Tweaked wording

haileytap · haileytap · commit e74c0df47575 · 2025-07-16T18:27:36.000-04:00
diff --git a/articles/search/search-get-started-portal-image-search.md b/articles/search/search-get-started-portal-image-search.md
@@ -62,7 +62,7 @@ For content embedding, you can choose either image verbalization (followed by te
 
 <sup>4</sup> `phi-4` is only available to Azure AI Foundry projects.
 
-<sup>5</sup> The Azure portal doesn't support `embed-v-4-0` for vectorization, so don't use it for this quickstart. Instead, use the [AML skill](cognitive-search-aml-skill.md) to programmatically specify this model. You can then use the portal to view and manage the skillset.
+<sup>5</sup> The Azure portal doesn't support `embed-v-4-0` for vectorization, so don't use it for this quickstart. Instead, use the [AML skill](cognitive-search-aml-skill.md) or [Azure AI Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) to programmatically specify this model. You can then use the portal to manage the skillset or vectorizer.
 
 ### Public endpoint requirements
 
diff --git a/articles/search/search-get-started-portal-import-vectors.md b/articles/search/search-get-started-portal-import-vectors.md
@@ -59,7 +59,7 @@ For integrated vectorization, you must use one of the following embedding models
 
 <sup>4</sup> The Azure AI Vision multimodal embedding model is available in [select regions](/azure/ai-services/computer-vision/overview-image-analysis#region-availability).
 
-<sup>5</sup> The Azure portal doesn't support `embed-v-4-0` for vectorization, so don't use it for this quickstart. Instead, use the [AML skill](cognitive-search-aml-skill.md) to programmatically specify this model. You can then use the portal to view and manage the skillset.
+<sup>5</sup> The Azure portal doesn't support `embed-v-4-0` for vectorization, so don't use it for this quickstart. Instead, use the [AML skill](cognitive-search-aml-skill.md) or [Azure AI Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) to programmatically specify this model. You can then use the portal to manage the skillset or vectorizer.
 
 ### Public endpoint requirements
 
diff --git a/articles/search/tutorial-rag-build-solution-models.md b/articles/search/tutorial-rag-build-solution-models.md
@@ -58,7 +58,7 @@ Azure AI Search provides skill and vectorizer support for the following embeddin
 
 <sup>1</sup> Supports image and text vectorization.
 
-<sup>2</sup> At this time, you can only specify `embed-v-4-0` programmatically through the [AML skill](cognitive-search-aml-skill.md), not through the Azure portal. However, you can use the portal to view and manage the skillset afterward.
+<sup>2</sup> At this time, you can only specify `embed-v-4-0` programmatically through the [AML skill](cognitive-search-aml-skill.md) or [Azure AI Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md), not through the Azure portal. However, you can use the portal to manage the skillset or vectorizer afterward.
 
 <sup>3</sup> Deployed models in the model catalog are accessed over an AML endpoint. We use the existing AML skill for this connection.
 
diff --git a/articles/search/vector-search-how-to-configure-vectorizer.md b/articles/search/vector-search-how-to-configure-vectorizer.md
@@ -48,7 +48,7 @@ The following table lists the embedding models that can be used with a vectorize
 | [`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 services 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) |
 
-<sup>1</sup> At this time, you can only specify `embed-v-4-0` programmatically through the [AML skill](cognitive-search-aml-skill.md), not through the Azure portal. However, you can use the portal to view and manage the skillset afterward.
+<sup>1</sup> At this time, you can only specify `embed-v-4-0` programmatically through the [AML skill](cognitive-search-aml-skill.md) or [Azure AI Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md), not through the Azure portal. However, you can use the portal to manage the skillset or vectorizer afterward.
 
 ## Try a vectorizer with sample data
 
diff --git a/articles/search/vector-search-integrated-vectorization-ai-studio.md b/articles/search/vector-search-integrated-vectorization-ai-studio.md
@@ -41,7 +41,7 @@ Integrated vectorization and the [Import and vectorize data wizard](search-impor
 | Image | Facebook-DinoV2-Image-Embeddings-ViT-Base, Facebook-DinoV2-Image-Embeddings-ViT-Giant |
 | Multimodal (text and image) | embed-v-4-0 <sup>1</sup> |
 
-<sup>1</sup> At this time, you can only specify `embed-v-4-0` programmatically through the [AML skill](cognitive-search-aml-skill.md), not through the Azure portal. However, you can use the portal to view and manage the skillset afterward.
+<sup>1</sup> At this time, you can only specify `embed-v-4-0` programmatically through the [AML skill](cognitive-search-aml-skill.md) or [Azure AI Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md), not through the Azure portal. However, you can use the portal to manage the skillset or vectorizer afterward.
 
 ## Deploy an embedding model from the Azure AI Foundry model catalog
 
diff --git a/articles/search/vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md b/articles/search/vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md
@@ -16,7 +16,7 @@ ms.date: 07/16/2024
 > [!IMPORTANT]
 > This vectorizer is in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). To use this feature, we recommend the latest preview version of [Indexes - Create Or Update](/rest/api/searchservice/indexes/create-or-update) (REST API).
 
-The **Azure AI Foundry model catalog** vectorizer connects to an embedding model that was deployed via [the Azure AI Foundry model catalog](/azure/ai-foundry/how-to/model-catalog-overview) to an Azure Machine Learning endpoint. Your data is processed in the [Geo](https://azure.microsoft.com/explore/global-infrastructure/data-residency/) where your model is deployed. 
+The **Azure AI Foundry model catalog** vectorizer connects to an embedding model that was deployed via the [Azure AI Foundry model catalog](/azure/ai-foundry/how-to/model-catalog-overview) to an Azure Machine Learning endpoint. Your data is processed in the [Geo](https://azure.microsoft.com/explore/global-infrastructure/data-residency/) where your model is deployed. 
 
 If you used integrated vectorization to create the vector arrays, the skillset should include an [AML skill pointing to the model catalog in Azure AI Foundry portal](cognitive-search-aml-skill.md).
 
