139 lines
4.6 KiB
Python
139 lines
4.6 KiB
Python
"""Expert 配置与模板 - ExpertConfig, ExpertTemplate"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any
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from agentkit.core.config_driven import AgentConfig
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class ExpertConfig(AgentConfig):
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"""扩展 AgentConfig，新增 Expert 专属字段
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Expert 是比 Skill 更高层的角色抽象，一个 Expert 聚合多个 Skill，
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并包含 persona、thinking_style、collaboration_strategy 等角色属性。
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"""
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def __init__(
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self,
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name: str,
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agent_type: str,
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version: str = "1.0.0",
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description: str = "",
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task_mode: str = "llm_generate",
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supported_tasks: list[str] | None = None,
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max_concurrency: int = 1,
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input_schema: dict[str, Any] | None = None,
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output_schema: dict[str, Any] | None = None,
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prompt: dict[str, str] | None = None,
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llm: dict[str, Any] | None = None,
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tools: list[str] | None = None,
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memory: dict[str, Any] | None = None,
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custom_handler: str | None = None,
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# Expert 专属字段
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persona: str = "",
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thinking_style: str = "",
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collaboration_strategy: str = "cooperative",
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bound_skills: list[str] | None = None,
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avatar: str = "",
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color: str = "#1890ff",
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is_lead: bool = False,
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):
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super().__init__(
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name=name,
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agent_type=agent_type,
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version=version,
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description=description,
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task_mode=task_mode,
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supported_tasks=supported_tasks,
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max_concurrency=max_concurrency,
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input_schema=input_schema,
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output_schema=output_schema,
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prompt=prompt,
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llm=llm,
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tools=tools,
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memory=memory,
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custom_handler=custom_handler,
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)
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self.persona = persona
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self.thinking_style = thinking_style
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self.collaboration_strategy = collaboration_strategy
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self.bound_skills = bound_skills or []
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self.avatar = avatar
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self.color = color
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self.is_lead = is_lead
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@classmethod
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def from_dict(cls, data: dict[str, Any]) -> ExpertConfig:
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"""从字典创建配置"""
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return cls(
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name=data["name"],
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agent_type=data["agent_type"],
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version=data.get("version", "1.0.0"),
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description=data.get("description", ""),
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task_mode=data.get("task_mode", "llm_generate"),
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supported_tasks=data.get("supported_tasks"),
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max_concurrency=data.get("max_concurrency", 1),
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input_schema=data.get("input_schema"),
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output_schema=data.get("output_schema"),
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prompt=data.get("prompt"),
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llm=data.get("llm"),
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tools=data.get("tools"),
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memory=data.get("memory"),
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custom_handler=data.get("custom_handler"),
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persona=data.get("persona", ""),
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thinking_style=data.get("thinking_style", ""),
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collaboration_strategy=data.get("collaboration_strategy", "cooperative"),
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bound_skills=data.get("bound_skills"),
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avatar=data.get("avatar", ""),
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color=data.get("color", "#1890ff"),
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is_lead=data.get("is_lead", False),
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)
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def to_dict(self) -> dict[str, Any]:
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"""序列化为字典，包含 Expert 专属字段"""
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d = super().to_dict()
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d["persona"] = self.persona
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d["thinking_style"] = self.thinking_style
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d["collaboration_strategy"] = self.collaboration_strategy
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d["bound_skills"] = self.bound_skills
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d["avatar"] = self.avatar
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d["color"] = self.color
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d["is_lead"] = self.is_lead
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return d
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@dataclass
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class ExpertTemplate:
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"""Expert 模板 - 可复用的 Expert 配置模板
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用于预定义 Expert 角色配置，支持内置模板和用户自定义模板。
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"""
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name: str
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config: ExpertConfig
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is_builtin: bool = False
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description: str = ""
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def to_dict(self) -> dict[str, Any]:
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"""序列化为字典"""
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return {
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"name": self.name,
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"config": self.config.to_dict(),
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"is_builtin": self.is_builtin,
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"description": self.description,
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}
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@classmethod
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def from_dict(cls, data: dict[str, Any]) -> ExpertTemplate:
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"""从字典创建模板"""
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config_data = data["config"]
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config = ExpertConfig.from_dict(config_data)
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return cls(
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name=data["name"],
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config=config,
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is_builtin=data.get("is_builtin", False),
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description=data.get("description", ""),
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)
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