Updated v4 nomenclature

Author: haileytap

articles/search/search-get-started-portal-image-search.md

@@ -52,7 +52,7 @@ For content embedding, you can choose either image verbalization (followed by te
 | Method | Description | Supported models |
 |--|--|--|
 | Image verbalization | Uses an LLM to generate natural-language descriptions of images, and then uses an embedding model to vectorize plain text and verbalized images.<br><br>Requires an [Azure OpenAI resource](/azure/ai-services/openai/how-to/create-resource) <sup>1, 2</sup> or [Azure AI Foundry project](/azure/ai-foundry/how-to/create-projects).<br><br>For text vectorization, you can also use an [Azure AI services multi-service resource](/azure/ai-services/multi-service-resource#azure-ai-multi-services-resource-for-azure-ai-search-skills) <sup>3</sup> in a [supported region](cognitive-search-skill-vision-vectorize.md). | LLMs:<br>GPT-4o<br>GPT-4o-mini<br>phi-4 <sup>4</sup><br><br>Embedding models:<br>text-embedding-ada-002<br>text-embedding-3-small<br>text-embedding-3-large |
-| Multimodal embeddings | Uses an embedding model to directly vectorize both text and images.<br><br>Requires an [Azure AI Foundry project](/azure/ai-foundry/how-to/create-projects) or [Azure AI services multi-service resource](/azure/ai-services/multi-service-resource#azure-ai-multi-services-resource-for-azure-ai-search-skills) <sup>3</sup> in a [supported region](cognitive-search-skill-vision-vectorize.md). | Cohere-embed-v3-english<br>Cohere-embed-v3-multilingual<br>embed-v-4-0 <sup>5</sup> |
+| Multimodal embeddings | Uses an embedding model to directly vectorize both text and images.<br><br>Requires an [Azure AI Foundry project](/azure/ai-foundry/how-to/create-projects) or [Azure AI services multi-service resource](/azure/ai-services/multi-service-resource#azure-ai-multi-services-resource-for-azure-ai-search-skills) <sup>3</sup> in a [supported region](cognitive-search-skill-vision-vectorize.md). | Cohere-embed-v3-english<br>Cohere-embed-v3-multilingual<br>Cohere-embed-v4 <sup>5</sup> |
 
 <sup>1</sup> The endpoint of your Azure OpenAI resource must have a [custom subdomain](/azure/ai-services/cognitive-services-custom-subdomains), such as `https://my-unique-name.openai.azure.com`. If you created your resource in the [Azure portal](https://portal.azure.com/), this subdomain was automatically generated during resource setup.
 

articles/search/search-get-started-portal-import-vectors.md

@@ -49,7 +49,7 @@ For integrated vectorization, you must use one of the following embedding models
 |--|--|
 | [Azure OpenAI in Azure AI Foundry Models](/azure/ai-services/openai/how-to/create-resource) <sup>1, 2</sup> | text-embedding-ada-002<br>text-embedding-3-small<br>text-embedding-3-large |
 | [Azure AI services multi-service resource](/azure/ai-services/multi-service-resource#azure-ai-multi-services-resource-for-azure-ai-search-skills) <sup>3</sup> | For text and images: [Azure AI Vision multimodal](/azure/ai-services/computer-vision/how-to/image-retrieval) <sup>4</sup></li> |
-| [Azure AI Foundry model catalog](/azure/ai-foundry/what-is-azure-ai-foundry) | For images:<br>Facebook-DinoV2-Image-Embeddings-ViT-Base<br>Facebook-DinoV2-Image-Embeddings-ViT-Giant<br><br>For text and images:<br>Cohere-embed-v3-english<br>Cohere-embed-v3-multilingual<br>embed-v-4-0 <sup>5</sup> |
+| [Azure AI Foundry model catalog](/azure/ai-foundry/what-is-azure-ai-foundry) | For images:<br>Facebook-DinoV2-Image-Embeddings-ViT-Base<br>Facebook-DinoV2-Image-Embeddings-ViT-Giant<br><br>For text and images:<br>Cohere-embed-v3-english<br>Cohere-embed-v3-multilingual<br>Cohere-embed-v4 <sup>5</sup> |
 
 <sup>1</sup> The endpoint of your Azure OpenAI resource must have a [custom subdomain](/azure/ai-services/cognitive-services-custom-subdomains), such as `https://my-unique-name.openai.azure.com`. If you created your resource in the [Azure portal](https://portal.azure.com/), this subdomain was automatically generated during resource setup.
 

articles/search/search-how-to-integrated-vectorization.md

@@ -48,7 +48,7 @@ For integrated vectorization, you must use one of the following embedding models
 |--|--|
 | [Azure OpenAI in Azure AI Foundry Models](/azure/ai-services/openai/how-to/create-resource) <sup>1, 2</sup> | text-embedding-ada-002<br>text-embedding-3-small<br>text-embedding-3-large |
 | [Azure AI services multi-service resource](/azure/ai-services/multi-service-resource#azure-ai-services-resource-for-azure-ai-search-skills) <sup>3</sup> | For text and images: [Azure AI Vision multimodal](/azure/ai-services/computer-vision/how-to/image-retrieval) <sup>4</sup></li> |
-<!--| [Azure AI Foundry model catalog](/azure/ai-foundry/what-is-azure-ai-foundry) | For images:<br>Facebook-DinoV2-Image-Embeddings-ViT-Base<br>Facebook-DinoV2-Image-Embeddings-ViT-Giant<br>For text and images:<br>Cohere-embed-v3-english<br>Cohere-embed-v3-multilingual<br>embed-v-4-0|-->
+<!--| [Azure AI Foundry model catalog](/azure/ai-foundry/what-is-azure-ai-foundry) | For images:<br>Facebook-DinoV2-Image-Embeddings-ViT-Base<br>Facebook-DinoV2-Image-Embeddings-ViT-Giant<br>For text and images:<br>Cohere-embed-v3-english<br>Cohere-embed-v3-multilingual<br>Cohere-embed-v4 |-->
 
 <sup>1</sup> The endpoint of your Azure OpenAI resource must have a [custom subdomain](/azure/ai-services/cognitive-services-custom-subdomains), such as `https://my-unique-name.openai.azure.com`. If you created your resource in the [Azure portal](https://portal.azure.com/), this subdomain was automatically generated during resource setup.
 

articles/search/tutorial-rag-build-solution-models.md

@@ -54,7 +54,7 @@ Azure AI Search provides skill and vectorizer support for the following embeddin
 |--------|------------------|-------|------------|
 | Azure OpenAI | text-embedding-ada-002<br>text-embedding-3-large<br>text-embedding-3-small | [AzureOpenAIEmbedding](cognitive-search-skill-azure-openai-embedding.md) | [AzureOpenAIEmbedding](vector-search-vectorizer-azure-open-ai.md) |
 | Azure AI Vision | multimodal 4.0 <sup>1</sup> | [AzureAIVision](cognitive-search-skill-vision-vectorize.md) | [AzureAIVision](vector-search-vectorizer-ai-services-vision.md) |
-| Azure AI Foundry model catalog | Facebook-DinoV2-Image-Embeddings-ViT-Base<br>Facebook-DinoV2-Image-Embeddings-ViT-Giant<br>Cohere-embed-v3-english <sup>1</sup><br>Cohere-embed-v3-multilingual <sup>1</sup><br>embed-v-4-0 <sup>1, 2</sup> | [AML](cognitive-search-aml-skill.md) <sup>3</sup> | [Azure AI Foundry model catalog](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) |
+| Azure AI Foundry model catalog | Facebook-DinoV2-Image-Embeddings-ViT-Base<br>Facebook-DinoV2-Image-Embeddings-ViT-Giant<br>Cohere-embed-v3-english <sup>1</sup><br>Cohere-embed-v3-multilingual <sup>1</sup><br>Cohere-embed-v4 <sup>1, 2</sup> | [AML](cognitive-search-aml-skill.md) <sup>3</sup> | [Azure AI Foundry model catalog](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) |
 
 <sup>1</sup> Supports text and image vectorization.
 

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

@@ -44,7 +44,7 @@ The following table lists the embedding models that can be used with a vectorize
 | Vectorizer kind | Model names | Model provider | Associated skill |
 |-----------------|------------|----------------|------------------|
 | [`azureOpenAI`](vector-search-vectorizer-azure-open-ai.md) | text-embedding-ada-002<br>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<br>Cohere-embed-v3<br>embed-v-4-0 <sup>1</sup> | [Azure AI Foundry model catalog](vector-search-integrated-vectorization-ai-studio.md)  | [AML skill](cognitive-search-aml-skill.md) |
+| [`aml`](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) | Facebook-DinoV2-Image-Embeddings<br>Cohere-embed-v3<br>Cohere-embed-v4 <sup>1</sup> | [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 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) |
 

articles/search/vector-search-integrated-vectorization-ai-studio.md

@@ -38,7 +38,7 @@ Integrated vectorization and the [Import and vectorize data wizard](search-impor
 | Embedding type | Supported models |
 |--|--|
 | Image | Facebook-DinoV2-Image-Embeddings-ViT-Base<br>Facebook-DinoV2-Image-Embeddings-ViT-Giant |
-| Text and image (multimodal) | Cohere-embed-v3-english<br>Cohere-embed-v3-multilingual<br>embed-v-4-0 <sup>1</sup> |
+| Text and image (multimodal) | Cohere-embed-v3-english<br>Cohere-embed-v3-multilingual<br>Cohere-embed-v4 <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) 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.
 
@@ -175,7 +175,7 @@ This AML skill payload works with the following text embedding models from Azure
 
 + Cohere-embed-v3-english
 + Cohere-embed-v3-multilingual
-+ embed-v-4-0
++ Cohere-embed-v4
 
 It assumes that you're chunking your content using the Text Split skill and therefore your text to be vectorized is in the `/document/pages/*` path. If your text comes from a different path, update all references to the `/document/pages/*` path accordingly.