Merge pull request #6204 from laujan/454011-update-images-text-clu-loop

update create project

Author: AnnaMHuff

articles/ai-services/language-service/conversational-language-understanding/concepts/data-formats.md

@@ -17,7 +17,7 @@ If you're uploading your data into conversational language understanding, it mus
 
 ## Import project file format
 
-If you're [importing a project](../how-to/create-project.md#import-a-project) into conversational language understanding, the file uploaded must be in the following format:
+If you're [importing a project](../how-to/create-project.md#import-an-existing-azure-ai-project) into conversational language understanding, the file uploaded must be in the following format:
 
 ```json
 {
@@ -101,7 +101,7 @@ If you're [importing a project](../how-to/create-project.md#import-a-project) in
 |Key  |Placeholder  |Value  | Example |
 |---------|---------|----------|--|
 |`{API-VERSION}`     | The [version](../../concepts/model-lifecycle.md#api-versions) of the API you're calling. | `2023-04-01` |
-|`confidenceThreshold`|`{CONFIDENCE-THRESHOLD}`|This is the threshold score below which the intent is predicted as [None intent](none-intent.md). Values are from `0` to `1`.|`0.7`|
+|`confidenceThreshold`|`{CONFIDENCE-THRESHOLD}`|The threshold score for which the intent is predicted as [None intent](none-intent.md). Values are from `0` to `1`.|`0.7`|
 | `projectName` | `{PROJECT-NAME}` | The name of your project. This value is case sensitive. | `EmailApp` |
 | `multilingual` | `true`| A Boolean value that enables you to have utterances in multiple languages in your dataset. When your model is deployed, you can query the model in any supported language (not necessarily included in your training documents. For more information about supported language codes, see [Language support](../language-support.md#multi-lingual-option). | `true`|
 |`sublists`|`[]`|Array that contains sublists. Each sublist is a key and its associated values.|`[]`|
@@ -110,7 +110,7 @@ If you're [importing a project](../how-to/create-project.md#import-a-project) in
 | `language` | `{LANGUAGE-CODE}` |  A string specifying the language code for the utterances, synonyms, and regular expressions used in your project. If your project is a multilingual project, choose the [language code](../language-support.md) of most the utterances. |`en-us`|
 | `intents` | `[]` | Array that contains all the intents you have in the project. These intents are classified from your utterances.| `[]` |
 | `entities` | `[]` | Array that contains all the entities in your project. These entities are extracted from your utterances. Every entity can have other optional components defined with them: list, prebuilt, or regex. | `[]` |
-| `dataset` | `{DATASET}` |  The test set to which this utterance goes to when it's split before training. To learn more about data splitting, see [Train your conversational language understanding model](../how-to/train-model.md#data-splitting). Possible values for this field are `Train` and `Test`.      |`Train`|
+| `dataset` | `{DATASET}` |  The test set that this utterance is assigned to when the data is split before training. To learn more about data splitting, see [Train your conversational language understanding model](../how-to/train-model.md#data-splitting). Possible values for this field are `Train` and `Test`.      |`Train`|
 | `category` | ` ` |  The type of entity associated with the span of text specified. | `Entity1`|
 | `offset` | ` ` |  The inclusive character position of the start of the entity.      |`5`|
 | `length` | ` ` |  The character length of the entity.      |`5`|
@@ -166,7 +166,7 @@ Conversational language understanding offers the option to upload your utterance
 |---------|---------|----------|--|
 |`text`|`{Utterance-Text}`|Your utterance text.|Testing|
 | `language` | `{LANGUAGE-CODE}` |  A string that specifies the language code for the utterances used in your project. If your project is a multilingual project, choose the language code of most of the utterances. For more information about supported language codes, see [Language support](../language-support.md). |`en-us`|
-| `dataset` | `{DATASET}` |  The test set to which this utterance goes to when it's split before training. To learn more about data splitting, see [Train your conversational language understanding model](../how-to/train-model.md#data-splitting). Possible values for this field are `Train` and `Test`.      |`Train`|
+| `dataset` | `{DATASET}` |  The test set that this utterance is assigned to when the data is split before training. To learn more about data splitting, see [Train your conversational language understanding model](../how-to/train-model.md#data-splitting). Possible values for this field are `Train` and `Test`.      |`Train`|
 |`intent`|`{intent}`|The assigned intent.| intent1|
 |`entity`|`{entity}`|The entity to be extracted.| entity1|
 | `category` | ` ` |  The type of entity associated with the span of text specified. | `Entity1`|
@@ -175,5 +175,5 @@ Conversational language understanding offers the option to upload your utterance
 
 ## Related content
 
-* For more information on importing your labeled data into your project directly, see [Import project](../how-to/create-project.md#import-a-project).
+* For more information on importing your labeled data into your project directly, see [Import project](../how-to/create-project.md#import-an-existing-azure-ai-project).
 * For more information about labeling your data, see [Label your utterances in Language Studio](../how-to/tag-utterances.md). After you label your data, you can [train your model](../how-to/train-model.md).

articles/ai-services/language-service/conversational-language-understanding/how-to/create-project.md

@@ -6,64 +6,45 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: how-to
-ms.date: 05/01/2025
+ms.date: 07/23/2025
 ms.author: lajanuar
 ms.custom: language-service-clu
 ---
 
-# Create a CLU fine-tuning task
+# Create a CLU project in Azure AI Foundry
 
-Use this article to learn how to set up these requirements and create a project.
+Azure AI Foundry projects help you organize your work when exploring new ideas or developing prototypes for specific use cases. A Foundry project is created on an Azure AI Foundry resource. This type of project offers an easy setup and provides access to agents and Azure AI models.
+
+If you already have an Azure AI Language or multi-service resource—whether used on its own or through Language Studio—you can continue to use those existing Language resources within the Azure AI Foundry portal. For more information, see [How to use Azure AI services in the Azure AI Foundry portal](../../../../ai-services/connect-services-ai-foundry-portal.md).
 
 ## Prerequisites
 
 * An Azure subscription. If you don't have one, you can [create one for free](https://azure.microsoft.com/free/cognitive-services).
-* An Azure AI Language resource.
-
-### Create a Language resource
-
-Before you start using conversational language understanding (CLU), you need an Azure AI Language resource.
-
-> [!NOTE]
-> You need to have an Owner role assigned for the resource group to create a Language resource.
-
-[!INCLUDE [create a new resource from the Azure portal](../includes/resource-creation-azure-portal.md)]
-
-[!INCLUDE [create a new resource from Language Studio](../includes/resource-creation-language-studio.md)]
-
-## Sign in to Language Studio
+* **Requisite permissions**. Make sure the person establishing the account and project is assigned as the Azure AI Account Owner role at the subscription level. Alternatively, having either the **Contributor** or **Cognitive Services Contributor** role at the subscription scope also meets this requirement. For more information, *see* [Role based access control (RBAC)](../../../openai/how-to/role-based-access-control.md#cognitive-services-contributor).
+* An [Azure AI Foundry resource](../../../multi-service-resource.md)
+  * For more information, *see* [Configure an Azure AI Foundry resource](configure-azure-resources.md#option-1-configure-an-azure-ai-foundry-resource).
+* After you create an Azure AI Foundry resource, [create a CLU project](#create-a-clu-project).
 
-[!INCLUDE [Sign in to Language studio](../includes/language-studio/sign-in-studio.md)]
+## Create a CLU project
 
-## Create a conversation project
-
-After you create a Language resource, create a CLU project.
+ An Azure AI Foundry project is created using an Azure AI Foundry resource. Projects are designed to help you organize your work. They offer various tools and resources that support the development, customization, and management of AI applications all within a centralized environment.
 
 ### [Azure AI Foundry](#tab/azure-ai-foundry)
 
-[!INCLUDE [Create project](../includes/language-studio/create-project.md)]
+ To learn how to create a CLU Foundry project, *see* [Create an AI Foundry project](../../../../ai-foundry/how-to/create-projects.md).
+
 
 ### [REST APIs](#tab/rest-api)
 
 [!INCLUDE [create project](../includes/rest-api/create-project.md)]
 
 ---
 
-## Import a project
+## Import an existing Azure AI project
 
 ### [Azure AI Foundry](#tab/azure-ai-foundry)
 
-You can export a CLU project as a JSON file at any time. On the conversation projects page, select a project, and on the top menu, select **Export**.
-
-:::image type="content" source="../media/export.png" alt-text="A screenshot that shows the CLU Export button." lightbox="../media/export.png":::
-
-You can reimport that project as a new project. If you import a project with the exact same name, it replaces the project's data with the newly imported project's data.
-
-If you have an existing Language Understanding (LUIS) application, you can _import_ the LUIS application JSON to CLU directly. It creates a Conversation project with all the pieces that are currently available: intents, machine learning entities, and utterances. For more information, see [Migrate from Language Understanding (LUIS) to conversational language understanding (CLU)](../how-to/migrate-from-luis.md).
-
-To import a project, select the arrow button next to **Create a new project** and select **Import**. Then select the LUIS or CLU JSON file.
-
-:::image type="content" source="../media/import.png" alt-text="A screenshot that shows the CLU Import button." lightbox="../media/import.png":::
+To import an existing Azure AI services project with Azure AI Foundry, you need to create a connection to the Azure AI services resource within your Azure AI Foundry project. For more information, *see* [Connect Azure AI Services projects to Azure AI Foundry](../../../../ai-services/connect-services-ai-foundry-portal.md)
 
 ### [REST APIs](#tab/rest-api)
 
@@ -77,7 +58,13 @@ You can import a CLU JSON into the service.
 
 ### [Azure AI Foundry](#tab/azure-ai-foundry)
 
-You can export a CLU project as a JSON file at any time. On the conversation projects page, select a project, and then select **Export**.
+You can download a CLU project as a **config.json** file:
+
+1. Navigate to your project home page.
+1. At the top of the page, select your project from the right page ribbon area.
+1. Select **Download config file**.
+
+    :::image type="content" source="../media/create-project/download-config-json.png" alt-text="Screenshot of project drop-down menu with the download config file hyperlink in the Azure AI Foundry.":::
 
 ### [REST APIs](#tab/rest-api)
 
@@ -87,11 +74,18 @@ You can export a CLU project as a JSON file at any time.
 
 ---
 
-## Get CLU project details
+## View and manage project details
 
 ### [Azure AI Foundry](#tab/azure-ai-foundry)
 
-[!INCLUDE [Language Studio project details](../includes/language-studio/project-details.md)]
+* On the project Home page, information about the project is found in the **Project details** section.
+* To view project settings, select **Management center** from the bottom of the left navigation pane, then select one of the following tabs:
+   *  **Overview** to view project details.
+   *  **Users** to manage users and roles.
+   *  **Models + endpoints** to manage deployments of your models and services.
+   *  **Connected resources** to manage connected resources for the project.
+
+   :::image type="content" source="../media/create-project/project-details.png" alt-text="Screenshot of the project details list in the Azure AI Foundry.":::
 
 ### [REST APIs](#tab/rest-api)
 
@@ -103,11 +97,30 @@ You can export a CLU project as a JSON file at any time.
 
 ### [Azure AI Foundry](#tab/azure-ai-foundry)
 
-[!INCLUDE [Delete project](../includes/language-studio/delete-project.md)]
+
+If you no longer need your project, you can delete it from the Azure AI Foundry.
+
+1. Navigate to the [Azure AI Foundry](https://ai.azure.com/) home page. Initiate the authentication process by signing in, unless you already completed this step and your session is active.
+1. Select the project that you want to delete from the **Keep building with Azure AI Foundry**
+1. Select **Management center**.
+1. Select **Delete project**.
+
+   :::image type="content" source="../media/create-project/delete-project.png" alt-text="Screenshot of the Delete project button in the Azure AI Foundry.":::
+
+To delete the hub along with all its projects:
+
+1. Navigate to the **Overview** tab inn the **Hub** section.
+
+   :::image type="content" source="../media/create-project/hub-details.png" alt-text="Screenshot of the hub details list in the Azure AI Foundry.":::
+
+1. On the right, select **Delete hub**. 
+1. The link opens the Azure portal for you to delete the hub there.
+
+   :::image type="content" source="../media/create-project/delete-hub.png" alt-text="Screenshot of the Delete hub button in the Azure AI Foundry.":::
 
 ### [REST APIs](#tab/rest-api)
 
-When you don't need your project anymore, you can use the APIs to delete your project.
+If you no longer need your project, delete it using the REST API.
 
 [!INCLUDE [Delete project](../includes/rest-api/delete-project.md)]
 

articles/ai-services/language-service/conversational-language-understanding/how-to/tag-utterances.md

@@ -15,7 +15,7 @@ ms.custom: language-service-clu
 
 After you [build a schema](build-schema.md) for your fine-tuning task, you add training utterances to your project. The utterances should be similar to what your users use when they interact with the project. When you add an utterance, you have to assign which intent it belongs to. After the utterance is added, label the words within your utterance that you want to extract as entities.
 
-Data labeling is a crucial step in the conversational language understanding (CLU) trained development lifecycle. This data is used in the next step when you train your model so that your model can learn from the labeled data. If you already labeled utterances, you can directly [import them into your project](create-project.md#import-a-project), if your data follows the [accepted data format](../concepts/data-formats.md). To learn more about importing labeled data, see [Create a CLU fine-tuning task](create-project.md#import-a-project). Labeled data informs the model about how to interpret text and is used for training and evaluation.
+Data labeling is a crucial step in the conversational language understanding (CLU) trained development lifecycle. This data is used in the next step when you train your model so that your model can learn from the labeled data. If you already labeled utterances, you can directly [import them into your project](create-project.md#import-an-existing-azure-ai-project), if your data follows the [accepted data format](../concepts/data-formats.md). To learn more about importing labeled data, see [Create a CLU fine-tuning task](create-project.md#import-an-existing-azure-ai-project). Labeled data informs the model about how to interpret text and is used for training and evaluation.
 
 > [!TIP]
 > Use the **Quick Deploy** option to implement custom CLU intent routing, which is powered by your own large language model deployment without adding or labeling any training data.
@@ -43,9 +43,9 @@ As you add utterances and label them, keep in mind:
 
 * For [multilingual projects](../language-support.md#multi-lingual-option), adding utterances in other languages increases the model's performance in these languages. Avoid duplicating your data across all the languages that you want to support. For example, to improve a calender bot's performance with users, a developer might add examples mostly in English and a few in Spanish or French. They might add utterances such as:
 
-  * "Set a meeting with **Matt** and **Kevin** **tomorrow** at **12 PM**." (English)
-  * "Reply as **tentative** to the **weekly update** meeting." (English)
-  * "Cancelar mi **próxima** reunión." (Spanish)
+  * `Set a meeting with **Matt** and **Kevin** **tomorrow** at **12 PM**.` (English)
+  * `Reply as **tentative** to the **weekly update** meeting.` (English)
+  * `Cancelar mi **próxima** reunión.` (Spanish)
 
 ## Label your utterances
 
@@ -111,7 +111,7 @@ Before you get started, the suggested utterances feature is available only if yo
 
 On the **Data labeling** page:
 
-1. Select **Suggest utterances**. A pane opens on the right and prompts you to select your Azure OpenAI resource and deployment.
+1. Select **Suggest utterances**. A pane opens and prompts you to select your Azure OpenAI resource and deployment.
 1. After you select an Azure OpenAI resource, select **Connect** so that your Language resource has direct access to your Azure OpenAI resource. It assigns your Language resource the **Cognitive Services User** role to your Azure OpenAI resource. Now your current Language resource has access to Azure OpenAI. If the connection fails, follow [these steps](#add-required-configurations-to-azure-openai-resource) to manually add the correct role to your Azure OpenAI resource.
 1. After the resource is connected, select the deployment. The model that we recommend for the Azure OpenAI deployment is `gpt-35-turbo-instruct`.
 1. Select the intent for which you want to get suggestions. Make sure the intent that you selected has at least five saved utterances to be enabled for utterance suggestions. The suggestions provided by Azure OpenAI are based on the most recent utterances that you added for that intent.

articles/ai-services/language-service/conversational-language-understanding/how-to/train-model.md

@@ -28,8 +28,8 @@ Model evaluation is triggered automatically after training is completed successf
 ## Prerequisites
 
 * **An active Azure subscription**. If you don't have one, you can [create one for free](https://azure.microsoft.com/free/cognitive-services).
-* **Requisite permissions**. Make sure the person establishing the account and project is assigned as the Azure AI Account Owner role at the subscription level. Alternatively, having either the **Contributor** or **Cognitive Services Contributor** role at the subscription scope also meets this requirement. For more information, *see* [Role based access control (RBAC)](../../../openai/how-to/role-based-access-control.md#cognitive-services-contributor)
-* **A project created in the Azure AI Foundry**. For more information, *see* [Create an AI Foundry project](../../../../ai-foundry/how-to/create-projects.md)
+* **Requisite permissions**. Make sure the person establishing the account and project is assigned as the Azure AI Account Owner role at the subscription level. Alternatively, having either the **Contributor** or **Cognitive Services Contributor** role at the subscription scope also meets this requirement. For more information, *see* [Role based access control (RBAC)](../../../openai/how-to/role-based-access-control.md#cognitive-services-contributor).
+* **A project created in the Azure AI Foundry**. For more information, *see* [Create an AI Foundry project](../../../../ai-foundry/how-to/create-projects.md).
 * [**Your labeled utterances**](tag-utterances.md) tagged for your fine tuning task.