Merge pull request #3817 from HeidiSteen/release-azs-4

[release-azure-search] Edits for readability, prep for broad distribution

Author: prmerger-automator[bot]

articles/search/search-faceted-navigation-examples.md

@@ -16,7 +16,7 @@ ms.date: 03/31/2025
 
 This section extends [faceted navigation configuration](search-faceted-navigation.md) with examples that demonstrate basic usage and other scenarios.
 
-Facetable fields are defined in an index, but facet parameters and expressions are defined in query requests. If you have an index with facetable fields, you can try new features like [facet hierarchs](#facet-hierarchy-example) and [aggregations](#facet-aggregation-example) on existing indexes.
+Facetable fields are defined in an index, but facet parameters and expressions are defined in query requests. If you have an index with facetable fields, you can try new preview features like [facet hierarchies](#facet-hierarchy-example), [facet aggregations](#facet-aggregation-example), and [facet filters](#facet-filtering-example) on existing indexes.
 
 ## Facet parameters and syntax
 

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

@@ -47,20 +47,20 @@ Two types of quantization are supported:
 
 ## Recommended rescoring techniques
 
-Rescoring is technique used to offset information loss due to vector compression. It uses oversampling to pick up extra vectors, and supplemental information to rescore initial results found by the query. Supplemental information is either uncompressed original full-precision vectors, or for binary quantization only, you have an alternative option of rescoring using the binary quantized document candidates against the query vector.
+Rescoring is a technique used to offset information loss due to vector compression. It uses oversampling to pick up extra vectors, and supplemental information to rescore initial results found by the query. Supplemental information is either uncompressed original full-precision vectors - or for binary quantization only - you have the option of rescoring using the binary quantized document candidates against the query vector. Rescoring options are specified in the index, but you can invoke rescoring at query time if the index supports it.
 
-Rescoring applies to:
+API versions determine which rescoring behavior is operational for your code. The most recent preview API supports a new rescoring approach for binary quantization. Indexes created with `2025-03-01-preview` can use the new rescoring behaviors.
 
-- scalar quantization using Hierarchical Navigable Small World (HNSW) graphs for similarity search
-- binary quantization, also using HNSW graphs
+| API version | Quantization type | Rescoring properties |
+|-------------|-------------------|------------------|
+| [2024-07-01](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-07-01&preserve-view=true) | Scalar and binary quantization, on vector indexes built using Hierarchical Navigable Small World (HNSW) graphs for similarity search | `rerankWithOriginalVectors` |
+| [2024-11-01-preview](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-11-01-preview&preserve-view=true) | Scalar and binary quantization on HNSW graphs | `rescoringOptions.enableRescoring` and `rescoreStorageMethod.preserveOriginals` |
+| [2025-03-01-preview](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2025-03-01-preview&preserve-view=true) | Binary quantization on HNSW graphs | Previous parameter combinations are still supported but binary quantization can now be rescored if original embeddings are deleted: `rescoringOptions.enableRescoring` and `rescoringOptions.rescoreStorageMethod=discardOriginals` |
 
-Exhaustive K Nearest Neighbors (eKNN) doesn't support rescoring.
+Only HNSW graphs allow rescoring. Exhaustive K Nearest Neighbors (eKNN) doesn't support rescoring.
 
-Rescoring occurs when you set a rescoring option in the index vector configuration:
-
-- In version 2024-07-01, set `rerankWithOriginalVectors`
-- In version 2024-11-01-preview, set `rescoringOptions.enableRescoring` and `rescoreStorageMethod.preserveOriginals`
-- In version 2025-03-01-preview, set `rescoringOptions.enableRescoring` and `rescoringOptions.rescoreStorageMethod=preserveOriginals` for scalar or binary quantization, or `rescoringOptions.enableRescoring` and `rescoringOptions.rescoreStorageMethod=discardOriginals` for binary quantization only
+<!-- - In version 2024-11-01-preview, set `rescoringOptions.enableRescoring` and `rescoreStorageMethod.preserveOriginals`
+- In version 2025-03-01-preview, set `rescoringOptions.enableRescoring` and `rescoringOptions.rescoreStorageMethod=preserveOriginals` for scalar or binary quantization, or `rescoringOptions.enableRescoring` and `rescoringOptions.rescoreStorageMethod=discardOriginals` for binary quantization only -->
 
 The generalized process for rescoring is:
 

articles/search/whats-new.md

@@ -24,10 +24,11 @@ Learn about the latest updates to Azure AI Search functionality, docs, and sampl
 
 | Item                         | Type |  Description |
 |-----------------------------|------|--------------|
-| [Service upgrade (preview)](search-how-to-upgrade.md) | Feature | Upgrade your search service to higher storage limits in your region. With an in-place upgrade, you no longer need to recreate your service. Available in [Upgrade Service (2025-02-01-preview)](/rest/api/searchmanagement/services/upgrade?view=rest-searchmanagement-2025-02-01-preview&preserve-view=true) and the Azure portal. |
-| [Service tier change (preview)](search-capacity-planning.md#change-your-service-tier) | Feature | Change the [pricing tier](search-sku-tier.md) of your search service. This provides flexibility to scale storage, increase request throughput, and decrease latency based on your needs. In this preview, you can only change between Basic and Standard (S1, S2, and S3) tiers. Available in [Update Service (2025-02-01-preview)](/rest/api/searchmanagement/services/update?view=rest-searchmanagement-2025-02-01-preview&preserve-view=true#searchupdateservicewithsku) and the Azure portal. |
-| [Facet hierarchies, aggregations, and facet filters (preview)](search-faceted-navigation-examples.md) | Feature | New facet query parameters support nested facets. For numeric facetable fields, you can sum the values of each field. You can also specify filters on a facet to add inclusion or exclusion criteria. |
-| [Rescore vector queries over binary quantization using full precision vectors (preview)](vector-search-how-to-quantization.md#recommended-rescoring-techniques) | Feature | For vector indexes that contain binary quantization, you can rescore query results using a full precision vector query. The query engine uses the dot product of the binary embeddings and the vector query for rescoring, which improves the quality of search results.  Set `enableRescoring` and `discardOriginals` to use this feature.|
+| [Service upgrade (preview)](search-how-to-upgrade.md) | Service | Upgrade your search service to higher storage limits in your region. With an in-place upgrade, you no longer need to recreate your service. Available in [Upgrade Service (2025-02-01-preview)](/rest/api/searchmanagement/services/upgrade?view=rest-searchmanagement-2025-02-01-preview&preserve-view=true) and the Azure portal. |
+| [Service tier change (preview)](search-capacity-planning.md#change-your-service-tier) | Service | Change the [pricing tier](search-sku-tier.md) of your search service. This provides flexibility to scale storage, increase request throughput, and decrease latency based on your needs. In this preview, you can only change between Basic and Standard (S1, S2, and S3) tiers. Available in [Update Service (2025-02-01-preview)](/rest/api/searchmanagement/services/update?view=rest-searchmanagement-2025-02-01-preview&preserve-view=true#searchupdateservicewithsku) and the Azure portal. |
+| [Facet hierarchies, aggregations, and facet filters (preview)](search-faceted-navigation-examples.md) | Queries | New facet query parameters support nested facets. For numeric facetable fields, you can sum the values of each field. You can also specify filters on a facet to add inclusion or exclusion criteria. Available in [Search Documents (2025-03-01-preview)](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2025-03-01-preview&preserve-view=true) and the Azure portal.|
+| [Rescore vector queries over binary quantization using full precision vectors (preview)](vector-search-how-to-quantization.md#recommended-rescoring-techniques) | Queries | For vector indexes that contain binary quantization, you can rescore query results using a full precision vector query. The query engine uses the dot product of the binary embeddings and the vector query for rescoring, which improves the quality of search results.  Set `enableRescoring` and `discardOriginals` to use this feature, and call the latest preview API version on the request.|
+| [Semantic ranker pre-release models (preview)](semantic-how-to-configure.md#opt-in-for-prerelease-semantic-ranking-models) | Index | Opt in to use pre-release semantic ranker models if one happens to be available in your region. Available in [Create or Update Index (2025-03-01-preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2025-03-01-preview#semanticconfiguration&preserve-view=true).|
 | [Search Service REST 2025-03-01-preview](/rest/api/searchservice/search-service-api-versions?view=rest-searchservice-2025-03-01-preview&preserve-view=true) | REST | Public preview release of REST APIs for data plane operations. Adds support for multi-vector embeddings, hierarchical facets, facet aggregation, and facet filters. |
 | [Search Management 2025-02-01-preview](/rest/api/searchmanagement/management-api-versions?view=rest-searchmanagement-2025-02-01-preview&preserve-view=true) | REST | Public review release of REST APIs for control plane operations. Adds support for in-place upgrade to higher capacity partitions, in-place upgrade to higher tiers, and Azure Confidential Compute. |