Merge pull request #554 from HeidiSteen/heidist-fresh

[azure search] port scoring profile updates from release to main

Author: denrea

articles/search/index-add-scoring-profiles.md

@@ -10,7 +10,7 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 09/23/2024
+ms.date: 10/01/2024
 ---
 
 # Add scoring profiles to boost search scores
@@ -89,14 +89,19 @@ See the [Extended example for vector and hybrid search](#extended-example-for-ve
 
 ## How search scoring works in Azure AI Search
 
-Scoring profiles supplement the default scoring algorithm by boosting the scores of matches that meet the profile's criteria. Scoring functions apply to keyword search, pure vector queries, and on hybrid queries. 
+Scoring profiles supplement the default scoring algorithm by boosting the scores of matches that meet the profile's criteria. Scoring functions apply to:
 
-When you use scoring profiles or any other boosting features in Azure AI Search, the [Reciprocal Ranking Function (RRF)](hybrid-search-ranking.md) algorithm assigns the score, including for standalone text and vector queries. Post-RRF, all scoring/boosting, [semantic ranking](semantic-search-overview.md), and [vector weighting](vector-search-how-to-query.md#vector-weighting) adjustments occur.
++ [Text (keyword) search](search-query-create.md)
++ [Pure vector queries](vector-search-how-to-query.md)
++ [Hybrid queries](hybrid-search-how-to-query.md), with text and vector subqueries execute in parallel
 
-:::image type="content" source="media/scoring-profiles/scoring-over-ranked-results.png" alt-text="Diagram showing which fields have a scoring profile and when ranking occurs.":::
+For standalone text queries, scoring profiles identify the maximum 1,000 matches in a [BM25-ranked search](index-similarity-and-scoring.md), and the top 50 are returned in results.
+
+For pure vectors, the query is vector-only, but the [*k*-matching documents](vector-search-ranking.md) include alphanumeric fields that a scoring profile can process. The scoring profile revised the result set by boosting documents that match criteria in the profile.
 
-> [!TIP]
-> You can use the [featuresMode (preview)](index-similarity-and-scoring.md#featuresmode-parameter-preview) parameter to request extra scoring details with the search results (including the field level scores).
+For text queries in a hybrid query, scoring profiles identify the maximum 1,000 matches in a BM25-ranked search. However, once those 1,000 results are identified, they're restored to their original BM25 order so that they can be rescored alongside vectors results in the final [Reciprocal Ranking Function (RRF)](hybrid-search-ranking.md) ordering, where the scoring profile (document boosting) is applied to the merged results, followed by [vector weighting](vector-search-how-to-query.md#vector-weighting), and [semantic ranking](semantic-search-overview.md) as the last step.
+
+:::image type="content" source="media/scoring-profiles/scoring-over-ranked-results.png" alt-text="Diagram showing which fields have a scoring profile and when ranking occurs.":::
 
 ## Add a scoring profile to a search index
 

articles/search/index-similarity-and-scoring.md

@@ -131,13 +131,13 @@ In Azure AI Search, you can configure BM25 algorithm parameters, and tune search
 | [Scoring algorithm configuration](index-ranking-similarity.md) | Search index |  |
 | [Scoring profiles](index-add-scoring-profiles.md) | Search index | Provide criteria for boosting the search score of a match based on content characteristics. For example, you might want to boost matches based on their revenue potential, promote newer items, or perhaps boost items that have been in inventory too long. A scoring profile is part of the index definition, composed of weighted fields, functions, and parameters. You can update an existing index with scoring profile changes, without incurring an index rebuild.|
 | [Semantic ranking](semantic-search-overview.md) | Query request | Applies machine reading comprehension to search results, promoting more semantically relevant results to the top. |
-| [featuresMode parameter](#featuresmode-parameter-preview) | Query request | This parameter is mostly used for unpacking a score, but it can be used for in code that provides a [custom scoring solution](https://github.com/Azure-Samples/search-ranking-tutorial). |
+| [featuresMode parameter](#featuresmode-parameter-preview) | Query request | This parameter is mostly used for unpacking a BM25-ranked score, but it can be used for in code that provides a [custom scoring solution](https://github.com/Azure-Samples/search-ranking-tutorial). |
 
 <a name="featuresMode-param"></a>
 
 ## featuresMode parameter (preview)
 
-[Search Documents](/rest/api/searchservice/documents/search-post) requests support a featuresMode parameter that provides more detail about a relevance score at the field level. Whereas the `@searchScore` is calculated for the document all-up (how relevant is this document in the context of this query), featuresMode reveals information about individual fields, as expressed in a `@search.features` structure. The structure contains all fields used in the query (either specific fields through **searchFields** in a query, or all fields attributed as **searchable** in an index). 
+[Search Documents](/rest/api/searchservice/documents/search-post) requests support a featuresMode parameter that provides more detail about a BM25 relevance score at the field level. Whereas the `@searchScore` is calculated for the document all-up (how relevant is this document in the context of this query), featuresMode reveals information about individual fields, as expressed in a `@search.features` structure. The structure contains all fields used in the query (either specific fields through **searchFields** in a query, or all fields attributed as **searchable** in an index). 
 
 For each field, `@search.features` give you the following values:
 
@@ -169,7 +169,7 @@ For a query that targets the "description" and "title" fields, a response that i
 
 You can consume these data points in [custom scoring solutions](https://github.com/Azure-Samples/search-ranking-tutorial) or use the information to debug search relevance problems.
 
-The featuresMode parameter isn't documented in the REST APIs, but you can use it on a preview REST API call to Search Documents.
+The featuresMode parameter isn't documented in the REST APIs, but you can use it on a preview REST API call to Search Documents for text (Keyword) search that's BM25-ranked.
 
 ## Number of ranked results in a full text query response