r2

api/services/rag_pipeline/rag_pipeline.py

@@ -443,21 +443,23 @@ class RagPipelineService:
         repository.save(workflow_node_execution)
 
         # Convert node_execution to WorkflowNodeExecution after save
-        workflow_node_execution = repository.to_db_model(workflow_node_execution)
+        workflow_node_execution_db_model = repository.to_db_model(workflow_node_execution)
 
         with Session(bind=db.engine) as session, session.begin():
             draft_var_saver = DraftVariableSaver(
                 session=session,
                 app_id=pipeline.id,
-                node_id=workflow_node_execution.node_id,
+                node_id=workflow_node_execution_db_model.node_id,
-                node_type=NodeType(workflow_node_execution.node_type),
+                node_type=NodeType(workflow_node_execution_db_model.node_type),
                 enclosing_node_id=enclosing_node_id,
                 node_execution_id=workflow_node_execution.id,
             )
-            draft_var_saver.save(process_data=workflow_node_execution.process_data,
+            draft_var_saver.save(
-                                 outputs=workflow_node_execution.outputs)
+                process_data=workflow_node_execution.process_data,
+                outputs=workflow_node_execution.outputs,
+            )
             session.commit()
-        return workflow_node_execution
+        return workflow_node_execution_db_model
 
     def run_datasource_workflow_node(
         self,

api/services/rag_pipeline/rag_pipeline_dsl_service.py

@@ -231,6 +231,9 @@ class RagPipelineDslService:
                         status=ImportStatus.FAILED,
                         error="Pipeline not found",
                     )
+                dataset = pipeline.dataset
+                if dataset:
+                    dataset_name = dataset.name
 
             # If major version mismatch, store import info in Redis
             if status == ImportStatus.PENDING:
@@ -285,7 +288,7 @@ class RagPipelineDslService:
                         and dataset.chunk_structure != knowledge_configuration.chunk_structure
                     ):
                         raise ValueError("Chunk structure is not compatible with the published pipeline")
-                    else:
+                    if not dataset:
                         dataset = Dataset(
                             tenant_id=account.current_tenant_id,
                             name=name,