Edits for readability

Author: HeidiSteen

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

@@ -40,7 +40,7 @@ You can also start a new file on your local system and create requests manually
 
 ## Get a search endpoint and an API key
 
-You can find the search service endpoint and API keys in the Azure portal.
+You can find the search service endpoint and API keys in the Azure portal. You're pasting these values into a `.rest` or `.http` file in the next step.
 
 Requests to the search endpoint must be authenticated and authorized. You can use API keys or roles for this task. Keys are easier to start with, but roles are more secure. Although we use API keys for this quickstart, we recommend [switching to a keyless connection](search-get-started-rbac.md).
 
@@ -50,12 +50,10 @@ Requests to the search endpoint must be authenticated and authorized. You can us
 
    :::image type="content" source="media/search-get-started-rest/get-endpoint.png" lightbox="media/search-get-started-rest/get-endpoint.png" alt-text="Screenshot of the URL property on the overview page.":::
 
-1. Select **Settings** > **Keys** and then copy an admin key. [Admin API keys](search-security-api-keys.md) are used to add, modify, and delete objects. There are two interchangeable admin keys. Copy either one.
+1. Select **Settings** > **Keys**. Either **API keys** or **Both** must be enabled. [Admin API keys](search-security-api-keys.md) are used to add, modify, and delete objects. There are two interchangeable admin keys. Copy either one.
 
    :::image type="content" source="media/search-get-started-rest/get-api-key.png" lightbox="media/search-get-started-rest/get-api-key.png" alt-text="Screenshot that shows the API keys in the Azure portal.":::
 
-You're pasting these values into a `.rest` or `.http` file in the next step.
-
 ## Create a vector index
 
 [Create Index (REST)](/rest/api/searchservice/indexes/create) creates a vector index and sets up the physical data structures on your search service.
@@ -212,13 +210,16 @@ The index schema is organized around hotel content. Sample data consists of vect
     }
     ```
 
-1. Save the file, and then 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.
+1. Save the file again, and then 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.
 
     Key takeaways about this 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.
+
     - 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).
+
     - [Optional]: The `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).
 
 ## Upload documents
@@ -369,6 +370,7 @@ api-key: {{apiKey}}
 Key takeaways about this REST API:
 
 - Documents in the payload consist of fields defined in the index schema.
+
 - Vector fields contain floating point values. The dimensions attribute has a minimum of 2 and a maximum of 3,072 floating point values each. This quickstart sets the dimensions attribute to 1,536 because that's the size of embeddings generated by the Azure OpenAI **text-embedding-ada-002** model.
 
 ## Run queries
@@ -786,10 +788,12 @@ Here's the last query in the collection. This hybrid query with semantic ranking
     }
     ```
 
-    Key points about this REST API:
+    Key takeaways about this REST API:
 
     - Vector search is specified through the `vectors.value` property. Keyword search is specified through the `search` property.
+
     - In a hybrid search, you can integrate vector search with full-text search over keywords. Filters, spell check, and semantic ranking apply to textual content only, and not vectors. In this final query, there's no semantic `answer` because the system didn't produce one that was sufficiently strong.
+
     - Actual results include more detail, including semantic captions and highlights. Results were modified for readability. To get the full structure of the response, run the request in the REST client.
 
 ## Clean up