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100 changes: 25 additions & 75 deletions src/sempy_labs/tom/_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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.
Expand All @@ -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
-------
Expand All @@ -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}"}
Expand Down Expand Up @@ -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.
Expand Down
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