Updated content about Cohere models

haileytap · haileytap · commit 67e1deb8dbfd · 2025-07-17T17:01:28.000-04:00
diff --git a/articles/search/search-get-started-portal-import-vectors.md b/articles/search/search-get-started-portal-import-vectors.md
@@ -10,7 +10,7 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: quickstart
-ms.date: 07/16/2025
+ms.date: 07/17/2025
 ---
 
 # Quickstart: Vectorize text in the Azure portal
@@ -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 text:<br>Cohere-embed-v3-english<br>Cohere-embed-v3-multilingual<br><br>For images:<br>Facebook-DinoV2-Image-Embeddings-ViT-Base<br>Facebook-DinoV2-Image-Embeddings-ViT-Giant<br><br>For text and images:<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>embed-v-4-0 <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.
 
diff --git a/articles/search/search-how-to-integrated-vectorization.md b/articles/search/search-how-to-integrated-vectorization.md
@@ -7,7 +7,7 @@ author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 07/16/2025
+ms.date: 07/17/2025
 ---
 
 # Set up integrated vectorization in Azure AI Search using REST
@@ -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 text:<br>Cohere-embed-v3-english<br>Cohere-embed-v3-multilingual<br><br>For images:<br>Facebook-DinoV2-Image-Embeddings-ViT-Base<br>Facebook-DinoV2-Image-Embeddings-ViT-Giant<br>For text and images:<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>embed-v-4-0|-->
 
 <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.
 
diff --git a/articles/search/tutorial-rag-build-solution-models.md b/articles/search/tutorial-rag-build-solution-models.md
@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.update-cycle: 180-days
 ms.topic: tutorial
 ms.custom: references_regions
-ms.date: 07/16/2025
+ms.date: 07/17/2025
 
 ---
 
@@ -52,11 +52,11 @@ Azure AI Search provides skill and vectorizer support for the following embeddin
 
 | Client | Embedding models | Skill | Vectorizer |
 |--------|------------------|-------|------------|
-| 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 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, <br>Cohere-embed-v3-multilingual, <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>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) |
 
-<sup>1</sup> Supports image and text vectorization.
+<sup>1</sup> Supports text and image 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) 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.
 
diff --git a/articles/search/vector-search-how-to-configure-vectorizer.md b/articles/search/vector-search-how-to-configure-vectorizer.md
@@ -10,7 +10,7 @@ ms.update-cycle: 180-days
 ms.custom:
   - build-2024
 ms.topic: how-to
-ms.date: 07/16/2025
+ms.date: 07/17/2025
 ---
 
 # Configure a vectorizer in a search index
@@ -43,8 +43,8 @@ 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, 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, 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) |
+| [`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) |
 | [`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) |
 
diff --git a/articles/search/vector-search-integrated-vectorization-ai-studio.md b/articles/search/vector-search-integrated-vectorization-ai-studio.md
@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - build-2024
 ms.topic: how-to
-ms.date: 07/16/2025
+ms.date: 07/17/2025
 ---
 
 # Use embedding models from Azure AI Foundry model catalog for integrated vectorization
@@ -37,9 +37,8 @@ Integrated vectorization and the [Import and vectorize data wizard](search-impor
 
 | Embedding type | Supported models |
 |--|--|
-| Text | Cohere-embed-v3-english, Cohere-embed-v3-multilingual|
-| Image | Facebook-DinoV2-Image-Embeddings-ViT-Base, Facebook-DinoV2-Image-Embeddings-ViT-Giant |
-| Multimodal (text and image) | embed-v-4-0 <sup>1</sup> |
+| 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> |
 
 <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.
 
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
@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - build-2024
 ms.topic: reference
-ms.date: 07/16/2024
+ms.date: 07/17/2024
 ---
 
 #	Azure AI Foundry model catalog vectorizer
@@ -53,9 +53,9 @@ Which vector query types are supported by the Azure AI Foundry model catalog vec
 |--------------------|-------------|-------------|-------------|
 | Facebook-DinoV2-Image-Embeddings-ViT-Base |  | X | X |
 | Facebook-DinoV2-Image-Embeddings-ViT-Giant |  | X | X |
-| Cohere-embed-v3-english | X |  |  |
-| Cohere-embed-v3-multilingual | X |  |  |
-| Cohere-embed-v4 | X | X | X |
+| Cohere-embed-v3-english | X |  | X |
+| Cohere-embed-v3-multilingual | X |  | X |
+| Cohere-embed-v4 | X |  | X |
 
 ## Expected field dimensions
 
