Merge pull request #6235 from HeidiSteen/heidist-freshness

[azure search] Concept article placement

Author: v-ccolin

articles/search/search-blob-indexer-role-based-access.md

@@ -168,9 +168,10 @@ JSON schema example:
 
 To effectively manage blob deletion, ensure that you have enabled [deletion tracking](search-howto-index-changed-deleted-blobs.md) before your indexer runs for the first time. This feature allows the system to detect deleted blobs from your source and have them deleted from the index.  
 
-## Related content
+## See also
 
++ [Connect to Azure AI Search using roles](search-security-rbac.md)
+- [Query-Time ACL and RBAC enforcement](search-query-access-control-rbac-enforcement.md)
+- [azure-search-python-samples/Quickstart-Document-Permissions-Push-API](https://github.com/Azure-Samples/azure-search-python-samples/blob/main/Quickstart-Document-Permissions-Push-API)
 + [Search over Azure Blob Storage content](search-blob-storage-integration.md)
 + [Configure a blob indexer](search-howto-indexing-azure-blob-storage.md)
-+ [Change and delete detection using indexers for Azure Storage](search-howto-index-changed-deleted-blobs.md)
-+ [Connect to Azure AI Search using roles](search-security-rbac.md)

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

@@ -122,7 +122,8 @@ This example illustrates how the document access rules are resolved based on the
 | 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
+## See also
 
+- [Connect to Azure AI Search using roles](search-security-rbac.md)
 - [Query-Time ACL and RBAC enforcement](search-query-access-control-rbac-enforcement.md)
 - [azure-search-python-samples/Quickstart-Document-Permissions-Push-API](https://github.com/Azure-Samples/azure-search-python-samples/blob/main/Quickstart-Document-Permissions-Push-API)

articles/search/search-indexer-access-control-lists-and-role-based-access.md

@@ -298,3 +298,8 @@ Choose one of the following mechanisms, depending on how many items changed:
 
 To effectively manage blob deletion, ensure that you have enabled [deletion tracking](search-howto-index-changed-deleted-blobs.md) before your indexer runs for the first time. This feature allows the system to detect deleted blobs from your source and have them deleted from the index.
 
+## See also
+
++ [Connect to Azure AI Search using roles](search-security-rbac.md)
++ [Query-Time ACL and RBAC enforcement](search-query-access-control-rbac-enforcement.md)
++ [azure-search-python-samples/Quickstart-Document-Permissions-Push-API](https://github.com/Azure-Samples/azure-search-python-samples/blob/main/Quickstart-Document-Permissions-Push-API)

articles/search/search-query-access-control-rbac-enforcement.md

@@ -76,7 +76,7 @@ Content-Type: application/json
 }
 ```
 
-## Related content
+## See also
 
 - [Tutorial: Index ADLS Gen2 permission metadata](tutorial-adls-gen2-indexer-acls.md) 
 

articles/search/search-relevance-overview.md

@@ -17,7 +17,7 @@ In a query operation, the relevance of any given result is measured by a ranking
 
 Ranking occurs whenever the query request includes full text or vector queries. It doesn't occur if the query invokes strict pattern matching, such as a filter-only query or a specialized query form like autocomplete, suggestions, geospatial search, fuzzy search, or regular expression search. A uniform search score of 1.0 indicates the absence of a ranking algorithm.
 
-In Azure AI Search, ***relevance tuning*** is primarily centered on textual content, applying scoring profiles or semantic ranking to enhance the quality of results. For vectors, you can experiment between Hierarchical Navigable Small World (HNSW) and exhaustive K-nearest neighbors (KNN) to see if one algorithm outperforms the other for your scenario. HNSW graphing with an exhaustive KNN override at query time is the most flexible approach for testing. You can also experiment with various embedding models to see which ones produce higher quality results.
+***Relevance tuning*** is primarily directed at textual content, applying scoring profiles or semantic ranking to enhance the quality of search results. For vector content, there's no explicit relevance tuning capabilities, but you can experiment between Hierarchical Navigable Small World (HNSW) and exhaustive K-nearest neighbors (KNN) to see if one algorithm outperforms the other for your scenario. HNSW graphing with an exhaustive KNN override at query time is the most flexible approach for comparison testing. You can also experiment with various embedding models to see which ones produce higher quality results.
 
 ## Levels of ranking
 

articles/search/search-security-overview.md

@@ -11,7 +11,7 @@ ms.update-cycle: 180-days
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 02/28/2025
+ms.date: 07/26/2025
 ---
 
 # Security in Azure AI Search
@@ -155,9 +155,11 @@ For multitenancy solutions requiring security boundaries at the index level, it'
 
 ### Restricting access to documents
 
-User permissions at the document level, also known as *row-level security*, isn't natively supported in Azure AI Search. If you import data from an external system that provides row-level security, such as Azure Cosmos DB, those permissions won't transfer with the data as its being indexed by Azure AI Search.
+User permissions at the document level, also known as *row-level security*, is available as a preview feature and depends on the data source. If content originates from [Azure Data Lake Storage (ADLS) Gen2](search-indexer-access-control-lists-and-role-based-access.md) or [Azure blobs](search-blob-indexer-role-based-access.md), user permission metadata that originates in Azure Storage is preserved in indexer-generated indexes and enforced at query time so that only authorized content is included in search results.
 
-If you require permissioned access over content in search results, there's a technique for applying filters that include or exclude documents based on user identity. This workaround adds a string field in the data source that represents a group or user identity, which you can make filterable in your index. For more information about this pattern, see [Security trimming based on identity filters](search-security-trimming-for-azure-search.md).
+For other data sources, you can [push a document payload that includes user or group permission metadata](search-index-access-control-lists-and-rbac-push-api.md), and those permissions are retained in indexed content and also enforced at query time. This capability is also in preview.
+
+If you can't use preview features and you require permissioned access over content in search results, there's a technique for applying filters that include or exclude documents based on user identity. This workaround adds a string field in the data source that represents a group or user identity, which you can make filterable in your index. For more information about this pattern, see [Security trimming based on identity filters](search-security-trimming-for-azure-search.md). For more information about document access, see [Document-level access control](search-document-level-access-overview.md).
 
 ## Data residency
 

articles/search/search-what-is-an-index.md

@@ -16,7 +16,7 @@ ms.date: 06/20/2025
 
 # Search indexes in Azure AI Search
 
-In Azure AI Search, a *search index* is your searchable content, available to the search engine for indexing, full-text search, vector search, hybrid search, and filtered queries. An index is defined by a schema and saved to the search service, with data import following as a second step. This content exists within your search service, apart from your primary data stores, which is necessary for the millisecond response times expected in modern search applications. Except for indexer-driven indexing scenarios, the search service never connects to or queries your source data.
+In Azure AI Search, a *search index* is your searchable content, available to the search engine for indexing, agentic search, full-text search, vector search, hybrid search, and filtered queries. An index is defined by a schema and saved to the search service, with data ingestion following as a second step. Indexed content exists within your search service, apart from your primary external data stores, which is necessary for the millisecond response times expected in modern search applications. Except for indexer-driven indexing scenarios, the search service never connects to or queries your external source data.
 
 This article covers the key concepts for creating and managing a search index, including:
 

articles/search/toc.yml

@@ -18,28 +18,22 @@ items:
   items:
   - name: Data
     items:
+    - name: Data import strategies
+      href: search-what-is-data-import.md
     - name: Search index
       href: search-what-is-an-index.md
     - name: Vector index
       href: vector-store.md
     - name: Knowledge store
       href: knowledge-store-concept-intro.md
-    - name: Data import strategies
-      href: search-what-is-data-import.md
     - name: Indexers
       href: search-indexer-overview.md
   - name: Applied AI
     items:
-    - name: Multimodal search
-      href: multimodal-search-overview.md
-    - name: Built-in vectorization
-      href: vector-search-integrated-vectorization.md
     - name: AI enrichment during indexing
       href: cognitive-search-concept-intro.md
-    - name: Enrichment cache
-      href: cognitive-search-incremental-indexing-conceptual.md
-    - name: Skillsets
-      href: cognitive-search-working-with-skillsets.md
+    - name: Built-in vectorization
+      href: vector-search-integrated-vectorization.md
   - name: Retrieval
     items:
     - name: Agentic search
@@ -50,6 +44,8 @@ items:
       href: vector-search-overview.md
     - name: Hybrid search
       href: hybrid-search-overview.md
+    - name: Multimodal search
+      href: multimodal-search-overview.md
     - name: Retrieval Augmented Generation (RAG)
       href: retrieval-augmented-generation-overview.md
     - name: Other query types
@@ -348,6 +344,8 @@ items:
         href: search-howto-index-sharepoint-online.md
     - name: Skillsets (indexers)
       items:
+      - name: Skillsets overview
+        href: cognitive-search-working-with-skillsets.md
       - name: Create a skillset
         href: cognitive-search-defining-skillset.md
       - name: Attach an Azure AI resource
@@ -364,6 +362,8 @@ items:
         href: cognitive-search-output-field-mapping.md
       - name: Process image files
         href: cognitive-search-concept-image-scenarios.md
+      - name: Enrichment cache
+        href: cognitive-search-incremental-indexing-conceptual.md
       - name: Cache (incremental) enrichment
         href: search-howto-incremental-index.md
       - name: Design tips