Add Chinese comments for Temporal workflow flow
This commit is contained in:
Codex
@@ -1,4 +1,10 @@
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"""Temporal workflow application service."""
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"""Temporal 工作流服务层。
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这一层位于 API 和 Temporal 之间，负责：
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1. 选择该启动哪个 workflow
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2. 发送 signal
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3. 查询已持久化的 workflow 状态
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"""
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from datetime import timedelta
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@@ -17,18 +23,22 @@ from app.workers.workflows.types import PipelineWorkflowInput, ReviewSignalPaylo
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class WorkflowService:
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"""Application service for Temporal workflow orchestration."""
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"""Temporal 编排服务。"""
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@staticmethod
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def workflow_type_for_mode(service_mode: ServiceMode) -> str:
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"""Return the workflow class name for a service mode."""
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"""根据服务模式返回对应的 workflow 类型名。"""
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if service_mode == ServiceMode.AUTO_BASIC:
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return LowEndPipelineWorkflow.__name__
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return MidEndPipelineWorkflow.__name__
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async def start_workflow(self, workflow_input: PipelineWorkflowInput) -> None:
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"""Start the appropriate Temporal workflow for an order."""
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"""为订单启动对应的 Temporal workflow。
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这里做的只是“发起执行”：
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真正的流水线顺序仍然在 workflow 类里定义。
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"""
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client = await get_temporal_client()
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workflow_id = f"order-{workflow_input.order_id}"
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@@ -40,6 +50,7 @@ class WorkflowService:
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await client.start_workflow(
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workflow_callable,
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workflow_input,
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# workflow_id 固定为 order-{order_id}，方便 API 后续按订单回查。
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id=workflow_id,
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task_queue=IMAGE_PIPELINE_CONTROL_TASK_QUEUE,
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run_timeout=timedelta(minutes=30),
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@@ -47,14 +58,19 @@ class WorkflowService:
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)
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async def signal_review(self, workflow_id: str, payload: ReviewSignalPayload) -> None:
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"""Send a review signal to a running Temporal workflow."""
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"""向运行中的 workflow 发送审核 signal。"""
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client = await get_temporal_client()
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handle = client.get_workflow_handle(workflow_id=workflow_id)
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# "submit_review" 对应 workflow 里用 @workflow.signal 标记的方法名。
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await handle.signal("submit_review", payload)
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async def get_workflow_status(self, session, order_id: int) -> WorkflowStatusResponse:
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"""Return persisted workflow execution state for an order."""
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"""返回订单对应的已持久化 workflow 状态。
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这里查的是我们自己数据库里的状态镜像，不是直接去 Temporal history 现查。
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这么做更适合业务 API 对外暴露。
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"""
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result = await session.execute(
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select(WorkflowRunORM)
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@@ -1,4 +1,8 @@
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"""Temporal client helpers."""
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"""Temporal client 辅助函数。
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API 和 worker 都需要连接 Temporal Server。
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这里做了一个简单的单例缓存，避免重复建立连接。
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"""
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import asyncio
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@@ -11,13 +15,17 @@ _client_lock = asyncio.Lock()
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async def get_temporal_client() -> Client:
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"""Return a cached Temporal client."""
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"""返回缓存后的 Temporal Client。
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第一次调用时才真正连接 Temporal；后续复用同一个 client。
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"""
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global _client
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if _client is not None:
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return _client
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async with _client_lock:
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# 双重检查，避免并发场景下重复 connect。
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if _client is None:
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settings = get_settings()
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_client = await Client.connect(
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@@ -28,8 +36,10 @@ async def get_temporal_client() -> Client:
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def set_temporal_client(client: Client | None) -> None:
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"""Override the cached Temporal client, primarily for tests."""
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"""覆盖缓存的 Temporal Client。
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主要用于测试场景，把真实连接替换成 Temporal 测试环境里的 client。
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"""
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global _client
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_client = client
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@@ -1,4 +1,8 @@
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"""Review state management mock activities."""
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"""审核相关 activity。
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这里的函数都运行在 worker 侧，可以安全地做数据库 I/O。
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workflow 本身只负责调用这些 activity，不直接写数据库。
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"""
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from sqlalchemy import select
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@@ -18,7 +22,13 @@ from app.workers.workflows.types import (
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@activity.defn
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async def mark_waiting_for_review_activity(payload: ReviewWaitActivityInput) -> None:
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"""Mark a workflow as waiting for a human review."""
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"""把 workflow 标记为等待人工审核。
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这一步会做三件事：
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1. 新增一条 review 类型的 workflow_step，状态是 waiting
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2. 新增一条 review_task，供 API 查询待审核列表
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3. 把订单和 workflow_run 都切到 waiting_review
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"""
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async with get_session_factory()() as session:
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order, workflow_run = await load_order_and_run(session, payload.order_id, payload.workflow_run_id)
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@@ -47,7 +57,12 @@ async def mark_waiting_for_review_activity(payload: ReviewWaitActivityInput) ->
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@activity.defn
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async def complete_review_wait_activity(payload: ReviewResolutionActivityInput) -> None:
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"""Resolve the current waiting-review step before the next branch runs."""
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"""收口当前这次 waiting_review。
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这里的职责不是决定后续怎么跑，而是把“等待审核”这个数据库状态结束掉：
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- approve / rerun -> review step 记为 succeeded
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- reject -> review step 记为 failed
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"""
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async with get_session_factory()() as session:
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order, workflow_run = await load_order_and_run(session, payload.order_id, payload.workflow_run_id)
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@@ -62,6 +77,8 @@ async def complete_review_wait_activity(payload: ReviewResolutionActivityInput)
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)
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review_step = step_result.scalars().first()
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if review_step is not None:
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# 只处理仍处于 waiting 的那条 review_step，
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# 避免重复 signal 把历史 review 记录覆盖掉。
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review_step.step_status = (
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StepStatus.FAILED if payload.decision == ReviewDecision.REJECT else StepStatus.SUCCEEDED
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)
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@@ -82,7 +99,11 @@ async def complete_review_wait_activity(payload: ReviewResolutionActivityInput)
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@activity.defn
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async def mark_workflow_failed_activity(payload: WorkflowFailureActivityInput) -> None:
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"""Mark the persisted workflow state as failed."""
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"""把订单和 workflow_run 持久化为失败。
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这个 activity 是 workflow 的“兜底收尾器”：
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当任意步骤抛异常时，workflow 调它把数据库状态补完整。
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"""
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async with get_session_factory()() as session:
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order, workflow_run = await load_order_and_run(session, payload.order_id, payload.workflow_run_id)
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@@ -1,4 +1,8 @@
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"""Temporal worker runner."""
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"""Temporal worker 启动入口。
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可以把 worker 理解成 Temporal 的“执行器进程”：
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它负责监听 task queue，然后真正执行 workflow / activity。
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"""
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import asyncio
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from contextlib import AsyncExitStack
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@@ -31,9 +35,18 @@ from app.workers.workflows.mid_end_pipeline import MidEndPipelineWorkflow
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def build_workers(client: Client) -> list[Worker]:
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"""Create the worker set needed for the task queues in this MVP."""
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"""创建本项目需要的 worker 集合。
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这里按 task queue 拆 worker，目的是把不同类型的任务分开：
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- control: 流程控制、review、失败收尾
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- image-gen: tryon / scene
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- post-process: texture / face / fusion
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- qc: 质检
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- export: 导出
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"""
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return [
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# control 队列负责 workflow 本体，以及少量“流程状态管理型” activity。
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Worker(
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client,
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task_queue=IMAGE_PIPELINE_CONTROL_TASK_QUEUE,
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@@ -45,11 +58,13 @@ def build_workers(client: Client) -> list[Worker]:
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mark_workflow_failed_activity,
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],
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),
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# image-gen 队列放生成类步骤，便于后续横向扩容。
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Worker(
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client,
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task_queue=IMAGE_PIPELINE_IMAGE_GEN_TASK_QUEUE,
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activities=[run_tryon_activity, run_scene_activity],
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),
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# post-process 队列放增强/融合类步骤。
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Worker(
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client,
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task_queue=IMAGE_PIPELINE_POST_PROCESS_TASK_QUEUE,
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@@ -69,16 +84,17 @@ def build_workers(client: Client) -> list[Worker]:
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async def run_workers() -> None:
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"""Start all Temporal workers and keep the process alive."""
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"""启动全部 worker，并保持进程常驻。"""
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client = await get_temporal_client()
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workers = build_workers(client)
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async with AsyncExitStack() as stack:
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for worker in workers:
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# 逐个把 worker 注册到上下文里，退出时会自动优雅关闭。
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await stack.enter_async_context(worker)
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# 用一个永不触发的 Event 让进程保持存活。
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await asyncio.Event().wait()
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if __name__ == "__main__":
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asyncio.run(run_workers())
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@@ -1,10 +1,18 @@
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"""Low-end image pipeline workflow."""
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"""低端图片流水线工作流。
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这个文件里的 `workflow` 只负责“编排顺序”和“等待结果”，
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不直接访问数据库、不做真实 I/O。
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真正的落库和 mock 执行都放在 activity 里完成。
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"""
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from datetime import timedelta
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from temporalio import workflow
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from temporalio.common import RetryPolicy
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# Temporal workflow 代码需要尽量保持可重放（deterministic）。
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# 这里导入的模块会在 workflow 外部执行，所以用 imports_passed_through
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# 明确告诉 SDK：这些导入不是 workflow 重放逻辑的一部分。
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with workflow.unsafe.imports_passed_through():
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from app.domain.enums import OrderStatus, WorkflowStepName
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from app.infra.temporal.task_queues import (
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@@ -35,14 +43,27 @@ ACTIVITY_RETRY_POLICY = RetryPolicy(
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@workflow.defn
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class LowEndPipelineWorkflow:
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"""Low-end fully automated image pipeline."""
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"""低端全自动工作流。
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它对应的是一条从头跑到尾、不需要人工介入的流水线：
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prepare_model -> tryon -> scene -> qc -> export
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"""
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@workflow.run
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async def run(self, payload: PipelineWorkflowInput) -> dict[str, int | str | None]:
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"""Execute the low-end workflow from start to finish."""
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"""执行低端工作流主流程。
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可以把这里理解成“时序控制器”：
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1. 按顺序调 activity
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2. 把上一步产物传给下一步
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3. 出错时统一标记 workflow 失败
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"""
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# current_step 用来在异常时把失败位置持久化到数据库。
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current_step = WorkflowStepName.PREPARE_MODEL
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try:
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# 每个步骤都通过 execute_activity 触发真正执行。
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# workflow 自己不做计算，只负责调度。
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prepared = await workflow.execute_activity(
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prepare_model_activity,
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StepActivityInput(
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@@ -60,6 +81,7 @@ class LowEndPipelineWorkflow:
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)
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current_step = WorkflowStepName.TRYON
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# 下游步骤通过 source_asset_id 引用上一步生成的资产。
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tryon_result = await workflow.execute_activity(
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run_tryon_activity,
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StepActivityInput(
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@@ -90,6 +112,8 @@ class LowEndPipelineWorkflow:
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)
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current_step = WorkflowStepName.QC
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# QC 是流程里的“闸门”。
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# 如果这里不通过，低端流程直接失败，不会再 export。
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qc_result = await workflow.execute_activity(
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run_qc_activity,
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StepActivityInput(
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@@ -108,6 +132,8 @@ class LowEndPipelineWorkflow:
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return {"order_id": payload.order_id, "status": OrderStatus.FAILED.value, "final_asset_id": None}
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current_step = WorkflowStepName.EXPORT
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# candidate_asset_ids 是 QC 推荐可导出的候选资产。
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# 当前 MVP 只会返回一个候选；如果没有，就退回 scene 结果导出。
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final_result = await workflow.execute_activity(
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run_export_activity,
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StepActivityInput(
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@@ -126,6 +152,7 @@ class LowEndPipelineWorkflow:
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"final_asset_id": final_result.asset_id,
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}
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except Exception as exc:
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# workflow 出异常时，额外调一个 activity 把数据库状态补齐。
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await self._mark_failed(payload, current_step, str(exc))
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raise
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@@ -135,7 +162,11 @@ class LowEndPipelineWorkflow:
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current_step: WorkflowStepName,
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message: str,
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) -> None:
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"""Persist workflow failure state."""
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"""持久化失败状态。
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注意这里仍然通过 activity 落库，而不是在 workflow 里直连数据库。
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这样能保持 workflow 的职责单一：只编排，不做外部副作用。
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"""
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await workflow.execute_activity(
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mark_workflow_failed_activity,
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@@ -149,4 +180,3 @@ class LowEndPipelineWorkflow:
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start_to_close_timeout=ACTIVITY_TIMEOUT,
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retry_policy=ACTIVITY_RETRY_POLICY,
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)
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@@ -1,10 +1,18 @@
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"""Mid-end image pipeline workflow with review signal support."""
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"""中端图片流水线工作流。
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和低端流程相比，这里最大的区别是：
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1. 会在 QC 之后停在 waiting_review
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2. 通过 Temporal signal 接收人工审核结果
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3. 可以按审核意见回流到 scene / face / fusion 重新跑
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"""
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from datetime import timedelta
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from temporalio import workflow
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from temporalio.common import RetryPolicy
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# 这些导入属于 workflow 外部世界的对象，明确标记为 pass-through，
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# 避免把它们当成需要重放的 workflow 逻辑一部分。
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with workflow.unsafe.imports_passed_through():
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from app.domain.enums import OrderStatus, ReviewDecision, WorkflowStepName
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from app.infra.temporal.task_queues import (
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@@ -47,21 +55,36 @@ ACTIVITY_RETRY_POLICY = RetryPolicy(
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@workflow.defn
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class MidEndPipelineWorkflow:
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"""Mid-end workflow that pauses for human review and supports reruns."""
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"""中端半自动工作流。
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这个 workflow 会经历“自动生成 -> 等待人工审核 -> 按审核意见继续”。
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"""
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def __init__(self) -> None:
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# signal 到达后，先暂存在 workflow 内存里，
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# 主流程再通过 wait_condition 继续往下走。
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self._review_payload: ReviewSignalPayload | None = None
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@workflow.signal
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def submit_review(self, payload: ReviewSignalPayload) -> None:
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"""Receive a review decision from the API layer."""
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"""接收 API 层发来的审核 signal。
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这一步不会直接继续执行，只是把审核结果写进 workflow 内存状态。
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真正恢复主流程是在 `_wait_for_review` 里。
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"""
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self._review_payload = payload
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@workflow.run
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async def run(self, payload: PipelineWorkflowInput) -> dict[str, int | str | None]:
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"""Execute the mid-end workflow with a human review loop."""
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"""执行中端工作流主流程。
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主线是：
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prepare_model -> tryon -> scene -> texture -> face -> fusion -> qc
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-> waiting_review -> approve/export 或 rerun
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"""
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# current_step 用于失败时记录“最后跑到哪一步”。
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current_step = WorkflowStepName.PREPARE_MODEL
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try:
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prepared = await workflow.execute_activity(
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@@ -113,8 +136,14 @@ class MidEndPipelineWorkflow:
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await self._mark_failed(payload, current_step, qc_result.message)
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return {"order_id": payload.order_id, "status": OrderStatus.FAILED.value, "final_asset_id": None}
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# 中端流程会一直循环到：
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# 1. 审核 approve 然后 export 成功
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# 2. 审核 reject 直接结束
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# 3. rerun 后再次回到 waiting_review，继续等下一次人工输入
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while True:
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current_step = WorkflowStepName.REVIEW
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# 这里通过 activity 把数据库里的订单状态更新成 waiting_review，
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# 同时创建 review_task，供 API 查询待审核列表。
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await workflow.execute_activity(
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mark_waiting_for_review_activity,
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ReviewWaitActivityInput(
|
||||
@@ -127,7 +156,11 @@ class MidEndPipelineWorkflow:
|
||||
retry_policy=ACTIVITY_RETRY_POLICY,
|
||||
)
|
||||
|
||||
# workflow 在这里“停住”，直到外部 signal 进来。
|
||||
review_payload = await self._wait_for_review()
|
||||
|
||||
# signal 到达后，先把 review 这一步的等待态收口成已处理，
|
||||
# 这样数据库里的 review_step / review_task 状态是完整的。
|
||||
await workflow.execute_activity(
|
||||
complete_review_wait_activity,
|
||||
ReviewResolutionActivityInput(
|
||||
@@ -145,6 +178,8 @@ class MidEndPipelineWorkflow:
|
||||
|
||||
if review_payload.decision == ReviewDecision.APPROVE:
|
||||
current_step = WorkflowStepName.EXPORT
|
||||
# 如果审核人显式选了资产，就导出该资产；
|
||||
# 否则默认导出 QC 候选资产。
|
||||
export_source_id = review_payload.selected_asset_id
|
||||
if export_source_id is None:
|
||||
export_source_id = (qc_result.candidate_asset_ids or [fusion_result.asset_id])[0]
|
||||
@@ -167,8 +202,11 @@ class MidEndPipelineWorkflow:
|
||||
}
|
||||
|
||||
if review_payload.decision == ReviewDecision.REJECT:
|
||||
# reject 不再重跑，直接结束。
|
||||
return {"order_id": payload.order_id, "status": OrderStatus.FAILED.value, "final_asset_id": None}
|
||||
|
||||
# rerun 的核心思想是：
|
||||
# 把指定节点后的链路重新跑一遍，然后再次进入 QC 和 waiting_review。
|
||||
if review_payload.decision == ReviewDecision.RERUN_SCENE:
|
||||
current_step = WorkflowStepName.SCENE
|
||||
scene_result = await self._run_scene(payload, tryon_result.asset_id)
|
||||
@@ -197,7 +235,11 @@ class MidEndPipelineWorkflow:
|
||||
raise
|
||||
|
||||
async def _wait_for_review(self) -> ReviewSignalPayload:
|
||||
"""Suspend the workflow until a review signal arrives."""
|
||||
"""等待人工审核 signal。
|
||||
|
||||
`workflow.wait_condition` 是 Temporal 里很常见的等待方式：
|
||||
workflow 会被安全地挂起，不会像普通 while + sleep 那样空转占资源。
|
||||
"""
|
||||
|
||||
if self._review_payload is None:
|
||||
await workflow.wait_condition(lambda: self._review_payload is not None)
|
||||
@@ -207,7 +249,10 @@ class MidEndPipelineWorkflow:
|
||||
return review_payload
|
||||
|
||||
async def _run_scene(self, payload: PipelineWorkflowInput, source_asset_id: int | None) -> MockActivityResult:
|
||||
"""Execute the scene activity."""
|
||||
"""执行 scene activity。
|
||||
|
||||
抽成私有方法后，rerun_scene 时可以直接复用，不需要复制整段 activity 调用代码。
|
||||
"""
|
||||
|
||||
return await workflow.execute_activity(
|
||||
run_scene_activity,
|
||||
@@ -224,7 +269,7 @@ class MidEndPipelineWorkflow:
|
||||
)
|
||||
|
||||
async def _run_texture(self, payload: PipelineWorkflowInput, source_asset_id: int | None) -> MockActivityResult:
|
||||
"""Execute the texture activity."""
|
||||
"""执行 texture activity。"""
|
||||
|
||||
return await workflow.execute_activity(
|
||||
run_texture_activity,
|
||||
@@ -240,7 +285,7 @@ class MidEndPipelineWorkflow:
|
||||
)
|
||||
|
||||
async def _run_face(self, payload: PipelineWorkflowInput, source_asset_id: int | None) -> MockActivityResult:
|
||||
"""Execute the face activity."""
|
||||
"""执行 face activity。"""
|
||||
|
||||
return await workflow.execute_activity(
|
||||
run_face_activity,
|
||||
@@ -261,7 +306,7 @@ class MidEndPipelineWorkflow:
|
||||
source_asset_id: int | None,
|
||||
face_asset_id: int | None,
|
||||
) -> MockActivityResult:
|
||||
"""Execute the fusion activity."""
|
||||
"""执行 fusion activity。"""
|
||||
|
||||
return await workflow.execute_activity(
|
||||
run_fusion_activity,
|
||||
@@ -278,7 +323,7 @@ class MidEndPipelineWorkflow:
|
||||
)
|
||||
|
||||
async def _run_qc(self, payload: PipelineWorkflowInput, source_asset_id: int | None) -> MockActivityResult:
|
||||
"""Execute the QC activity."""
|
||||
"""执行 QC activity。"""
|
||||
|
||||
return await workflow.execute_activity(
|
||||
run_qc_activity,
|
||||
@@ -299,7 +344,7 @@ class MidEndPipelineWorkflow:
|
||||
current_step: WorkflowStepName,
|
||||
message: str,
|
||||
) -> None:
|
||||
"""Persist workflow failure state."""
|
||||
"""持久化 workflow 失败状态。"""
|
||||
|
||||
await workflow.execute_activity(
|
||||
mark_workflow_failed_activity,
|
||||
|
||||
@@ -1,4 +1,8 @@
|
||||
"""Shared workflow and activity payload types."""
|
||||
"""workflow / activity 之间共享的数据类型。
|
||||
|
||||
这些 dataclass 是 Temporal 编排层最关键的“消息格式”：
|
||||
workflow 把它们发给 activity，activity 再把结果返回给 workflow。
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
from enum import Enum
|
||||
@@ -8,7 +12,11 @@ from app.domain.enums import CustomerLevel, OrderStatus, ReviewDecision, Service
|
||||
|
||||
|
||||
def _coerce_enum(value: Any, enum_cls: type[Enum]) -> Any:
|
||||
"""Coerce raw Temporal payload values back into enum instances."""
|
||||
"""把 Temporal 反序列化后的原始值转回枚举。
|
||||
|
||||
在某些序列化场景下，枚举值可能先变成字符串，甚至被拆成字符列表。
|
||||
这里统一做一次归一化，避免后面写数据库时类型不对。
|
||||
"""
|
||||
|
||||
if value is None or isinstance(value, enum_cls):
|
||||
return value
|
||||
@@ -19,7 +27,10 @@ def _coerce_enum(value: Any, enum_cls: type[Enum]) -> Any:
|
||||
|
||||
@dataclass(slots=True)
|
||||
class PipelineWorkflowInput:
|
||||
"""Temporal workflow input for an image pipeline order."""
|
||||
"""工作流启动输入。
|
||||
|
||||
这是一张订单进入 workflow 时携带的最小上下文。
|
||||
"""
|
||||
|
||||
order_id: int
|
||||
workflow_run_id: int
|
||||
@@ -31,7 +42,7 @@ class PipelineWorkflowInput:
|
||||
scene_ref_asset_id: int
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
"""Normalize enum-like values after Temporal deserialization."""
|
||||
"""在反序列化后把枚举字段修正回来。"""
|
||||
|
||||
self.customer_level = _coerce_enum(self.customer_level, CustomerLevel)
|
||||
self.service_mode = _coerce_enum(self.service_mode, ServiceMode)
|
||||
@@ -39,7 +50,14 @@ class PipelineWorkflowInput:
|
||||
|
||||
@dataclass(slots=True)
|
||||
class StepActivityInput:
|
||||
"""Input payload shared by the mock pipeline activities."""
|
||||
"""通用 activity 输入。
|
||||
|
||||
大多数图片处理步骤都只需要：
|
||||
- 当前订单
|
||||
- 当前 workflow_run
|
||||
- 这一步叫什么
|
||||
- 上一步产出的 asset_id
|
||||
"""
|
||||
|
||||
order_id: int
|
||||
workflow_run_id: int
|
||||
@@ -53,14 +71,14 @@ class StepActivityInput:
|
||||
metadata: dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
"""Normalize enum-like values after Temporal deserialization."""
|
||||
"""在反序列化后把枚举字段修正回来。"""
|
||||
|
||||
self.step_name = _coerce_enum(self.step_name, WorkflowStepName)
|
||||
|
||||
|
||||
@dataclass(slots=True)
|
||||
class MockActivityResult:
|
||||
"""Common mock activity result structure."""
|
||||
"""通用 mock activity 返回结构。"""
|
||||
|
||||
step_name: WorkflowStepName
|
||||
success: bool
|
||||
@@ -73,14 +91,14 @@ class MockActivityResult:
|
||||
metadata: dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
"""Normalize enum-like values after Temporal deserialization."""
|
||||
"""在反序列化后把枚举字段修正回来。"""
|
||||
|
||||
self.step_name = _coerce_enum(self.step_name, WorkflowStepName)
|
||||
|
||||
|
||||
@dataclass(slots=True)
|
||||
class ReviewSignalPayload:
|
||||
"""Signal payload sent from the API to the mid-end workflow."""
|
||||
"""API 发给中端 workflow 的审核 signal 载荷。"""
|
||||
|
||||
decision: ReviewDecision
|
||||
reviewer_id: int
|
||||
@@ -88,14 +106,14 @@ class ReviewSignalPayload:
|
||||
comment: str | None = None
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
"""Normalize enum-like values after Temporal deserialization."""
|
||||
"""在反序列化后把枚举字段修正回来。"""
|
||||
|
||||
self.decision = _coerce_enum(self.decision, ReviewDecision)
|
||||
|
||||
|
||||
@dataclass(slots=True)
|
||||
class ReviewWaitActivityInput:
|
||||
"""Input for marking a workflow as waiting for review."""
|
||||
"""把流程切到 waiting_review 时传给 activity 的输入。"""
|
||||
|
||||
order_id: int
|
||||
workflow_run_id: int
|
||||
@@ -105,7 +123,7 @@ class ReviewWaitActivityInput:
|
||||
|
||||
@dataclass(slots=True)
|
||||
class ReviewResolutionActivityInput:
|
||||
"""Input for completing a waiting review state."""
|
||||
"""审核结果到达后，用于结束 waiting_review 的输入。"""
|
||||
|
||||
order_id: int
|
||||
workflow_run_id: int
|
||||
@@ -115,14 +133,14 @@ class ReviewResolutionActivityInput:
|
||||
comment: str | None = None
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
"""Normalize enum-like values after Temporal deserialization."""
|
||||
"""在反序列化后把枚举字段修正回来。"""
|
||||
|
||||
self.decision = _coerce_enum(self.decision, ReviewDecision)
|
||||
|
||||
|
||||
@dataclass(slots=True)
|
||||
class WorkflowFailureActivityInput:
|
||||
"""Input for marking a workflow as failed."""
|
||||
"""流程失败收尾 activity 的输入。"""
|
||||
|
||||
order_id: int
|
||||
workflow_run_id: int
|
||||
@@ -131,7 +149,7 @@ class WorkflowFailureActivityInput:
|
||||
status: OrderStatus = OrderStatus.FAILED
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
"""Normalize enum-like values after Temporal deserialization."""
|
||||
"""在反序列化后把枚举字段修正回来。"""
|
||||
|
||||
self.current_step = _coerce_enum(self.current_step, WorkflowStepName)
|
||||
self.status = _coerce_enum(self.status, OrderStatus)
|
||||
|
||||
Reference in New Issue
Block a user