Hartmut
b41e70cdad
- workflow_schema.py: WorkflowConfig Pydantic model validates all node
step values against StepName enum, edges reference declared node IDs,
unique node IDs enforced; WorkflowEdge uses "from"/"to" aliases
- workflow_executor.py: dispatch_workflow() validates config, topological-
sorts nodes (Kahn's algorithm, raises on cycles), maps StepName →
Celery task name via STEP_TASK_MAP (all 15 StepName values covered),
dispatches via celery_app.send_task()
- workflow_router.py: config validation on POST/PUT (422 on invalid);
POST /{id}/dispatch?context_id= endpoint (PM+) dispatches workflow
steps as Celery tasks for a given entity
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
76 lines
2.2 KiB
Python
76 lines
2.2 KiB
Python
"""Pydantic schema for validated WorkflowDefinition.config JSONB.
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A workflow config is a versioned DAG description stored as JSONB. Before
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being dispatched (or saved), the raw dict must pass this schema.
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Example config::
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{
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"version": 1,
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"nodes": [
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{"id": "n1", "step": "resolve_step_path", "params": {}},
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{"id": "n2", "step": "blender_still", "params": {"engine": "cycles"}}
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],
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"edges": [
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{"from": "n1", "to": "n2"}
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]
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}
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"""
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from pydantic import BaseModel, Field, field_validator, model_validator
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from app.core.process_steps import StepName
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class WorkflowNode(BaseModel):
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id: str
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step: StepName # validated against the StepName StrEnum
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params: dict = {}
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class WorkflowEdge(BaseModel):
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# "from" is a Python keyword, so we alias it.
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from_node: str = Field(alias="from")
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to_node: str = Field(alias="to")
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model_config = {"populate_by_name": True}
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class WorkflowConfig(BaseModel):
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version: int = 1
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nodes: list[WorkflowNode]
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edges: list[WorkflowEdge] = []
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@field_validator("nodes")
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@classmethod
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def nodes_not_empty(cls, v: list[WorkflowNode]) -> list[WorkflowNode]:
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if not v:
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raise ValueError("workflow must have at least one node")
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return v
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@field_validator("edges")
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@classmethod
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def edges_reference_valid_nodes(
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cls, edges: list[WorkflowEdge], info
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) -> list[WorkflowEdge]:
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if "nodes" in info.data:
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node_ids = {n.id for n in info.data["nodes"]}
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for edge in edges:
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if edge.from_node not in node_ids:
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raise ValueError(
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f"edge references unknown node id: {edge.from_node!r}"
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)
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if edge.to_node not in node_ids:
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raise ValueError(
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f"edge references unknown node id: {edge.to_node!r}"
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)
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return edges
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@model_validator(mode="after")
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def node_ids_are_unique(self) -> "WorkflowConfig":
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seen: set[str] = set()
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for node in self.nodes:
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if node.id in seen:
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raise ValueError(f"duplicate node id: {node.id!r}")
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seen.add(node.id)
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return self
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