feat: Enhance OpenAI compatibility and introduce unified LLM service data models
- Refactored model fetching logic to support various authentication methods for OpenAI-compatible APIs. - Introduced new data models for LLM service requests and responses to standardize interactions across layers. - Added an adapter base class for unified request execution across different providers. - Implemented utility functions for building OpenAI-compatible client configurations and request overrides.
This commit is contained in:
DrSmoothl
@@ -1,12 +1,13 @@
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import copy
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import warnings
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from dataclasses import dataclass, field, fields
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from enum import Enum
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from typing import Any, Dict, List, Optional, Tuple
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from typing import Any, Dict, List, Optional
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import copy
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import warnings
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from maim_message import Seg
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from src.llm_models.payload_content.tool_option import ToolParamType as ToolParamType
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from src.llm_models.payload_content.tool_option import ToolCall as ToolCall
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from src.llm_models.payload_content.tool_option import ToolCall
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# from src.common.data_models.message_data_model import ReplyContentType as ReplyContentType
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# from src.common.data_models.message_data_model import ReplyContent as ReplyContent
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# from src.common.data_models.message_data_model import ForwardNode as ForwardNode
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@@ -15,49 +16,42 @@ from src.llm_models.payload_content.tool_option import ToolCall as ToolCall
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# 组件类型枚举
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class ComponentType(Enum):
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"""组件类型枚举"""
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"""Host 内部使用的组件类型枚举。"""
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ACTION = "action" # 动作组件
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COMMAND = "command" # 命令组件
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TOOL = "tool" # 服务组件（预留）
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SCHEDULER = "scheduler" # 定时任务组件（预留）
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EVENT_HANDLER = "event_handler" # 事件处理组件（预留）
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TOOL = "tool" # 工具组件
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def __str__(self) -> str:
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"""返回枚举值字符串。
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Returns:
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str: 当前组件类型对应的字符串值。
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"""
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return self.value
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# 动作激活类型枚举
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class ActionActivationType(Enum):
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"""动作激活类型枚举"""
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"""动作激活类型枚举。"""
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NEVER = "never" # 从不激活（默认关闭）
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ALWAYS = "always" # 默认参与到planner
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RANDOM = "random" # 随机启用action到planner
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KEYWORD = "keyword" # 关键词触发启用action到planner
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def __str__(self):
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return self.value
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def __str__(self) -> str:
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"""返回枚举值字符串。
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# 聊天模式枚举
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class ChatMode(Enum):
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"""聊天模式枚举"""
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FOCUS = "focus" # Focus聊天模式
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NORMAL = "normal" # Normal聊天模式
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PRIORITY = "priority" # 优先级聊天模式
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ALL = "all" # 所有聊天模式
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def __str__(self):
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Returns:
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str: 当前激活类型对应的字符串值。
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"""
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return self.value
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# 事件类型枚举
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class EventType(Enum):
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"""
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事件类型枚举类
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"""
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"""事件类型枚举。"""
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ON_START = "on_start" # 启动事件，用于调用按时任务
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ON_STOP = "on_stop" # 停止事件，用于调用按时任务
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@@ -72,185 +66,96 @@ class EventType(Enum):
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UNKNOWN = "unknown" # 未知事件类型
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def __str__(self) -> str:
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"""返回枚举值字符串。
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Returns:
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str: 当前事件类型对应的字符串值。
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"""
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return self.value
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@dataclass
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class PythonDependency:
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"""Python包依赖信息"""
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package_name: str # 包名称
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version: str = "" # 版本要求，例如: ">=1.0.0", "==2.1.3", ""表示任意版本
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optional: bool = False # 是否为可选依赖
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description: str = "" # 依赖描述
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install_name: str = "" # 安装时的包名（如果与import名不同）
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def __post_init__(self):
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if not self.install_name:
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self.install_name = self.package_name
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def get_pip_requirement(self) -> str:
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"""获取pip安装格式的依赖字符串"""
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if self.version:
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return f"{self.install_name}{self.version}"
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return self.install_name
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@dataclass
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@dataclass(slots=True)
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class ComponentInfo:
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"""组件信息"""
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"""Host 内部使用的组件信息快照。"""
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name: str # 组件名称
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component_type: ComponentType # 组件类型
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description: str = "" # 组件描述
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enabled: bool = True # 是否启用
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plugin_name: str = "" # 所属插件名称
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is_built_in: bool = False # 是否为内置组件
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metadata: Dict[str, Any] = field(default_factory=dict) # 额外元数据
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name: str
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"""组件名称。"""
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def __post_init__(self):
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if self.metadata is None:
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self.metadata = {}
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description: str = ""
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"""组件描述。"""
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enabled: bool = True
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"""组件是否启用。"""
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plugin_name: str = ""
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"""所属插件 ID。"""
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component_type: ComponentType = field(init=False)
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"""组件类型。"""
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@dataclass
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@dataclass(slots=True)
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class ActionInfo(ComponentInfo):
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"""动作组件信息"""
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"""供 Planner 与回复链使用的动作信息快照。"""
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action_parameters: Dict[str, str] = field(
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default_factory=dict
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) # 动作参数与描述，例如 {"param1": "描述1", "param2": "描述2"}
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action_require: List[str] = field(default_factory=list) # 动作需求说明
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associated_types: List[str] = field(default_factory=list) # 关联的消息类型
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# 激活类型相关
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focus_activation_type: ActionActivationType = ActionActivationType.ALWAYS # 已弃用
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normal_activation_type: ActionActivationType = ActionActivationType.ALWAYS # 已弃用
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activation_type: ActionActivationType = ActionActivationType.ALWAYS
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random_activation_probability: float = 0.0
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activation_keywords: List[str] = field(default_factory=list) # 激活关键词列表
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keyword_case_sensitive: bool = False
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# 模式和并行设置
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parallel_action: bool = False
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component_type: ComponentType = field(init=False, default=ComponentType.ACTION)
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"""组件类型。"""
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def __post_init__(self):
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super().__post_init__()
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if self.activation_keywords is None:
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self.activation_keywords = []
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if self.action_parameters is None:
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self.action_parameters = {}
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if self.action_require is None:
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self.action_require = []
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if self.associated_types is None:
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self.associated_types = []
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self.component_type = ComponentType.ACTION
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def __post_init__(self) -> None:
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"""归一化动作快照中的集合字段。"""
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self.action_parameters = dict(self.action_parameters or {})
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self.action_require = list(self.action_require or [])
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self.associated_types = list(self.associated_types or [])
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self.activation_keywords = list(self.activation_keywords or [])
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@dataclass
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@dataclass(slots=True)
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class CommandInfo(ComponentInfo):
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"""命令组件信息"""
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"""供命令处理链使用的命令信息快照。"""
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command_pattern: str = "" # 命令匹配模式（正则表达式）
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def __post_init__(self):
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super().__post_init__()
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self.component_type = ComponentType.COMMAND
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component_type: ComponentType = field(init=False, default=ComponentType.COMMAND)
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"""组件类型。"""
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@dataclass
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@dataclass(slots=True)
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class ToolInfo(ComponentInfo):
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"""工具组件信息"""
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"""供工具执行链使用的工具信息快照。"""
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tool_parameters: List[Tuple[str, ToolParamType, str, bool, List[str] | None]] = field(
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default_factory=list
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) # 工具参数定义
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tool_description: str = "" # 工具描述
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parameters_schema: Dict[str, Any] | None = None
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"""对象级工具参数 Schema。"""
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def __post_init__(self):
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super().__post_init__()
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self.component_type = ComponentType.TOOL
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component_type: ComponentType = field(init=False, default=ComponentType.TOOL)
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"""组件类型。"""
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def get_llm_definition(self) -> dict:
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"""生成 LLM function-calling 所需的工具定义"""
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return {
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def get_llm_definition(self) -> Dict[str, Any]:
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"""生成供 LLM 使用的规范化工具定义。
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Returns:
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Dict[str, Any]: 统一工具定义字典。
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"""
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definition: Dict[str, Any] = {
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"name": self.name,
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"description": self.tool_description,
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"parameters": self.tool_parameters,
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"description": self.description,
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}
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if self.parameters_schema is not None:
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definition["parameters_schema"] = copy.deepcopy(self.parameters_schema)
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return definition
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@dataclass
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class EventHandlerInfo(ComponentInfo):
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"""事件处理器组件信息"""
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event_type: EventType | str = EventType.ON_MESSAGE # 监听事件类型
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intercept_message: bool = False # 是否拦截消息处理（默认不拦截）
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weight: int = 0 # 事件处理器权重，决定执行顺序
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def __post_init__(self):
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super().__post_init__()
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self.component_type = ComponentType.EVENT_HANDLER
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@dataclass
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class PluginInfo:
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"""插件信息"""
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display_name: str # 插件显示名称
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name: str # 插件名称
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description: str # 插件描述
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version: str = "1.0.0" # 插件版本
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author: str = "" # 插件作者
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enabled: bool = True # 是否启用
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is_built_in: bool = False # 是否为内置插件
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components: List[ComponentInfo] = field(default_factory=list) # 包含的组件列表
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dependencies: List[str] = field(default_factory=list) # 依赖的其他插件
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python_dependencies: List[PythonDependency] = field(default_factory=list) # Python包依赖
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config_file: str = "" # 配置文件路径
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metadata: Dict[str, Any] = field(default_factory=dict) # 额外元数据
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# 新增：manifest相关信息
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manifest_data: Dict[str, Any] = field(default_factory=dict) # manifest文件数据
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license: str = "" # 插件许可证
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homepage_url: str = "" # 插件主页
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repository_url: str = "" # 插件仓库地址
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keywords: List[str] = field(default_factory=list) # 插件关键词
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categories: List[str] = field(default_factory=list) # 插件分类
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min_host_version: str = "" # 最低主机版本要求
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max_host_version: str = "" # 最高主机版本要求
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def __post_init__(self):
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if self.components is None:
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self.components = []
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if self.dependencies is None:
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self.dependencies = []
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if self.python_dependencies is None:
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self.python_dependencies = []
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if self.metadata is None:
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self.metadata = {}
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if self.manifest_data is None:
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self.manifest_data = {}
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if self.keywords is None:
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self.keywords = []
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if self.categories is None:
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self.categories = []
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def get_missing_packages(self) -> List[PythonDependency]:
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"""检查缺失的Python包"""
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missing = []
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for dep in self.python_dependencies:
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try:
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__import__(dep.package_name)
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except ImportError:
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if not dep.optional:
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missing.append(dep)
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return missing
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def get_pip_requirements(self) -> List[str]:
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"""获取所有pip安装格式的依赖"""
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return [dep.get_pip_requirement() for dep in self.python_dependencies]
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@dataclass
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@dataclass(slots=True)
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class ModifyFlag:
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"""消息修改标记集合。"""
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modify_message_segments: bool = False
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modify_plain_text: bool = False
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modify_llm_prompt: bool = False
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@@ -258,9 +163,9 @@ class ModifyFlag:
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modify_llm_response_reasoning: bool = False
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@dataclass
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@dataclass(slots=True)
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class MaiMessages:
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"""MaiM插件消息"""
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"""核心事件系统使用的统一消息模型。"""
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message_segments: List[Seg] = field(default_factory=list)
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"""消息段列表，支持多段消息"""
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@@ -306,11 +211,17 @@ class MaiMessages:
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_modify_flags: ModifyFlag = field(default_factory=ModifyFlag)
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def __post_init__(self):
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def __post_init__(self) -> None:
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"""归一化消息段列表。"""
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if self.message_segments is None:
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self.message_segments = []
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def deepcopy(self):
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def deepcopy(self) -> "MaiMessages":
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"""深拷贝当前消息对象。
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Returns:
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MaiMessages: 深拷贝后的消息对象。
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"""
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return copy.deepcopy(self)
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def to_transport_dict(self) -> Dict[str, Any]:
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@@ -347,6 +258,14 @@ class MaiMessages:
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@staticmethod
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def _serialize_transport_value(value: Any) -> Any:
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"""递归序列化字段值为可传输结构。
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Args:
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value: 任意字段值。
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Returns:
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Any: 可用于 IPC 传输的纯 Python 值。
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"""
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if isinstance(value, (str, int, float, bool)) or value is None:
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return value
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if isinstance(value, Enum):
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@@ -367,13 +286,22 @@ class MaiMessages:
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@staticmethod
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def _deserialize_transport_field(field_name: str, value: Any) -> Any:
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"""反序列化特定字段的传输值。
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|
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Args:
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field_name: 字段名称。
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value: 传输层返回的字段值。
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||||
|
||||
Returns:
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Any: 反序列化后的字段值。
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||||
"""
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if field_name == "message_segments" and isinstance(value, list):
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deserialized_segments: List[Seg] = []
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for segment in value:
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if isinstance(segment, Seg):
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deserialized_segments.append(segment)
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elif isinstance(segment, dict) and "type" in segment:
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deserialized_segments.append(Seg(type=segment.get("type", "text"), data=segment.get("data")))
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deserialized_segments.append(Seg(type=segment.get("type", "text"), data=segment.get("data", "")))
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return deserialized_segments
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|
||||
if field_name == "llm_response_tool_call" and isinstance(value, list):
|
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@@ -393,15 +321,15 @@ class MaiMessages:
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||||
|
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return value
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||||
|
||||
def modify_message_segments(self, new_segments: List[Seg], suppress_warning: bool = False):
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"""
|
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修改消息段列表
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def modify_message_segments(self, new_segments: List[Seg], suppress_warning: bool = False) -> None:
|
||||
"""修改消息段列表。
|
||||
|
||||
Warning:
|
||||
在生成了plain_text的情况下调用此方法，可能会导致plain_text内容与消息段不一致
|
||||
在生成了 ``plain_text`` 的情况下调用此方法，可能会导致文本与消息段不一致。
|
||||
|
||||
Args:
|
||||
new_segments (List[Seg]): 新的消息段列表
|
||||
new_segments: 新的消息段列表。
|
||||
suppress_warning: 是否抑制潜在不一致警告。
|
||||
"""
|
||||
if self.plain_text and not suppress_warning:
|
||||
warnings.warn(
|
||||
@@ -412,15 +340,15 @@ class MaiMessages:
|
||||
self.message_segments = new_segments
|
||||
self._modify_flags.modify_message_segments = True
|
||||
|
||||
def modify_llm_prompt(self, new_prompt: str, suppress_warning: bool = False):
|
||||
"""
|
||||
修改LLM提示词
|
||||
def modify_llm_prompt(self, new_prompt: str, suppress_warning: bool = False) -> None:
|
||||
"""修改 LLM 提示词。
|
||||
|
||||
Warning:
|
||||
在没有生成llm_prompt的情况下调用此方法，可能会导致修改无效
|
||||
在没有生成 ``llm_prompt`` 的情况下调用此方法，可能会导致修改无效。
|
||||
|
||||
Args:
|
||||
new_prompt (str): 新的提示词内容
|
||||
new_prompt: 新的提示词内容。
|
||||
suppress_warning: 是否抑制潜在无效修改警告。
|
||||
"""
|
||||
if self.llm_prompt is None and not suppress_warning:
|
||||
warnings.warn(
|
||||
@@ -431,15 +359,15 @@ class MaiMessages:
|
||||
self.llm_prompt = new_prompt
|
||||
self._modify_flags.modify_llm_prompt = True
|
||||
|
||||
def modify_plain_text(self, new_text: str, suppress_warning: bool = False):
|
||||
"""
|
||||
修改生成的plain_text内容
|
||||
def modify_plain_text(self, new_text: str, suppress_warning: bool = False) -> None:
|
||||
"""修改生成的纯文本内容。
|
||||
|
||||
Warning:
|
||||
在未生成plain_text的情况下调用此方法，可能会导致plain_text为空或者修改无效
|
||||
在未生成 ``plain_text`` 的情况下调用此方法，可能会导致修改无效。
|
||||
|
||||
Args:
|
||||
new_text (str): 新的纯文本内容
|
||||
new_text: 新的纯文本内容。
|
||||
suppress_warning: 是否抑制潜在无效修改警告。
|
||||
"""
|
||||
if not self.plain_text and not suppress_warning:
|
||||
warnings.warn(
|
||||
@@ -450,15 +378,15 @@ class MaiMessages:
|
||||
self.plain_text = new_text
|
||||
self._modify_flags.modify_plain_text = True
|
||||
|
||||
def modify_llm_response_content(self, new_content: str, suppress_warning: bool = False):
|
||||
"""
|
||||
修改生成的llm_response_content内容
|
||||
def modify_llm_response_content(self, new_content: str, suppress_warning: bool = False) -> None:
|
||||
"""修改生成的 LLM 响应正文。
|
||||
|
||||
Warning:
|
||||
在未生成llm_response_content的情况下调用此方法，可能会导致llm_response_content为空或者修改无效
|
||||
在未生成 ``llm_response_content`` 的情况下调用此方法，可能会导致修改无效。
|
||||
|
||||
Args:
|
||||
new_content (str): 新的LLM响应内容
|
||||
new_content: 新的 LLM 响应内容。
|
||||
suppress_warning: 是否抑制潜在无效修改警告。
|
||||
"""
|
||||
if not self.llm_response_content and not suppress_warning:
|
||||
warnings.warn(
|
||||
@@ -469,15 +397,15 @@ class MaiMessages:
|
||||
self.llm_response_content = new_content
|
||||
self._modify_flags.modify_llm_response_content = True
|
||||
|
||||
def modify_llm_response_reasoning(self, new_reasoning: str, suppress_warning: bool = False):
|
||||
"""
|
||||
修改生成的llm_response_reasoning内容
|
||||
def modify_llm_response_reasoning(self, new_reasoning: str, suppress_warning: bool = False) -> None:
|
||||
"""修改生成的 LLM 推理内容。
|
||||
|
||||
Warning:
|
||||
在未生成llm_response_reasoning的情况下调用此方法，可能会导致llm_response_reasoning为空或者修改无效
|
||||
在未生成 ``llm_response_reasoning`` 的情况下调用此方法，可能会导致修改无效。
|
||||
|
||||
Args:
|
||||
new_reasoning (str): 新的LLM响应推理内容
|
||||
new_reasoning: 新的 LLM 推理内容。
|
||||
suppress_warning: 是否抑制潜在无效修改警告。
|
||||
"""
|
||||
if not self.llm_response_reasoning and not suppress_warning:
|
||||
warnings.warn(
|
||||
@@ -487,10 +415,3 @@ class MaiMessages:
|
||||
)
|
||||
self.llm_response_reasoning = new_reasoning
|
||||
self._modify_flags.modify_llm_response_reasoning = True
|
||||
|
||||
|
||||
@dataclass
|
||||
class CustomEventHandlerResult:
|
||||
message: str = ""
|
||||
timestamp: float = 0.0
|
||||
extra_info: Optional[Dict] = None
|
||||
|
||||
Reference in New Issue
Block a user