feat: 修复了一些bug
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@ -20,6 +20,12 @@ import asyncio
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workflow_router = APIRouter(prefix="/api/v1/workflow", tags=["workflow"])
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@workflow_router.get("/list")
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async def get_workflow_list():
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global_state_machine = ray_actor_hook("global_state_machine").global_state_machine
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events = await global_state_machine.list_events.remote()
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return events
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@workflow_router.get("/sse/{trace_id}")
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async def get_workflow_sse(trace_id: str, request: Request):
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global_state_machine = ray_actor_hook("global_state_machine").global_state_machine
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@ -139,6 +139,20 @@ class GlobalStateMachine:
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def get_workflow(self, trace_id: str) -> PretorWorkflow:
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return self.event_dict[trace_id].workflow
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def list_events(self) -> list[dict]:
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result = []
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for trace_id, event in self.event_dict.items():
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workflow_title = event.workflow.title if event.workflow else None
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workflow_status = event.workflow.status.status if event.workflow and event.workflow.status else None
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result.append({
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"event_id": trace_id,
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"workflow_title": workflow_title,
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"status": workflow_status,
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"user_name": event.user_name,
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"message": event.message,
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})
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return result
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async def put_pending(self, trace_id, item) -> None:
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await self.event_dict[trace_id].pending_queue.put(item)
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@ -80,7 +80,7 @@ class ConsciousnessNode:
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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 composite_individual 或 primary_individual,并将 agent_id 设置为对应的 Skill Individual 名称),作为可调用的工具。\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 设置为对应的 Skill Individual 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"- 名称: {skill['name']}\n 描述: {skill['description']}\n"
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@ -18,8 +18,7 @@ from pydantic import BaseModel, Field, model_validator
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from pretor.utils.logger import get_logger
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logger = get_logger('workflow')
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NodeType = Literal[
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"consciousness_node", "control_node", "supervisory_node",
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"composite_individual", "primary_individual"
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"consciousness_node", "control_node", "supervisory_node", "skill_individual"
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]
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class EventInfo(BaseModel):
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@ -38,17 +37,13 @@ class WorkStep(BaseModel):
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desc: str = Field(..., description="动作细节的自然语言描述,包含人工规范指导")
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inputs: Optional[Union[str, List[str]]] = Field(default=None, description="前置依赖输出")
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outputs: Optional[str] = Field(default=None, description="当前步骤产出物变量名")
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agent_id: Optional[str] = Field(default=None, description="分配给 skill_individual 的 Skill Individual 名称")
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logic_gate: Optional[LogicGate] = Field(default=None, description="逻辑跳转控制")
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status: Literal["waiting", "running", "completed", "failed"] = Field(
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default="waiting",
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description="执行状态 (LLM建议保留默认值)"
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)
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class WorkerGroup(BaseModel):
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name: str = Field(..., description="工作组名称,如 'coding_squad'")
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primary_individual: Dict[str, int] = Field(..., description="基础子个体配置,例如 {'coder': 2, 'tester': 1}")
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composite_individual: Dict[str, int] = Field(..., description="复合子个体配置,例如 {'code_reviewer': 1}")
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class WorkflowStatus(BaseModel):
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step: int = Field(default=1, gt=0, description="当前运行到的工作流步数")
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@ -59,7 +54,6 @@ class WorkflowStatus(BaseModel):
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class PretorWorkflow(BaseModel):
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title: str = Field(..., description="工作流的标题")
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workgroup_list: List[WorkerGroup] = Field(..., description="工作组资源编排列表")
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work_link: List[WorkStep] = Field(..., description="工作链逻辑定义")
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# ---------------- 以下为系统级管控字段,LLM 无需关心 ---------------- #
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trace_id: str | None = Field(description="系统自动生成的追溯ID")
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@ -199,15 +199,13 @@ class WorkflowEngine:
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return result_obj.output, True
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return result_obj, True
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elif step.node in ["primary_individual", "composite_individual"]:
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self.logger.info(f"正在通过 WorkerCluster 调度 {step.node} 的 {step.action} 动作。")
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elif step.node == "skill_individual":
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self.logger.info(f"正在通过 WorkerCluster 调度 skill_individual 执行 {step.action}。")
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try:
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from pretor.utils.ray_hook import ray_actor_hook
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worker_cluster = ray_actor_hook("worker_cluster").worker_cluster
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task_id = f"{self.workflow.trace_id}_step_{step.step}"
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agent_id = getattr(step, 'agent_id', f"default_{step.node}")
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if isinstance(input_data, dict) and "agent_id" in input_data:
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agent_id = input_data.get("agent_id")
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agent_id = step.agent_id or f"default_{step.node}"
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task_event = {
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"action": step.action,
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"description": step.desc,
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