style: 项目重构
1.项目改名为kilostar（千星） 2.后端部分进行大规模重构 3.node功能进行大规模重新设计
This commit is contained in:
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# Copyright 2026 zhaoxi826
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from kilostar.worker_individual.base_individual import BaseIndividual
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from kilostar.worker_individual.skill_individual import SkillIndividual
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from kilostar.worker_individual.ordinary_individual import OrdinaryIndividual
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from kilostar.worker_individual.special_individual import SpecialIndividual
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__all__ = [
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"BaseIndividual",
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"SkillIndividual",
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"OrdinaryIndividual",
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"SpecialIndividual",
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]
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# Copyright 2026 zhaoxi826
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from pydantic_ai import Agent, RunContext
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from pydantic import Field
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from kilostar.adapter.model_adapter.agent_factory import AgentFactory
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from kilostar.core.global_state_machine.model_provider.base_provider import Provider
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from kilostar.utils.agent_model import ResponseModel, InputModel, DepsModel
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from kilostar.utils.ray_hook import ray_actor_hook
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from kilostar.utils.logger import get_logger
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logger = get_logger("worker_individual")
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class WorkerIndividualResponse(ResponseModel):
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"""WorkerIndividualResponse 核心组件类。
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这是一个具体的 Worker 智能体实体类，代表着具备特定人设、领域技能或长文本处理能力的数字员工。它可以被控制器动态拉起，并在安全沙箱内执行复杂的工作流指令与多步骤推理任务。"""
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output: str = Field(..., description="Worker执行任务的输出结果")
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class WorkerIndividualDeps(DepsModel):
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"""WorkerIndividualDeps 核心组件类。
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这是一个具体的 Worker 智能体实体类，代表着具备特定人设、领域技能或长文本处理能力的数字员工。它可以被控制器动态拉起，并在安全沙箱内执行复杂的工作流指令与多步骤推理任务。"""
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task_event: dict
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class WorkerIndividualInput(InputModel):
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"""WorkerIndividualInput 核心组件类。
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这是一个具体的 Worker 智能体实体类，代表着具备特定人设、领域技能或长文本处理能力的数字员工。它可以被控制器动态拉起，并在安全沙箱内执行复杂的工作流指令与多步骤推理任务。"""
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task_event: dict
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class BaseIndividual:
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"""
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Worker Individual 的基类
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"""
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def __init__(self, agent_config: dict):
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self.agent_config = agent_config
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self.agent_id = agent_config.get("agent_id")
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self.agent: Agent | None = None
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async def _init_agent(self, agent_name: str, system_prompt: str):
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"""完成 agent 模块的启动与依赖初始化。
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在系统引导或服务拉起阶段被调用，负责建立网络连接、分配基础内存资源及注册核心服务组件。
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Args: agent_name (str): 赋予该实体的人类可读名称或标题字符串，主要用于前端 UI 展示、日志记录或模糊检索。 system_prompt (str): 控制逻辑流向的具体字符串参数，指定了期望的 system prompt 内容。
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Returns: : 经由当前业务模型加工处理后所输出的具体数据实例或领域模型对象。"""
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from kilostar.utils.get_tool import load_tools_from_list
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global_state_machine = ray_actor_hook(
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"global_state_machine"
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).global_state_machine
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provider_title = self.agent_config.get(
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"provider_title", "openai"
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) # default fallback
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model_id = self.agent_config.get("model_id", "gpt-4o") # default fallback
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tools_list = self.agent_config.get("tools", None)
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provider: Provider = await global_state_machine.get_provider.remote(
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provider_title
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)
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agent_factory = AgentFactory()
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callables = load_tools_from_list(tools_list)
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self.agent = agent_factory.create_agent(
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provider=provider,
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model_id=model_id,
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output_type=WorkerIndividualResponse,
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system_prompt=system_prompt,
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deps_type=WorkerIndividualDeps,
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agent_name=agent_name,
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tools=callables,
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)
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@self.agent.system_prompt
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async def dynamic_prompt(ctx: RunContext[WorkerIndividualDeps]):
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"""执行与 dynamic prompt 相关的核心业务流转操作。
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该方法封装了具体的算法策略或状态控制逻辑，确保操作能够在事务上下文中被原子且一致地执行。
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Args: ctx (RunContext[WorkerIndividualDeps]): 参与 dynamic prompt 逻辑运算或数据构建的上下文依赖对象。
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Returns: : 经由当前业务模型加工处理后所输出的具体数据实例或领域模型对象。"""
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prompt = system_prompt + "\n\n"
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prompt += f"=== 当前任务上下文 ===\n{ctx.deps.task_event}\n"
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return prompt
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async def run(self, task_event: dict) -> dict:
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"""执行与 run 相关的核心业务流转操作。
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该方法封装了具体的算法策略或状态控制逻辑，确保操作能够在事务上下文中被原子且一致地执行。
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Args: task_event (dict): 由事件总线或工作流引擎分发过来的事件载荷，封装了触发此次调用的上下文快照与任务目标指令。
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Returns: (dict): 高度聚合的字典结构数据，将多维度的属性特征或统计指标组合后一并返回。"""
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raise NotImplementedError("子类必须实现 run 方法")
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worker_individual
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---
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**worker_individual**是kilostar中的基础工作对象，主要分为三类:**skill_individual**,**ordinary_individual**和**special_individual**,庞大的**worker_individual**将负责具体的生产工作。
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---
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## worker_individual分类
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### skill_individual（专家子个体）
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**skill_individual（专家子个体）** 是拥有专业**skill**的agent，通常使用MoE（混合专家模型）或者大参数的专家模型来作为agent的模型。通过装配专业化的知识从而实现完成复杂任务。
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### ordinary_individual（普通子个体）
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**ordinary_individual（普通子个体）** 是普通的agent，通常使用小参数微调专家模型来作为agent的模型。通过专业化数据的微调，在一定程度上实现比大参数MoE模型在单一方面上的能力。
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### special_individual（特殊子个体）
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**special_individual（特殊子个体）** 是特殊的agent，这类agent一般不承担普通的生成任务，更多是实现一些特殊的任务，比如生成语音生成视频等。
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# Copyright 2026 zhaoxi826
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from kilostar.worker_individual.base_individual import (
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BaseIndividual,
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WorkerIndividualDeps,
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)
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from kilostar.utils.logger import get_logger
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logger = get_logger("ordinary_individual")
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class OrdinaryIndividual(BaseIndividual):
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"""
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普通子个体：普通的 agent。
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"""
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def __init__(self, agent_config: dict):
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super().__init__(agent_config)
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async def run(self, task_event: dict) -> dict:
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"""执行与 run 相关的核心业务流转操作。
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该方法封装了具体的算法策略或状态控制逻辑，确保操作能够在事务上下文中被原子且一致地执行。
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Args: task_event (dict): 由事件总线或工作流引擎分发过来的事件载荷，封装了触发此次调用的上下文快照与任务目标指令。
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Returns: (dict): 高度聚合的字典结构数据，将多维度的属性特征或统计指标组合后一并返回。"""
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if self.agent is None:
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system_prompt = self.agent_config.get(
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"prompt", "你是一个普通的AI助手，请尽力完成给定的任务。"
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)
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await self._init_agent("ordinary_individual", system_prompt)
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deps = WorkerIndividualDeps(task_event=task_event)
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self.agent.retries = 3
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try:
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result = await self.agent.run(f"请执行以下任务：\n{task_event}", deps=deps)
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return {"output": result.data.output}
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except Exception as e:
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logger.exception(f"OrdinaryIndividual {self.agent_id} 执行失败: {e}")
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raise
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# Copyright 2026 zhaoxi826
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from kilostar.worker_individual.base_individual import (
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BaseIndividual,
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WorkerIndividualDeps,
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)
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from kilostar.utils.logger import get_logger
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import os
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import json
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from pydantic_ai import Tool
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import importlib.util
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logger = get_logger("skill_individual")
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class SkillIndividual(BaseIndividual):
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"""
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专家子个体：拥有专业 skill 的 agent。
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"""
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def __init__(self, agent_config: dict):
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super().__init__(agent_config)
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async def _load_skill_tools(self):
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"""动态加载已绑定的 skill 工具。"""
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tools = []
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bound_skill = self.agent_config.get("bound_skill", "")
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# bound_skill can be string or dict {"skill_name": ["file1", "file2"]}
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skill_mapper = {}
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if isinstance(bound_skill, str) and bound_skill:
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try:
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skill_mapper = json.loads(bound_skill)
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except json.JSONDecodeError:
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pass
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elif isinstance(bound_skill, dict):
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skill_mapper = bound_skill
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skill_base_dir = os.path.abspath(
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os.path.join(os.path.dirname(__file__), "..", "plugin", "skill")
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)
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for skill_name, _ in skill_mapper.items():
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skill_path = os.path.join(skill_base_dir, skill_name)
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metadata_path = os.path.join(skill_path, "metadata.json")
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if not os.path.exists(metadata_path):
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continue
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try:
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with open(metadata_path, "r", encoding="utf-8") as f:
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metadata = json.load(f)
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except Exception as e:
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logger.error(f"Failed to load metadata for skill {skill_name}: {e}")
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continue
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if "functions" in metadata:
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for func_info in metadata["functions"]:
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# Ensure path is absolute
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script_path = func_info.get("file_path", "")
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if not os.path.isabs(script_path):
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script_path = os.path.join(skill_path, script_path)
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if not os.path.exists(script_path):
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logger.warning(f"Skill script not found: {script_path}")
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continue
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func_name = func_info.get("name")
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try:
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# Dynamically load the python module
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spec = importlib.util.spec_from_file_location(
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func_name, script_path
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)
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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func = getattr(module, func_name)
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if callable(func):
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# Convert to PydanticAI Tool
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tool = Tool(
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func,
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name=func_name,
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description=func_info.get("docstring", ""),
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)
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tools.append(tool)
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logger.info(
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f"Loaded skill tool: {func_name} from {skill_name}"
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)
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except Exception as e:
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logger.error(
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f"Failed to load function {func_name} from {script_path}: {e}"
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)
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return tools
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async def run(self, task_event: dict) -> dict:
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"""执行与 run 相关的核心业务流转操作。
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该方法封装了具体的算法策略或状态控制逻辑，确保操作能够在事务上下文中被原子且一致地执行。
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Args: task_event (dict): 由事件总线或工作流引擎分发过来的事件载荷，封装了触发此次调用的上下文快照与任务目标指令。
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Returns: (dict): 高度聚合的字典结构数据，将多维度的属性特征或统计指标组合后一并返回。"""
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if self.agent is None:
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system_prompt = self.agent_config.get(
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"prompt",
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"你是一个拥有专业技能的专家级AI助手，请利用你的专业知识完成给定的任务。",
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)
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await self._init_agent("skill_individual", system_prompt)
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deps = WorkerIndividualDeps(task_event=task_event)
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self.agent.retries = 3
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tools = await self._load_skill_tools()
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try:
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result = await self.agent.run(
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f"请执行以下任务：\n{task_event}",
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deps=deps,
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tools=tools if tools else None,
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)
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return {"output": result.data.output}
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except Exception as e:
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logger.exception(f"SkillIndividual {self.agent_id} 执行失败: {e}")
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raise
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@@ -0,0 +1,50 @@
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# Copyright 2026 zhaoxi826
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
|
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# You may obtain a copy of the License at
|
||||
#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
|
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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# See the License for the specific language governing permissions and
|
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# limitations under the License.
|
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from kilostar.worker_individual.base_individual import (
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BaseIndividual,
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WorkerIndividualDeps,
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)
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from kilostar.utils.logger import get_logger
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logger = get_logger("special_individual")
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class SpecialIndividual(BaseIndividual):
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"""
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特殊子个体：执行特殊任务的 agent，如生成语音、视频等。
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"""
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def __init__(self, agent_config: dict):
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super().__init__(agent_config)
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async def run(self, task_event: dict) -> dict:
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"""执行与 run 相关的核心业务流转操作。
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该方法封装了具体的算法策略或状态控制逻辑，确保操作能够在事务上下文中被原子且一致地执行。
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Args: task_event (dict): 由事件总线或工作流引擎分发过来的事件载荷，封装了触发此次调用的上下文快照与任务目标指令。
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Returns: (dict): 高度聚合的字典结构数据，将多维度的属性特征或统计指标组合后一并返回。"""
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if self.agent is None:
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system_prompt = self.agent_config.get(
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"prompt", "你是一个特殊的AI助手，负责处理特殊类型的任务。"
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)
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await self._init_agent("special_individual", system_prompt)
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deps = WorkerIndividualDeps(task_event=task_event)
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self.agent.retries = 3
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try:
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result = await self.agent.run(f"请执行以下任务：\n{task_event}", deps=deps)
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return {"output": result.data.output}
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except Exception as e:
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logger.exception(f"SpecialIndividual {self.agent_id} 执行失败: {e}")
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raise
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