Merge pull request #270393 from gmndrg/main

Update vector-search-how-to-create-index.md

Author: prmerger-automator[bot]

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

@@ -9,7 +9,7 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 01/29/2024
+ms.date: 03/27/2024
 ---
 
 # Create a vector store
@@ -48,7 +48,7 @@ Make sure your documents:
 
 1. Provide vector data (an array of single-precision floating point numbers) in source fields.
 
-   Vector fields contain numeric 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.
+   Vector fields contain numeric 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. Only index top-level vector fields are supported: Vector sub-fields are not currently supported.
 
 1. Provide other fields with human-readable alphanumeric content for the query response, and for hybrid query scenarios that include full text search or semantic ranking in the same request.