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Add Chinese comments for Temporal workflow flow

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2026-03-27 00:13:54 +08:00
parent cc03da8a94
commit d02fc8565f
7 changed files with 205 additions and 49 deletions
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67
app/workers/workflows/mid_end_pipeline.py
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@@ -1,10 +1,18 @@
"""Mid-end image pipeline workflow with review signal support."""
"""中端图片流水线工作流。
和低端流程相比，这里最大的区别是：
1. 会在 QC 之后停在 waiting_review
2. 通过 Temporal signal 接收人工审核结果
3. 可以按审核意见回流到 scene / face / fusion 重新跑
"""
from datetime import timedelta
from temporalio import workflow
from temporalio.common import RetryPolicy
# 这些导入属于 workflow 外部世界的对象，明确标记为 pass-through，
# 避免把它们当成需要重放的 workflow 逻辑一部分。
with workflow.unsafe.imports_passed_through():
from app.domain.enums import OrderStatus, ReviewDecision, WorkflowStepName
from app.infra.temporal.task_queues import (
@@ -47,21 +55,36 @@ ACTIVITY_RETRY_POLICY = RetryPolicy(
@workflow.defn
class MidEndPipelineWorkflow:
"""Mid-end workflow that pauses for human review and supports reruns."""
"""中端半自动工作流。
这个 workflow 会经历“自动生成 -> 等待人工审核 -> 按审核意见继续”。
"""
def __init__(self) -> None:
# signal 到达后，先暂存在 workflow 内存里，
# 主流程再通过 wait_condition 继续往下走。
self._review_payload: ReviewSignalPayload | None = None
@workflow.signal
def submit_review(self, payload: ReviewSignalPayload) -> None:
"""Receive a review decision from the API layer."""
"""接收 API 层发来的审核 signal。
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。
真正恢复主流程是在 `_wait_for_review` 里。
"""
self._review_payload = payload
@workflow.run
async def run(self, payload: PipelineWorkflowInput) -> dict[str, int | str | None]:
"""Execute the mid-end workflow with a human review loop."""
"""执行中端工作流主流程。
主线是：
prepare_model -> tryon -> scene -> texture -> face -> fusion -> qc
-> waiting_review -> approve/export 或 rerun
"""
# current_step 用于失败时记录“最后跑到哪一步”。
current_step = WorkflowStepName.PREPARE_MODEL
try:
prepared = await workflow.execute_activity(
@@ -113,8 +136,14 @@ class MidEndPipelineWorkflow:
await self._mark_failed(payload, current_step, qc_result.message)
return {"order_id": payload.order_id, "status": OrderStatus.FAILED.value, "final_asset_id": None}
# 中端流程会一直循环到：
# 1. 审核 approve 然后 export 成功
# 2. 审核 reject 直接结束
# 3. rerun 后再次回到 waiting_review，继续等下一次人工输入
while True:
current_step = WorkflowStepName.REVIEW
# 这里通过 activity 把数据库里的订单状态更新成 waiting_review，
# 同时创建 review_task，供 API 查询待审核列表。
await workflow.execute_activity(
mark_waiting_for_review_activity,
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,