@@ -1,5 +1,15 @@
 {
     "redirections": [
+        {
+            "source_path_from_root": "/articles/search/cognitive-search-incremental-indexing-conceptual.md",
+            "redirect_url": "/azure/search/enrichment-cache-how-to-manage",
+            "redirect_document_id": true
+        },
+                {
+            "source_path_from_root": "/articles/search/search-howto-incremental-index.md",
+            "redirect_url": "/azure/search/enrichment-cache-how-to-configure",
+            "redirect_document_id": true
+        },
         {
             "source_path_from_root": "/articles/search/search-security-network-security-perimiter.md",
             "redirect_url": "/azure/search/search-security-network-security-perimeter",

@@ -321,7 +321,7 @@ A [query-time vectorizer](vector-search-how-to-configure-vectorizer.md) backed b
 Image extraction is an Azure AI Search operation that occurs when documents are cracked prior to enrichment. Image extraction is billable on all tiers, except for 20 free daily extractions on the free tier. Image extraction costs apply to image files inside blobs, embedded images in other files (PDF and other app files), and for images extracted using [Document Extraction](cognitive-search-skill-document-extraction.md). For image extraction pricing, see the [Azure AI Search pricing page](https://azure.microsoft.com/pricing/details/search/).
 
 > [!TIP]
-> To lower the cost of skillset processing, enable [incremental enrichment](cognitive-search-incremental-indexing-conceptual.md) to cache and reuse any enrichments that are unaffected by changes made to a skillset. Caching requires Azure Storage (see [pricing](https://azure.microsoft.com/pricing/details/storage/blobs/) but the cumulative cost of skillset execution is lower if existing enrichments can be reused, especially for skillsets that use image extraction and analysis.
+> To lower the cost of skillset processing, enable [incremental enrichment](enrichment-cache-how-to-configure.md) to cache and reuse any enrichments that are unaffected by changes made to a skillset. Caching requires Azure Storage (see [pricing](https://azure.microsoft.com/pricing/details/storage/blobs/) but the cumulative cost of skillset execution is lower if existing enrichments can be reused, especially for skillsets that use image extraction and analysis.
 
 ## Example: Estimate costs
 

@@ -43,7 +43,7 @@ The following diagram shows the progression of AI enrichment:
 
 + Enrichment starts when the indexer ["cracks documents"](search-indexer-overview.md#document-cracking) and extracts images and text. The kind of processing that occurs next depends on your data and which skills you've added to a skillset. If you have images, they can be forwarded to skills that perform image processing. Text content is queued for text and natural language processing. Internally, skills create an ["enriched document"](cognitive-search-working-with-skillsets.md#enrichment-tree) that collects the transformations as they occur.
 
-+ Enriched content is generated during skillset execution, and is temporary unless you save it. You can enable an [enrichment cache](cognitive-search-incremental-indexing-conceptual.md) to persist cracked documents and skill outputs for subsequent reuse during future skillset executions.
++ Enriched content is generated during skillset execution, and is temporary unless you save it. You can enable an [enrichment cache](enrichment-cache-how-to-configure.md) to persist cracked documents and skill outputs for subsequent reuse during future skillset executions.
 
 + To get content into a search index, the indexer must have mapping information for sending enriched content to target field. [Field mappings](search-indexer-field-mappings.md) (explicit or implicit) set the data path from source data to a search index. [Output field mappings](cognitive-search-output-field-mapping.md) set the data path from enriched documents to an index.
 
@@ -84,7 +84,7 @@ In Azure AI Search, an indexer saves the output it creates. A single indexer run
 |------------|----------|----------|-------------|
 | [**searchable index**](search-what-is-an-index.md) | Required | Search service | Used for full text search and other query forms. Specifying an index is an indexer requirement. Index content is populated from skill outputs, plus any source fields that are mapped directly to fields in the index. |
 | [**knowledge store**](knowledge-store-concept-intro.md) | Optional | Azure Storage | Used for downstream apps like knowledge mining or data science. A knowledge store is defined within a skillset. Its definition determines whether your enriched documents are projected as tables or objects (files or blobs) in Azure Storage. |
-| [**enrichment cache**](cognitive-search-incremental-indexing-conceptual.md) | Optional | Azure Storage | Used for caching enrichments for reuse in subsequent skillset executions. The cache stores imported, unprocessed content (cracked documents). It also stores the enriched documents created during skillset execution. Caching is helpful if you're using image analysis or OCR, and you want to avoid the time and expense of reprocessing image files. |
+| [**enrichment cache**](enrichment-cache-how-to-configure.md) | Optional | Azure Storage | Used for caching enrichments for reuse in subsequent skillset executions. The cache stores imported, unprocessed content (cracked documents). It also stores the enriched documents created during skillset execution. Caching is helpful if you're using image analysis or OCR, and you want to avoid the time and expense of reprocessing image files. |
 
 Indexes and knowledge stores are fully independent of each other. While you must attach an index to satisfy indexer requirements, if your sole objective is a knowledge store, you can ignore the index after it's populated.
 
@@ -126,7 +126,7 @@ Start with a subset of data in a [supported data source](search-indexer-overview
 
    An indexer is also where you specify field mappings and output field mappings that set up the data path to a search index.
 
-   Optionally, [enable enrichment caching](cognitive-search-incremental-indexing-conceptual.md) in the indexer configuration. This step allows you to reuse existing enrichments later on.
+   Optionally, [enable enrichment caching](enrichment-cache-how-to-configure.md) in the indexer configuration. This step allows you to reuse existing enrichments later on.
 
 1. [Run queries](search-query-create.md) to evaluate results or [start a debug session](cognitive-search-how-to-debug-skillset.md) to work through any skillset issues.
 

@@ -30,7 +30,7 @@ Rules for skillset definition include:
 Indexers drive skillset execution. You need an [indexer](search-howto-create-indexers.md), [data source](search-data-sources-gallery.md), and [index](search-what-is-an-index.md) before you can test your skillset.
 
 > [!TIP]
-> Enable [enrichment caching](cognitive-search-incremental-indexing-conceptual.md) to reuse the content you've already processed and lower the cost of development.
+> Enable [enrichment caching](enrichment-cache-how-to-configure.md) to reuse the content you've already processed and lower the cost of development.
 
 ## Add a skillset definition
 

@@ -270,4 +270,4 @@ This tutorial touched on various aspects of skillset definition and processing.
 
 + [Skillsets in Azure AI Search](cognitive-search-working-with-skillsets.md)
 
-+ [How to configure caching for incremental enrichment](cognitive-search-incremental-indexing-conceptual.md)
++ [How to configure caching for incremental enrichment](enrichment-cache-how-to-configure.md)

@@ -126,7 +126,7 @@ Notice how the output of the first skill ("pages") is used in sentiment analysis
 
 An enriched document is a temporary, tree-like data structure created during skillset execution that collects all of the changes introduced through skills. Collectively, enrichments are represented as a hierarchy of addressable nodes. Nodes also include any unenriched fields that are passed in verbatim from the external data source. The best approach for examining the structure and content of an enrichment tree is through a [debug session](cognitive-search-debug-session.md) in the Azure portal.
 
-An enriched document exists for the duration of skillset execution, but can be [cached](cognitive-search-incremental-indexing-conceptual.md) or sent to a [knowledge store](knowledge-store-concept-intro.md). 
+An enriched document exists for the duration of skillset execution, but can be [cached](enrichment-cache-how-to-configure.md) or sent to a [knowledge store](knowledge-store-concept-intro.md). 
 
 Initially, an enriched document is simply the content extracted from a data source during [*document cracking*](search-indexer-overview.md#document-cracking), where text and images are extracted from the source and made available for language or image analysis. 
 

@@ -1,23 +1,23 @@
 ---
-title: Enable caching for incremental enrichment (preview)
+title: Configure enrichment caching (preview)
 titleSuffix: Azure AI Search
-description: Enable caching of enriched content for potential reuse when modifying downstream skills and projections in an AI enrichment pipeline.
+description: Cache enriched content for potential reuse when modifying downstream skills and projections in an AI enrichment pipeline.
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 02/24/2025
+ms.date: 07/28/2025
 ms.custom:
   - ignite-2023
   - sfi-ropc-nochange
 ---
 
-# Enable caching for incremental enrichment in Azure AI Search
+# Configure an enrichment cache
 
 > [!IMPORTANT] 
 > This feature is in public preview under [supplemental terms of use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). [Preview REST APIs](/rest/api/searchservice/index-preview) support this feature.
 
-This article explains how to add caching to an enrichment pipeline so that you can modify downstream enrichment steps without having to rebuild in full every time. By default, a skillset is stateless, and changing any part of its composition requires a full rerun of the indexer. With an [**enrichment cache**](cognitive-search-incremental-indexing-conceptual.md), the indexer can determine which parts of the document tree must be refreshed based on changes detected in the skillset or indexer definitions. Existing processed output is preserved and reused wherever possible. 
+This article explains how to add caching to an enrichment pipeline so that you can modify downstream enrichment steps without having to rebuild in full every time. By default, a skillset is stateless, and changing any part of its composition requires a full rerun of the indexer. With an *enrichment cache*, the indexer can determine which parts of the document tree must be refreshed based on changes detected in the skillset or indexer definitions. Existing processed output is preserved and reused wherever possible. 
 
 Cached content is placed in Azure Storage using account information that you provide. The container, named `ms-az-search-indexercache-<alpha-numerc-string>`, is created when you run the indexer. It should be considered an internal component managed by your search service and must not be modified.
 
@@ -27,18 +27,20 @@ Cached content is placed in Azure Storage using account information that you pro
 
 + [For blob indexing only](search-howto-indexing-azure-blob-storage.md), if you need synchronized document removal from both the cache and index when blobs are deleted from your data source, enable a [deletion policy](search-howto-index-changed-deleted-blobs.md) in the indexer. Without this policy, document deletion from the cache isn't supported.
 
-You should be familiar with setting up indexers. Start with [indexer overview](search-indexer-overview.md) and then continue on to [skillsets](cognitive-search-working-with-skillsets.md) to learn about enrichment pipelines. For more background on key concepts, see [incremental enrichment](cognitive-search-incremental-indexing-conceptual.md).
+You should be familiar with setting up indexers and skillsets. Start with [indexer overview](search-indexer-overview.md) and then continue on to [skillsets](cognitive-search-working-with-skillsets.md) to learn about enrichment pipelines. 
+
+## Limitations
 
 > [!CAUTION]
-> Avoid enrichment caching for data originating from the [SharePoint Online indexer (Preview)](search-howto-index-sharepoint-online.md). Under certain circumstances, the cache becomes invalid, requiring a [full indexer reset and run](search-howto-run-reset-indexers.md), should you choose to reload it.
+> If you're using the [SharePoint Online indexer (Preview)](search-howto-index-sharepoint-online.md), you should avoid incremental enrichment. Under certain circumstances, the cache becomes invalid, requiring an [indexer reset and full rebuild](search-howto-run-reset-indexers.md), should you choose to reload it.
 
 ## Permissions
 
-Azure AI Search needs write-access to Azure Storage. If you're using a managed identity for your search service, make sure it's assigned to the **Storage Blob Data Contributor** and **Storage Table Data Reader** roles. For more information, see [Connect to Azure Storage using a managed identity (Azure AI Search)](search-howto-managed-identities-storage.md).
+Azure AI Search needs write-access to Azure Storage. If you're using a managed identity for your search service, make sure it's assigned to the **Storage Blob Data Contributor** and **Storage Table Data Contributor** roles. For more information, see [Connect to Azure Storage using a managed identity (Azure AI Search)](search-howto-managed-identities-storage.md).
 
 ## Enable on new indexers
 
-You can use the Azure portal, preview APIs, or beta Azure SDKs are required to enable an enrichment cache on an indexer.
+You can use the Azure portal, preview APIs, or preview Azure SDK packages to enable an enrichment cache on an indexer.
 
 ### [**Azure portal**](#tab/portal)
 
@@ -50,18 +52,18 @@ You can use the Azure portal, preview APIs, or beta Azure SDKs are required to e
 
 ### [**REST**](#tab/rest)
 
-On new indexers, add the "cache" property in the indexer definition payload when calling Create or Update Indexer. 
+On new indexers, add the `cache` property in the indexer definition payload when calling Create or Update Indexer. 
 
-You can use preview API versions 2021-04-30-preview and later. We recommend the latest preview for [Create or Update Indexer (2024-05-01-preview)](/rest/api/searchservice/indexers/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true).
+We recommend the latest preview for [Create or Update Indexer (2025-05-01-preview)](/rest/api/searchservice/indexers/create-or-update?view=rest-searchservice-2025-05-01-preview&preserve-view=true).
 
 ```https
-POST https://[service name].search.windows.net/indexers?api-version=2024-05-01-preview
+POST https://[service name].search.windows.net/indexers?api-version=2025-05-01-preview
     {
         "name": "<YOUR-INDEXER-NAME>",
         "targetIndexName": "<YOUR-INDEX-NAME>",
         "dataSourceName": "<YOUR-DATASOURCE-NAME>",
         "skillsetName": "<YOUR-SKILLSET-NAME>",
-        "cache" : {
+        `cache` : {
             "storageConnectionString" : "<YOUR-STORAGE-ACCOUNT-CONNECTION-STRING>",
             "enableReprocessing": true
         },
@@ -79,29 +81,29 @@ For existing indexers that already have a skillset, use the following steps to a
 
 ### Step 1: Get the indexer definition
 
-Start with a valid, work indexer that has these components: data source, skillset, index. Using an API client, send a [GET Indexer](/rest/api/searchservice/indexers/get?view=rest-searchservice-2024-05-01-preview&preserve-view=true) request to retrieve the indexer. When you use the preview API version to the GET the indexer, a "cache" property set to null is added to the definition automatically.
+Start with a valid, work indexer that has these components: data source, skillset, index. Using an API client, send a [GET Indexer](/rest/api/searchservice/indexers/get?view=rest-searchservice-2025-05-01-preview&preserve-view=true) request to retrieve the indexer. When you use the preview API version to the GET the indexer, a `cache` property set to null is added to the definition automatically.
 
 ```http
-GET https://[YOUR-SEARCH-SERVICE].search.windows.net/indexers/[YOUR-INDEXER-NAME]?api-version=2024-05-01-preview
+GET https://[YOUR-SEARCH-SERVICE].search.windows.net/indexers/[YOUR-INDEXER-NAME]?api-version=2025-05-01-preview
     Content-Type: application/json
     api-key: [YOUR-ADMIN-KEY]
 ```
 
 ### Step 2: Set the cache property
 
-In the index definition, modify "cache" to include the following required and optional properties:
+In the index definition, modify `cache` to include the following required and optional properties:
 
 + (Required) `storageConnectionString` must be set to an Azure Storage connection string.
 + (Optional) `enableReprocessing` boolean property (`true` by default), indicates that incremental enrichment is enabled. Set to `false` if you want to suspend incremental processing while other resource-intensive operations, such as indexing new documents, are underway and then switch back to `true` later.
 
 ```http
-POST https://[service name].search.windows.net/indexers?api-version=2024-05-01-preview
+POST https://[service name].search.windows.net/indexers?api-version=2025-05-01-preview
     {
         "name": "<YOUR-INDEXER-NAME>",
         "targetIndexName": "<YOUR-INDEX-NAME>",
         "dataSourceName": "<YOUR-DATASOURCE-NAME>",
         "skillsetName": "<YOUR-SKILLSET-NAME>",
-        "cache" : {
+        `cache` : {
             "storageConnectionString" : "<YOUR-STORAGE-ACCOUNT-CONNECTION-STRING>",
             "enableReprocessing": true
         },
@@ -116,23 +118,23 @@ POST https://[service name].search.windows.net/indexers?api-version=2024-05-01-p
 [Reset Indexer](/rest/api/searchservice/indexers/reset) is required when setting up incremental enrichment for existing indexers to ensure all documents are in a consistent state. You can use the Azure portal or an API client for this task.
 
 ```https
-POST https://[YOUR-SEARCH-SERVICE].search.windows.net/indexers/[YOUR-INDEXER-NAME]/reset?api-version=2024-05-01-preview
+POST https://[YOUR-SEARCH-SERVICE].search.windows.net/indexers/[YOUR-INDEXER-NAME]/reset?api-version=2025-05-01-preview
     Content-Type: application/json
     api-key: [YOUR-ADMIN-KEY]
 ```
 
 ### Step 4: Save the indexer
 
-[Update Indexer](/rest/api/searchservice/indexers/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true) with a PUT request, where the body of the request includes "cache".
+[Update Indexer](/rest/api/searchservice/indexers/create-or-update?view=rest-searchservice-2025-05-01-preview&preserve-view=true) with a PUT request, where the body of the request includes `cache`.
 
 ```http
-PUT https://[YOUR-SEARCH-SERVICE].search.windows.net/indexers/[YOUR-INDEXER-NAME]?api-version=2024-05-01-preview
+PUT https://[YOUR-SEARCH-SERVICE].search.windows.net/indexers/[YOUR-INDEXER-NAME]?api-version=2025-05-01-preview
     Content-Type: application/json
     api-key: [YOUR-ADMIN-KEY]
     {
         "name" : "<YOUR-INDEXER-NAME>",
         ...
-        "cache": {
+        `cache`: {
             "storageConnectionString": "<YOUR-STORAGE-ACCOUNT-CONNECTION-STRING>",
             "enableReprocessing": true
         }
@@ -142,7 +144,7 @@ PUT https://[YOUR-SEARCH-SERVICE].search.windows.net/indexers/[YOUR-INDEXER-NAME
 If you now issue another GET request on the indexer, the response from the service includes an `ID` property in the cache object. The string is appended to the name of the container containing all the cached results and intermediate state of each document processed by this indexer. The ID is used to uniquely name the cache in Blob storage.
 
 ```http
-    "cache": {
+    `cache`: {
         "ID": "<ALPHA-NUMERIC STRING>",
         "enableReprocessing": true,
         "storageConnectionString": "DefaultEndpointsProtocol=https;AccountName=<YOUR-STORAGE-ACCOUNT>;AccountKey=<YOUR-STORAGE-KEY>;EndpointSuffix=core.windows.net"
@@ -156,7 +158,7 @@ To run indexer, you can use the Azure portal or the API. In the Azure portal, fr
 Alternatively, you can use REST to [run the indexer](/rest/api/searchservice/indexers/run):
 
 ```http
-POST https://[YOUR-SEARCH-SERVICE].search.windows.net/indexers/[YOUR-INDEXER-NAME]/run?api-version=2024-05-01-preview
+POST https://[YOUR-SEARCH-SERVICE].search.windows.net/indexers/[YOUR-INDEXER-NAME]/run?api-version=2025-05-01-preview
 Content-Type: application/json
 api-key: [YOUR-ADMIN-KEY]
 ```
@@ -185,11 +187,9 @@ The following error occurs if you forget to specify a preview API version on the
 
 A 400 Bad Request error will also occur if you're missing an indexer requirement. The error message specifies any missing dependencies.
 
-## Next steps
+## Next step
 
-Incremental enrichment is applicable on indexers that contain skillsets, providing reusable content for both indexes and knowledge stores. The following links provide more information about caching and skillsets.
+Incremental enrichment is applicable on indexers that contain skillsets, providing reusable content for both indexes and knowledge stores. The following link provides more information about cache management.
 
-+ [Incremental enrichment (lifecycle and management)](cognitive-search-incremental-indexing-conceptual.md)
-+ [Skillset concepts and composition](cognitive-search-working-with-skillsets.md)
-+ [Create a skillset](cognitive-search-defining-skillset.md)
-+ [Tutorial: Use REST and AI to generate searchable content from Azure blobs](tutorial-skillset.md)
+> [!div class="checklist"]
+> + [Manage an enrichment cache](enrichment-cache-how-to-manage.md)

@@ -1,44 +1,48 @@
 ---
-title: Incremental enrichment concepts (preview)
+title: Manage enrichment caching
 titleSuffix: Azure AI Search
 description: Cache intermediate content and incremental changes from AI enrichment pipeline in Azure Storage to preserve investments in existing processed documents. This feature is currently in public preview.
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
-ms.topic: conceptual
-ms.date: 07/11/2025
+ms.topic: how-to
+ms.date: 07/28/2025
 ms.custom:
   - ignite-2023
   - sfi-ropc-nochange
 ---
 
-# Incremental enrichment and caching in Azure AI Search
+# Manage an enrichment cache
 
 > [!IMPORTANT] 
 > This feature is in public preview under [supplemental terms of use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). The [preview REST API](/rest/api/searchservice/search-service-api-versions#preview-versions) supports this feature.
 
-*Incremental enrichment* refers to the use of cached enrichments during [skillset execution](cognitive-search-working-with-skillsets.md) so that only new and changed skills and documents incur standard processing charges for API calls to Azure AI services. The cache contains the output from [document cracking](search-indexer-overview.md#document-cracking), plus the outputs of each skill for every document. Although caching is billable (it uses Azure Storage), the overall cost of enrichment is reduced because the costs of storage are less than image extraction and AI processing.
+An *enrichment cache* is an optional feature that stores reusable enriched content created during [skillset execution](cognitive-search-working-with-skillsets.md) so that only new and changed skills and documents incur standard processing charges during future indexer and skillset processing. 
 
-To ensure synchronization between your data source data and your index, it's important to understand your unique [data source](search-data-sources-gallery.md) change and deletion tracking prerequisites. This guide specifically addresses how to manage incremental modifications in terms of your skills processing and how to utilize cache for this purpose.
+The cache contains the output from [document cracking](search-indexer-overview.md#document-cracking), plus the outputs of each skill for every document. Although caching is billable (it uses Azure Storage), the overall cost of enrichment is reduced because the costs of storage are less than image extraction and AI processing.
 
-When you enable caching, the indexer evaluates your updates to determine whether existing enrichments can be pulled from the cache. Image and text content from the document cracking phase, plus skill outputs that are upstream or orthogonal to your edits, are likely to be reusable.
+If you have configured an enrichment cache, this article explains how to manage skill and data source updates so that you get maximum utility from cached enrichments.
 
-After skillset processing is finished, the refreshed results are written back to the cache, and also to the search index or knowledge store.
+## Prerequisites
+
++ An [indexer](search-indexer-overview.md) and [skillset](cognitive-search-working-with-skillsets.md)
+
++ An [enrichment cache](enrichment-cache-how-to-configure.md)
 
 ## Limitations
 
 > [!CAUTION]
-> If you're using the [SharePoint Online indexer (Preview)](search-howto-index-sharepoint-online.md), you should avoid incremental enrichment. Under certain circumstances, the cache becomes invalid, requiring an [indexer reset and run](search-howto-run-reset-indexers.md), should you choose to reload it.
+> If you're using the [SharePoint Online indexer (Preview)](search-howto-index-sharepoint-online.md), you should avoid incremental enrichment. Under certain circumstances, the cache becomes invalid, requiring an [indexer reset and full rebuild](search-howto-run-reset-indexers.md), should you choose to reload it.
 
 ## Cache configuration
 
-Physically, the cache is stored in a blob container in your Azure Storage account, one per indexer. Each indexer is assigned a unique and immutable cache identifier that corresponds to the container it's using.
+Physically, the cache is stored in a blob container or table in your Azure Storage account, one per indexer. Each indexer is assigned a unique and immutable cache identifier that corresponds to the container it's using.
 
 The cache is created when you specify the "cache" property and run the indexer. Only enriched content can be cached. If your indexer doesn't have an attached skillset, then caching doesn't apply. 
 
-The following example illustrates an indexer with caching enabled. See [Enable enrichment caching](search-howto-incremental-index.md) for full instructions. 
+The following example illustrates an indexer with caching enabled. See [Configure enrichment caching](enrichment-cache-how-to-configure.md) for full instructions. 
 
-To use the cache property, you can use 2020-06-30-preview or later when you [create or update an indexer](/rest/api/searchservice/indexers/create-or-update?view=rest-searchservice-2025-05-01-preview&preserve-view=true). We recommend the latest preview API.
+To set the cache property, use a preview REST API [Create or Update Indexer](/rest/api/searchservice/indexers/create-or-update?view=rest-searchservice-2025-05-01-preview&preserve-view=true) or a preview Azure SDK package that provides the feature. You can also enable enrichment caching in the Import data wizard in the Azure portal.
 
 ```json
 POST https://[YOUR-SEARCH-SERVICE-NAME].search.windows.net/indexers?api-version=2025-05-01-preview
@@ -180,9 +184,9 @@ Incremental processing evaluates your skillset definition and determines which s
 
 ## APIs used for caching
 
-REST API version `2020-06-30-Preview` or later provides incremental enrichment through extra properties on indexers. We recommend the latest preview API.
+Preview APIs provide extra properties on indexers. We recommend the latest preview API.
 
-Skillsets and data sources can use the generally available version. In addition to the reference documentation, see  [Configure caching for incremental enrichment](search-howto-incremental-index.md) for details about order of operations.
+Skillsets and data sources can use the generally available version. In addition to the reference documentation, see  [Configure caching for incremental enrichment](enrichment-cache-how-to-configure.md) for details about order of operations.
 
 + [Create or Update Indexer (api-version=2025-05-01-preview)](/rest/api/searchservice/indexers/create-or-update?view=rest-searchservice-2025-05-01-preview&preserve-view=true) 
 
@@ -191,10 +195,3 @@ Skillsets and data sources can use the generally available version. In addition
 + [Create or Update Skillset (api-version=2025-05-01-preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2025-05-01-preview&preserve-view=true) (New URI parameter on the request)
 
 + [Create or Update Data Source (api-version=2025-05-01-preview)](/rest/api/searchservice/data-sources/create-or-update?view=rest-searchservice-2025-05-01-preview&preserve-view=true), when called with a preview API version, provides a new parameter named "ignoreResetRequirement", which should be set to true when your update action shouldn't invalidate the cache. Use "ignoreResetRequirement" sparingly as it could lead to unintended inconsistency in your data that won't be detected easily.
-
-## Next steps
-
-Incremental enrichment is a powerful feature that extends change tracking to skillsets and AI enrichment. Incremental enrichment enables reuse of existing processed content as you iterate over skillset design. As a next step, enable caching on your indexers.
-
-> [!div class="nextstepaction"]
-> [Enable caching for incremental enrichment](search-howto-incremental-index.md)

@@ -138,7 +138,7 @@ For data sources that support change tracking, an indexer will process new and c
 
 ### Changes to a skillset
 
-If you're making changes to a skillset, you should [enable caching of enriched documents](cognitive-search-incremental-indexing-conceptual.md) to reuse existing enrichments where possible.
+If you're making changes to a skillset, you should [enable caching of enriched documents](enrichment-cache-how-to-configure.md) to reuse existing enrichments where possible.
 
 Without incremental caching, the indexer will always process documents in order of the high water mark, without going backwards. For blobs, the indexer would process blobs sorted by `lastModified`, regardless of any changes to indexer settings or the skillset. If you change a skillset, previously processed documents aren't updated to reflect the new skillset. Documents processed after the skillset change will use the new skillset, resulting in index documents being a mix of old and new skillsets.
 

@@ -657,4 +657,4 @@ When building projections of different types, file and object projections are ge
 The example in this article demonstrates common patterns on how to create projections. Now that you have a good understanding of the concepts, you're better equipped to build projections for your specific scenario.
 
 > [!div class="nextstepaction"]
-> [Configure caching for incremental enrichment](search-howto-incremental-index.md)
+> [Configure caching for incremental enrichment](enrichment-cache-how-to-configure.md)

@@ -157,7 +157,7 @@ Recall that projections are exclusive to knowledge stores, and aren't used to st
 
 1. While in Azure Storage, familiarize yourself with existing content in containers and tables so that you choose nonconflicting names for the projections. A knowledge store is a loose collection of tables and containers. Consider adopting a naming convention to keep track of related objects.
 
-1. In Azure AI Search, [enable enrichment caching (preview)](search-howto-incremental-index.md) in the indexer and then [run the indexer](search-howto-run-reset-indexers.md) to execute the skillset and populate the cache. This is a preview feature, so be sure to use the preview REST API  on the indexer request. Once the cache is populated, you can modify projection definitions in a knowledge store free of charge (as long as the skills themselves aren't modified).
+1. In Azure AI Search, [enable enrichment caching (preview)](enrichment-cache-how-to-configure.md) in the indexer and then [run the indexer](search-howto-run-reset-indexers.md) to execute the skillset and populate the cache. This is a preview feature, so be sure to use the preview REST API  on the indexer request. Once the cache is populated, you can modify projection definitions in a knowledge store free of charge (as long as the skills themselves aren't modified).
 
 1. In your code, all projections are defined solely in a skillset. There are no indexer properties (such as field mappings or output field mappings) that apply to projections. Within a skillset definition, you'll focus on two areas: knowledgeStore property and skills array.
 

@@ -42,7 +42,7 @@ Recall that projections are defined under the `knowledgeStore` property of a ski
 If you need more background before getting started, review [this check list](knowledge-store-projection-overview.md#checklist-for-getting-started) for tips and workflow.
 
 > [!TIP]
-> When developing projections, [enable enrichment caching (preview)](search-howto-incremental-index.md) so that you can reuse existing enrichments while editing projection definitions. Enrichment caching is a preview feature, so be sure to use the preview REST API on the indexer request. Without caching, simple edits to a projection will result in a full reprocess of enriched content. By caching the enrichments, you can iterate over projections without incurring any skillset processing charges.
+> When developing projections, [enable enrichment caching (preview)](enrichment-cache-how-to-configure.md) so that you can reuse existing enrichments while editing projection definitions. Enrichment caching is a preview feature, so be sure to use the preview REST API on the indexer request. Without caching, simple edits to a projection will result in a full reprocess of enriched content. By caching the enrichments, you can iterate over projections without incurring any skillset processing charges.
 
 ## Requirements
 

@@ -7,13 +7,22 @@ author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: conceptual
-ms.date: 06/02/2025
+ms.date: 07/28/2025
 ---
 
 # Productivity tools and accelerators for Azure AI Search
 
 Microsoft engineers build productivity tools that aren't part of the Azure AI Search service and aren't covered by service-level agreements (SLAs). You can download, modify, and build these tools to create an app that helps you develop or maintain a search solution.
 
+## Accelerators
+
+| Accelerator | Description |
+|--|--|
+| [RAG Experiment Accelerator](https://github.com/microsoft/rag-experiment-accelerator) | Conduct experiments and evaluations using Azure AI Search and the RAG pattern. This accelerator has code for loading multiple data sources, using a variety of models, and creating a variety of search indexes and queries. |
+| [Build your own copilot solution accelerator](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator) | Code and docs to build a copilot for specific use cases. |
+| [Chat with your data solution accelerator](https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator/blob/main/README.md) | Code and docs to create interactive search solution in production environments. |
+| [Document knowledge mining solution accelerator](https://github.com/microsoft/Document-Knowledge-Mining-Solution-Accelerator/blob/main/README.md) | Code and docs built on Azure OpenAI in Azure AI Foundry Models and Azure AI Document Intelligence. It processes and extracts summaries, entities, and metadata from unstructured, multimodal documents to enable searching and chatting over this data. |
+
 ## Tools
 
 | Tool name | Description |
@@ -24,11 +33,3 @@ Microsoft engineers build productivity tools that aren't part of the Azure AI Se
 | [Performance testing solution](https://github.com/Azure-Samples/azure-search-performance-testing/blob/main/README.md) | This solution helps you load test Azure AI Search. It uses Apache JMeter as an open source load and performance testing tool and Terraform to dynamically provision and destroy the required infrastructure on Azure. |
 | [Visual Studio Code extension](https://github.com/microsoft/vscode-azurecognitivesearch) | Although the extension is no longer available on the Visual Studio Code Marketplace, the code is open source. You can clone and modify the tool for your own use. |
 
-## Accelerators
-
-| Accelerator | Description |
-|--|--|
-| [Build your own copilot solution accelerator](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator) | Code and docs to build a copilot for specific use cases. |
-| [Chat with your data solution accelerator](https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator/blob/main/README.md) | Code and docs to create interactive search solution in production environments. |
-| [Document knowledge mining solution accelerator](https://github.com/microsoft/Document-Knowledge-Mining-Solution-Accelerator/blob/main/README.md) | Code and docs built on Azure OpenAI in Azure AI Foundry Models and Azure AI Document Intelligence. It processes and extracts summaries, entities, and metadata from unstructured, multimodal documents to enable searching and chatting over this data. |
-| [Knowledge mining accelerator](https://github.com/Azure-Samples/azure-search-knowledge-mining/blob/main/README.md) | Code and docs to jump start a knowledge store using your data. |

@@ -276,6 +276,8 @@ Check out the following GitHub repositories for code, documentation, and video d
 
 ## See also
 
++ [RAG Experiment Accelerator](https://github.com/microsoft/rag-experiment-accelerator)
+
 + [Retrieval Augmented Generation: Streamlining the creation of intelligent natural language processing models](https://ai.meta.com/blog/retrieval-augmented-generation-streamlining-the-creation-of-intelligent-natural-language-processing-models/)
-+ [Retrieval Augmented Generation using Azure Machine Learning prompt flow](/azure/machine-learning/concept-retrieval-augmented-generation)
+
 + [Azure Cognitive Search and LangChain: A Seamless Integration for Enhanced Vector Search Capabilities](https://techcommunity.microsoft.com/t5/azure-ai-services-blog/azure-cognitive-search-and-langchain-a-seamless-integration-for/ba-p/3901448)

@@ -72,7 +72,6 @@ An accelerator is an end-to-end solution that includes code and documentation th
 | Samples | Repository | Description |
 |---------|------------|-------------|
 | Search + QnA Maker Accelerator | [search-qna-maker-accelerator](https://github.com/Azure-Samples/search-qna-maker-accelerator)| A [solution](https://techcommunity.microsoft.com/t5/azure-ai/qna-with-azure-cognitive-search/ba-p/2081381) combining the power of Search and QnA Maker. See the live [demo site](https://aka.ms/qnaWithAzureSearchDemo). |
-| [Knowledge Mining Solution Accelerator](/shows/ai-show/knowledge-mining-with-azure-search) | [azure-search-knowledge-mining](https://github.com/azure-samples/azure-search-knowledge-mining/tree/main/) | Includes templates, support files, and analytical reports to help you prototype an end-to-end knowledge mining solution.  |
 
 ## Demos
 

@@ -54,6 +54,14 @@ Code samples from the Azure AI Search team demonstrate features and workflows. M
 
 [**aisearch-openai-rag-audio**](https://github.com/Azure-Samples/aisearch-openai-rag-audio) is "voice to RAG". This sample demonstrates a simple architecture for voice-based generative AI applications that enables Azure AI Search RAG on top of the real-time audio API with full-duplex audio streaming from client devices, while securely handling access to both the model and retrieval system. Backend code is written in Python, while frontend code is written in JavaScript. For an introduction, watch this [video](https://www.youtube.com/watch?v=vXJka8xZ9Ko).
 
+## Accelerators
+
+An accelerator is an end-to-end solution that includes code and documentation that you can adapt for your own implementation of a specific scenario.
+
+| Repository | Description |
+|------------|-------------|
+| [RAG Experiment Accelerator](https://github.com/microsoft/rag-experiment-accelerator) | Conduct experiments and evaluations using Azure AI Search and the RAG pattern. This accelerator has code for loading multiple data sources, using a variety of models, and creating a variety of search indexes and queries. |
+
 ## Other samples
 
 The following samples are also published by the Azure AI Search team but aren't referenced in documentation. Associated readme files provide usage instructions.

@@ -48,7 +48,7 @@ Preview features are removed from this list if they're retired or transition to
 | [**Text Split skill (token chunking)**](cognitive-search-skill-textsplit.md) | Applied AI (skills) | This skill has new parameters that improve data chunking for embedding models. A new `unit` parameter lets you specify token chunking. You can now chunk by token length, setting the length to a value that makes sense for your embedding model. You can also specify the tokenizer and any tokens that shouldn't be split during data chunking. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-09-01-preview&preserve-view=true). |
 | [**Azure AI Vision multimodal embedding skill**](cognitive-search-skill-vision-vectorize.md) | Applied AI (skills) | A new skill type that calls Azure AI Vision multimodal API to generate embeddings for text or images during indexing. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |
 | [**Azure Machine Learning (AML) skill**](cognitive-search-aml-skill.md) | Applied AI (skills) | AML skill integrates an inferencing endpoint from Azure Machine Learning. In previous preview APIs, it supports connections to deployed custom models in an AML workspace. Starting in the 2024-05-01-preview, you can use this skill in workflows that connect to embedding models in the Azure AI Foundry model catalog. It's also available in the Azure portal, in skillset design, assuming Azure AI Search and Azure Machine Learning services are deployed in the same subscription. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |
-| [**Incremental enrichment cache**](cognitive-search-incremental-indexing-conceptual.md) | Applied AI (skills) | Adds caching to an enrichment pipeline, allowing you to reuse existing output if a targeted modification, such as an update to a skillset or another object, doesn't change the content. Caching applies only to enriched documents produced by a skillset.| [Create or Update Indexer (preview)](/rest/api/searchservice/indexers/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |
+| [**Incremental enrichment cache**](enrichment-cache-how-to-configure.md) | Applied AI (skills) | Adds caching to an enrichment pipeline, allowing you to reuse existing output if a targeted modification, such as an update to a skillset or another object, doesn't change the content. Caching applies only to enriched documents produced by a skillset.| [Create or Update Indexer (preview)](/rest/api/searchservice/indexers/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |
 |  [**OneLake files indexer**](search-how-to-index-onelake-files.md) | Indexer data source | New data source for extracting searchable data and metadata data from a [lakehouse](/fabric/onelake/create-lakehouse-onelake) on top of [OneLake](/fabric/onelake/onelake-overview) | [Create or Update Data Source (preview)](/rest/api/searchservice/data-sources/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |
 |  [**Azure Files indexer**](search-file-storage-integration.md) | Indexer data source | New data source for indexer-based indexing from [Azure Files](https://azure.microsoft.com/services/storage/files/) | [Create or Update Data Source (preview)](/rest/api/searchservice/data-sources/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |
 | [**SharePoint Online indexer**](search-howto-index-sharepoint-online.md) | Indexer data source | New data source for indexer-based indexing of SharePoint content. | [Sign up](https://aka.ms/azure-cognitive-search/indexer-preview) to enable the feature. [Create or Update Data Source (preview)](/rest/api/searchservice/data-sources/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true) or the Azure portal. |

@@ -49,7 +49,7 @@ The following table summarizes features by category. There's feature parity in a
 |-------------------|----------|
 |AI processing during indexing | [**AI enrichment**](cognitive-search-concept-intro.md) refers to embedded image and natural language processing in an indexer pipeline that extracts text and information from content that can't otherwise be indexed for full text search. AI processing is achieved by adding and combining skills in a skillset, which is then attached to an indexer. AI can be either [built-in skills](cognitive-search-predefined-skills.md) from Microsoft, such as text translation or Optical Character Recognition (OCR), or [custom skills](cognitive-search-create-custom-skill-example.md) that you provide. |
 | Storing enriched content for analysis and consumption in non-search scenarios | [**Knowledge store**](knowledge-store-concept-intro.md) is persistent storage of enriched content, intended for non-search scenarios like knowledge mining and data science processing. A knowledge store is defined in a skillset, but created in Azure Storage as objects or tabular rowsets.|
-| Cached enrichments | [**Enrichment caching (preview)**](cognitive-search-incremental-indexing-conceptual.md) refers to cached enrichments that can be reused during skillset execution. Caching is particularly valuable in skillsets that include OCR and image analysis, which are expensive to process. |
+| Cached enrichments | [**Enrichment caching (preview)**](enrichment-cache-how-to-configure.md) refers to cached enrichments that can be reused during skillset execution. Caching is particularly valuable in skillsets that include OCR and image analysis, which are expensive to process. |
 
 ## Full text and other query forms
 

@@ -48,7 +48,7 @@ You can use this indexer for the following tasks:
 + **Deletion detection:** The indexer can [detect deletions through custom metadata](search-howto-index-changed-deleted-blobs.md).
 + **Applied AI through skillsets:** [Skillsets](cognitive-search-concept-intro.md) are fully supported by the indexer. This includes key features like [integrated vectorization](vector-search-integrated-vectorization.md) that adds data chunking and embedding steps.
 + **Parsing modes:** The indexer supports [JSON parsing modes](search-howto-index-json-blobs.md) if you want to parse JSON arrays or lines into individual search documents. It also supports [Markdown parsing mode](search-how-to-index-markdown-blobs.md).
-+ **Compatibility with other features:** The indexer is designed to work seamlessly with other indexer features, such as [debug sessions](cognitive-search-debug-session.md), [indexer cache for incremental enrichments](search-howto-incremental-index.md), and [knowledge store](knowledge-store-concept-intro.md).
++ **Compatibility with other features:** The indexer is designed to work seamlessly with other indexer features, such as [debug sessions](cognitive-search-debug-session.md), [indexer cache for incremental enrichments](enrichment-cache-how-to-configure.md), and [knowledge store](knowledge-store-concept-intro.md).
 
 ## Supported document formats
 

@@ -107,7 +107,7 @@ Skills-based indexing uses [AI enrichment](cognitive-search-concept-intro.md) to
 }
 ```
 
-AI enrichment is its own subject area and is out of scope for this article. For more information, start with [AI enrichment](cognitive-search-concept-intro.md), [Skillsets in Azure AI Search](cognitive-search-working-with-skillsets.md), [Create a skillset](cognitive-search-defining-skillset.md), [Map enriched output fields](cognitive-search-output-field-mapping.md), and [Enable caching for AI enrichment](search-howto-incremental-index.md).
+AI enrichment is its own subject area and is out of scope for this article. For more information, start with [AI enrichment](cognitive-search-concept-intro.md), [Skillsets in Azure AI Search](cognitive-search-working-with-skillsets.md), [Create a skillset](cognitive-search-defining-skillset.md), [Map enriched output fields](cognitive-search-output-field-mapping.md), and [Enable caching for AI enrichment](enrichment-cache-how-to-configure.md).
 
 ## Prepare external data
 
@@ -255,7 +255,7 @@ Change detection logic is built into the data platforms. How an indexer supports
 
 Indexers keep track of the last document it processed from the data source through an internal *high water mark*. The marker is never exposed in the API, but internally the indexer keeps track of where it stopped. When indexing resumes, either through a scheduled run or an on-demand invocation, the indexer references the high water mark so that it can pick up where it left off.
 
-If you need to clear the high water mark to reindex in full, you can use [Reset Indexer](/rest/api/searchservice/indexers/reset). For more selective reindexing, use [Reset Skills](/rest/api/searchservice/skillsets/reset-skills?view=rest-searchservice-2024-05-01-preview&preserve-view=true) or [Reset Documents](/rest/api/searchservice/indexers/reset-docs?view=rest-searchservice-2024-05-01-preview&preserve-view=true). Through the reset APIs, you can clear internal state, and also flush the cache if you enabled [incremental enrichment](search-howto-incremental-index.md). For more background and comparison of each reset option, see [Run or reset indexers, skills, and documents](search-howto-run-reset-indexers.md).
+If you need to clear the high water mark to reindex in full, you can use [Reset Indexer](/rest/api/searchservice/indexers/reset). For more selective reindexing, use [Reset Skills](/rest/api/searchservice/skillsets/reset-skills?view=rest-searchservice-2024-05-01-preview&preserve-view=true) or [Reset Documents](/rest/api/searchservice/indexers/reset-docs?view=rest-searchservice-2024-05-01-preview&preserve-view=true). Through the reset APIs, you can clear internal state, and also flush the cache if you enabled [incremental enrichment](enrichment-cache-how-to-configure.md). For more background and comparison of each reset option, see [Run or reset indexers, skills, and documents](search-howto-run-reset-indexers.md).
 
 ## Related content
 

@@ -56,7 +56,7 @@ You can use this indexer for the following tasks:
 + **Deletion detection:** The indexer can [detect deletions via custom metadata](#detect-deletions-via-custom-metadata) for most files and shortcuts. This requires adding metadata to files to signify that they have been "soft deleted", enabling their removal from the search index. Currently, it's not possible to detect deletions in Google Cloud Storage or Amazon S3 shortcut files because custom metadata isn't supported for those data sources.
 + **Applied AI through skillsets:** [Skillsets](cognitive-search-concept-intro.md) are fully supported by the OneLake files indexer. This includes key features like [integrated vectorization](vector-search-integrated-vectorization.md) that adds data chunking and embedding steps.
 + **Parsing modes:** The indexer supports [JSON parsing modes](search-howto-index-json-blobs.md) if you want to parse JSON arrays or lines into individual search documents. It also supports [Markdown parsing mode](search-how-to-index-markdown-blobs.md).
-+ **Compatibility with other features:** The OneLake indexer is designed to work seamlessly with other indexer features, such as [debug sessions](cognitive-search-debug-session.md), [indexer cache for incremental enrichments](search-howto-incremental-index.md), and [knowledge store](knowledge-store-concept-intro.md).
++ **Compatibility with other features:** The OneLake indexer is designed to work seamlessly with other indexer features, such as [debug sessions](cognitive-search-debug-session.md), [indexer cache for incremental enrichments](enrichment-cache-how-to-configure.md), and [knowledge store](knowledge-store-concept-intro.md).
 
 <a name="SupportedFormats"></a>
 

@@ -43,7 +43,7 @@ For this deletion detection approach, Azure AI Search depends on the [native blo
 
 ### Requirements for native soft delete
 
-+ Blobs must be in an Azure Blob Storage container. The Azure AI Search native blob soft delete policy isn't supported for blobs in ADLS Gen2 or Azure Files.
++ Blobs must be in an Azure Blob Storage container, including ADLS Gen2 Blob container. The Azure AI Search native blob soft delete policy isn't supported for Azure Files.
 
 + [Enable soft delete for blobs](/azure/storage/blobs/soft-delete-blob-enable).
 

@@ -53,7 +53,7 @@ You can use this indexer for the following tasks:
 + **Deletion detection:** The indexer can [detect deletions through native soft delete or through custom metadata](search-howto-index-changed-deleted-blobs.md).
 + **Applied AI through skillsets:** [Skillsets](cognitive-search-concept-intro.md) are fully supported by the indexer. This includes key features like [integrated vectorization](vector-search-integrated-vectorization.md) that adds data chunking and embedding steps.
 + **Parsing modes:** The indexer supports [JSON parsing modes](search-howto-index-json-blobs.md) if you want to parse JSON arrays or lines into individual search documents. It also supports [Markdown parsing mode](search-how-to-index-markdown-blobs.md).
-+ **Compatibility with other features:** The indexer is designed to work seamlessly with other indexer features, such as [debug sessions](cognitive-search-debug-session.md), [indexer cache for incremental enrichments](search-howto-incremental-index.md), and [knowledge store](knowledge-store-concept-intro.md).
++ **Compatibility with other features:** The indexer is designed to work seamlessly with other indexer features, such as [debug sessions](cognitive-search-debug-session.md), [indexer cache for incremental enrichments](enrichment-cache-how-to-configure.md), and [knowledge store](knowledge-store-concept-intro.md).
 
 <a name="SupportedFormats"></a>
 

@@ -47,7 +47,7 @@ A search service uses Azure Storage as an indexer data source and as a data sink
 | [Indexer connections to supported Azure data sources](search-indexer-overview.md) <sup>1</sup>| Yes | Yes |
 | [Azure Key Vault for customer-managed keys](search-security-manage-encryption-keys.md) | Yes | Yes |
 | [Debug sessions (hosted in Azure Storage)](cognitive-search-debug-session.md)	<sup>1</sup> | Yes | No |
-| [Enrichment cache (hosted in Azure Storage)](search-howto-incremental-index.md) <sup>1,</sup> <sup>2</sup> | Yes | Yes |
+| [Enrichment cache (hosted in Azure Storage)](enrichment-cache-how-to-configure.md) <sup>1,</sup> <sup>2</sup> | Yes | Yes |
 | [Knowledge Store (hosted in Azure Storage)](knowledge-store-create-rest.md) <sup>1</sup>| Yes | Yes |
 | Connections to Azure OpenAI, Azure AI Foundry and Azure Functions via skills/vectorizers <sup>3</sup> | Yes | Yes |
 
@@ -274,7 +274,7 @@ A knowledge store definition includes a connection string to Azure Storage. The
 }
 ```
 
-[**Enrichment cache:**](search-howto-incremental-index.md)
+[**Enrichment cache:**](enrichment-cache-how-to-configure.md)
 
 An indexer creates, uses, and remembers the container used for the cached enrichments. It's not necessary to include the container in the cache connection string. You can find the object ID on the **Identity** page of your search service in the Azure portal.
 

@@ -45,7 +45,7 @@ You can use a system-assigned managed identity or a user-assigned managed identi
    | Table indexing using an indexer | Add **Storage Table Data Reader** |
    | File indexing using an indexer | Add **Reader and Data Access** |
    | Write to a [knowledge store](knowledge-store-concept-intro.md) | Add **Storage Blob Data Contributor** for object and file projections, and **Reader and Data Access** for table projections. |
-   | Write to an [enrichment cache](cognitive-search-incremental-indexing-conceptual.md) | Add **Storage Blob Data Contributor** and **Storage Table Data Reader** |
+   | Write to an [enrichment cache](enrichment-cache-how-to-configure.md) | Add **Storage Blob Data Contributor** and **Storage Table Data Contributor** |
    | Save [debug session state](cognitive-search-debug-session.md) | Add **Storage Blob Data Contributor**  |
 
 1. Select **Next**.

@@ -102,7 +102,7 @@ After reset, follow with a Run command to reprocess new and existing documents.
 
 ## How to reset and run indexers
 
-Reset clears the high-water mark. All documents in the search index are flagged for full overwrite, without inline updates or merging into existing content. For indexers with a skillset and [enrichment caching](cognitive-search-incremental-indexing-conceptual.md), resetting the index also implicitly resets the skillset. 
+Reset clears the high-water mark. All documents in the search index are flagged for full overwrite, without inline updates or merging into existing content. For indexers with a skillset and [enrichment caching](enrichment-cache-how-to-configure.md), resetting the index also implicitly resets the skillset. 
 
 The actual work occurs when you follow a reset with a Run command:
 
@@ -182,7 +182,7 @@ catch (RequestFailedException ex) when (ex.Status == 429)
 
 ## How to reset skills (preview)
 
-For indexers that have skillsets, you can reset individual skills to force processing of just that skill and any downstream skills that depend on its output. The [enrichment cache](search-howto-incremental-index.md), if you enabled it, is also refreshed. 
+For indexers that have skillsets, you can reset individual skills to force processing of just that skill and any downstream skills that depend on its output. The [enrichment cache](enrichment-cache-how-to-configure.md), if you enabled it, is also refreshed. 
 
 [Reset Skills](/rest/api/searchservice/skillsets/reset-skills?view=rest-searchservice-2024-05-01-preview&preserve-view=true) is currently REST-only, available through 2020-06-30-preview or later. We recommend the latest preview API.
 

@@ -50,7 +50,7 @@ This article supplements [**Index data from ADLS  Gen2**](search-howto-index-azu
   + [Custom Web API skill](cognitive-search-custom-skill-web-api.md)
   + [GenAI Prompt skill](cognitive-search-skill-genai-prompt.md)
   + [Knowledge store](knowledge-store-concept-intro.md)
-  + [Indexer enrichment cache](search-howto-incremental-index.md)
+  + [Indexer enrichment cache](enrichment-cache-how-to-configure.md)
   + [Debug sessions](cognitive-search-debug-session.md)
 
 ## Support for the permission model

@@ -66,7 +66,7 @@ A system managed identity is a Microsoft Entra service principal. The assignment
 
 1. Add **Storage Blob Data Contributor** if write access is required.
 
-   Features that require write access include [enrichment caching](cognitive-search-incremental-indexing-conceptual.md), [debug sessions](cognitive-search-debug-session.md), and [knowledge store](knowledge-store-concept-intro.md).
+   Features that require write access include [enrichment caching](enrichment-cache-how-to-configure.md), [debug sessions](cognitive-search-debug-session.md), and [knowledge store](knowledge-store-concept-intro.md).
 
 ## Set up and test the connection
 

@@ -75,7 +75,7 @@ AzureDiagnostics
 
 In some cases, it can be useful to test individual queries to see how they're performing. To do this, it's important to be able to see how long the search service takes to complete the work, as well as how long it takes to make the round-trip request from the client and back to the client. The diagnostics logs could be used to look up individual operations, but it might be easier to do this all from a REST client.
 
-In the example below, a REST-based search query was executed. Azure AI Search includes in every response the number of milliseconds it takes to complete the query, visible in the Headers tab, in "elapsed-time". Next to Status at the top of the response, you'll find the round-trip duration, in this case, 418 milliseconds (ms). In the results section, the “Headers” tab was chosen. Using these two values, highlighted with a red box in the image below, we see the search service took 21 ms to complete the search query and the entire client round-trip request took 125 ms. By subtracting these two numbers we can determine that it took 104-ms additional time to transmit the search query to the search service and to transfer the search results back to the client.
+In the example below, a REST-based search query was executed. Azure AI Search includes in every response the number of milliseconds it takes to complete the query, visible in the Headers tab, in "elapsed-time". Next to Status at the top of the response, you'll find the round-trip duration, in this case, 125 milliseconds (ms). In the results section, the “Headers” tab was chosen. Using these two values, highlighted with a red box in the image below, we see the search service took 21 ms to complete the search query and the entire client round-trip request took 125 ms. By subtracting these two numbers we can determine that it took 104-ms additional time to transmit the search query to the search service and to transfer the search results back to the client.
 
 This technique helps you isolate network latencies from other factors impacting query performance.
 

@@ -17,7 +17,7 @@ In a query operation, the relevance of any given result is determined by a ranki
 
 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.
 
-You can enhance the quality of ranked results through ***relevance tuning*** that boosts search scores based on extra criteria or analysis. In Azure AI Search, relevance tuning is primarily directed at textual and numeric (nonvector) content when you apply a [scoring profile](#custom-boosting-logic-using-scoring-profiles) or invoke the [semantic ranker](semantic-search-overview.md). 
+***Relevance tuning*** can be used to boost search scores based on extra criteria such as freshness or proximity. In Azure AI Search, relevance tuning is primarily directed at textual and numeric (nonvector) content when you apply a [scoring profile](#custom-boosting-logic-using-scoring-profiles) or invoke the [semantic ranker](semantic-search-overview.md). 
 
 > [!NOTE]
 > In Azure AI Search, there's no explicit relevance tuning capabilities for vector content, 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.

@@ -174,7 +174,7 @@ Traffic Manager doesn't provide an endpoint for a direct connection to Azure AI
 
 When you deploy multiple search services in various geographic regions, your content is stored in the region you chose for each search service.
 
-Azure AI Search doesn't store data outside of your specified region without your authorization. Authorization is implicit when you use features that write to an Azure Storage resource: [enrichment cache](cognitive-search-incremental-indexing-conceptual.md), [debug session](cognitive-search-debug-session.md), [knowledge store](knowledge-store-concept-intro.md). In all cases, the storage account is one that you provide, in the region of your choice. 
+Azure AI Search doesn't store data outside of your specified region without your authorization. Authorization is implicit when you use features that write to an Azure Storage resource: [enrichment cache](enrichment-cache-how-to-configure.md), [debug session](cognitive-search-debug-session.md), [knowledge store](knowledge-store-concept-intro.md). In all cases, the storage account is one that you provide, in the region of your choice. 
 
 > [!NOTE]
 > If both the storage account and the search service are in the same region, network traffic between search and storage uses a private IP address and occurs over the Microsoft backbone network. Because private IP addresses are used, you can't configure IP firewalls or a private endpoint for network security. Instead, use the [trusted service exception](search-indexer-howto-access-trusted-service-exception.md) as an alternative when both services are in the same region. 

@@ -65,7 +65,7 @@ The following list is a full enumeration of the outbound requests for which you
 | Operation | Scenario |
 | ----------| -------- |
 | Indexers | Connect to external data sources to retrieve data. For more information, see [Indexer access to content protected by Azure network security](search-indexer-securing-resources.md). |
-| Indexers | Connect to Azure Storage to persist [knowledge stores](knowledge-store-concept-intro.md), [cached enrichments](cognitive-search-incremental-indexing-conceptual.md), [debug sessions](cognitive-search-debug-session.md). |
+| Indexers | Connect to Azure Storage to persist [knowledge stores](knowledge-store-concept-intro.md), [cached enrichments](enrichment-cache-how-to-configure.md), [debug sessions](cognitive-search-debug-session.md). |
 | Custom skills | Connect to Azure functions, Azure web apps, or other apps running external code that's hosted off-service. The request for external processing is sent during skillset execution. |
 | Indexers and [integrated vectorization](vector-search-integrated-vectorization.md) | Connect to Azure OpenAI and a deployed embedding model, or it goes through a custom skill to connect to an embedding model that you provide. The search service sends text to embedding models for vectorization during indexing. |
 | Vectorizers | Connect to Azure OpenAI or other embedding models at query time to [convert user text strings to vectors](vector-search-how-to-configure-vectorizer.md) for vector search. |
@@ -167,7 +167,7 @@ When you set up a search service, you choose a region that determines where cust
 
 Currently, the only external resource that a search service writes to is Azure Storage. The storage account is one that you provide, and it could be in any region. A search service writes to Azure Storage if you use any of the following features:
 
-+ [enrichment cache](cognitive-search-incremental-indexing-conceptual.md)
++ [enrichment cache](enrichment-cache-how-to-configure.md)
 + [debug session](cognitive-search-debug-session.md)
 + [knowledge store](knowledge-store-concept-intro.md)
 

@@ -59,7 +59,7 @@ Depending on your configuration and usage, the following charges might apply:
 
 + Data traffic might incur networking costs. See the [bandwidth pricing](https://azure.microsoft.com/pricing/details/bandwidth/).
 
-+ Several premium features, such as [knowledge stores](knowledge-store-concept-intro.md), [debug sessions](cognitive-search-debug-session.md), and [enrichment caches](cognitive-search-incremental-indexing-conceptual.md), depend on Azure Storage and incur storage costs. Charges for these features appear on your Azure Storage bill.
++ Several premium features, such as [knowledge stores](knowledge-store-concept-intro.md), [debug sessions](cognitive-search-debug-session.md), and [enrichment caches](enrichment-cache-how-to-configure.md), depend on Azure Storage and incur storage costs. Charges for these features appear on your Azure Storage bill.
 
 + [Customer-managed keys](search-security-manage-encryption-keys.md), which provide double encryption of sensitive content, require a billable [Azure Key Vault](https://azure.microsoft.com/pricing/details/key-vault/).
 
@@ -114,7 +114,7 @@ To minimize the costs of your Azure AI Search solution, use the following strate
 
 + Use [incremental indexing](search-howto-reindex.md) to process only new or changed data.
 
-+ Use [enrichment caching](cognitive-search-incremental-indexing-conceptual.md) and a [knowledge store](knowledge-store-concept-intro.md) to reuse previously enriched content. Although caching incurs a storage charge, it lowers the cumulative cost of [AI enrichment](cognitive-search-concept-intro.md).
++ Use [enrichment caching](enrichment-cache-how-to-configure.md) and a [knowledge store](knowledge-store-concept-intro.md) to reuse previously enriched content. Although caching incurs a storage charge, it lowers the cumulative cost of [AI enrichment](cognitive-search-concept-intro.md).
 
 + Keep vector payloads compact. For vector search, see the [vector  compression best practices](https://techcommunity.microsoft.com/blog/azure-ai-services-blog/azure-ai-search-cut-vector-costs-up-to-92-5-with-new-compression-techniques/4404866).
 

@@ -348,6 +348,8 @@ items:
         href: cognitive-search-working-with-skillsets.md
       - name: Create a skillset
         href: cognitive-search-defining-skillset.md
+      - name: Design tips
+        href: cognitive-search-concept-troubleshooting.md
       - name: Attach an Azure AI resource
         href: cognitive-search-attach-cognitive-services.md
       - name: Define an index projection
@@ -362,12 +364,10 @@ 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
-        href: cognitive-search-concept-troubleshooting.md
+      - name: Configure an enrichment cache
+        href: enrichment-cache-how-to-configure.md
+      - name: Manage an enrichment cache
+        href: enrichment-cache-how-to-manage.md
       - name: Best practices - GenAI Prompt skill
         href: responsible-ai-best-practices-genai-prompt-skill.md
       - name: GenAI Prompt Skill - Example Usage Guide

@@ -334,7 +334,7 @@ Consider revisiting the [queries from the previous tutorial](tutorial-rag-build-
 
 ## Next step
 
-There are code samples in all of the Azure SDKs that provide Azure AI Search programmability. You can also review vector sample code for specific use cases and technology combinations.
+We recommend this accelerator for you next step:
 
 > [!div class="nextstepaction"]
-> [azure-search-vector-samples](https://github.com/Azure/azure-search-vector-samples)
\ No newline at end of file
+> [RAG Experiment Accelerator](https://github.com/microsoft/rag-experiment-accelerator)
\ No newline at end of file

@@ -10,16 +10,16 @@ ms.date: 7/11/2025
 zone_pivot_groups: tutorial-create-skillset
 ---
 
-# Quickstart: Skillsets in Azure AI Search
+# Tutorial: Skillsets in Azure AI Search
 
 ::: zone pivot="csharp"
 
-[!INCLUDE [C# quickstart](includes/tutorials/skillset-csharp.md)]
+[!INCLUDE [C# tutorial](includes/tutorials/skillset-csharp.md)]
 
 ::: zone-end
 
 ::: zone pivot="rest"
 
-[!INCLUDE [REST quickstart](includes/tutorials/skillset-rest.md)]
+[!INCLUDE [REST tutorial](includes/tutorials/skillset-rest.md)]
 
 ::: zone-end