More multimodal intro differentiation

Author: HeidiSteen

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

@@ -10,32 +10,32 @@ ms.service: azure-ai-search
 ms.update-cycle: 180-days
 ms.custom:
 ms.topic: tutorial
-ms.date: 05/29/2025
+ms.date: 07/30/2025
 
 ---
 
 # Tutorial: Verbalize images using generative AI
 
-Azure AI Search can extract and index both text and images from PDF documents stored in Azure Blob Storage. This tutorial shows you how to build a multimodal indexing pipeline that includes steps for describing visual content in natural language and using the generated descriptions in your searchable index.
+Azure AI Search can extract and index both text and images from PDF documents stored in Azure Blob Storage. This tutorial shows you how to build a multimodal indexing pipeline that *chunks data  using the built-in Text Split skill* and uses *image verbalization* to describe images. Cropped images are stored in a knowledge store, and visual content is described in natural language and ingested alongside text in a searchable index.
 
-From the source document, each image is passed to the [GenAI Prompt skill (preview)](cognitive-search-skill-genai-prompt.md) that calls a chat completion model to generate a concise textual description. These descriptions, along with the original document text, are then embedded into vector representations using Azure OpenAI’s text-embedding-3-large model. The result is a single index containing semantically searchable content from both modalities: text and verbalized images.
+To get image verbalizations, each extracted image is passed to the [GenAI Prompt skill (preview)](cognitive-search-skill-genai-prompt.md) that calls a chat completion model to generate a concise textual description. These descriptions, along with the original document text, are then embedded into vector representations using Azure OpenAI’s text-embedding-3-large model. The result is a single index containing searchable content from both modalities: text and verbalized images.
 
 In this tutorial, you use:
 
 + A 36-page PDF document that combines rich visual content, such as charts, infographics, and scanned pages, with traditional text.
 
 + An indexer and skillset to create an indexing pipeline that includes AI enrichment through skills.
 
-+ The [Document Extraction skill](cognitive-search-skill-document-extraction.md) for extracting normalized images and text.
++ The [Document Extraction skill](cognitive-search-skill-document-extraction.md) for extracting normalized images and text. The [Text Split skill](cognitive-search-skill-textsplit.md) chunks the data.
 
 + The [GenAI Prompt skill (preview)](cognitive-search-skill-genai-prompt.md) that calls a chat completion model to create descriptions of visual content.
 
-+ A search index configured to store text and image verbalizations.
++ A search index configured to store text and image verbalizations. Some content is vectorized for vector-based similarity search.
 
 This tutorial demonstrates a lower-cost approach for indexing multimodal content using the Document Extraction skill and image captioning. It enables extraction and search over both text and images from documents in Azure Blob Storage. However, it doesn't include locational metadata for text, such as page numbers or bounding regions. For a more comprehensive solution that includes structured text layout and spatial metadata, see [Tutorial: Verbalize images from a structured document layout](tutorial-document-layout-image-verbalization.md).
 
 > [!NOTE]
-> Setting `imageAction` to `generateNormalizedImages` results in image extraction, which is an extra charge. For more information, see [Azure AI Search pricing](https://azure.microsoft.com/pricing/details/search/) for image extraction.
+> Image extraction by the Document Extraction skill isn't free. Setting `imageAction` to `generateNormalizedImages` in the skillset triggers image extraction, which is an extra charge. For billing information, see [Azure AI Search pricing](https://azure.microsoft.com/pricing/details/search/).
 
 ## Prerequisites
 

articles/search/tutorial-document-extraction-multimodal-embeddings.md

@@ -10,41 +10,47 @@ ms.service: azure-ai-search
 ms.update-cycle: 180-days
 ms.custom:
 ms.topic: tutorial
-ms.date: 06/11/2025
+ms.date: 07/30/2025
 
 ---
-<!-- # Tutorial: Index mixed content using multimodal embeddings and the Document Extraction skill -->
+
 # Tutorial: Vectorize images and text
 
-Azure AI Search can extract and index both text and images from PDF documents stored in Azure Blob Storage. This tutorial shows you how to build a multimodal indexing pipeline by embedding both text and images into a unified semantic search index.
+Azure AI Search can extract and index both text and images from PDF documents stored in Azure Blob Storage. This tutorial shows you how to build a multimodal indexing pipeline in Azure AI Search that *chunks data using the built-in Text Split skill* and *uses multimodal embeddings* to vectorize text and images from the same document. Cropped images are stored in a knowledge store, and both text and visual content are vectorized and ingested in a searchable index.
 
 In this tutorial, you use:
 
 + A 36-page PDF document that combines rich visual content, such as charts, infographics, and scanned pages, with traditional text.
 
 + An indexer and skillset to create an indexing pipeline that includes AI enrichment through skills.
 
-+ The [Document Extraction skill](cognitive-search-skill-document-extraction.md) for extracting normalized images and text.
++ The [Document Extraction skill](cognitive-search-skill-document-extraction.md) for extracting normalized images and text. The [Text Split skill](cognitive-search-skill-textsplit.md) chunks the data.
 
 + The [Azure AI Vision multimodal embeddings skill](cognitive-search-skill-vision-vectorize.md) to vectorize text and images.
 
-+ A search index configured to store text and image embeddings and support for vector-based similarity search.
++ A search index configured to store extracted text and image content. Some content is vectorized for vector-based similarity search.
 
-This tutorial demonstrates a lower-cost approach for indexing multimodal content using the Document Extraction skill and image captioning. It enables extraction and search over both text and images from documents in Azure Blob Storage. However, it doesn't include locational metadata for text, such as page numbers or bounding regions. For a more comprehensive solution that includes structured text layout and spatial metadata, see [Tutorial: Verbalize images from a structured document layout](tutorial-document-layout-image-verbalization.md).
+This tutorial demonstrates a lower-cost approach for indexing multimodal content using the Document Extraction skill. It enables extraction and search over both text and images from documents pulled from Azure Blob Storage. However, it doesn't include locational metadata for text, such as page numbers or bounding regions. For a more comprehensive solution that includes structured text layout and spatial metadata, see [Tutorial: Vectorize from a structured document layout](tutorial-document-layout-multimodal-embeddings.md).
 
 > [!NOTE]
-> Setting `imageAction` to `generateNormalizedImages` results in image extraction, which is an extra charge. For more information, see [Azure AI Search pricing](https://azure.microsoft.com/pricing/details/search/) for image extraction.
+> Image extraction by the Document Extraction skill isn't free. Setting `imageAction` to `generateNormalizedImages` in the skillset triggers image extraction, which is an extra charge. For billing information, see [Azure AI Search pricing](https://azure.microsoft.com/pricing/details/search/).
 
 ## Prerequisites
 
-+ [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. It must also be in the same region as your multi-service account.
++ An [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 both the Azure AI Vision multimodal embedding model and the Document Intelligence Layout model used by the skills in this tutorial. You must use an Azure AI multi-service account for skillset access to these resources.
 
-+ [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 AI Search](search-create-service-portal.md). [Configure your search service](search-manage.md) for role-based access control and a managed identity for connections to Azure Storage and Azure AI Vision. Your service must be on the Basic tier or higher. This tutorial isn't supported on the Free tier. The search service must also be in the same region as your multi-service account.
 
-+ An [Azure AI services multi-service account](/azure/ai-services/multi-service-resource#azure-ai-services-resource-for-azure-ai-search-skills) that provides Azure AI Vision for multimodal embeddings. You must use an Azure AI multi-service account for this task. For an updated list of regions that provide multimodal embeddings, see the [Azure AI Vision documentation](/azure/ai-services/computer-vision/overview-image-analysis#region-availability).
++ [Azure Storage](/azure/storage/common/storage-account-create), used for storing sample data and for creating a [knowledge store](knowledge-store-concept-intro.md).
 
 + [Visual Studio Code](https://code.visualstudio.com/download) with a [REST client](https://marketplace.visualstudio.com/items?itemName=humao.rest-client).
 
+## Limitations
+
++ The [Document Layout skill](cognitive-search-skill-document-intelligence-layout.md) has limited regional availability. For a list of supported regions, see [Document Layout skill> Supported regions](cognitive-search-skill-document-intelligence-layout.md#supported-regions).
+
++ The [Azure AI Vision multimodal embeddings skill](cognitive-search-skill-vision-vectorize.md) also has limited regional availability. For an updated list of regions that provide multimodal embeddings, see the [Azure AI Vision documentation](/azure/ai-services/computer-vision/overview-image-analysis#region-availability).
+
 ## Prepare data
 
 Download the following sample PDF:

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

@@ -10,15 +10,15 @@ ms.service: azure-ai-search
 ms.update-cycle: 180-days
 ms.custom:
 ms.topic: tutorial
-ms.date: 05/29/2025
+ms.date: 07/30/2025
 
 ---
 
 # Tutorial: Verbalize images from a structured document layout
 
-In this Azure AI Search tutorial, learn how to build a multimodal indexing pipeline that chunks data based on document structure and uses image verbalization to describe images. Cropped images are stored in a knowledge store, and visual content is described in natural language and ingested alongside text in a searchable index.
+Azure AI Search can extract and index both text and images from PDF documents stored in Azure Blob Storage. This tutorial shows you how to build a multimodal indexing pipeline that *chunks data based on document structure* and uses *image verbalization* to describe images. Cropped images are stored in a knowledge store, and visual content is described in natural language and ingested alongside text in a searchable index. Chunking is based on the Azure AI Document Intelligence Layout model that recognizes document structure.
 
-From the source document, each image is passed to the [GenAI Prompt skill (preview)](cognitive-search-skill-genai-prompt.md) to generate a concise textual description. These descriptions, along with the original document text, are then embedded into vector representations using Azure OpenAI’s text-embedding-3-large model. The result is a single index containing semantically searchable content from both modalities: text and verbalized images.
+To get image verbalizations, each extracted image is passed to the [GenAI Prompt skill (preview)](cognitive-search-skill-genai-prompt.md) that calls a chat completion model to generate a concise textual description. These descriptions, along with the original document text, are then embedded into vector representations using Azure OpenAI’s text-embedding-3-large model. The result is a single index containing searchable content from both modalities: text and verbalized images.
 
 In this tutorial, you use:
 

articles/search/tutorial-document-layout-multimodal-embeddings.md

@@ -10,14 +10,13 @@ ms.service: azure-ai-search
 ms.update-cycle: 180-days
 ms.custom:
 ms.topic: tutorial
-ms.date: 06/11/2025
+ms.date: 07/30/2025
 
 ---
 
 # Tutorial: Vectorize from a structured document layout
 
-<!-- Multimodal plays an essential role in generative AI apps and the user experience as it enables the extraction of information not only from text but also from complex images embedded within documents.  -->
-In this Azure AI Search tutorial, learn how to build a multimodal indexing pipeline that chunks data based on document structure, and uses a multimodal embedding model to vectorize text and images in a searchable index.
+Azure AI Search can extract and index both text and images from PDF documents stored in Azure Blob Storage. This tutorial shows you how to build a multimodal indexing pipeline that *chunks data based on document structure* and *uses multimodal embeddings* to vectorize text and images from the same document. Cropped images are stored in a knowledge store, and both text and visual content are vectorized and ingested in a searchable index. Chunking is based on the Azure AI Document Intelligence Layout model that recognizes document structure.
 
 In this tutorial, you use:
 
@@ -29,21 +28,23 @@ In this tutorial, you use:
 
 + The [Azure AI Vision multimodal embeddings skill](cognitive-search-skill-vision-vectorize.md) to vectorize text and images.
 
-+ A search index configured to store extracted text and image verbalizations. Some content is vectorized for vector-based similarity search.
++ A search index configured to store extracted text and image content. Some content is vectorized for vector-based similarity search.
 
 ## Prerequisites
 
++ An [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 both the Azure AI Vision multimodal embedding model and the Document Intelligence Layout model used by the skills in this tutorial. You must use an Azure AI multi-service account for skillset access to these resources. 
+
 + [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. It must also be in the same region as your multi-service account.
 
 + [Azure Storage](/azure/storage/common/storage-account-create), used for storing sample data and for creating a [knowledge store](knowledge-store-concept-intro.md).
 
-+ An [Azure AI services multi-service account](/azure/ai-services/multi-service-resource#azure-ai-services-resource-for-azure-ai-search-skills) that provides Azure AI Vision for multimodal embeddings. You must use an Azure AI multi-service account for this task. For an updated list of regions that provide multimodal embeddings, see the [Azure AI Vision documentation](/azure/ai-services/computer-vision/overview-image-analysis#region-availability).
-
 + [Visual Studio Code](https://code.visualstudio.com/download) with a [REST client](https://marketplace.visualstudio.com/items?itemName=humao.rest-client).
 
 ## Limitations
 
-The [Document Layout skill](cognitive-search-skill-document-intelligence-layout.md) has limited regional availability, is bound to Azure AI services, and requires a [billable resource](cognitive-search-attach-cognitive-services.md) for transactions that exceed 20 documents per indexer per day. For a lower-cost solution to indexing multimodal content, see [Tutorial: Verbalize images using generative AI](tutorial-document-extraction-image-verbalization.md).
++ The [Document Layout skill](cognitive-search-skill-document-intelligence-layout.md) has limited regional availability. For a list of supported regions, see [Document Layout skill> Supported regions](cognitive-search-skill-document-intelligence-layout.md#supported-regions).
+
++ The [Azure AI Vision multimodal embeddings skill](cognitive-search-skill-vision-vectorize.md) also has limited regional availability. For an updated list of regions that provide multimodal embeddings, see the [Azure AI Vision documentation](/azure/ai-services/computer-vision/overview-image-analysis#region-availability).
 
 ## Prepare data