Parity between versions

Author: HeidiSteen

articles/search/vector-search-how-to-create-index.md

@@ -201,7 +201,7 @@ Be sure to have a strategy for [vectorizing your content](vector-search-how-to-g
 
 [**2024-05-01-preview**](/rest/api/searchservice/search-service-api-versions#2024-05-01-preview) is the most recent preview version. It's inclusive of previous preview versions.
 
-Preview and stable API versions support the same `"vectorSearch"` configurations. You would choose the preview over the stable version for other reasons, such as newer [vectorizers](vector-search-how-to-configure-vectorizer.md) invoked at query time.
+Preview and stable API versions support the same `"vectorSearch"` configurations. You would choose the preview over the stable version for other reasons, such as [more compression options](vector-search-how-to-quantization.md) or [newer vectorizers](vector-search-how-to-configure-vectorizer.md) invoked at query time.
 
 1. Use the [Create or Update Index Preview REST API](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true) to create the index.
 
@@ -405,9 +405,26 @@ Vector fields are characterized by [their data type](/rest/api/searchservice/sup
 
 [**2024-05-01-preview**](/rest/api/searchservice/search-service-api-versions#2024-05-01-preview) is the most recent preview version. It supports the same vector field definitions as the stable version, including support for all [vector data types](/rest/api/searchservice/supported-data-types#edm-data-types-for-vector-fields).
 
-1. Use the [Create or Update Index Preview REST API](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true) to define the fields collection of an index.
+1. Use the [Create or Update Index Preview REST API](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true) to create the index and add a vector field to the fields collection.
 
-1. Add vector fields to the fields collection. You can store one generated embedding per document field. For each vector field:
+    ```json
+    {
+      "name": "example-index",
+      "fields": [
+        {
+            "name": "contentVector",
+            "type": "Collection(Edm.Single)",
+            "searchable": true,
+            "retrievable": false,
+            "stored": false,
+            "dimensions": 1536,
+            "vectorSearchProfile": "vector-profile-1"
+        }
+      ]
+    }
+    ```
+
+1. Specify a vector field with the following attributes. You can store one generated embedding per document field. For each vector field:
 
    + `type` can be `Collection(Edm.Single)`, `Collection(Edm.Half)`, `Collection(Edm.Int16)`, `Collection(Edm.SByte)`
    + `dimensions` is the number of dimensions generated by the embedding model. For text-embedding-ada-002, it's 1536.
@@ -648,6 +665,6 @@ Key points include:
 
 As a next step, we recommend [Query vector data in a search index](vector-search-how-to-query.md).
 
-Code samples in the [azure-search-vector](https://github.com/Azure/azure-search-vector-samples) repository demonstrate end-to-end workflows that include schema definition, vectorization, indexing, and queries.
+Code samples in the [azure-search-vector-samples](https://github.com/Azure/azure-search-vector-samples) repository demonstrate end-to-end workflows that include schema definition, vectorization, indexing, and queries.
 
 There's demo code for [Python](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-python), [C#](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-dotnet), and [JavaScript](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-javascript).