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articles/ai-services/computer-vision/how-to/find-similar-faces.md

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[!code-csharp[](~/cognitive-services-quickstart-code/dotnet/Face/FaceQuickstart.cs?name=snippet_loadfaces)]
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#### [JavaScript](#tab/javascript)
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The following face detection method is optimized for comparison operations. It doesn't extract detailed face attributes, and it uses an optimized recognition model.
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:::code language="js" source="~/cognitive-services-quickstart-code/javascript/Face/sdk_quickstart.js" id="recognize":::
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The following code uses the above method to get face data from a series of images.
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:::code language="js" source="~/cognitive-services-quickstart-code/javascript/Face/sdk_quickstart.js" id="snippet_loadfaces":::
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#### [REST API](#tab/rest)
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Copy the following cURL command and insert your key and endpoint where appropriate. Then run the command to detect one of the target faces.
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[!code-csharp[](~/cognitive-services-quickstart-code/dotnet/Face/FaceQuickstart.cs?name=snippet_find_similar_print)]
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#### [JavaScript](#tab/javascript)
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The following method takes a set of target faces and a single source face. Then, it compares them and finds all the target faces that are similar to the source face. Finally, it prints the match details to the console.
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:::code language="js" source="~/cognitive-services-quickstart-code/javascript/Face/sdk_quickstart.js" id="find_similar":::
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#### [REST API](#tab/rest)
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articles/ai-services/computer-vision/includes/quickstarts-sdk/identity-javascript-sdk.md

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Get started with facial recognition using the Face client library for JavaScript. Follow these steps to install the package and try out the example code for basic tasks. The Face service provides you with access to advanced algorithms for detecting and recognizing human faces in images. Follow these steps to install the package and try out the example code for basic face identification using remote images.
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[Reference documentation](/javascript/api/overview/azure/cognitiveservices-face-readme) | [Package (npm)](https://www.npmjs.com/package/@azure/cognitiveservices-face) | [Samples](/samples/browse/?products=azure&term=face&languages=javascript)
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[Reference documentation](https://aka.ms/azsdk-javascript-face-ref) | [Library source code](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/face/ai-vision-face-rest) | [Package (npm)](https://www.npmjs.com/package/@azure-rest/ai-vision-face) | [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/face/ai-vision-face-rest/samples)
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## Prerequisites
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npm init
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```
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1. Install the `ms-rest-azure` and `azure-cognitiveservices-face` npm packages:
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1. Install the `@azure-rest/ai-vision-face` npm packages:
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```console
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npm install @azure/cognitiveservices-face @azure/ms-rest-js uuid
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npm install @azure-rest/ai-vision-face
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```
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Your app's `package.json` file is updated with the dependencies.
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Done adding faces to person group.
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Training person group: c08484e0-044b-4610-8b7e-c957584e5d2d.
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Waiting 10 seconds...
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Training status: succeeded.
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No persons identified for face with ID 259dd648-be70-499c-9942-3512594e21eb
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Person: Family1-Mom is identified for face in: identification1.jpg with ID: b7f7f542-c338-4a40-ad52-e61772bc6e14. Confidence: 0.96921.
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Person: Family1-Son is identified for face in: identification1.jpg with ID: 600dc1b4-b2c4-4516-87de-edbbdd8d7632. Confidence: 0.92886.
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Person: Family1-Dad is identified for face in: identification1.jpg with ID: e83b494f-9ad2-473f-9d86-3de79c01e345. Confidence: 0.96725.
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Verification result between face bb7f7f542-c338-4a40-ad52-e61772bc6e14 and person de1d7dea-a393-4f69-9062-10cb66d4cf17: true with confidence: 0.96921
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Verification result between face 600dc1b4-b2c4-4516-87de-edbbdd8d7632 and person 05fd84e4-41b0-4716-b767-4376e33fa207: true with confidence: 0.92886
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Verification result between face e83b494f-9ad2-473f-9d86-3de79c01e345 and person c5124fe2-39dd-47ba-9163-1ed2998fdeb2: true with confidence: 0.96725
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Deleting person group: c08484e0-044b-4610-8b7e-c957584e5d2d
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Done.
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```
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> [Specify a face detection model version](../../how-to/specify-detection-model.md)
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* [What is the Face service?](../../overview-identity.md)
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* More extensive sample code can be found on [GitHub](https://github.com/Azure-Samples/cognitive-services-quickstart-code/blob/master/javascript/Face/sdk_quickstart.js).
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* More extensive sample code can be found on [GitHub](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/face/ai-vision-face-rest/samples).

articles/ai-services/openai/concepts/use-your-data.md

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* Reuse an index created before by ingesting data from other data sources.
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> [!NOTE]
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> To use an existing index, it must have at least one searchable field.
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> * To use an existing index, it must have at least one searchable field.
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> * Set the CORS **Allow Origin Type** option to `all` and the **Allowed origins** option to `*`.
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### Search types
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[!INCLUDE [ai-search-ingestion](../includes/ai-search-ingestion.md)]
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[!INCLUDE [authentication](../includes/on-your-data-authentication.md)]
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# [Vector Database in Azure Cosmos DB for MongoDB](#tab/mongo-db)
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[!INCLUDE [ai-search-ingestion](../includes/ai-search-ingestion.md)]
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[!INCLUDE [authentication](../includes/on-your-data-authentication.md)]
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# [Upload files (preview)](#tab/file-upload)
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Using Azure OpenAI Studio, you can upload files from your machine to try Azure OpenAI On Your Data. You also have the option to create a new Azure Blob Storage account and Azure AI Search resource. The service then stores the files to an Azure storage container and performs ingestion from the container. You can use the [quickstart](../use-your-data-quickstart.md) article to learn how to use this data source option.
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[!INCLUDE [ai-search-ingestion](../includes/ai-search-ingestion.md)]
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[!INCLUDE [authentication](../includes/on-your-data-authentication.md)]
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# [URL/Web address (preview)](#tab/web-pages)
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You can paste URLs and the service will store the webpage content, using it when generating responses from the model. The content in URLs/web addresses that you use need to have the following characteristics to be properly ingested:
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Once you have added the URL/web address for data ingestion, the web pages from your URL are fetched and saved to Azure Blob Storage with a container name: `webpage-<index name>`. Each URL will be saved into a different container within the account. Then the files are indexed into an Azure AI Search index, which is used for retrieval when you’re chatting with the model.
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[!INCLUDE [ai-search-ingestion](../includes/ai-search-ingestion.md)]
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[!INCLUDE [authentication](../includes/on-your-data-authentication.md)]
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# [Elasticsearch (preview)](#tab/elasticsearch)
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You can connect to your [Elasticsearch vector database](https://www.elastic.co/guide/en/elasticsearch/reference/current/elasticsearch-intro.html) and chat with your data.

articles/ai-services/openai/includes/assistants-ai-studio.md

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[!INCLUDE [Feature preview](../../../ai-studio/includes/feature-preview.md)]
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[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
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## Prerequisites
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articles/ai-services/openai/includes/fine-tuning-openai-in-ai-studio.md

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[!INCLUDE [Feature preview](../../../ai-studio/includes/feature-preview.md)]
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[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
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---
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manager: nitinme
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author: aahill
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ms.author: aahi
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ms.service: azure-ai-openai
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ms.topic: include
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ms.date: 03/27/2024
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---
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## Data connection
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You need to select how you want to authenticate the connection from Azure OpenAI, Azure AI Search, and Azure blob storage. You can choose a *System assigned managed identity* or an *API key*. By selecting *API key* as the authentication type, the system will automatically populate the API key for you to connect with your Azure AI Search, Azure OpenAI, and Azure Blob Storage resources. By selecting *System assigned managed identity*, the authentication will be based on the [role assignment](../how-to/use-your-data-securely.md#role-assignments) you have. *System assigned managed identity* is selected by default for security.
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:::image type="content" source="../media/use-your-data/data-connection-authentication.png" alt-text="A screenshot showing the managed identity option in Azure OpenAI Studio." lightbox="../media/use-your-data/data-connection-authentication.png":::
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Once you select the **next** button, it will automatically validate your setup to use the selected authentication method. If you encounter an error, see the [role assignments article](../how-to/use-your-data-securely.md#role-assignments) to update your setup.
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Once you have fixed the setup, select **next** again to validate and proceed. API users can also [configure authentication](../references/azure-search.md#api-key-authentication-options) with assigned managed identity and API keys.
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articles/ai-services/openai/references/azure-search.md

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| `allow_partial_result` | integer | False | If specified as true, the system will allow partial search results to be used and the request fails if all the queries fail. If not specified, or specified as false, the request will fail if any search query fails. |
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| `include_contexts` | array | False | The included properties of the output context. If not specified, the default value is `citations` and `intent`. Values can be `citations`,`intent`, `all_retrieved_documents`.|
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The authentication options for Azure OpenAI On Your Data when using an API key.

articles/ai-services/speech-service/includes/quickstarts/speech-to-text-basics/ai-studio.md

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[!INCLUDE [Feature preview](../../../../../ai-studio/includes/feature-preview.md)]
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[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
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In this quickstart, you try real-time speech to text in [Azure AI Studio](https://ai.azure.com).
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articles/ai-services/speech-service/pronunciation-assessment-tool.md

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# Pronunciation assessment in AI Studio
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[!INCLUDE [Feature preview](../../ai-studio/includes/feature-preview.md)]
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[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
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Pronunciation assessment uses the speech to text capability to provide subjective and objective feedback for language learners. Practicing pronunciation and getting timely feedback are essential for improving language skills. Assessments driven by experienced teachers can take much time and effort and makes a high-quality assessment expensive for learners. Pronunciation assessment can help make the language assessment more engaging and accessible to learners of all backgrounds.
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