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。
2026-04-29 10:09:07 +08:00
commit d84212f780
163 changed files with 19251 additions and 0 deletions
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+#  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 .control_node import ControlNode
+__all__ = ["ControlNode"]
@@ -0,0 +1,102 @@
+#  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 ray
+from pydantic_ai import Agent, RunContext
+from pretor.core.global_state_machine.global_state_machine import GlobalStateMachine
+from pretor.core.global_state_machine.model_provider.base_provider import Provider
+from pretor.adapter.model_adapter.agent_factory import AgentFactory
+from pretor.core.individual.control_node.template import ForWorkflow, ForWorkflowInput, ControlNodeDeps
+
+
+
+@ray.remote
+class ControlNode:
+    def __init__(self):
+        from pretor.utils.logger import get_logger
+        self.logger = get_logger('control_node')
+        self.agent: Agent | None = 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Control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【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"
+            "你的工作职责是：\n"
+            "1. 仔细分析分配给你的工作流步骤 (workflow_step) 的目标和要求。\n"
+            "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"
+            "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"
+            "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。"
+        )
+        output_type = ForWorkflow
+        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=ControlNodeDeps,
+                                                agent_name="control_node",
+                                                tools=callables)
+        @self.agent.system_prompt
+        async def dynamic_prompt(ctx: RunContext[ControlNodeDeps]):
+            prompt = system_prompt + "\n\n"
+            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
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+#  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