feat(phase7.3-ext): workflow executor + config validation

- 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>
2026-03-08 20:41:26 +01:00
parent 1cc10d4bbb
commit b41e70cdad
3 changed files with 290 additions and 3 deletions
@@ -0,0 +1,75 @@
+"""Pydantic schema for validated WorkflowDefinition.config JSONB.
+
+A workflow config is a versioned DAG description stored as JSONB.  Before
+being dispatched (or saved), the raw dict must pass this schema.
+
+Example config::
+
+    {
+        "version": 1,
+        "nodes": [
+            {"id": "n1", "step": "resolve_step_path", "params": {}},
+            {"id": "n2", "step": "blender_still",      "params": {"engine": "cycles"}}
+        ],
+        "edges": [
+            {"from": "n1", "to": "n2"}
+        ]
+    }
+"""
+from pydantic import BaseModel, Field, field_validator, model_validator
+
+from app.core.process_steps import StepName
+
+
+class WorkflowNode(BaseModel):
+    id: str
+    step: StepName  # validated against the StepName StrEnum
+    params: dict = {}
+
+
+class WorkflowEdge(BaseModel):
+    # "from" is a Python keyword, so we alias it.
+    from_node: str = Field(alias="from")
+    to_node: str = Field(alias="to")
+
+    model_config = {"populate_by_name": True}
+
+
+class WorkflowConfig(BaseModel):
+    version: int = 1
+    nodes: list[WorkflowNode]
+    edges: list[WorkflowEdge] = []
+
+    @field_validator("nodes")
+    @classmethod
+    def nodes_not_empty(cls, v: list[WorkflowNode]) -> list[WorkflowNode]:
+        if not v:
+            raise ValueError("workflow must have at least one node")
+        return v
+
+    @field_validator("edges")
+    @classmethod
+    def edges_reference_valid_nodes(
+        cls, edges: list[WorkflowEdge], info
+    ) -> list[WorkflowEdge]:
+        if "nodes" in info.data:
+            node_ids = {n.id for n in info.data["nodes"]}
+            for edge in edges:
+                if edge.from_node not in node_ids:
+                    raise ValueError(
+                        f"edge references unknown node id: {edge.from_node!r}"
+                    )
+                if edge.to_node not in node_ids:
+                    raise ValueError(
+                        f"edge references unknown node id: {edge.to_node!r}"
+                    )
+        return edges
+
+    @model_validator(mode="after")
+    def node_ids_are_unique(self) -> "WorkflowConfig":
+        seen: set[str] = set()
+        for node in self.nodes:
+            if node.id in seen:
+                raise ValueError(f"duplicate node id: {node.id!r}")
+            seen.add(node.id)
+        return self