Enrichment cache update

Author: HeidiSteen

articles/search/.openpublishing.redirection.search.json

@@ -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",

articles/search/cognitive-search-attach-cognitive-services.md

@@ -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
 

articles/search/cognitive-search-concept-intro.md

@@ -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.
 

articles/search/cognitive-search-defining-skillset.md

@@ -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
 

articles/search/cognitive-search-tutorial-debug-sessions.md

@@ -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)

articles/search/cognitive-search-working-with-skillsets.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.