chore: initial commit for Pretor v0.1.0-alpha
正式发布 Pretor 平台的首个 alpha 版本。本项目旨在构建一个基于分布式架构的多智能体协同工作流水线。 核心功能实现： 1. 建立基于 BaseIndividual 的动态插件加载机制。 2. 实现三类核心 worker_individual 子个体。 3. 集成 Ray 框架支持分布式集群调度。 4. 基于 PostgreSQL 的全量持久化存储方案。 5. 提供完整的 FastAPI 后端与 React 前端交互界面。
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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# 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 pretor.core.individual.consciousness_node.template import (ConsciousnessNodeDeps, ForSupervisoryNode, ForWorkflow,\
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ForWorkflowEngine, ForWorkflowInput, ForSupervisoryInput, ForWorkflowEngineInput)
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from pydantic_ai import Agent, RunContext
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from pretor.core.global_state_machine.global_state_machine import GlobalStateMachine
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from pretor.core.global_state_machine.model_provider.base_provider import Provider
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from pretor.adapter.model_adapter.agent_factory import AgentFactory
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@ray.remote
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class ConsciousnessNode:
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def __init__(self) -> None:
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from pretor.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(self, global_state_machine: GlobalStateMachine, provider_title: str, model_id: str, tools_list: list[str] = None) -> 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Pretor，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 (PretorWorkflow)，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 (Supervisory 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 ForSupervisoryNode 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[ForSupervisoryNode, ForWorkflow, ForWorkflowEngine]
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from pretor.utils.get_tool import load_tools_from_list
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provider: Provider = await global_state_machine.get_provider.remote( provider_title)
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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(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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@self.agent.system_prompt
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async def dynamic_prompt(ctx: RunContext[ConsciousnessNodeDeps]):
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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.workflow_template:
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prompt += f"- 选定工作流模板 (Workflow Template): {ctx.deps.workflow_template}\n"
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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(self, payload: Union[ForWorkflowEngineInput, ForWorkflowInput, ForSupervisoryInput]) -> Union[ForWorkflowEngine, ForWorkflow, ForSupervisoryNode, None]:
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try:
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result = await self._run(payload)
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if isinstance(result, (ForWorkflowEngine, ForWorkflow, ForSupervisoryNode)):
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return result
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else:
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self.logger.error(f"ConsciousnessNode: 未知或不匹配的返回类型: {type(result)}")
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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supervisory_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workflow_template和event对象
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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: ForSupervisoryInput) -> ForSupervisoryNode:
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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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ForSupervisory对象，作为ConsciousnessNode对于全工作流的技术性总结，返回给SupervisoryNode
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"""
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pass
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async def _run(self, payload: Union[ForSupervisoryInput, ForWorkflowInput, ForWorkflowEngineInput]) -> Union[ForSupervisoryNode, ForWorkflow, ForWorkflowEngine]:
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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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workflow_template=payload.workflow_template,
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command="拆解原始命令变成一个工作流",
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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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if payload.workflow_template:
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prompt += "，可以学习并参考workflow_template的设计理念"
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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("ConsciousnessNode: 开始处理工作流节点任务 (原生重试开启)")
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result = await self.agent.run(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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return result.output
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elif isinstance(payload, ForSupervisoryInput):
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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("ConsciousnessNode: 开始生成技术总结报告 (原生重试开启)")
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result = await self.agent.run(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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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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@@ -0,0 +1,67 @@
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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 pretor.core.workflow.workflow import PretorWorkflow, WorkStep
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from pretor.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: PretorWorkflow = Field(..., description="生成好的符合规范的完整工作流对象。")
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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 ForSupervisoryNode(ConsciousnessNodeResponse):
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"""工作流完成后进行校验并返回给SupervisoryNode"""
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output: str = Field(..., 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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class ConsciousnessNodeDeps(DepsModel):
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original_command: str
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workflow_template: str | None = None
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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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pass
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class ForWorkflowEngineInput(ConsciousnessNodeInput):
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workflow_template: str | None = None
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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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workflow_step: WorkStep
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original_command: str
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class ForSupervisoryInput(ConsciousnessNodeInput):
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workflow: PretorWorkflow
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original_command: str
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@@ -0,0 +1,16 @@
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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 .control_node import ControlNode
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__all__ = ["ControlNode"]
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@@ -0,0 +1,102 @@
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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.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
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# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
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import ray
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from pydantic_ai import Agent, RunContext
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from pretor.core.global_state_machine.global_state_machine import GlobalStateMachine
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from pretor.core.global_state_machine.model_provider.base_provider import Provider
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from pretor.adapter.model_adapter.agent_factory import AgentFactory
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from pretor.core.individual.control_node.template import ForWorkflow, ForWorkflowInput, ControlNodeDeps
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@ray.remote
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class ControlNode:
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def __init__(self):
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from pretor.utils.logger import get_logger
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self.logger = get_logger('control_node')
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self.agent: Agent | None = None
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async def create_agent(self, global_state_machine: GlobalStateMachine, provider_title: str, model_id: str, tools_list: list[str] = None) -> None:
|
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"""
|
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create_agent方法，将agent对象装配到Control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，
|
||||
并挂载到self.agent属性
|
||||
Args:
|
||||
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:
|
||||
无返回
|
||||
"""
|
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system_prompt: str = (
|
||||
"你叫Pretor，是一个多智能体AI助手系统中的【控制节点 (Control Node)】。\n"
|
||||
"你是系统的'执行者'和'车间主任'，专门负责执行工作流中分配给你的具体子任务。\n"
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||||
"你的工作职责是：\n"
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||||
"1. 仔细分析分配给你的工作流步骤 (workflow_step) 的目标和要求。\n"
|
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"2. 运用你被分配的工具 (如有) 或者依靠自身的知识和推理能力，精准、高效地完成该任务。\n"
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||||
"3. 将执行的结果、产生的数据或者具体的输出，严格按照 ForWorkflow 格式返回。\n"
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"请注意：你的输出应当具体、实用，直接提供任务所要求的结果，不要做过多无关的寒暄。"
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)
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output_type = ForWorkflow
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from pretor.utils.get_tool import load_tools_from_list
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provider: Provider = await global_state_machine.get_provider.remote( provider_title)
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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(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=ControlNodeDeps,
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agent_name="control_node",
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tools=callables)
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@self.agent.system_prompt
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async def dynamic_prompt(ctx: RunContext[ControlNodeDeps]):
|
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prompt = system_prompt + "\n\n"
|
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prompt += (
|
||||
f"=== 当前任务步骤上下文 ===\n"
|
||||
f"- 步骤名称 (Name): {ctx.deps.workflow_step.name}\n"
|
||||
f"- 步骤目标/描述 (Description): {ctx.deps.workflow_step.desc}\n"
|
||||
f"- 前置输入（input）: {ctx.deps.workflow_step.inputs}\n"
|
||||
)
|
||||
return prompt
|
||||
|
||||
async def working(self, payload: ForWorkflowInput) -> str:
|
||||
try:
|
||||
result: ForWorkflow = await self._run(payload)
|
||||
return result
|
||||
except Exception:
|
||||
self.logger.exception("ControlNode在执行working时发生严重错误")
|
||||
return None
|
||||
|
||||
async def _run(self, payload: ForWorkflowInput) -> ForWorkflow:
|
||||
try:
|
||||
self.agent.retries = 3
|
||||
deps = ControlNodeDeps(
|
||||
workflow_step=payload.workflow_step
|
||||
)
|
||||
self.logger.debug(f"ControlNode: 开始执行工作流节点 [{payload.workflow_step.name}] (原生重试开启)")
|
||||
|
||||
result = await self.agent.run(
|
||||
f"请根据提供的 workflow_step 上下文，执行此步骤并输出结果。\n详细指令或附加数据：{payload.workflow_step.model_dump_json()}",
|
||||
deps=deps
|
||||
)
|
||||
return result.output
|
||||
except Exception as e:
|
||||
self.logger.exception(f"ControlNode 在执行步骤 [{payload.workflow_step.name}] 时最终失败: {str(e)}")
|
||||
raise RuntimeError(f"ControlNode 执行步骤失败: {str(e)}") from e
|
||||
@@ -0,0 +1,39 @@
|
||||
# Copyright 2026 zhaoxi826
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
from pydantic import Field
|
||||
from pretor.core.workflow.workflow import WorkStep
|
||||
from pretor.utils.agent_model import ResponseModel, InputModel, DepsModel
|
||||
|
||||
class ControlNodeResponse(ResponseModel):
|
||||
"""控制节点回复的基类"""
|
||||
pass
|
||||
|
||||
|
||||
class ControlNodeInput(InputModel):
|
||||
pass
|
||||
|
||||
|
||||
class ControlNodeDeps(DepsModel):
|
||||
workflow_step: WorkStep
|
||||
# In the future, this can be dynamically populated with tools specific to the current task execution
|
||||
|
||||
|
||||
class ForWorkflow(ControlNodeResponse):
|
||||
output: str = Field(..., description="控制节点执行特定工作流步骤的结果。包含执行细节和输出数据。")
|
||||
|
||||
|
||||
class ForWorkflowInput(ControlNodeInput):
|
||||
workflow_step: WorkStep
|
||||
@@ -0,0 +1,14 @@
|
||||
# Copyright 2026 zhaoxi826
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
@@ -0,0 +1,14 @@
|
||||
# Copyright 2026 zhaoxi826
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
@@ -0,0 +1,16 @@
|
||||
# Copyright 2026 zhaoxi826
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from .supervisory_node import SupervisoryNode
|
||||
__all__ = ["SupervisoryNode"]
|
||||
@@ -0,0 +1,192 @@
|
||||
# Copyright 2026 zhaoxi826
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import datetime
|
||||
import ray
|
||||
from typing import Union, overload
|
||||
from pretor.api.platform.event import PretorEvent
|
||||
from pretor.adapter.model_adapter.agent_factory import AgentFactory
|
||||
from pretor.core.global_state_machine.global_state_machine import GlobalStateMachine
|
||||
from pretor.core.global_state_machine.model_provider import Provider
|
||||
from pretor.core.individual.supervisory_node.template import ForConsciousnessNode, ForUser, SupervisoryNodeDeps, TerminationMessage
|
||||
from pydantic_ai import RunContext, Agent
|
||||
from pretor.utils.ray_hook import ray_actor_hook
|
||||
|
||||
|
||||
@ray.remote
|
||||
class SupervisoryNode:
|
||||
def __init__(self) -> None:
|
||||
from pretor.utils.logger import get_logger
|
||||
self.logger = get_logger('supervisory_node')
|
||||
self.agent: None | Agent = None
|
||||
|
||||
|
||||
async def create_agent(self, global_state_machine: GlobalStateMachine, provider_title: str, model_id: str, tools_list: list[str] = None) -> None:
|
||||
"""
|
||||
create_agent方法，将agent对象装配到SupervisoryNode的属性内
|
||||
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Agent实例，
|
||||
并挂载到self.agent属性
|
||||
Args:
|
||||
global_state_machine: 全局状态机
|
||||
provider_title: 供应商名
|
||||
model_id: 模型id
|
||||
|
||||
Returns:
|
||||
无返回
|
||||
"""
|
||||
system_prompt: str = (
|
||||
"你叫Pretor，是一个多智能体AI助手系统中的【监控节点 (Supervisory Node)】。\n"
|
||||
"你是系统的'前台接待'和'大脑皮层'，负责接收用户的初始请求或工作流的最终报告。\n"
|
||||
"你的核心职责是进行【意图识别与路由】。请仔细阅读用户的请求：\n"
|
||||
"1. 如果用户只是进行简单的问候、闲聊或查询非常基础的信息，请直接生成友好的回复，使用 ForUser 格式。\n"
|
||||
"2. 如果用户提出的是复杂任务（如需要编写代码、多步骤规划、数据处理等），请务必将其判定为需要工作流处理的任务，"
|
||||
" 并使用 ForConsciousnessNode 格式。若提供的【可用模板列表】中有合适的模板请选用，若都不匹配则 workflow_template 设为 null。\n"
|
||||
"3. 如果你收到的是 TerminationMessage（代表工作流已完成并生成了报告），请将报告内容转化为友好的面向用户的回复，使用 ForUser 格式。\n"
|
||||
"请保持冷静、专业，并严格遵循上述路由规则。"
|
||||
)
|
||||
output_type = Union[ForConsciousnessNode, ForUser]
|
||||
from pretor.utils.get_tool import load_tools_from_list
|
||||
provider: Provider = await global_state_machine.get_provider.remote( provider_title)
|
||||
agent_factory = AgentFactory()
|
||||
|
||||
callables = load_tools_from_list(tools_list)
|
||||
self.agent = agent_factory.create_agent(provider=provider,
|
||||
model_id=model_id,
|
||||
output_type=output_type,
|
||||
system_prompt=system_prompt,
|
||||
deps_type=SupervisoryNodeDeps,
|
||||
agent_name="supervisory_node",
|
||||
tools=callables)
|
||||
|
||||
@self.agent.system_prompt
|
||||
async def dynamic_prompt(ctx: RunContext[SupervisoryNodeDeps]):
|
||||
prompt = system_prompt + "\n\n"
|
||||
prompt += (
|
||||
f"=== 当前上下文 ===\n"
|
||||
f"- 平台 (Platform): {ctx.deps.platform}\n"
|
||||
f"- 用户名 (User): {ctx.deps.user_name}\n"
|
||||
f"- 当前时间 (Time): {ctx.deps.time}\n"
|
||||
f"- 可用工作流模板 (Available Templates): {ctx.deps.available_templates}\n"
|
||||
)
|
||||
# 修改 system_prompt 变量
|
||||
prompt += (
|
||||
"\n\n注意：你必须调用且只能调用一个函数（工具）来输出结果。"
|
||||
"如果你想直接回复用户，请调用 ForUser；"
|
||||
"如果你想移交给工作流，请调用 ForConsciousnessNode（若没有合适的模板，workflow_template 填 null）。"
|
||||
"严禁返回纯文本，必须使用工具格式！"
|
||||
)
|
||||
if ctx.deps.error_history:
|
||||
prompt += (
|
||||
f"\n=== 错误重试指示 ===\n"
|
||||
f"警告：前一次尝试失败，错误信息如下：\n{ctx.deps.error_history}\n"
|
||||
f"请务必修正该错误并按照要求的 Pydantic 格式输出。"
|
||||
)
|
||||
return prompt
|
||||
|
||||
###工作函数
|
||||
async def working(self, payload: Union[PretorEvent, TerminationMessage]) -> str:
|
||||
"""
|
||||
working方法，是节点唯一的调用方法，对于_run函数的结果进行判断并实现最终回复
|
||||
Args:
|
||||
payload: 消息载荷，包含所有信息
|
||||
|
||||
Returns:
|
||||
str,监控节点对于用户的回复
|
||||
"""
|
||||
try:
|
||||
result = await self._run(payload)
|
||||
if isinstance(result, ForConsciousnessNode):
|
||||
self.logger.info(f"SupervisoryNode: 任务已分配给工作流引擎处理，选用模板 [{result.workflow_template}]")
|
||||
if isinstance(payload, PretorEvent):
|
||||
payload.context["workflow_template"] = result.workflow_template
|
||||
try:
|
||||
global_state_machine = ray_actor_hook("global_state_machine").global_state_machine
|
||||
await global_state_machine.add_event.remote(payload)
|
||||
workflow_running_engine = ray_actor_hook("workflow_running_engine").workflow_running_engine
|
||||
await workflow_running_engine.put_event.remote(payload)
|
||||
except Exception as e:
|
||||
self.logger.error(f"SupervisoryNode: 无法将事件放入 WorkflowRunningEngine: {e}")
|
||||
return "抱歉，任务提交失败，系统内部错误。"
|
||||
return f"任务已创建，准备创建工作流。原因：{result.reasoning}"
|
||||
elif isinstance(result, ForUser):
|
||||
self.logger.info("SupervisoryNode: 直接向用户返回简单回复。")
|
||||
return result.context
|
||||
else:
|
||||
self.logger.error(f"SupervisoryNode: 未知响应类型: {type(result)}")
|
||||
return "抱歉，系统内部遇到未知错误，无法正确处理您的请求。"
|
||||
except Exception:
|
||||
self.logger.exception("SupervisoryNode在处理请求时发生未捕获的严重错误")
|
||||
return "抱歉，监控节点处理请求时发生严重错误，请联系管理员。"
|
||||
|
||||
@overload
|
||||
async def _run(self, payload: PretorEvent) -> Union[ForConsciousnessNode, ForUser]:
|
||||
"""
|
||||
_run方法
|
||||
Args:
|
||||
payload: PretorEvent的实例，是用户输入时对于消息的封装
|
||||
|
||||
Returns:
|
||||
ForUser对象，监控节点对于用户进行的简单回答
|
||||
ForConsciousnessNode对象，监控节点将用户的请求判断为复杂任务，将PretorEvent传递给意识节点，并且给选择好的工作流模板
|
||||
"""
|
||||
...
|
||||
|
||||
@overload
|
||||
async def _run(self, payload: TerminationMessage) -> ForUser:
|
||||
"""
|
||||
_run方法
|
||||
Args:
|
||||
payload: Termination的实例，是工作流结束后到达监控节点的最后结果
|
||||
|
||||
Returns:
|
||||
ForUser对象，工作流结束后给用户的返回
|
||||
"""
|
||||
...
|
||||
|
||||
async def _run(self, payload: Union[PretorEvent, TerminationMessage]) -> Union[ForConsciousnessNode, ForUser]:
|
||||
"""
|
||||
_run方法，将payload转化为对llm发送的消息并发送
|
||||
Args:
|
||||
payload: 消息载荷
|
||||
|
||||
Returns:
|
||||
ForConsciousnessNode对象，对意识节点发送的消息
|
||||
ForUser对象，对用户发送到消息
|
||||
"""
|
||||
platform = payload.platform
|
||||
user_name = payload.user_name
|
||||
message = payload.message
|
||||
time_str = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
try:
|
||||
global_state_machine = ray_actor_hook("global_state_machine").global_state_machine
|
||||
workflow_template_dict = await global_state_machine.get_all_workflow_templates.remote()
|
||||
available_templates_str = "\n".join([f"- 名称: {k}, 描述/内容: {v}" for k, v in
|
||||
workflow_template_dict.items()]) if workflow_template_dict else "暂无注册的工作流模板"
|
||||
deps = SupervisoryNodeDeps(
|
||||
platform=platform,
|
||||
user_name=user_name,
|
||||
time=time_str,
|
||||
available_templates=available_templates_str
|
||||
)
|
||||
self.logger.debug("SupervisoryNode 开始生成 (启用原生 Pydantic-AI 重试)")
|
||||
prompt_message = message
|
||||
if isinstance(payload, TerminationMessage):
|
||||
prompt_message = f"【工作流执行结束报告】\n请将以下技术报告转化为对用户的友好回复：\n{message}"
|
||||
self.agent.retries = 3
|
||||
result = await self.agent.run(prompt_message,
|
||||
deps=deps)
|
||||
return result.output
|
||||
except Exception as e:
|
||||
self.logger.exception(f"SupervisoryNode 模型生成或解析最终失败: {str(e)}")
|
||||
return ForUser(context="系统当前负载过高或遇到复杂内部错误，请稍后再试。")
|
||||
@@ -0,0 +1,40 @@
|
||||
# Copyright 2026 zhaoxi826
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from pydantic import Field
|
||||
from pretor.utils.agent_model import ResponseModel, DepsModel
|
||||
from pydantic import BaseModel
|
||||
|
||||
class SupervisoryNodeResponse(ResponseModel):
|
||||
pass
|
||||
|
||||
class ForUser(SupervisoryNodeResponse):
|
||||
context: str = Field(..., description="对用户的回复，应当使用和蔼的语气进行回复。用于直接解答简单问题或返回最终报告。")
|
||||
|
||||
class ForConsciousnessNode(SupervisoryNodeResponse):
|
||||
workflow_template: str | None = Field(default=None, description="选择的工作流模板的名称，用于处理复杂任务。若无需模板则为 None。")
|
||||
reasoning: str = Field(..., description="选择将任务移交意识节点并选用该模板的简短原因。")
|
||||
|
||||
class TerminationMessage(BaseModel):
|
||||
platform: str
|
||||
user_name: str
|
||||
message: str
|
||||
|
||||
class SupervisoryNodeDeps(DepsModel):
|
||||
platform: str
|
||||
user_name: str
|
||||
time: str
|
||||
retry_count: int = 0
|
||||
error_history: str = ""
|
||||
available_templates: str = "默认工作流 (default_workflow)"
|
||||
Reference in New Issue
Block a user