microsoft/semantic-link-labs 0.15.0

semantic-link-labs 0.15.0

Repository: microsoft/semantic-link-labs

Tag: 0.15.0

Published: 2026-05-14T13:15:25Z

Prerelease: no

Release notes:

New Functions

• sempy_labs
• ConnectMirroredAzureDatabricksCatalog Write SQL or T-SQL statements against a Mirrored Azure Databricks Catalog.
• sempy_labs.lakehouse
• create_schema Creates a schema within a schema-enabled lakehouse (thanks @pawarbi!).
• create_materialized_lake_view Creates a materialized lake view in a Fabric lakehouse. Only supported in schema-enabled lakehouses.
• sempy_labs.mirrored_azure_databricks_catalog
• list_mirrored_azure_databricks_catalogs
• delete_mirrored_azure_databricks_catalog
• create_mirrored_azure_databricks_catalog
• get_mirrored_azure_databricks_catalog
• update_mirrored_azure_databricks_catalog
• sempy_labs.semantic_model
• perspective_editor An interactive UI which makes it easy to create/manage perspectives
• sempy_labs.sql_database
• revalidate_cmk
• list_restorable_deleted_databases
• sempy_labs.tom
• get_direct_lake_sources Now available directly in the tom package. Shows a list of all sources used for Direct Lake.
• add_direct_lake_tables Adds specified tables (in Direct Lake mode) to a Direct Lake semantic model.
• hide_key_columns Hides Integer columns used in relationships.
• mark_primary_keys Sets the IsKey property to True for columns on the One side of relationships.
• get_mini_model_properties Shows the properties of the mini model if the semantic model is a mini model (see the deploy_semantic_model function).
• sempy_labs.workspace
• apply_workspace_tags
• unapply_workspace_tags

Updated Functions

• sempy_labs
• list_data_access_roles Now supports 3 views using the 'view' parameter (Rules, MicrosoftEntraMembers, FabricItemMembers).
• create_blank_semantic_model Now returns the ID of the created semantic model.
• deploy_semantic_model added the 'filters' parameter which allows deploying a mini model based on the objects in a perspective as well as filters applied to the underlying tables. This is only supported for single-lakehouse-sourced Direct Lake semantic models.
• generate_direct_lake_semantic_model Generates a Direct Lake semantic model based on any viable source (Lakehouse, Warehouse, SQL Database, Mirrored Azure Databricks Catalog, or Mirrored Database).
• sempy_labs.admin
• list_tenant_settings Additional columns added (#1177).
• sempy_labs.sql_database
• list_sql_databases Added the column 'Sensitivity Label Id' .
• sempy_labs.tom
• 'Add' functions (i.e. add_table) now return the object that was created. As an example, add_table now returns the created table.

Bug Fixes

• #1163
• #1168
• #1180
• #1220
• Fixed bug in remove_vertipaq_annotations