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
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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 .consciousness_node import ConsciousnessNode
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__all__ = ["ConsciousnessNode"]
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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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import ray
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from typing import Union, overload
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from kilostar.core.individual.consciousness_node.template import (
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ConsciousnessNodeDeps,
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ForregulatoryNode,
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ForWorkflow,
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ForWorkflowEngine,
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ForWorkflowInput,
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ForregulatoryInput,
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ForWorkflowEngineInput,
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)
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from pydantic_ai import Agent, RunContext
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from kilostar.core.global_state_machine.global_state_machine import GlobalStateMachine
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from kilostar.core.global_state_machine.model_provider.base_provider import Provider
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from kilostar.adapter.model_adapter.agent_factory import AgentFactory
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@ray.remote
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class ConsciousnessNode:
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"""ConsciousnessNode 核心组件类。
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这是一个系统执行节点类，作为多智能体架构中的独立处理单元。它能够接收工作流上下文，根据内置的大模型策略进行意图理解和自主决策，从而驱动特定阶段的任务闭环。"""
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def __init__(self) -> None:
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from kilostar.utils.logger import get_logger
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self.logger = get_logger("consciousness_node")
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self.agent: None | Agent = None
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async def create_agent(
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self,
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global_state_machine: GlobalStateMachine,
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provider_title: str,
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model_id: str,
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tools_list: list[str] = None,
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) -> None:
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"""
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create_agent方法，将agent对象装配到ConsciousnessNode的属性内
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该方法通过provider_title从global_state_machine中获取provider对象，然后从provider对象中取出供应商形象，装配为pydantic_ai的
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Agent实例，
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并挂载到self.agent属性
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Args:
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global_state_machine: 全局状态机
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provider_title: 供应商名
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model_id: 模型id
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Returns:
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无返回
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"""
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system_prompt: str = (
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"你叫kilostar，是一个多智能体AI助手系统中的【意识节点 (Consciousness Node)】。\n"
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"你是系统的'高级规划师'和'架构师'，负责处理监控节点分配过来的复杂任务。\n"
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"你的主要工作场景包括：\n"
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"1. 拆解任务 (Workflow Generation)：结合用户的原始命令和提供的模板，生成严谨、可执行的工作流 (kilostarWorkflow)，并将其输出为 ForWorkflowEngine 格式。拆解时步骤应清晰连贯。\n"
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"2. 中途指导 (Workflow Execution)：在工作流执行中，如果某一步骤指派给你，你需要对控制节点的结果进行分析或提供下一步的指导，输出 ForWorkflow 格式。\n"
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"3. 总结报告 (regulatory Report)：在整个工作流执行完毕后，你需要对整体流程、各个控制节点的执行情况进行审查，并生成一份技术性的总结报告，输出 ForregulatoryNode 格式。\n"
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"请确保所有的思考和生成过程符合逻辑，严密且高质量。"
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)
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output_type = Union[ForregulatoryNode, ForWorkflow, ForWorkflowEngine]
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from kilostar.utils.get_tool import load_tools_from_list
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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=output_type,
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system_prompt=system_prompt,
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deps_type=ConsciousnessNodeDeps,
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agent_name="consciousness_node",
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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[ConsciousnessNodeDeps]):
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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[ConsciousnessNodeDeps]): 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 += (
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f"=== 当前任务上下文 ===\n"
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f"- 当前指令 (Command): {ctx.deps.command}\n"
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f"- 原始用户命令 (Original Command): {ctx.deps.original_command}\n"
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)
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if ctx.deps.available_skills:
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prompt += "\n=== 当前可用 Skill Individual ===\n"
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prompt += "你可以直接将以下 Skill Individual 安排进工作流的步骤中（设置 node 为 skill_individual，并将 agent_id 设置为对应 Skill Individual 的真实 agent_id，不要用名称！），作为可调用的工具。\n"
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for skill in ctx.deps.available_skills:
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prompt += f"- 真实 agent_id: {skill.get('agent_id')}\n 名称: {skill['name']}\n 描述: {skill['description']}\n"
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return prompt
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async def working(
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self,
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payload: Union[ForWorkflowEngineInput, ForWorkflowInput, ForregulatoryInput],
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) -> Union[ForWorkflowEngine, ForWorkflow, ForregulatoryNode, None]:
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"""执行与 working 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: payload (Union[ForWorkflowEngineInput, ForWorkflowInput, ForregulatoryInput]): 从客户端传递过来或由上游组件生成的核心业务数据体，通常需要进一步的清洗和结构化解析。
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Returns: (Union[ForWorkflowEngine, ForWorkflow, ForregulatoryNode, None]): 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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try:
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result = await self._run(payload)
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if isinstance(result, (ForWorkflowEngine, ForWorkflow, ForregulatoryNode)):
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return result
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else:
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self.logger.error(
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f"ConsciousnessNode: 未知或不匹配的返回类型: {type(result)}"
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)
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return None
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except Exception:
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self.logger.exception("ConsciousnessNode在执行working时发生严重错误")
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return None
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@overload
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async def _run(self, payload: ForWorkflowEngineInput) -> ForWorkflowEngine:
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"""
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_run方法
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该分支应当在regulatory_node简单处理用户命令后，工作流创建前调用！
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Args:
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payload: 应当包含原始命令和可用技能等信息
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Returns:
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ForWorkflowEngine对象，将被放到全局状态机后丢入WorkflowEngine的异步队列
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"""
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pass
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@overload
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async def _run(self, payload: ForWorkflow) -> ForWorkflow:
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"""
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_run方法
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该分支应当在workflow运行时，由WorkflowEngine进行调用！
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Args:
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payload: 应当包含workflow中的WorkStep对象
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Returns:
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ForWorkflow对象，作为ConsciousnessNode执行Workflow中的WorkStep的结果
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"""
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pass
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@overload
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async def _run(self, payload: ForregulatoryInput) -> ForregulatoryNode:
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"""
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_run方法
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该分支应当在workflow运行完全结束后，由WorkflowEngine进行调用！
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Args:
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payload: 应当包含整个Workflow的情况
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Returns:
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Forregulatory对象，作为ConsciousnessNode对于全工作流的技术性总结，返回给regulatoryNode
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"""
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pass
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async def _run(
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self,
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payload: Union[ForregulatoryInput, ForWorkflowInput, ForWorkflowEngineInput],
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) -> Union[ForregulatoryNode, ForWorkflow, ForWorkflowEngine]:
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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: payload (Union[ForregulatoryInput, ForWorkflowInput, ForWorkflowEngineInput]): 从客户端传递过来或由上游组件生成的核心业务数据体，通常需要进一步的清洗和结构化解析。
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Returns: (Union[ForregulatoryNode, ForWorkflow, ForWorkflowEngine]): 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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try:
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self.agent.retries = 3
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if isinstance(payload, ForWorkflowEngineInput):
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deps = ConsciousnessNodeDeps(
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original_command=payload.original_command,
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command="自主分析并拆解原始命令，生成严密可执行的工作流",
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available_skills=payload.available_skills,
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)
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self.logger.debug("ConsciousnessNode: 开始生成工作流 (原生重试开启)")
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prompt = "根据original_command制定严密的可执行workflow"
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result = await self.agent.run(prompt, deps=deps)
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return result.output
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elif isinstance(payload, ForWorkflowInput):
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deps = ConsciousnessNodeDeps(
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original_command=payload.original_command,
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command="完成workflow step中分配给意识节点的特定任务或指导",
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)
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self.logger.debug(
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"ConsciousnessNode: 开始处理工作流节点任务 (原生重试开启)"
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)
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result = await self.agent.run(
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f"处理此工作流步骤信息：\n{payload.workflow_step.model_dump_json()}",
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deps=deps,
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)
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return result.output
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elif isinstance(payload, ForregulatoryInput):
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deps = ConsciousnessNodeDeps(
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original_command=payload.original_command,
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command="对于工作流整体执行结果进行检查，并且生成一份专业的技术性总结报告",
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)
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self.logger.debug(
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"ConsciousnessNode: 开始生成技术总结报告 (原生重试开启)"
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)
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result = await self.agent.run(
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f"基于以下工作流的执行记录，生成技术报告：\n{payload.workflow.model_dump_json()}",
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deps=deps,
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)
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return result.output
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except Exception as e:
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self.logger.exception(f"ConsciousnessNode 模型生成最终失败: {str(e)}")
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raise RuntimeError(f"ConsciousnessNode 无法完成任务: {str(e)}") from e
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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.core.workflow_running_engine.workflow import kilostarWorkflow, WorkStep
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from kilostar.utils.agent_model import ResponseModel, DepsModel, InputModel
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from pydantic import Field
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# 意识节点回复类
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class ConsciousnessNodeResponse(ResponseModel):
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"""Consciousness response model,是意识节点所有回复类型的父类"""
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pass
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class ForWorkflowEngine(ConsciousnessNodeResponse):
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"""生成workflow并放入WorkflowEngine"""
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workflow: kilostarWorkflow = Field(
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..., description="生成好的符合规范的完整工作流对象。"
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)
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reasoning: str = Field(..., description="生成此工作流的原因和思路简述。")
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class ForWorkflow(ConsciousnessNodeResponse):
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"""处理workflow中需要ConsciousnessNode的工作"""
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output: str = Field(..., description="对当前工作流步骤的具体处理结果或指导意见。")
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class ForregulatoryNode(ConsciousnessNodeResponse):
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"""工作流完成后进行校验并返回给regulatoryNode"""
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output: str = Field(
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..., description="为监控节点提供的全工作流执行情况的技术性总结报告。"
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)
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class ConsciousnessNodeDeps(DepsModel):
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"""ConsciousnessNodeDeps 核心组件类。
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这是一个系统执行节点类，作为多智能体架构中的独立处理单元。它能够接收工作流上下文，根据内置的大模型策略进行意图理解和自主决策，从而驱动特定阶段的任务闭环。"""
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original_command: str
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command: str
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available_skills: list[dict] | None = None
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class ConsciousnessNodeInput(InputModel):
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"""ConsciousnessNodeInput 核心组件类。
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这是一个系统执行节点类，作为多智能体架构中的独立处理单元。它能够接收工作流上下文，根据内置的大模型策略进行意图理解和自主决策，从而驱动特定阶段的任务闭环。"""
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pass
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class ForWorkflowEngineInput(ConsciousnessNodeInput):
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"""ForWorkflowEngineInput 核心组件类。
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这是一个领域数据模型或功能封装类，承载了 ForWorkflowEngineInput 相关的内聚属性定义与状态维护。它的存在隔离了局部的业务复杂性，并对外提供了类型安全的访问接口。"""
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original_command: str
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available_skills: list[dict] | None = None
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class ForWorkflowInput(ConsciousnessNodeInput):
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"""ForWorkflowInput 核心组件类。
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这是一个领域数据模型或功能封装类，承载了 ForWorkflowInput 相关的内聚属性定义与状态维护。它的存在隔离了局部的业务复杂性，并对外提供了类型安全的访问接口。"""
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workflow_step: WorkStep
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original_command: str
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class ForregulatoryInput(ConsciousnessNodeInput):
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"""ForregulatoryInput 核心组件类。
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这是一个领域数据模型或功能封装类，承载了 ForregulatoryInput 相关的内聚属性定义与状态维护。它的存在隔离了局部的业务复杂性，并对外提供了类型安全的访问接口。"""
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workflow: kilostarWorkflow
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original_command: str
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