Merge pull request #2302 from HeidiSteen/heidist-uuf

[azure search] updates per UUF

Author: prmerger-automator[bot]

articles/search/vector-search-how-to-configure-vectorizer.md

@@ -9,14 +9,14 @@ ms.service: azure-ai-search
 ms.custom:
   - build-2024
 ms.topic: how-to
-ms.date: 08/05/2024
+ms.date: 01/14/2025
 ---
 
 # Configure a vectorizer in a search index
 
-In Azure AI Search a *vectorizer* is software that performs vectorization, such as a deployed embedding model on Azure OpenAI, that converts text (or images) to vectors during query execution.
+In Azure AI Search a *vectorizer* is a component that performs vectorization using a deployed embedding model on Azure OpenAI or Azure AI Vision. It converts text (or images) to vectors during query execution.
 
-It's defined in a [search index](search-what-is-an-index.md), it applies to searchable vector fields, and it's used at query time to generate an embedding for a text or image query input. If instead you need to vectorize content as part of the indexing process, refer to [Integrated Vectorization (Preview)](vector-search-integrated-vectorization.md). For built-in vectorization during indexing, you can configure an indexer and skillset that calls an embedding model for your raw text content.
+It's defined in a [search index](search-what-is-an-index.md), it applies to searchable vector fields, and it's used at query time to generate an embedding for a text or image query input. If instead you need to vectorize content as part of the indexing process, refer to [integrated vectorization](vector-search-integrated-vectorization.md). For built-in vectorization during indexing, you can configure an indexer and skillset that calls an embedding model for your raw text or image content.
 
 To add a vectorizer to search index, you can use the index designer in Azure portal, or call the [Create or Update Index](/rest/api/searchservice/indexes/create-or-update) REST API, or use any Azure  SDK package that's updated to provide this feature.
 
@@ -28,14 +28,25 @@ Vectorizers are now generally available as long as you use a generally available
 
 + [An index with searchable vector fields](vector-search-how-to-create-index.md) on Azure AI Search.
 
-+ A deployed embedding model, such as **text-embedding-ada-002**, **text-embedding-3-small**, or **text-embedding-3-large** on Azure OpenAI. It's used to vectorize a query. It must be [identical to the embedding model used for the vector field](vector-search-integrated-vectorization.md#using-integrated-vectorization-in-queries) in your index. You can also use [models deployed from the Azure AI Foundry model catalog](vector-search-integrated-vectorization-ai-studio.md) or an [Azure AI Vision model](/azure/ai-services/computer-vision/concept-image-retrieval).
++ A deployed embedding model (see the next section).
 
-+ Permissions to use the embedding model. If you're using Azure OpenAI, the caller must have [Cognitive Services OpenAI User](/azure/ai-services/openai/how-to/role-based-access-control#azure-openai-roles) permissions. Or, you can provide an API key.
++ Permissions to use the embedding model. On Azure OpenAI, the caller must have [Cognitive Services OpenAI User](/azure/ai-services/openai/how-to/role-based-access-control#azure-openai-roles) permissions. Or, you can provide an API key.
 
 + [Visual Studio Code](https://code.visualstudio.com/download) with a [REST client](https://marketplace.visualstudio.com/items?itemName=humao.rest-client) to send the query and accept a response.
 
 We recommend that you enable diagnostic logging on your search service to confirm vector query execution.
 
+## Supported embedding models
+
+The following table lists the embedding models that can be used with a vectorizer. Because you must use the [same embedding model for indexing and queries](vector-search-integrated-vectorization.md#using-integrated-vectorization-in-queries), vectorizers are paired with skills that generate embeddings during indexing. The table lists the skill associated with a particular vectorizer.
+
+| Vectorizer kind | Model names | Model provider | Associated skill |
+|-----------------|------------|----------------|------------------|
+| [`azureOpenAI`](vector-search-vectorizer-azure-open-ai.md) | text-embedding-ada-002, text-embedding-3 | Azure OpenAI | [AzureOpenAIEmbedding skill](cognitive-search-skill-azure-openai-embedding.md) |
+| [`aml`](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) | Facebook-DinoV2-Image-Embeddings, Cohere-embed-v3 | [Azure AI Foundry model catalog](vector-search-integrated-vectorization-ai-studio.md)  | [AML skill](cognitive-search-aml-skill.md) |
+| [`aiServicesVision`](vector-search-vectorizer-ai-services-vision.md) | [Multimodal embeddings 4.0 API](/azure/ai-services/computer-vision/concept-image-retrieval) | Azure AI Vision (through an Azure AI multi-service account) | [Azure AI Vision multimodal embeddings skill](cognitive-search-skill-vision-vectorize.md) |
+| [`customWebApi`](vector-search-vectorizer-custom-web-api.md) | Any embedding model | Hosted externally | [Custom Web API skill](cognitive-search-custom-skill-web-api.md) |
+
 ## Try a vectorizer with sample data
 
 The [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) reads files from Azure Blob storage, creates an index with chunked and vectorized fields, and adds a vectorizer. By design, the vectorizer that's created by the wizard is set to the same embedding model used to index the blob content.