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.openpublishing.publish.config.json

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"branch": "docs-snippets",
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"branch_mapping": {}
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},
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{
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"path_to_root": "functions-openai-extension",
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"url": "https://github.com/Azure/azure-functions-openai-extension",
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"branch": "main",
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"branch_mapping": {}
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},
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{
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"path_to_root": "cosmos-db-nosql-dotnet-samples",
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"url": "https://github.com/azure-samples/cosmos-db-nosql-dotnet-samples",

.openpublishing.redirection.azure-kubernetes-service.json

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"redirect_url": "/azure/aks/create-node-pools",
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"redirect_document_id": false
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},
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{
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"source_path_from_root": "/articles/copilot/limited-access.md",
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"redirect_url": "/azure/copilot/manage-access",
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"redirect_document_id": false
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},
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{
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"source_path_from_root": "/articles/aks/command-invoke.md",
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"redirect_url": "/azure/aks/access-private-cluster",

.openpublishing.redirection.json

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"redirect_url": "/azure/azure-maps/power-bi-visual-conversion",
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"redirect_document_id": false
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},
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{
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"source_path_from_root": "/articles/azure-maps/migrate-from-bing-maps-web-services.md",
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"redirect_url": "/azure/azure-maps/migrate-bing-maps-overview",
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"redirect_document_id": false
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},
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{
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"source_path_from_root": "/articles/azure-maps/migrate-from-bing-maps.md",
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"redirect_url": "/azure/azure-maps/migrate-bing-maps-overview",
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"redirect_document_id": false
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},
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{
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"source_path_from_root": "/articles/azure-portal/resource-group-portal-linked-resources.md",
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"redirect_url": "/azure/azure-portal/azure-portal-dashboards",

articles/active-directory-b2c/microsoft-graph-operations.md

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1. Initializes the auth provider using [OAuth 2.0 client credentials grant](../active-directory/develop/v2-oauth2-client-creds-grant-flow.md) flow. With the client credentials grant flow, the app is able to get an access token to call the Microsoft Graph API.
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1. Sets up the Microsoft Graph service client with the auth provider:
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:::code language="csharp" source="~/ms-identity-dotnetcore-b2c-account-management/src/Program.cs" id="ms_docref_set_auth_provider":::
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The previously published sample code is not available at this time.
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<!--:::code language="csharp" source="~/ms-identity-dotnetcore-b2c-account-management/src/Program.cs" id="ms_docref_set_auth_provider":::-->
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The initialized _GraphServiceClient_ is then used in _UserService.cs_ to perform the user management operations. For example, getting a list of the user accounts in the tenant:
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:::code language="csharp" source="~/ms-identity-dotnetcore-b2c-account-management/src/Services/UserService.cs" id="ms_docref_get_list_of_user_accounts":::
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The previously published sample code is not available at this time.
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<!--:::code language="csharp" source="~/ms-identity-dotnetcore-b2c-account-management/src/Services/UserService.cs" id="ms_docref_get_list_of_user_accounts":::-->
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[Make API calls using the Microsoft Graph SDKs](/graph/sdks/create-requests) includes information on how to read and write information from Microsoft Graph, use `$select` to control the properties returned, provide custom query parameters, and use the `$filter` and `$orderBy` query parameters.
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---
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title: include file
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description: include file
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author: eur
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ms.reviewer: eur
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ms.author: eric-urban
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ms.service: azure-ai-studio
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ms.topic: include
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ms.date: 2/22/2024
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ms.custom: include, build-2024
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---
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> [!NOTE]
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> This feature is currently in public preview. This preview is provided without a service-level agreement, and we don't recommend it for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see [Supplemental Terms of Use for Microsoft Azure Previews](https://azure.microsoft.com/support/legal/preview-supplemental-terms/).
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---
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author: eric-urban
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ms.service: azure-ai-services
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ms.custom:
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- build-2024
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ms.date: 5/21/2024
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ms.topic: include
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ms.author: eur
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---
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> [!div class="checklist"]
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> - Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services).
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> - Some AI services features are free to try in AI Studio. For access to all capabilities described in this article, you need to [connect AI services to your hub in AI Studio](../../../ai-studio/ai-services/connect-ai-services.md#connect-to-ai-services).

articles/ai-services/language-service/personally-identifiable-information/concepts/conversations-entity-categories.md

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---
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title: Entity categories recognized by Conversational Personally Identifiable Information (detection) in Azure AI Language
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titleSuffix: Azure AI services
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description: Learn about the entities the Conversational PII feature (preview) can recognize from conversation inputs.
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description: Learn about the entities the Conversational PII feature can recognize from conversation inputs.
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#services: cognitive-services
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author: jboback
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manager: nitinme
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ms.service: azure-ai-language
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ms.topic: conceptual
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ms.date: 12/19/2023
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ms.custom: language-service-pii
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ms.custom: language-service-pii, build-2024
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---
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# Supported customer content (PII) entity categories in conversations

articles/ai-services/language-service/personally-identifiable-information/language-support.md

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ms.custom: language-service-pii
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ms.custom: language-service-pii, build-2024
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---
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# Personally Identifiable Information (PII) detection language support
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Use this article to learn which natural languages are supported by the PII and conversation PII (preview) features of Azure AI Language.
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Use this article to learn which natural languages are supported by the PII and conversation PII features of Azure AI Language.
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# [PII for documents](#tab/documents)
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articles/ai-services/language-service/personally-identifiable-information/overview.md

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---
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# What is Personally Identifiable Information (PII) detection in Azure AI Language?
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PII detection 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 PII detection feature can **identify, categorize, and redact** sensitive information in unstructured text. For example: phone numbers, email addresses, and forms of identification. The method for utilizing PII in conversations is different than other use cases, and articles for this use are separate.
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As of June 2024, we now provide General Availability support for the Conversational PII service (English-language only).
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Customers can now redact transcripts, chats, and other text written in a conversational style (i.e. text with “um”s, “ah”s, multiple speakers, and the spelling out of words for more clarity) with better confidence in AI quality, Azure SLA support and production environment support, and enterprise-grade security in mind.
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PII detection 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 PII detection feature can **identify, categorize, and redact** sensitive information in unstructured text. For example: phone numbers, email addresses, and forms of identification. Azure AI Language supports general text PII redaction, as well as [Conversational PII](how-to-call-for-conversations.md), a specialized model for handling speech transcriptions and the more informal, conversational tone of meeting and call transcripts. The service also supports [Native Document PII redaction](#native-document-support), where the input and output are structured document files.
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* [**Quickstarts**](quickstart.md) are getting-started instructions to guide you through making requests to the service.
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* [**How-to guides**](how-to-call.md) contain instructions for using the service in more specific or customized ways.
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* The [**conceptual articles**](concepts/entity-categories.md) provide in-depth explanations of the service's functionality and features.
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PII comes into two shapes:
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* [PII](how-to-call.md) - works on unstructured text.
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* [Conversation PII (preview)](how-to-call-for-conversations.md) - tailored model to work on conversation transcription.
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[!INCLUDE [Typical workflow for pre-configured language features](../includes/overview-typical-workflow.md)]
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## Native document support

articles/ai-services/language-service/summarization/custom/how-to/data-formats.md

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- build-2024
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## Custom summarization document sample format
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In the abstractive document summarization scenario, each document (whether it has a provided label or not) is expected to be provided in a plain .txt file. The file contains one or more lines. If multiple lines are provided, each is assumed to be a paragraph of the document. The following is an example document with three paragraphs.
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In the abstractive text summarization scenario, each document (whether it has a provided label or not) is expected to be provided in a plain .txt file. The file contains one or more lines. If multiple lines are provided, each is assumed to be a paragraph of the document. The following is an example document with three paragraphs.
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*At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic, human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI services, I have been working with a team of amazing scientists and engineers to turn this quest into a reality.*
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## Sample mapping JSON format
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In both document and conversation summarization scenarios, a set of documents and corresponding labels can be provided in a single JSON file that references individual document/conversation and summary files.
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In both text and conversation summarization scenarios, a set of documents and corresponding labels can be provided in a single JSON file that references individual document/conversation and summary files.
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```
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## Custom document summarization mapping sample
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The following is an example mapping file for the abstractive document summarization scenario with three documents and corresponding labels.
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The following is an example mapping file for the abstractive text summarization scenario with three documents and corresponding labels.
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```json
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{

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