mirror of
https://github.com/AstrBotDevs/AstrBot
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cdf72b75a4
新增模块: - api/basic/: AstrBotConfig, BaseConversationManager, Conversation - api/event/: AstrMessageEvent, AstrBotMessage, EventType, MessageType, MessageSession, EventResult 等核心事件类型 - api/message/: MessageChain 及所有消息组件 (Plain, At, Image, File 等) - api/platform/: PlatformMetadata 平台元数据类型 - api/provider/: LLMResponse 提供者响应实体 - api/star/star.py: StarMetadata 插件元数据类型 更新模块: - api/__init__.py: 导出所有子模块 - api/components/: 扩展 Command 兼容性 - api/event/filter.py: 增强 filter 装饰器兼容性 - context.py, decorators.py, events.py: 顶层兼容入口 测试更新: - test_api_event_filter.py: 验证 filter 兼容性 - test_api_modules.py: 验证新模块可导入 - test_handler_dispatcher.py: 验证处理器分发 文档更新: - AGENTS.md/CLAUDE.md: 添加兼容层设计说明，避免重复造轮子 设计原则: - 兼容层通过 thin re-export 方式暴露旧版 API - 不复制独立运行时逻辑，保持架构清晰 - 新版推荐使用顶层模块导入路径
111 lines
3.3 KiB
Python
111 lines
3.3 KiB
Python
"""旧版 Provider 实体兼容类型。"""
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from __future__ import annotations
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import json
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from dataclasses import dataclass
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from typing import Any
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from ..message import components as Comp
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from ..message.chain import MessageChain
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try:
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from astr_agent_sdk.message import ToolCall as _ToolCall
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except ImportError:
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@dataclass(slots=True)
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class _ToolCallFunctionBody:
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name: str
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arguments: str
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@dataclass(slots=True)
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class _ToolCall:
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id: str
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function: _ToolCallFunctionBody
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FunctionBody = _ToolCallFunctionBody
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@dataclass(init=False)
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class LLMResponse:
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"""兼容旧版 LLM 响应对象。"""
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role: str
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result_chain: MessageChain | None
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tools_call_args: list[dict[str, Any]]
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tools_call_name: list[str]
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tools_call_ids: list[str]
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raw_completion: Any | None
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_new_record: dict[str, Any] | None
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_completion_text: str
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is_chunk: bool
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def __init__(
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self,
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role: str,
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completion_text: str = "",
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result_chain: MessageChain | None = None,
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tools_call_args: list[dict[str, Any]] | None = None,
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tools_call_name: list[str] | None = None,
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tools_call_ids: list[str] | None = None,
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raw_completion: Any | None = None,
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_new_record: dict[str, Any] | None = None,
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is_chunk: bool = False,
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) -> None:
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self.role = role
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self.result_chain = result_chain
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self.tools_call_args = list(tools_call_args or [])
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self.tools_call_name = list(tools_call_name or [])
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self.tools_call_ids = list(tools_call_ids or [])
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self.raw_completion = raw_completion
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self._new_record = _new_record
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self._completion_text = completion_text
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self.is_chunk = is_chunk
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@property
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def completion_text(self) -> str:
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if self.result_chain:
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return self.result_chain.get_plain_text()
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return self._completion_text
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@completion_text.setter
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def completion_text(self, value: str) -> None:
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if self.result_chain:
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self.result_chain.chain = [
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component
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for component in self.result_chain.chain
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if not isinstance(component, Comp.Plain)
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]
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self.result_chain.chain.insert(0, Comp.Plain(text=value))
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return
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self._completion_text = value
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def to_openai_tool_calls(self) -> list[dict[str, Any]]:
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ret: list[dict[str, Any]] = []
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for idx, tool_call_arg in enumerate(self.tools_call_args):
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ret.append(
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{
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"id": self.tools_call_ids[idx],
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"function": {
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"name": self.tools_call_name[idx],
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"arguments": json.dumps(tool_call_arg),
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},
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"type": "function",
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}
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)
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return ret
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def to_openai_to_calls_model(self) -> list[_ToolCall]:
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ret: list[_ToolCall] = []
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for idx, tool_call_arg in enumerate(self.tools_call_args):
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ret.append(
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_ToolCall(
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id=self.tools_call_ids[idx],
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function=_ToolCall.FunctionBody(
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name=self.tools_call_name[idx],
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arguments=json.dumps(tool_call_arg),
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),
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)
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)
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return ret
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