@@ -10,14 +10,14 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 09/04/2024
+ms.date: 02/24/2025
 ---
 
 # AI enrichment in Azure AI Search
 
 In Azure AI Search, *AI enrichment* refers to integration with [Azure AI services](/azure/ai-services/what-are-ai-services) to process content that isn't searchable in its raw form. Through enrichment, analysis and inference are used to create searchable content and structure where none previously existed. 
 
-Because Azure AI Search is used for text and vector queries, the purpose of AI enrichment is to improve the utility of your content in search-related scenarios. Raw content must be text or images (you can't enrich vectors), but the output of an enrichment pipeline can be vectorized and indexed in a vector index using skills like [Text Split skill](cognitive-search-skill-textsplit.md) for chunking and [AzureOpenAIEmbedding skill](cognitive-search-skill-azure-openai-embedding.md) for encoding. For more information about using skills in vector scenarios, see [Integrated data chunking and embedding](vector-search-integrated-vectorization.md).
+Because Azure AI Search is used for text and vector queries, the purpose of AI enrichment is to improve the utility of your content in search-related scenarios. Raw content must be text or images (you can't enrich vectors), but the output of an enrichment pipeline can be vectorized and indexed in a search index using skills like [Text Split skill](cognitive-search-skill-textsplit.md) for chunking and [AzureOpenAIEmbedding skill](cognitive-search-skill-azure-openai-embedding.md) for vector encoding. For more information about using skills in vector scenarios, see [Integrated data chunking and embedding](vector-search-integrated-vectorization.md).
 
 AI enrichment is based on [*skills*](cognitive-search-working-with-skillsets.md).
 

@@ -32,7 +32,7 @@ For the recommended keyless authentication with Microsoft Entra ID, you need to:
 1. Create a new folder `full-text-quickstart` to contain the application and open Visual Studio Code in that folder with the following command:
 
     ```shell
-    mkdir full-text-quickstart && code full-text-quickstart
+    mkdir full-text-quickstart && cd full-text-quickstart
     ```
 
 1. Create a new console application with the following command:

@@ -32,7 +32,7 @@ For the recommended keyless authentication with Microsoft Entra ID, you need to:
 1. Create a new folder `full-text-quickstart` to contain the application and open Visual Studio Code in that folder with the following command:
 
     ```shell
-    mkdir full-text-quickstart && code full-text-quickstart
+    mkdir full-text-quickstart && cd full-text-quickstart
     ```
 
 1. Create the `package.json` with the following command:

@@ -32,7 +32,7 @@ For the recommended keyless authentication with Microsoft Entra ID, you need to:
 1. Create a new folder `full-text-quickstart` to contain the application and open Visual Studio Code in that folder with the following command:
 
     ```shell
-    mkdir full-text-quickstart && code full-text-quickstart
+    mkdir full-text-quickstart && cd full-text-quickstart
     ```
 
 1. Create the `package.json` with the following command:

@@ -10,14 +10,22 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 10/01/2024
+ms.date: 02/25/2025
 ---
 
 # Add scoring profiles to boost search scores
 
-Scoring profiles allow you to boost the ranking of matching documents based on criteria. In this article, learn how to specify and assign a scoring profile that boosts a search score based on parameters that you provide. 
+Scoring profiles are used to boost the ranking of matching documents based on criteria. In this article, learn how to specify and assign a scoring profile that boosts a search score based on parameters that you provide. 
 
-You can use scoring profiles for [keyword search](search-lucene-query-architecture.md), [vector search](vector-search-overview.md), and [hybrid search](hybrid-search-overview.md). However, scoring profiles only apply to nonvector fields, so make sure your index has text or numeric fields that can be used in a scoring profile. Scoring profile support for vector and hybrid search is available in 2024-05-01-preview and 2024-07-01 REST APIs and in Azure SDK packages that targeting those releases.
+You can use scoring profiles for [keyword search](search-lucene-query-architecture.md), [vector search](vector-search-overview.md), and [hybrid search](hybrid-search-overview.md). However, scoring profiles only apply to nonvector fields, so make sure your index has text or numeric fields that can be used in a scoring profile. 
+
+## Prerequisites
+
++ A new or existing search index with text or numeric fields.
+
+You can specify a scoring profile in the index designer in the Azure portal or through APIs like [Create or Update Index REST](/rest/api/searchservice/indexes/create-or-update) or equivalent APIs in any Azure SDK.
+
+Scoring profile support for vector and hybrid search is available in 2024-05-01-preview and 2024-07-01 REST APIs and in Azure SDK packages that targeting those releases.
 
 ## Key points about scoring profiles
 

@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 07/22/2024
+ms.date: 02/24/2025
 ---
 
 # Configure BM25 relevance scoring

@@ -12,7 +12,7 @@ metadata:
   ms.topic: landing-page
   author: HeidiSteen
   ms.author: heidist
-  ms.date: 09/04/2024
+  ms.date: 02/024/2025
 # linkListType: architecture | concept | deploy | download | get-started | how-to-guide | learn | overview | quickstart | reference | tutorial | video | whats-new
 
 landingContent:

@@ -109,8 +109,6 @@ The following table lists common and recommended alert rules for Azure AI Search
 - [Monitor Azure resources with Azure Monitor](/azure/azure-monitor/essentials/monitor-azure-resource)
 - [Monitor queries](search-monitor-queries.md)
 - [Monitor indexer-based indexing](search-howto-monitor-indexers.md)
-- [Monitor client-side interactions](search-traffic-analytics.md)
 - [Visualize resource logs](search-monitor-logs-powerbi.md)
 - [Analyze performance in Azure AI Search](search-performance-analysis.md)
-- [Performance benchmarks](performance-benchmarks.md)
 - [Tips for better performance](search-performance-tips.md)

@@ -1,224 +0,0 @@
----
-title: Performance benchmarks
-titleSuffix: Azure AI Search
-description: Learn about the performance of Azure AI Search through various performance benchmarks
-author: gmndrg
-ms.author: gimondra
-ms.service: azure-ai-search
-ms.custom:
-  - ignite-2023
-ms.topic: conceptual
-ms.date: 04/22/2024
----
-
-# Azure AI Search performance benchmarks
-
-> [!IMPORTANT]
-> These benchmarks apply to search services created *before April 3, 2024* on deployments that run on older infrastructure. The benchmarks also apply to nonvector workloads only. Updates are pending for services and workloads on the new limits. 
-
-Performance benchmarks are useful for estimating potential performance under similar configurations. Actual performance depends on a [variety of factors](search-performance-tips.md), including the size of your search service and the types of queries you're sending. 
-
-To help you estimate the size of search service needed for your workload, we ran several benchmarks to document the performance for different search services and configurations. 
-
-To cover a range of different use cases, we ran benchmarks for two main scenarios:
-
-* **E-commerce search** - This benchmark emulates a real e-commerce scenario and is based on the Nordic e-commerce company [CDON](https://cdon.com).
-* **Document search** - This scenario is comprised of keyword search over full text documents from [Semantic Scholar](https://www.aclweb.org/anthology/2020.acl-main.447/). This emulates a typical document search solution.
-
-While these scenarios reflect different use cases, every scenario is different so we always recommend performance testing your individual workload. We've published a [performance testing solution using JMeter](https://github.com/Azure-Samples/azure-search-performance-testing) so you can run similar tests against your own service.
-
-## Testing methodology
-
-To benchmark Azure AI Search's performance, we ran tests for two different scenarios at different tiers and replica/partition combinations.
-
-To create these benchmarks, the following methodology was used:
-
-1. The test begins at `X` queries per second (QPS) for 180 seconds. This was usually 5 or 10 QPS.
-2. QPS then increased by `X` and ran for another 180 seconds
-3. Every 180 seconds, the test increased by `X` QPS until average latency increased above 1000 ms or less than 99% of queries succeeded.
-
-The following graph gives a visual example of what the test's query load looks like:
-
-![Example test](./media/performance-benchmarks/example-test.png)
-
-Each scenario used at least 10,000 unique queries to avoid tests being overly skewed by caching.
-
-> [!IMPORTANT]
-> These tests only include query workloads. If you expect to have a high volume of indexing operations, be sure to factor that into your estimation and performance testing. Sample code for simulating indexing can be found in this [tutorial](tutorial-optimize-indexing-push-api.md).
-
-### Definitions
-
-- **Maximum QPS** -  the maximum QPS numbers are based on the highest QPS achieved in a test where 99% of queries completed successfully without throttling and average latency stayed under 1000 ms.
-
-- **Percentage of max QPS** - A percentage of the maximum QPS achieved for a particular test. For example, if a given test reached a maximum of 100 QPS, 20% of max QPS would be 20 QPS.
-
-- **Latency** - The server's latency for a query; these numbers don't include [round trip delay (RTT)](https://en.wikipedia.org/wiki/Round-trip_delay). Values are in milliseconds (ms).
-
-## Testing disclaimer
-
-The code we used to run these benchmarks is available on the [azure-search-performance-testing](https://github.com/Azure-Samples/azure-search-performance-testing/tree/main/other_tools) repository. It's worth noting that we observed slightly lower QPS levels with the [JMeter performance testing solution](https://github.com/Azure-Samples/azure-search-performance-testing) than in the benchmarks. The differences can be attributed to differences in the style of the tests. This speaks to the importance of making your performance tests as similar to your production workload as possible.
-
-> [!IMPORTANT]
-> These benchmarks in no way guarantee a certain level of performance from your service but can give you an idea of the performance you can expect based on your scenario.
-
-If you have any questions or concerns, reach out to us at azuresearch_contact@microsoft.com.
-
-## Benchmark 1: E-commerce search
-
-:::row:::
-   :::column span="1":::
-      ![CDON Logo](./media/performance-benchmarks/cdon-logo-160px2.png)
-   :::column-end:::
-   :::column span="3":::
-      This benchmark was created in partnership with the e-commerce company, [CDON](https://cdon.com), the Nordic region's largest online marketplace with operations in Sweden, Finland, Norway, and Denmark. Through its 1,500 merchants, CDON offers a wide range assortment that includes over 8 million products. In 2020, CDON had over 120 million visitors and 2 million active customers. You can learn more about CDON's use of Azure AI Search in [this article](https://pulse.microsoft.com/transform/na/fa1-how-cdon-has-been-using-technology-to-become-the-leading-marketplace-in-the-nordics/).
-   :::column-end:::
-:::row-end:::
-
-To run these tests, we used a snapshot of CDON's production search index and thousands of unique queries from their [website](https://cdon.com).
-
-### Scenario Details
-
-- **Document Count**: 6,000,000 
-- **Index Size**: 20 GB
-- **Index Schema**: a wide index with 250 fields total, 25 searchable fields, and 200 facetable/filterable fields
-- **Query Types**: full text search queries including facets, filters, ordering, and scoring profiles
-
-### S1 Performance
-
-#### Queries per second
-
-The following chart shows the highest query load a service could handle for an extended period of time in terms of queries per second (QPS).
-
-![Highest maintainable QPS ecommerce s1](./media/performance-benchmarks/s1-ecom-qps.png)
-
-#### Query latency
-
-Query latency varies based on the load of the service and services under higher stress have a higher average query latency. The following table shows the 25th, 50th, 75th, 90th, 95th, and 99th percentiles of query latency for three different usage levels.
-
-| Percentage of max QPS  | Average latency | 25% | 75% | 90% | 95% | 99%|
-|---|---|---|---| --- | --- | --- | 
-| 20%  | 104 ms  | 35 ms  | 115 ms   | 177 ms | 257 ms | 738 ms |
-| 50%  | 140 ms  | 47 ms  | 144 ms   | 241 ms | 400 ms | 1175 ms |
-| 80%  | 239 ms  | 77 ms  | 248 ms   | 466 ms | 763 ms | 1752 ms | 
-
-### S2 Performance
-
-#### Queries per second
-
-The following chart shows the highest query load a service could handle for an extended period of time in terms of queries per second (QPS).
-
-![Highest maintainable QPS ecommerce s2](./media/performance-benchmarks/s2-ecom-qps.png)
-
-#### Query latency
-
-Query latency varies based on the load of the service and services under higher stress have a higher average query latency. The following table shows the 25th, 50th, 75th, 90th, 95th, and 99th percentiles of query latency for three different usage levels.
-
-| Percentage of max QPS  | Average latency | 25% | 75% | 90% | 95% | 99%|
-|---|---|---|---| --- | --- | --- | 
-| 20%  | 56 ms | 21 ms | 68 ms  | 106 ms  | 132 ms | 210 ms | 
-| 50%  | 71 ms  | 26 ms  | 83 ms   | 132 ms | 177 ms | 329 ms |
-| 80%  | 140 ms  | 47 ms  | 153 ms   | 293 ms | 452 ms | 924 ms | 
-
-### S3 Performance
-
-#### Queries per second
-
-The following chart shows the highest query load a service could handle for an extended period of time in terms of queries per second (QPS).
-
-![Highest maintainable QPS ecommerce s3](./media/performance-benchmarks/s3-ecom-qps.png)
-
-In this case, we see that adding a second partition significantly increases the maximum QPS but adding a third partition provides diminishing marginal returns. The smaller improvement is likely because all of the data is already being pulled into the S3's active memory with just two partitions.
-
-#### Query latency
-
-Query latency varies based on the load of the service and services under higher stress have a higher average query latency. The following table shows the 25th, 50th, 75th, 90th, 95th, and 99th percentiles of query latency for three different usage levels.
-
-| Percentage of max QPS  | Average latency | 25% | 75% | 90% | 95% | 99%|
-|---|---|---|---| --- | --- | --- |
-| 20%  | 50 ms  | 20 ms  | 64 ms   | 83 ms | 98 ms | 160 ms |
-| 50%  | 62 ms  | 24 ms  | 80 ms   | 107 ms | 130 ms | 253 ms |
-| 80%  | 115 ms  | 38 ms  | 121 ms   | 218 ms | 352 ms | 828 ms |
-
-## Benchmark 2: Document search
-
-### Scenario Details
-
-- **Document Count**: 7.5 million
-- **Index Size**: 22 GB
-- **Index Schema**: 23 fields; 8 searchable, 10 filterable/facetable
-- **Query Types**: keyword searches with facets and hit highlighting
-
-### S1 Performance
-
-#### Queries per second
-
-The following chart shows the highest query load a service could handle for an extended period of time in terms of queries per second (QPS).
-
-![Highest maintainable QPS doc search s1](./media/performance-benchmarks/s1-docsearch-qps.png)
-
-#### Query latency
-
-Query latency varies based on the load of the service and services under higher stress have a higher average query latency. The following table shows the 25th, 50th, 75th, 90th, 95th, and 99th percentiles of query latency for three different usage levels.
-
-| Percentage of max QPS  | Average latency | 25% | 75% | 90% | 95% | 99%|
-|---|---|---|---| --- | --- | --- |
-| 20%  | 67 ms  | 44 ms  | 77 ms   | 103 ms | 126 ms | 216 ms |
-| 50%  | 93 ms  | 59 ms  | 110 ms   | 150 ms | 184 ms | 304 ms |
-| 80%  | 150 ms  | 96 ms  | 184 ms   | 248 ms | 297 ms | 424 ms |
-
-### S2 Performance
-
-#### Queries per second
-
-The following chart shows the highest query load a service could handle for an extended period of time in terms of queries per second (QPS).
-
-![Highest maintainable QPS doc search s2](./media/performance-benchmarks/s2-docsearch-qps.png)
-
-#### Query latency
-
-Query latency varies based on the load of the service and services under higher stress have a higher average query latency. The following table shows the 25th, 50th, 75th, 90th, 95th, and 99th percentiles of query latency for three different usage levels.
-
-| Percentage of max QPS  | Average latency | 25% | 75% | 90% | 95% | 99%|
-|---|---|---|---| --- | --- | --- |
-| 20%  | 45 ms  | 31 ms  | 55 ms   | 73 ms | 84 ms | 109 ms |
-| 50%  | 63 ms  | 39 ms  | 81 ms   | 106 ms | 123 ms | 163 ms |
-| 80%  | 115 ms  | 73 ms  | 145 ms   | 191 ms | 224 ms | 291 ms |
-
-### S3 Performance
-
-#### Queries per second
-
-The following chart shows the highest query load a service could handle for an extended period of time in terms of queries per second (QPS).
-
-![Highest maintainable QPS doc search s3](./media/performance-benchmarks/s3-docsearch-qps.png)
-
-#### Query latency
-
-Query latency varies based on the load of the service and services under higher stress have a higher average query latency. The following table shows the 25th, 50th, 75th, 90th, 95th, and 99th percentiles of query latency for three different usage levels.
-
-| Percentage of max QPS  | Average latency | 25% | 75% | 90% | 95% | 99%|
-|---|---|---|---| --- | --- | --- |
-| 20%  | 43 ms  | 29 ms  | 53 ms   | 74 ms | 86 ms | 111 ms |
-| 50%  | 65 ms  | 37 ms  | 85 ms   | 111 ms | 128 ms | 164 ms |
-| 80%  | 126 ms  | 83 ms  | 162 ms   | 205 ms | 233 ms | 281 ms |
-
-## Takeaways
-
-Through these benchmarks, you can get an idea of the performance Azure AI Search offers. You can also see difference between services at different tiers.
-
-Some key take ways from these benchmarks are:
-
-* An S2 can typically handle at least four times the query volume as an S1
-* An S2 typically has lower latency than an S1 at comparable query volumes
-* As you add replicas, the QPS a service can handle typically scales linearly (for example, if one replica can handle 10 QPS then five replicas can usually handle 50 QPS)
-* The higher the load on the service, the higher the average latency
-
-You can also see that performance can vary drastically between scenarios. If you're not getting the performance you expect, check out the [tips for better performance](search-performance-tips.md).
-
-## Next steps
-
-Now that you've seen the performance benchmarks, you can learn more about how to analyze Azure AI Search's performance and key factors that influence performance.
-
-+ [Analyze performance](search-performance-analysis.md)
-+ [Tips for better performance](search-performance-tips.md)
-+ [Case Study: Use Cognitive Search to Support Complex AI Scenarios](https://techcommunity.microsoft.com/t5/azure-ai/case-study-effectively-using-cognitive-search-to-support-complex/ba-p/2804078)

@@ -12,7 +12,7 @@ ms.custom:
   - devx-track-dotnet
   - ignite-2023
 ms.topic: conceptual
-ms.date: 07/22/2024
+ms.date: 02/24/2025
 ---
 
 # Upgrade versions of the Azure Search .NET Management SDK

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2024
 ms.topic: conceptual
-ms.date: 09/04/2024
+ms.date: 02/24/2025
 ---
 
 # Features of Azure AI Search

@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 12/10/2024
+ms.date: 02/24/2025
 ---
 
 # Enable caching for incremental enrichment in Azure AI Search
@@ -29,7 +29,11 @@ Cached content is placed in Azure Storage using account information that you pro
 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).
 
 > [!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.
+> 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.
+
+## 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).
 
 ## Enable on new indexers
 

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 08/05/2024
+ms.date: 02/24/2025
 ---
 
 # Change and delete detection using indexers for Azure Storage in Azure AI Search
@@ -24,7 +24,7 @@ There are two ways to implement a soft delete strategy:
 + [Native blob soft delete (preview)](#native-blob-soft-delete), applies to Blob Storage only
 + [Soft delete using custom metadata](#soft-delete-using-custom-metadata)
 
-The deletion detection strategy should be applied from the very first indexer run. If you didn't establish the deletion policy prior to the initial run, any documents that were deleted before the policy was implemented will remain in your index, even if you add the policy to the indexer later and reset it. If this has occurred, it is suggested that you create a new index using a new indexer, ensuring the deletion policy is in place from the beginning.
+The deletion detection strategy must be applied from the very first indexer run. If you didn't establish the deletion policy prior to the initial run, any documents that were deleted before the policy was implemented will remain in your index, even if you add the policy to the indexer later and reset it. If this has occurred, it's suggested that you create a new index using a new indexer, ensuring the deletion policy is in place from the beginning.
 
 ## Prerequisites
 
@@ -33,7 +33,7 @@ The deletion detection strategy should be applied from the very first indexer ru
 + Use consistent document keys and file structure. Changing document keys or directory names and paths (applies to ADLS Gen2) breaks the internal tracking information used by indexers to know which content was indexed, and when it was last indexed.
 
 > [!NOTE]
-> ADLS Gen2 allows directories to be renamed. When a directory is renamed, the timestamps for the blobs in that directory do not get updated. As a result, the indexer will not re-index those blobs. If you need the blobs in a directory to be reindexed after a directory rename because they now have new URLs, you will need to update the `LastModified` timestamp for all the blobs in the directory so that the indexer knows to re-index them during a future run. The virtual directories in Azure Blob Storage cannot be changed, so they do not have this issue.
+> ADLS Gen2 allows directories to be renamed. When a directory is renamed, the timestamps for the blobs in that directory don't get updated. As a result, the indexer won't reindex those blobs. If you need the blobs in a directory to be reindexed after a directory rename because they now have new URLs, you need to update the `LastModified` timestamp for all the blobs in the directory so that the indexer knows to reindex them during a future run. The virtual directories in Azure Blob Storage can't be changed, so they don't have this issue.
 
 ## Native blob soft delete
 

@@ -11,7 +11,7 @@ ms.custom:
   - ignite-2023
 ms.service: azure-ai-search
 ms.topic: tutorial
-ms.date: 09/04/2024
+ms.date: 02/24/2025
 ---
 
 # Tutorial: Index and enrich encrypted blobs for full-text search in Azure AI Search

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 07/25/2024
+ms.date: 02/24/2025
 ---
 
 # Index plain text blobs and files in Azure AI Search

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 08/20/2024
+ms.date: 02/24/2025
 ---
 
 # Index data from SharePoint document libraries

@@ -11,7 +11,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 08/09/2024
+ms.date: 02/24/2025
 ---
 
 # Field mappings and transformations using Azure AI Search indexers

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 08/19/2024
+ms.date: 02/24/2025
 ---
 
 # Full text search in Azure AI Search

@@ -17,8 +17,6 @@ This article describes the tools, behaviors, and approaches for analyzing query
 
 In any large implementation, it's critical to do a performance benchmarking test of your Azure AI Search service before you roll it into production. You should test both the search query load that you expect, but also the expected data ingestion workloads (if possible, run both workloads simultaneously). Having benchmark numbers helps to validate the proper [search tier](search-sku-tier.md), [service configuration](search-capacity-planning.md), and expected [query latency](search-performance-analysis.md#average-query-latency).
 
-<!-- To develop benchmarks, we recommend the [azure-search-performance-testing (GitHub)](https://github.com/Azure-Samples/azure-search-performance-testing) tool. -->
-
 To isolate the effects of a distributed service architecture, try testing on service configurations of one replica and one partition.
 
 > [!NOTE]

@@ -58,9 +58,9 @@ AI service integration refers to internal connections to an Azure AI multi-servi
 
 | Region | AI service integration | Semantic ranker | Availability zones | Capacity constrained |
 |--|--|--|--|--|
-| North Europe​​ | ✅ | ✅ | ✅ | All tiers|
+| North Europe​​ | ✅ | ✅ | ✅ | S2, S3, L1, L2|
 | West Europe​​ | ✅ | ✅ | ✅ |  |
-| France Central​​ | ✅ | ✅ | ✅ | All Tiers|
+| France Central​​ | ✅ | ✅ | ✅ | S2, S3, L1, L2|
 | Germany West Central​ ​| ✅ |  | ✅ | |
 | Italy North​​ |  |  | ✅ | |
 | Norway East​​ | ✅ |  | ✅ |  |

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 12/10/2024
+ms.date: 02/25/2025
 ---
 
 # Plan and manage costs of an Azure AI Search service
@@ -42,15 +42,15 @@ Billing is based on capacity (SUs) and the costs of running premium features, su
 
 | Meter | Unit |
 |-------|------|
-| Image extraction (AI enrichment) <sup>1, 2</sup> | Per 1000 images. See the [pricing page](https://azure.microsoft.com/pricing/details/search/#pricing). |
+| Image extraction (AI enrichment) <sup>1, 2</sup> | Per 1000 images or files. See the [pricing page](https://azure.microsoft.com/pricing/details/search/#pricing). |
 | Custom Entity Lookup skill (AI enrichment) <sup>1</sup> | Per 1000 text records. See the [pricing page](https://azure.microsoft.com/pricing/details/search/#pricing) |
 | [Built-in skills](cognitive-search-predefined-skills.md)  (AI enrichment) <sup>1</sup> | Number of transactions, billed at the same rate as if you had performed the task by calling Azure AI services directly. You can process 20 documents per indexer per day for free. Larger or more frequent workloads require a multi-resource Azure AI services key. |
 | [Semantic ranker](semantic-search-overview.md) <sup>1</sup> | Number of queries of "queryType=semantic", billed at a progressive rate. See the [pricing page](https://azure.microsoft.com/pricing/details/search/#pricing). |
 | [Shared private link](search-indexer-howto-access-private.md) <sup>1</sup> | [Billed for bandwidth](https://azure.microsoft.com/pricing/details/private-link/) as long as the shared private link exists and is used. |
 
 <sup>1</sup> Applies only if you use or enable the feature.
 
-<sup>2</sup> In an [indexer configuration](/rest/api/searchservice/indexers/create#indexer-parameters), `imageAction` is the parameter that triggers image extraction. If `imageAction` is set to "none" (the default), you won't be charged for image extraction. Costs are incurred when `imageAction` parameter is set *and* you include OCR, Image Analysis, or Document Extraction in a skillset.
+<sup>2</sup> Extracts images from a file within the indexer pipeline. Text extraction is free. Image extraction is billed during the initial document cracking step and when invoking the Document Extraction skill. In an [indexer configuration](/rest/api/searchservice/indexers/create#indexer-parameters), `imageAction` is the parameter that triggers image extraction. If `imageAction` is set to "none" (the default), there's no charge. If set to "generateNormalizedImages" or "generateNormalizedImagePerPage" and the document contains images, you're charged for each image extraction. If the document contains no images, you're still billed for the action because the indexer has to crack the file to look for images.
 
 You aren't billed on the number of full text or vector queries, query responses, or documents ingested, although [service limits](search-limits-quotas-capacity.md) do apply at each tier.
 

@@ -59,8 +59,8 @@ Currently, several regions are capacity-constrained for specific tiers and can't
 
 | Region | Disabled tier (SKU) due to over-capacity | Suggested alternative |
 |--------|------------------------------------------|-----------------------|
-| France Central | All tiers| Sweden Central, West Europe|
-| North Europe | All tiers | Sweden Central, West Europe|
+| France Central | S2, S3, L1, L2| Sweden Central, West Europe|
+| North Europe | S2, S3, L1, L2 | Sweden Central, West Europe|
 
 ## Feature availability by tier
 

@@ -1,222 +0,0 @@
----
-title: Telemetry for search traffic analytics
-titleSuffix: Azure AI Search
-description: Enable search traffic analytics for Azure AI Search, collect telemetry and user-initiated events using Application Insights.
-author: HeidiSteen
-manager: nitinme
-ms.author: heidist
-
-ms.service: azure-ai-search
-ms.topic: how-to
-ms.date: 10/29/2024
----
-
-# Collect telemetry data for search traffic analytics
-
-Search traffic analytics is a pattern for collecting telemetry about user interactions with your Azure AI Search application, such as user-initiated clickstream events and keyboard inputs. Using this information, you can determine the effectiveness of your search solution, including clickthrough rate and which query inputs yield zero results.
-
-Instrumentation has the following parts:
-
-+ Add a telemetry client
-+ Modify a search request to include a correlation Id that maps search results to user actions
-+ Create and send a custom event to Application Insights and use the visualization and reporting tools to view event data
-
-This pattern takes a dependency on [Application Insights](/azure/azure-monitor/app/app-insights-overview) (a feature of [Azure Monitor](/azure/azure-monitor/)) to collect user data. It requires that you add instrumentation to your application code, as described in this article. Finally, you need a reporting mechanism to analyze the data. You can use any visualization tool that connects to Application Insights.
-
-> [!NOTE]
-> The pattern described in this article is for advanced scenarios and clickstream data generated by code you add to your client. In contrast, service logs are easy to set up, provide a range of metrics including search terms, and can be done in the Azure portal with no code required. We recommend that you enable logging for all scenarios. For more information, see [Collect and analyze log data](monitor-azure-cognitive-search.md).
-
-## Prerequisites
-
-+ [Azure AI Search](search-create-service-portal.md), any region, basic tier and above.
-
-+ [Application Insights](/azure/azure-monitor/app/create-workspace-resource).
-
-+ A rich client application providing an interactive search experience that includes clickstream events or other user actions that you want to correlate to search result selections.
-
-## Identify relevant search data
-
-To collect useful metrics for search traffic analytics, it's necessary to log some signals from the users of your search application. These signals signify content that users are interested in and that they consider relevant. For search traffic analytics, these include:
-
-+ User-generated search events: Only search queries initiated by a user are interesting. Other search requests, such as those used to populate facets or retrieve internal information, aren't important. Be sure to only instrument user-initiated events to avoid skew or bias in your results.
-
-+ User-generated clickstream events: On a search results page, a clickstream event generally means that a document is a relevant result for a specific search query.
-
-In your application code, you should correlate these events with the search results returned from a given query. By linking search and clickstream events with a correlation ID, you can gain a deeper understanding of how well your application's search functionality is performing.
-
-## Add search traffic analytics
-
-For Azure AI Search, the Azure [portal](https://portal.azure.com) provides a Search Traffic Analytics page that has C# and JavaScript code snippets for adding a telemetry client, request headers, and properties necessary for custom log events. 
-
-> [!IMPORTANT]
-> The Search traffic analytics portal page is currently outdated and references an obsolete client libary. The workaround is to use code snippets from the [azure-search-traffic-analytics](https://github.com/Azure-Samples/azure-search-traffic-analytics) GitHub repository. This article includes code snippets from the GitHub repository.
-
-:::image type="content" source="media/search-traffic-analytics/azuresearch-trafficanalytics.png" alt-text="Screenshot of the Azure portal command and page for setting up Application Insights.":::
-
-## Step 1: Set up Application Insights
-
-Create an object that sends events to Application Insights. You can add instrumentation to your server-side application code or client-side code running in a browser, expressed here as C# and JavaScript variants. For other languages, see [supported platforms and frameworks](/azure/azure-monitor/app/app-insights-overview#supported-languages).
-
-Server-side telemetry captures metrics at the application layer, for example in applications running as a web service on Azure, or as an on-premises app on a corporate network. Server-side telemetry captures search and clickstream events, the position of a document in results, and query information, but your data collection will be scoped to whatever information is available at that layer.
-
-On the client, you might have other code that manipulates query inputs, adds navigation, or includes context (for example, queries initiated from a home page versus a product page). If this describes your solution, you might opt for client-side instrumentation so that your telemetry reflects the extra detail. How this extra detail is collected goes beyond the scope of this pattern, but you can review [Application Insights for web pages](/azure/azure-monitor/app/javascript#explore-browserclient-side-data) for help with that decision.
-
-In this step, provide a [connection string to Application Insights](/azure/azure-monitor/app/migrate-from-instrumentation-keys-to-connection-strings).
-
-### [**Visual Studio**](#tab/visual-studio-telemetry-client)
-
-A shortcut that works for some Visual Studio project types is reflected in the following steps.
-
-1. Open your solution in Visual Studio.
-
-1. On the **Project** menu, select **Connected services** > **Add** > **Azure Application Insights**.
-
-1. In Connect to dependency, select **Azure Application Insights**, and then select **Next**.
-
-1. Select your Azure subscription, your Application Insights resource, and then select **Finish**.
-
-At this point, your application is set up for application monitoring, which means all page loads in your client app are tracked with default metrics.
-
-If this shortcut didn't work for you, see [Enable Application Insights server-side telemetry](/azure/azure-monitor/app/asp-net-core#enable-application-insights-server-side-telemetry-visual-studio) or refer to code snippets in the adjacent tabs.
-
-### [**.NET**](#tab/dotnet-telemetry-client)
-
-```csharp
-var telemetryConfiguration = new TelemetryConfiguration
-{
-    ConnectionString = "<PUT YOUR CONNECTION STRING HERE>"
-};
-var telemetryClient = new TelemetryClient(telemetryConfiguration);
-```
-
-### [**JavaScript**](#tab/javascript-telemetry-client)
-
-```javascript
-const appInsights = new ApplicationInsights({ config: {
-  connectionString: '<PUT YOUR CONNECTION STRING HERE>'
-  /* ...Other Configuration Options... */
-} });
-appInsights.loadAppInsights();
-```
-
----
-
-## Step 2: Add instrumentation
-
-Add instrumentation code to your client application.
-
-### Correlate clickstream events with search results
-
-To correlate search requests with clicks, it's necessary to have a correlation ID that relates these two distinct events. Azure AI Search provides you with a search ID when you request it with an HTTP header.
-
-Having the search ID allows correlation of the metrics emitted by Azure AI Search for the request itself, with the custom metrics you're logging in Application Insights.
-
-### [**.NET**](#tab/dotnet-correlation)
-
-```csharp
-var client = new SearchClient(new Uri("https://contoso.search.windows.net"), "hotels-sample-index", new DefaultAzureCredential());
-
-// Generate a new correlation id for logs
-string searchId = Guid.NewGuid().ToString();
-string searchText = "*";
-SearchResults<SearchDocument> searchResults;
-
-// Set correlation id for search request
-using (HttpPipeline.CreateClientRequestIdScope(clientRequestId: searchId))
-{
-    searchResults = client.Search<SearchDocument>(searchText, options: new SearchOptions { IncludeTotalCount = true } );
-}
-```
-
-### [**JavaScript**](#tab/javascript-correlation)
-
-```javascript
-const searchId = uuidv4();
-const searchText = "*";
-const searchResults = await searchClient.search(searchText, { includeTotalCount: true, customHeaders: { "x-ms-client-request-id": searchId }});
-const properties = {
-    searchId: searchId,
-    serviceName: "<PUT YOUR SEARCH SERVICE NAME HERE, example contoso-search>",
-    indexName: "<PUT YOUR INDEX NAME HERE>",
-    searchText: searchText,
-    resultsCount: searchResults.count
-};
-appInsights.trackEvent({ name: "search" }, properties);
-```
-
----
-
-### Log custom events
-
-Every time that a search request is issued by a user, you should log that as a search event with the following schema on an [Application Insights custom event](/azure/azure-monitor/app/api-custom-events-metrics). Remember to log only user-generated search queries.
-
-+ **SearchId**: (guid) unique identifier of the search query (built into the search response)
-+ **SearchServiceName**: (string) search service name
-+ **IndexName**: (string) search service index to be queried
-+ **SearchText**: (string) search terms entered by the user
-+ **ResultCount**: (int) number of documents that were returned (built into the search response)
-
-> [!NOTE]
-> Request the count of user generated queries by adding `$count=true` to your search query. For more information, see [Search Documents (REST)](/rest/api/searchservice/documents/search-post#searchrequest).
->
-
-### [**.NET**](#tab/dotnet-properties)
-
-```csharp
-// Create properties for telemetry
-var properties = new Dictionary<string, string>
-{
-    ["searchId"] = searchId,
-    ["serviceName"] = "<PUT YOUR SEARCH SERVICE NAME HERE, example: contoso-search>",
-    ["indexName"] = "<PUT YOUR INDEX NAME HERE>",
-    ["searchText"] = searchText,
-    ["resultsCount"] = searchResults.TotalCount?.ToString()
-};
-```
-
-### [**JavaScript**](#tab/javascript-properties)
-
-```javascript
-const properties = {
-    searchId: searchId,
-    serviceName: "<PUT YOUR SEARCH SERVICE NAME HERE, example contoso-search>",
-    indexName: "<PUT YOUR INDEX NAME HERE>",
-    searchText: searchText,
-    resultsCount: searchResults.count
-};
-```
-
----
-
-### Send the custom event to Application Insights
-
-Add the custom even to the *custom events* table in Application Insights. For more information, see [Application Insights API for custom events and metrics](/azure/azure-monitor/app/api-custom-events-metrics).
-
-### [**.NET**](#tab/dotnet-custom-events)
-
-```csharp
-telemetryClient.TrackEvent("search", properties);
-telemetryClient.Flush();
-```
-
-### [**JavaScript**](#tab/javascript-custom-events)
-
-```javascript
-appInsights.trackEvent({ name: "search" }, properties);
-```
-
----
-
-## Step 3: Review logs
-
-Use any of the approaches supported by Application Insights for viewing custom events.
-
-+ [Create or edit an Azure Workbook](/azure/azure-monitor/visualize/workbooks-create-workbook)
-+ [Create and share dashboards of Log Analytics data](/azure/azure-monitor/visualize/tutorial-logs-dashboards)
-+ [Integrate Log Analytics with Power BI](/azure/azure-monitor/logs/log-powerbi)
-
-## Next steps
-
-You can find more information on [Application Insights](/azure/azure-monitor/app/app-insights-overview) and visit the [pricing page](https://azure.microsoft.com/pricing/details/application-insights/) to learn more about their different service tiers.
-
-Learn more about creating reports. See [Getting started with Power BI Desktop](/power-bi/fundamentals/desktop-getting-started) for details.

@@ -2,7 +2,6 @@ items:
 - name: Azure AI Search Documentation
   href: index.yml
 - name: Overview
-  expanded: true
   items:
   - name: What's Azure AI Search?
     href: search-what-is-azure-search.md
@@ -14,6 +13,7 @@ items:
     href: search-try-for-free.md
   - name: FAQ
     href: search-faq-frequently-asked-questions.yml
+  expanded: true
 - name: Quickstarts
   items:
   - name: Vector search
@@ -24,7 +24,7 @@ items:
     href: search-get-started-text.md
   - name: Semantic ranking
     href: search-get-started-semantic.md
-  - name: Chat with your data 
+  - name: Chat with your data
     href: /azure/ai-services/openai/use-your-data-quickstart?context=/azure/search/context/context
   - name: Connect without keys
     href: search-get-started-rbac.md
@@ -51,7 +51,7 @@ items:
   - name: Deployment
     items:
     - name: ARM template
-      displayName: Resource Manager 
+      displayName: Resource Manager
       href: search-get-started-arm.md
     - name: Bicep
       displayName: ARM, Resource Manager, Template
@@ -65,17 +65,17 @@ items:
     - name: Add search to ASP.NET Core (MVC)
       href: tutorial-csharp-create-mvc-app.md
     - name: Add search to static web apps
-      items: 
-        - name: Overview
-          href: tutorial-csharp-overview.md
-        - name: Create a search index
-          href: tutorial-csharp-create-load-index.md
-        - name: Deploy static web app
-          href: tutorial-csharp-deploy-static-web-app.md
-        - name: Explore the code
-          href: tutorial-csharp-search-query-integration.md          
-    - name: Query from Power Apps   
-      href: search-howto-powerapps.md     
+      items:
+      - name: Overview
+        href: tutorial-csharp-overview.md
+      - name: Create a search index
+        href: tutorial-csharp-create-load-index.md
+      - name: Deploy static web app
+        href: tutorial-csharp-deploy-static-web-app.md
+      - name: Explore the code
+        href: tutorial-csharp-search-query-integration.md
+    - name: Query from Power Apps
+      href: search-howto-powerapps.md
   - name: Indexing tutorials
     items:
     - name: Index any data
@@ -88,7 +88,7 @@ items:
       href: search-semi-structured-data.md
     - name: Index Markdown in Azure blobs
       href: search-markdown-data-tutorial.md
-    - name: Index multiple Azure data sources 
+    - name: Index multiple Azure data sources
       href: tutorial-multiple-data-sources.md
     - name: Index encrypted blobs
       href: search-howto-index-encrypted-blobs.md
@@ -140,7 +140,7 @@ items:
       href: search-what-is-an-index.md
     - name: Vector store
       href: vector-store.md
-    - name: Knowledge store 
+    - name: Knowledge store
       href: knowledge-store-concept-intro.md
     - name: Data import strategies
       href: search-what-is-data-import.md
@@ -267,44 +267,44 @@ items:
         href: cognitive-search-common-errors-warnings.md
     - name: Data sources (indexers)
       items:
-        - name: Data sources gallery
-          href: search-data-sources-gallery.md
-        - name: Azure Storage
-          items:
-          - name: Search over blobs
-            href: search-blob-storage-integration.md
-          - name: ADLS Gen2
-            href: search-howto-index-azure-data-lake-storage.md
-          - name: Blobs
-            href: search-howto-indexing-azure-blob-storage.md
-          - name: Files
-            href: search-file-storage-integration.md
-          - name: Tables
-            href: search-howto-indexing-azure-tables.md
-          - name: Index changed and deleted content
-            href: search-howto-index-changed-deleted-blobs.md
-        - name: Azure Cosmos DB
-          items:
-          - name: Azure Cosmos DB for NoSQL
-            href: search-howto-index-cosmosdb.md
-          - name: Azure Cosmos DB for MongoDB
-            href: search-howto-index-cosmosdb-mongodb.md
-          - name: Azure Cosmos DB for Apache Gremlin
-            href: search-howto-index-cosmosdb-gremlin.md
-        - name: Azure DB for MySQL
-          href: search-howto-index-mysql.md
-        - name: Azure SQL
-          items:
-          - name: Azure SQL Databases
-            href: search-how-to-index-sql-database.md
-          - name: Azure SQL Managed Instances
-            href: search-how-to-index-sql-managed-instance.md
-          - name: Azure SQL Server VMs
-            href: search-how-to-index-sql-server.md
-        - name: OneLake files
-          href: search-how-to-index-onelake-files.md
-        - name: SharePoint Online
-          href: search-howto-index-sharepoint-online.md
+      - name: Data sources gallery
+        href: search-data-sources-gallery.md
+      - name: Azure Storage
+        items:
+        - name: Search over blobs
+          href: search-blob-storage-integration.md
+        - name: ADLS Gen2
+          href: search-howto-index-azure-data-lake-storage.md
+        - name: Blobs
+          href: search-howto-indexing-azure-blob-storage.md
+        - name: Files
+          href: search-file-storage-integration.md
+        - name: Tables
+          href: search-howto-indexing-azure-tables.md
+        - name: Index changed and deleted content
+          href: search-howto-index-changed-deleted-blobs.md
+      - name: Azure Cosmos DB
+        items:
+        - name: Azure Cosmos DB for NoSQL
+          href: search-howto-index-cosmosdb.md
+        - name: Azure Cosmos DB for MongoDB
+          href: search-howto-index-cosmosdb-mongodb.md
+        - name: Azure Cosmos DB for Apache Gremlin
+          href: search-howto-index-cosmosdb-gremlin.md
+      - name: Azure DB for MySQL
+        href: search-howto-index-mysql.md
+      - name: Azure SQL
+        items:
+        - name: Azure SQL Databases
+          href: search-how-to-index-sql-database.md
+        - name: Azure SQL Managed Instances
+          href: search-how-to-index-sql-managed-instance.md
+        - name: Azure SQL Server VMs
+          href: search-how-to-index-sql-server.md
+      - name: OneLake files
+        href: search-how-to-index-onelake-files.md
+      - name: SharePoint Online
+        href: search-howto-index-sharepoint-online.md
     - name: Skillsets
       items:
       - name: Create a skillset
@@ -342,7 +342,7 @@ items:
     - name: Chunk documents
       href: vector-search-how-to-chunk-documents.md
     - name: Chunk and vectorize by document layout
-      href: search-how-to-semantic-chunking.md   
+      href: search-how-to-semantic-chunking.md
     - name: Generate embeddings
       href: vector-search-how-to-generate-embeddings.md
     - name: Use embedding models from Azure AI Foundry
@@ -412,7 +412,7 @@ items:
         href: search-query-troubleshoot-collection-filters.md
       - name: Normalize text for filters
         href: search-normalizers.md
-    - name: Search results 
+    - name: Search results
       items:
       - name: Page, sort, and shape results
         href: search-pagination-page-layout.md
@@ -435,7 +435,7 @@ items:
     - name: Configure BM25 ranking
       href: index-ranking-similarity.md
     - name: Enable or disable semantic ranker
-      href: semantic-how-to-enable-disable.md      
+      href: semantic-how-to-enable-disable.md
     - name: Configure semantic ranker
       href: semantic-how-to-configure.md
     - name: Add semantic ranking to queries
@@ -522,14 +522,10 @@ items:
       href: search-monitor-queries.md
     - name: Monitor indexer-based indexing
       href: search-howto-monitor-indexers.md
-    - name: Monitor client-side interactions
-      href: search-traffic-analytics.md
     - name: Visualize resource logs
       href: search-monitor-logs-powerbi.md
     - name: Performance analysis
       href: search-performance-analysis.md
-    - name: Performance benchmarks
-      href: performance-benchmarks.md
     - name: Tips for better performance
       href: search-performance-tips.md
   - name: Knowledge store
@@ -543,7 +539,7 @@ items:
     - name: Shape data
       href: knowledge-store-projection-shape.md
     - name: Define projections
-      href: knowledge-store-projections-examples.md 
+      href: knowledge-store-projections-examples.md
     - name: Projection example
       href: knowledge-store-projection-example-long.md
     - name: Connect with Power BI
@@ -633,7 +629,7 @@ items:
     - name: Azure CLI
       href: /cli/azure/search
     - name: Azure PowerShell
-      href: /powershell/module/az.search 
+      href: /powershell/module/az.search
     - name: Azure Resource Manager template
       href: /azure/templates/microsoft.search/searchservices
     - name: Azure Policy built-ins
@@ -650,27 +646,27 @@ items:
     - name: Azure AI resource skills
       items:
       - name: Document Layout skill
-        href: cognitive-search-skill-document-intelligence-layout.md  
+        href: cognitive-search-skill-document-intelligence-layout.md
       - name: Entity Linking (v3)
         href: cognitive-search-skill-entity-linking-v3.md
       - name: Entity Recognition (v3)
         href: cognitive-search-skill-entity-recognition-v3.md
       - name: Image Analysis
         href: cognitive-search-skill-image-analysis.md
-      - name: Key Phrase Extraction 
+      - name: Key Phrase Extraction
         href: cognitive-search-skill-keyphrases.md
       - name: Language Detection
         href: cognitive-search-skill-language-detection.md
       - name: OCR
         href: cognitive-search-skill-ocr.md
       - name: PII Detection
-        href: cognitive-search-skill-pii-detection.md  
+        href: cognitive-search-skill-pii-detection.md
       - name: Sentiment (v3)
         href: cognitive-search-skill-sentiment-v3.md
       - name: Text Translation
         href: cognitive-search-skill-text-translation.md
       - name: AI Vision multimodal embeddings
-        href: cognitive-search-skill-vision-vectorize.md      
+        href: cognitive-search-skill-vision-vectorize.md
     - name: Azure AI Search utility skills (nonbillable)
       items:
       - name: Conditional
@@ -686,7 +682,7 @@ items:
     - name: Azure OpenAI skills
       items:
       - name: Azure OpenAI Embedding
-        href: cognitive-search-skill-azure-openai-embedding.md 
+        href: cognitive-search-skill-azure-openai-embedding.md
     - name: Custom skills
       items:
       - name: Azure Machine Learning (AML)

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 08/19/2024
+ms.date: 02/24/2025
 ---
 
 # Add a filter in a vector query in Azure AI Search
@@ -26,7 +26,7 @@ You can also use [Search Explorer](search-get-started-portal-import-vectors.md#c
 
 ## How filtering works in a vector query
 
-Filters apply to `filterable` nonvector fields, either a string field or numeric, to include or exclude search documents based on filter criteria. Although a vector field isn't filterable itself, filters can be applied to other fields in the same index, including or excluding the documents that also contain vector fields.
+Filters apply to `filterable` *nonvector* fields, either a string field or numeric, to include or exclude search documents based on filter criteria. Although a vector field isn't filterable itself, filters can be applied to other nonvector fields in the same index, including or excluding the documents that also happen to contain vector fields you're searching on.
 
 Filters are applied before or after query execution based on the `vectorFilterMode` parameter.
 

@@ -1,38 +1,37 @@
 ---
 title: Integrated vectorization
 titleSuffix: Azure AI Search
-description: Add a data chunking and embedding step in an Azure AI Search skillset to vectorize content during indexing.
+description: Add a vector embedding step in an Azure AI Search skillset to vectorize content during indexing or queries.
 
 author: heidisteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 09/04/2024
+ms.date: 02/24/2025
 ---
 
-# Integrated data chunking and embedding in Azure AI Search
+# Integrated vector embedding in Azure AI Search
 
 Integrated vectorization is an extension of the indexing and query pipelines in Azure AI Search. It adds the following capabilities:
 
-+ Data chunking during indexing
-+ Text-to-vector conversion during indexing
-+ Text-to-vector conversion during queries
++ Vector encoding during indexing
++ Vector encoding during queries
 
-Data chunking isn't a hard requirement, but unless your raw documents are small, chunking is necessary for meeting the token input requirements of embedding models.
+[Data chunking](vector-search-how-to-chunk-documents.md) isn't a hard requirement, but unless your raw documents are small, chunking is necessary for meeting the token input requirements of embedding models.
 
-Vector conversions are one-way: text-to-vector. There's no vector-to-text conversion for queries or results (for example, you can't convert a vector result to a human-readable string).
+Vector conversions are one-way: nonvector-to-vector. For example, there's no vector-to-text conversion for queries or results, such as converting a vector result to a human-readable string, which is why indexes contain both vector and nonvector fields.
 
-Integrated data chunking and vectorization speeds up the development and minimizes maintenance tasks during data ingestion and query time because there are fewer external components to configure and manage. This capability is now generally available.
+Integrated vectorization speeds up the development and minimizes maintenance tasks during data ingestion and query time because there are fewer operations that you have to implement manually. This capability is now generally available.
 
 ## Using integrated vectorization during indexing
 
-For data chunking and text-to-vector conversions, you're taking a dependency on the following components:
+For integrated data chunking and vector conversions, you're taking a dependency on the following components:
 
-+ [An indexer](search-indexer-overview.md), which retrieves raw data from a [supported data source](search-indexer-overview.md#supported-data-sources) and serves as the pipeline engine.
++ [An indexer](search-indexer-overview.md), which retrieves raw data from a [supported data source](search-indexer-overview.md#supported-data-sources) and drives the pipeline engine.
 
-+ [A vector index](search-what-is-an-index.md) to receive the chunked and vectorized content.
++ [A search index](search-what-is-an-index.md) to receive the chunked and vectorized content.
 
 + [A skillset](cognitive-search-working-with-skillsets.md) configured for:
 
@@ -94,10 +93,6 @@ Data chunking (Text Split skill) is free and available on all Azure AI services
 
 + Combine vector and text fields for hybrid search, with or without semantic ranking. Integrated vectorization simplifies all of the [scenarios supported by vector search](vector-search-overview.md#what-scenarios-can-vector-search-support).
 
-## When to use integrated vectorization
-
-We recommend using the built-in vectorization support of Azure AI Foundry. If this approach doesn't meet your needs, you can create indexers and skillsets that invoke integrated vectorization using the programmatic interfaces of Azure AI Search.
-
 ## How to use integrated vectorization
 
 For query-only vectorization:
@@ -146,7 +141,7 @@ Here are some of the key benefits of the integrated vectorization:
 
 + Automate indexing end-to-end. When data changes in the source (such as in Azure Storage, Azure SQL, or Cosmos DB), the indexer can move those updates through the entire pipeline, from retrieval, to document cracking, through optional AI-enrichment, data chunking, vectorization, and indexing.
 
-+ Batching and retry logic is built in (non-configurable). Azure AI Search has internal retry policies for throttling errors that surface due to the Azure OpenAI endpoint maxing out on token quotas for the embedding model. We recommend putting the indexer on a schedule (for example, every 5 minutes) so the indexer can process any calls that were throttled by the Azure OpenAI endpoint despite of the retry policies.
++ Batching and retry logic is built in (non-configurable). Azure AI Search has internal retry policies for throttling errors that surface due to the Azure OpenAI endpoint maxing out on token quotas for the embedding model. We recommend putting the indexer on a schedule (for example, every 5 minutes) so the indexer can process any calls that are throttled by the Azure OpenAI endpoint despite of the retry policies.
 
 + Projecting chunked content to secondary indexes. Secondary indexes are created as you would any search index (a schema with fields and other constructs), but they're populated in tandem with a primary index by an indexer. Content from each source document flows to fields in primary and secondary indexes during the same indexing run. 
 

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 08/05/2024
+ms.date: 02/24/2025
 ---
 
 # Vectors in Azure AI Search
@@ -20,7 +20,7 @@ Vector search is an approach in information retrieval that supports indexing and
 + multilingual content ("dog" in English and "hund" in German)
 + multiple content types ("dog" in plain text and a photograph of a dog in an image file)
 
-This article provides [a high-level introduction to vectors](#vector-search-concepts) in Azure AI Search. It also explains integration with other Azure services and covers [terminology and concepts](#vector-search-concepts) related to vector search development.
+This article provides [a high-level introduction to vector support](#vector-search-concepts) in Azure AI Search. It also explains integration with other Azure services and covers [terminology and concepts](#vector-search-concepts) related to vector search development.
 
 We recommend this article for background, but if you'd rather get started, follow these steps:
 
@@ -39,9 +39,9 @@ Scenarios for vector search include:
 
 + **Search across different content types (multimodal)**. Encode images and text using multimodal embeddings (for example, with [OpenAI CLIP](https://github.com/openai/CLIP) or [GPT-4 Turbo with Vision](/azure/ai-services/openai/whats-new#gpt-4-turbo-with-vision-now-available) in Azure OpenAI) and query an embedding space composed of vectors from both content types.
 
-+ [**Hybrid search**](hybrid-search-overview.md). In Azure AI Search, hybrid search refers to vector and keyword query execution in the same request. Vector support is implemented at the field level, with an index containing both vector fields and searchable text fields. The queries execute in parallel and the results are merged into a single response. Optionally, add [semantic ranking](semantic-search-overview.md) for more accuracy with L2 reranking using the same language models that power Bing.
++ [**Hybrid search**](hybrid-search-overview.md). In Azure AI Search, we define hybrid search as dual vector and keyword query execution in the same request. Vector support is implemented at the field level. If an index contains both vector and non-vector fields, you can write a query that targets both. The queries execute in parallel and the results are merged into a single response and ranked accordingly.
 
-+ **Multilingual search**. Providing a search experience in the users own language is possible through embedding models and chat models trained in multiple languages. If you need more control over translation, you can supplement with the [multi-language capabilities](search-language-support.md) that Azure AI Search supports for nonvector content, in hybrid search scenarios.
++ **Multilingual search**. Azure AI Search is designed for extensibility. If you have embedding models and chat models trained in multiple languages, you can call them through custom or built-in skills on the indexing side, or vectorizers on the query side. If you need more control over text translation, you can supplement with the [multi-language capabilities](search-language-support.md) that Azure AI Search supports for nonvector content, in hybrid search scenarios.
 
 + **Filtered vector search**. A query request can include a vector query and a [filter expression](search-filters.md). Filters apply to text and numeric fields, and are useful for metadata filters, and including or excluding search results based on filter criteria. Although a vector field isn't filterable itself, you can set up a filterable text or numeric field. The search engine can process the filter before or after the vector query executes.
 
@@ -57,11 +57,11 @@ The following diagram shows the indexing and query workflows for vector search.
 
 On the indexing side, Azure AI Search takes vector embeddings and uses a [nearest neighbors algorithm](vector-search-ranking.md) to place similar vectors close together in an index. Internally, it creates vector indexes for each vector field.
 
-How you get embeddings from your source content into Azure AI Search depends on whether you want to perform the work within an Azure AI Search indexing pipeline, or externally.  Azure AI Search offers [integrated data chunking and vectorization](vector-search-integrated-vectorization.md) in an indexer pipeline. You still provide the resources (endpoints and connection information to Azure OpenAI), but Azure AI Search makes all of the calls and handles the transitions. This approach requires an indexer, a supported data source, and a skillset that drives chunking and embedding. Otherwise, you can handle all vectorization separately, and then push prevectorized content to [vector fields](vector-search-how-to-create-index.md) in a vector store.
+How you get embeddings from your source content into Azure AI Search depends on whether you want to perform the work within an Azure AI Search indexing pipeline, or externally.  Azure AI Search offers [integrated data chunking and vectorization](vector-search-integrated-vectorization.md) in an indexer pipeline. You still provide the resources (endpoints and connection information to Azure OpenAI), but Azure AI Search makes all of the calls and handles the transitions. This approach requires an indexer, a supported data source, and a skillset that drives chunking and embedding. If you don't want to use indexers, you can handle all vectorization externally, and then push prevectorized content into [vector fields](vector-search-how-to-create-index.md) in the search index.
 
 On the query side, in your client application, you collect the query input from a user, usually through a prompt workflow. You can then add an encoding step that converts the input into a vector, and then send the vector query to your index on Azure AI Search for a similarity search. As with indexing, you can deploy the [integrated vectorization](vector-search-integrated-vectorization.md) to convert the question into a vector. For either approach, Azure AI Search returns documents with the requested `k` nearest neighbors (kNN) in the results.
 
-Azure AI Search supports [hybrid scenarios](hybrid-search-overview.md) that run vector and keyword search in parallel, returning a unified result set that often provides better results than just vector or keyword search alone. For hybrid, vector and nonvector content is ingested into the same index, for queries that run side by side.
+Azure AI Search supports [hybrid scenarios](hybrid-search-overview.md) that run vector and keyword search in parallel, returning a unified result set that often provides better results than just vector or keyword search alone. For hybrid, vector and non-vector content is ingested into the same index, for queries that run side by side.
 
 ## Availability and pricing
 
@@ -88,7 +88,7 @@ Azure AI Search is deeply integrated across the Azure AI platform. The following
 | Azure AI Foundry | In the chat with your data playground, **Add your own data** uses Azure AI Search for grounding data and conversational search. This is the easiest and fastest approach for chatting with your data. |
 | Azure OpenAI | Azure OpenAI provides embedding models and chat models. Demos and samples target the [text-embedding-ada-002](/azure/ai-services/openai/concepts/models#embeddings-models). We recommend Azure OpenAI for generating embeddings for text. |
 | Azure AI Services | [Image Retrieval Vectorize Image API(Preview)](/azure/ai-services/computer-vision/how-to/image-retrieval#call-the-vectorize-image-api) supports vectorization of image content. We recommend this API for generating embeddings for images. |
-| Azure data platforms: Azure Blob Storage, Azure Cosmos DB | You can use [indexers](search-indexer-overview.md) to automate data ingestion, and then use [integrated vectorization](vector-search-integrated-vectorization.md) to generate embeddings. Azure AI Search can automatically index vector data from two data sources: [Azure blob indexers](search-howto-indexing-azure-blob-storage.md) and [Azure Cosmos DB for NoSQL indexers](search-howto-index-cosmosdb.md). For more information, see [Add vector fields to a search index.](vector-search-how-to-create-index.md). |
+| Azure data platforms: Azure Blob Storage, Azure Cosmos DB, Azure SQL, OneLake | You can use [indexers](search-indexer-overview.md) to automate data ingestion, and then use [integrated vectorization](vector-search-integrated-vectorization.md) to generate embeddings. Azure AI Search can automatically index vector data from [Azure blob indexers](search-howto-indexing-azure-blob-storage.md), [Azure Cosmos DB for NoSQL indexers](search-howto-index-cosmosdb.md), [Azure Data Lake Storage Gen2](search-howto-index-azure-data-lake-storage.md), [Azure Table Storage](search-howto-indexing-azure-tables.md), [Fabric OneLake](search-how-to-index-onelake-files.md). For more information, see [Add vector fields to a search index.](vector-search-how-to-create-index.md). |
 
 It's also commonly used in open-source frameworks like [LangChain](https://js.langchain.com/docs/integrations/vectorstores/azure_aisearch).
 
@@ -98,7 +98,7 @@ If you're new to vectors, this section explains some core concepts.
 
 ### About vector search
 
-Vector search is a method of information retrieval where documents and queries are represented as vectors instead of plain text. In vector search, machine learning models generate the vector representations of source inputs, which can be text, images, or other content. Having a mathematic representation of content provides a common basis for search scenarios. If everything is a vector, a query can find a match in vector space, even if the associated original content is in different media or language than the query.
+Vector search is a method of information retrieval where documents and queries are represented as vectors instead of plain text. In vector search, machine learning models generate the vector representations of source inputs, which can be text, images, or other content. Having a mathematic representation of content provides a common language for comparing disparate content. If everything is a vector, a query can find a match in vector space, even if the associated original content is in different media or language than the query.
 
 ### Why use vector search