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>

backend/app/domains/rendering/workflow_executor.py

@@ -0,0 +1,155 @@
+"""Execute a validated WorkflowDefinition config as individual Celery tasks.
+
+Usage::
+
+    from app.domains.rendering.workflow_executor import dispatch_workflow
+
+    task_ids = dispatch_workflow(workflow_definition.config, context_id=str(order_line_id))
+
+The function:
+1. Validates the raw config dict via WorkflowConfig.
+2. Sorts nodes into dependency order (topological sort, Kahn's algorithm).
+3. Maps each StepName to the corresponding Celery task name string.
+4. Sends each task independently (not chained) so that every node gets its own
+   Celery task ID and can be tracked individually.
+
+Nodes whose StepName has no mapping in STEP_TASK_MAP are skipped with a
+warning — this lets you add new StepName values to the enum without breaking
+existing dispatchers.
+"""
+import logging
+from collections import deque
+
+from app.domains.rendering.workflow_schema import WorkflowConfig, WorkflowNode
+from app.core.process_steps import StepName
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Step → Celery task name mapping
+#
+# Values are the full dotted task name strings passed to celery_app.send_task().
+# Keep this in sync with the @celery_app.task(name=...) decorators in the
+# various tasks modules.
+# ---------------------------------------------------------------------------
+STEP_TASK_MAP: dict[StepName, str] = {
+    # ── STEP file processing ─────────────────────────────────────────────
+    StepName.RESOLVE_STEP_PATH: "app.tasks.step_tasks.process_step_file",
+    StepName.OCC_OBJECT_EXTRACT: "app.tasks.step_tasks.process_step_file",
+    StepName.OCC_GLB_EXPORT: "app.tasks.step_tasks.generate_gltf_geometry_task",
+    StepName.STL_CACHE_GENERATE: "app.tasks.step_tasks.process_step_file",
+    # ── Thumbnail generation ─────────────────────────────────────────────
+    StepName.BLENDER_RENDER: "app.tasks.step_tasks.render_step_thumbnail",
+    StepName.THUMBNAIL_SAVE: "app.tasks.step_tasks.render_step_thumbnail",
+    # ── Order line stills & turntables ──────────────────────────────────
+    StepName.BLENDER_STILL: "app.domains.rendering.tasks.render_order_line_still_task",
+    StepName.BLENDER_TURNTABLE: "app.domains.rendering.tasks.render_turntable_task",
+    # ── GLB / asset export ───────────────────────────────────────────────
+    StepName.EXPORT_GLB_GEOMETRY: "app.domains.rendering.tasks.export_gltf_for_order_line_task",
+    StepName.EXPORT_BLEND: "app.domains.rendering.tasks.export_blend_for_order_line_task",
+    # ── Steps without a dedicated standalone task (no mapping) ───────────
+    # StepName.GLB_BBOX              — computed inline inside process_step_file
+    # StepName.MATERIAL_MAP_RESOLVE  — computed inline inside render tasks
+    # StepName.AUTO_POPULATE_MATERIALS — computed inline inside process_step_file
+    # StepName.THREEJS_RENDER        — no standalone task exists yet
+    # StepName.ORDER_LINE_SETUP      — computed inline inside render_order_line_task
+    # StepName.RESOLVE_TEMPLATE      — computed inline inside render_order_line_task
+    # StepName.OUTPUT_SAVE           — handled via publish_asset after render tasks
+    # StepName.EXPORT_GLB_PRODUCTION — app.tasks.step_tasks.generate_gltf_production_task
+    StepName.EXPORT_GLB_PRODUCTION: "app.tasks.step_tasks.generate_gltf_production_task",
+    # StepName.NOTIFY                — emitted inline via notification_service
+}
+
+
+def dispatch_workflow(workflow_config: dict, context_id: str) -> list[str]:
+    """Execute workflow nodes in topological order as individual Celery tasks.
+
+    Args:
+        workflow_config: Raw dict from WorkflowDefinition.config JSONB.
+        context_id: UUID string of the entity being processed.  Depending on
+            the StepName, this is interpreted as a ``cad_file_id`` (for STEP
+            processing / thumbnail tasks) or an ``order_line_id`` (for render
+            tasks).
+
+    Returns:
+        List of dispatched Celery task ID strings (one per mapped node).
+
+    Raises:
+        pydantic.ValidationError: if *workflow_config* does not conform to
+            WorkflowConfig schema.
+        ValueError: if the node graph contains a cycle.
+    """
+    from app.tasks.celery_app import celery_app
+
+    # Validate + parse config
+    config = WorkflowConfig.model_validate(workflow_config)
+
+    # Topological sort so that dependency nodes dispatch first
+    ordered_nodes = _topological_sort(config)
+
+    task_ids: list[str] = []
+    for node in ordered_nodes:
+        task_name = STEP_TASK_MAP.get(node.step)
+        if task_name is None:
+            logger.warning(
+                "[WORKFLOW] No Celery task mapping for step %r — skipping node %r",
+                node.step,
+                node.id,
+            )
+            continue
+
+        result = celery_app.send_task(task_name, args=[context_id], kwargs=node.params)
+        task_ids.append(result.id)
+        logger.info(
+            "[WORKFLOW] Dispatched node %r (step=%s) → Celery task %s",
+            node.id,
+            node.step,
+            result.id,
+        )
+
+    logger.info(
+        "[WORKFLOW] dispatch_workflow complete: %d task(s) dispatched for context %s",
+        len(task_ids),
+        context_id,
+    )
+    return task_ids
+
+
+# ---------------------------------------------------------------------------
+# Internal helpers
+# ---------------------------------------------------------------------------
+
+def _topological_sort(config: WorkflowConfig) -> list[WorkflowNode]:
+    """Return nodes in dependency order using Kahn's algorithm.
+
+    Nodes with no incoming edges are processed first.  If the graph contains a
+    cycle a ``ValueError`` is raised (the caller should treat this as a bad
+    workflow config).
+    """
+    node_map: dict[str, WorkflowNode] = {n.id: n for n in config.nodes}
+    in_degree: dict[str, int] = {n.id: 0 for n in config.nodes}
+    adjacency: dict[str, list[str]] = {n.id: [] for n in config.nodes}
+
+    for edge in config.edges:
+        adjacency[edge.from_node].append(edge.to_node)
+        in_degree[edge.to_node] += 1
+
+    queue: deque[str] = deque(nid for nid, deg in in_degree.items() if deg == 0)
+    result: list[WorkflowNode] = []
+
+    while queue:
+        nid = queue.popleft()
+        result.append(node_map[nid])
+        for neighbor in adjacency[nid]:
+            in_degree[neighbor] -= 1
+            if in_degree[neighbor] == 0:
+                queue.append(neighbor)
+
+    if len(result) != len(config.nodes):
+        raise ValueError(
+            "Workflow config contains a cycle — topological sort failed. "
+            f"Processed {len(result)}/{len(config.nodes)} nodes."
+        )
+
+    return result