949

Author: m-kovalsky

src/sempy_labs/tom/_model.py

@@ -4660,7 +4660,7 @@ def remove_changed_property(self, object, property: str):
     def generate_measure_descriptions(
         self,
         measure_name: Optional[str | List[str]] = None,
-        max_batch_size: Optional[int] = 5,
+        max_batch_size: Optional[int] = 1,
     ) -> pd.DataFrame:
         """
         Auto-generates descriptions for measures using an LLM. This function requires a paid F-sku (Fabric) of F64 or higher.
@@ -4672,8 +4672,8 @@ def generate_measure_descriptions(
         measure_name : str | List[str], default=None
             The measure name (or a list of measure names).
             Defaults to None which generates descriptions for all measures in the semantic model.
-        max_batch_size : int, default=5
-            Sets the max batch size for each API call.
+        max_batch_size : int, default=1
+            Sets the max batch size for each API call. This parameter has been decomissioned.
 
         Returns
         -------
@@ -4698,45 +4698,30 @@ def generate_measure_descriptions(
         if isinstance(measure_name, str):
             measure_name = [measure_name]
 
-        if len(measure_name) > max_batch_size:
-            measure_lists = [
-                measure_name[i : i + max_batch_size]
-                for i in range(0, len(measure_name), max_batch_size)
-            ]
-        else:
-            measure_lists = [measure_name]
-
-        # Each API call can have a max of 5 measures
-        for measure_list in measure_lists:
-            payload = {
-                "scenarioDefinition": {
-                    "generateModelItemDescriptions": {
-                        "modelItems": [],
-                    },
+        payload = {
+            "scenarioDefinition": {
+                "generateModelItemDescriptions": {
+                    "modelItems": [],
                 },
-                "workspaceId": self._workspace_id,
-                "artifactInfo": {"artifactType": "SemanticModel"},
-            }
-            for m_name in measure_list:
-                expr, t_name = next(
-                    (ms.Expression, ms.Parent.Name)
-                    for ms in self.all_measures()
-                    if ms.Name == m_name
-                )
-                if t_name is None:
-                    raise ValueError(
-                        f"{icons.red_dot} The '{m_name}' measure does not exist in the '{self._dataset_name}' semantic model within the '{self._workspace_name}' workspace."
-                    )
+            },
+            "workspaceId": self._workspace_id,
+            "artifactInfo": {"artifactType": "SemanticModel"},
+        }
 
-                new_item = {
-                    "urn": m_name,
-                    "type": 1,
-                    "name": m_name,
-                    "expression": expr,
-                }
-                payload["scenarioDefinition"]["generateModelItemDescriptions"][
-                    "modelItems"
-                ].append(new_item)
+        for m in measure_name:
+            (table_name, expr) = next(
+                (ms.Parent.Name, ms.Expression)
+                for ms in self.all_measures()
+                if ms.Name == m
+            )
+            payload["scenarioDefinition"]["generateModelItemDescriptions"][
+                "modelItems"
+            ] = {
+                "urn": m,
+                "type": 1,
+                "name": m,
+                "expression": expr,
+            }
 
             token = notebookutils.credentials.getToken("pbi")
             headers = {"Authorization": f"Bearer {token}"}
@@ -4775,41 +4760,6 @@ def generate_measure_descriptions(
 
         return df
 
-        # def process_measure(m):
-        #     table_name = m.Parent.Name
-        #     m_name = m.Name
-        #     m_name_fixed = "1"
-        #     expr = m.Expression
-        #     if measure_name is None or m_name in measure_name:
-        #         payload = {
-        #             "scenarioDefinition": {
-        #                 "generateModelItemDescriptions": {
-        #                     "modelItems": [
-        #                         {
-        #                             "urn": f"modelobject://Table/{table_name}/Measure/{m_name_fixed}",
-        #                             "type": 1,
-        #                             "name": m_name,
-        #                             "expression": expr,
-        #                         }
-        #                     ]
-        #                 }
-        #             },
-        #             "workspaceId": workspace_id,
-        #             "artifactInfo": {"artifactType": "SemanticModel"},
-        #         }
-
-        #         response = client.post(
-        #             "/explore/v202304/nl2nl/completions", json=payload
-        #         )
-        #         if response.status_code != 200:
-        #             raise FabricHTTPException(response)
-
-        #         desc = response.json()["modelItems"][0]["description"]
-        #         m.Description = desc
-
-        # with concurrent.futures.ThreadPoolExecutor() as executor:
-        #     executor.map(process_measure, self.all_measures())
-
     def set_value_filter_behavior(self, value_filter_behavior: str = "Automatic"):
         """
         Sets the `Value Filter Behavior <https://learn.microsoft.com/power-bi/transform-model/value-filter-behavior>`_ property for the semantic model.