Revising for readability

Author: HeidiSteen

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

@@ -23,21 +23,21 @@ In Azure Cognitive Search, vector data is represented in fields in a [search ind
 
   Most existing services support vector search. For a small subset of services created prior to January 2019, an index containing vector fields will fail on creation. In this situation, a new service must be created.
 
-+ Pre-existing embeddings in your source documents. Cognitive Search doesn't generate embeddings. We recommend Azure OpenAI but you can use any model for vectorization. For more information, see [Create and use embeddings for search queries and documents](vector-search-how-to-generate-embeddings.md).
++ Pre-existing vector embeddings in your source documents. Cognitive Search doesn't generate vectors. We recommend Azure OpenAI but you can use any model for vectorization. For more information, see [Create and use embeddings for search queries and documents](vector-search-how-to-generate-embeddings.md).
 
-  Be sure to use the same embedding model for both indexing and queries. At query time, you must include a step that converts the user's query into a vector. 
+  Be sure to use the same embedding model for both indexing and queries. At query time, you must include a step that converts the user's query string into a vector. 
 
 ## Prepare documents for indexing
 
-Prior to indexing, assemble a document payload that includes vector data. The document structure must conform to the index schema. Make sure your documents include the following elements:
+Prior to indexing, assemble a document payload that includes vector data. The document structure must conform to the index schema. Make sure your documents:
 
-1. Provide a unique value or a metadata property that uniquely identifies each source document. All search indexes require a document key as a unique identifier, which means all documents must have one field that can be mapped to type `Edm.String` and `key=true` in the search index. 
+1. Provide a field or a metadata property that uniquely identifies each document. All search indexes require a document key. Your documents must have one field or property that can be mapped to type `Edm.String` and `key=true` in the search index. 
 
 1. Provide vector data (an array of single-precision floating point numbers) in source fields.
 
-   Vector fields contain vector data generated by embedding models. We recommend the embedding models in [Azure OpenAI](https://aka.ms/oai/access), such as **text-embedding-ada-002** for text documents or the [Image Retrieval REST API](/rest/api/computervision/2023-02-01-preview/image-retrieval/vectorize-image) for images.
+   Vector fields contain vector data generated by embedding models, one embedding per field. We recommend the embedding models in [Azure OpenAI](https://aka.ms/oai/access), such as **text-embedding-ada-002** for text documents or the [Image Retrieval REST API](/rest/api/computervision/2023-02-01-preview/image-retrieval/vectorize-image) for images.
 
-1. Provide any other fields with alphanumeric content for any nonvector queries you want to support, as well as for hybrid query scenarios that include full text search or semantic ranking in the same request. 
+1. Provide other fields with alphanumeric content for the search response and for hybrid query scenarios that include full text search or semantic ranking in the same request. 
 
 Your search index should include fields and content for all of the query scenarios you want to support. Suppose you want to search or filter over product names, versions, metadata, or addresses. In this case, similarity search isn't especially helpful. Keyword search, geo-search, or filters would be a better choice. A search index that includes a comprehensive field collection of vector and non-vector data provides maximum flexibility for query construction and response composition.