@@ -28,7 +28,7 @@ To improve relevance, use these parameters:
 
 + A search index containing `searchable` vector and nonvector fields. See [Create an index](search-how-to-create-search-index.md) and [Add vector fields to a search index](vector-search-how-to-create-index.md).
 
-+ (Optional) If you want [semantic ranking](semantic-how-to-configure.md), your search service must be Basic tier or higher, with [semantic ranking enabled](semantic-how-to-enable-disable.md).
++ (Optional) If you want the [semantic ranker](semantic-search-overview.md), your search service must be Basic tier or higher, with [semantic ranker enabled](semantic-how-to-enable-disable.md).
 
 + (Optional) If you want text-to-vector conversion of a query string, [create and assign a vectorizer](vector-search-how-to-configure-vectorizer.md) to vector fields in the search index.
 
@@ -167,7 +167,7 @@ api-key: {{admin-api-key}}
 
 ## Semantic hybrid search
 
-Assuming that you [enabled semantic ranking](semantic-how-to-enable-disable.md) and your index definition includes a [semantic configuration](semantic-how-to-query-request.md), you can formulate a query that includes vector search and keyword search, with semantic ranking over the merged result set. Optionally, you can add captions and answers. 
+Assuming that you [enabled semantic ranker](semantic-how-to-enable-disable.md) and your index definition includes a [semantic configuration](semantic-how-to-query-request.md), you can formulate a query that includes vector search and keyword search, with semantic ranking over the merged result set. Optionally, you can add captions and answers. 
 
 ```http
 POST https://{{search-service-name}}.search.windows.net/indexes/{{index-name}}/docs/search?api-version=2024-07-01
@@ -200,7 +200,7 @@ api-key: {{admin-api-key}}
 
 **Key points:**
 
-+ Semantic ranking accepts up to 50 results from the merged response.
++ Semantic ranker accepts up to 50 results from the merged response.
 
 + "queryType" and "semanticConfiguration" are required.
 
@@ -353,9 +353,9 @@ Both "k" and "top" are optional. Unspecified, the default number of results in a
 > [!NOTE]
 > The semantic ranker can take up to 50 results. 
 
-If you're using semantic ranking in 2024-05-01-preview API, it's a best practice to set "k" and "maxTextRecallSize" to sum to at least 50 total.  You can then restrict the results returned to the user with the "top" parameter. 
+If you're using semantic ranker in 2024-05-01-preview API, it's a best practice to set "k" and "maxTextRecallSize" to sum to at least 50 total.  You can then restrict the results returned to the user with the "top" parameter. 
 
-If you're using semantic ranking in previous APIs do the following:
+If you're using semantic ranker in previous APIs do the following:
 
 + if doing keyword-only search (no vector) set "top" to 50
 + if doing hybrid search set "k" to 50, to ensure that the semantic ranker gets at least 50 results. 

@@ -68,7 +68,7 @@ Key points include:
 + `select` specifies which fields to return in results, which can be text fields that are human readable.
 + `filters` can specify geospatial search or other include and exclude criteria, such as whether parking is included. The geospatial query in this example finds hotels within a 300-kilometer radius of Washington D.C.
 + `facets` can be used to compute facet buckets over results that are returned from hybrid queries.
-+ `queryType=semantic` invokes semantic ranking, applying machine reading comprehension to surface more relevant search results.
++ `queryType=semantic` invokes semantic ranker, applying machine reading comprehension to surface more relevant search results.
 
 Filters and facets target data structures within the index that are distinct from the inverted indexes used for full text search and the vector indexes used for vector search. As such, when filters and faceted operations execute, the search engine can apply the operational result to the hybrid search results in the response.
 
@@ -124,7 +124,7 @@ A response from the above query might look like this:
 
 Hybrid search combines the strengths of vector search and keyword search. The advantage of vector search is finding information that's conceptually similar to your search query, even if there are no keyword matches in the inverted index. The advantage of keyword or full text search is precision, with the ability to apply semantic ranking that improves the quality of the initial results. Some scenarios - such as querying over product codes, highly specialized jargon, dates, and people's names - can perform better with keyword search because it can identify exact matches.
 
-Benchmark testing on real-world and benchmark datasets indicates that hybrid retrieval with semantic ranking offers significant benefits in search relevance.
+Benchmark testing on real-world and benchmark datasets indicates that hybrid retrieval with semantic ranker offers significant benefits in search relevance.
 
 The following video explains how hybrid retrieval gives you optimal grounding data for generating useful AI responses.
 

@@ -9,7 +9,7 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 06/12/2024
+ms.date: 09/24/2024
 ---
 
 # Relevance scoring in hybrid search using Reciprocal Rank Fusion (RRF)
@@ -55,7 +55,7 @@ The following chart identifies the scoring property returned on each match, algo
 | hybrid search | `@search.score` | RRF algorithm | Upper limit is bounded by the number of queries being fused, with each query contributing a maximum of approximately 1 to the RRF score. For example, merging three queries would produce higher RRF scores than if only two search results are merged. |
 | semantic ranking | `@search.rerankerScore` | Semantic ranking | 0.00 - 4.00 |
 
-Semantic ranking doesn't participate in RRF. Its score (`@search.rerankerScore`) is always reported separately in the query response. Semantic ranking can rerank full text and hybrid search results, assuming those results include fields having semantically rich content.
+Semantic ranking occurs after RRF merging of results. Its score (`@search.rerankerScore`) is always reported separately in the query response. Semantic ranker can rerank full text and hybrid search results, assuming those results include fields having semantically rich content. It can rerank pure vector queries if the search documents include text fields that contain semantically relevant content.
 
 ## Weighted scores
 

@@ -115,7 +115,7 @@ Azure AI Search searches over content stored in the service. The code for upload
 
 #### Search an index
 
-Here's a query that invokes semantic ranking, with search options for specifying parameters:
+Here's a query that invokes semantic ranker, with search options for specifying parameters:
 
 ```csharp
 Console.WriteLine("Example of a semantic query.");

@@ -285,7 +285,7 @@ for result in results:
 
 In this final query, return semantic answers.
 
-Semantic ranking can generate answers to a query string that has the characteristics of a question. The generated answer is extracted verbatim from your content. To get a semantic answer, the question and answer must be closely aligned, and the model must find content that clearly answers the question. If potential answers fail to meet a confidence threshold, the model doesn't return an answer. For demonstration purposes, the question in this example is designed to get a response so that you can see the syntax.
+Semantic ranker can generate answers to a query string that has the characteristics of a question. The generated answer is extracted verbatim from your content. To get a semantic answer, the question and answer must be closely aligned, and the model must find content that clearly answers the question. If potential answers fail to meet a confidence threshold, the model doesn't return an answer. For demonstration purposes, the question in this example is designed to get a response so that you can see the syntax.
 
 ```python
 # Run a semantic query that returns semantic answers  

@@ -10,22 +10,34 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 11/06/2023
+ms.date: 09/23/2024
 ---
 
 # Add scoring profiles to boost search scores
 
-In this article, you'll learn how to define a scoring profile. A scoring profile is critera for boosting a search score based on parameters that you provide. For example, you might want matches found in a "tags" field to be more relevant than the same match found in "descriptions". Criteria can be a weighted field (such as the "tags" example) or a function. 
+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 defined in a search index and invoked on non-vector fields in query requests. You can create multiple profiles and then modify query logic to choose which one is used.
+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.
+
+## Key points about scoring profiles
+
+Scoring profile parameters are either:
+
++ Weighted fields, where a match is found in a specific string field. For example, you might want matches found in a "summary" field to be more relevant than the same match found in a "content" field.
+
++ Functions for numeric data, including dates, ranges, and geographic coordinates. There's also a Tags function that operates on a field providing an arbitrary collection of strings. You can choose this approach over weighted fields if you want to boost a score based on whether a match is found in a tags field.
+
+You can create multiple profiles and then modify query logic to choose which one is used.
+
+You can have up to 100 scoring profiles within an index (see [service Limits](search-limits-quotas-capacity.md)), but you can only specify one profile at time in any given query.
 
 > [!NOTE]
-> Unfamiliar with relevance concepts? The following [video segment on YouTube](https://www.youtube.com/embed/Y_X6USgvB1g?version=3&start=463&end=970) fast-forwards to how scoring profiles work in Azure AI Search. You can also visit [Relevance and scoring in Azure AI Search](index-similarity-and-scoring.md) for more background.
+> Unfamiliar with relevance concepts? Visit [Relevance and scoring in Azure AI Search](index-similarity-and-scoring.md) for background. You can also watch this [video segment on YouTube](https://www.youtube.com/embed/Y_X6USgvB1g?version=3&start=463&end=970) for scoring profiles over BM25-ranked results.
 >
 
 ## Scoring profile definition
 
-A scoring profile is named object defined in an index schema. A profile can be composed of weighted fields, functions, and parameters.
+A scoring profile is named object defined in an index schema. A scoring profile is composed of weighted fields, functions, and parameters.
 
 The following definition shows a simple profile named "geo". This example boosts results that have the search term in the hotelName field. It also uses the `distance` function to favor results that are within 10 kilometers of the current location. If someone searches on the term 'inn', and 'inn' happens to be part of the hotel name, documents that include hotels with 'inn' within a 10 KM radius of the current location will appear higher in the search results.  
 
@@ -73,52 +85,45 @@ POST /indexes/hotels/docs&api-version=2024-07-01
 
 This query searches on the term "inn" and passes in the current location. Notice that this query includes other parameters, such as scoringParameter. Query parameters, including "scoringParameter", are described in [Search Documents (REST API)](/rest/api/searchservice/documents/search-post).  
 
-See the [Extended example](#bkmk_ex) to review a more detailed example of a scoring profile.  
-
-<a name=what-is-default-scoring></a>
-
-## How scores are computed
-
-Scores are computed for full text search queries. Matches are scored based on how relevant the match is, and the highest scoring matches are returned in the query response. The overall score for each document is an aggregation of the individual scores for each field, where the individual score of each field is computed based on the term frequency and document frequency of the searched terms within that field (known as [TF-IDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) or term frequency-inverse document frequency). 
+See the [Extended example for vector and hybrid search](#extended-example-for-vector-and-hybrid-search) and [Extended example for keyword search](#extended-example-for-keyword-search) for more scenarios.
 
-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).
+## How search scoring works in Azure AI Search
 
-## When to add scoring logic
+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. 
 
-You should create one or more scoring profiles when the default ranking behavior doesn’t go far enough in meeting your business objectives. For example, you might decide that search relevance should favor newly added items. Likewise, you might have a field that contains profit margin, or some other field indicating revenue potential. Boosting results that are more meaningful to your users or the business is often the deciding factor in adoption of scoring profiles.
+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.
 
-Relevancy-based ordering in a search page is also implemented through scoring profiles. Consider search results pages you’ve used in the past that let you sort by price, date, rating, or relevance. In Azure AI Search, scoring profiles can be used to drive the ‘relevance’ option. The definition of relevance is user-defined, predicated on business objectives and the type of search experience you want to deliver.  
+:::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.":::
 
-## Steps for adding a scoring 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).
 
-To implement custom scoring behavior, add a scoring profile to the schema that defines the index. You can have up to 100 scoring profiles within an index (see [Service Limits](search-limits-quotas-capacity.md)), but you can only specify one profile at time in any given query.
+## Add a scoring profile to a search index
 
-1. Start with an index definition. You can add and update scoring profiles on an existing index without having to rebuild it. Use an [Update Index](/rest/api/searchservice/indexes/create-or-update) request to post your revision.
+1. Start with an [index definition](/rest/api/searchservice/indexes/create). You can add and update scoring profiles on an existing index without having to rebuild it. Use an [Create or Update Index](/rest/api/searchservice/indexes/create-or-update) request to post a revision.
 
-1. Paste in the [Template](#bkmk_template) provided in this article.  
+1. Paste in the [template](#template) provided in this article.  
 
-1. Provide a name. Scoring profiles are optional, but if you add one, the name is required. Be sure to follow Azure AI Search [naming conventions](/rest/api/searchservice/naming-rules) for fields (starts with a letter, avoids special characters and reserved words).  
+1. Provide a name that adheres to [naming conventions](/rest/api/searchservice/naming-rules).
 
-1. Specify boosting criteria. A single profile can contain [weighted fields](#weighted-fields), [functions](#functions), or both. 
+1. Specify boosting criteria. A single profile can contain [text weighted fields](#use-text-weighted-fields), [functions](#use-functions), or both. 
 
 You should work iteratively, using a data set that will help you prove or disprove the efficacy of a given profile.
 
 Scoring profiles can be defined in Azure portal as shown in the following screenshot, or programmatically through [REST APIs](/rest/api/searchservice/indexes/create-or-update) or in Azure SDKs, such as the [ScoringProfile](/dotnet/api/azure.search.documents.indexes.models.scoringprofile) class in the Azure SDK for .NET.
 
    :::image type="content" source="media/scoring-profiles/portal-add-scoring-profile-small.png" alt-text="Add scoring profiles page" lightbox="media/scoring-profiles/portal-add-scoring-profile.png" border="true":::
 
-<a name="weighted-fields"></a>
+## Use text-weighted fields
 
-### Using weighted fields
+Use text-weighted fields when field context is important and queries include `searchable` string fields. For example, if a query includes the term "airport", you might want "airport" in the Description field to have more weight than in the HotelName. 
 
-Use weighted fields when field context is important and queries are full text search. For example, if a query includes the term "airport", you might want "airport" in the Description field to have more weight than in the HotelName. 
-
-Weighted fields are composed of a searchable field and a positive number that is used as a multiplier. If the original field score of HotelName is 3, the boosted score for that field becomes 6, contributing to a higher overall score for the parent document itself.
+Weighted fields are name-value pairs composed of a `searchable` field and a positive number that is used as a multiplier. If the original field score of HotelName is 3, the boosted score for that field becomes 6, contributing to a higher overall score for the parent document itself.
 
 ```json
 "scoringProfiles": [  
     {  
-      "name": "boostKeywords",  
+      "name": "boostSearchTerms",  
       "text": {  
         "weights": {  
           "HotelName": 2,  
@@ -129,30 +134,27 @@ Weighted fields are composed of a searchable field and a positive number that is
 ]
 ```
 
-<a name="functions"></a>
-
-### Using functions
+## Use functions
 
 Use functions when simple relative weights are insufficient or don't apply, as is the case of distance and freshness, which are calculations over numeric data. You can specify multiple functions per scoring profile. For more information about the EDM data types used in Azure AI Search, see [Supported data types](/rest/api/searchservice/supported-data-types).
 
-| Function | Description |
+| Function | Description | Use cases |
 |-|-|
-| "freshness" | Boosts by values in a datetime field (`Edm.DateTimeOffset`). This function has a "boostingDuration" attribute so that you can specify a value representing a timespan over which boosting occurs. | 
-| "magnitude" | Boosts based on how high or low a numeric value is. Scenarios that call for this function include boosting by profit margin, highest price, lowest price, or a count of downloads. This function can only be used with `Edm.Double` and `Edm.Int` fields. For the magnitude function, you can reverse the range, high to low, if you want the inverse pattern (for example, to boost lower-priced items more than higher-priced items). Given a range of prices from $100 to $1, you would set "boostingRangeStart" at 100 and "boostingRangeEnd" at 1 to boost the lower-priced items. | 
-| "distance"  | Boosts by proximity or geographic location. This function can only be used with `Edm.GeographyPoint` fields. | 
-| "tag"  | Boosts by tags that are common to both search documents and query strings. Tags are provided in a "tagsParameter". This function can only be used with search fields of type `Edm.String` and `Collection(Edm.String)`. | 
+| distance  | Boost by proximity or geographic location. This function can only be used with `Edm.GeographyPoint` fields. | Use for "find near me" scenarios. |
+| freshness | Boost by values in a datetime field (`Edm.DateTimeOffset`). [Set boostingDuration](#set-boostingduration-for-freshness-function) to specify a value representing a timespan over which boosting occurs. | Use when you want to boost by newer or older dates. Rank items like calendar events with future dates such that items closer to the present can be ranked higher than items further in the future. One end of the range is fixed to the current time. To boost a range of times in the past, use a positive boostingDuration. To boost a range of times in the future, use a negative boostingDuration. |
+| magnitude | Alter rankings based on the range of values for a numeric field. The value must be an integer or floating-point number. For star ratings of 1 through 4, this would be 1. For margins over 50%, this would be 50. This function can only be used with `Edm.Double` and `Edm.Int` fields. For the magnitude function, you can reverse the range, high to low, if you want the inverse pattern (for example, to boost lower-priced items more than higher-priced items). Given a range of prices from $100 to $1, you would set `boostingRangeStart` at 100 and `boostingRangeEnd` at 1 to boost the lower-priced items. | Use when you want to boost by profit margin, ratings, clickthrough counts, number of downloads, highest price, lowest price, or a count of downloads. When two items are relevant, the item with the higher rating will be displayed first. |
+| tag  | Boost by tags that are common to both search documents and query strings. Tags are provided in a `tagsParameter`. This function can only be used with search fields of type `Edm.String` and `Collection(Edm.String)`. | Use when you have tag fields. If a given tag within the list is itself a comma-delimited list, you can [use a text normalizer](search-normalizers.md) on the field to strip out the commas at query time (map the comma character to a space). This approach will "flatten" the list so that all terms are a single, long string of comma-delimited terms. | 
 
 ### Rules for using functions
 
-+ Functions can only be applied to fields that are attributed as filterable.
++ Functions can only be applied to fields that are attributed as `filterable`.
 + Function type ("freshness", "magnitude", "distance", "tag") must be lower case.
 + Functions can't include null or empty values.
-
-<a name="bkmk_template"></a>
++ Functions can only have a single field per function definition. To use magnitude twice in the same profile, provide two definitions magnitude, one for each field.
 
 ## Template
 
- This section shows the syntax and template for scoring profiles. Refer to [Property reference](#bkmk_indexref) in the next section for descriptions of the scoring profile attributes.  
+ This section shows the syntax and template for scoring profiles. For a description of properties, see the [REST API reference](/rest/api/searchservice/indexes/create?view=rest-searchservice-2024-07-01&preserve-view=true#scoringfunctionaggregation).
 
 ```json
 "scoringProfiles": [  
@@ -201,54 +203,22 @@ Use functions when simple relative weights are insufficient or don't apply, as i
   }   
 ],   
 "defaultScoringProfile": (optional) "...", 
-```  
-
-<a name="bkmk_indexref"></a> 
-
-## Property reference
-
-|Attribute|Description|  
-|---------------|-----------------|  
-| name | Required. This is the name of the scoring profile. It follows the same naming conventions of a field. It must start with a letter, can't contain dots, colons or @ symbols, and can't start with the phrase azureSearch (case-sensitive).|  
-| text | Contains the weights property.|  
-| weights | Optional. Name-value pairs that specify a searchable field and a positive integer or floating-point number by which to boost a field's score. The positive integer or number becomes a multiplier for the original field score generated by the ranking algorithm. For example, if a field score is 2 and the weight value is 3, the boosted score for the field becomes 6. Individual field scores are then aggregated to create a document field score, which is then used to rank the document in the result set. |  
-| functions | Optional. A scoring function can only be applied to fields that are filterable.|  
-| functions > type | Required for scoring functions. Indicates the type of function to use. Valid values include magnitude, freshness, distance, and tag. You can include more than one function in each scoring profile. The function name must be lower case.|  
-| functions > boost | Required for scoring functions. A positive number used as multiplier for raw score. It can't be equal to 1.|  
-| functions > fieldname | Required for scoring functions. A scoring function can only be applied to fields that are part of the field collection of the index, and that are filterable. In addition, each function type introduces additional restrictions (freshness is used with datetime fields, magnitude with integer or double fields, and distance with location fields). You can only specify a single field per function definition. For example, to use magnitude twice in the same profile, you would need to include two definitions magnitude, one for each field.|  
-| functions > interpolation | Required for scoring functions. Defines the slope for which the score boosting increases from the start of the range to the end of the range. Valid values include Linear (default), Constant, Quadratic, and Logarithmic. See [Set interpolations](#bkmk_interpolation) for details.|  
-| functions > magnitude | The magnitude scoring function is used to alter rankings based on the range of values for a numeric field. Some of the most common usage examples of this are: </br></br>"Star ratings:" Alter the scoring based on the value within the "Star Rating" field. When two items are relevant, the item with the higher rating will be displayed first. </br>"Margin:" When two documents are relevant, a retailer may wish to boost documents that have higher margins first. </br>"Click counts:" For applications that track click through actions to products or pages, you could use magnitude to boost items that tend to get the most traffic. </br>"Download counts:" For applications that track downloads, the magnitude function lets you boost items that have the most downloads.|  
-| functions > magnitude > boostingRangeStart | Sets the start value of the range over which magnitude is scored. The value must be an integer or floating-point number. For star ratings of 1 through 4, this would be 1. For margins over 50%, this would be 50.|  
-| functions > magnitude > boostingRangeEnd | Sets the end value of the range over which magnitude is scored. The value must be an integer or floating-point number. For star ratings of 1 through 4, this would be 4.|  
-| functions > magnitude > constantBoostBeyondRange | Valid values are true or false (default). When set to true, the full boost will continue to apply to documents that have a value for the target field that’s higher than the upper end of the range. If false, the boost of this function won’t be applied to documents having a value for the target field that falls outside of the range.|  
-| functions > freshness | The freshness scoring function is used to alter ranking scores for items based on values in DateTimeOffset fields. For example, an item with a more recent date can be ranked higher than older items. </br></br>It's also possible to rank items like calendar events with future dates such that items closer to the present can be ranked higher than items further in the future. </br></br>In the current service release, one end of the range will be fixed to the current time. The other end is a time in the past based on the boostingDuration. To boost a range of times in the future, use a negative boostingDuration. </br></br>The rate at which the boosting changes from a maximum and minimum range is determined by the Interpolation applied to the scoring profile (see the figure below). To reverse the boosting factor applied, choose a boost factor of less than 1.|  
-| functions > freshness > boostingDuration | Sets an expiration period after which boosting will stop for a particular document. See [Set boostingDuration](#bkmk_boostdur) in the following section for syntax and examples.|  
-| functions > distance | The distance scoring function is used to affect the score of documents based on how close or far they're relative to a reference geographic location. The reference location is given as part of the query in a parameter (using the scoringParameter query parameter) as a `lon,lat` argument.|  
-|functions >  distance > referencePointParameter | A parameter to be passed in queries to use as reference location (using the scoringParameter query parameter). |  
-| functions > distance > boostingDistance | A number that indicates the distance in kilometers from the reference location where the boosting range ends.|  
-| functions > tag | The tag scoring function is used to affect the score of documents based on tags in documents and search queries. Documents that have tags in common with the search query will be boosted. The tags for the search query are provided as a scoring parameter in each search request (using the scoringParameter query parameter). |  
-| functions > tag > tagsParameter | A parameter to be passed in queries to specify tags for a particular request (using the scoringParameter query parameter). The parameter consists of a comma-delimited list of whole terms. If a given tag within the list is itself a comma-delimited list, you can [use a text normalizer](search-normalizers.md) on the field to strip out the commas at query time (map the comma character to a space). This approach will "flatten" the list so that all terms are a single, long string of comma-delimited terms. |  
-| functionAggregation | Optional. Applies only when functions are specified. Valid values include: sum (default), average, minimum, maximum, and firstMatching. A search score is single value that is computed from multiple variables, including multiple functions. This attribute indicates how the boosts of all the functions are combined into a single aggregate boost that then is applied to the base document score. The base score is based on the [tf-idf](https://wikipedia.org/wiki/Tf%E2%80%93idf) value computed from the document and the search query.|  
-| defaultScoringProfile | When executing a search request, if no scoring profile is specified, then default scoring is used ([tf-idf](https://wikipedia.org/wiki/Tf%E2%80%93idf) only). </br></br>You can override the built-in default, substituting a custom profile as the one to use when no specific profile is given in the search request.|  
-
-<a name="bkmk_interpolation"></a> 
+```
 
 ## Set interpolations
 
-Interpolations allow you to set the shape of the slope used for scoring. Because scoring is high to low, the slope is always decreasing, but the interpolation determines the curve of the downward slope. The following interpolations can be used:  
+Interpolations set the shape of the slope used for scoring. Because scoring is high to low, the slope is always decreasing, but the interpolation determines the curve of the downward slope. The following interpolations can be used:  
 
 | Interpolation | Description |  
 |-|-|  
-|`linear`|For items that are within the max and min range, the boost applied to the item will be done in a constantly decreasing amount. Linear is the default interpolation for a scoring profile.|  
-|`constant`|For items that are within the start and ending range, a constant boost will be applied to the rank results.|  
-|`quadratic`|In comparison to a Linear interpolation that has a constantly decreasing boost, Quadratic will initially decrease at smaller pace and then as it approaches the end range, it decreases at a much higher interval. This interpolation option isn't allowed in tag scoring functions.|  
-|`logarithmic`|In comparison to a Linear interpolation that has a constantly decreasing boost, Logarithmic will initially decrease at higher pace and then as it approaches the end range, it decreases at a much smaller interval. This interpolation option isn't allowed in tag scoring functions.|  
+|`linear`|For items that are within the max and min range, boosting is applied in a constantly decreasing amount. Linear is the default interpolation for a scoring profile.|  
+|`constant`|For items that are within the start and ending range, a constant boost is applied to the rank results.|  
+|`quadratic`|In comparison to a linear interpolation that has a constantly decreasing boost, Quadratic initially decreases at smaller pace and then as it approaches the end range, it decreases at a much higher interval. This interpolation option isn't allowed in tag scoring functions.|  
+|`logarithmic`|In comparison to a linear interpolation that has a constantly decreasing boost, logarithmic initially decreases at higher pace and then as it approaches the end range, it decreases at a much smaller interval. This interpolation option isn't allowed in tag scoring functions.|  
 
  ![Constant, linear, quadratic, log10 lines on graph](media/scoring-profiles/azuresearch_scorefunctioninterpolationgrapht.png "AzureSearch_ScoreFunctionInterpolationGrapht")  
 
-<a name="bkmk_boostdur"></a> 
-
-## Set boostingDuration
+## Set boostingDuration for freshness function
 
 `boostingDuration` is an attribute of the `freshness` function. You use it to set an expiration period after which boosting will stop for a particular document. For example, to boost a product line or brand for a 10-day promotional period, you would specify the 10-day period as "P10D" for those documents.  
 
@@ -263,11 +233,13 @@ The following table provides several examples.
 |15 minutes|"PT15M"|  
 |30 days, 5 hours, 10 minutes, and 6.334 seconds|"P30DT5H10M6.334S"|  
 
-For more examples, see [XML Schema: Datatypes (W3.org web site)](https://www.w3.org/TR/xmlschema11-2/#dayTimeDuration).  
+For more examples, see [XML Schema: Datatypes (W3.org web site)](https://www.w3.org/TR/xmlschema11-2/#dayTimeDuration).
+
+## Extended example for vector and hybrid search
 
-<a name="bkmk_ex"></a>
+See this [blog post](https://farzzy.hashnode.dev/enhance-azure-ai-search-document-boosting) and [notebook](https://github.com/farzad528/azure-ai-search-python-playground/blob/main/azure-ai-search-document-boosting.ipynb) for a demonstration of using scoring profiles and document boosting in vector and generative AI scenarios.
 
-## Extended example
+## Extended example for keyword search
 
 The following example shows the schema of an index with two scoring profiles (`boostGenre`, `newAndHighlyRated`). Any query against this index that includes either profile as a query parameter will use the profile to score the result set. 
 

@@ -61,7 +61,7 @@ Because the data is multiple rows in one CSV file, set the *parsing mode* to get
 
    A connection string can be full access, having the following format: `DefaultEndpointsProtocol=https;AccountName=<YOUR-ACCOUNT-NAME>;AccountKey=<YOUR-ACCOUNT-KEY>;EndpointSuffix=core.windows.net`
 
-   Or, a connection string can reference a managed identity, assuming it's [configured and assigned a role](search-howto-managed-identities-data-sources.md) in Azure Storage: `ResourceId=/subscriptions/{YOUR-SUBSCRIPTION-ID}/resourceGroups/{YOUR-RESOURCE-GROUP-NAME}/providers/Microsoft.Storage/storageAccounts/{YOUR-ACCOUNT-NAME};`
+   Or, a connection string can reference a managed identity, assuming it's [configured and assigned a role](search-howto-managed-identities-data-sources.md) in Azure Storage: `ResourceId=/subscriptions/<YOUR-SUBSCRIPTION-ID>/resourceGroups/<YOUR-RESOURCE-GROUP-NAME>/providers/Microsoft.Storage/storageAccounts/<YOUR-ACCOUNT-NAME>;`
 
 1. In **Containers**, enter the name of the blob container holding the data ("hotel-reviews").
 

@@ -130,7 +130,7 @@ Proximity searches are used to find terms that are near each other in a document
 
 Term boosting refers to ranking a document higher if it contains the boosted term, relative to documents that don't contain the term. This differs from scoring profiles in that scoring profiles boost certain fields, rather than specific terms.  
 
-The following example helps illustrate the differences. Suppose that there's a scoring profile that boosts matches in a certain field, say *genre* in the  [musicstoreindex example](index-add-scoring-profiles.md#bkmk_ex). Term boosting could be used to further boost certain search terms higher than others. For example, `rock^2 electronic` boosts documents that contain the search terms in the genre field higher than other searchable fields in the index. Further, documents that contain the search term *rock* are ranked higher than the other search term *electronic* as a result of the term boost value (2).  
+The following example helps illustrate the differences. Suppose that there's a scoring profile that boosts matches in a certain field, say *genre* in the  [musicstoreindex example](index-add-scoring-profiles.md#extended-example-for-keyword-search). Term boosting could be used to further boost certain search terms higher than others. For example, `rock^2 electronic` boosts documents that contain the search terms in the genre field higher than other searchable fields in the index. Further, documents that contain the search term *rock* are ranked higher than the other search term *electronic* as a result of the term boost value (2).  
 
  To boost a term, use the caret, `^`, symbol with a boost factor (a number) at the end of the term you're searching. You can also boost phrases. The higher the boost factor, the more relevant the term is relative to other search terms. By default, the boost factor is 1. Although the boost factor must be positive, it can be less than 1 (for example, 0.20).  
 

@@ -116,7 +116,7 @@ There's no query type in Azure AI Search - not even semantic or vector search -
 |---------------|---------|------------|
 | [Simple or full Lucene syntax](search-query-create.md) | Query execution over text and nonvector numeric content | Full text search is best for exact matches, rather than similar matches. Full text search queries are ranked using the [BM25 algorithm](index-similarity-and-scoring.md) and support relevance tuning through scoring profiles. It also supports filters and facets. |
 | [Filters](search-filters.md) and [facets](search-faceted-navigation.md) | Applies to text or numeric (nonvector) fields only. Reduces the search surface area based on inclusion or exclusion criteria. | Adds precision to your queries. |
-| [Semantic ranking](semantic-how-to-query-request.md) | Re-ranks a BM25 result set using semantic models. Produces short-form captions and answers that are useful as LLM inputs. | Easier than scoring profiles, and depending on your content, a more reliable technique for relevance tuning. |
+| [Semantic ranker](semantic-how-to-query-request.md) | Re-ranks a BM25 result set using semantic models. Produces short-form captions and answers that are useful as LLM inputs. | Easier than scoring profiles, and depending on your content, a more reliable technique for relevance tuning. |
   [Vector search](vector-search-how-to-query.md) | Query execution over vector fields for similarity search, where the query string is one or more vectors. | Vectors can represent all types of content, in any language. |
 | [Hybrid search](hybrid-search-how-to-query.md) | Combines any or all of the above query techniques. Vector and nonvector queries execute in parallel and are returned in a unified result set. | The most significant gains in precision and recall are through hybrid queries. |
 
@@ -145,7 +145,7 @@ Here are some tips for maximizing relevance and recall:
 
   + [Scoring profiles](index-add-scoring-profiles.md) that boost the search score if matches are found in a specific search field or on other criteria.
 
-  + [Semantic ranking](semantic-ranking.md) that re-ranks an initial results set, using semantic models from Bing to reorder results for a better semantic fit to the original query.
+  + [Semantic ranker](semantic-ranking.md) that re-ranks an initial results set, using semantic models from Bing to reorder results for a better semantic fit to the original query.
 
   + Query parameters for fine-tuning. You can [bump up the importance of vector queries](vector-search-how-to-query.md#vector-weighting) or [adjust the amount of BM25-ranked results](vector-search-how-to-query.md#maxtextsizerecall-for-hybrid-search-preview) in a hybrid query. You can also [set minimum thresholds to exclude low scoring results](vector-search-how-to-query.md#set-thresholds-to-exclude-low-scoring-results-preview) from a vector query.
 

@@ -39,7 +39,7 @@ Code samples from the Azure SDK development team demonstrate API usage. You can
 | [Indexing documents (push model)](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/search/Azure.Search.Documents/samples/Sample05_IndexingDocuments.md) | "Push" model indexing, where you send a JSON payload to an index on a service.   |
 | [Encryption key sample](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/search/Azure.Search.Documents/samples/Sample06_EncryptedIndex.md) | Demonstrates using a customer-managed encryption key to add an extra layer of protection over sensitive content.  |
 | [Vector search sample](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/search/Azure.Search.Documents/samples/Sample07_VectorSearch.md) | Shows you how to index a vector field and perform vector search using the Azure SDK for .NET. |
-| [Semantic ranking sample](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/search/Azure.Search.Documents/samples/Sample08_SemanticSearch.md) | Shows you how to configure semantic ranking in an index and invoke semantic queries using the Azure SDK for .NET. |
+| [Semantic ranking sample](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/search/Azure.Search.Documents/samples/Sample08_SemanticSearch.md) | Shows you how to configure semantic ranker in an index and invoke semantic queries using the Azure SDK for .NET. |
 
 ## Doc samples
 
@@ -52,7 +52,7 @@ Code samples from the Azure AI Search team demonstrate features and workflows. A
 |-------------|------------------|---------|
 | [create-mvc-app](https://github.com/Azure-Samples/azure-search-dotnet-samples/tree/main/create-mvc-app) |  [Tutorial: Add search to an ASP.NET Core (MVC) app](tutorial-csharp-create-mvc-app.md) | While most samples are console applications, this MVC sample uses a web page to front the sample Hotels index, demonstrating basic search, pagination, and other server-side behaviors.|
 | [quickstart](https://github.com/Azure-Samples/azure-search-dotnet-samples/tree/main/quickstart/v11) | [Quickstart: Full text search using the Azure SDKs](search-get-started-text.md) | Covers the basic workflow for creating, loading, and querying a search index in C# using sample data. |
-| [quickstart-semantic-search](https://github.com/Azure-Samples/azure-search-dotnet-samples/blob/main/quickstart-semantic-search/) | [Quickstart: Semantic ranking using the Azure SDKs](search-get-started-semantic.md) | Shows the index schema and query request for invoking semantic ranking. |
+| [quickstart-semantic-search](https://github.com/Azure-Samples/azure-search-dotnet-samples/blob/main/quickstart-semantic-search/) | [Quickstart: Semantic ranking using the Azure SDKs](search-get-started-semantic.md) | Shows the index schema and query request for invoking semantic ranker. |
 | [search-website](https://github.com/Azure-Samples/azure-search-static-web-app) | [Tutorial: Add search to web apps](tutorial-csharp-overview.md) | Demonstrates an end-to-end search app that includes bulk upload using the push APIs and a rich client for hosting the app and handling search requests.|
 | [tutorial-ai-enrichment](https://github.com/Azure-Samples/azure-search-dotnet-samples/tree/main/tutorial-ai-enrichment)  | [Tutorial: AI-generated searchable content from Azure blobs](cognitive-search-tutorial-blob-dotnet.md) | Shows how to configure an indexer and skillset. |
 | [multiple-data-sources](https://github.com/Azure-Samples/azure-search-dotnet-scale/tree/main/multiple-data-sources)  | [Tutorial: Index from multiple data sources](tutorial-multiple-data-sources.md). | Merges content from two data sources into one search index.

@@ -39,7 +39,7 @@ Code samples from the Azure AI Search team demonstrate features and workflows. M
 | [Tutorial-RAG](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Tutorial-RAG) | Source code for the Python portion of [How to build a RAG solution using Azure AI Search](tutorial-rag-build-solution.md).|
 | [Quickstart](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart) | Source code for the Python portion of [Quickstart: Full text search using the Azure SDKs](search-get-started-text.md). This article covers the basic workflow for creating, loading, and querying a search index using sample data. |
 | [Quickstart-RAG](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-RAG) | Source code for the Python portion of [Quickstart: Generative search (RAG) with grounding data from Azure AI Search](search-get-started-rag.md). |
-| [Quickstart-Semantic-Search](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Semantic-Search) | Source code for the Python portion of [Quickstart: Semantic ranking using the Azure SDKs](search-get-started-semantic.md). It shows the index schema and query request for invoking semantic ranking. |
+| [Quickstart-Semantic-Search](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Semantic-Search) | Source code for the Python portion of [Quickstart: Semantic ranking using the Azure SDKs](search-get-started-semantic.md). It shows the index schema and query request for invoking semantic ranker. |
 | [bulk-insert](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/bulk-insert) | Source code for the Python example of how to [use the push APIs](search-how-to-load-search-index.md) to upload and index documents. |
 | [azure-function-search](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/azure-function-search) | Source code for the Python example of an Azure function that sends queries to a search service. You can substitute this Python version of the `api` code used in the [Add search to web sites](tutorial-csharp-overview.md) C# sample. |
 

@@ -35,7 +35,7 @@ We recommend upgrading API versions in succession, working through each version
 
 Azure AI Search breaks backward compatibility as a last resort. Upgrade is necessary when:
 
-+ Your code references a retired or unsupported API version and is subject to one or more breaking changes. You must address breaking changes if your code targets [`2023-07-10-preview`](#code-upgrade-for-vector-indexes-and-queries) for vectors, [`2020-06-01-preview`](#breaking-change-for-semantic-ranking) for semantic ranking, and [`2019-05-06`](#upgrade-to-2019-05-06) for obsolete skills and workarounds. 
++ Your code references a retired or unsupported API version and is subject to one or more breaking changes. You must address breaking changes if your code targets [`2023-07-10-preview`](#code-upgrade-for-vector-indexes-and-queries) for vectors, [`2020-06-01-preview`](#breaking-change-for-semantic-ranker) for semantic ranker, and [`2019-05-06`](#upgrade-to-2019-05-06) for obsolete skills and workarounds. 
 
 + Your code fails when unrecognized properties are returned in an API response. As a best practice, your application should ignore properties that it doesn't understand.
 
@@ -63,13 +63,13 @@ Effective March 29, 2024 and applicable to all [supported REST APIs](/rest/api/s
 
 + If you need to retrieve connection strings of another Azure resource such as Azure Storage or Azure Cosmos DB, use the APIs of that resource and published guidance to obtain the information. 
 
-## Breaking change for semantic ranking
+## Breaking change for semantic ranker
 
-[Semantic ranking](semantic-search-overview.md) is generally available in `2023-11-01`. Here are the breaking changes for semantic ranking from earlier releases:
+[Semantic ranker](semantic-search-overview.md) is generally available in `2023-11-01`. Here are the breaking changes for semantic ranker from earlier releases:
 
 + In all versions after `2020-06-01-preview`: `semanticConfiguration` replaces `searchFields` as the mechanism for specifying which fields to use for L2 ranking.
 
-+ For all API versions, updates on July 14, 2023 to the Microsoft-hosted semantic models made semantic ranking language-agnostic, effectively decommissioning the `queryLanguage` property. There's no "breaking change" in code, but the property is ignored.
++ For all API versions, updates on July 14, 2023 to the Microsoft-hosted semantic models made semantic ranker language-agnostic, effectively decommissioning the `queryLanguage` property. There's no "breaking change" in code, but the property is ignored.
 
 See [Migrate from preview version](semantic-how-to-configure.md#migrate-from-preview-versions) to transition your code to use `semanticConfiguration`.
 
@@ -103,7 +103,7 @@ If you're upgrading from `2023-10-01-preview`, there are no breaking changes. Ho
 
 ## Upgrade to 2023-11-01
 
-[`2023-11-01`](/rest/api/searchservice/search-service-api-versions#2023-11-01) is a general release. The former preview features are now generally available: semantic ranking, vector index and query support.
+[`2023-11-01`](/rest/api/searchservice/search-service-api-versions#2023-11-01) is a general release. The former preview features are now generally available: semantic ranker, vector index and query support.
 
 There are no breaking changes from `2023-10-01-preview`, but there are multiple breaking changes from `2023-07-01-preview` to `2023-11-01`. For more information, see [Upgrade from 2023-07-01-preview](#upgrade-from-2023-07-01-preview).
 

@@ -30,15 +30,15 @@ A few service properties are fixed for the lifetime of the service. Before creat
 
 + [Service name](#name-the-service) becomes part of the URL endpoint. The name must be unique and it must conform to naming rules.
 
-+ [Region](search-region-support.md) determines data residency and the availability of certain features. Semantic ranking and Azure AI integration come with region requirements. Make sure your region of choice supports the features you need.
++ [Region](search-region-support.md) determines data residency and the availability of certain features. Semantic ranker and Azure AI integration come with region requirements. Make sure your region of choice supports the features you need.
 
 + [Service tier](search-sku-tier.md) determines infrastructure, service limits, and billing. Some features aren't available on lower or specialized tiers.
 
 ## Subscribe (free or paid)
 
 Paid (or billable) search occurs when you choose a billable tier (Basic or higher) when creating the resource on a billable Azure subscription.
 
-To try Azure AI Search for free, [open a trial subscription](https://azure.microsoft.com/pricing/free-trial/?WT.mc_id=A261C142F) and then create your search service by choosing the **Free** tier. You can have one free search service per Azure subscription. Free search services are intended for short-term evaluation of the product for nonproduction applications. Generally, you can complete all of the quickstarts and most tutorials, except for those featuring semantic ranking (it requires a billable service).
+To try Azure AI Search for free, [open a trial subscription](https://azure.microsoft.com/pricing/free-trial/?WT.mc_id=A261C142F) and then create your search service by choosing the **Free** tier. You can have one free search service per Azure subscription. Free search services are intended for short-term evaluation of the product for nonproduction applications. Generally, you can complete all of the quickstarts and most tutorials, except for those featuring semantic ranker (it requires a billable service).
 
 Alternatively, you can use free credits to try out paid Azure services. With this approach, you can create your search service at **Basic** or higher to get more capacity. Your credit card is never charged unless you explicitly change your settings and ask to be charged. Another approach is to [activate Azure credits in a Visual Studio subscription](https://azure.microsoft.com/pricing/member-offers/msdn-benefits-details/?WT.mc_id=A261C142F). A Visual Studio subscription gives you credits every month you can use for paid Azure services. 
 

@@ -77,9 +77,9 @@ For more secure connections:
 
 If you're starting with the free service, you're limited to 3 indexes, data sources, skillsets, and indexers. Basic limits you to 15. Make sure you have room for extra items before you begin. This quickstart creates one of each object.
 
-### Check for semantic ranking
+### Check for semantic ranker
 
-The wizard supports semantic ranking, but only on the Basic tier and higher, and only if semantic ranking is already [enabled on your search service](semantic-how-to-enable-disable.md). If you're using a billable tier, check whether semantic ranking is enabled.
+The wizard supports semantic ranking, but only on the Basic tier and higher, and only if semantic ranker is already [enabled on your search service](semantic-how-to-enable-disable.md). If you're using a billable tier, check whether semantic ranker is enabled.
 
 ## Prepare sample data
 

@@ -17,7 +17,7 @@ This quickstart shows you how to send queries to a Large Language Model (LLM) fo
 
 - An Azure subscription. [Create one for free](https://azure.microsoft.com/free/).
 
-- [Azure AI Search](search-create-service-portal.md), Basic tier or higher so that you can [enable semantic ranking](semantic-how-to-enable-disable.md). Region must be the same one used for Azure OpenAI.
+- [Azure AI Search](search-create-service-portal.md), Basic tier or higher so that you can [enable semantic ranker](semantic-how-to-enable-disable.md). Region must be the same one used for Azure OpenAI.
 
 - [Azure OpenAI](https://aka.ms/oai/access) resource with a deployment of `gpt-35-turbo`, `gpt-4`, or equivalent model, in the same region as Azure AI Search.
 
@@ -124,7 +124,7 @@ We recommend the hotels-sample-index, which can be created in minutes and runs o
 
 1. Run the following query in [Search Explorer](search-explorer.md) to test your index: `hotels near the ocean with beach access and good views`.
 
-   Output should look similar to the following example. Results that are returned directly from the search engine consist of fields and their verbatim values, along with metadata like a search score and a semantic ranking score and caption if you use semantic ranking.
+   Output should look similar to the following example. Results that are returned directly from the search engine consist of fields and their verbatim values, along with metadata like a search score and a semantic ranking score and caption if you use semantic ranker.
 
    ```
       "@search.score": 5.600783,
@@ -183,7 +183,7 @@ This section uses Visual Studio Code and Python to call the chat completion APIs
     AZURE_DEPLOYMENT_MODEL: str = "gpt-35-turbo"
    ```
 
-1. Run the following code to set query parameters. The query is a keyword search using semantic ranking. In a keyword search, the search engine returns up to 50 matches, but only the top 5 are provided to the model. If you can't [enable semantic ranking](semantic-how-to-enable-disable.md) on your search service, set the value to false.
+1. Run the following code to set query parameters. The query is a keyword search using semantic ranking. In a keyword search, the search engine returns up to 50 matches, but only the top 5 are provided to the model. If you can't [enable semantic rankersemantic-how-to-enable-disable.md) on your search service, set the value to false.
 
    ```python
    # Set query parameters for grounding the conversation on your search index

@@ -1,7 +1,7 @@
 ---
 title: 'Quickstart: semantic ranking'
 titleSuffix: Azure AI Search
-description: Change an existing index to use semantic ranking.
+description: Change an existing index to use semantic ranker.
 author: HeidiSteen
 manager: nitinme
 ms.author: heidist
@@ -16,9 +16,9 @@ ms.date: 03/11/2024
 
 # Quickstart: Semantic ranking with .NET or Python
 
-In Azure AI Search, [semantic ranking](semantic-search-overview.md) is query-side functionality that uses machine reading comprehension from Microsoft to rescore search results, promoting the most semantically relevant matches to the top of the list. Depending on the content and the query, semantic ranking can [significantly improve search relevance](https://techcommunity.microsoft.com/t5/azure-ai-services-blog/azure-cognitive-search-outperforming-vector-search-with-hybrid/ba-p/3929167), with minimal work for the developer.
+In Azure AI Search, [semantic ranker](semantic-search-overview.md) is query-side functionality that uses machine reading comprehension from Microsoft to rescore search results, promoting the most semantically relevant matches to the top of the list. Depending on the content and the query, semantic ranking can [significantly improve search relevance](https://techcommunity.microsoft.com/t5/azure-ai-services-blog/azure-cognitive-search-outperforming-vector-search-with-hybrid/ba-p/3929167), with minimal work for the developer.
 
-This quickstart walks you through the index and query modifications that invoke semantic ranking.
+This quickstart walks you through the index and query modifications that invoke semantic ranker.
 
 > [!NOTE]
 > Looking for an Azure AI Search solution with ChatGPT interaction? See [this demo](https://github.com/Azure-Samples/azure-search-openai-demo/blob/main/README.md) or [this accelerator](https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator) for details.
@@ -27,7 +27,7 @@ This quickstart walks you through the index and query modifications that invoke
 
 + An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/).
 
-+ Azure AI Search, at Basic tier or higher, with [semantic ranking enabled](semantic-how-to-enable-disable.md).
++ Azure AI Search, at Basic tier or higher, with [semantic ranker enabled](semantic-how-to-enable-disable.md).
 
 + An API key and search service endpoint. Sign in to the [Azure portal](https://portal.azure.com) and [find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices).
 
@@ -39,11 +39,11 @@ This quickstart walks you through the index and query modifications that invoke
 
 ## Add semantic ranking
 
-To use semantic ranking, add a *semantic configuration* to a search index, and add parameters to a query. If you have an existing index, you can make these changes without having to reindex your content because there's no impact on the structure of your searchable content.
+To use semantic ranker, add a *semantic configuration* to a search index, and add parameters to a query. If you have an existing index, you can make these changes without having to reindex your content because there's no impact on the structure of your searchable content.
 
 + A semantic configuration sets a priority order for fields that contribute a title, keywords, and content used in semantic reranking. Field prioritization allows for faster processing.
 
-+ Queries that invoke semantic ranking include parameters for query type and whether captions and answers are returned. You can add these parameters to your existing query logic. There's no conflict with other parameters.
++ Queries that invoke semantic ranker include parameters for query type and whether captions and answers are returned. You can add these parameters to your existing query logic. There's no conflict with other parameters.
 
 ### [**.NET**](#tab/dotnet)
 
@@ -63,7 +63,7 @@ You can find and manage resources in the portal, using the **All resources** or
 
 ## Next steps
 
-In this quickstart, you learned how to invoke semantic ranking on an existing index. We recommend trying semantic ranking on your own indexes as a next step. However, if you want to continue with demos, visit the following link.
+In this quickstart, you learned how to invoke semantic ranker on an existing index. We recommend trying semantic ranker on your own indexes as a next step. However, if you want to continue with demos, visit the following link.
 
 > [!div class="nextstepaction"]
 > [Tutorial: Add search to web apps](tutorial-csharp-overview.md)

@@ -30,7 +30,7 @@ If you don't have an Azure subscription, create a [free account](https://azure.m
 
   Most existing services support vector search. For a small subset of services created prior to January 2019, an index that contains vector fields fails on creation. In this situation, a new service must be created.
 
-- Optionally, to run the query example that invokes [semantic reranking](semantic-search-overview.md), your search service must be the Basic tier or higher, with [semantic ranking enabled](semantic-how-to-enable-disable.md).
+- Optionally, to run the query example that invokes [semantic reranking](semantic-search-overview.md), your search service must be the Basic tier or higher, with [semantic ranker enabled](semantic-how-to-enable-disable.md).
 
 - Optionally, an [Azure OpenAI](https://aka.ms/oai/access) resource with a deployment of `text-embedding-ada-002`. The source `.rest` file includes an optional step for generating new text embeddings, but we provide pregenerated embeddings so that you can omit this dependency.
 

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: tutorial
-ms.date: 01/18/2024
+ms.date: 09/23/2024
 ms.custom:
   - devx-track-csharp
   - devx-track-dotnet
@@ -87,7 +87,7 @@ If you have an existing Azure SQL Database resource, you can add the hotels tabl
 1. Copy the ADO.NET connection string for the database. Under **Settings** > **Connection Strings**, copy the ADO.NET connection string, similar to the example below.
 
     ```sql
-    Server=tcp:{your_dbname}.database.windows.net,1433;Initial Catalog=hotels-db;Persist Security Info=False;User ID={your_username};Password={your_password};MultipleActiveResultSets=False;Encrypt=True;TrustServerCertificate=False;Connection Timeout=30;
+    Server=tcp:<YOUR-DATABASE-NAME>.database.windows.net,1433;Initial Catalog=hotels-db;Persist Security Info=False;User ID=<YOUR-USER-NAME>;Password=<YOUR-PASSWORD>;MultipleActiveResultSets=False;Encrypt=True;TrustServerCertificate=False;Connection Timeout=30;
     ```
 
 You'll need this connection string in the next exercise, setting up your environment.
@@ -114,7 +114,7 @@ API calls require the service URL and an access key. A search service is created
 
 1. For `SearchServiceEndPoint`, if the full URL on the service overview page is "https://my-demo-service.search.windows.net", then the value to provide is the entire URL.
 
-1. For `AzureSqlConnectionString`, the string format is similar to this: `"Server=tcp:{your_dbname}.database.windows.net,1433;Initial Catalog=hotels-db;Persist Security Info=False;User ID={your_username};Password={your_password};MultipleActiveResultSets=False;Encrypt=True;TrustServerCertificate=False;Connection Timeout=30;"`
+1. For `AzureSqlConnectionString`, the string format is similar to this: `"Server=tcp:<your-database-name>.database.windows.net,1433;Initial Catalog=hotels-db;Persist Security Info=False;User ID=<your-user-name>;Password=<your-password>;MultipleActiveResultSets=False;Encrypt=True;TrustServerCertificate=False;Connection Timeout=30;"`
 
     ```json
     {

@@ -27,7 +27,7 @@ The Management REST API is available in stable and preview versions. Be sure to
 > * [Create or update a service](#create-or-update-a-service)
 > * [Enable Azure role-based access control for data plane](#enable-rbac)
 > * [Enforce a customer-managed key policy](#enforce-cmk)
-> * [Disable semantic ranking](#disable-semantic-search)
+> * [Disable semantic ranker](#disable-semantic-ranker)
 > * [Disable workloads that push data to external resources](#disable-external-access)
 > * [Create a query key](#create-query-api-keys)
 > * [Regenerate an admin key](#regenerate-admin-api-keys)
@@ -228,14 +228,12 @@ PATCH https://management.azure.com/subscriptions/{{subscriptionId}}/resourcegrou
     }
 ```
 
-<a name="disable-semantic-search"></a>
+## Disable semantic ranker
 
-## Disable semantic ranking
-
-Although [semantic ranking isn't enabled](semantic-how-to-enable-disable.md) by default, you could lock down the feature at the service level.
+Although [semantic ranker isn't enabled](semantic-how-to-enable-disable.md) by default, you could lock down the feature at the service level for greater certainty it can't be used.
 
 ```http
-### disable semantic ranking
+### disable semantic ranker
 PATCH https://management.azure.com/subscriptions/{{subscriptionId}}/resourcegroups/{{resource-group}}/providers/Microsoft.Search/searchServices/{{search-service-name}}?api-version=2023-11-01 HTTP/1.1
      Content-type: application/json
      Authorization: Bearer {{token}}

@@ -94,7 +94,7 @@ By default, a search service is created in a minimum configuration of one replic
 Some features add to the cost of running the service:
 
 + [How you're charged for Azure AI Search](search-sku-manage-costs.md#how-youre-charged-for-azure-ai-search) explains which features have billing impact.
-+ [(Optional) disable semantic ranking](semantic-how-to-enable-disable.md) at the service level to prevent usage of the feature.
++ [(Optional) disable semantic ranker](semantic-how-to-enable-disable.md) at the service level to prevent usage of the feature.
 
 ### Enable diagnostic logging
 
@@ -107,9 +107,9 @@ Internally, Microsoft collects telemetry data about your service and the platfor
 > [!NOTE]
 > See the ["Data residency"](search-security-overview.md#data-residency) section of the security overview article for more information about data location and privacy.
 
-### Enable semantic ranking
+### Enable semantic ranker
 
-Semantic ranking is free for the first 1,000 requests per month, but you must opt in to get the free quota. 
+Semantic ranker is free for the first 1,000 requests per month, but you must opt in to get the free quota. 
 
 In Azure portal, under **Settings** on the leftmost pane, select **Semantic ranker** and then choose the Free plan. For more information, see [Enable semantic ranker](semantic-how-to-enable-disable.md).
 

@@ -165,7 +165,7 @@ For either algorithm, a "@search.score" equal to 1.00 indicates an unscored or u
 
 ### Order by the semantic reranker
 
-If you're using [semantic ranking](semantic-search-overview.md), the "@search.rerankerScore" determines the sort order of your results. 
+If you're using [semantic ranker](semantic-search-overview.md), the "@search.rerankerScore" determines the sort order of your results. 
 
 The "@search.rerankerScore" range is 1 to 4.00, where a higher score indicates a stronger semantic match.
 

@@ -27,30 +27,15 @@ This article identifies the cloud regions in which Azure AI Search is available.
 | [Azure OpenAI integration](vector-search-integrated-vectorization.md)  | Refers to skills and vectorizers that make internal calls to deployed embedding and chat models on Azure OpenAI. Check [Azure OpenAI model region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability) for the most current list of regions for each embedding and chat model. Specific Azure OpenAI models are in fewer regions, so be sure to check for joint regional availability before installing.|
 | [Azure AI Studio integration](vector-search-integrated-vectorization-ai-studio.md) | Refers to skills and vectorizers that make internal calls to the models hosted in the model catalog. Check [Azure AI Studio region availability](/azure/ai-studio/reference/region-support) for the most current list of regions. |
 | [Azure AI Vision 4.0 multimodal APIs for image vectorization](search-get-started-portal-image-search.md) | Refers to skills and vectorizers that call the multimodal embedding API. Check the [Azure AI Vision region list](/azure/ai-services/computer-vision/overview-image-analysis#region-availability) for joint regional availability. |
-| [Semantic ranking](semantic-search-overview.md) | Takes a dependency on Microsoft-hosted models in specific regions. Regional support is noted in this article. |
-
-<!-- Each cloud region noted in this article includes a column indicating support for the following features.
-
-- [Semantic ranking](semantic-search-overview.md) depends on models hosted in specific regions.
-- [AI enrichment](cognitive-search-concept-intro.md) refers to skills and vectorizers that make internal calls to Azure AI and Azure OpenAI. Integration requires that Azure AI Search coexist with an [Azure AI multi-service account](/azure/ai-services/multi-service-resource) in the same physical region.
-- [Availability zones](search-reliability.md#availability-zone-support) are an Azure platform capability that divides a region's data centers into distinct physical location groups to provide high-availability, within the same region.
-
-We recommend that you check [Azure AI Studio region availability](/azure/ai-studio/reference/region-support) and [Azure OpenAI model region availability](/azure/reliability/availability-zones-service-support#azure-regions-with-availability-zone-support) for the most current list of regions for those features. 
-
-Also, if you plan to use Azure AI Vision 4.0 multimodal APIs for image vectorization, it's available in a reduced list of regions. [Check the Azure AI Vision region list for multimodal embeddings](/azure/ai-services/computer-vision/overview-image-analysis#region-availability) and be sure to create both your Azure AI multi-service account and Azure AI Search service in one of those supported regions.
-
-> [!NOTE]
-> Higher capacity partitions became available in selected regions starting in April 2024. A second wave of higher capacity partitions released in May 2024. Currently, there are just a few regions that *don't* offer higher capacity patitions, and those are indicated in footnotes.
->
-> If you're using an older search service, consider creating a new search service in a supported region to benefit from more capacity at the same billing rate as before. For more information, see [Service limits](search-limits-quotas-capacity.md#service-limits) and [How to check service creation date](vector-search-index-size.md#how-to-check-service-creation-date). -->
+| [Semantic ranker](semantic-search-overview.md) | Takes a dependency on Microsoft-hosted models in specific regions. Regional support is noted in this article. |
 
 ## Azure Public regions
 
 You can create an Azure AI Search resource in any of the following Azure public regions. Almost all of these regions support [higher capacity tiers](search-limits-quotas-capacity.md#service-limits). Exceptions are noted where they apply.
 
 ### Americas
 
-| Region | AI integration | Semantic ranking | Availability zones |
+| Region | AI integration | Semantic ranker | Availability zones |
 |--|--|--|--|
 | Brazil South​​ ​ | ✅ | ✅ | |
 | Canada Central​​ | ✅ | ✅ | ✅ |
@@ -71,7 +56,7 @@ You can create an Azure AI Search resource in any of the following Azure public
 
 ### Europe
 
-| Region | AI integration | Semantic ranking | Availability zones |
+| Region | AI integration | Semantic ranker | Availability zones |
 |--|--|--|--|
 | North Europe​​ | ✅ | ✅ | ✅ |
 | West Europe​​ <sup>1, 2</sup>| ✅ | ✅ | ✅ |
@@ -93,7 +78,7 @@ You can create an Azure AI Search resource in any of the following Azure public
 
 ### Middle East
 
-| Region | AI integration | Semantic ranking | Availability zones |
+| Region | AI integration | Semantic ranker | Availability zones |
 |--|--|--|--|
 | Israel Central​ <sup>2</sup> |  |  | ✅  |
 | Qatar Central​ <sup>1, 2</sup> |  |  | ✅ |
@@ -105,13 +90,13 @@ You can create an Azure AI Search resource in any of the following Azure public
 
 ### Africa
 
-| Region | AI integration | Semantic ranking | Availability zones |
+| Region | AI integration | Semantic ranker | Availability zones |
 |--|--|--|--|
 | South Africa North​ | ✅ |  | ✅ |
 
 ### Asia Pacific
 
-| Region | AI integration | Semantic ranking | Availability zones |
+| Region | AI integration | Semantic ranker | Availability zones |
 |--|--|--|--|
 | Australia East​ ​ | ✅ | ✅ | ✅ |
 | Australia Southeast​​​ |  | ✅ |  |
@@ -131,7 +116,7 @@ You can create an Azure AI Search resource in any of the following Azure public
 
 ## Azure Government regions
 
-| Region | AI integration | Semantic ranking | Availability zones |
+| Region | AI integration | Semantic ranker | Availability zones |
 |--|--|--|--|
 | Arizona | ✅ | ✅  | |
 | Texas |  |  |  |
@@ -141,7 +126,7 @@ You can create an Azure AI Search resource in any of the following Azure public
 
 ## Azure operated by 21Vianet
 
-| Region | AI integration | Semantic ranking | Availability zones |
+| Region | AI integration | Semantic ranker | Availability zones |
 |--|--|--|--|
 | China East |  |  |  |
 | China East 2 <sup>1</sup> | ✅  | | |

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: how-to
-ms.date: 08/05/2024
+ms.date: 09/23/2024
 ms.custom:
   - references_regions
   - ignite-2023
@@ -207,19 +207,19 @@ Conditions that prevent you from adopting this approach include:
     Content-Type: application/json
 
     {
-      "location": "[region]",
+      "location": "<your-region>",
       "sku": {
-        "name": "[sku]"
+        "name": "<your-sku>"
       },
       "properties": {
-        "replicaCount": [replica count],
-        "partitionCount": [partition count],
+        "replicaCount": <your-replica-count>,
+        "partitionCount": <your-partition count>,
         "hostingMode": "default"
       },
       "identity": {
         "type": "UserAssigned",
         "userAssignedIdentities": {
-          "/subscriptions/[subscription ID]/resourcegroups/[resource group name]/providers/Microsoft.ManagedIdentity/userAssignedIdentities/[managed identity name]": {}
+          "/subscriptions/<your-subscription-ID>/resourcegroups/<your-resource-group-name>/providers/Microsoft.ManagedIdentity/userAssignedIdentities/<your-managed-identity-name>": {}
         }
       }
     } 
@@ -230,12 +230,12 @@ Conditions that prevent you from adopting this approach include:
     ```json
     {
       "encryptionKey": {
-        "keyVaultUri": "https://[key vault name].vault.azure.net",
-        "keyVaultKeyName": "[key vault key name]",
-        "keyVaultKeyVersion": "[key vault key version]",
+        "keyVaultUri": "<YOUR-KEY-VAULT-URI>",
+        "keyVaultKeyName": "<YOUR-ENCRYPTION-KEY-NAME>",
+        "keyVaultKeyVersion": "<YOUR-ENCRYPTION-KEY-VERSION>",
         "identity" : { 
             "@odata.type": "#Microsoft.Azure.Search.DataUserAssignedIdentity",
-            "userAssignedIdentity" : "/subscriptions/[subscription ID]/resourceGroups/[resource group name]/providers/Microsoft.ManagedIdentity/userAssignedIdentities/[managed identity name]"
+            "userAssignedIdentity" : "/subscriptions/<your-subscription-ID>/resourceGroups/<your-resource-group-name>/providers/Microsoft.ManagedIdentity/userAssignedIdentities/<your-managed-identity-name>"
         }
       }
     }
@@ -312,9 +312,9 @@ Encryption keys are added when you create an object. To add a customer-managed k
     ```json
     {
       "encryptionKey": {
-        "keyVaultUri": "https://demokeyvault.vault.azure.net",
-        "keyVaultKeyName": "myEncryptionKey",
-        "keyVaultKeyVersion": "eaab6a663d59439ebb95ce2fe7d5f660"
+        "keyVaultUri": "<YOUR-KEY-VAULT-URI>",
+        "keyVaultKeyName": "<YOUR-ENCRYPTION-KEY-NAME>",
+        "keyVaultKeyVersion": "<YOUR-ENCRYPTION-KEY-VERSION>"
       }
     }
     ```
@@ -324,12 +324,12 @@ Encryption keys are added when you create an object. To add a customer-managed k
     ```json
     {
       "encryptionKey": {
-        "keyVaultUri": "https://demokeyvault.vault.azure.net",
-        "keyVaultKeyName": "myEncryptionKey",
-        "keyVaultKeyVersion": "eaab6a663d59439ebb95ce2fe7d5f660",
+        "keyVaultUri": "<YOUR-KEY-VAULT-URI>",
+        "keyVaultKeyName": "<YOUR-ENCRYPTION-KEY-NAME>",
+        "keyVaultKeyVersion": "<YOUR-ENCRYPTION-KEY-VERSION>",
         "accessCredentials": {
-          "applicationId": "00000000-0000-0000-0000-000000000000",
-          "applicationSecret": "myApplicationSecret"
+          "applicationId": "<YOUR-APPLICATION-ID>",
+          "applicationSecret": "<YOUR-APPLICATION-SECRET>"
         }
       }
     }
@@ -363,7 +363,7 @@ In this section, you set the policy that defines a CMK standard for your search
 1. Call the [Services - Update API](/rest/api/searchmanagement/services/update) to enable CMK policy enforcement at the service level.
 
 ```http
-PATCH https://management.azure.com/subscriptions/[subscriptionId]/resourceGroups/[resourceGroupName]/providers/Microsoft.Search/searchServices/[serviceName]?api-version=2023-11-01
+PATCH https://management.azure.com/subscriptions/<your-subscription-Id>/resourceGroups/<your-resource-group-name>/providers/Microsoft.Search/searchServices/<your-search-service-name>?api-version=2023-11-01
 
 {
     "properties": {
@@ -399,12 +399,12 @@ The details of creating a new index via the REST API could be found at [Create I
   {"name": "Location", "type": "Edm.GeographyPoint", "filterable": true, "sortable": true}
  ],
   "encryptionKey": {
-    "keyVaultUri": "https://demokeyvault.vault.azure.net",
-    "keyVaultKeyName": "myEncryptionKey",
-    "keyVaultKeyVersion": "eaab6a663d59439ebb95ce2fe7d5f660",
+    "keyVaultUri": "<YOUR-KEY-VAULT-URI>",
+    "keyVaultKeyName": "<YOUR-ENCRYPTION-KEY-NAME>",
+    "keyVaultKeyVersion": "<YOUR-ENCRYPTION-KEY-VERSION>",
     "accessCredentials": {
-      "applicationId": "00000000-0000-0000-0000-000000000000",
-      "applicationSecret": "myApplicationSecret"
+      "applicationId": "<YOUR-APPLICATION-ID>",
+      "applicationSecret": "<YOUR-APPLICATION-SECRET>"
     }
   }
 }
@@ -423,12 +423,12 @@ Create an encrypted synonym map using the [Create Synonym Map Azure AI Search RE
   "synonyms" : "United States, United States of America, USA\n
   Washington, Wash. => WA",
   "encryptionKey": {
-    "keyVaultUri": "https://demokeyvault.vault.azure.net",
-    "keyVaultKeyName": "myEncryptionKey",
-    "keyVaultKeyVersion": "eaab6a663d59439ebb95ce2fe7d5f660",
+    "keyVaultUri": "<YOUR-KEY-VAULT-URI>",
+    "keyVaultKeyName": "<YOUR-ENCRYPTION-KEY-NAME>",
+    "keyVaultKeyVersion": "<YOUR-ENCRYPTION-KEY-VERSION>",
     "accessCredentials": {
-      "applicationId": "00000000-0000-0000-0000-000000000000",
-      "applicationSecret": "myApplicationSecret"
+      "applicationId": "<YOUR-APPLICATION-ID>",
+      "applicationSecret": "<YOUR-APPLICATION-SECRET>"
     }
   }
 }
@@ -449,12 +449,12 @@ Create an encrypted data source using the [Create Data Source (REST API)](/rest/
   },
   "container" : { "name" : "containername" },
   "encryptionKey": {
-    "keyVaultUri": "https://demokeyvault.vault.azure.net",
-    "keyVaultKeyName": "myEncryptionKey",
-    "keyVaultKeyVersion": "eaab6a663d59439ebb95ce2fe7d5f660",
+    "keyVaultUri": "<YOUR-KEY-VAULT-URI>",
+    "keyVaultKeyName": "<YOUR-ENCRYPTION-KEY-NAME>",
+    "keyVaultKeyVersion": "<YOUR-ENCRYPTION-KEY-VERSION>",
     "accessCredentials": {
-      "applicationId": "00000000-0000-0000-0000-000000000000",
-      "applicationSecret": "myApplicationSecret"
+      "applicationId": "<YOUR-APPLICATION-ID>",
+      "applicationSecret": "<YOUR-APPLICATION-SECRET>"
     }
   }
 }
@@ -473,12 +473,12 @@ Create an encrypted skillset using the [Create Skillset REST API](/rest/api/sear
     "cognitiveServices": { omitted for brevity },
       "knowledgeStore":  { omitted for brevity  },
     "encryptionKey": (optional) { 
-        "keyVaultKeyName": "myEncryptionKey",
-        "keyVaultKeyVersion": "eaab6a663d59439ebb95ce2fe7d5f660",
-        "keyVaultUri": "https://demokeyvault.vault.azure.net",
+        "keyVaultKeyName": "<YOUR-ENCRYPTION-KEY-NAME>",
+        "keyVaultKeyVersion": "<YOUR-ENCRYPTION-KEY-VERSION>",
+        "keyVaultUri": "<YOUR-KEY-VAULT-URI>",
         "accessCredentials": {
-            "applicationId": "00000000-0000-0000-0000-000000000000",
-            "applicationSecret": "myApplicationSecret"}
+          "applicationId": "<YOUR-APPLICATION-ID>",
+          "applicationSecret": "<YOUR-APPLICATION-SECRET>"
     }
 }
 ```
@@ -500,12 +500,12 @@ Create an encrypted indexer using the [Create Indexer REST API](/rest/api/search
       }
   },
   "encryptionKey": {
-    "keyVaultUri": "https://demokeyvault.vault.azure.net",
-    "keyVaultKeyName": "myEncryptionKey",
-    "keyVaultKeyVersion": "eaab6a663d59439ebb95ce2fe7d5f660",
+    "keyVaultUri": "<YOUR-KEY-VAULT-URI>",
+    "keyVaultKeyName": "<YOUR-ENCRYPTION-KEY-NAME>",
+    "keyVaultKeyVersion": "<YOUR-ENCRYPTION-KEY-VERSION>",
     "accessCredentials": {
-      "applicationId": "00000000-0000-0000-0000-000000000000",
-      "applicationSecret": "myApplicationSecret"
+      "applicationId": "<YOUR-APPLICATION-ID>",
+      "applicationSecret": "<YOUR-APPLICATION-SECRET>"
     }
   }
 }

@@ -165,7 +165,7 @@ HTTP/1.1 201 Created
 Transfer-Encoding: chunked
 Content-Type: application/json; odata.metadata=minimal; odata.streaming=true; charset=utf-8
 ETag: "0x8DC43A5FDB8448F"
-Location: https://free-demo-search-svc.search.windows.net:443/datasources('ny-philharmonic-ds')?api-version=2024-07-01
+Location: https://<YOUR-SEARCH-SERVICE-NAME>.search.windows.net:443/datasources('ny-philharmonic-ds')?api-version=2024-07-01
 Server: Microsoft-IIS/10.0
 Strict-Transport-Security: max-age=2592000, max-age=15724800; includeSubDomains
 Preference-Applied: odata.include-annotations="*"
@@ -176,7 +176,7 @@ Date: Wed, 13 Mar 2024 21:38:58 GMT
 Connection: close
 
 {
-  "@odata.context": "https://free-demo-search-svc.search.windows.net/$metadata#datasources/$entity",
+  "@odata.context": "https://<YOUR-SEARCH-SERVICE-NAME>.search.windows.net/$metadata#datasources/$entity",
   "@odata.etag": "\"0x8DC43A5FDB8448F\"",
   "name": "ny-philharmonic-ds",
   "description": null,
@@ -308,7 +308,7 @@ Date: Wed, 13 Mar 2024 22:09:59 GMT
 Connection: close
 
 {
-  "@odata.context": "https://free-demo-search-svc.search.windows.net/indexes('ny-philharmonic-index')/$metadata#docs(*)",
+  "@odata.context": "https://<YOUR-SEARCH-SERVICE-NAME>.search.windows.net/indexes('ny-philharmonic-index')/$metadata#docs(*)",
   "@odata.count": 1521,
   "@search.nextPageParameters": {
     "search": "*",
@@ -317,7 +317,7 @@ Connection: close
   },
   "value": [
   ],
-  "@odata.nextLink": "https://free-demo-search-svc.search.windows.net/indexes/ny-philharmonic-index/docs/search?api-version=2024-07-01"
+  "@odata.nextLink": "https://<YOUR-SEARCH-SERVICE-NAME>.search.windows.net/indexes/ny-philharmonic-index/docs/search?api-version=2024-07-01"
 }
 ```
 

@@ -128,7 +128,7 @@ Within @search.answers:
 
 + **"score"** is a confidence score that reflects the strength of the answer. If there are multiple answers in the response, this score is used to determine the order. Top answers and top captions can be derived from different search documents, where the top answer originates from one document, and the top caption from another, but in general the same documents appear in the top positions within each array.
 
-Answers are followed by the **"value"** array, which always includes scores, captions, and any fields that are retrievable by default. If you specified the select parameter, the "value" array is limited to the fields that you specified. See [Configure semantic ranking](semantic-how-to-configure.md) for details.
+Answers are followed by the **"value"** array, which always includes scores, captions, and any fields that are retrievable by default. If you specified the select parameter, the "value" array is limited to the fields that you specified. See [Configure semantic ranker](semantic-how-to-configure.md) for details.
 
 ## Tips for producing high-quality answers
 
@@ -144,4 +144,4 @@ For best results, return semantic answers on a document corpus having the follow
 
 + [Semantic ranking overview](semantic-search-overview.md)
 + [Configure BM25 ranking](index-ranking-similarity.md)
-+ [Configure semantic ranking](semantic-how-to-configure.md)
++ [Configure semantic ranker](semantic-how-to-configure.md)

@@ -13,7 +13,7 @@ ms.topic: how-to
 ms.date: 08/05/2024
 ---
 
-# Configure semantic ranking and return captions in search results
+# Configure semantic ranker and return captions in search results
 
 This article explains how to configure a search index for semantic reranking. 
 

@@ -21,7 +21,7 @@ Semantic ranker is a premium feature that's billed by usage. By default, semanti
 
 Check the [Products Available by Region](https://azure.microsoft.com/global-infrastructure/services/?products=search) page on the Azure web site to see if your region is listed.
 
-## Enable semantic ranking
+## Enable semantic ranker
 
 Follow these steps to enable [semantic ranker](semantic-search-overview.md) at the service level. Once enabled, it's available to all indexes. You can't turn it on or off for specific indexes.
 
@@ -41,7 +41,7 @@ The free plan is capped at 1,000 queries per month. After the first 1,000 querie
 
 ### [**REST**](#tab/enable-rest)
 
-To enable semantic ranking using the REST API, you can use the [Create or Update Service API](/rest/api/searchmanagement/services/create-or-update?view=rest-searchmanagement-2023-11-01&tabs=HTTP#searchsemanticsearch&preserve-view=true).
+To enable semantic ranker using the REST API, you can use the [Create or Update Service API](/rest/api/searchmanagement/services/create-or-update?view=rest-searchmanagement-2023-11-01&tabs=HTTP#searchsemanticsearch&preserve-view=true).
 
 Management REST API calls are authenticated through Microsoft Entra ID. See [Manage your Azure AI Search service with REST APIs](search-manage-rest.md) for instructions on how to authenticate.
 
@@ -63,9 +63,9 @@ PATCH https://management.azure.com/subscriptions/{{subscriptionId}}/resourcegrou
 
 ---
 
-## Disable semantic ranking using the REST API
+## Disable semantic ranker using the REST API
 
-To reverse feature enablement, or for full protection against accidental usage and charges, you can disable semantic ranking using the [Create or Update Service API](/rest/api/searchmanagement/services/create-or-update#searchsemanticsearch) on your search service. After the feature is disabled, any requests that include the semantic query type will be rejected.
+To reverse feature enablement, or for full protection against accidental usage and charges, you can disable semantic ranker using the [Create or Update Service API](/rest/api/searchmanagement/services/create-or-update#searchsemanticsearch) on your search service. After the feature is disabled, any requests that include the semantic query type will be rejected.
 
 Management REST API calls are authenticated through Microsoft Entra ID. See [Manage your Azure AI Search service with REST APIs](search-manage-rest.md) for instructions on how to authenticate.
 
@@ -78,8 +78,8 @@ PATCH https://management.azure.com/subscriptions/{{subscriptionId}}/resourcegrou
     }
 ```
 
-To re-enable semantic ranking, rerun the above request, setting "semanticSearch" to either "free" (default) or "standard".
+To re-enable semantic ranker, rerun the above request, setting "semanticSearch" to either "free" (default) or "standard".
 
 ## Next steps
 
-[Configure semantic ranking](semantic-how-to-configure.md) so that you can test out semantic ranking on your content.
+[Configure semantic ranker](semantic-how-to-configure.md) so that you can test out semantic ranking on your content.

@@ -1,7 +1,7 @@
 ---
 title: Add semantic ranking
 titleSuffix: Azure AI Search
-description: Set a semantic query type to attach the deep learning models of semantic ranking.
+description: Set a semantic query type to attach the deep learning models of semantic ranker.
 
 manager: nitinme
 author: HeidiSteen
@@ -17,11 +17,11 @@ ms.date: 07/24/2024
 
 This article explains how to invoke the semantic ranker on queries. You can apply semantic ranking to text queries, hybrid queries, and vector queries if your search documents contain string fields and the [vector query has a text representation](vector-search-how-to-query.md#query-with-integrated-vectorization).
 
-Semantic ranking iterates over an initial result set, applying an L2 ranking methodology that promotes the most semantically relevant results to the top of the stack. You can also get semantic captions, with highlights over the most relevant terms and phrases, and [semantic answers](semantic-answers.md).
+Semantic ranker iterates over an initial result set, applying an L2 ranking methodology that promotes the most semantically relevant results to the top of the stack. You can also get semantic captions, with highlights over the most relevant terms and phrases, and [semantic answers](semantic-answers.md).
 
 ## Prerequisites
 
-+ A search service, basic tier or higher, with [semantic ranking enabled](semantic-how-to-enable-disable.md).
++ A search service, basic tier or higher, with [semantic ranker enabled](semantic-how-to-enable-disable.md).
 
 + An existing search index with a [semantic configuration](semantic-how-to-configure.md) and rich text content.
 
@@ -136,7 +136,7 @@ The following example in this section uses the [hotels-sample-index](search-get-
 
 ### [**.NET SDK**](#tab/dotnet-query)
 
-Use QueryType or SemanticQuery to invoke semantic ranking on a semantic query. The [following example](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/search/Azure.Search.Documents/samples/Sample08_SemanticSearch.md) is from the Azure SDK team.
+Use QueryType or SemanticQuery to invoke semantic ranker on a semantic query. The [following example](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/search/Azure.Search.Documents/samples/Sample08_SemanticSearch.md) is from the Azure SDK team.
 
 ```csharp
 SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>(
@@ -237,4 +237,4 @@ If you anticipate consistent throughput requirements near, at, or higher than th
 Semantic ranking can be used in hybrid queries that combine keyword search and vector search into a single request and a unified response.
 
 > [!div class="nextstepaction"]
-> [Hybrid query with semantic ranking](hybrid-search-how-to-query.md#semantic-hybrid-search)
\ No newline at end of file
+> [Hybrid query with semantic ranker](hybrid-search-how-to-query.md#semantic-hybrid-search)
\ No newline at end of file

@@ -15,20 +15,20 @@ ms.date: 06/12/2024
 
 # Semantic ranking in Azure AI Search
 
-In Azure AI Search, *semantic ranking* is a feature that measurably improves search relevance by using Microsoft's language understanding models to rerank search results. This article is a high-level introduction. The section at the end covers [availability and pricing](#availability-and-pricing).
+In Azure AI Search, *semantic ranker* is a feature that measurably improves search relevance by using Microsoft's language understanding models to rerank search results. This article is a high-level introduction. The section at the end covers [availability and pricing](#availability-and-pricing).
 
 Semantic ranker is a premium feature, billed by usage. We recommend this article for background, but if you'd rather get started, follow these steps:
 
 > [!div class="checklist"]
 > * [Check regional availability](search-region-support.md)
 > * [Sign in to Azure portal](https://portal.azure.com) to verify your search service is Basic or higher
-> * [Enable semantic ranking and choose a pricing plan](semantic-how-to-enable-disable.md)
-> * [Set up a semantic configuration in a search index](semantic-how-to-configure.md)
+> * [Enable semantic ranker and choose a pricing plan](semantic-how-to-enable-disable.md)
+> * [Configure semantic ranker in a search index](semantic-how-to-configure.md)
 > * [Set up queries to return semantic captions and highlights](semantic-how-to-query-request.md)
 > * [Optionally, return semantic answers](semantic-answers.md)
 
 > [!NOTE]
-> Semantic ranking doesn't use generative AI or vectors. If you're looking for vector support and similarity search? See [Vector search in Azure AI Search](vector-search-overview.md) for details.
+> Semantic ranker doesn't use generative AI or vectors. If you're looking for vector support and similarity search? See [Vector search in Azure AI Search](vector-search-overview.md) for details.
 
 ## What is semantic ranking?
 
@@ -42,7 +42,7 @@ Here are the capabilities of the semantic reranker.
 
 | Feature | Description |
 |---------|-------------|
-| Semantic ranking | Uses the context or semantic meaning of a query to compute a new relevance score over preranked results. |
+| Semantic ranker | Uses the context or semantic meaning of a query to compute a new relevance score over preranked results. |
 | [Semantic captions and highlights](semantic-how-to-query-request.md) | Extracts verbatim sentences and phrases from a document that best summarize the content, with highlights over key passages for easy scanning. Captions that summarize a result are useful when individual content fields are too dense for the search results page. Highlighted text elevates the most relevant terms and phrases so that users can quickly determine why a match was considered relevant. |
 | [Semantic answers](semantic-answers.md) | An optional and extra substructure returned from a semantic query. It provides a direct answer to a query that looks like a question. It requires that a document has text with the characteristics of an answer. |
 
@@ -62,7 +62,7 @@ There are two steps to semantic ranking: summarization and scoring. Outputs cons
 
 In semantic ranking, the query subsystem passes search results as an input to summarization and ranking models. Because the ranking models have input size constraints and are processing intensive, search results must be sized and structured (summarized) for efficient handling.
 
-1. Semantic ranking starts with a [BM25-ranked result](index-ranking-similarity.md) from a text query or an [RRF-ranked result](hybrid-search-ranking.md) from a hybrid query. Only text fields are used in the reranking exercise, and only the top 50 results progress to semantic ranking, even if results include more than 50. Typically, fields used in semantic ranking are informational and descriptive.
+1. Semantic ranker starts with a [BM25-ranked result](index-ranking-similarity.md) from a text query or an [RRF-ranked result](hybrid-search-ranking.md) from a hybrid query. Only text fields are used in the reranking exercise, and only the top 50 results progress to semantic ranking, even if results include more than 50. Typically, fields used in semantic ranking are informational and descriptive.
 
 1. For each document in the search result, the summarization model accepts up to 2,000 tokens, where a token is approximately 10 characters. Inputs are assembled from the "title", "keyword", and "content" fields listed in the [semantic configuration](semantic-how-to-configure.md). 
 
@@ -111,11 +111,11 @@ Semantic ranker is a newer technology so it's important to set expectations abou
 
 * Find strings to use as captions and answers. Captions and answers are returned in the response and can be rendered on a search results page.
 
-What semantic ranking *can't* do is rerun the query over the entire corpus to find semantically relevant results. Semantic ranking reranks the existing result set, consisting of the top 50 results as scored by the default ranking algorithm. Furthermore, semantic ranking can't create new information or strings. Captions and answers are extracted verbatim from your content so if the results don't include answer-like text, the language models won't produce one.
+What semantic ranker *can't* do is rerun the query over the entire corpus to find semantically relevant results. Semantic ranking reranks the existing result set, consisting of the top 50 results as scored by the default ranking algorithm. Furthermore, semantic ranker can't create new information or strings. Captions and answers are extracted verbatim from your content so if the results don't include answer-like text, the language models won't produce one.
 
-Although semantic ranking isn't beneficial in every scenario, certain content can benefit significantly from its capabilities. The language models in semantic ranking work best on searchable content that is information-rich and structured as prose. A knowledge base, online documentation, or documents that contain descriptive content see the most gains from semantic ranking capabilities.
+Although semantic ranking isn't beneficial in every scenario, certain content can benefit significantly from its capabilities. The language models in semantic ranker work best on searchable content that is information-rich and structured as prose. A knowledge base, online documentation, or documents that contain descriptive content see the most gains from semantic ranker capabilities.
 
-The underlying technology is from Bing and Microsoft Research, and integrated into the Azure AI Search infrastructure as an add-on feature. For more information about the research and AI investments backing semantic ranking, see [How AI from Bing is powering Azure AI Search (Microsoft Research Blog)](https://www.microsoft.com/research/blog/the-science-behind-semantic-search-how-ai-from-bing-is-powering-azure-cognitive-search/).
+The underlying technology is from Bing and Microsoft Research, and integrated into the Azure AI Search infrastructure as an add-on feature. For more information about the research and AI investments backing semantic ranker, see [How AI from Bing is powering Azure AI Search (Microsoft Research Blog)](https://www.microsoft.com/research/blog/the-science-behind-semantic-search-how-ai-from-bing-is-powering-azure-cognitive-search/).
 
 The following video provides an overview of the capabilities.
 
@@ -132,10 +132,10 @@ When you enable semantic ranker, choose a pricing plan for the feature:
 
 The [Azure AI Search pricing page](https://azure.microsoft.com/pricing/details/search/) shows you the billing rate for different currencies and intervals.
 
-Charges for semantic ranking are levied when query requests include `queryType=semantic` and the search string isn't empty (for example, `search=pet friendly hotels in New York`). If your search string is empty (`search=*`), you aren't charged, even if the queryType is set to semantic.
+Charges for semantic ranker are levied when query requests include `queryType=semantic` and the search string isn't empty (for example, `search=pet friendly hotels in New York`). If your search string is empty (`search=*`), you aren't charged, even if the queryType is set to semantic.
 
 ## See also
 
-* [Enable semantic ranking](semantic-how-to-enable-disable.md)
-* [Configure semantic ranking](semantic-how-to-query-request.md)
+* [Enable semantic ranker](semantic-how-to-enable-disable.md)
+* [Configure semantic ranker](semantic-how-to-query-request.md)
 * [Blog: Outperforming vector search with hybrid retrieval and ranking capabilities](https://techcommunity.microsoft.com/t5/azure-ai-services-blog/azure-cognitive-search-outperforming-vector-search-with-hybrid/ba-p/3929167)

@@ -10,7 +10,7 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 06/27/2024
+ms.date: 09/24/2024
 ---
 
 # Configure network access and firewall rules for Azure AI Search
@@ -96,9 +96,12 @@ When IP rules are configured, some features of the Azure portal are disabled. Yo
 
 You can restore portal access to the full range of search service operations by adding the portal IP address.
 
-To get the portal's IP address, perform `nslookup` (or `ping`) on `stamp2.ext.search.windows.net`, which is the domain of the traffic manager. For nslookup, the IP address is visible in the "Non-authoritative answer" portion of the response.
+To get the portal's IP address, perform `nslookup` (or `ping`) on:
 
-In the following example, the IP address that you should copy is `52.252.175.48`.
++ `stamp2.ext.search.windows.net`, which is the domain of the traffic manager for the Azure public cloud.
++ `stamp2.ext.search.azure.us` for Azure Government cloud.
+
+For nslookup, the IP address is visible in the "Non-authoritative answer" portion of the response. In the following example, the IP address that you should copy is `52.252.175.48`.
 
 ```bash
 $ nslookup stamp2.ext.search.windows.net

@@ -77,7 +77,7 @@ POST https://[service name].search.windows.net/indexes/hotels-sample-index/docs/
 
 ## Spell correction with semantic ranking
 
-This query, with typos in every term except one, undergoes spelling corrections to return relevant results. To learn more, see [Configure semantic ranking](semantic-how-to-query-request.md).
+This query, with typos in every term except one, undergoes spelling corrections to return relevant results. To learn more, see [Configure semantic ranker](semantic-how-to-query-request.md).
 
 ```http
 POST https://[service name].search.windows.net/indexes/hotels-sample-index/docs/search?api-version=2024-05-01-preview    
@@ -105,7 +105,7 @@ Valid values for `queryLanguage` can be found in the following table, copied fro
 | Dutch [NL] | NL, NL-BE, NL-NL (default) |
 
 > [!NOTE]
-> Previously, while semantic ranking was in public preview, the `queryLanguage` parameter was also used for semantic ranking. Semantic ranking is now language-agnostic.
+> Previously, while semantic ranker was in public preview, the `queryLanguage` parameter was also used for semantic ranking. Semantic ranker is now language-agnostic.
 
 ### Language analyzer considerations
 

@@ -407,16 +407,16 @@ items:
     href: hybrid-search-how-to-query.md
   - name: Ranking and relevance
     items:
-    - name: Enable or disable semantic ranking
+    - name: Enable or disable semantic ranker
       href: semantic-how-to-enable-disable.md      
-    - name: Configure semantic ranking
+    - name: Configure semantic ranker
       href: semantic-how-to-configure.md
-    - name: Configure BM25 ranking
-      href: index-ranking-similarity.md
     - name: Add semantic ranking to queries
       href: semantic-how-to-query-request.md
     - name: Add a scoring profile
       href: index-add-scoring-profiles.md
+    - name: Configure BM25 ranking
+      href: index-ranking-similarity.md
   - name: Security
     items:
     - name: Configure network access

@@ -34,7 +34,7 @@ The function app authenticates through the SDK to the cloud-based Azure AI Searc
 
 ## Azure Function: Search the catalog
 
-The [Search API](https://github.com/Azure-Samples/azure-search-static-web-app/api/Search.cs) takes a search term and searches across the documents in the search index, returning a list of matches. 
+The [Search API](https://github.com/Azure-Samples/azure-search-static-web-app/blob/main/api/Search.cs) takes a search term and searches across the documents in the search index, returning a list of matches. 
 
 The Azure function pulls in the search configuration information, and fulfills the query.
 
@@ -48,9 +48,9 @@ Call the Azure Function in the React client with the following code.
 
 ## Azure Function: Suggestions from the catalog
 
-The [Suggest API](https://github.com/Azure-Samples/azure-search-static-web-app/api/Suggest.cs) takes a search term while a user is typing and suggests search terms such as book titles and authors across the documents in the search index, returning a small list of matches. 
+The [Suggest API](https://github.com/Azure-Samples/azure-search-static-web-app/blob/main/api/Suggest.cs) takes a search term while a user is typing and suggests search terms such as book titles and authors across the documents in the search index, returning a small list of matches. 
 
-The search suggester, `sg`, is defined in the [schema file](https://github.com/Azure-Samples/azure-search-static-web-app/bulk-insert/BookSearchIndex.cs) used during bulk upload.
+The search suggester, `sg`, is defined in the [schema file](https://github.com/Azure-Samples/azure-search-static-web-app/blob/main/bulk-insert/BookSearchIndex.cs) used during bulk upload.
 
 :::code language="csharp" source="~/azure-search-static-web-app/api/Suggest.cs"  :::
 
@@ -62,7 +62,7 @@ The Suggest function API is called in the React app at `\client\src\components\S
 
 ## Azure Function: Get specific document 
 
-The [Document Lookup API](https://github.com/Azure-Samples/azure-search-static-web-app/api/Lookup.cs) takes an ID and returns the document object from the Search Index. 
+The [Document Lookup API](https://github.com/Azure-Samples/azure-search-static-web-app/blob/main/api/Lookup.cs) takes an ID and returns the document object from the Search Index. 
 
 :::code language="csharp" source="~/azure-search-static-web-app/api/Lookup.cs"  :::
 

@@ -344,7 +344,7 @@ summer thaw. Nonetheless, this July 2001 image shows plenty of surface vegetatio
 shrubs, and grasses. The abundant fresh water also means the area is teeming with flies and mosquitoes.
 ```
 
-With this example, it's easier to spot how chunks are returned verbatim, and how keyword and similarity search identify top matches. This specific chunk definitely has information about water and coverage over the earth, but it's not exactly relevant to the query. Semantic ranking would find a better answer, but as a next step, let's see how to connect Azure AI Search to an LLM for conversational search.
+With this example, it's easier to spot how chunks are returned verbatim, and how keyword and similarity search identify top matches. This specific chunk definitely has information about water and coverage over the earth, but it's not exactly relevant to the query. Semantic ranker would find a better answer, but as a next step, let's see how to connect Azure AI Search to an LLM for conversational search.
 
 <!-- Objective:
 

@@ -9,7 +9,7 @@ ms.service: cognitive-search
 ms.custom:
   - build-2024
 ms.topic: how-to
-ms.date: 08/19/2024
+ms.date: 09/24/2024
 ---
 
 # Create a vector query in Azure AI Search
@@ -463,8 +463,6 @@ Weights are used when calculating the [reciprocal rank fusion](hybrid-search-ran
 
 The following example is a hybrid query with two vector query strings and one text string. Weights are assigned to the vector queries. The first query is 0.5 or half the weight, reducing its importance in the request. The second vector query is twice as important. 
 
-Text queries have no weight parameters, but you can increase or decrease their importance by setting [maxTextRecallSize](hybrid-search-how-to-query.md#set-maxtextrecallsize-and-countandfacetmode-preview).
-
 ```http
 POST https://[service-name].search.windows.net/indexes/[index-name]/docs/search?api-version=2024-07-01
 
@@ -489,6 +487,8 @@ POST https://[service-name].search.windows.net/indexes/[index-name]/docs/search?
     } 
 ```
 
+Vector weighting applies to vectors only. The text query in this example ("hello world") has an implicit weight of 1.0 or neutral weight. However, in a hybrid query, you can increase or decrease the importance of text fields by setting [maxTextRecallSize](hybrid-search-how-to-query.md#set-maxtextrecallsize-and-countandfacetmode-preview).
+
 ## Set thresholds to exclude low-scoring results (preview)
 
 Because nearest neighbor search always returns the requested `k` neighbors, it's possible to get multiple low scoring matches as part of meeting the `k` number requirement on search results. To exclude low-scoring search result, you can add a `threshold` query parameter that filters out results based on a minimum score. Filtering occurs before [fusing results](hybrid-search-ranking.md) from different recall sets. 

@@ -71,8 +71,8 @@ The URI and key are generated when you deploy the model from the catalog. For mo
 {
   "@odata.type": "#Microsoft.Skills.Custom.AmlSkill",
   "context": "/document/pages/*",
-  "uri": "{YOUR_MODEL_URL_HERE}",
-  "key": "{YOUR_MODEL_KEY_HERE}",
+  "uri": "<YOUR_MODEL_URL_HERE>",
+  "key": "<YOUR_MODEL_KEY_HERE>",
   "inputs": [
     {
       "name": "input_data",
@@ -118,8 +118,8 @@ The URI and key are generated when you deploy the model from the catalog. For mo
 {
   "@odata.type": "#Microsoft.Skills.Custom.AmlSkill",
   "context": "/document/normalized_images/*",
-  "uri": "{YOUR_MODEL_URL_HERE}",
-  "key": "{YOUR_MODEL_HERE}",
+  "uri": "<YOUR_MODEL_URL_HERE>",
+  "key": "<YOUR_MODEL_HERE>",
   "inputs": [
     {
       "name": "input_data",
@@ -165,8 +165,8 @@ The URI and key are generated when you deploy the model from the catalog. For mo
 {
   "@odata.type": "#Microsoft.Skills.Custom.AmlSkill",
   "context": "/document/pages/*",
-  "uri": "{YOUR_MODEL_URL_HERE}/v1/embed",
-  "key": "{YOUR_MODEL_KEY_HERE}",
+  "uri": "<YOUR_MODEL_URL_HERE>/v1/embed",
+  "key": "<YOUR_MODEL_KEY_HERE>",
   "inputs": [
     {
       "name": "texts",
@@ -202,7 +202,7 @@ If you selected a different `embedding_types` in your skill definition that you
 "indexProjections": {
   "selectors": [
     {
-      "targetIndexName": "{YOUR_TARGET_INDEX_NAME_HERE}",
+      "targetIndexName": "<YOUR_TARGET_INDEX_NAME_HERE>",
       "parentKeyFieldName": "ParentKey", // Change this to the name of the field in your index definition where the parent key will be stored
       "sourceContext": "/document/pages/*",
       "mappings": [
@@ -230,12 +230,12 @@ For Cohere models, you should NOT add the `/v1/embed` path to the end of your UR
 ```json
 "vectorizers": [
     {
-        "name": "{YOUR_VECTORIZER_NAME_HERE}",
+        "name": "<YOUR_VECTORIZER_NAME_HERE>",
         "kind": "aml",
         "amlParameters": {
-            "uri": "{YOUR_URL_HERE}",
-            "key": "{YOUR_PRIMARY_KEY_HERE}",
-            "modelName": "{YOUR_MODEL_ID_HERE}"
+            "uri": "<YOUR_URL_HERE>",
+            "key": "<YOUR_PRIMARY_KEY_HERE>",
+            "modelName": "<YOUR_MODEL_ID_HERE>"
         },
     }
 ]
@@ -246,8 +246,8 @@ For Cohere models, you should NOT add the `/v1/embed` path to the end of your UR
 If you can't use key-based authentication, you can instead configure the AML skill and AI Studio vectorizer connection for [token authentication](../machine-learning/how-to-authenticate-online-endpoint.md) via role-based access control on Azure. The search service must have a [system or user-assigned managed identity](search-howto-managed-identities-data-sources.md), and the identity must have Owner or Contributor permissions for your AML project workspace. You can then remove the key field from your skill and vectorizer definition, replacing it with the resourceId field. If your AML project and search service are in different regions, also provide the region field.
 
 ```json
-"uri": "{YOUR_URL_HERE}",
-"resourceId": "subscriptions/{YOUR_SUBSCRIPTION_ID_HERE/resourceGroups/{YOUR_RESOURCE_GROUP_NAME_HERE}/providers/Microsoft.MachineLearningServices/workspaces/{YOUR_AML_WORKSPACE_NAME_HERE}/onlineendpoints/{YOUR_AML_ENDPOINT_NAME_HERE}",
+"uri": "<YOUR_URL_HERE>",
+"resourceId": "subscriptions/<YOUR_SUBSCRIPTION_ID_HERE>/resourceGroups/<YOUR_RESOURCE_GROUP_NAME_HERE>/providers/Microsoft.MachineLearningServices/workspaces/<YOUR_AML_WORKSPACE_NAME_HERE>/onlineendpoints/<YOUR_AML_ENDPOINT_NAME_HERE>",
 "region": "westus", // Only need if AML project lives in different region from search service
 ```
 

@@ -86,7 +86,7 @@ We recommend the [Import and vectorize data wizard](search-get-started-portal-im
 
 The bias of this schema is that search documents are built around data chunks. If a language model formulates the response, as is typical for RAG apps, you want a schema designed around data chunks. 
 
-Data chunking is necessary for staying within the input limits of language models, but it also improves precision in similarity search when queries can be matched against smaller chunks of content pulled from multiple parent documents. Finally, if you're using semantic ranking, the semantic ranker also has token limits, which are more easily met if data chunking is part of your approach.
+Data chunking is necessary for staying within the input limits of language models, but it also improves precision in similarity search when queries can be matched against smaller chunks of content pulled from multiple parent documents. Finally, if you're using semantic ranker, the semantic ranker also has token limits, which are more easily met if data chunking is part of your approach.
 
 In the following example, for each search document, there's one chunk ID, chunk, title, and vector field. The chunkID and parent ID are populated by the wizard, using base 64 encoding of blob metadata (path). Chunk and title are derived from blob content and blob name. Only the vector field is fully generated. It's the vectorized version of the chunk field. Embeddings are generated by calling an Azure OpenAI embedding model that you provide.
 

@@ -90,12 +90,12 @@ ms.custom:
 | Month | Type | Announcement |
 |-------|------|-------------|
 | November | Feature | [**Vector search, generally available**](vector-search-overview.md). The previous restriction on customer-managed keys (CMK) is now lifted. [Prefiltering](vector-search-how-to-query.md) and [exhaustive K-nearest neighbor algorithm](vector-search-ranking.md) are also now generally available. |
-| November | Feature | [**Semantic ranking, generally available**](semantic-search-overview.md)|
+| November | Feature | [**Semantic ranker, generally available**](semantic-search-overview.md)|
 | November | Feature | [**Integrated vectorization (preview)**](vector-search-integrated-vectorization.md) adds data chunking and text-to-vector conversions during indexing, and also adds text-to-vector conversions at query time. |
 | November | Feature | [**Import and vectorize data wizard (preview)**](search-get-started-portal-import-vectors.md) automates data chunking and vectorization. It targets the [2023-10-01-Preview](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2023-10-01-preview&preserve-view=true) REST API. | 
 | November | Feature | [**Index projections (preview)**](index-projections-concept-intro.md) defines the shape of a secondary index, used for a one-to-many index pattern, where content from an enrichment pipeline can target multiple indexes. | 
 | November | API | [**2023-11-01 Search REST API**](/rest/api/searchservice/search-service-api-versions#2023-11-01) is stable version of the Search REST APIs for [vector search](vector-search-overview.md) and [semantic ranking](semantic-how-to-query-request.md). See [Upgrade REST APIs](search-api-migration.md) for migration steps to generally available features.|
-| November | API | [**2023-11-01 Management REST API**](/rest/api/searchmanagement/operation-groups?view=rest-searchmanagement-2023-11-01&preserve-view=true) adds APIs that [enable or disable semantic ranking](/rest/api/searchmanagement/services/create-or-update#searchsemanticsearch). |
+| November | API | [**2023-11-01 Management REST API**](/rest/api/searchmanagement/operation-groups?view=rest-searchmanagement-2023-11-01&preserve-view=true) adds APIs that [enable or disable semantic ranker](/rest/api/searchmanagement/services/create-or-update#searchsemanticsearch). |
 | November | Skill | [**Azure OpenAI Embedding skill (preview)**](cognitive-search-skill-azure-openai-embedding.md) connects to a deployed embedding model on your Azure OpenAI resource to generate embeddings during skillset execution.|
 | November | Skill | [**Text Split skill (preview)**](cognitive-search-skill-textsplit.md) updated in [2023-10-01-Preview](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2023-10-01-preview&preserve-view=true) to support native data chunking. |
 | November | Video | [**How vector search and semantic ranking improve your GPT prompts**](https://www.youtube.com/watch?v=Xwx1DJ0OqCk) explains how hybrid retrieval gives you optimal grounding data for generating useful AI responses and enables search over both concepts and keywords. |
@@ -139,4 +139,4 @@ This service has had multiple names over the years. Here they are in reverse chr
 
 ## Feature rename
 
-Semantic search was renamed to [semantic ranking](semantic-search-overview.md) in November 2023 to better describe the feature, which provides L2 ranking of an existing result set.
+Semantic search was renamed to [semantic ranker](semantic-search-overview.md) in November 2023 to better describe the feature, which provides L2 ranking of an existing result set.