fix: PR Comments

metadata-ingestion/docs/transformer/dataset_transformer.md

@@ -679,11 +679,11 @@ We can add glossary terms to datasets based on a regex filter.
 
 <br/>
 
-The `tags_to_term` transformer is designed to map specific tags to glossary terms within DataHub. It takes a configuration of tags should be translated into corresponding glossaryTerm. This transformer can apply these mappings to any tags found either at column level of dataset or dataset top level.
+The `tags_to_term` transformer is designed to map specific tags to glossary terms within DataHub. It takes a configuration of tags that should be translated into corresponding glossary terms. This transformer can apply these mappings to any tags found either at the column level of a dataset or at the dataset top level.
 
 When specifying tags in the configuration, use the tag's simple name rather than the full tag URN.
 
-For example, instead of using the tag URN `urn:li:tag:snowflakedb.snowflakeschema.tag_name:tag_value`, you should specify just the tag name `tag_name` in the mapping configuration
+For example, instead of using the tag URN `urn:li:tag:snowflakedb.snowflakeschema.tag_name:tag_value`, you should specify just the tag name `tag_name` in the mapping configuration.
 
 ```yaml
 transformers:
@@ -694,28 +694,29 @@ transformers:
         - "tag_name"
 ```
 
-`tags_to_term` can be configured in below different way
+The `tags_to_term` transformer can be configured in the following ways:
 
-- Add domains based on tags, however overwrite the domains available for the dataset on DataHub GMS
+- Add terms based on tags, however overwrite the terms available for the dataset on DataHub GMS
 ```yaml
     transformers:
-      - type: "domain_mapping_based_on_tags"
+      - type: "tags_to_term"
         config:
           semantics: OVERWRITE  # OVERWRITE is default behaviour
           tags:
             - "example1"
             - "example2"
             - "example3"
   ```
-- Add domains based on tags, however keep the domains available for the dataset on DataHub GMS
+- Add terms based on tags, however keep the terms available for the dataset on DataHub GMS
 ```yaml
     transformers:
-      - type: "domain_mapping_based_on_tags"
+      - type: "tags_to_term"
         config:
           semantics: PATCH
-          domain_mapping:
-            'example1': "urn:li:domain:engineering"
-            'example2': "urn:li:domain:hr"
+          tags:
+            - "example1"
+            - "example2"
+            - "example3"
   ```
 
 ## Pattern Add Dataset Schema Field glossaryTerms

metadata-ingestion/src/datahub/ingestion/graph/client.py

@@ -1279,17 +1279,15 @@ def create_tag(self, tag_name: str) -> str:
         return res["createTag"]
 
     def remove_tag(self, tag_urn: str, resource_urn: str) -> bool:
-        graph_query = """
+        graph_query = f"""
             mutation removeTag {{
                 removeTag(
                 input: {{
                     tagUrn: "{tag_urn}",
                     resourceUrn: "{resource_urn}"
                     }})
             }}
-        """.format(
-            tag_urn=tag_urn, resource_urn=resource_urn
-        )
+        """
 
         res = self.execute_graphql(query=graph_query)
         return res["removeTag"]

metadata-ingestion/tests/unit/test_transform_dataset.py

@@ -1898,9 +1898,11 @@ def run_dataset_transformer_pipeline(
     transformer_type: Type[Union[DatasetTransformer, TagTransformer]],
     aspect: Optional[builder.Aspect],
     config: dict,
-    pipeline_context: PipelineContext = PipelineContext(run_id="transformer_pipe_line"),
+    pipeline_context: Optional[PipelineContext] = None,
     use_mce: bool = False,
 ) -> List[RecordEnvelope]:
+    if pipeline_context is None:
+        pipeline_context = PipelineContext(run_id="transformer_pipe_line")
     transformer: DatasetTransformer = cast(
         DatasetTransformer, transformer_type.create(config, pipeline_context)
     )
@@ -3665,8 +3667,8 @@ def test_tags_to_terms_transformation(mock_datahub_graph):
     def fake_get_tags(entity_urn: str) -> models.GlobalTagsClass:
         return models.GlobalTagsClass(
             tags=[
-                TagAssociationClass(tag=builder.make_tag_urn("exmaple1")),
-                TagAssociationClass(tag=builder.make_tag_urn("exmaple2")),
+                TagAssociationClass(tag=builder.make_tag_urn("example1")),
+                TagAssociationClass(tag=builder.make_tag_urn("example2")),
             ]
         )
 
@@ -3729,7 +3731,7 @@ def fake_schema_metadata(entity_urn: str) -> models.SchemaMetadataClass:
     pipeline_context.graph.get_schema_metadata = fake_schema_metadata  # type: ignore
 
     # Configuring the transformer
-    config = {"tags": ["exmaple1", "exmaple2"]}
+    config = {"tags": ["example1", "example2"]}
 
     # Running the transformer within a test pipeline
     output = run_dataset_transformer_pipeline(
@@ -3753,8 +3755,8 @@ def fake_schema_metadata(entity_urn: str) -> models.SchemaMetadataClass:
     assert isinstance(terms_aspect, models.GlossaryTermsClass)
     assert len(terms_aspect.terms) == len(expected_terms)
     assert set(term.urn for term in terms_aspect.terms) == {
-        "urn:li:glossaryTerm:exmaple1",
-        "urn:li:glossaryTerm:exmaple2",
+        "urn:li:glossaryTerm:example1",
+        "urn:li:glossaryTerm:example2",
     }