Merge pull request #37 from Aignosi/fix/SIENTIAPDE-1478

Enhance API: Add optional operation_type to get_core_labels

laborious/activities/api.py

@@ -71,6 +71,38 @@ class API(SientiaMonitoring):
             },
         )
 
+    def get_pi_web_api_core_labels(
+        self,
+        metadata: dict[str, Any],
+        operation_type: str | None = None,
+    ) -> dict[str, Any]:
+        """
+        Generate core labels for metrics, optionally including operation_type.
+
+        This override keeps compatibility with the base implementation while adding
+        a convenience overload behavior:
+         - When operation_type is provided, it behaves exactly like the base class,
+           returning labels that include the operation_type key.
+         - When operation_type is omitted (None), it removes the operation_type key
+           from the resulting labels. This is useful for metrics, such as the PI Web
+           API metrics, that are defined without the operation_type label.
+
+        Args:
+         - metadata (dict[str, Any]): Workflow execution metadata used to derive labels
+         - operation_type (str | None): Optional operation type label. If None, the
+           operation_type key will be removed from the returned labels.
+
+        Return:
+         dict[str, Any]: Core labels dictionary, with operation_type only when provided
+        """
+        base_labels = super().get_core_labels(
+            metadata=metadata,
+            operation_type=operation_type or '-',
+        )
+        if operation_type is None:
+            base_labels.pop('operation_type', None)
+        return base_labels
+
     def close(self) -> None:
         """
         Close the PI Web API client and shutdown monitoring services.
@@ -202,7 +234,7 @@ class API(SientiaMonitoring):
         prediction_tags = list[str](raw_prediction_tags.values())
         confidence_tags = list(raw_confidence_tags.values())
 
-        core_labels = self.get_core_labels(metadata)
+        core_labels = self.get_pi_web_api_core_labels(metadata)
 
         prediction_value = data.head(1)['prediction'].values[0]
         confidence_value = data.head(1)['prediction_confidence'].values[0]