pre-commit-ci[bot]
132 lines
5.7 KiB
Python
132 lines
5.7 KiB
Python
import astrbot.api.message_components as Comp
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import copy
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import json
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from astrbot.api import logger
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from astrbot.api.event import AstrMessageEvent, filter
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from astrbot.api.star import Context, Star, register
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from astrbot.core.utils.session_waiter import (
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SessionWaiter,
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USER_SESSIONS,
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FILTERS,
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session_waiter,
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SessionController,
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)
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from sys import maxsize
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@register(
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"session_controller",
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"Cvandia & Soulter",
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"为插件支持会话控制",
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"v1.0.1",
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"https://astrbot.app",
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)
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class Waiter(Star):
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"""会话控制"""
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def __init__(self, context: Context):
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super().__init__(context)
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self.empty_mention_waiting = self.context.get_config()["platform_settings"][
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"empty_mention_waiting"
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]
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self.wake_prefix = self.context.get_config()["wake_prefix"]
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@filter.event_message_type(filter.EventMessageType.ALL, priority=maxsize)
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async def handle_session_control_agent(self, event: AstrMessageEvent):
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"""会话控制代理"""
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for session_filter in FILTERS:
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session_id = session_filter.filter(event)
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if session_id in USER_SESSIONS:
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await SessionWaiter.trigger(session_id, event)
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event.stop_event()
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@filter.event_message_type(filter.EventMessageType.ALL, priority=maxsize - 1)
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async def handle_empty_mention(self, event: AstrMessageEvent):
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"""实现了对只有一个 @ 的消息内容的处理"""
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try:
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messages = event.get_messages()
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if len(messages) == 1:
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if (
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isinstance(messages[0], Comp.At)
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and str(messages[0].qq) == str(event.get_self_id())
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and self.empty_mention_waiting
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) or (
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isinstance(messages[0], Comp.Plain)
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and messages[0].text.strip() in self.wake_prefix
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):
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try:
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# 尝试使用 LLM 生成更生动的回复
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func_tools_mgr = self.context.get_llm_tool_manager()
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# 获取用户当前的对话信息
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curr_cid = await self.context.conversation_manager.get_curr_conversation_id(
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event.unified_msg_origin
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)
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conversation = None
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context = []
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if curr_cid:
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conversation = await self.context.conversation_manager.get_conversation(
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event.unified_msg_origin, curr_cid
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)
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context = (
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json.loads(conversation.history)
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if conversation.history
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else []
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)
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else:
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# 创建新对话
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curr_cid = await self.context.conversation_manager.new_conversation(
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event.unified_msg_origin
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)
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# 使用 LLM 生成回复
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yield event.request_llm(
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prompt="用户只是@我或唤醒我，请友好地询问用户想要聊些什么或者需要什么帮助，回复要符合人设，不要太过机械化。",
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func_tool_manager=func_tools_mgr,
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session_id=curr_cid,
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contexts=context,
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system_prompt="",
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conversation=conversation,
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)
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except Exception as e:
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logger.error(f"LLM response failed: {str(e)}")
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# LLM 回复失败，使用原始预设回复
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yield event.plain_result("想要问什么呢？😄")
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@session_waiter(60)
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async def empty_mention_waiter(
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controller: SessionController, event: AstrMessageEvent
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):
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logger.info("empty_mention_waiter")
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event.message_obj.message.insert(
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0, Comp.At(qq=event.get_self_id(), name=event.get_self_id())
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)
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new_event = copy.copy(event)
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self.context.get_event_queue().put_nowait(
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new_event
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) # 重新推入事件队列
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event.stop_event()
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controller.stop()
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try:
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await empty_mention_waiter(event)
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except TimeoutError as _:
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try:
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# 超时时也尝试使用 LLM 生成回复
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yield event.request_llm(
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prompt="用户在提问后超时未回复，请生成一个温馨友好的提醒，告诉用户如果需要帮助可以再次提问，回答要符合人设。",
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func_tool_manager=self.context.get_llm_tool_manager(),
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system_prompt="",
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)
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except Exception:
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# LLM 回复失败，使用原始预设回复
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yield event.plain_result("如果需要帮助，请再次 @ 我哦~")
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except Exception as e:
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yield event.plain_result("发生错误，请联系管理员: " + str(e))
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finally:
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event.stop_event()
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except Exception as e:
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logger.error("handle_empty_mention error: " + str(e))
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