check formatting'

Author: HeidiSteen

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

@@ -46,9 +46,9 @@ Use an embedding model on an Azure AI platform in the [same region as Azure AI S
 
 | Provider | Supported models |
 |---|---|
-| [Azure OpenAI Service](https://aka.ms/oai/access) | text-embedding-ada-002, text-embedding-3-large, or text-embedding-3-small. |
-| [Azure AI Foundry model catalog](/azure/ai-studio/what-is-ai-studio) |  Azure, Cohere, and Facebook embedding models. |
-| [Azure AI services multi-service account](/azure/ai-services/multi-service-resource) | [Azure AI Vision multimodal](/azure/ai-services/computer-vision/how-to/image-retrieval) for image and text vectorization. Azure AI Vision multimodal is available in selected regions. [Check the documentation](/azure/ai-services/computer-vision/how-to/image-retrieval?tabs=csharp) for an updated list. Depending on how you [attach the multi-service resource](cognitive-search-attach-cognitive-services.md), the account might need to be in the same region as Azure AI Search. |
+| [Azure OpenAI Service](https://aka.ms/oai/access) | text-embedding-ada-002 <br>text-embedding-3-large <br>text-embedding-3-small |
+| [Azure AI Foundry model catalog](/azure/ai-studio/what-is-ai-studio) | For text: <br>Cohere-embed-v3-english <br>Cohere-embed-v3-multilingual <br>For images: <br>Facebook-DinoV2-Image-Embeddings-ViT-Base <br>Facebook-DinoV2-Image-Embeddings-ViT-Giant |
+| [Azure AI services multi-service account](/azure/ai-services/multi-service-resource) | [Azure AI Vision multimodal](/azure/ai-services/computer-vision/how-to/image-retrieval) for image and text vectorization, [available in selected regions](/azure/ai-services/computer-vision/how-to/image-retrieval?tabs=csharp). Depending on how you [attach the multi-service resource](cognitive-search-attach-cognitive-services.md), the multi-service account might need to be in the same region as Azure AI Search. |
 
 If you use the Azure OpenAI Service, the endpoint must have an associated [custom subdomain](/azure/ai-services/cognitive-services-custom-subdomains). A custom subdomain is an endpoint that includes a unique name (for example, `https://hereismyuniquename.cognitiveservices.azure.com`). If the service was created through the Azure portal, this subdomain is automatically generated as part of your service setup. Ensure that your service includes a custom subdomain before using it with the Azure AI Search integration.
 
@@ -202,13 +202,15 @@ After you finish these steps, you should be able to select the Azure AI Vision v
 
 ### [Azure AI Foundry model catalog](#tab/model-catalog)
 
-The wizard supports Azure, Cohere, and Facebook embedding models in the Azure AI Foundry model catalog, but it doesn't currently support the OpenAI CLIP model. Internally, the wizard calls the [AML skill](cognitive-search-aml-skill.md) to connect to the catalog.
+The wizard supports Azure, Cohere, and Facebook embedding models in the Azure AI Foundry model catalog, but it doesn't currently support the OpenAI CLIP models. Internally, the wizard calls the [AML skill](cognitive-search-aml-skill.md) to connect to the catalog.
 
 1. For the model catalog, you should have an [Azure OpenAI resource](/azure/ai-services/openai/how-to/create-resource), a [hub in Azure AI Foundry portal](/azure/ai-studio/how-to/create-projects), and a [project](/azure/ai-studio/how-to/create-projects). Hubs and projects having the same name can share connection information and permissions.
 
-1. Deploy a supported embedding model to the model catalog in your project.
+1. Deploy an embedding model to the model catalog in your project.
 
-1. For role-based connections, create two role assignments: one for Azure AI Search, and another for the AI Foundry project. Assign the [Cognitive Services OpenAI User](/azure/ai-services/openai/how-to/role-based-access-control) role for embeddings and vectorization.
+    1. Select **Models + Endpoints**, and then select **Deploy a model**. Choose **Deploy base model**.
+    1. Filter by inference task set to *Embeddings*.
+    1. Deploy one of the [supported embedding models](#supported-embedding-models).
 
 ---
 
@@ -321,7 +323,7 @@ Chunking is built in and nonconfigurable. The effective settings are:
 
    + For Azure OpenAI, choose an existing deployment of text-embedding-ada-002, text-embedding-3-large, or text-embedding-3-small.
 
-   + For AI Foundry catalog, choose an existing deployment of an Azure, Cohere, and Facebook embedding model.
+   + For AI Foundry catalog, choose an existing deployment of an Azure or Cohere embedding model.
 
    + For AI Vision multimodal embeddings, select the account.
 

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

@@ -8,7 +8,7 @@ ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: tutorial
 ms.custom: references_regions
-ms.date: 10/25/2024
+ms.date: 12/03/2024
 
 ---
 
@@ -66,7 +66,7 @@ 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 | OpenAI-CLIP-Image-Text-Embeddings-vit-base-patch32, <br>OpenAI-CLIP-Image-Text-Embeddings-ViT-Large-Patch14-336, <br>Facebook-DinoV2-Image-Embeddings-ViT-Base, <br>Facebook-DinoV2-Image-Embeddings-ViT-Giant, <br>Cohere-embed-v3-english, <br>Cohere-embed-v3-multilingual | [AML](cognitive-search-aml-skill.md) <sup>2</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 | [AML](cognitive-search-aml-skill.md) <sup>2</sup>  | [Azure AI Foundry model catalog](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) |
 
 <sup>1</sup> Supports image and text vectorization.
 

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: 10/29/2024
+ms.date: 12/03/2024
 ---
 
 # How to implement integrated vectorization using models from Azure AI Foundry
@@ -105,8 +105,6 @@ The URI and key are generated when you deploy the model from the catalog. For mo
 
 This AML skill payload works with the following models from AI Foundry:
 
-+ OpenAI-CLIP-Image-Text-Embeddings-vit-base-patch32
-+ OpenAI-CLIP-Image-Text-Embeddings-ViT-Large-Patch14-336
 + Facebook-DinoV2-Image-Embeddings-ViT-Base
 + Facebook-DinoV2-Image-Embeddings-ViT-Giant
 

articles/search/vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md

@@ -8,12 +8,12 @@ ms.service: azure-ai-search
 ms.custom:
   - build-2024
 ms.topic: reference
-ms.date: 08/05/2024
+ms.date: 12/03/2024
 ---
 
 #	Azure AI Foundry model catalog vectorizer
 
-> [!IMPORTANT] 
+> [!IMPORTANT]
 > This vectorizer is in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). The [2024-05-01-Preview REST API](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-05-01-Preview&preserve-view=true) supports this feature.
 
 The **Azure AI Foundry model catalog** vectorizer connects to an embedding model that was deployed via [the Azure AI Foundry model catalog](/azure/ai-studio/how-to/model-catalog) 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. 
@@ -27,7 +27,7 @@ Parameters are case-sensitive. Which parameters you choose to use depends on wha
 | Parameter name | Description |
 |--------------------|-------------|
 | `uri` | (Required) The [URI of the AML online endpoint](../machine-learning/how-to-authenticate-online-endpoint.md) to which the _JSON_ payload is sent. Only the **https** URI scheme is allowed. |
-| `modelName` | (Required) The model ID from the AI Foundry model catalog that is deployed at the provided endpoint. Currently supported values are <ul><li>OpenAI-CLIP-Image-Text-Embeddings-vit-base-patch32 </li><li>OpenAI-CLIP-Image-Text-Embeddings-ViT-Large-Patch14-336 </li><li>Facebook-DinoV2-Image-Embeddings-ViT-Base </li><li>Facebook-DinoV2-Image-Embeddings-ViT-Giant </li><li>Cohere-embed-v3-english </li><li>Cohere-embed-v3-multilingual</ul> |
+| `modelName` | (Required) The model ID from the AI Foundry model catalog that is deployed at the provided endpoint. Currently supported models are <ul><li>Facebook-DinoV2-Image-Embeddings-ViT-Base </li><li>Facebook-DinoV2-Image-Embeddings-ViT-Giant </li><li>Cohere-embed-v3-english </li><li>Cohere-embed-v3-multilingual</ul> |
 | `key` | (Required for [key authentication](#WhatParametersToUse)) The [key for the AML online endpoint](../machine-learning/how-to-authenticate-online-endpoint.md). |
 | `resourceId` | (Required for [token authentication](#WhatParametersToUse)). The Azure Resource Manager resource ID of the AML online endpoint. It should be in the format subscriptions/{guid}/resourceGroups/{resource-group-name}/Microsoft.MachineLearningServices/workspaces/{workspace-name}/onlineendpoints/{endpoint_name}. |
 | `region` | (Optional for [token authentication](#WhatParametersToUse)). The [region](https://azure.microsoft.com/global-infrastructure/regions/) the AML online endpoint is deployed in. Needed if the region is different from the region of the search service. |
@@ -51,8 +51,6 @@ Which vector query types are supported by the AI Foundry model catalog vectorize
 
 | `modelName` | Supports `text` query | Supports `imageUrl` query | Supports `imageBinary` query |
 |--------------------|-------------|-------------|-------------|
-| OpenAI-CLIP-Image-Text-Embeddings-vit-base-patch32 | X | X | X |
-| OpenAI-CLIP-Image-Text-Embeddings-ViT-Large-Patch14-336 | X | X | X |
 | Facebook-DinoV2-Image-Embeddings-ViT-Base |  | X | X |
 | Facebook-DinoV2-Image-Embeddings-ViT-Giant |  | X | X |
 | Cohere-embed-v3-english | X |  |  |
@@ -64,8 +62,6 @@ The expected field dimensions for a field configured with an AI Foundry model ca
 
 | `modelName` | Expected dimensions |
 |--------------------|-------------|
-| OpenAI-CLIP-Image-Text-Embeddings-vit-base-patch32 | 512 |
-| OpenAI-CLIP-Image-Text-Embeddings-ViT-Large-Patch14-336 | 768 |
 | Facebook-DinoV2-Image-Embeddings-ViT-Base | 768 |
 | Facebook-DinoV2-Image-Embeddings-ViT-Giant | 1536 |
 | Cohere-embed-v3-english | 1024 |