quickstart pivots for entra and apikey

Author: eric-urban

articles/search/search-get-started-vector.md

@@ -520,7 +520,7 @@ The index schema in this example is organized around hotel content. Sample data
 
 1. Select **Send request**. You should have an `HTTP/1.1 201 Created` response. 
 
----
+***
 
 The response body should include the JSON representation of the index schema.
 
@@ -628,15 +628,15 @@ The response body should include the JSON representation of the index schema.
     }
     ```
 
-    Key takeaways about the [Create Index](/rest/api/searchservice/indexes/create) REST API:
+Key takeaways about the [Create Index](/rest/api/searchservice/indexes/create) REST API:
 
-    - The `fields` collection includes a required key field and text and vector fields (such as `Description` and `DescriptionVector`) for text and vector search. Colocating vector and nonvector fields in the same index enables hybrid queries. For instance, you can combine filters, text search with semantic ranking, and vectors into a single query operation.
+- The `fields` collection includes a required key field and text and vector fields (such as `Description` and `DescriptionVector`) for text and vector search. Colocating vector and nonvector fields in the same index enables hybrid queries. For instance, you can combine filters, text search with semantic ranking, and vectors into a single query operation.
 
-    - Vector fields must be `type: Collection(Edm.Single)` with `dimensions` and `vectorSearchProfile` properties.
+- Vector fields must be `type: Collection(Edm.Single)` with `dimensions` and `vectorSearchProfile` properties.
 
-    - The `vectorSearch` section is an array of approximate nearest neighbor algorithm configurations and profiles. Supported algorithms include hierarchical navigable small world and exhaustive k-nearest neighbor. For more information, see [Relevance scoring in vector search](vector-search-ranking.md).
+- The `vectorSearch` section is an array of approximate nearest neighbor algorithm configurations and profiles. Supported algorithms include hierarchical navigable small world and exhaustive k-nearest neighbor. For more information, see [Relevance scoring in vector search](vector-search-ranking.md).
 
-    - The (optional) `semantic` configuration enables reranking of search results. You can rerank results in queries of type `semantic` for string fields that are specified in the configuration. To learn more, see [Semantic ranking overview](semantic-search-overview.md).
+- The (optional) `semantic` configuration enables reranking of search results. You can rerank results in queries of type `semantic` for string fields that are specified in the configuration. To learn more, see [Semantic ranking overview](semantic-search-overview.md).
 
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