@@ -81,7 +81,7 @@ To get the endpoints for this quickstart, select both of the following tabs.
 
 ## Deploy models
 
-To use agentic retrieval, you must deploy two Azure OpenAI models to your Azure AI Foundry resource:
+To use agentic retrieval, you must deploy two Azure OpenAI models to your Azure AI Foundry project:
 
 + An embedding model for text-to-vector conversion. This quickstart uses `text-embedding-3-large`, but you can use any `text-embedding` model.
 

@@ -9,7 +9,7 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: quickstart
-ms.date: 10/08/2025
+ms.date: 10/24/2025
 ---
 
 # Quickstart: Vectorize text in the Azure portal
@@ -47,7 +47,7 @@ For integrated vectorization, use one of the following embedding models. Deploym
 | Provider | Supported models |
 |--|--|
 | [Azure OpenAI in Azure AI Foundry Models resource](/azure/ai-services/openai/how-to/create-resource) <sup>1, 2</sup> | For text:<br>text-embedding-ada-002<br>text-embedding-3-small<br>text-embedding-3-large |
-| [Azure AI Foundry project](/azure/ai-foundry/how-to/create-projects) | For text:<br>text-embedding-ada-002<br>text-embedding-3-small<br>text-embedding-3-large<br><br>For images:<br>Facebook-DinoV2-Image-Embeddings-ViT-Base<br>Facebook-DinoV2-Image-Embeddings-ViT-Giant |
+| [Azure AI Foundry project](/azure/ai-foundry/how-to/create-projects) | For text:<br>text-embedding-ada-002<br>text-embedding-3-small<br>text-embedding-3-large |
 | [Azure AI Foundry hub-based project](/azure/ai-foundry/how-to/hub-create-projects) | For text:<br>text-embedding-ada-002<br>text-embedding-3-small<br>text-embedding-3-large<br><br>For text and images:<br>Cohere-embed-v3-english <sup>3</sup><br>Cohere-embed-v3-multilingual <sup>3</sup> |
 | [Azure AI services multi-service resource](/azure/ai-services/multi-service-resource#azure-ai-multi-services-resource-for-azure-ai-search-skills) <sup>4</sup> | For text and images: [Azure AI Vision multimodal](/azure/ai-services/computer-vision/how-to/image-retrieval) <sup>5</sup></li> |
 

@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 10/06/2025
+ms.date: 10/23/2025
 ms.update-cycle: 365-days
 ---
 
@@ -19,10 +19,20 @@ Occasionally, indexers run into problems that don't produce errors or that occur
 > [!NOTE]
 > If you have an Azure AI Search error to investigate, see [Troubleshooting common indexer errors and warnings](cognitive-search-common-errors-warnings.md) instead.
 
-## Best practice: indexers are designed to run on a schedule
-> For reliable indexing, configure your indexers to run on a [regular schedule](search-howto-schedule-indexers.md). Scheduled runs automatically pick up any documents missed in previous runs due to transient errors, network interruptions, or temporary service issues. This approach helps maintain data consistency and minimizes the need for manual intervention.  
->  
-> For [large data sources](search-how-to-large-index.md), the initial enumeration and indexing can take hours or even days. Running your indexer on a schedule allows that progress continues and errors are retried automatically. Avoid relying solely on manual or on-demand indexer runs, as these do not provide the same reliability or transient error recovery.  
+## Best practices
+
+These are some best practices and recommendations when working with indexers:
+
+### Indexers are designed to run on a schedule
+
+- For reliable indexing, configure your indexers to run on a [regular schedule](search-howto-schedule-indexers.md). Scheduled runs automatically pick up any documents missed in previous runs due to transient errors, network interruptions, or temporary service issues. This approach helps maintain data consistency and minimizes the need for manual intervention.  
+- For [large data sources](search-how-to-large-index.md), the initial enumeration and indexing can take hours or even days. Running your indexer on a schedule allows that progress continues and errors are retried automatically. Avoid relying solely on manual or on-demand indexer runs, as these do not provide the same reliability or transient error recovery.
+
+### Indexers provide best-effort indexing over time
+
+- Built-in indexers are designed to process all documents without permanent errors over time, if not in the current run, then in subsequent scheduled runs. They offer a convenient, low/no-code way to index data for common scenarios, enabling faster development and easier maintenance. However, if they have AI enrichment capabilities, they are not optimized for very large-scale workloads. For guidance on handling large datasets, see [how to index large data sets](search-how-to-large-index.md).
+- If your solution requires strict control over indexing timelines, use the Push APIs instead, such as the [Documents Index REST API](/rest/api/searchservice/documents) or the [IndexDocuments method (Azure SDK for .NET)](/dotnet/api/azure.search.documents.searchclient.indexdocuments). These options give you full control of the indexing pipeline.
+- Indexers can occasionally fall out of schedule. While this is uncommon and auto-recovery mechanisms exist, recovery may take time. This behavior is expected.
 
 <a name="connection-errors"></a>
 

@@ -57,7 +57,7 @@ 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<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) |
+| Azure AI Foundry model catalog | 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.
 

@@ -43,7 +43,7 @@ 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>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) |
+| [`aml`](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) | 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) |
 

@@ -33,15 +33,11 @@ After the model is deployed, you can use it for [integrated vectorization](vecto
 
 ## Supported embedding models
 
-Integrated vectorization and the [**Import data (new)** wizard](search-import-data-portal.md) support the following embedding models in the model catalog:
+Supported embedding models from the Azure AI Foundry model catalog vary by usage method:
 
-| 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-v4 <sup>1</sup> |
++ For the latest list of models supported programmatically, see the [AML skill](cognitive-search-aml-skill.md) and [Azure AI Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) references.
 
-<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.
++ For the latest list of models supported in the Azure portal, see [Quickstart: Vector search in the Azure portal](search-get-started-portal-import-vectors.md) and [Quickstart: Multimodal search in the Azure portal](search-get-started-portal-image-search.md).
 
 ## Deploy an embedding model from the Azure AI Foundry model catalog
 
@@ -75,9 +71,9 @@ Integrated vectorization and the [**Import data (new)** wizard](search-import-da
 
 ## Sample AML skill payloads
 
-When you deploy embedding models from the [Azure AI Foundry model catalog](https://ai.azure.com/explore/models) you connect to them using the [AML skill](cognitive-search-aml-skill.md) in Azure AI Search for indexing workloads.
+When you deploy embedding models from the Azure AI Foundry model catalog, you connect to them using the [AML skill](cognitive-search-aml-skill.md) in Azure AI Search for indexing workloads.
 
-This section describes the AML skill definition and index mappings. It includes sample payloads that are already configured to work with their corresponding deployed endpoints. For more technical details on how these payloads work, read about the [Skill context and input annotation language](cognitive-search-skill-annotation-language.md).
+This section describes the AML skill definition and index mappings. It includes sample payloads that are already configured to work with their corresponding deployed endpoints. For more technical details on how these payloads work, see the [Skill context and input annotation language](cognitive-search-skill-annotation-language.md).
 
 <!-- ### [**Text Input for "Inference" API**](#tab/inference-text)
 
@@ -124,6 +120,7 @@ The URI and key are generated when you deploy the model from the catalog. For mo
 }
 ``` -->
 
+<!--
 ### [**Facebook embedding models**](#tab/inference-image)
 
 This AML skill payload works with the following image embedding models from Azure AI Foundry:
@@ -171,8 +168,11 @@ 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 embedding models from Azure AI Foundry:
+### Cohere embedding models
+
+This AML skill payload works with the following embedding models:
 
 + Cohere-embed-v3-english
 + Cohere-embed-v3-multilingual
@@ -240,8 +240,6 @@ If you selected a different `embedding_types` in your skill definition, change `
 }
 ```
 
----
-
 ## Sample Azure AI Foundry vectorizer payload
 
 The [Azure AI Foundry vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md), unlike the AML skill, is tailored to work only with those embedding models that are deployable via the Azure AI Foundry model catalog. The main difference is that you don't have to worry about the request and response payload, but you do have to provide the `modelName`, which corresponds to the "Model ID" that you copied after deploying the model in [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs). 

@@ -28,7 +28,7 @@ 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 Azure AI Foundry model catalog that is deployed at the provided endpoint. Supported models are:<p><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</li><li>Cohere-embed-v4</li></ul> |
+| `modelName` | (Required) The model ID from the Azure AI Foundry model catalog that is deployed at the provided endpoint. Supported models are:<p><ul><li></li><li>Cohere-embed-v3-english </li><li>Cohere-embed-v3-multilingual</li><li>Cohere-embed-v4</li></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. |
@@ -52,24 +52,30 @@ Which vector query types are supported by the Azure AI Foundry model catalog vec
 
 | Embedding model | Supports `text` query | Supports `imageUrl` query | Supports `imageBinary` query |
 |--------------------|-------------|-------------|-------------|
-| Facebook-DinoV2-Image-Embeddings-ViT-Base |  | X | X |
-| Facebook-DinoV2-Image-Embeddings-ViT-Giant |  | X | X |
 | Cohere-embed-v3-english | X |  | X |
 | Cohere-embed-v3-multilingual | X |  | X |
 | Cohere-embed-v4 | X |  | X |
 
+<!--
+| Facebook-DinoV2-Image-Embeddings-ViT-Base |  | X | X |
+| Facebook-DinoV2-Image-Embeddings-ViT-Giant |  | X | X |
+-->
+
 ## Expected field dimensions
 
 The expected field dimensions for a vector field configured with an Azure AI Foundry model catalog vectorizer depend on the `modelName` that is configured.
 
 | `modelName` | Expected dimensions |
 |--------------------|-------------|
-| Facebook-DinoV2-Image-Embeddings-ViT-Base | 768 |
-| Facebook-DinoV2-Image-Embeddings-ViT-Giant | 1536 |
 | Cohere-embed-v3-english | 1024 |
 | Cohere-embed-v3-multilingual | 1024 |
 | Cohere-embed-v4 | 256–1536 |
 
+<!--
+| Facebook-DinoV2-Image-Embeddings-ViT-Base | 768 |
+| Facebook-DinoV2-Image-Embeddings-ViT-Giant | 1536 |
+-->
+
 ## Sample definition
 
 Suggested model names in the Azure AI Foundry model catalog consist of the base model plus a random three-letter suffix. The name of your model will be different from the one shown in this example.