Merge pull request #5061 from gmndrg/main

Update search documents with clarification or document bugs

Author: prmerger-automator[bot]

articles/search/multimodal-search-overview.md

@@ -4,7 +4,7 @@ titleSuffix: Azure AI Search
 description: Learn what multimodal search is, how Azure AI Search supports it for text + image content, and where to find detailed concepts, tutorials, and samples.
 ms.service: azure-ai-search
 ms.topic: conceptual
-ms.date: 05/12/2025
+ms.date: 05/19/2025
 author: gmndrg
 ms.author: gimondra
 ---
@@ -28,6 +28,9 @@ Azure AI Search simplifies the construction of a multimodal pipeline through a g
 The functionality behind the **Import and vectorize data** wizard's multimodality option is powered by managed, configurable AI skills and the Azure Search knowledge store:
 
 + [Document Intelligence layout skill](cognitive-search-skill-document-intelligence-layout.md) and [document extraction skill](cognitive-search-skill-document-extraction.md) obtain page text, inline images, and structural metadata. The Document Extraction skill doesn't support polygon extraction or page number extraction. Also, the range of supported file types may vary. To ensure optimal alignment with your specific use case, check each skill documentation for detailed information on compatibility and capabilities.
+The native document extraction mechanisms (document layout or document extraction skills) don't support either table extraction or the preservation of their structure. To extract tables and retain their structure, you can:
+  1. Build a [custom Web API skill](cognitive-search-custom-skill-web-api.md).
+  1. Use this skill to call the [Azure AI Content Understanding service](/azure/ai-services/content-understanding/tutorial/build-rag-solution), which supports content extraction, including tables.
 + [Split skill](cognitive-search-skill-textsplit.md) chunks the extracted text for utilization in the remaining pipeline functionality (such as embedding skills). 
 + [Gen AI prompt skill](cognitive-search-skill-genai-prompt.md) verbalizes images, producing concise natural-language descriptions suitable for text search and embedding using a Large Language Model (LLM). 
 + Text/image (or multimodal) embedding skills create embeddings for text and images, enabling similarity and hybrid retrieval. You can call [Azure OpenAI](cognitive-search-skill-azure-openai-embedding.md), [AI Foundry](cognitive-search-aml-skill.md), or [AI Vision](cognitive-search-skill-vision-vectorize.md) embedding models natively.
@@ -39,7 +42,9 @@ A multimodal pipeline begins by cracking each source document into chunks of tex
 
 | Characteristic | Document Intelligence layout skill | Document extraction skill |
 |----------------|------------------------------------|---------------------------|
-| Location metadata extraction (page, bounding polygon) | Yes | No |
+| Text location metadata extraction (page, bounding polygon) | Yes | No |
+| Image location metadata extraction (page, bounding polygon) | Yes | Yes |
+| Location metadata extraction based on file type  | Multiple file support according to [Azure AI Document Intelligence layout model (preview)](/azure/ai-services/document-intelligence/prebuilt/layout)  | PDF only |
 | Data-extraction billing | Billed according to [Document Intelligence layout-model pricing](https://azure.microsoft.com/pricing/details/ai-document-intelligence/). | Image extraction is billed as outlined in the [Azure AI Search pricing page](https://azure.microsoft.com/pricing/details/search/). |
 | Recommended scenarios | RAG pipelines and agent workflows that need precise page numbers, on-page highlights, or diagram overlays in client apps. | Rapid prototyping or production pipelines where the exact position or detailed layout information isn't required. |
 

articles/search/search-index-access-control-lists-and-rbac-push-api.md

@@ -4,7 +4,7 @@ titleSuffix: Azure AI Search
 description: Learn how to use the REST API for indexing documents with ACLs and RBAC metadata.  
 ms.service: azure-ai-search  
 ms.topic: conceptual  
-ms.date: 05/09/2025  
+ms.date: 05/19/2025  
 author: admayber
 ms.author: admayber  
 ---  
@@ -16,7 +16,7 @@ ms.author: admayber
 Indexing documents, along with their associated [Access Control Lists (ACLs)](/azure/storage/blobs/data-lake-storage-access-control) and container [Role-Based Access Control (RBAC) roles](/azure/role-based-access-control/overview), into an Azure AI Search index via the [REST API](/rest/api/searchservice/) offers fine-grained control over the indexing pipeline. This approach enables the inclusion of document entries with precise, document-level permissions directly within the index. This article explains how to use the REST API to index document-level permissions' metadata in Azure AI Search. This process prepares your index to query and enforce end-user permissions.
 
 ## Supported scenarios  
-- Indexing ACLs metadata from [ENTRA-based](/entra/fundamentals/whatis), POSIX-style ACL systems, such as [Azure Data Lake Storage (ADLS) Gen2].(/azure/storage/blobs/data-lake-storage-introduction)
+- Indexing ACLs metadata from [ENTRA-based](/entra/fundamentals/whatis), POSIX-style ACL systems, such as [Azure Data Lake Storage (ADLS) Gen2](/azure/storage/blobs/data-lake-storage-introduction)
 - Indexing RBAC container metadata from ADLS Gen2.
 
 ### Limitations
@@ -107,9 +107,9 @@ This example illustrates how the document access rules are resolved based on the
 | 3 | ["none"] | ["group1", "group2"] | Empty | Members of group1 or group2 | |
 | 4 | ["all"] | ["none"] | Empty | Any user | Any querying user matches the ACL filter "all", so all users have access |
 | 5 | ["all"] | ["group1", "group2"] | scope/to/container1 | Any user | Since all users match the "all" filter for userID, the groupID and RBAC filters don't have any impact |
-| 5 | ["user1", "user2"] | ["group1"] | Empty | User1, user2, or any member of group1 | |
-| 5 | ["user1", "user2"] | [] | Empty | User1, user2, or any user with RBAC permissions to container1 | |
+| 6 | ["user1", "user2"] | ["group1"] | Empty | User1, user2, or any member of group1 | |
+| 7 | ["user1", "user2"] | [] | Empty | User1, user2, or any user with RBAC permissions to container1 | |
 
 ## Next steps
-- [How to query the index using end user ENTRA-token to enforce document-level permissions](https://aka.ms/azs-query-preserving-permissions)
+- [How to query the index using end user ENTRA-token to enforce document-level permissions](search-query-access-control-rbac-enforcement.md)
 - [How to index ADLS Gen2 document-level permission information using indexers](tutorial-adls-gen2-indexer-acls.md)