
|
||||
|
||||
# 处理黑话缓存逻辑
|
||||
self._process_unknown_words_cache(actions)
|
||||
|
||||
logger.debug(f"{self.log_prefix}规划器选择了{len(actions)}个动作: {' '.join([a.action_type for a in actions])}")
|
||||
|
||||
return extracted_reasoning, actions, llm_content, llm_reasoning, llm_duration_ms
|
||||
|
||||
def _create_no_reply(self, reasoning: str, available_actions: Dict[str, ActionInfo]) -> List[ActionPlannerInfo]:
|
||||
"""创建no_reply"""
|
||||
return [
|
||||
ActionPlannerInfo(
|
||||
action_type="no_reply",
|
||||
reasoning=reasoning,
|
||||
action_data={},
|
||||
action_message=None,
|
||||
available_actions=available_actions,
|
||||
)
|
||||
]
|
||||
|
||||
def _extract_json_from_markdown(self, content: str) -> Tuple[List[dict], str]:
|
||||
# sourcery skip: for-append-to-extend
|
||||
"""从Markdown格式的内容中提取JSON对象和推理内容"""
|
||||
json_objects = []
|
||||
reasoning_content = ""
|
||||
|
||||
# 使用正则表达式查找```json包裹的JSON内容
|
||||
json_pattern = r"```json\s*(.*?)\s*```"
|
||||
markdown_matches = re.findall(json_pattern, content, re.DOTALL)
|
||||
|
||||
# 提取JSON之前的内容作为推理文本
|
||||
first_json_pos = len(content)
|
||||
if markdown_matches:
|
||||
# 找到第一个```json的位置
|
||||
first_json_pos = content.find("```json")
|
||||
if first_json_pos > 0:
|
||||
reasoning_content = content[:first_json_pos].strip()
|
||||
# 清理推理内容中的注释标记
|
||||
reasoning_content = re.sub(r"^//\s*", "", reasoning_content, flags=re.MULTILINE)
|
||||
reasoning_content = reasoning_content.strip()
|
||||
|
||||
# 处理```json包裹的JSON
|
||||
for match in markdown_matches:
|
||||
try:
|
||||
# 清理可能的注释和格式问题
|
||||
json_str = re.sub(r"//.*?\n", "\n", match) # 移除单行注释
|
||||
json_str = re.sub(r"/\*.*?\*/", "", json_str, flags=re.DOTALL) # 移除多行注释
|
||||
if json_str := json_str.strip():
|
||||
# 尝试按行分割，每行可能是一个JSON对象
|
||||
lines = [line.strip() for line in json_str.split("\n") if line.strip()]
|
||||
for line in lines:
|
||||
with contextlib.suppress(json.JSONDecodeError):
|
||||
json_obj = json.loads(repair_json(line))
|
||||
if isinstance(json_obj, dict):
|
||||
if json_obj:
|
||||
json_objects.append(json_obj)
|
||||
elif isinstance(json_obj, list):
|
||||
for item in json_obj:
|
||||
if isinstance(item, dict) and item:
|
||||
json_objects.append(item)
|
||||
|
||||
# 如果按行解析没有成功（或只得到空字典），尝试将整个块作为一个JSON对象或数组
|
||||
if not json_objects:
|
||||
json_obj = json.loads(repair_json(json_str))
|
||||
if isinstance(json_obj, dict):
|
||||
# 过滤掉空字典
|
||||
if json_obj:
|
||||
json_objects.append(json_obj)
|
||||
elif isinstance(json_obj, list):
|
||||
for item in json_obj:
|
||||
if isinstance(item, dict) and item:
|
||||
json_objects.append(item)
|
||||
except Exception as e:
|
||||
logger.warning(f"解析JSON块失败: {e}, 块内容: {match[:100]}...")
|
||||
continue
|
||||
|
||||
# 如果没有找到完整的```json```块，尝试查找不完整的代码块（缺少结尾```）
|
||||
if not json_objects:
|
||||
json_start_pos = content.find("```json")
|
||||
if json_start_pos != -1:
|
||||
# 找到```json之后的内容
|
||||
json_content_start = json_start_pos + 7 # ```json的长度
|
||||
# 提取从```json之后到内容结尾的所有内容
|
||||
incomplete_json_str = content[json_content_start:].strip()
|
||||
|
||||
# 提取JSON之前的内容作为推理文本
|
||||
if json_start_pos > 0:
|
||||
reasoning_content = content[:json_start_pos].strip()
|
||||
reasoning_content = re.sub(r"^//\s*", "", reasoning_content, flags=re.MULTILINE)
|
||||
reasoning_content = reasoning_content.strip()
|
||||
|
||||
if incomplete_json_str:
|
||||
try:
|
||||
# 清理可能的注释和格式问题
|
||||
json_str = re.sub(r"//.*?\n", "\n", incomplete_json_str)
|
||||
json_str = re.sub(r"/\*.*?\*/", "", json_str, flags=re.DOTALL)
|
||||
json_str = json_str.strip()
|
||||
|
||||
if json_str:
|
||||
# 尝试按行分割，每行可能是一个JSON对象
|
||||
lines = [line.strip() for line in json_str.split("\n") if line.strip()]
|
||||
for line in lines:
|
||||
try:
|
||||
json_obj = json.loads(repair_json(line))
|
||||
if isinstance(json_obj, dict):
|
||||
# 过滤掉空字典，避免单个 { 字符被错误修复为 {} 的情况
|
||||
if json_obj:
|
||||
json_objects.append(json_obj)
|
||||
elif isinstance(json_obj, list):
|
||||
for item in json_obj:
|
||||
if isinstance(item, dict) and item:
|
||||
json_objects.append(item)
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
# 如果按行解析没有成功（或只得到空字典），尝试将整个块作为一个JSON对象或数组
|
||||
if not json_objects:
|
||||
try:
|
||||
json_obj = json.loads(repair_json(json_str))
|
||||
if isinstance(json_obj, dict):
|
||||
# 过滤掉空字典
|
||||
if json_obj:
|
||||
json_objects.append(json_obj)
|
||||
elif isinstance(json_obj, list):
|
||||
for item in json_obj:
|
||||
if isinstance(item, dict) and item:
|
||||
json_objects.append(item)
|
||||
except Exception as e:
|
||||
logger.debug(f"尝试解析不完整的JSON代码块失败: {e}")
|
||||
except Exception as e:
|
||||
logger.debug(f"处理不完整的JSON代码块时出错: {e}")
|
||||
|
||||
return json_objects, reasoning_content
|
||||
@@ -10,8 +10,8 @@ from datetime import datetime
|
||||
from src.common.logger import get_logger
|
||||
from src.common.data_models.info_data_model import ActionPlannerInfo
|
||||
from src.common.data_models.llm_data_model import LLMGenerationDataModel
|
||||
from src.config.config import global_config, model_config
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
from src.services.llm_service import LLMServiceClient
|
||||
from maim_message import BaseMessageInfo, MessageBase, Seg, UserInfo as MaimUserInfo
|
||||
|
||||
from src.common.data_models.mai_message_data_model import MaiMessage
|
||||
@@ -56,14 +56,12 @@ class DefaultReplyer:
|
||||
chat_stream: 当前绑定的聊天会话。
|
||||
request_type: LLM 请求类型标识。
|
||||
"""
|
||||
self.express_model = LLMRequest(model_set=model_config.model_task_config.replyer, request_type=request_type)
|
||||
self.express_model = LLMServiceClient(
|
||||
task_name="replyer", request_type=request_type
|
||||
)
|
||||
self.chat_stream = chat_stream
|
||||
self.is_group_chat, self.chat_target_info = get_chat_type_and_target_info(self.chat_stream.session_id)
|
||||
|
||||
from src.chat.tool_executor import ToolExecutor
|
||||
|
||||
self.tool_executor = ToolExecutor(chat_id=self.chat_stream.session_id, enable_cache=True, cache_ttl=3)
|
||||
|
||||
async def generate_reply_with_context(
|
||||
self,
|
||||
extra_info: str = "",
|
||||
@@ -397,6 +395,11 @@ class DefaultReplyer:
|
||||
return f"{expression_habits_title}\n{expression_habits_block}", selected_ids
|
||||
|
||||
async def build_tool_info(self, chat_history: str, sender: str, target: str, enable_tool: bool = True) -> str:
|
||||
del chat_history
|
||||
del sender
|
||||
del target
|
||||
del enable_tool
|
||||
return ""
|
||||
"""构建工具信息块
|
||||
|
||||
Args:
|
||||
@@ -413,9 +416,7 @@ class DefaultReplyer:
|
||||
|
||||
try:
|
||||
# 使用工具执行器获取信息
|
||||
tool_results, _, _ = await self.tool_executor.execute_from_chat_message(
|
||||
sender=sender, target_message=target, chat_history=chat_history, return_details=False
|
||||
)
|
||||
tool_results = []
|
||||
|
||||
if tool_results:
|
||||
tool_info_str = "以下是你通过工具获取到的实时信息：\n"
|
||||
@@ -576,29 +577,6 @@ class DefaultReplyer:
|
||||
duration = end_time - start_time
|
||||
return name, result, duration
|
||||
|
||||
async def _build_disabled_jargon_explanation(self) -> str:
|
||||
"""当关闭黑话解释时使用的占位协程，避免额外的LLM调用"""
|
||||
return ""
|
||||
|
||||
async def _build_unknown_words_jargon(self, unknown_words: Optional[List[str]], chat_id: str) -> str:
|
||||
"""针对 Planner 提供的未知词语列表执行黑话检索"""
|
||||
if not unknown_words:
|
||||
return ""
|
||||
# 清洗未知词语列表，只保留非空字符串
|
||||
concepts: List[str] = []
|
||||
for item in unknown_words:
|
||||
if isinstance(item, str):
|
||||
s = item.strip()
|
||||
if s:
|
||||
concepts.append(s)
|
||||
if not concepts:
|
||||
return ""
|
||||
try:
|
||||
return await retrieve_concepts_with_jargon(concepts, chat_id)
|
||||
except Exception as e:
|
||||
logger.error(f"未知词语黑话检索失败: {e}")
|
||||
return ""
|
||||
|
||||
async def _build_jargon_explanation(
|
||||
self,
|
||||
chat_id: str,
|
||||
@@ -608,19 +586,14 @@ class DefaultReplyer:
|
||||
) -> str:
|
||||
"""
|
||||
统一的黑话解释构建函数：
|
||||
- 根据 enable_jargon_explanation / jargon_mode 决定具体策略
|
||||
- 根据 enable_jargon_explanation 决定是否启用
|
||||
"""
|
||||
del unknown_words
|
||||
enable_jargon_explanation = getattr(global_config.expression, "enable_jargon_explanation", True)
|
||||
if not enable_jargon_explanation:
|
||||
return ""
|
||||
|
||||
jargon_mode = getattr(global_config.expression, "jargon_mode", "context")
|
||||
|
||||
# planner 模式：仅使用 Planner 的 unknown_words
|
||||
if jargon_mode == "planner":
|
||||
return await self._build_unknown_words_jargon(unknown_words, chat_id)
|
||||
|
||||
# 默认 / context 模式：使用上下文自动匹配黑话
|
||||
# 使用上下文自动匹配黑话
|
||||
try:
|
||||
return await explain_jargon_in_context(chat_id, messages_short, chat_talking_prompt_short) or ""
|
||||
except Exception as e:
|
||||
@@ -1158,9 +1131,11 @@ class DefaultReplyer:
|
||||
# else:
|
||||
# logger.debug(f"\nreplyer_Prompt:{prompt}\n")
|
||||
|
||||
content, (reasoning_content, model_name, tool_calls) = await self.express_model.generate_response_async(
|
||||
prompt
|
||||
)
|
||||
generation_result = await self.express_model.generate_response(prompt)
|
||||
content = generation_result.response
|
||||
reasoning_content = generation_result.reasoning
|
||||
model_name = generation_result.model_name
|
||||
tool_calls = generation_result.tool_calls
|
||||
|
||||
# 移除 content 前后的换行符和空格
|
||||
content = content.strip()
|
||||
@@ -1169,6 +1144,10 @@ class DefaultReplyer:
|
||||
return content, reasoning_content, model_name, tool_calls
|
||||
|
||||
async def get_prompt_info(self, message: str, sender: str, target: str):
|
||||
del message
|
||||
del sender
|
||||
del target
|
||||
return ""
|
||||
related_info = ""
|
||||
start_time = time.time()
|
||||
try:
|
||||
@@ -1200,17 +1179,21 @@ class DefaultReplyer:
|
||||
template_prompt.add_context("sender", sender)
|
||||
template_prompt.add_context("target_message", target)
|
||||
prompt = await prompt_manager.render_prompt(template_prompt)
|
||||
_, _, _, _, tool_calls = await llm_api.generate_with_model_with_tools(
|
||||
prompt,
|
||||
model_config=model_config.model_task_config.tool_use,
|
||||
tool_options=[search_knowledge_tool.get_tool_definition()],
|
||||
generation_result = await llm_api.generate(
|
||||
llm_api.LLMServiceRequest(
|
||||
task_name="utils",
|
||||
request_type="replyer.lpmm_knowledge",
|
||||
prompt=prompt,
|
||||
tool_options=[search_knowledge_tool.get_tool_definition()],
|
||||
)
|
||||
)
|
||||
tool_calls = generation_result.completion.tool_calls
|
||||
|
||||
# logger.info(f"工具调用提示词: {prompt}")
|
||||
# logger.info(f"工具调用: {tool_calls}")
|
||||
|
||||
if tool_calls:
|
||||
result = await self.tool_executor.execute_tool_call(tool_calls[0])
|
||||
result = None
|
||||
end_time = time.time()
|
||||
if not result or not result.get("content"):
|
||||
logger.debug("从LPMM知识库获取知识失败，返回空知识...")
|
||||
|
||||
419
src/chat/replyer/maisaka_generator.py
Normal file
419
src/chat/replyer/maisaka_generator.py
Normal file
@@ -0,0 +1,419 @@
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
|
||||
import random
|
||||
import time
|
||||
|
||||
from sqlmodel import select
|
||||
|
||||
from src.chat.message_receive.chat_manager import BotChatSession
|
||||
from src.common.database.database import get_db_session
|
||||
from src.common.database.database_model import Expression
|
||||
from src.common.data_models.reply_generation_data_models import (
|
||||
GenerationMetrics,
|
||||
LLMCompletionResult,
|
||||
ReplyGenerationResult,
|
||||
)
|
||||
from src.common.logger import get_logger
|
||||
from src.common.prompt_i18n import load_prompt
|
||||
from src.config.config import global_config
|
||||
from src.core.types import ActionInfo
|
||||
from src.services.llm_service import LLMServiceClient
|
||||
|
||||
from src.chat.message_receive.message import SessionMessage
|
||||
from src.maisaka.context_messages import AssistantMessage, LLMContextMessage, ReferenceMessage, SessionBackedMessage, ToolResultMessage
|
||||
from src.maisaka.message_adapter import parse_speaker_content
|
||||
|
||||
logger = get_logger("replyer")
|
||||
|
||||
|
||||
@dataclass
|
||||
class MaisakaReplyContext:
|
||||
"""Maisaka replyer 使用的回复上下文。"""
|
||||
|
||||
expression_habits: str = ""
|
||||
selected_expression_ids: List[int] = field(default_factory=list)
|
||||
|
||||
|
||||
@dataclass
|
||||
class _ExpressionRecord:
|
||||
"""表达方式的轻量记录。"""
|
||||
|
||||
expression_id: Optional[int]
|
||||
situation: str
|
||||
style: str
|
||||
|
||||
|
||||
class MaisakaReplyGenerator:
|
||||
"""生成 Maisaka 的最终可见回复。"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
chat_stream: Optional[BotChatSession] = None,
|
||||
request_type: str = "maisaka_replyer",
|
||||
) -> None:
|
||||
self.chat_stream = chat_stream
|
||||
self.request_type = request_type
|
||||
self.express_model = LLMServiceClient(
|
||||
task_name="replyer",
|
||||
request_type=request_type,
|
||||
)
|
||||
self._personality_prompt = self._build_personality_prompt()
|
||||
|
||||
def _build_personality_prompt(self) -> str:
|
||||
"""构建 replyer 使用的人设描述。"""
|
||||
try:
|
||||
bot_name = global_config.bot.nickname
|
||||
alias_names = global_config.bot.alias_names
|
||||
bot_aliases = f"，也有人叫你{','.join(alias_names)}" if alias_names else ""
|
||||
|
||||
prompt_personality = global_config.personality.personality
|
||||
if (
|
||||
hasattr(global_config.personality, "states")
|
||||
and global_config.personality.states
|
||||
and hasattr(global_config.personality, "state_probability")
|
||||
and global_config.personality.state_probability > 0
|
||||
and random.random() < global_config.personality.state_probability
|
||||
):
|
||||
prompt_personality = random.choice(global_config.personality.states)
|
||||
|
||||
return f"你的名字是{bot_name}{bot_aliases}，你{prompt_personality};"
|
||||
except Exception as exc:
|
||||
logger.warning(f"构建 Maisaka 人设提示词失败: {exc}")
|
||||
return "你的名字是麦麦，你是一个活泼可爱的 AI 助手。"
|
||||
|
||||
@staticmethod
|
||||
def _normalize_content(content: str, limit: int = 500) -> str:
|
||||
normalized = " ".join((content or "").split())
|
||||
if len(normalized) > limit:
|
||||
return normalized[:limit] + "..."
|
||||
return normalized
|
||||
|
||||
@staticmethod
|
||||
def _format_message_time(message: LLMContextMessage) -> str:
|
||||
return message.timestamp.strftime("%H:%M:%S")
|
||||
|
||||
@staticmethod
|
||||
def _extract_visible_assistant_reply(message: AssistantMessage) -> str:
|
||||
del message
|
||||
return ""
|
||||
|
||||
def _extract_guided_bot_reply(self, message: SessionBackedMessage) -> str:
|
||||
speaker_name, body = parse_speaker_content(message.processed_plain_text.strip())
|
||||
bot_nickname = global_config.bot.nickname.strip() or "Bot"
|
||||
if speaker_name == bot_nickname:
|
||||
return self._normalize_content(body.strip())
|
||||
return ""
|
||||
|
||||
@staticmethod
|
||||
def _split_user_message_segments(raw_content: str) -> List[tuple[Optional[str], str]]:
|
||||
"""按说话人拆分用户消息。"""
|
||||
segments: List[tuple[Optional[str], str]] = []
|
||||
current_speaker: Optional[str] = None
|
||||
current_lines: List[str] = []
|
||||
|
||||
for raw_line in raw_content.splitlines():
|
||||
speaker_name, content_body = parse_speaker_content(raw_line)
|
||||
if speaker_name is not None:
|
||||
if current_lines:
|
||||
segments.append((current_speaker, "\n".join(current_lines)))
|
||||
current_speaker = speaker_name
|
||||
current_lines = [content_body]
|
||||
continue
|
||||
|
||||
current_lines.append(raw_line)
|
||||
|
||||
if current_lines:
|
||||
segments.append((current_speaker, "\n".join(current_lines)))
|
||||
|
||||
return segments
|
||||
|
||||
def _format_chat_history(self, messages: List[LLMContextMessage]) -> str:
|
||||
"""格式化 replyer 使用的可见聊天记录。"""
|
||||
bot_nickname = global_config.bot.nickname.strip() or "Bot"
|
||||
parts: List[str] = []
|
||||
|
||||
for message in messages:
|
||||
timestamp = self._format_message_time(message)
|
||||
|
||||
if isinstance(message, (ReferenceMessage, ToolResultMessage)):
|
||||
continue
|
||||
|
||||
if isinstance(message, SessionBackedMessage):
|
||||
guided_reply = self._extract_guided_bot_reply(message)
|
||||
if guided_reply:
|
||||
parts.append(f"{timestamp} {bot_nickname}(you): {guided_reply}")
|
||||
continue
|
||||
|
||||
raw_content = message.processed_plain_text
|
||||
for speaker_name, content_body in self._split_user_message_segments(raw_content):
|
||||
content = self._normalize_content(content_body)
|
||||
if not content:
|
||||
continue
|
||||
visible_speaker = speaker_name or global_config.maisaka.user_name.strip() or "User"
|
||||
parts.append(f"{timestamp} {visible_speaker}: {content}")
|
||||
continue
|
||||
|
||||
if isinstance(message, AssistantMessage):
|
||||
visible_reply = self._extract_visible_assistant_reply(message)
|
||||
if visible_reply:
|
||||
parts.append(f"{timestamp} {bot_nickname}(you): {visible_reply}")
|
||||
|
||||
return "\n".join(parts)
|
||||
|
||||
def _build_prompt(
|
||||
self,
|
||||
chat_history: List[LLMContextMessage],
|
||||
reply_reason: str,
|
||||
expression_habits: str = "",
|
||||
) -> str:
|
||||
"""构建 Maisaka replyer 提示词。"""
|
||||
current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
formatted_history = self._format_chat_history(chat_history)
|
||||
|
||||
try:
|
||||
system_prompt = load_prompt(
|
||||
"maidairy_replyer",
|
||||
bot_name=global_config.bot.nickname,
|
||||
time_block=f"当前时间：{current_time}",
|
||||
identity=self._personality_prompt,
|
||||
reply_style=global_config.personality.reply_style,
|
||||
)
|
||||
except Exception:
|
||||
system_prompt = "你是一个友好的 AI 助手，请根据聊天记录自然回复。"
|
||||
|
||||
extra_sections: List[str] = []
|
||||
if expression_habits.strip():
|
||||
extra_sections.append(expression_habits.strip())
|
||||
|
||||
user_sections = [
|
||||
f"当前时间：{current_time}",
|
||||
f"【聊天记录】\n{formatted_history}",
|
||||
]
|
||||
if extra_sections:
|
||||
user_sections.append("\n\n".join(extra_sections))
|
||||
user_sections.append(f"【你的想法】\n{reply_reason}")
|
||||
user_sections.append("现在，你说：")
|
||||
|
||||
user_prompt = "\n\n".join(user_sections)
|
||||
return f"System: {system_prompt}\n\nUser: {user_prompt}"
|
||||
|
||||
def _resolve_session_id(self, stream_id: Optional[str]) -> str:
|
||||
"""解析当前回复使用的会话 ID。"""
|
||||
if stream_id:
|
||||
return stream_id
|
||||
if self.chat_stream is not None:
|
||||
return self.chat_stream.session_id
|
||||
return ""
|
||||
|
||||
async def _build_reply_context(
|
||||
self,
|
||||
chat_history: List[LLMContextMessage],
|
||||
reply_message: Optional[SessionMessage],
|
||||
reply_reason: str,
|
||||
stream_id: Optional[str],
|
||||
) -> MaisakaReplyContext:
|
||||
"""在 replyer 内部构建表达习惯和黑话解释。"""
|
||||
session_id = self._resolve_session_id(stream_id)
|
||||
if not session_id:
|
||||
logger.warning("构建 Maisaka 回复上下文失败：缺少会话标识")
|
||||
return MaisakaReplyContext()
|
||||
|
||||
expression_habits, selected_expression_ids = self._build_expression_habits(
|
||||
session_id=session_id,
|
||||
chat_history=chat_history,
|
||||
reply_message=reply_message,
|
||||
reply_reason=reply_reason,
|
||||
)
|
||||
return MaisakaReplyContext(
|
||||
expression_habits=expression_habits,
|
||||
selected_expression_ids=selected_expression_ids,
|
||||
)
|
||||
|
||||
def _build_expression_habits(
|
||||
self,
|
||||
session_id: str,
|
||||
chat_history: List[LLMContextMessage],
|
||||
reply_message: Optional[SessionMessage],
|
||||
reply_reason: str,
|
||||
) -> tuple[str, List[int]]:
|
||||
"""查询并格式化适合当前会话的表达习惯。"""
|
||||
del chat_history
|
||||
del reply_message
|
||||
del reply_reason
|
||||
|
||||
expression_records = self._load_expression_records(session_id)
|
||||
if not expression_records:
|
||||
return "", []
|
||||
|
||||
lines: List[str] = []
|
||||
selected_ids: List[int] = []
|
||||
for expression in expression_records:
|
||||
if expression.expression_id is not None:
|
||||
selected_ids.append(expression.expression_id)
|
||||
lines.append(f"- 当{expression.situation}时，可以自然地用{expression.style}这种表达习惯。")
|
||||
|
||||
block = "【表达习惯参考】\n" + "\n".join(lines)
|
||||
logger.info(
|
||||
f"已构建 Maisaka 表达习惯: 会话标识={session_id} "
|
||||
f"数量={len(selected_ids)} 表达编号={selected_ids!r}"
|
||||
)
|
||||
return block, selected_ids
|
||||
|
||||
def _load_expression_records(self, session_id: str) -> List[_ExpressionRecord]:
|
||||
"""提取表达方式静态数据，避免 detached ORM 对象。"""
|
||||
with get_db_session(auto_commit=False) as session:
|
||||
query = select(Expression).where(Expression.rejected.is_(False)) # type: ignore[attr-defined]
|
||||
if global_config.expression.expression_checked_only:
|
||||
query = query.where(Expression.checked.is_(True)) # type: ignore[attr-defined]
|
||||
|
||||
query = query.where(
|
||||
(Expression.session_id == session_id) | (Expression.session_id.is_(None)) # type: ignore[attr-defined]
|
||||
).order_by(Expression.count.desc(), Expression.last_active_time.desc()) # type: ignore[attr-defined]
|
||||
|
||||
expressions = session.exec(query.limit(5)).all()
|
||||
return [
|
||||
_ExpressionRecord(
|
||||
expression_id=expression.id,
|
||||
situation=expression.situation,
|
||||
style=expression.style,
|
||||
)
|
||||
for expression in expressions
|
||||
]
|
||||
|
||||
async def generate_reply_with_context(
|
||||
self,
|
||||
extra_info: str = "",
|
||||
reply_reason: str = "",
|
||||
available_actions: Optional[Dict[str, ActionInfo]] = None,
|
||||
chosen_actions: Optional[List[object]] = None,
|
||||
enable_tool: bool = True,
|
||||
from_plugin: bool = True,
|
||||
stream_id: Optional[str] = None,
|
||||
reply_message: Optional[SessionMessage] = None,
|
||||
reply_time_point: Optional[float] = None,
|
||||
think_level: int = 1,
|
||||
unknown_words: Optional[List[str]] = None,
|
||||
log_reply: bool = True,
|
||||
chat_history: Optional[List[LLMContextMessage]] = None,
|
||||
expression_habits: str = "",
|
||||
selected_expression_ids: Optional[List[int]] = None,
|
||||
) -> Tuple[bool, ReplyGenerationResult]:
|
||||
"""结合上下文生成 Maisaka 的最终可见回复。"""
|
||||
del available_actions
|
||||
del chosen_actions
|
||||
del enable_tool
|
||||
del extra_info
|
||||
del from_plugin
|
||||
del log_reply
|
||||
del reply_time_point
|
||||
del think_level
|
||||
del unknown_words
|
||||
|
||||
result = ReplyGenerationResult()
|
||||
if chat_history is None:
|
||||
result.error_message = "聊天历史为空"
|
||||
return False, result
|
||||
|
||||
logger.info(
|
||||
f"Maisaka 回复器开始生成: 会话流标识={stream_id} 回复原因={reply_reason!r} "
|
||||
f"历史消息数={len(chat_history)} 目标消息编号="
|
||||
f"{reply_message.message_id if reply_message else None}"
|
||||
)
|
||||
|
||||
filtered_history = [
|
||||
message
|
||||
for message in chat_history
|
||||
if not isinstance(message, (ReferenceMessage, ToolResultMessage))
|
||||
]
|
||||
|
||||
logger.debug(f"Maisaka 回复器过滤后历史消息数={len(filtered_history)}")
|
||||
|
||||
# Validate that express_model is properly initialized
|
||||
if self.express_model is None:
|
||||
logger.error("Maisaka 回复器的回复模型未初始化")
|
||||
result.error_message = "回复模型尚未初始化"
|
||||
return False, result
|
||||
|
||||
try:
|
||||
reply_context = await self._build_reply_context(
|
||||
chat_history=filtered_history,
|
||||
reply_message=reply_message,
|
||||
reply_reason=reply_reason or "",
|
||||
stream_id=stream_id,
|
||||
)
|
||||
except Exception as exc:
|
||||
import traceback
|
||||
logger.error(f"Maisaka 回复器构建回复上下文失败: {exc}\n{traceback.format_exc()}")
|
||||
result.error_message = f"构建回复上下文失败: {exc}"
|
||||
return False, result
|
||||
|
||||
merged_expression_habits = expression_habits.strip() or reply_context.expression_habits
|
||||
result.selected_expression_ids = (
|
||||
list(selected_expression_ids)
|
||||
if selected_expression_ids is not None
|
||||
else list(reply_context.selected_expression_ids)
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"Maisaka 回复上下文构建完成: 会话流标识={stream_id} "
|
||||
f"已选表达编号={result.selected_expression_ids!r}"
|
||||
)
|
||||
|
||||
try:
|
||||
prompt = self._build_prompt(
|
||||
chat_history=filtered_history,
|
||||
reply_reason=reply_reason or "",
|
||||
expression_habits=merged_expression_habits,
|
||||
)
|
||||
except Exception as exc:
|
||||
import traceback
|
||||
logger.error(f"Maisaka 回复器构建提示词失败: {exc}\n{traceback.format_exc()}")
|
||||
result.error_message = f"构建提示词失败: {exc}"
|
||||
return False, result
|
||||
|
||||
result.completion.request_prompt = prompt
|
||||
|
||||
if global_config.debug.show_replyer_prompt:
|
||||
logger.info(f"\nMaisaka 回复器提示词：\n{prompt}\n")
|
||||
|
||||
started_at = time.perf_counter()
|
||||
try:
|
||||
generation_result = await self.express_model.generate_response(prompt)
|
||||
except Exception as exc:
|
||||
logger.exception("Maisaka 回复器调用失败")
|
||||
result.error_message = str(exc)
|
||||
result.metrics = GenerationMetrics(
|
||||
overall_ms=round((time.perf_counter() - started_at) * 1000, 2),
|
||||
)
|
||||
return False, result
|
||||
|
||||
response_text = (generation_result.response or "").strip()
|
||||
result.success = bool(response_text)
|
||||
result.completion = LLMCompletionResult(
|
||||
request_prompt=prompt,
|
||||
response_text=response_text,
|
||||
reasoning_text=generation_result.reasoning or "",
|
||||
model_name=generation_result.model_name or "",
|
||||
tool_calls=generation_result.tool_calls or [],
|
||||
)
|
||||
result.metrics = GenerationMetrics(
|
||||
overall_ms=round((time.perf_counter() - started_at) * 1000, 2),
|
||||
)
|
||||
|
||||
if global_config.debug.show_replyer_reasoning and result.completion.reasoning_text:
|
||||
logger.info(f"Maisaka 回复器思考内容：\n{result.completion.reasoning_text}")
|
||||
|
||||
if not result.success:
|
||||
result.error_message = "回复器返回了空内容"
|
||||
logger.warning("Maisaka 回复器返回了空内容")
|
||||
return False, result
|
||||
|
||||
logger.info(
|
||||
f"Maisaka 回复器生成成功: 回复文本={response_text!r} "
|
||||
f"总耗时毫秒={result.metrics.overall_ms} "
|
||||
f"已选表达编号={result.selected_expression_ids!r}"
|
||||
)
|
||||
result.text_fragments = [response_text]
|
||||
return True, result
|
||||
@@ -9,8 +9,8 @@ from datetime import datetime
|
||||
from src.common.logger import get_logger
|
||||
from src.common.data_models.info_data_model import ActionPlannerInfo
|
||||
from src.common.data_models.llm_data_model import LLMGenerationDataModel
|
||||
from src.config.config import global_config, model_config
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
from src.services.llm_service import LLMServiceClient
|
||||
from maim_message import BaseMessageInfo, MessageBase, Seg, UserInfo as MaimUserInfo
|
||||
|
||||
from src.common.data_models.mai_message_data_model import MaiMessage
|
||||
@@ -52,15 +52,13 @@ class PrivateReplyer:
|
||||
chat_stream: 当前绑定的聊天会话。
|
||||
request_type: LLM 请求类型标识。
|
||||
"""
|
||||
self.express_model = LLMRequest(model_set=model_config.model_task_config.replyer, request_type=request_type)
|
||||
self.express_model = LLMServiceClient(
|
||||
task_name="replyer", request_type=request_type
|
||||
)
|
||||
self.chat_stream = chat_stream
|
||||
self.is_group_chat, self.chat_target_info = get_chat_type_and_target_info(self.chat_stream.session_id)
|
||||
# self.memory_activator = MemoryActivator()
|
||||
|
||||
from src.chat.tool_executor import ToolExecutor
|
||||
|
||||
self.tool_executor = ToolExecutor(chat_id=self.chat_stream.session_id, enable_cache=True, cache_ttl=3)
|
||||
|
||||
async def generate_reply_with_context(
|
||||
self,
|
||||
extra_info: str = "",
|
||||
@@ -290,6 +288,11 @@ class PrivateReplyer:
|
||||
return f"{expression_habits_title}\n{expression_habits_block}", selected_ids
|
||||
|
||||
async def build_tool_info(self, chat_history: str, sender: str, target: str, enable_tool: bool = True) -> str:
|
||||
del chat_history
|
||||
del sender
|
||||
del target
|
||||
del enable_tool
|
||||
return ""
|
||||
"""构建工具信息块
|
||||
|
||||
Args:
|
||||
@@ -306,9 +309,7 @@ class PrivateReplyer:
|
||||
|
||||
try:
|
||||
# 使用工具执行器获取信息
|
||||
tool_results, _, _ = await self.tool_executor.execute_from_chat_message(
|
||||
sender=sender, target_message=target, chat_history=chat_history, return_details=False
|
||||
)
|
||||
tool_results = []
|
||||
|
||||
if tool_results:
|
||||
tool_info_str = "以下是你通过工具获取到的实时信息：\n"
|
||||
@@ -997,9 +998,11 @@ class PrivateReplyer:
|
||||
else:
|
||||
logger.debug(f"\n{prompt}\n")
|
||||
|
||||
content, (reasoning_content, model_name, tool_calls) = await self.express_model.generate_response_async(
|
||||
prompt
|
||||
)
|
||||
generation_result = await self.express_model.generate_response(prompt)
|
||||
content = generation_result.response
|
||||
reasoning_content = generation_result.reasoning
|
||||
model_name = generation_result.model_name
|
||||
tool_calls = generation_result.tool_calls
|
||||
|
||||
content = content.strip()
|
||||
|
||||
|
||||
@@ -1,65 +1,82 @@
|
||||
from typing import Dict, Optional
|
||||
from typing import TYPE_CHECKING, Any, Dict, Optional
|
||||
|
||||
from src.common.logger import get_logger
|
||||
from src.chat.message_receive.chat_manager import BotChatSession, chat_manager as _chat_manager
|
||||
from src.chat.replyer.group_generator import DefaultReplyer
|
||||
from src.chat.replyer.private_generator import PrivateReplyer
|
||||
from src.common.logger import get_logger
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from src.chat.replyer.group_generator import DefaultReplyer
|
||||
from src.chat.replyer.maisaka_generator import MaisakaReplyGenerator
|
||||
from src.chat.replyer.private_generator import PrivateReplyer
|
||||
|
||||
logger = get_logger("ReplyerManager")
|
||||
|
||||
|
||||
class ReplyerManager:
|
||||
def __init__(self):
|
||||
self._repliers: Dict[str, DefaultReplyer | PrivateReplyer] = {}
|
||||
"""统一管理不同类型的回复生成器。"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._repliers: Dict[str, Any] = {}
|
||||
|
||||
def get_replyer(
|
||||
self,
|
||||
chat_stream: Optional[BotChatSession] = None,
|
||||
chat_id: Optional[str] = None,
|
||||
request_type: str = "replyer",
|
||||
) -> Optional[DefaultReplyer | PrivateReplyer]:
|
||||
"""
|
||||
获取或创建回复器实例。
|
||||
|
||||
model_configs 仅在首次为某个 chat_id/stream_id 创建实例时有效。
|
||||
后续调用将返回已缓存的实例，忽略 model_configs 参数。
|
||||
"""
|
||||
replyer_type: str = "default",
|
||||
) -> Optional["DefaultReplyer | MaisakaReplyGenerator | PrivateReplyer"]:
|
||||
"""按会话和 replyer 类型获取实例。"""
|
||||
stream_id = chat_stream.session_id if chat_stream else chat_id
|
||||
if not stream_id:
|
||||
logger.warning("[ReplyerManager] 缺少 stream_id，无法获取回复器。")
|
||||
logger.warning("[ReplyerManager] 缺少 stream_id，无法获取 replyer")
|
||||
return None
|
||||
|
||||
# 如果已有缓存实例，直接返回
|
||||
if stream_id in self._repliers:
|
||||
logger.debug(f"[ReplyerManager] 为 stream_id '{stream_id}' 返回已存在的回复器实例。")
|
||||
return self._repliers[stream_id]
|
||||
cache_key = f"{replyer_type}:{stream_id}"
|
||||
if cache_key in self._repliers:
|
||||
logger.info(f"[ReplyerManager] 命中缓存 replyer: cache_key={cache_key}")
|
||||
return self._repliers[cache_key]
|
||||
|
||||
# 如果没有缓存，则创建新实例（首次初始化）
|
||||
logger.debug(f"[ReplyerManager] 为 stream_id '{stream_id}' 创建新的回复器实例并缓存。")
|
||||
|
||||
target_stream = chat_stream
|
||||
target_stream = chat_stream or _chat_manager.get_session_by_session_id(stream_id)
|
||||
if not target_stream:
|
||||
target_stream = _chat_manager.get_session_by_session_id(stream_id)
|
||||
|
||||
if not target_stream:
|
||||
logger.warning(f"[ReplyerManager] 未找到 stream_id='{stream_id}' 的聊天流，无法创建回复器。")
|
||||
logger.warning(f"[ReplyerManager] 未找到会话，stream_id={stream_id}")
|
||||
return None
|
||||
|
||||
# model_configs 只在此时（初始化时）生效
|
||||
if target_stream.is_group_session:
|
||||
replyer = DefaultReplyer(
|
||||
chat_stream=target_stream,
|
||||
request_type=request_type,
|
||||
)
|
||||
else:
|
||||
replyer = PrivateReplyer(
|
||||
chat_stream=target_stream,
|
||||
request_type=request_type,
|
||||
)
|
||||
logger.info(
|
||||
f"[ReplyerManager] 开始创建 replyer: cache_key={cache_key}, "
|
||||
f"replyer_type={replyer_type}, is_group_session={target_stream.is_group_session}"
|
||||
)
|
||||
|
||||
self._repliers[stream_id] = replyer
|
||||
try:
|
||||
if replyer_type == "maisaka":
|
||||
logger.info("[ReplyerManager] importing MaisakaReplyGenerator")
|
||||
from src.chat.replyer.maisaka_generator import MaisakaReplyGenerator
|
||||
|
||||
replyer = MaisakaReplyGenerator(
|
||||
chat_stream=target_stream,
|
||||
request_type=request_type,
|
||||
)
|
||||
elif target_stream.is_group_session:
|
||||
logger.info("[ReplyerManager] importing DefaultReplyer")
|
||||
from src.chat.replyer.group_generator import DefaultReplyer
|
||||
|
||||
replyer = DefaultReplyer(
|
||||
chat_stream=target_stream,
|
||||
request_type=request_type,
|
||||
)
|
||||
else:
|
||||
logger.info("[ReplyerManager] importing PrivateReplyer")
|
||||
from src.chat.replyer.private_generator import PrivateReplyer
|
||||
|
||||
replyer = PrivateReplyer(
|
||||
chat_stream=target_stream,
|
||||
request_type=request_type,
|
||||
)
|
||||
except Exception:
|
||||
logger.exception(f"[ReplyerManager] 创建 replyer 失败: cache_key={cache_key}")
|
||||
raise
|
||||
|
||||
self._repliers[cache_key] = replyer
|
||||
logger.info(f"[ReplyerManager] replyer 创建完成: cache_key={cache_key}")
|
||||
return replyer
|
||||
|
||||
|
||||
# 创建一个全局实例
|
||||
replyer_manager = ReplyerManager()
|
||||
|
||||
@@ -1,248 +0,0 @@
|
||||
"""工具执行器。
|
||||
|
||||
独立的工具执行组件，可以直接输入聊天消息内容，
|
||||
自动判断并执行相应的工具，返回结构化的工具执行结果。
|
||||
"""
|
||||
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
import hashlib
|
||||
import time
|
||||
|
||||
from src.common.logger import get_logger
|
||||
from src.config.config import global_config, model_config
|
||||
from src.core.announcement_manager import global_announcement_manager
|
||||
from src.llm_models.payload_content import ToolCall
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.plugin_runtime.component_query import component_query_service
|
||||
from src.prompt.prompt_manager import prompt_manager
|
||||
|
||||
logger = get_logger("tool_use")
|
||||
|
||||
|
||||
class ToolExecutor:
|
||||
"""独立的工具执行器组件
|
||||
|
||||
可以直接输入聊天消息内容，自动判断并执行相应的工具，返回结构化的工具执行结果。
|
||||
"""
|
||||
|
||||
def __init__(self, chat_id: str, enable_cache: bool = True, cache_ttl: int = 3):
|
||||
from src.chat.message_receive.chat_manager import chat_manager as _chat_manager
|
||||
|
||||
self.chat_id = chat_id
|
||||
self.chat_stream = _chat_manager.get_session_by_session_id(self.chat_id)
|
||||
self.log_prefix = f"[{_chat_manager.get_session_name(self.chat_id) or self.chat_id}]"
|
||||
|
||||
self.llm_model = LLMRequest(model_set=model_config.model_task_config.tool_use, request_type="tool_executor")
|
||||
|
||||
self.enable_cache = enable_cache
|
||||
self.cache_ttl = cache_ttl
|
||||
self.tool_cache: Dict[str, dict] = {}
|
||||
|
||||
logger.info(f"{self.log_prefix}工具执行器初始化完成，缓存{'启用' if enable_cache else '禁用'}，TTL={cache_ttl}")
|
||||
|
||||
async def execute_from_chat_message(
|
||||
self, target_message: str, chat_history: str, sender: str, return_details: bool = False
|
||||
) -> Tuple[List[Dict[str, Any]], List[str], str]:
|
||||
"""从聊天消息执行工具"""
|
||||
|
||||
cache_key = self._generate_cache_key(target_message, chat_history, sender)
|
||||
if cached_result := self._get_from_cache(cache_key):
|
||||
logger.info(f"{self.log_prefix}使用缓存结果，跳过工具执行")
|
||||
if not return_details:
|
||||
return cached_result, [], ""
|
||||
used_tools = [result.get("tool_name", "unknown") for result in cached_result]
|
||||
return cached_result, used_tools, ""
|
||||
|
||||
tools = self._get_tool_definitions()
|
||||
if not tools:
|
||||
logger.debug(f"{self.log_prefix}没有可用工具，直接返回空内容")
|
||||
return [], [], ""
|
||||
|
||||
prompt_template = prompt_manager.get_prompt("tool_executor")
|
||||
prompt_template.add_context("target_message", target_message)
|
||||
prompt_template.add_context("chat_history", chat_history)
|
||||
prompt_template.add_context("sender", sender)
|
||||
prompt_template.add_context("bot_name", global_config.bot.nickname)
|
||||
prompt_template.add_context("time_now", time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
|
||||
prompt = await prompt_manager.render_prompt(prompt_template)
|
||||
|
||||
logger.debug(f"{self.log_prefix}开始LLM工具调用分析")
|
||||
|
||||
response, (reasoning_content, model_name, tool_calls) = await self.llm_model.generate_response_async(
|
||||
prompt=prompt, tools=tools, raise_when_empty=False
|
||||
)
|
||||
|
||||
tool_results, used_tools = await self.execute_tool_calls(tool_calls)
|
||||
|
||||
if tool_results:
|
||||
self._set_cache(cache_key, tool_results)
|
||||
|
||||
if used_tools:
|
||||
logger.info(f"{self.log_prefix}工具执行完成，共执行{len(used_tools)}个工具: {used_tools}")
|
||||
|
||||
if return_details:
|
||||
return tool_results, used_tools, prompt
|
||||
return tool_results, [], ""
|
||||
|
||||
def _get_tool_definitions(self) -> List[Dict[str, Any]]:
|
||||
"""获取 LLM 可用的工具定义列表"""
|
||||
all_tools = component_query_service.get_llm_available_tools()
|
||||
user_disabled_tools = global_announcement_manager.get_disabled_chat_tools(self.chat_id)
|
||||
return [info.get_llm_definition() for name, info in all_tools.items() if name not in user_disabled_tools]
|
||||
|
||||
async def execute_tool_calls(self, tool_calls: Optional[List[ToolCall]]) -> Tuple[List[Dict[str, Any]], List[str]]:
|
||||
"""执行工具调用列表"""
|
||||
tool_results: List[Dict[str, Any]] = []
|
||||
used_tools: List[str] = []
|
||||
|
||||
if not tool_calls:
|
||||
logger.debug(f"{self.log_prefix}无需执行工具")
|
||||
return [], []
|
||||
|
||||
func_names = [call.func_name for call in tool_calls if call.func_name]
|
||||
logger.info(f"{self.log_prefix}开始执行工具调用: {func_names}")
|
||||
|
||||
for tool_call in tool_calls:
|
||||
tool_name = tool_call.func_name
|
||||
try:
|
||||
logger.debug(f"{self.log_prefix}执行工具: {tool_name}")
|
||||
result = await self.execute_tool_call(tool_call)
|
||||
|
||||
if result:
|
||||
tool_info = {
|
||||
"type": result.get("type", "unknown_type"),
|
||||
"id": result.get("id", f"tool_exec_{time.time()}"),
|
||||
"content": result.get("content", ""),
|
||||
"tool_name": tool_name,
|
||||
"timestamp": time.time(),
|
||||
}
|
||||
content = tool_info["content"]
|
||||
if not isinstance(content, (str, list, tuple)):
|
||||
tool_info["content"] = str(content)
|
||||
content_check = tool_info["content"]
|
||||
if (isinstance(content_check, str) and not content_check.strip()) or (
|
||||
isinstance(content_check, (list, tuple)) and len(content_check) == 0
|
||||
):
|
||||
logger.debug(f"{self.log_prefix}工具{tool_name}无有效内容，跳过展示")
|
||||
continue
|
||||
|
||||
tool_results.append(tool_info)
|
||||
used_tools.append(tool_name)
|
||||
preview = str(content)[:200]
|
||||
logger.debug(f"{self.log_prefix}工具{tool_name}结果内容: {preview}...")
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix}工具{tool_name}执行失败: {e}")
|
||||
error_info = {
|
||||
"type": "tool_error",
|
||||
"id": f"tool_error_{time.time()}",
|
||||
"content": f"工具{tool_name}执行失败: {str(e)}",
|
||||
"tool_name": tool_name,
|
||||
"timestamp": time.time(),
|
||||
}
|
||||
tool_results.append(error_info)
|
||||
|
||||
return tool_results, used_tools
|
||||
|
||||
async def execute_tool_call(self, tool_call: ToolCall) -> Optional[Dict[str, Any]]:
|
||||
"""执行单个工具调用"""
|
||||
function_name = tool_call.func_name
|
||||
function_args = tool_call.args or {}
|
||||
function_args["llm_called"] = True
|
||||
|
||||
executor = component_query_service.get_tool_executor(function_name)
|
||||
if not executor:
|
||||
logger.warning(f"未知工具名称: {function_name}")
|
||||
return None
|
||||
|
||||
result = await executor(function_args)
|
||||
if result:
|
||||
return {
|
||||
"tool_call_id": tool_call.call_id,
|
||||
"role": "tool",
|
||||
"name": function_name,
|
||||
"type": "function",
|
||||
"content": result["content"],
|
||||
}
|
||||
return None
|
||||
|
||||
async def execute_specific_tool_simple(self, tool_name: str, tool_args: Dict) -> Optional[Dict]:
|
||||
"""直接执行指定工具"""
|
||||
try:
|
||||
tool_call = ToolCall(
|
||||
call_id=f"direct_tool_{time.time()}",
|
||||
func_name=tool_name,
|
||||
args=tool_args,
|
||||
)
|
||||
|
||||
logger.info(f"{self.log_prefix}直接执行工具: {tool_name}")
|
||||
result = await self.execute_tool_call(tool_call)
|
||||
|
||||
if result:
|
||||
tool_info = {
|
||||
"type": result.get("type", "unknown_type"),
|
||||
"id": result.get("id", f"direct_tool_{time.time()}"),
|
||||
"content": result.get("content", ""),
|
||||
"tool_name": tool_name,
|
||||
"timestamp": time.time(),
|
||||
}
|
||||
logger.info(f"{self.log_prefix}直接工具执行成功: {tool_name}")
|
||||
return tool_info
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix}直接工具执行失败 {tool_name}: {e}")
|
||||
|
||||
return None
|
||||
|
||||
# === 缓存方法 ===
|
||||
|
||||
def _generate_cache_key(self, target_message: str, chat_history: str, sender: str) -> str:
|
||||
content = f"{target_message}_{chat_history}_{sender}"
|
||||
return hashlib.md5(content.encode()).hexdigest()
|
||||
|
||||
def _get_from_cache(self, cache_key: str) -> Optional[List[Dict]]:
|
||||
if not self.enable_cache or cache_key not in self.tool_cache:
|
||||
return None
|
||||
cache_item = self.tool_cache[cache_key]
|
||||
if cache_item["ttl"] <= 0:
|
||||
del self.tool_cache[cache_key]
|
||||
return None
|
||||
cache_item["ttl"] -= 1
|
||||
return cache_item["result"]
|
||||
|
||||
def _set_cache(self, cache_key: str, result: List[Dict]):
|
||||
if not self.enable_cache:
|
||||
return
|
||||
self.tool_cache[cache_key] = {"result": result, "ttl": self.cache_ttl, "timestamp": time.time()}
|
||||
|
||||
def _cleanup_expired_cache(self):
|
||||
if not self.enable_cache:
|
||||
return
|
||||
expired = [k for k, v in self.tool_cache.items() if v["ttl"] <= 0]
|
||||
for key in expired:
|
||||
del self.tool_cache[key]
|
||||
|
||||
def clear_cache(self):
|
||||
if self.enable_cache:
|
||||
self.tool_cache.clear()
|
||||
|
||||
def get_cache_status(self) -> Dict:
|
||||
if not self.enable_cache:
|
||||
return {"enabled": False, "cache_count": 0}
|
||||
self._cleanup_expired_cache()
|
||||
ttl_distribution: Dict[int, int] = {}
|
||||
for item in self.tool_cache.values():
|
||||
ttl = item["ttl"]
|
||||
ttl_distribution[ttl] = ttl_distribution.get(ttl, 0) + 1
|
||||
return {
|
||||
"enabled": True,
|
||||
"cache_count": len(self.tool_cache),
|
||||
"cache_ttl": self.cache_ttl,
|
||||
"ttl_distribution": ttl_distribution,
|
||||
}
|
||||
|
||||
def set_cache_config(self, enable_cache: Optional[bool] = None, cache_ttl: int = -1):
|
||||
if enable_cache is not None:
|
||||
self.enable_cache = enable_cache
|
||||
if cache_ttl > 0:
|
||||
self.cache_ttl = cache_ttl
|
||||
@@ -12,7 +12,7 @@ from sqlmodel import col, select
|
||||
|
||||
from src.common.logger import get_logger
|
||||
from src.common.database.database import get_db_session
|
||||
from src.common.database.database_model import OnlineTime, ModelUsage, Messages, ActionRecord
|
||||
from src.common.database.database_model import Messages, ModelUsage, OnlineTime, ToolRecord
|
||||
from src.manager.async_task_manager import AsyncTask
|
||||
from src.manager.local_store_manager import local_storage
|
||||
from src.config.config import global_config
|
||||
@@ -648,7 +648,7 @@ class StatisticOutputTask(AsyncTask):
|
||||
def _collect_message_count_for_period(
|
||||
self,
|
||||
collect_period: list[tuple[str, datetime]],
|
||||
) -> StatPeriodMapping:
|
||||
) -> dict[str, dict[str, object]]:
|
||||
"""
|
||||
收集指定时间段的消息统计数据
|
||||
|
||||
@@ -659,8 +659,13 @@ class StatisticOutputTask(AsyncTask):
|
||||
|
||||
collect_period.sort(key=lambda x: x[1], reverse=True)
|
||||
|
||||
stats: StatPeriodMapping = {
|
||||
period_key: StatisticOutputTask._build_stat_period_data() for period_key, _ in collect_period
|
||||
stats: dict[str, dict[str, object]] = {
|
||||
period_key: {
|
||||
TOTAL_MSG_CNT: 0,
|
||||
MSG_CNT_BY_CHAT: defaultdict(int),
|
||||
TOTAL_REPLY_CNT: 0,
|
||||
}
|
||||
for period_key, _ in collect_period
|
||||
}
|
||||
|
||||
query_start_timestamp = collect_period[-1][1]
|
||||
@@ -710,24 +715,24 @@ class StatisticOutputTask(AsyncTask):
|
||||
StatisticOutputTask._add_defaultdict_int(stats[period_key], MSG_CNT_BY_CHAT, chat_id, 1)
|
||||
break
|
||||
|
||||
# 使用 ActionRecords 中的 reply 动作次数作为回复数基准
|
||||
# 使用 ToolRecord 中的 reply 工具次数作为回复数基准
|
||||
try:
|
||||
action_query_start_timestamp = collect_period[-1][1]
|
||||
tool_query_start_timestamp = collect_period[-1][1]
|
||||
with get_db_session(auto_commit=False) as session:
|
||||
statement = select(ActionRecord).where(col(ActionRecord.timestamp) >= action_query_start_timestamp)
|
||||
actions = session.exec(statement).all()
|
||||
for action in actions:
|
||||
if action.action_name != "reply":
|
||||
statement = select(ToolRecord).where(col(ToolRecord.timestamp) >= tool_query_start_timestamp)
|
||||
tool_records = session.exec(statement).all()
|
||||
for tool_record in tool_records:
|
||||
if tool_record.tool_name != "reply":
|
||||
continue
|
||||
|
||||
action_time_ts = action.timestamp.timestamp()
|
||||
action_time_ts = tool_record.timestamp.timestamp()
|
||||
for idx, (_, period_start_dt) in enumerate(collect_period):
|
||||
if action_time_ts >= period_start_dt.timestamp():
|
||||
for period_key, _ in collect_period[idx:]:
|
||||
StatisticOutputTask._add_int_stat(stats[period_key], TOTAL_REPLY_CNT, 1)
|
||||
break
|
||||
except Exception as e:
|
||||
logger.warning(f"统计 reply 动作次数失败，将回复数视为 0，错误信息：{e}")
|
||||
logger.warning(f"统计 reply 工具次数失败，将回复数视为 0，错误信息：{e}")
|
||||
|
||||
return stats
|
||||
|
||||
|
||||
@@ -13,8 +13,8 @@ import jieba
|
||||
from src.chat.message_receive.chat_manager import chat_manager as _chat_manager
|
||||
from src.chat.message_receive.message import SessionMessage
|
||||
from src.common.logger import get_logger
|
||||
from src.config.config import global_config, model_config
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
from src.services.llm_service import LLMServiceClient
|
||||
from src.person_info.person_info import Person
|
||||
|
||||
from .typo_generator import ChineseTypoGenerator
|
||||
@@ -235,10 +235,11 @@ def is_mentioned_bot_in_message(message: SessionMessage) -> tuple[bool, bool, fl
|
||||
|
||||
async def get_embedding(text, request_type="embedding") -> Optional[List[float]]:
|
||||
"""获取文本的embedding向量"""
|
||||
# 每次都创建新的LLMRequest实例以避免事件循环冲突
|
||||
llm = LLMRequest(model_set=model_config.model_task_config.embedding, request_type=request_type)
|
||||
# 每次都创建新的服务层实例以避免事件循环冲突
|
||||
llm = LLMServiceClient(task_name="embedding", request_type=request_type)
|
||||
try:
|
||||
embedding, _ = await llm.get_embedding(text)
|
||||
embedding_result = await llm.embed_text(text)
|
||||
embedding = embedding_result.embedding
|
||||
except Exception as e:
|
||||
logger.error(f"获取embedding失败: {str(e)}")
|
||||
embedding = None
|
||||
|
||||
Reference in New Issue
Block a user