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https://github.com/jxxghp/MoviePilot.git
synced 2026-06-11 18:50:59 +08:00
feat(workflow): add execution configuration and structured execution state to workflow
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jxxghp
@@ -1,28 +1,32 @@
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import base64
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import pickle
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import threading
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import time
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from types import SimpleNamespace
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from app.chain import workflow as workflow_module
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from app.schemas import ActionContext, ActionResult
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from app.schemas import Action, ActionContext, ActionResult
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from app.schemas.types import EventType
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from app import workflow as workflow_package
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def _build_workflow(current_action=None, context=None, actions=None, flows=None):
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def _build_workflow(current_action=None, context=None, actions=None, flows=None,
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execution_config=None, execution_state=None):
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"""构造最小工作流对象。"""
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return SimpleNamespace(
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id=1,
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name="测试工作流",
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actions=actions or [
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actions=actions if actions is not None else [
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{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}},
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{"id": "B", "type": "FakeAction", "name": "动作B", "data": {}},
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],
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flows=flows or [
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flows=flows if flows is not None else [
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{"id": "flow-1", "source": "A", "target": "B", "animated": True},
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],
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current_action=current_action,
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context=context,
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execution_config=execution_config or {},
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execution_state=execution_state or {},
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)
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@@ -39,9 +43,12 @@ class _FakeWorkflowManager:
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def __init__(self, calls, results=None):
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self.calls = calls
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self.results = results or {}
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self.received_inputs = []
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def execute(self, workflow_id, action, context=None):
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def execute(self, workflow_id, action, context=None, inputs=None, runtime=None, cancel_token=None):
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"""执行伪动作并记录新版输入。"""
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self.calls.append(action.id)
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self.received_inputs.append((action.id, inputs or {}, runtime or {}, cancel_token))
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result = self.results.get(action.id)
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if callable(result):
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return result(action, context or ActionContext())
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@@ -262,6 +269,220 @@ def test_workflow_executor_all_done_join_can_continue_after_failure(monkeypatch)
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assert executor.success is True
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def test_workflow_executor_exclusive_branch_uses_first_matching_flow(monkeypatch):
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"""互斥分支应只执行第一条满足条件的出边。"""
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calls = []
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fake_manager = _FakeWorkflowManager(
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calls,
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results={
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"A": lambda action, context: ActionResult(
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success=True,
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message=f"{action.name}完成",
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context=context,
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outputs={"count": 2}
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)
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}
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)
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workflow = _build_workflow(
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actions=[
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{"id": "A", "type": "FakeAction", "name": "动作A", "data": {"branch_policy": "exclusive"}},
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{"id": "B", "type": "FakeAction", "name": "动作B", "data": {}},
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{"id": "C", "type": "FakeAction", "name": "动作C", "data": {}},
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],
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flows=[
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{"id": "flow-ab", "source": "A", "target": "B", "condition": "outputs.A.count > 0"},
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{"id": "flow-ac", "source": "A", "target": "C", "condition": "outputs.A.count > 1"},
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],
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)
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monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
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monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
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monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: False)
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executor = workflow_module.WorkflowExecutor(workflow)
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executor.execute()
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assert calls == ["A", "B"]
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assert executor.node_states["C"] == "skipped"
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def test_workflow_executor_passes_declared_inputs(monkeypatch):
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"""动作输入声明应从 node_outputs 中读取指定路径。"""
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calls = []
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fake_manager = _FakeWorkflowManager(
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calls,
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results={
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"A": lambda action, context: ActionResult(
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success=True,
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message=f"{action.name}完成",
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context=context,
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outputs={"torrents": ["a", "b"]}
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)
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}
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)
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workflow = _build_workflow(
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actions=[
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{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}},
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{
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"id": "B",
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"type": "FakeAction",
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"name": "动作B",
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"data": {"inputs": ["A.torrents", "outputs.A.torrents.count"]},
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},
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],
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)
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monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
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monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
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monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: False)
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executor = workflow_module.WorkflowExecutor(workflow)
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executor.execute()
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b_inputs = [item for action_id, item, _, _ in fake_manager.received_inputs if action_id == "B"][0]
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assert b_inputs == {
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"A.torrents": ["a", "b"],
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"outputs.A.torrents.count": 2,
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}
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def test_workflow_executor_persists_structured_state(monkeypatch):
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"""步骤回调应收到可持久化的结构化执行状态。"""
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calls = []
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states = []
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fake_manager = _FakeWorkflowManager(
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calls,
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results={
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"A": lambda action, context: ActionResult(
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success=True,
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message=f"{action.name}完成",
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context=context,
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outputs={"items": ["movie"]}
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)
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}
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)
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monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
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monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
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monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: False)
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executor = workflow_module.WorkflowExecutor(
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_build_workflow(actions=[{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}}], flows=[]),
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step_callback=lambda action, context, execution_state, completed: states.append(execution_state),
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)
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executor.execute()
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assert states[-1]["nodes"]["A"]["state"] == "success"
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assert states[-1]["outputs"]["A"]["items"] == ["movie"]
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assert states[-1]["runtime"]["progress"] == 100
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def test_workflow_executor_restores_outputs_from_execution_state(monkeypatch):
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"""恢复执行时应从结构化状态读取节点输出并继续判断条件边。"""
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calls = []
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fake_manager = _FakeWorkflowManager(calls)
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workflow = _build_workflow(
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execution_state={
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"nodes": {
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"A": {"state": "success", "attempt": 1},
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},
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"outputs": {
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"A": {"torrents": ["movie"]},
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},
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},
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flows=[
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{"id": "flow-ab", "source": "A", "target": "B", "condition": "A.torrents.count > 0"},
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],
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)
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monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
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monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
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monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: False)
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executor = workflow_module.WorkflowExecutor(workflow)
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executor.execute()
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assert calls == ["B"]
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assert executor.context.node_outputs["A"]["torrents"] == ["movie"]
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def test_workflow_executor_concurrency_key_serializes_parallel_nodes(monkeypatch):
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"""相同 concurrency_key 的并行节点不应同时运行。"""
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calls = []
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active_count = 0
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max_active_count = 0
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lock = threading.Lock()
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def run_action(action, context):
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"""记录同一并发键下的同时运行数量。"""
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nonlocal active_count, max_active_count
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with lock:
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active_count += 1
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max_active_count = max(max_active_count, active_count)
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time.sleep(0.05)
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with lock:
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active_count -= 1
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return ActionResult(success=True, message=f"{action.name}完成", context=context)
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fake_manager = _FakeWorkflowManager(calls, results={"A": run_action, "B": run_action})
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workflow = _build_workflow(
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actions=[
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{"id": "A", "type": "FakeAction", "name": "动作A", "data": {"concurrency_key": "download"}},
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{"id": "B", "type": "FakeAction", "name": "动作B", "data": {"concurrency_key": "download"}},
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],
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flows=[],
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execution_config={"max_workers": 2},
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)
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monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
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monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
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monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: False)
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executor = workflow_module.WorkflowExecutor(workflow)
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executor.execute()
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assert set(calls) == {"A", "B"}
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assert max_active_count == 1
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def test_workflow_executor_filter_action_replaces_artifact_outputs(monkeypatch):
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"""过滤类动作默认应替换列表输出，避免把过滤前数据重新合并回来。"""
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calls = []
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fake_manager = _FakeWorkflowManager(
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calls,
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results={
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"A": lambda action, context: ActionResult(
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success=True,
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message=f"{action.name}完成",
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context=context,
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outputs={"torrents": ["old", "keep"]}
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),
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"B": lambda action, context: ActionResult(
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success=True,
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message=f"{action.name}完成",
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context=context,
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outputs={"torrents": ["keep"]}
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),
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}
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)
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workflow = _build_workflow(
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actions=[
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{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}},
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{"id": "B", "type": "FilterTorrentsAction", "name": "过滤资源", "data": {}},
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],
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)
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monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
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monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
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monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: False)
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executor = workflow_module.WorkflowExecutor(workflow)
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executor.execute()
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assert executor.context.torrents == ["keep"]
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assert executor.context.artifacts["torrents"] == ["keep"]
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def test_workflow_executor_stop_is_not_success(monkeypatch):
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"""停止信号不应被执行器汇报为成功完成。"""
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calls = []
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@@ -328,3 +549,43 @@ def test_workflow_event_listener_keeps_shared_handler_until_last_workflow(monkey
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assert fake_eventmanager.removed == [EventType.DownloadAdded]
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assert manager.get_event_workflows() == {}
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def test_workflow_manager_retries_action_until_success(monkeypatch):
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"""动作管理器应按 retry 配置重试失败动作。"""
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class RetryAction:
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"""模拟第二次才成功的动作。"""
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call_count = 0
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def __init__(self, action_id):
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self.action_id = action_id
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def execute_with_inputs(self, workflow_id, params, inputs, runtime, context):
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"""执行动作并在第二次返回成功。"""
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_ = workflow_id, params, inputs, runtime
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RetryAction.call_count += 1
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if RetryAction.call_count == 1:
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return ActionResult(success=False, message="第一次失败", context=context)
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return ActionResult(success=True, message="第二次成功", context=context, outputs={"ok": True})
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manager = object.__new__(workflow_package.WorkFlowManager)
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manager._actions = {"RetryAction": RetryAction}
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monkeypatch.setattr(workflow_package.global_vars, "is_workflow_stopped", lambda workflow_id: False)
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result = manager.execute(
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workflow_id=1,
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action=Action(
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id="retry",
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type="RetryAction",
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name="重试动作",
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data={"retry": {"max_attempts": 2, "interval": 0}},
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),
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context=ActionContext(),
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)
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assert result.success is True
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assert result.attempts == 2
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assert result.outputs == {"ok": True}
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assert RetryAction.call_count == 2
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