Merge pull request #7338 from HeidiSteen/heidist-0901

bulk edit fix

Author: prmerger-automator[bot]

articles/search/cognitive-search-aml-skill.md

@@ -30,7 +30,7 @@ Like other built-in skills, a custom **AML** skill has inputs and outputs. The i
 > * `503 Service Unavailable`
 > * `429 Too Many Requests`
 
-You can call the **AML** skill with the 2024-07-01 stable API version or an equivalent Azure SDK. For connections to the model catalog in the Azure AI Foundry portal, use the 2024-05-01-preview API version or later.
+You can call the **AML** skill with the stable REST API version or an equivalent Azure SDK. For connections to the model catalog in the Azure AI Foundry portal, use a preview API version.
 
 ## AML skill for models in Azure AI Foundry
 

articles/search/cognitive-search-attach-cognitive-services.md

@@ -235,7 +235,7 @@ Enrichments are billable operations. If you no longer need to call Azure AI serv
 1. Remove the key in the body of the definition, and then send the request:
 
     ```http
-    PUT https://[servicename].search.windows.net/skillsets/[skillset name]?api-version=@search.rerankerBoostedScore
+    PUT https://[servicename].search.windows.net/skillsets/[skillset name]?api-version=2025-09-01
     api-key: [admin key]
     Content-Type: application/json
     {

articles/search/cognitive-search-skill-azure-openai-embedding.md

@@ -45,12 +45,12 @@ Parameters are case-sensitive.
 
 | Inputs | Description |
 |---------------------|-------------|
-| `resourceUri` | The URI of the model provider. This parameter only supports URLs with the `openai.azure.com` domain, such as `https://<resourcename>.openai.azure.com`. If your Azure OpenAI endpoint has a URL with the `cognitiveservices.azure.com` domain, such as `https://<resourcename>.cognitiveservices.azure.com`, you must create a [custom subdomain](/azure/ai-services/openai/how-to/use-your-data-securely#enabled-custom-subdomain) with `openai.azure.com` for the Azure OpenAI resource and use `https://<resourcename>.openai.azure.com` instead. This field is required if your Azure OpenAI resource is deployed behind a private endpoint or uses Virtual Network (VNet) integration. [Azure API Management](/azure/api-management/api-management-key-concepts) endpoints are supported with URL `https://<resourcename>.azure-api.net `. Shared private links aren't supported for API Management endpoints.
+| `resourceUri` | Required. The URI of the model provider. This parameter only supports URLs with the `openai.azure.com` domain, such as `https://<resourcename>.openai.azure.com`. If your Azure OpenAI endpoint has a URL with the `cognitiveservices.azure.com` domain, such as `https://<resourcename>.cognitiveservices.azure.com`, you must create a [custom subdomain](/azure/ai-services/openai/how-to/use-your-data-securely#enabled-custom-subdomain) with `openai.azure.com` for the Azure OpenAI resource and use `https://<resourcename>.openai.azure.com` instead. This field is required if your Azure OpenAI resource is deployed behind a private endpoint or uses Virtual Network (VNet) integration. [Azure API Management](/azure/api-management/api-management-key-concepts) endpoints are supported with URL `https://<resourcename>.azure-api.net `. Shared private links aren't supported for API Management endpoints.
 | `apiKey`   |  The secret key used to access the model. If you provide a key, leave `authIdentity` empty. If you set both the `apiKey` and `authIdentity`, the `apiKey` is used on the connection. |
-| `deploymentId`   | The name of the deployed Azure OpenAI embedding model. The model should be an embedding model, such as text-embedding-ada-002. See the [List of Azure OpenAI models](/azure/ai-services/openai/concepts/models) for supported models.|
+| `deploymentId`   | Required. The name of the deployed Azure OpenAI embedding model. The model should be an embedding model, such as text-embedding-ada-002. See the [List of Azure OpenAI models](/azure/ai-services/openai/concepts/models) for supported models.|
 | `authIdentity`   | A user-managed identity used by the search service for connecting to Azure OpenAI. You can use either a [system or user managed identity](search-how-to-managed-identities.md). To use a system managed identity, leave `apiKey` and `authIdentity` blank. The system-managed identity is used automatically. A managed identity must have [Cognitive Services OpenAI User](/azure/ai-services/openai/how-to/role-based-access-control#azure-openai-roles) permissions to send text to Azure OpenAI. |
-| `modelName` | This property is required if your skillset is created using the 2024-05-01-preview or 2024-07-01 REST API. Set this property to the deployment name of an Azure OpenAI embedding model deployed on the provider specified through `resourceUri` and identified through `deploymentId`. Currently, the supported values are `text-embedding-ada-002`, `text-embedding-3-large`, and `text-embedding-3-small`.  |
-| `dimensions` | Optional, starting in the 2024-05-01-preview REST API, the dimensions of embeddings that you would like to generate, assuming the model supports a range of dimensions. Supported ranges are listed below, and currently only apply to the text-embedding-3 model series. The default is the maximum dimensions for each model. For skillsets created using earlier REST API versions dating back to the 2023-10-01-preview, dimensions are fixed at 1536. When setting the dimensions property on a skill, make sure to set the `dimensions` property on the [vector field definition](vector-search-how-to-create-index.md#add-a-vector-field-to-the-fields-collection) to the same value. |
+| `modelName` | Required. Set this property to the deployment name of an Azure OpenAI embedding model deployed on the provider specified through `resourceUri` and identified through `deploymentId`. Currently, the supported values are `text-embedding-ada-002`, `text-embedding-3-large`, and `text-embedding-3-small`.  |
+| `dimensions` | Optional. The dimensions of embeddings that you would like to generate, assuming the model supports a range of dimensions. Supported ranges are listed below, and currently only apply to the text-embedding-3 model series. The default is the maximum dimensions for each model. For skillsets created using earlier REST API versions dating back to the 2023-10-01-preview, dimensions are fixed at 1536. When setting the dimensions property on a skill, make sure to set the `dimensions` property on the [vector field definition](vector-search-how-to-create-index.md#add-a-vector-field-to-the-fields-collection) to the same value. |
 
 ## Supported dimensions by `modelName`
 

articles/search/cognitive-search-skill-textsplit.md

@@ -33,15 +33,15 @@ Microsoft.Skills.Text.SplitSkill
 
 Parameters are case-sensitive. 
 
-| Parameter name     | Version   | Description |
+| Parameter name     | Description |
 |--------------------|-------------|-------------|
-| `textSplitMode`    | All versions | Either `pages` or `sentences`. Pages have a configurable maximum length, but the skill attempts to avoid truncating a sentence so the actual length might be smaller. Sentences are a string that terminates at sentence-ending punctuation, such as a period, question mark, or exclamation point, assuming the language has sentence-ending punctuation. | 
-| `maximumPageLength` | All versions | Only applies if `textSplitMode` is set to `pages`. For `unit` set to `characters`, this parameter refers to the maximum page length in characters as measured by `String.Length`. The minimum value is 300, the maximum is 50000, and the default value is 5000.  The algorithm does its best to break the text on sentence boundaries, so the size of each chunk might be slightly less than `maximumPageLength`. <br><br>For `unit` set to `azureOpenAITokens`, the maximum page length is the token length limit of the model. For text embedding models, a general recommendation for page length is 512 tokens. |
-| `defaultLanguageCode`	| All versions | (optional) One of the following language codes: `am, bs, cs, da, de, en, es, et, fr, he, hi, hr, hu, fi, id, is, it, ja, ko, lv, no, nl, pl, pt-PT, pt-BR, ru, sk, sl, sr, sv, tr, ur, zh-Hans`. Default is English (en). A few things to consider: <ul><li>Providing a language code is useful to avoid cutting a word in half for nonwhitespace languages such as Chinese, Japanese, and Korean.</li><li>If you don't know the language  in advance (for example, if you're using the [LanguageDetectionSkill](cognitive-search-skill-language-detection.md) to detect language), we recommend the `en` default. </li></ul>  |
-| `pageOverlapLength` | [2024-07-01](/rest/api/searchservice/skillsets/create-or-update) | Only applies if `textSplitMode` is set to `pages`. Each page starts with this number of characters or tokens from the end of the previous page. If this parameter is set to 0, there's no overlapping text on successive pages. This [example](#example-for-chunking-and-vectorization) includes the parameter. |
-| `maximumPagesToTake` | [2024-07-01](/rest/api/searchservice/skillsets/create-or-update) | Only applies if `textSplitMode` is set to `pages`. Number of pages to return. The default is 0, which means to return all pages. You should set this value if only a subset of pages are needed. This [example](#example-for-chunking-and-vectorization) includes the parameter.|
-| `unit` | [2024-09-01-preview](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-09-01-preview&preserve-view=true) | **New**. Only applies if `textSplitMode` is set to `pages`. Specifies whether to chunk by `characters` (default) or `azureOpenAITokens`. Setting the unit affects `maximumPageLength` and `pageOverlapLength`. |
-| `azureOpenAITokenizerParameters` | [2024-09-01-preview](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-09-01-preview&preserve-view=true) | **New**. An object providing extra parameters for the `azureOpenAITokens` unit. <br><br>`encoderModelName` is a designated tokenizer used for converting text into tokens, essential for natural language processing (NLP) tasks. Different models use different tokenizers. Valid values include cl100k_base (default) used by GPT-35-Turbo and GPT-4. Other valid values are r50k_base, p50k_base, and p50k_edit. The skill implements the tiktoken library by way of [SharpToken](https://www.nuget.org/packages/SharpToken) and `Microsoft.ML.Tokenizers` but doesn't support every encoder. For example, there's currently no support for o200k_base encoding used by GPT-4o. <br><br>`allowedSpecialTokens` defines a collection of special tokens that are permitted within the tokenization process. Special tokens are  string that you want to treat uniquely, ensuring they aren't split during tokenization. For example ["[START"], "[END]"]. If the `tiktoken` library doesn't perform tokenization as expected, either due to language-specific limitations or other unexpected behaviors, it's recommended to use text splitting instead.|
+| `textSplitMode`    | Either `pages` or `sentences`. Pages have a configurable maximum length, but the skill attempts to avoid truncating a sentence so the actual length might be smaller. Sentences are a string that terminates at sentence-ending punctuation, such as a period, question mark, or exclamation point, assuming the language has sentence-ending punctuation. | 
+| `maximumPageLength` | Only applies if `textSplitMode` is set to `pages`. For `unit` set to `characters`, this parameter refers to the maximum page length in characters as measured by `String.Length`. The minimum value is 300, the maximum is 50000, and the default value is 5000.  The algorithm does its best to break the text on sentence boundaries, so the size of each chunk might be slightly less than `maximumPageLength`. <br><br>For `unit` set to `azureOpenAITokens`, the maximum page length is the token length limit of the model. For text embedding models, a general recommendation for page length is 512 tokens. |
+| `defaultLanguageCode`	| (optional) One of the following language codes: `am, bs, cs, da, de, en, es, et, fr, he, hi, hr, hu, fi, id, is, it, ja, ko, lv, no, nl, pl, pt-PT, pt-BR, ru, sk, sl, sr, sv, tr, ur, zh-Hans`. Default is English (en). A few things to consider: <ul><li>Providing a language code is useful to avoid cutting a word in half for nonwhitespace languages such as Chinese, Japanese, and Korean.</li><li>If you don't know the language  in advance (for example, if you're using the [LanguageDetectionSkill](cognitive-search-skill-language-detection.md) to detect language), we recommend the `en` default. </li></ul>  |
+| `pageOverlapLength` | Only applies if `textSplitMode` is set to `pages`. Each page starts with this number of characters or tokens from the end of the previous page. If this parameter is set to 0, there's no overlapping text on successive pages. This [example](#example-for-chunking-and-vectorization) includes the parameter. |
+| `maximumPagesToTake` | Only applies if `textSplitMode` is set to `pages`. Number of pages to return. The default is 0, which means to return all pages. You should set this value if only a subset of pages are needed. This [example](#example-for-chunking-and-vectorization) includes the parameter.|
+| `unit` | Only applies if `textSplitMode` is set to `pages`. Specifies whether to chunk by `characters` (default) or `azureOpenAITokens`. Setting the unit affects `maximumPageLength` and `pageOverlapLength`. |
+| `azureOpenAITokenizerParameters` An object providing extra parameters for the `azureOpenAITokens` unit. <br><br>`encoderModelName` is a designated tokenizer used for converting text into tokens, essential for natural language processing (NLP) tasks. Different models use different tokenizers. Valid values include cl100k_base (default) used by GPT-35-Turbo and GPT-4. Other valid values are r50k_base, p50k_base, and p50k_edit. The skill implements the tiktoken library by way of [SharpToken](https://www.nuget.org/packages/SharpToken) and `Microsoft.ML.Tokenizers` but doesn't support every encoder. For example, there's currently no support for o200k_base encoding used by GPT-4o. <br><br>`allowedSpecialTokens` defines a collection of special tokens that are permitted within the tokenization process. Special tokens are  string that you want to treat uniquely, ensuring they aren't split during tokenization. For example ["[START"], "[END]"]. If the `tiktoken` library doesn't perform tokenization as expected, either due to language-specific limitations or other unexpected behaviors, it's recommended to use text splitting instead.|
 
 ## Skill Inputs
 

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

@@ -105,7 +105,7 @@ The following example shows a hybrid query request using the REST API.
 This example is from the [vector quickstart](https://raw.githubusercontent.com/Azure-Samples/azure-search-rest-samples/refs/heads/main/Quickstart-vectors/az-search-quickstart-vectors.rest) that has vector and nonvector content, and several query examples. For brevity, the vector is truncated in this article. 
 
 ```http
-POST https://{{search-service-name}}.search.windows.net/indexes/{{index-name}}/docs/search?api-version=@search.rerankerBoostedScore
+POST https://{{search-service-name}}.search.windows.net/indexes/{{index-name}}/docs/search?api-version=2025-09-01
 Content-Type: application/json
 api-key: {{admin-api-key}}
 {
@@ -240,7 +240,7 @@ This section has multiple query examples that illustrate hybrid query patterns.
 This example adds a filter, which is applied to the `filterable` nonvector fields of the search index.
 
 ```http
-POST https://{{search-service-name}}.search.windows.net/indexes/{{index-name}}/docs/search?api-version=@search.rerankerBoostedScore
+POST https://{{search-service-name}}.search.windows.net/indexes/{{index-name}}/docs/search?api-version=2025-09-01
 Content-Type: application/json
 api-key: {{admin-api-key}}
 {
@@ -324,7 +324,7 @@ Assuming that you [have semantic ranker](semantic-how-to-enable-disable.md) and
 Whenever you use semantic ranking with vectors, make sure `k` is set to 50. Semantic ranker uses up to 50 matches as input. Specifying less than 50 deprives the semantic ranking models of necessary inputs.
 
 ```http
-POST https://{{search-service-name}}.search.windows.net/indexes/{{index-name}}/docs/search?api-version=@search.rerankerBoostedScore
+POST https://{{search-service-name}}.search.windows.net/indexes/{{index-name}}/docs/search?api-version=2025-09-01
 Content-Type: application/json
 api-key: {{admin-api-key}}
 {
@@ -365,7 +365,7 @@ api-key: {{admin-api-key}}
 Here's the last query in the collection. It's the same semantic hybrid query as the previous example, but with a filter.
 
 ```http
-POST https://{{search-service-name}}.search.windows.net/indexes/{{index-name}}/docs/search?api-version=@search.rerankerBoostedScore
+POST https://{{search-service-name}}.search.windows.net/indexes/{{index-name}}/docs/search?api-version=2025-09-01
 Content-Type: application/json
 api-key: {{admin-api-key}}
 {

articles/search/hybrid-search-overview.md

@@ -34,7 +34,7 @@ Hybrid search is predicated on having a search index that contains fields of var
 A representative hybrid query might be as follows (notice that the vector queries have placeholder values for brevity):
 
 ```http
-POST https://{{searchServiceName}}.search.windows.net/indexes/hotels-vector-quickstart/docs/search?api-version=@search.rerankerBoostedScore
+POST https://{{searchServiceName}}.search.windows.net/indexes/hotels-vector-quickstart/docs/search?api-version=2025-09-01
   content-type: application/JSON
 {
     "count": true,