Merge remote-tracking branch 'upstream/main' into azcmagent-1-40

Author: Ryan Willis

.acrolinx-config.edn

@@ -1,2 +1,2 @@
 {:allowed-branchname-matches ["main" "release-.*"]
- :allowed-filename-matches ["(?i)articles/(?:(?!active-directory/saas-apps/toc.yml|role-based-access-control/resource-provider-operations.md|.*policy/samples/|.*resource-graph/samples/))" "(?i)includes/(?:(?!policy/reference/|policy/standards/))"]}
+ :allowed-filename-matches ["(?i)articles/(?:(?!active-directory/saas-apps/toc.yml|role-based-access-control/resource-provider-operations.md|.*policy/samples/|.*resource-graph/samples/))" "(?i)includes/(?:(?!policy/reference/|policy/standards/|resource-graph/samples/))"]}

.openpublishing.redirection.json

@@ -1070,6 +1070,11 @@
             "redirect_url": "/azure/container-instances/container-instances-resource-and-quota-limits",
             "redirect_document_id": false
         },
+        {
+            "source_path_from_root": "/articles/container-instances/tutorial-docker-compose.md",
+            "redirect_url": "/azure/container-instances/container-instances-multi-container-group",
+            "redirect_document_id": false
+        },
         {
             "source_path_from_root": "/articles/cassandra-managed-instance/compare-cosmosdb-managed-instance.md",
             "redirect_url": "/azure/managed-instance-apache-cassandra/compare-cosmosdb-managed-instance",
@@ -3964,6 +3969,11 @@
             "source_path_from_root":"/articles/virtual-network-manager/concept-security-admin-rules-network-groups.md",
             "redirect_url":"/azure/virtual-network-manager/overview",
             "redirect_document_id":false 
+        },
+        {
+            "source_path_from_root":"/articles/trusted-signing/concept.md",
+            "redirect_url":"/azure/trusted-signing/concept-trustedsigning-resources-roles",
+            "redirect_document_id":false 
         }
     ]
 }

.openpublishing.redirection.virtual-desktop.json

@@ -404,6 +404,11 @@
             "source_path_from_root": "/articles/virtual-desktop/whats-new-client-microsoft-store.md",
             "redirect_url": "/azure/virtual-desktop/whats-new-client-windows",
             "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/virtual-desktop/autoscale-scaling-plan.md",
+            "redirect_url": "/azure/virtual-desktop/autoscale-create-assign-scaling-plan",
+            "redirect_document_id": true
         }
     ]
 }

articles/ai-services/content-safety/concepts/groundedness.md

@@ -65,8 +65,8 @@ To use this API, you must create your Azure AI Content Safety resource in the su
 
 | Pricing Tier | Requests per 10 seconds |
 | :----------- | :--------------------------- |
-| F0           | 10                           |
-| S0           | 10                           |
+| F0           | 50                           |
+| S0           | 50                           |
 
 If you need a higher rate, [contact us](mailto:[email protected]) to request it.
 

articles/ai-services/language-service/named-entity-recognition/concepts/entity-resolutions.md

@@ -16,11 +16,15 @@ ms.custom: language-service-ner
 
 A resolution is a standard format for an entity. Entities can be expressed in various forms and resolutions provide standard predictable formats for common quantifiable types. For example, "eighty" and "80" should both resolve to the integer `80`.
 
-You can use NER resolutions to implement actions or retrieve further information. For example, your service can extract datetime entities to extract dates and times that will be provided to a meeting scheduling system. 
+You can use NER resolutions to implement actions or retrieve further information. For example, your service can extract datetime entities to extract dates and times that will be provided to a meeting scheduling system.
+
+> [!IMPORTANT]
+> Starting from version 2023-04-15-preview, the entity resolution feature is replaced by [entity metadata](entity-metadata.md)
 
 > [!NOTE]
 > Entity resolution responses are only supported starting from **_api-version=2022-10-01-preview_** and **_"modelVersion": "2022-10-01-preview"_**.
 
+
 This article documents the resolution objects returned for each entity category or subcategory.
 
 ## Age

articles/ai-services/language-service/named-entity-recognition/concepts/ga-preview-mapping.md

@@ -14,7 +14,7 @@ ms.custom: language-service-ner
 
 # Preview API changes
 
-Use this article to get an overview of the new API changes starting from `2023-04-15-preview` version. This API change mainly introduces two new concepts (`entity types` and `entity tags`) replacing the `category` and `subcategory` fields in the current Generally Available API.
+Use this article to get an overview of the new API changes starting from `2023-04-15-preview` version. This API change mainly introduces two new concepts (`entity types` and `entity tags`) replacing the `category` and `subcategory` fields in the current Generally Available API. A detailed overview of each API parameter and the supported API versions it corresponds to can be found on the [Skill Parameters][../how-to/skill-parameters.md] page
 
 ## Entity types
 Entity types represent the lowest (or finest) granularity at which the entity has been detected and can be considered to be the base class that has been detected.
@@ -23,8 +23,16 @@ Entity types represent the lowest (or finest) granularity at which the entity ha
 Entity tags are used to further identify an entity where a detected entity is tagged by the entity type and additional tags to differentiate the identified entity. The entity tags list could be considered to include categories, subcategories, sub-subcategories, and so on.
 
 ## Changes from generally available API to preview API
-The changes introduce better flexibility for named entity recognition, including:
-* More granular entity recognition through introducing the tags list where an entity could be tagged by more than one entity tag.
+The changes introduce better flexibility for the named entity recognition service, including:
+
+Updates to the structure of input formats:
+•	InclusionList
+•	ExclusionList
+•	Overlap policy
+
+Updates to the handling of output formats:
+
+* More granular entity recognition outputs through introducing the tags list where an entity could be tagged by more than one entity tag.
 * Overlapping entities where entities could be recognized as more than one entity type and if so, this entity would be returned twice. If an entity was recognized to belong to two entity tags under the same entity type, both entity tags are returned in the tags list.
 * Filtering entities using entity tags, you can learn more about this by navigating to [this article](../how-to-call.md#select-which-entities-to-be-returned-preview-api-only).
 * Metadata Objects which contain additional information about the entity but currently only act as a wrapper for the existing entity resolution feature. You can learn more about this new feature [here](entity-metadata.md).
@@ -54,23 +62,24 @@ You can see a comparison between the structure of the entity categories/types in
 | Age            | Numeric, Age                           |
 | Currency       | Numeric, Currency                      |
 | Number         | Numeric, Number                        |
+| PhoneNumber    | PhoneNumber                            |
 | NumberRange    | Numeric, NumberRange                   |
 | Percentage     | Numeric, Percentage                    |
 | Ordinal        | Numeric, Ordinal                       |
-| Temperature    | Numeric, Dimension, Temperature         |
-| Speed          | Numeric, Dimension, Speed               |
-| Weight         | Numeric, Dimension, Weight              |
-| Height         | Numeric, Dimension, Height              |
-| Length         | Numeric, Dimension, Length              |
-| Volume         | Numeric, Dimension, Volume              |
-| Area           | Numeric, Dimension, Area                |
-| Information    | Numeric, Dimension, Information         |
+| Temperature    | Numeric, Dimension, Temperature        |
+| Speed          | Numeric, Dimension, Speed              |
+| Weight         | Numeric, Dimension, Weight             |
+| Height         | Numeric, Dimension, Height             |
+| Length         | Numeric, Dimension, Length             |
+| Volume         | Numeric, Dimension, Volume             |
+| Area           | Numeric, Dimension, Area               |
+| Information    | Numeric, Dimension, Information        |
 | Address        | Address                                |
 | Person         | Person                                 |
 | PersonType     | PersonType                             |
 | Organization   | Organization                           |
 | Product        | Product                                |
-| ComputingProduct | Product, ComputingProduct             |
+| ComputingProduct | Product, ComputingProduct            |
 | IP             | IP                                     |
 | Email          | Email                                  |
 | URL            | URL                                    |

articles/ai-services/language-service/named-entity-recognition/how-to/skill-parameters.md

@@ -0,0 +1,49 @@
+---
+title: Named entity recognition skill parameters
+titleSuffix: Azure AI services
+description: Learn about skill parameters for named entity recognition.
+#services: cognitive-services
+author: jboback
+manager: nitinme
+ms.service: azure-ai-language
+ms.custom:
+ms.topic: how-to
+ms.date: 03/21/2024
+ms.author: jboback
+---
+
+# Learn about named entity recognition skill parameters
+
+Use this article to get an overview of the different API parameters used to adjust the input to a NER API call.
+
+## InclusionList parameter
+
+The “inclusionList” parameter allows for you to specify which of the NER entity tags, listed here [link to Preview API table], you would like included in the entity list output in your inference JSON listing out all words and categorizations recognized by the NER service. By default, all recognized entities will be listed.
+
+## ExclusionList parameter
+
+The “exclusionList” parameter allows for you to specify which of the NER entity tags, listed here [link to Preview API table], you would like excluded in the entity list output in your inference JSON listing out all words and categorizations recognized by the NER service. By default, all recognized entities will be listed.
+
+## Example
+
+To do: work with Bidisha & Mikael to update with a good example
+
+## overlapPolicy parameter
+
+The “overlapPolicy” parameter allows for you to specify how you like the NER service to respond to recognized words/phrases that fall into more than one category. 
+
+By default, the overlapPolicy parameter will be set to “matchLongest”. This option will categorize the extracted word/phrase under the entity category that can encompass the longest span of the extracted word/phrase (longest defined by the most number of characters included).
+
+The alternative option for this parameter is “allowOverlap”, where all possible entity categories will be listed. 
+Parameters by supported API version
+
+|Parameter                                                   |API versions which support            |
+|------------------------------------------------------------|--------------------------------------|
+|inclusionList                                               |2023-04-15-preview, 2023-11-15-preview|
+|exclusionList                                               |2023-04-15-preview, 2023-11-15-preview|
+|Overlap policy                                              |2023-04-15-preview, 2023-11-15-preview|
+|[Entity resolution](link to archived Entity Resolution page)|2022-10-01-preview                    |
+
+## Next steps
+
+* See [Configure containers](../../concepts/configure-containers.md) for configuration settings.

articles/ai-services/language-service/named-entity-recognition/overview.md

@@ -14,14 +14,16 @@ ms.custom: language-service-ner
 
 # What is Named Entity Recognition (NER) in Azure AI Language?
 
-Named Entity Recognition (NER) is one of the features offered by [Azure AI Language](../overview.md), a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. The NER feature can identify and categorize entities in unstructured text. For example: people, places, organizations, and quantities.
+Named Entity Recognition (NER) is one of the features offered by [Azure AI Language](../overview.md), a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. The NER feature can identify and categorize entities in unstructured text. For example: people, places, organizations, and quantities. The prebuilt NER feature has a pre-set list of [recognized entities](concepts/named-entity-categories.md). The custom NER feature allows you to train the model to recognize specialized entities specific to your use case.
 
 * [**Quickstarts**](quickstart.md) are getting-started instructions to guide you through making requests to the service.
 * [**How-to guides**](how-to-call.md) contain instructions for using the service in more specific or customized ways.
 * The [**conceptual articles**](concepts/named-entity-categories.md) provide in-depth explanations of the service's functionality and features.
 
-[!INCLUDE [Typical workflow for pre-configured language features](../includes/overview-typical-workflow.md)]
+> [!NOTE]
+> [Entity Resolution](concepts/entity-resolutions.md) was upgraded to the [Entity Metadata](concepts/entity-metadata.md) starting in API version 2023-04-15-preview. If you are calling the preview version of the API equal or newer than 2023-04-15-preview, please check out the [Entity Metadata](concepts/entity-metadata.md) article to use the resolution feature.
 
+[!INCLUDE [Typical workflow for pre-configured language features](../includes/overview-typical-workflow.md)]
 
 ## Get started with named entity recognition
 

articles/ai-services/language-service/question-answering/how-to/azure-openai-integration.md

@@ -74,4 +74,4 @@ At the same time, customers often require a custom answer authoring experience t
 You can now start exploring Azure OpenAI capabilities with a no-code approach through the chat playground. It's simply a text box where you can submit a prompt to generate a completion. From this page, you can quickly iterate and experiment with the capabilities. You can also launch a [web app](../../../openai/how-to/use-web-app.md) to chat with the model over the web.
 
 ## Next steps
-* [Using Azure OpenAI on your data](../../../openai/concepts/use-your-data.md) 
+* [Using Azure OpenAI on your data](../../../openai/concepts/use-your-data.md)

articles/ai-services/language-service/question-answering/overview.md

@@ -13,6 +13,9 @@ ms.custom: language-service-question-answering
 
 # What is custom question answering?
 
+> [!NOTE]
+> [Azure Open AI On Your Data](../../openai/concepts/use-your-data.md) utilizes large language models (LLMs) to produce similar results to Custom Question Answering. If you wish to connect an existing Custom Question Answering project to Azure Open AI On Your Data, please check out our [guide]( how-to/azure-openai-integration.md).
+
 Custom question answering provides cloud-based Natural Language Processing (NLP) that allows you to create a natural conversational layer over your data. It is used to find appropriate answers from customer input or from a project.
 
 Custom question answering is commonly used to build conversational client applications, which include social media applications, chat bots, and speech-enabled desktop applications. This offering includes features like enhanced relevance using a deep learning ranker, precise answers, and end-to-end region support.