Merge branch 'm-kovalsky/listitemlabels'

Author: m-kovalsky

src/sempy_labs/__init__.py

@@ -1,3 +1,6 @@
+from ._labels import (
+    list_item_labels,
+)
 from ._sql_endpoints import (
     list_sql_endpoints,
     refresh_sql_endpoint_metadata,
@@ -601,4 +604,5 @@
     "list_semantic_model_datasources",
     "upgrade_dataflow",
     "get_dataflow_definition",
+    "list_item_labels",
 ]

src/sempy_labs/_labels.py

@@ -0,0 +1,126 @@
+import sempy.fabric as fabric
+import requests
+import pandas as pd
+from typing import Optional, Union
+from uuid import UUID
+from sempy.fabric.exceptions import FabricHTTPException
+from sempy._utils._log import log
+
+
+@log
+def list_item_labels(workspace: Optional[Union[str, UUID]] = None) -> pd.DataFrame:
+    """
+    List all items within a workspace and shows their sensitivity labels.
+
+    NOTE: This function uses an internal API and is subject to change/break without notice.
+
+    Parameters
+    ----------
+    workspace : str | uuid.UUID, default=None
+        The Fabric workspace name or ID.
+        Defaults to None which resolves to the workspace of the attached lakehouse
+        or if no lakehouse attached, resolves to the workspace of the notebook.
+    Returns
+    -------
+    pandas.DataFrame
+        A pandas dataframe showing a list of all items within a workspace and their sensitivity labels.
+    """
+
+    import notebookutils
+
+    token = notebookutils.credentials.getToken("pbi")
+    headers = {"Authorization": f"Bearer {token}"}
+
+    # Item types handled in special payload fields
+    grouped_types = {
+        "dashboards": "Dashboard",
+        "reports": "Report",
+        "models": "SemanticModel",
+        "dataflows": "Dataflow",
+        "datamarts": "Datamart",
+    }
+
+    # All other item types go into 'artifacts'
+    fabric_items = [
+        "Datamart",
+        "Lakehouse",
+        "Eventhouse",
+        "Environment",
+        "KQLDatabase",
+        "KQLQueryset",
+        "KQLDashboard",
+        "DataPipeline",
+        "Notebook",
+        "SparkJobDefinition",
+        "MLExperiment",
+        "MLModel",
+        "Warehouse",
+        "Eventstream",
+        "SQLEndpoint",
+        "MirroredWarehouse",
+        "MirroredDatabase",
+        "Reflex",
+        "GraphQLApi",
+        "MountedDataFactory",
+        "SQLDatabase",
+        "CopyJob",
+        "VariableLibrary",
+        "Dataflow",
+        "ApacheAirflowJob",
+        "WarehouseSnapshot",
+        "DigitalTwinBuilder",
+        "DigitalTwinBuilderFlow",
+        "MirroredAzureDatabricksCatalog",
+        "DataAgent",
+        "UserDataFunction",
+    ]
+
+    dfI = fabric.list_items(workspace=workspace)
+
+    payload = {
+        key: [{"artifactId": i} for i in dfI[dfI["Type"] == value]["Id"].tolist()]
+        for key, value in grouped_types.items()
+    }
+
+    # Add generic artifact types
+    artifact_ids = dfI[dfI["Type"].isin(fabric_items)]["Id"].tolist()
+    if artifact_ids:
+        payload["artifacts"] = [{"artifactId": i} for i in artifact_ids]
+
+    client = fabric.PowerBIRestClient()
+    response = client.get("/v1.0/myorg/capacities")
+    if response.status_code != 200:
+        raise FabricHTTPException("Failed to retrieve URL prefix.")
+    context = response.json().get("@odata.context")
+    prefix = context.split("/v1.0")[0]
+
+    response = requests.post(
+        f"{prefix}/metadata/informationProtection/artifacts",
+        json=payload,
+        headers=headers,
+    )
+    if response.status_code != 200:
+        raise FabricHTTPException(f"Failed to retrieve labels: {response.text}")
+    result = response.json()
+
+    label_keys = [
+        "artifactInformationProtections",
+        "datasetInformationProtections",
+        "reportInformationProtections",
+        "dashboardInformationProtections",
+    ]
+
+    rows = [
+        {
+            "Id": item.get("artifactObjectId"),
+            "Label Id": item.get("labelId"),
+            "Label Name": item.get("name"),
+            "Parent Label Name": item.get("parent", {}).get("name"),
+            "Label Description": item.get("tooltip"),
+        }
+        for key in label_keys
+        for item in result.get(key, [])
+    ]
+
+    df_labels = pd.DataFrame(rows)
+    return dfI.merge(df_labels, on="Id", how="left")