304 lines
11 KiB
Python
304 lines
11 KiB
Python
from asyncio import Queue
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from typing import List, Union
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from astrbot.core import sp
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from astrbot.core.provider.provider import Provider, TTSProvider, STTProvider
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from astrbot.core.db import BaseDatabase
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from astrbot.core.config.astrbot_config import AstrBotConfig
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from astrbot.core.provider.func_tool_manager import FuncCall
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from astrbot.core.platform.astr_message_event import MessageSesion
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from astrbot.core.message.message_event_result import MessageChain
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from astrbot.core.provider.manager import ProviderManager
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from astrbot.core.platform import Platform
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from astrbot.core.platform.manager import PlatformManager
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from .star import star_registry, StarMetadata, star_map
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from .star_handler import star_handlers_registry, StarHandlerMetadata, EventType
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from .filter.command import CommandFilter
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from .filter.regex import RegexFilter
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from typing import Awaitable
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from astrbot.core.rag.knowledge_db_mgr import KnowledgeDBManager
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from astrbot.core.conversation_mgr import ConversationManager
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from astrbot.core.star.filter.platform_adapter_type import (
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PlatformAdapterType,
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ADAPTER_NAME_2_TYPE,
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)
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class Context:
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"""
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暴露给插件的接口上下文。
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"""
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_event_queue: Queue = None
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"""事件队列。消息平台通过事件队列传递消息事件。"""
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_config: AstrBotConfig = None
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"""AstrBot 配置信息"""
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_db: BaseDatabase = None
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"""AstrBot 数据库"""
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provider_manager: ProviderManager = None
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platform_manager: PlatformManager = None
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# back compatibility
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_register_tasks: List[Awaitable] = []
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_star_manager = None
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def __init__(
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self,
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event_queue: Queue,
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config: AstrBotConfig,
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db: BaseDatabase,
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provider_manager: ProviderManager = None,
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platform_manager: PlatformManager = None,
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conversation_manager: ConversationManager = None,
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knowledge_db_manager: KnowledgeDBManager = None,
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):
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self._event_queue = event_queue
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self._config = config
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self._db = db
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self.provider_manager = provider_manager
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self.platform_manager = platform_manager
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self.knowledge_db_manager = knowledge_db_manager
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self.conversation_manager = conversation_manager
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def get_registered_star(self, star_name: str) -> StarMetadata:
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"""根据插件名获取插件的 Metadata"""
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for star in star_registry:
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if star.name == star_name:
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return star
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def get_all_stars(self) -> List[StarMetadata]:
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"""获取当前载入的所有插件 Metadata 的列表"""
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return star_registry
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def get_llm_tool_manager(self) -> FuncCall:
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"""获取 LLM Tool Manager，其用于管理注册的所有的 Function-calling tools"""
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return self.provider_manager.llm_tools
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def activate_llm_tool(self, name: str) -> bool:
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"""激活一个已经注册的函数调用工具。注册的工具默认是激活状态。
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Returns:
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如果没找到，会返回 False
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"""
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func_tool = self.provider_manager.llm_tools.get_func(name)
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if func_tool is not None:
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if func_tool.handler_module_path in star_map:
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if not star_map[func_tool.handler_module_path].activated:
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raise ValueError(
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f"此函数调用工具所属的插件 {star_map[func_tool.handler_module_path].name} 已被禁用，请先在管理面板启用再激活此工具。"
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)
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func_tool.active = True
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inactivated_llm_tools: list = sp.get("inactivated_llm_tools", [])
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if name in inactivated_llm_tools:
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inactivated_llm_tools.remove(name)
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sp.put("inactivated_llm_tools", inactivated_llm_tools)
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return True
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return False
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def deactivate_llm_tool(self, name: str) -> bool:
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"""停用一个已经注册的函数调用工具。
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Returns:
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如果没找到，会返回 False"""
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func_tool = self.provider_manager.llm_tools.get_func(name)
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if func_tool is not None:
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func_tool.active = False
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inactivated_llm_tools: list = sp.get("inactivated_llm_tools", [])
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if name not in inactivated_llm_tools:
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inactivated_llm_tools.append(name)
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sp.put("inactivated_llm_tools", inactivated_llm_tools)
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return True
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return False
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def register_provider(self, provider: Provider):
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"""
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注册一个 LLM Provider(Chat_Completion 类型)。
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"""
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self.provider_manager.provider_insts.append(provider)
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def get_provider_by_id(self, provider_id: str) -> Provider:
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"""通过 ID 获取用于文本生成任务的 LLM Provider(Chat_Completion 类型)。"""
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for provider in self.provider_manager.provider_insts:
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if provider.meta().id == provider_id:
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return provider
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return None
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def get_all_providers(self) -> List[Provider]:
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"""获取所有用于文本生成任务的 LLM Provider(Chat_Completion 类型)。"""
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return self.provider_manager.provider_insts
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def get_all_tts_providers(self) -> List[TTSProvider]:
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"""获取所有用于 TTS 任务的 Provider。"""
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return self.provider_manager.tts_provider_insts
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def get_all_stt_providers(self) -> List[STTProvider]:
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"""获取所有用于 STT 任务的 Provider。"""
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return self.provider_manager.stt_provider_insts
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def get_using_provider(self) -> Provider:
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"""
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获取当前使用的用于文本生成任务的 LLM Provider(Chat_Completion 类型)。
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通过 /provider 指令切换。
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"""
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return self.provider_manager.curr_provider_inst
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def get_using_tts_provider(self) -> TTSProvider:
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"""
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获取当前使用的用于 TTS 任务的 Provider。
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"""
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return self.provider_manager.curr_tts_provider_inst
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def get_using_stt_provider(self) -> STTProvider:
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"""
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获取当前使用的用于 STT 任务的 Provider。
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"""
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return self.provider_manager.curr_stt_provider_inst
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def get_config(self) -> AstrBotConfig:
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"""获取 AstrBot 的配置。"""
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return self._config
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def get_db(self) -> BaseDatabase:
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"""获取 AstrBot 数据库。"""
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return self._db
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def get_event_queue(self) -> Queue:
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"""
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获取事件队列。
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"""
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return self._event_queue
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def get_platform(self, platform_type: Union[PlatformAdapterType, str]) -> Platform:
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"""
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获取指定类型的平台适配器。
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"""
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for platform in self.platform_manager.platform_insts:
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name = platform.meta().name
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if isinstance(platform_type, str):
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if name == platform_type:
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return platform
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else:
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if (
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name in ADAPTER_NAME_2_TYPE
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and ADAPTER_NAME_2_TYPE[name] & platform_type
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):
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return platform
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async def send_message(
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self, session: Union[str, MessageSesion], message_chain: MessageChain
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) -> bool:
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"""
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根据 session(unified_msg_origin) 主动发送消息。
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@param session: 消息会话。通过 event.session 或者 event.unified_msg_origin 获取。
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@param message_chain: 消息链。
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@return: 是否找到匹配的平台。
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当 session 为字符串时，会尝试解析为 MessageSesion 对象，如果解析失败，会抛出 ValueError 异常。
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NOTE: qq_official(QQ 官方 API 平台) 不支持此方法
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"""
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if isinstance(session, str):
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try:
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session = MessageSesion.from_str(session)
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except BaseException as e:
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raise ValueError("不合法的 session 字符串: " + str(e))
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for platform in self.platform_manager.platform_insts:
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if platform.meta().name == session.platform_name:
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await platform.send_by_session(session, message_chain)
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return True
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return False
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"""
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以下的方法已经不推荐使用。请从 AstrBot 文档查看更好的注册方式。
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"""
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def register_llm_tool(
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self, name: str, func_args: list, desc: str, func_obj: Awaitable
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) -> None:
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"""
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为函数调用（function-calling / tools-use）添加工具。
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@param name: 函数名
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@param func_args: 函数参数列表，格式为 [{"type": "string", "name": "arg_name", "description": "arg_description"}, ...]
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@param desc: 函数描述
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@param func_obj: 异步处理函数。
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异步处理函数会接收到额外的的关键词参数：event: AstrMessageEvent, context: Context。
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"""
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md = StarHandlerMetadata(
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event_type=EventType.OnLLMRequestEvent,
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handler_full_name=func_obj.__module__ + "_" + func_obj.__name__,
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handler_name=func_obj.__name__,
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handler_module_path=func_obj.__module__,
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handler=func_obj,
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event_filters=[],
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desc=desc,
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)
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star_handlers_registry.append(md)
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self.provider_manager.llm_tools.add_func(
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name, func_args, desc, func_obj, func_obj
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)
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def unregister_llm_tool(self, name: str) -> None:
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"""删除一个函数调用工具。如果再要启用，需要重新注册。"""
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self.provider_manager.llm_tools.remove_func(name)
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def register_commands(
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self,
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star_name: str,
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command_name: str,
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desc: str,
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priority: int,
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awaitable: Awaitable,
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use_regex=False,
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ignore_prefix=False,
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):
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"""
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注册一个命令。
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[Deprecated] 推荐使用装饰器注册指令。该方法将在未来的版本中被移除。
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@param star_name: 插件（Star）名称。
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@param command_name: 命令名称。
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@param desc: 命令描述。
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@param priority: 优先级。1-10。
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@param awaitable: 异步处理函数。
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"""
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md = StarHandlerMetadata(
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event_type=EventType.AdapterMessageEvent,
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handler_full_name=awaitable.__module__ + "_" + awaitable.__name__,
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handler_name=awaitable.__name__,
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handler_module_path=awaitable.__module__,
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handler=awaitable,
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event_filters=[],
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desc=desc,
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)
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if use_regex:
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md.event_filters.append(RegexFilter(regex=command_name))
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else:
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md.event_filters.append(
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CommandFilter(command_name=command_name, handler_md=md)
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)
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star_handlers_registry.append(md)
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def register_task(self, task: Awaitable, desc: str):
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"""
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注册一个异步任务。
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"""
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self._register_tasks.append(task)
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