more prereq + rbac alignment

Author: HeidiSteen

articles/search/tutorial-document-extraction-image-verbalization.md

@@ -43,9 +43,11 @@ This tutorial demonstrates a lower-cost approach for indexing multimodal content
 
 + [Azure Storage](/azure/storage/common/storage-account-create), used for storing sample data and for creating a [knowledge store](knowledge-store-concept-intro.md).
 
-+ [Azure OpenAI](/azure/ai-foundry/openai/how-to/create-resource) with a deployment of a chat completion model hosted in Azure AI Foundry or another source. The model is used to verbalize image content. You provide the URI to the hosted model in the GenAI Prompt skill definition.
++ [Azure OpenAI](/azure/ai-foundry/openai/how-to/create-resource) with a deployment of
 
-+ A text embedding model deployed in Azure AI Foundry. The model is used to vectorize text content pull from source documents and the image descriptions generated by the chat completion model. For integrated vectorization, the embedding model must be located in Azure AI Foundry, and it must be either text-embedding-ada-002, text-embedding-3-large, or text-embedding-3-small. If you want to use an external embedding model, use a custom skill instead of the Azure OpenAI embedding skill.
+  + A chat completion model hosted in Azure AI Foundry or another source. The model is used to verbalize image content. You provide the URI to the hosted model in the GenAI Prompt skill definition. You can use [any chat completion model](cognitive-search-skill-genai-prompt.md#supported-models).
+
+  + A text embedding model deployed in Azure AI Foundry. The model is used to vectorize text content pull from source documents and the image descriptions generated by the chat completion model. For integrated vectorization, the embedding model must be located in Azure AI Foundry, and it must be either text-embedding-ada-002, text-embedding-3-large, or text-embedding-3-small. If you want to use an external embedding model, use a custom skill instead of the Azure OpenAI embedding skill.
 
 + [Visual Studio Code](https://code.visualstudio.com/download) with a [REST client](https://marketplace.visualstudio.com/items?itemName=humao.rest-client).
 

articles/search/tutorial-document-layout-image-verbalization.md

@@ -34,13 +34,17 @@ In this tutorial, you use:
 
 ## Prerequisites
 
++ [Azure AI services multi-service account](/azure/ai-services/multi-service-resource#azure-ai-services-resource-for-azure-ai-search-skills). This account provides access to the Document Intelligence Layout model used in this tutorial. You must use an Azure AI multi-service account for skillset access to this resource.
+
 + [Azure AI Search](search-create-service-portal.md). [Configure your search service](search-manage.md) for role-based access control and a managed identity. Your service must be on the Basic tier or higher. This tutorial isn't supported on the Free tier.
 
 + [Azure Storage](/azure/storage/common/storage-account-create), used for storing sample data and for creating a [knowledge store](knowledge-store-concept-intro.md).
 
-+ [Azure OpenAI](/azure/ai-foundry/openai/how-to/create-resource) with a deployment of a chat completion model hosted in Azure AI Foundry or another source. The model is used to verbalize image content. You provide the URI to the hosted model in the GenAI Prompt skill definition.
++ [Azure OpenAI](/azure/ai-foundry/openai/how-to/create-resource) with a deployment of
+
+  + A chat completion model hosted in Azure AI Foundry or another source. The model is used to verbalize image content. You provide the URI to the hosted model in the GenAI Prompt skill definition. You can use [any chat completion model](cognitive-search-skill-genai-prompt.md#supported-models).
 
-+ A text embedding model deployed in Azure AI Foundry. The model is used to vectorize text content pull from source documents and the image descriptions generated by the chat completion model. For integrated vectorization, the embedding model must be located in Azure AI Foundry, and it must be either text-embedding-ada-002, text-embedding-3-large, or text-embedding-3-small. If you want to use an external embedding model, use a custom skill instead of the Azure OpenAI embedding skill.
+  + A text embedding model deployed in Azure AI Foundry. The model is used to vectorize text content pull from source documents and the image descriptions generated by the chat completion model. For integrated vectorization, the embedding model must be located in Azure AI Foundry, and it must be either text-embedding-ada-002, text-embedding-3-large, or text-embedding-3-small. If you want to use an external embedding model, use a custom skill instead of the Azure OpenAI embedding skill.
 
 + [Visual Studio Code](https://code.visualstudio.com/download) with a [REST client](https://marketplace.visualstudio.com/items?itemName=humao.rest-client).
 
@@ -84,7 +88,23 @@ The following instructions apply to Azure Storage which provides the sample data
 
 ## Prepare models
 
-This tutorial assumes you have an existing Azure OpenAI resource through which the skills call the text embedding model and chat completion models. The search service connects to the models during skillset processing and during query execution using its managed identity. This section gives you guidance and links for assigning roles for authorized access.
+This tutorial assumes you have an existing Azure OpenAI resource through which the skills a chat completion model for GenAI Prompt and also a text embedding model for vectorization. The search service connects to the models during skillset processing and during query execution using its managed identity. This section gives you guidance and links for assigning roles for authorized access.
+
+You also need a role assignment for accessing the Document Intelligence Layout model via an Azure AI multi-service account.
+
+### Assign roles in Azure AI multi-service
+
+1. Sign in to the Azure portal (not the Foundry portal) and find the Azure AI multi=service account. Make sure it's in a region that provides the [Document Intelligence Layout model](cognitive-search-skill-document-intelligence-layout.md#supported-regions).
+
+1. Select **Access control (IAM)**.
+
+1. Select **Add** and then **Add role assignment**.
+
+1. Search for **Cognitive Services User** and then select it.
+
+1. Choose **Managed identity** and then assign your [search service managed identity](search-howto-managed-identities-data-sources.md).
+
+### Assign roles in Azure OpenAI
 
 1. Sign in to the Azure portal (not the Foundry portal) and find the Azure OpenAI resource.