Merge pull request #6139 from haileytap/multimodal

prmerger-automator[bot] · web-flow · commit 4cd95efb19da · 2025-07-21T19:19:54.000Z
[Azure Search] Restore Cohere-embed-v3 content
diff --git a/articles/search/search-get-started-portal-import-vectors.md b/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>Cohere-embed-v4 <sup>5</sup> |
+| [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>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.
 
diff --git a/articles/search/search-how-to-integrated-vectorization.md b/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>Cohere-embed-v4 |-->
+<!--| [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>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.
 
diff --git a/articles/search/tutorial-rag-build-solution-models.md b/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>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) |
+| 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>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.
 
diff --git a/articles/search/vector-search-integrated-vectorization-ai-studio.md b/articles/search/vector-search-integrated-vectorization-ai-studio.md
@@ -37,8 +37,9 @@ Integrated vectorization and the [Import and vectorize data wizard](search-impor
 
 | Embedding type | Supported models |
 |--|--|
+| Text | Cohere-embed-v3-english<br>Cohere-embed-v3-multilingual |
 | 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>Cohere-embed-v4 <sup>1</sup> |
+| Text and image (multimodal) | 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.
 
@@ -171,15 +172,15 @@ The URI and key are generated when you deploy the model from the catalog. For mo
 
 ### [**Cohere embedding models**](#tab/cohere)
 
-This AML skill payload works with the following text embedding models from Azure AI Foundry:
+This AML skill payload works with the following embedding models from Azure AI Foundry:
 
 + Cohere-embed-v3-english
 + Cohere-embed-v3-multilingual
 + 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.
 
-You must add the `/v1/embed` path onto the end of the URL that you copied from your Azure AI Foundry deployment. You might also change the values for the `input_type`, `truncate` and `embedding_types` inputs to better fit your use case. For more information on the available options, review the [Cohere Embed API reference](/azure/ai-foundry/how-to/deploy-models-cohere-embed).
+You must add the `/v1/embed` path onto the end of the URL that you copied from your Azure AI Foundry deployment. You might also change the values for the `input_type`, `truncate`, and `embedding_types` inputs to better fit your use case. For more information on the available options, review the [Cohere Embed API reference](/azure/ai-foundry/how-to/deploy-models-cohere-embed).
 
 The URI and key are generated when you deploy the model from the catalog. For more information about these values, see [How to deploy Cohere Embed models with Azure AI Foundry](/azure/ai-foundry/how-to/deploy-models-cohere-embed).
 
