edits for readability

Author: HeidiSteen

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

@@ -24,9 +24,7 @@ In Azure AI Search, you can store vectors in a search index. A vector store on A
 > + Add vector field definitions
 > + Load prevectorized data [as a separate step](#load-vector-data-for-indexing), or use [integrated vectorization](vector-search-integrated-vectorization.md) for data chunking and encoding during indexing
 
-This article explains the workflow and uses REST for illustration. 
-
-Once you understand the basic workflow, continue with the Azure SDK code samples in the [azure-search-vector-samples](https://github.com/Azure/azure-search-vector-samples) repository for guidance on using vectors in test and production code.
+This article explains the workflow using the REST API for illustration. Once you understand the basic workflow, continue with the Azure SDK code samples in the [azure-search-vector-samples](https://github.com/Azure/azure-search-vector-samples) repository for guidance on using vectors in test and production code.
 
 > [!TIP]
 > You can also use the Azure portal to [create a vector index](search-get-started-portal-import-vectors.md) and try out integrated data chunking and vectorization.
@@ -55,7 +53,7 @@ Make sure your documents provide the following content:
 
 | Content | Description |
 |---------|-------------|
-| Unique identifier | A field or a metadata property that uniquely identifies each document. All search indexes require a document key. To satisfy document key requirements, a source document must have one field or property uniquely identifies it in the index. If you're indexing blobs, it might be the metadata_storage_path. This source field must be mapped to an index field of type `Edm.String` and `key=true` in the search index.|
+| Unique identifier | A field or a metadata property that uniquely identifies each document. All search indexes require a document key. To satisfy document key requirements, a source document must have one field or property uniquely identifies it in the index. If you're indexing blobs, it might be the metadata_storage_path that uniquely identifies each blob. If you're indexing from a database, it might be primary key. This source field must be mapped to an index field of type `Edm.String` and `key=true` in the search index.|
 | Non-vector content | Provide other fields with human-readable content. Human readable content is useful for the query response, and for hybrid query scenarios that include full text search or semantic ranking in the same request. If you're using a chat completion model, the content you provide to the model is in plain text. |
 | Vector content| A vectorized version of your non-vector content. A vector is an array of single-precision floating point numbers generated by an embedding model. Each vector field contains an array generated by an embedding model, one embedding per field, where the field is a top-level field (not part of a nested or complex type). For the simplest integration, we recommend the embedding models in [Azure OpenAI](https://aka.ms/oai/access), such as an **text-embedding-3** model for text documents or the [Image Retrieval REST API](/rest/api/computervision/image-retrieval/vectorize-image) for images. <br><br>If you can take a dependency on indexers and skillsets, consider using [integrated vectorization](vector-search-integrated-vectorization.md) that encodes images and textual content during indexing. Your field definitions are for vector fields, but incoming source data can be text or images, which are converted to vector arrays during indexing. |