updates for readability

Author: HeidiSteen

articles/search/hybrid-search-how-to-query.md

@@ -14,11 +14,14 @@ ms.date: 06/12/2024
 
 # Create a hybrid query in Azure AI Search
 
-[Hybrid search](hybrid-search-overview.md) combines one or more keyword queries with one or more vector queries in a single search request. The queries execute in parallel. The results are merged and reordered by new search scores, using [Reciprocal Rank Fusion (RRF)](hybrid-search-ranking.md) to return a single ranked result set.
+[Hybrid search](hybrid-search-overview.md) combines one or more text (keyword) queries with one or more vector queries in a single search request. The queries execute in parallel. The results are merged and reordered by new search scores, using [Reciprocal Rank Fusion (RRF)](hybrid-search-ranking.md) to return a unified result set.
 
-In most cases, [per benchmark tests](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/azure-ai-search-outperforming-vector-search-with-hybrid/ba-p/3929167), hybrid queries with semantic ranking return the most relevant results.
+In many cases, [per benchmark tests](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/azure-ai-search-outperforming-vector-search-with-hybrid/ba-p/3929167), hybrid queries with semantic ranking return the most relevant results.
 
-New parameters [maxTextRecallSize and countAndFacetMode(preview)](#set-maxtextrecallsize-and-countandfacetmode-preview) give you more control over inputs into a hybrid query. You can also use [vector weighting](vector-search-how-to-query.md#vector-weighting-preview) on the vector side of a hybrid query to specify the relative weight of the vector query. This feature is particularly useful in complex queries where two or more distinct result sets need to be combined, as is the case for hybrid search. 
+To improve relevance:
+
++ New parameters [maxTextRecallSize and countAndFacetMode(preview)](#set-maxtextrecallsize-and-countandfacetmode-preview) give you more control over text inputs into a hybrid query. 
++ New [vector weighting](vector-search-how-to-query.md#vector-weighting-preview) lets you set the relative weight of the vector query. This feature is particularly useful in complex queries where two or more distinct result sets need to be combined, as is the case for hybrid search. 
 
 ## Prerequisites
 
@@ -235,19 +238,21 @@ api-key: {{admin-api-key}}
 
 ## Set maxTextRecallSize and countAndFacetMode (preview)
 
-In a `hybridSearch` query parameter object, specify the maximum number of documents recalled through the text portion of a hybrid (text and vector) query. You can also set `countAndFacetMode` property to report the counts for the text query (and for facets if you're using them) to the document limits set by `maxTextRecallSize`.
+This section explains how to adjust the inputs to a hybrid query by controlling the amount BM25-ranked results contributed to the query. Adjustments on the BM25-ranked results can improve hybrid query performance. Another option to consider is a supplemental or replacement technique, is [vector weighting](vector-search-how-to-query.md#vector-weighting-preview), which increases the importance of vector queries in the request.
+
+1. Use [Search - POST](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-05-01-preview&preserve-view=true) or [Search - GET](/rest/api/searchservice/documents/search-get?view=rest-searchservice-2024-05-01-preview&preserve-view=true) in `2024-05-01-Preview` to specify these parameters.
 
-The default maximum is `1000` documents. 
+1. Add a `hybridSearch` query parameter object to set the maximum number of documents recalled through the BM25-ranked results of a hybrid query. It has two properties:
 
-With `maxTextRecallSize`, you can increase or decrease the number of BM25-ranked results returned in hybrid queries. The default is `1000`. The maximum is `10000`.
+   + `maxTextRecallSize` specifies the number of BM25-ranked results to provide to the Reciprocal Rank Fusion (RRF) ranker used in hybrid queries. The default is 1,000. The maximum is 10,000.
 
-+ Reducing `maxTextRecallSize` below the default an significantly improve performance, assuming vector similarity search is generally performing better. You can also consider [vector weighting](vector-search-how-to-query.md#vector-weighting-preview) as an alternate approach for increasing the importance of vector queries.
+   + `countAndFacetMode` reports the counts for the BM25-ranked results (and for facets if you're using them). The default is all documents that match the query. Optionally, you can scope "count" to the `maxTextRecallSize`.
 
-+ Increasing `maxTextRecallSize` is useful if you have a large index, and the default isn't capturing a sufficient number of results. With a larger BM25-ranked result set, you can also set `top`, `skip`, and `next` to retrieve portions of those results.
+1. Lower `maxTextRecallSize` below the default to improve performance, assuming vector similarity search is generally outperforming the text-side of the the hybrid query.
 
-Use [Search - POST](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-05-01-preview&preserve-view=true) or [Search - GET](/rest/api/searchservice/documents/search-get?view=rest-searchservice-2024-05-01-preview&preserve-view=true) in `2024-05-01-Preview` to specify these parameters.
+1. Raise `maxTextRecallSize` if you have a large index, and the default isn't capturing a sufficient number of results. With a larger BM25-ranked result set, you can also set `top`, `skip`, and `next` to retrieve portions of those results.
 
-The first example lowers `maxTextRecallSize` to 100, limiting the text side of the hybrid query to just 100 document. It also sets `countAndFacetMode` to include only those results from `maxTextRecallSize`.
+The following REST examples show two use-cases for setting `maxTextRecallSize`. The first example lowers `maxTextRecallSize` to 100, limiting the text side of the hybrid query to just 100 document. It also sets `countAndFacetMode` to include only those results from `maxTextRecallSize`.
 
 ```http
 POST https://[service-name].search.windows.net/indexes/[index-name]/docs/search?api-version=2024-05-01-Preview 

articles/search/vector-search-how-to-query.md

@@ -597,36 +597,12 @@ POST https://[service-name].search.windows.net/indexes/[index-name]/docs/search?
 
 Filtering occurs before [fusing results](hybrid-search-ranking.md) from different recall sets. 
 
- <!-- This section should be in hybrid search only, but keeping a copy here to avoid broken links in a blog post. -->
+ <!-- Keep H2 as-is. Direct link from a blog post. Bulk of maxtextsizerecall has moved to hybrid query doc-->
 ## MaxTextSizeRecall for hybrid search (preview)
 
-In a `hybridSearch` query parameter object, specify the maximum number of documents recalled through the text portion of a hybrid (text and vector) query. The default is `1000` documents. With this parameter, you can increase or decrease the number of results returned in hybrid queries. Reducing the number of text documents retrieved can significantly improve performance. 
+Vector queries are often used in hybrid constructs that include nonvector fields. If you discover that BM25-ranked results are over or under represented in a hybrid query results, you can [set `maxTextRecallSize`](hybrid-search-how-to-query.md#set-maxtextrecallsize-and-countandfacetmode-preview) to increase or decrease the BM25-ranked results provided for hybrid ranking.
 
-You can set `countAndFacetMode` property to report the counts for the text query (and for facets if you're using them) to the document limits set by `maxTextRecallSize`.
-
-Use [Search - POST](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-05-01-preview&preserve-view=true) or [Search - GET](/rest/api/searchservice/documents/search-get?view=rest-searchservice-2024-05-01-preview&preserve-view=true) in `2024-05-01-Preview` to specify these parameters.
-
-The following example sets `maxTextRecallSize` to 100, which limits the results from the text side of the hybrid query to just 100 documents instead of all matches (up to 1,000 document default). It also sets `countAndFacetMode` to include only those results from `maxTextRecallSize`.
-
-```http
-POST https://[service-name].search.windows.net/indexes/[index-name]/docs/search?api-version=2024-05-01-Preview 
-
-    { 
-      "vectorQueries": [ 
-        { 
-          "kind": "vector", 
-          "vector": [1.0, 2.0, 3.0], 
-          "fields": "my_vector_field", 
-          "k": 10 
-        } 
-      ], 
-      "search": "hello world", 
-      "hybridSearch": { 
-        "maxTextRecallSize": 100, 
-        "countAndFacetMode": "countRetrievableResults" 
-      } 
-    } 
-```
+You can only set this property in hybrid requests that include both "search" and "vectorQueries" components.
 
 ## Vector weighting (preview)
 

articles/search/whats-new.md

@@ -7,7 +7,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: overview
-ms.date: 06/12/2024
+ms.date: 06/13/2024
 ms.custom:
   - references_regions
 ---
@@ -25,7 +25,7 @@ ms.custom:
 |-----------------------------|------|--------------|
 | [Higher capacity and more vector quota at every tier (same billing rate)](search-limits-quotas-capacity.md#service-limits) | Infrastructure | Partition sizes are now even larger for Standard 2 (S2), Standard 3 (S3), and Standard 3 High Density (S3 HD) for all services created after April 3, 2024. If you create a new service now, you get the larger partitions. If you created a new service between April 3 and May 17, you get the larger partitions automatically. <br><br>Storage Optimized tiers (L1 and L2) also have more capacity. L1 and L2 customers must create a new service to benefit from the higher capacity. There's no in-place upgrade at this time. <br><br>Extra capacity is now available in [more regions](search-limits-quotas-capacity.md#supported-regions-with-higher-storage-limits): Germany North​, Germany West Central​, South Africa North​, Switzerland West​, and Azure Government (Texas, Arizona, and Virginia).|
 | [OneLake integration (preview)](search-how-to-index-onelake-files.md) | Feature | New indexer for OneLake files and OneLake shortcuts. If you use Microsoft Fabric and OneLake for data access to Amazon Web Services (AWS) and Google data sources, use this indexer to import external data into a search index. This indexer is available through the Azure portal, the [2024-05-01-preview REST API](/rest/api/searchservice/data-sources/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true), and Azure SDK beta packages. |
-| [Vector results customization](vector-search-how-to-query.md) and [hybrid query relevance tuning](hybrid-search-how-to-query.md) | Feature | Three enhancements improve vector search relevance. <br><br>First, you can now set thresholds on vector search results to exclude low-scoring results. <br><br>Second, you can set `MaxTextRecallSize` and `countAndFacetMode` in hybrid queries to specify the maximum number of documents that can be recalled using text query in hybrid (text and vector) search. Previously, the maximum was fixed at `1000`, and the default is still `1000`. However, if you have more matches, you can now specify a higher limit on top and skip to get more results back. <br><br>Third, for hybrid queries queries, you can set a weight on vector queries to have more or less importance than the nonvector query. |
+| [Vector relevance](vector-search-how-to-query.md) <br>[hybrid query relevance](hybrid-search-how-to-query.md) | Feature | Four enhancements improve vector and hybrid search relevance. <br><br>First, you can now set thresholds on vector search results to exclude low-scoring results. <br><br>Second, changes in the query architecture apply scoring profiles at the end of the query pipeline for every query type. Document boosting is a common scoring profile, and it now works as expected on vector and hybrid queries.<br><br>Third, you can set `MaxTextRecallSize` and `countAndFacetMode` in hybrid queries to control the quantity of BM25-ranked search results to contribute to hybrid ranking. <br><br>Fourth, for vector and hybrid search, you can weight a vector queries to have boost or diminish its importance in a multi-query request. |
 | [Binary vectors support](/rest/api/searchservice/supported-data-types) | Feature | `Collection(Edm.Byte)` is a new supported data type. This data type opens up integration with the [Cohere v3 binary embedding models](https://cohere.com/blog/int8-binary-embeddings) and custom binary quantization. Narrow data types lower the cost of large vector datasets. See [Index binary data for vector search](vector-search-how-to-index-binary-data.md) for more information.| 
 | [Azure AI Vision multimodal embeddings skill (preview)](cognitive-search-skill-vision-vectorize.md) | Skill | New skill that's bound to the [multimodal embeddings API of Azure AI Vision](../ai-services/computer-vision/concept-image-retrieval.md). You can generate embeddings for text or images during indexing. This skill is available through the Azure portal and the [2024-05-01-preview REST API](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-05-01-preview&preserve-view=true).|
 | [Azure AI Vision vectorizer (preview)](vector-search-vectorizer-ai-services-vision.md) | Vectorizer | New vectorizer connects to an Azure AI Vision resource using the [multimodal embeddings API](../ai-services/computer-vision/concept-image-retrieval.md) to generate embeddings at query time. This vectorizer is available through the Azure portal and the [2024-05-01-preview REST API](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |
@@ -42,7 +42,7 @@ ms.custom:
 | Item&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | Type |  Description |
 |-----------------------------|------|--------------|
 | [Security update addressing information disclosure](https://msrc.microsoft.com/update-guide/vulnerability/CVE-2024-29063) | API | GET responses [no longer return connection strings or keys](search-api-migration.md#breaking-change-for-client-code-that-reads-connection-information). Applies to GET Skillset, GET Index, and GET Indexer. This change helps protect your Azure assets integrated with AI Search from unauthorized access. |
-| [**More storage on Basic and Standard tiers**](search-limits-quotas-capacity.md#service-limits) | Infrastructure |  Basic now supports up to three partitions and three replicas. Basic and Standard (S1, S2, S3) tiers have significantly more storage per partition, at the same per-partition billing rate. Extra capacity is subject to [regional availability](search-limits-quotas-capacity.md#supported-regions-with-higher-storage-limits) and applies to new search services created after April 3, 2024. Currently, there's no in-place upgrade, so please create a new search service to get the extra storage. |
+| [**More storage on Basic and Standard tiers**](search-limits-quotas-capacity.md#service-limits) | Infrastructure |  Basic now supports up to three partitions and three replicas. Basic and Standard (S1, S2, S3) tiers have significantly more storage per partition, at the same per-partition billing rate. Extra capacity is subject to [regional availability](search-limits-quotas-capacity.md#supported-regions-with-higher-storage-limits) and applies to new search services created after April 3, 2024. Currently, there's no in-place upgrade, so you must create a new search service to get the extra storage. |
 | [**More quota for vectors**](search-limits-quotas-capacity.md#vector-limits-on-services-created-between-april-3-2024-and-may-17-2024) | Infrastructure | Vector quotas are also higher on new services created after April 3, 2024 in selected regions. |
 | [**Vector quantization, narrow vector data types, and a new `stored` property (preview)**](vector-search-how-to-configure-compression-storage.md) | Feature | Collectively, these three features add vector compression and smarter storage options. First, *scalar quantization* reduces vector index size in memory and on disk. Second, [narrow data types](/rest/api/searchservice/supported-data-types) reduce per-field storage by storing smaller values. Third, you can use `stored` to opt-out of storing the extra copy of a vector that's used only for search results. If you don't need vectors in a query response, you can set `stored` to false to save on space. |
 | [**2024-03-01-preview Search REST API**](/rest/api/searchservice/search-service-api-versions#2024-03-01-preview) | API | New preview version of the Search REST APIs for the new data types, vector compression properties, and vector storage options. |