add python

website/docs/docs/build/metrics-overview.md

@@ -7,7 +7,7 @@ tags: [Metrics, Semantic Layer]
 pagination_next: "docs/build/cumulative"
 ---
 
-Once you've created your semantic models, it's time to start adding metrics! Metrics can be defined in the same YAML files as your semantic models, or split into separate YAML files into any other subdirectories (provided that these subdirectories are also within the same dbt project repo)
+Once you've created your semantic models, it's time to start adding metrics. Metrics can be defined in the same YAML files as your semantic models, or split into separate YAML files into any other subdirectories (provided that these subdirectories are also within the same dbt project repo).
 
 The keys for metrics definitions are:
 

website/docs/docs/build/semantic-models.md

@@ -14,9 +14,9 @@ Semantic models are the foundation for data definition in MetricFlow, which powe
 - Think of semantic models as nodes connected by entities in a semantic graph.
 - MetricFlow uses YAML configuration files to create this graph for querying metrics.
 - Each semantic model corresponds to a dbt model in your DAG, requiring a unique YAML configuration for each semantic model.
-- You can create multiple semantic models from a single dbt model, as long as you give each semantic model a unique name.
+- You can create multiple semantic models from a single dbt model (SQL or Python), as long as you give each semantic model a unique name.
 - Configure semantic models in a YAML file within your dbt project directory.
-- Organize them under a `metrics:` folder or within project sources as needed. 
+- Organize them under a `metrics:` folder or within project sources as needed.
 
 <Lightbox src="/img/docs/dbt-cloud/semantic-layer/semantic_foundation.jpg" width="70%" title="A semantic model is made up of different components: Entities, Measures, and Dimensions."/>