@@ -13,7 +13,7 @@ ms.devlang: csharp
 # ms.devlang: csharp, java, javascript, python
 ms.custom: language-service-entity-linking, mode-api, devx-track-extended-java, devx-track-js, devx-track-python
 keywords: text mining, entity linking
-zone_pivot_groups: programming-languages-text-analytics
+zone_pivot_groups: programming-languages-entity-linking
 ---
 
 # Quickstart: Entity Linking using the client library and REST API

@@ -19,7 +19,7 @@ The key phrase extraction feature can evaluate unstructured text, and for each d
 This feature is useful if you need to quickly identify the main points in a collection of documents. For example, given input text "*The food was delicious and the staff was wonderful*", the service returns the main topics: "*food*" and "*wonderful staff*".
 
 > [!TIP]
-> If you want to start using this feature, you can follow the [quickstart article](../quickstart.md) to get started. You can also make example requests using [Language Studio](../../language-studio.md) without needing to write code.
+> If you want to start using this feature, you can follow the [quickstart article](../quickstart.md) to get started. You can also make requests using [Azure AI Foundry](../../../../ai-studio//what-is-ai-studio.md) without needing to write code.
 
 ## Development options
 
@@ -29,20 +29,20 @@ This feature is useful if you need to quickly identify the main points in a coll
 
 ### Specify the key phrase extraction model
 
-By default, key phrase extraction will use the latest available AI model on your text. You can also configure your API requests to use a specific [model version](../../concepts/model-lifecycle.md).
+By default, key phrase extraction uses the latest available AI model on your text. You can also configure your API requests to use a specific [model version](../../concepts/model-lifecycle.md).
 
 ### Input languages
 
-When you submit documents to be processed by key phrase extraction, you can specify which of [the supported languages](../language-support.md) they're written in. if you don't specify a language, key phrase extraction will default to English. The API may return offsets in the response to support different [multilingual and emoji encodings](../../concepts/multilingual-emoji-support.md). 
+When you submit documents to be processed by key phrase extraction, you can specify which of [the supported languages](../language-support.md) they're written in. if you don't specify a language, key phrase extraction defaults to English. The API may return offsets in the response to support different [multilingual and emoji encodings](../../concepts/multilingual-emoji-support.md). 
 
 ## Submitting data
 
 Key phrase extraction works best when you give it bigger amounts of text to work on. This is opposite from sentiment analysis, which performs better on smaller amounts of text. To get the best results from both operations, consider restructuring the inputs accordingly.
 
-To send an API request, You will need your Language resource endpoint and key.
+To send an API request, You need your Language resource endpoint and key.
 
 > [!NOTE]
-> You can find the key and endpoint for your Language resource on the Azure portal. They will be located on the resource's **Key and endpoint** page, under **resource management**. 
+> You can find the key and endpoint for your Language resource on the Azure portal. They are located on the resource's **Key and endpoint** page, under **resource management**. 
 
 Analysis is performed upon receipt of the request. Using the key phrase extraction feature synchronously is stateless. No data is stored in your account, and results are returned immediately in the response.
 

@@ -13,6 +13,6 @@ To use key phrase extraction, you submit raw unstructured text for analysis and
 
 |Development option  |Description  |
 |---------|---------|
-|Language studio     | Language Studio is a web-based platform that lets you try entity linking with text examples without an Azure account, and your own data when you sign up. For more information, see the [Language Studio website](https://language.cognitive.azure.com/tryout/keyPhrases) or [language studio quickstart](../../language-studio.md).         |
+|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use entity linking with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-studio/what-is-ai-studio.md).         |
 |REST API or Client library (Azure SDK)      | Integrate key phrase extraction into your applications using the REST API, or the client library available in a variety of languages. For more information, see the [key phrase extraction quickstart](../quickstart.md).        |
 | Docker container | Use the available Docker container to [deploy this feature on-premises](../how-to/use-containers.md). These docker containers enable you to bring the service closer to your data for compliance, security, or other operational reasons. |

@@ -0,0 +1,49 @@
+---
+author: jboback
+manager: nitinme
+ms.service: azure-ai-language
+ms.topic: include
+ms.date: 02/16/2025
+ms.author: jboback
+---
+
+## Prerequisites
+
+* [Create a Project in Foundry in the Azure AI Foundry Portal](../../../../../ai-studio/how-to/create-projects.md)
+
+## Navigate to the Azure AI Foundry Playground
+
+Using the left side pane, select **Playgrounds**. Then select the **Try the Language Playground** button.
+
+:::image type="content" source="../../media/quickstarts/azure-ai-foundry/foundry-playground-navigation.png" alt-text="The development lifecycle" lightbox="../../media/quickstarts/azure-ai-foundry/foundry-playground-navigation.png":::
+
+## Use Key Phrase Extraction in the Azure AI Foundry Playground
+
+The **Language Playground** consists of four sections:
+
+* Top banner: You can select any of the currently available Language services here.
+* Right pane: This pane is where you can find the **Configuration** options for the service, such as the API and model version, along with features specific to the service.
+* Center pane: This pane is where you enter your text for processing. After the operation is run, some results are shown here.
+* Right pane: This pane is where **Details** of the run operation are shown.
+
+Here you can select the Key Phrase Extraction capability by choosing the top banner tile, **Extract key phrases**.
+
+## Use Extract key phrases
+
+**Extract key phrases** is designed to extract key phrases from text.
+
+In **Configuration** there are the following options:
+
+|Option              |Description                              |
+|--------------------|-----------------------------------------|
+|Select API version  | Select which version of the API to use.    |
+|Select model version| Select which version of the model to use.|
+|Select text language| Select the language of the input text. |
+
+After your operation is completed, each entity is underlined in the center pane and the **Details** section contains the following fields for the overall sentiment and the sentiment of each sentence:
+
+|Field | Description                |
+|------|----------------------------|
+|Extracted key phrases|A list of the extracted key phrases.|
+
+:::image type="content" source="../../media/quickstarts/azure-ai-foundry/key-phrase-extraction.png" alt-text="A screenshot of an example of Extract key phrases in azure AI studio." lightbox="../../media/quickstarts/azure-ai-foundry/key-phrase-extraction.png":::

@@ -7,14 +7,14 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 09/27/2024
+ms.date: 02/17/2025
 ms.author: jboback
 ms.custom: language-service-key-phrase
 ---
 
 # What is key phrase extraction in Azure AI Language?
 
-Key phrase extraction 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. Use key phrase extraction to quickly identify the main concepts in text. For example, in the text "*The food was delicious and the staff were wonderful.*", key phrase extraction will return the main topics: "*food*" and "*wonderful staff*".
+Key phrase extraction 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. Use key phrase extraction to quickly identify the main concepts in text. For example, in the text "*The food was delicious and the staff were wonderful.*", key phrase extraction returns the main topics: "*food*" and "*wonderful staff*."
 
 This documentation contains the following types of articles:
 
@@ -32,12 +32,12 @@ This documentation contains the following types of articles:
 
 ## Responsible AI 
 
-An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it is deployed. Read the [transparency note for key phrase extraction](/legal/cognitive-services/language-service/transparency-note-key-phrase-extraction?context=/azure/ai-services/language-service/context/context) to learn about responsible AI use and deployment in your systems. You can also see the following articles for more information:
+An AI system includes not only the technology, but also the people who use it, the people who are affected by it, and the environment in which it's deployed. Read the [transparency note for key phrase extraction](/legal/cognitive-services/language-service/transparency-note-key-phrase-extraction?context=/azure/ai-services/language-service/context/context) to learn about responsible AI use and deployment in your systems. For more information, see the following articles:
 
 [!INCLUDE [Responsible AI links](../includes/overview-responsible-ai-links.md)]
 
 ## Next steps
 
 There are two ways to get started using the entity linking feature:
-* [Language Studio](../language-studio.md), which is a web-based platform that enables you to try several Azure AI Language features without needing to write code.
+* [Azure AI Foundry](../../../ai-studio/what-is-ai-studio.md) is a web-based platform that lets you use several Azure AI Language features without needing to write code.
 * The [quickstart article](quickstart.md) for instructions on making requests to the service using the REST API and client library SDK.  

@@ -7,7 +7,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: quickstart
-ms.date: 11/21/2024
+ms.date: 02/17/2025
 ms.author: jboback
 ms.devlang: csharp
 # ms.devlang: csharp, java, javascript, python
@@ -48,6 +48,12 @@ zone_pivot_groups: programming-languages-text-analytics
 
 ::: zone-end
 
+::: zone pivot="azure-ai-foundry"
+
+[!INCLUDE [Azure AI Foundry quickstart](includes/quickstarts/azure-ai-foundry.md)]
+
+::: zone-end
+
 ## Clean up resources
 
 If you want to clean up and remove an Azure AI services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

@@ -13,6 +13,6 @@ To use language detection, you submit raw unstructured text for analysis and han
 
 |Development option  |Description  |
 |---------|---------|
-|Language studio     | Language Studio is a web-based platform that lets you try entity linking with text examples without an Azure account, and your own data when you sign up. For more information, see the [Language Studio website](https://language.cognitive.azure.com/tryout/detectLanguage) or [language studio quickstart](../../language-studio.md).         |
+|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use entity linking with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-studio/what-is-ai-studio.md).         |
 |REST API or Client library (Azure SDK)      | Integrate language detection into your applications using the REST API, or the client library available in a variety of languages. For more information, see the [language detection quickstart](../quickstart.md).        |
 | Docker container | Use the available Docker container to [deploy this feature on-premises](../how-to/use-containers.md). These docker containers enable you to bring the service closer to your data for compliance, security, or other operational reasons. |

@@ -0,0 +1,53 @@
+---
+author: jboback
+manager: nitinme
+ms.service: azure-ai-language
+ms.topic: include
+ms.date: 02/16/2025
+ms.author: jboback
+---
+
+## Prerequisites
+
+* [Create a Project in Foundry in the Azure AI Foundry Portal](../../../../../ai-studio/how-to/create-projects.md)
+
+## Navigate to the Azure AI Foundry Playground
+
+Using the left side pane, select **Playgrounds**. Then select the **Try the Language Playground** button.
+
+:::image type="content" source="../../media/quickstarts/azure-ai-foundry/foundry-playground-navigation.png" alt-text="The development lifecycle" lightbox="../../media/quickstarts/azure-ai-foundry/foundry-playground-navigation.png":::
+
+## Use Language Detection in the Azure AI Foundry Playground
+
+The **Language Playground** consists of four sections:
+
+* Top banner: You can select any of the currently available Language services here.
+* Right pane: This pane is where you can find the **Configuration** options for the service, such as the API and model version, along with features specific to the service.
+* Center pane: This pane is where you enter your text for processing. After the operation is run, some results are shown here.
+* Right pane: This pane is where **Details** of the run operation are shown.
+
+Here you can select the Language Detection capability by choosing the top banner tile, **Detect language**.
+
+## Use Detect language
+
+**Detect language** is designed to identify the language typed in text.
+
+In **Configuration** there are the following options:
+
+|Option              |Description                              |
+|--------------------|-----------------------------------------|
+|Select API version  | Select which version of the API to use.    |
+|Select model version| Select which version of the model to use.|
+|Select country hint| Select the origin country of the input text. |
+
+After your operation is completed, the **Details** section contains the following fields for the most detected language and script:
+
+|Field | Description                |
+|------|----------------------------|
+|Sentence|
+|Iso 639-1 Code| The ISE 639-1 code for the most detected language.|
+|Confidence Score| How confident the model is in the correctness of identification of the most typed language.|
+|Script Name| The name of the most detected script in the text.
+|Iso 15924 Script Code| The ISO 15924 script code for the most detected script.|
+
+:::image type="content" source="../../media/quickstarts/azure-ai-foundry/language-detection.png" alt-text="A screenshot of an example of detect language in azure AI studio." lightbox="../../media/quickstarts/azure-ai-foundry/language-detection.png":::

@@ -7,7 +7,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 09/27/2024
+ms.date: 02/17/2025
 ms.author: jboback
 ms.custom: language-service-language-detection
 ---
@@ -45,5 +45,5 @@ An AI system includes not only the technology, but also the people who will use
 ## Next steps
 
 There are two ways to get started using the entity linking feature:
-* [Language Studio](../language-studio.md), which is a web-based platform that enables you to try several Azure AI Language features without needing to write code.
+* [Azure AI Foundry](../../../ai-studio/what-is-ai-studio.md) is a web-based platform that lets you use several Language service features without needing to write code.
 * The [quickstart article](quickstart.md) for instructions on making requests to the service using the REST API and client library SDK.  

@@ -7,7 +7,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: quickstart
-ms.date: 10/21/2024
+ms.date: 02/17/2025
 ms.author: jboback
 ms.devlang: csharp
 # ms.devlang: csharp, java, javascript, python
@@ -48,6 +48,12 @@ zone_pivot_groups: programming-languages-text-analytics
 
 ::: zone-end
 
+::: zone pivot="azure-ai-foundry"
+
+[!INCLUDE [Azure AI Foundry quickstart](includes/quickstarts/azure-ai-foundry.md)]
+
+::: zone-end
+
 ## Clean up resources
 
 If you want to clean up and remove an Azure AI services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

@@ -7,7 +7,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: how-to
-ms.date: 11/21/2024
+ms.date: 02/15/2025
 ms.author: jboback
 ms.custom: language-service-ner, ignite-2024
 ---
@@ -25,23 +25,23 @@ The NER feature can evaluate unstructured text, and extract named entities from
 
 ### Input languages
 
-When you submit documents to be processed, you can specify which of [the supported languages](language-support.md) they're written in. if you don't specify a language, key phrase extraction defaults to English. The API may return offsets in the response to support different [multilingual and emoji encodings](../concepts/multilingual-emoji-support.md). 
+When you submit input text to be processed, you can specify which of [the supported languages](language-support.md) they're written in. if you don't specify a language, key phrase extraction defaults to English. The API may return offsets in the response to support different [multilingual and emoji encodings](../concepts/multilingual-emoji-support.md). 
 
 ## Submitting data
 
 Analysis is performed upon receipt of the request. Using the NER feature synchronously is stateless. No data is stored in your account, and results are returned immediately in the response.
 
 [!INCLUDE [asynchronous-result-availability](../includes/async-result-availability.md)]
 
-The API attempts to detect the [defined entity categories](concepts/named-entity-categories.md) for a given document language. 
+The API attempts to detect the [defined entity categories](concepts/named-entity-categories.md) for a given input text language. 
 
 ## Getting NER results
 
 When you get results from NER, you can stream the results to an application or save the output to a file on the local system. The API response includes [recognized entities](concepts/named-entity-categories.md), including their categories and subcategories, and confidence scores. 
 
 ## Select which entities to be returned
 
-The API attempts to detect the [defined entity types and tags](concepts/named-entity-categories.md) for a given document language. The entity types and tags replace the categories and subcategories structure the older models use to define entities for more flexibility. You can also specify which entities are detected and returned, use the optional `includeList` and `excludeList` parameters with the appropriate entity types. The following example would detect only `Location`. You can specify one or more [entity types](concepts/named-entity-categories.md) to be returned. Given the types and tags hierarchy introduced for this version, you have the flexibility to filter on different granularity levels as so:
+The API attempts to detect the [defined entity types and tags](concepts/named-entity-categories.md) for a given input text language. The entity types and tags replace the categories and subcategories structure the older models use to define entities for more flexibility. You can also specify which entities are detected and returned, use the optional `includeList` and `excludeList` parameters with the appropriate entity types. The following example would detect only `Location`. You can specify one or more [entity types](concepts/named-entity-categories.md) to be returned. Given the types and tags hierarchy introduced for this version, you have the flexibility to filter on different granularity levels as so:
 
 **Input:**
 
@@ -115,7 +115,7 @@ In order to provide users with more insight into an entity's types and provide i
 |Name of the attribute|Type        |Definition                               |
 |---------------------|------------|-----------------------------------------|
 |`type`               |String      |The most specific type of detected entity.<br><br>For example, “Seattle” is a `City`, a `GPE` (Geo Political Entity) and a `Location`. The most granular classification for “Seattle” is that it is a `City`. The type would be `City` for the text “Seattle".|
-|`tags`               |List (tags) |A list of tag objects which expresses the affinity of the detected entity to a hierarchy or any other grouping.<br><br>A tag contains two fields:<br>1. `name`: A unique name for the tag.<br>2. `confidenceScore`: The associated confidence score for a tag ranging from 0 to 1.<br><br>This unique tagName is be used to filter in the `inclusionList` and `exclusionList` parameters.
+|`tags`               |List (tags) |A list of tag objects which expresses the affinity of the detected entity to a hierarchy or any other grouping.<br><br>A tag contains two fields:<br>1. `name`: A unique name for the tag.<br>2. `confidenceScore`: The associated confidence score for a tag ranging from 0 to 1.<br><br>This unique tagName is used to filter in the `inclusionList` and `exclusionList` parameters.
 |`metadata`           |Object      |Metadata is an object containing more data about the entity type detected. It changes based on the field `metadataKind`.
 
 ## Sample output

@@ -13,5 +13,5 @@ To use named entity recognition, you submit raw unstructured text for analysis a
 
 |Development option  |Description  |
 |---------|---------|
-|Language studio     | Language Studio is a web-based platform that lets you try entity linking with text examples without an Azure account, and your own data when you sign up. For more information, see the [Language Studio website](https://language.cognitive.azure.com/tryout/namedEntities) or [language studio quickstart](../../language-studio.md).         |
+|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use named entity recognition with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-studio/what-is-ai-studio.md).         |
 |REST API or Client library (Azure SDK)      | Integrate named entity recognition into your applications using the REST API, or the client library available in a variety of languages. For more information, see the [named entity recognition quickstart](../quickstart.md).        |

@@ -0,0 +1,56 @@
+---
+author: jboback
+manager: nitinme
+ms.service: azure-ai-language
+ms.topic: include
+ms.date: 02/16/2025
+ms.author: jboback
+---
+
+## Prerequisites
+
+* [Create a Project in Foundry in the Azure AI Foundry Portal](../../../../../ai-studio/how-to/create-projects.md)
+
+## Navigate to the Azure AI Foundry Playground
+
+Using the left side pane, select **Playgrounds**. Then select the **Try the Language Playground** button.
+
+:::image type="content" source="../../media/quickstarts/azure-ai-foundry/foundry-playground-navigation.png" alt-text="The development lifecycle" lightbox="../../media/quickstarts/azure-ai-foundry/foundry-playground-navigation.png":::
+
+## Use NER in the Azure AI Foundry Playground
+
+The **Language Playground** consists of four sections:
+
+* Top banner: You can select any of the currently available Language services here.
+* Right pane: This pane is where you can find the **Configuration** options for the service, such as the API and model version, along with features specific to the service.
+* Center pane: This pane is where you enter your text for processing. After the operation is run, some results are shown here.
+* Right pane: This pane is where **Details** of the run operation are shown.
+
+Here you can select the Named Entity Recognition capability by choosing the top banner tile, **Extract Named Entities**.
+
+## Use Extract Named Entities
+
+**Extract Named Entities** is designed to identify named entities in text.
+
+In **Configuration** there are the following options:
+
+|Option              |Description                              |
+|--------------------|-----------------------------------------|
+|Select API version  | Select which version of the API to use.    |
+|Select model version| Select which version of the model to use.|
+|Select text language| Select which language the language is input in.|
+|Select types to include| Select they types of information you want to extract.|
+|Overlap policy| Select the policy for overlapping entities.|
+|Inference options| Additional options to customize the return of the processed data.|
+
+After your operation is completed, the type of entity is displayed beneath each entity in the center pane and the **Details** section contains the following fields for each entity:
+
+|Field | Description                |
+|------|----------------------------|
+|Entity|The detected entity.|
+|Category| The type of entity that was detected.|
+|Offset| The number of characters that the entity was detected from the beginning of the line.|
+|Length| The character length of the entity.|
+|Confidence| How confident the model is in the correctness of identification of entity's type.|
+
+:::image type="content" source="../../media/quickstarts/azure-ai-foundry/named-entity-recognition.png" alt-text="A screenshot of an example of extract named entities in azure AI studio." lightbox="../../media/quickstarts/azure-ai-foundry/named-entity-recognition.png":::

@@ -7,21 +7,21 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 09/27/2024
+ms.date: 02/15/2025
 ms.author: jboback
 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. 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.
+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 preset 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.
 
 > [!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.
+> [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're calling the preview version of the API equal or newer than 2023-04-15-preview, 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)]
 
@@ -33,7 +33,7 @@ Named Entity Recognition (NER) is one of the features offered by [Azure AI Langu
 
 ## Responsible AI 
 
-An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it is deployed. Read the [transparency note for NER](/legal/cognitive-services/language-service/transparency-note-named-entity-recognition?context=/azure/ai-services/language-service/context/context) to learn about responsible AI use and deployment in your systems. You can also see the following articles for more information:
+An AI system includes not only the technology, but also the people who use it, the people who are affected by it, and the environment in which it's deployed. Read the [transparency note for NER](/legal/cognitive-services/language-service/transparency-note-named-entity-recognition?context=/azure/ai-services/language-service/context/context) to learn about responsible AI use and deployment in your systems. You can also see the following articles for more information:
 
 [!INCLUDE [Responsible AI links](../includes/overview-responsible-ai-links.md)]
 
@@ -46,5 +46,5 @@ An AI system includes not only the technology, but also the people who will use
 ## Next steps
 
 There are two ways to get started using the Named Entity Recognition (NER) feature:
-* [Language Studio](../language-studio.md), which is a web-based platform that enables you to try several Azure AI Language features without needing to write code.
+* [Azure AI Foundry](../../../ai-studio/what-is-ai-studio.md) is a web-based platform that lets you use several Language service features without needing to write code.
 * The [quickstart article](quickstart.md) for instructions on making requests to the service using the REST API and client library SDK.  

@@ -48,6 +48,12 @@ zone_pivot_groups: programming-languages-text-analytics
 
 ::: zone-end
 
+::: zone pivot="azure-ai-foundry"
+
+[!INCLUDE [Azure AI Foundry quickstart](includes/quickstarts/azure-ai-foundry.md)]
+
+::: zone-end
+
 ## Clean up resources
 
 If you want to clean up and remove an Azure AI services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

@@ -30,10 +30,10 @@ By default, this feature uses the latest available AI model on your text. You ca
 
 ### Input languages
 
-When you submit documents to be processed, you can specify which of [the supported languages](language-support.md) they're written in. If you don't specify a language, extraction defaults to English. The API may return offsets in the response to support different [multilingual and emoji encodings](../concepts/multilingual-emoji-support.md). 
+When you submit input text to be processed, you can specify which of [the supported languages](language-support.md) they're written in. If you don't specify a language, extraction defaults to English. The API may return offsets in the response to support different [multilingual and emoji encodings](../concepts/multilingual-emoji-support.md). 
 
 ### Redaction Policy (version 2024-11-5-preview only)
-In version 2024-11-5-preview, you're able to define the `redactionPolicy` parameter to reflect the redaction policy to be used when redacting text. The policy field supports three policy types:
+In version `2024-11-5-preview`, you're able to define the `redactionPolicy` parameter to reflect the redaction policy to be used when redacting text. The policy field supports three policy types:
 
 - `DoNotRedact` 
 - `MaskWithCharacter` (default) 
@@ -55,7 +55,7 @@ Analysis is performed upon receipt of the request. Using the PII detection featu
 
 ## Select which entities to be returned
 
-The API attempts to detect the [defined entity categories](concepts/entity-categories.md) for a given document language. If you want to specify which entities are detected and returned, use the optional `piiCategories` parameter with the appropriate entity categories. This parameter can also let you detect entities that aren't enabled by default for your document language. The following example would detect only `Person`. You can specify one or more [entity types](concepts/entity-categories.md) to be returned.
+The API attempts to detect the [defined entity categories](concepts/entity-categories.md) for a given input text language. If you want to specify which entities are detected and returned, use the optional `piiCategories` parameter with the appropriate entity categories. This parameter can also let you detect entities that aren't enabled by default for your input text language. The following example would detect only `Person`. You can specify one or more [entity types](concepts/entity-categories.md) to be returned.
 
 > [!TIP]
 > If you don't include `default` when specifying entity categories, The API only returns the entity categories you specify.
@@ -138,4 +138,4 @@ When you get results from PII detection, you can stream the results to an applic
 
 ## Next steps
 
-[Named Entity Recognition overview](overview.md)
+[Personally Identifying Information (PII) overview](overview.md)

@@ -13,5 +13,5 @@ To use PII detection, you submit text for analysis and handle the API output in
 
 |Development option  |Description  |
 |---------|---------|
-|Language studio     | Language Studio is a web-based platform that lets you try entity linking with text examples without an Azure account, and your own data when you sign up. For more information, see the [Language Studio website](https://language.cognitive.azure.com/tryout/pii) or [language studio quickstart](../../language-studio.md).         |
+|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use personally identifying information detection with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-studio/what-is-ai-studio.md).         |
 |REST API or Client library (Azure SDK)      | Integrate PII detection into your applications using the REST API, or the client library available in various languages. For more information, see the [PII detection quickstart](../quickstart.md).        |

@@ -0,0 +1,86 @@
+---
+author: jboback
+manager: nitinme
+ms.service: azure-ai-language
+ms.topic: include
+ms.date: 02/16/2025
+ms.author: jboback
+ms.custom: language-service-pii
+---
+
+## Prerequisites
+
+* [Create a Project in Foundry in the Azure AI Foundry Portal](../../../../../ai-studio/how-to/create-projects.md)
+
+## Navigate to the Azure AI Foundry Playground
+
+Using the left side pane, select **Playgrounds**. Then select the **Try the Language Playground** button.
+
+:::image type="content" source="../../media/quickstarts/azure-ai-foundry/foundry-playground-navigation.png" alt-text="The development lifecycle" lightbox="../../media/quickstarts/azure-ai-foundry/foundry-playground-navigation.png":::
+
+## Use PII in the Azure AI Foundry Playground
+
+The **Language Playground** consists of four sections:
+
+* Top banner: You can select any of the currently available Language services here.
+* Right pane: This pane is where you can find the **Configuration** options for the service, such as the API and model version, along with features specific to the service.
+* Center pane: This pane is where you enter your text for processing. After the operation is run, some results will be shown here.
+* Right pane: This pane is where **Details** of the run operation are shown.
+
+Here you can select from two Personally Identifying Information (PII) detection capabilities by choosing the top banner tiles, **Extract PII from conversation** or **Extract PII from text**. Each is for a different scenario.
+
+### Extract PII from conversation
+
+**Extract PII from conversation** is designed to identify and mask personally identifying information in conversational text.
+
+In **Configuration** there are the following options:
+
+|Option              |Description                              |
+|--------------------|-----------------------------------------|
+|Select API version  | Select which version of the API to use.    |
+|Select model version| Select which version of the model to use.|
+|Select text language| Select which language the language is input in.|
+|Select types to include| Select they types of information you want to redact.|
+|Specify redaction policy| Select the method of redaction.|
+|Specify redaction character| Select which character is used for redaction. Only available with the **CharacterMask** redaction policy.|
+
+After your operation is completed, the type of entity is displayed beneath each entity in the center pane and the **Details** section contains the following fields for each entity:
+
+|Field | Description                |
+|------|----------------------------|
+|Entity|The detected entity.|
+|Category| The type of entity that was detected.|
+|Offset| The number of characters that the entity was detected from the beginning of the line.|
+|Length| The character length of the entity.|
+|Confidence| How confident the model is in the correctness of identification of entity's type.|
+
+:::image type="content" source="../../media/quickstarts/azure-ai-foundry/conversation-pii.png" alt-text="A screenshot of an example of extract PII from conversation in azure AI studio." lightbox="../../media/quickstarts/azure-ai-foundry/conversation-pii.png":::
+
+### Extract PII from text
+
+**Extract PII from text** is designed to identify and mask personally identifying information in text.
+
+In **Configuration** there are the following options:
+
+|Option              |Description                              |
+|--------------------|-----------------------------------------|
+|Select API version  | Select which version of the API to use.    |
+|Select model version| Select which version of the model to use.|
+|Select text language| Select which language the language is input in.|
+|Select types to include| Select they types of information you want to redact.|
+|Specify redaction policy| Select the method of redaction.|
+|Specify redaction character| Select which character is used for redaction. Only available with the **CharacterMask** redaction policy.|
+
+After your operation is completed, the type of entity is displayed beneath each entity in the center pane and the **Details** section contains the following fields for each entity:
+
+|Field | Description                |
+|------|----------------------------|
+|Entity|The detected entity.|
+|Category| The type of entity that was detected.|
+|Offset| The number of characters that the entity was detected from the beginning of the line.|
+|Length| The character length of the entity.|
+|Confidence| How confident the model is in the correctness of identification of entity's type.|
+|Tags| How confident the model is in the correctness for each identified entity type.|
+
+:::image type="content" source="../../media/quickstarts/azure-ai-foundry/text-pii.png" alt-text="A screenshot of an example of extract PII from text in azure AI studio." lightbox="../../media/quickstarts/azure-ai-foundry/text-pii.png":::
+

@@ -73,5 +73,5 @@ An AI system includes not only the technology, but also the people who use it, t
 ## Next steps
 
 There are two ways to get started using the entity linking feature:
-* [Language Studio](../language-studio.md), which is a web-based platform that enables you to try several Language service features without needing to write code.
+* [Azure AI Foundry](../../../ai-studio/what-is-ai-studio.md) is a web-based platform that lets you use several Language service features without needing to write code.
 * The [quickstart article](quickstart.md) for instructions on making requests to the service using the REST API and client library SDK.

@@ -50,6 +50,12 @@ zone_pivot_groups: programming-languages-text-analytics
 
 ::: zone-end
 
+::: zone pivot="azure-ai-foundry"
+
+[!INCLUDE [Azure AI Foundry quickstart](includes/quickstarts/azure-ai-foundry.md)]
+
+::: zone-end
+
 ## Clean up resources
 
 If you want to clean up and remove an Azure AI services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

@@ -13,6 +13,6 @@ To use sentiment analysis, you submit raw unstructured text for analysis and han
 
 |Development option  |Description  |
 |---------|---------|
-|Language studio     | Language Studio is a web-based platform that lets you try entity linking with text examples without an Azure account, and your own data when you sign up. For more information, see the [Language Studio website](https://language.cognitive.azure.com/tryout/sentiment) or [language studio quickstart](../../language-studio.md).         |
+|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use entity linking with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-studio/what-is-ai-studio.md).         |
 |REST API or Client library (Azure SDK)      | Integrate sentiment analysis into your applications using the REST API, or the client library available in a variety of languages. For more information, see the [sentiment analysis quickstart](../quickstart.md).        |
 | Docker container | Use the available Docker container to [deploy this feature on-premises](../how-to/use-containers.md). These docker containers enable you to bring the service closer to your data for compliance, security, or other operational reasons. |
\ No newline at end of file

@@ -0,0 +1,59 @@
+---
+author: jboback
+manager: nitinme
+ms.service: azure-ai-language
+ms.topic: include
+ms.date: 02/16/2025
+ms.author: jboback
+---
+
+## Prerequisites
+
+* [Create a Project in Foundry in the Azure AI Foundry Portal](../../../../../ai-studio/how-to/create-projects.md)
+
+## Navigate to the Azure AI Foundry Playground
+
+Using the left side pane, select **Playgrounds**. Then select the **Try the Language Playground** button.
+
+:::image type="content" source="../../media/quickstarts/azure-ai-foundry/foundry-playground-navigation.png" alt-text="The development lifecycle" lightbox="../../media/quickstarts/azure-ai-foundry/foundry-playground-navigation.png":::
+
+## Use Sentiment Analysis in the Azure AI Foundry Playground
+
+The **Language Playground** consists of four sections:
+
+* Top banner: You can select any of the currently available Language services here.
+* Right pane: This pane is where you can find the **Configuration** options for the service, such as the API and model version, along with features specific to the service.
+* Center pane: This pane is where you enter your text for processing. After the operation is run, some results are shown here.
+* Right pane: This pane is where **Details** of the run operation are shown.
+
+Here you can select the Sentiment Analysis capability by choosing the top banner tile, **Analyze sentiment**.
+
+## Use Analyze sentiment
+
+**Analyze sentiment** is designed to identify positive, negative and neutral sentiment in text.
+
+In **Configuration** there are the following options:
+
+|Option              |Description                              |
+|--------------------|-----------------------------------------|
+|Select API version  | Select which version of the API to use.    |
+|Select model version| Select which version of the model to use.|
+|Select text language| Select the language of the input text. |
+|Enable opinion mining| Enables or disables the opinion mining skill.|
+
+After your operation is completed, in the center pane, each sentence will be numbered and opinions will be labeled if **Enable opinion mining** was checked and the **Details** section contains the following fields for the overall sentiment and the sentiment of each sentence:
+
+|Field | Description                |
+|------|----------------------------|
+|Sentence number|The number of the sentence in the order it was typed. This field is not present for **Overall sentiment**.|
+|Sentiment| The detected overall sentiment for the segment of text.|
+|Scores| The amount of positive, neutral and negative sentiment detected in the text segment.|
+
+The following fields are only present if opinion mining is enabled:
+
+|Field | Description                |
+|------|----------------------------|
+|Target|The target of the detected opinion.|
+|Assessments| The detected opinion and the detected persuasion (positive, neutral, negative), as well as the percent of detected persuasion.|
+
+:::image type="content" source="../../media/quickstarts/azure-ai-foundry/sentiment-opinion-mining.png" alt-text="An example of Analyze sentiment in azure AI studio" lightbox="../../media/quickstarts/azure-ai-foundry/sentiment-opinion-mining.png":::

@@ -7,7 +7,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 09/27/2024
+ms.date: 02/17/2025
 ms.author: jboback
 ms.custom: language-service-sentiment-opinion-mining
 ---

@@ -7,7 +7,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: quickstart
-ms.date: 10/21/2024
+ms.date: 02/17/2025
 ms.author: jboback
 ms.devlang: csharp
 # ms.devlang: csharp, java, javascript, python
@@ -47,3 +47,9 @@ zone_pivot_groups: programming-languages-text-analytics
 [!INCLUDE [REST API quickstart](includes/quickstarts/rest-api.md)]
 
 ::: zone-end
+
+::: zone pivot="azure-ai-foundry"
+
+[!INCLUDE [Azure AI Foundry quickstart](includes/quickstarts/azure-ai-foundry.md)]
+
+::: zone-end

@@ -25,11 +25,11 @@ ms.custom:
 
 - Issue and resolution (call center focused) is designed to summarize text chat logs between customers and customer-service agents. This feature is capable of providing both issues and resolutions present in these logs, which occur between two parties.
 
-- Narrative is designed to summarize the narrative of a conversation.
+- **Narrative** is designed to summarize the narrative of a conversation.
 
-- Recap is designed to condense lengthy meetings or conversations into a concise one-paragraph summary to provide a quick overview.
+- **Recap** is designed to condense lengthy meetings or conversations into a concise one-paragraph summary to provide a quick overview.
 
-- Follow-up tasks is designed to summarize action items and tasks that arise during a meeting.
+- **Follow-up tasks** is designed to summarize action items and tasks that arise during a meeting.
 
 :::image type="content" source="../media/conversation-summary-diagram.svg" alt-text="A diagram for sending data to the conversation summarization issues and resolution feature.":::
 
@@ -54,9 +54,6 @@ There's another feature in Azure AI Language named [text summarization](../overv
 
 ## Submitting data
 
-> [!NOTE]
-> See the [Language Studio](../../language-studio.md#valid-text-formats-for-conversation-features) article for information on formatting conversational text to submit using Language Studio. 
-
 You submit documents to the API as strings of text. Analysis is performed upon receipt of the request. Because the API is [asynchronous](../../concepts/use-asynchronously.md), there might be a delay between sending an API request and receiving the results.  For information on the size and number of requests you can send per minute and second, see the data limits below.
 
 When you use this feature, the API results are available for 24 hours from the time the request was ingested, and is indicated in the response. After this time period, the results are purged and are no longer available for retrieval.

@@ -40,9 +40,6 @@ For easier navigation, here are links to the corresponding sections for each ser
 
 ## Features
 
-> [!TIP]
-> If you want to start using these features, you can follow the [quickstart article](../quickstart.md) to get started. You can also make example requests using [Language Studio](../../language-studio.md) without needing to write code.
-
 The extractive summarization API uses natural language processing techniques to locate key sentences in an unstructured text document. These sentences collectively convey the main idea of the document.
 
 Extractive summarization returns a rank score as a part of the system response along with extracted sentences and their position in the original documents. A rank score is an indicator of how relevant a sentence is determined to be, to the main idea of a document. The model gives a score between 0 and 1 (inclusive) to each sentence and returns the highest scored sentences per request. For example, if you request a three-sentence summary, the service returns the three highest scored sentences.

@@ -16,7 +16,7 @@ To use summarization, you submit for analysis and handle the API output in your
 
 |Development option  |Description  |
 |---------|---------|
-|Language studio     | Language Studio is a web-based platform that lets you try entity linking with text examples without an Azure account, and your own data when you sign up. For more information, see the [Language Studio website](https://language.cognitive.azure.com/tryout/summarization) or [language studio quickstart](../../language-studio.md).         |
+|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use entity linking with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-studio/what-is-ai-studio.md).         |
 |REST API or Client library (Azure SDK)      | Integrate text summarization into your applications using the REST API, or the client library available in various languages. For more information, see the [summarization quickstart](../quickstart.md).        |
 
 
@@ -30,7 +30,7 @@ To use summarization, you submit for analysis and handle the API output in your
 
 |Development option  |Description  |
 |---------|---------|
-|Language studio     | Language Studio is a web-based platform that lets you try entity linking with text examples without an Azure account, and your own data when you sign up. For more information, see the [Language Studio website](https://language.cognitive.azure.com/tryout/summarization) or [language studio quickstart](../../language-studio.md).         |
+|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use entity linking with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-studio/what-is-ai-studio.md).         |
 |REST API or Client library (Azure SDK)      | Integrate text summarization into your applications using the REST API, or the client library available in various languages. For more information, see the [summarization quickstart](../quickstart.md).  
 
 ---

@@ -0,0 +1,98 @@
+---
+author: jboback
+manager: nitinme
+ms.service: azure-ai-language
+ms.topic: include
+ms.date: 02/16/2025
+ms.author: jboback
+---
+
+## Prerequisites
+
+* [Create a Project in Foundry in the Azure AI Foundry Portal](../../../../../ai-studio/how-to/create-projects.md)
+
+## Navigate to the Azure AI Foundry Playground
+
+Using the left side pane, select **Playgrounds**. Then select the **Try the Language Playground** button.
+
+:::image type="content" source="../../media/quickstarts/azure-ai-foundry/foundry-playground-navigation.png" alt-text="The development lifecycle" lightbox="../../media/quickstarts/azure-ai-foundry/foundry-playground-navigation.png":::
+
+## Use Summarization in the Azure AI Foundry Playground
+
+The **Language Playground** consists of four sections:
+
+* Top banner: You can select any of the currently available Language services here.
+* Right pane: This pane is where you can find the **Configuration** options for the service, such as the API and model version, along with features specific to the service.
+* Center pane: This pane is where you enter your text for processing. After the operation is run, some results are shown here.
+* Right pane: This pane is where **Details** of the run operation are shown.
+
+Here you can select the Summarization capability that you want to use by choosing one of these top banner tiles: **Summarize conversation**, **Summarize for call center**, or **Summarize text**.
+
+### Use Summarize conversation
+
+**Summarize conversation** is designed to recap conversations and segment long meetings into timestamped chapters.
+
+In **Configuration** there are the following options:
+
+|Option              |Description                              |
+|--------------------|-----------------------------------------|
+|Select API version  | Select which version of the API to use.    |
+|Select text language| Select the language of the input text.|
+|Summarization Aspects| Different methods of summarization that are returned. At least one must be selected.|
+
+After your operation is completed, the **Details** section contains the following fields for the selected methods of summarization:
+
+|Field | Description                |
+|------|----------------------------|
+|Sentence|
+|Recap| A recap of the processed text. The **Recap** Summarization aspect must be toggled on for this to appear.|
+|Chapter Title|  A list of titles for semantically segmented chapters with corresponding timestamps. The **Chapter title** Summarization aspect must be toggled on for this to appear.|
+|Narrative|  A list of narrative summaries for semantically segmented chapters with corresponding timestamps. The **Narrative** Summarization aspect must be toggled on for this to appear.|
+
+:::image type="content" source="../../media/quickstarts/azure-ai-foundry/conversation-summarization.png" alt-text="A screenshot of an example of summarize conversation in azure AI studio." lightbox="../../media/quickstarts/azure-ai-foundry/conversation-summarization.png":::
+
+### Use Summarize for call center
+
+**Summarize for call center** is designed to recap calls and summarize them for customer issues and resolutions.
+
+In **Configuration** there are the following options:
+
+|Option              |Description                              |
+|--------------------|-----------------------------------------|
+|Select API version  | Select which version of the API to use.    |
+|Select text language| Select the language of the input text.|
+|Summarization Aspects| Different methods of summarization that are returned. At least one must be selected.|
+
+After your operation is completed, the **Details** section contains the following fields for the selected methods of summarization:
+
+|Field | Description                |
+|------|----------------------------|
+|Sentence|
+|Recap| A recap of the processed text. The **Recap** Summarization aspect must be toggled on for this to appear.|
+|Issue|  A summary of the customer issue in the customer-and-agent conversation. The **Issue** Summarization aspect must be toggled on for this to appear.|
+|Resolution|  A summary of the solutions tried in the customer-and-agent conversation. The **Resolution** Summarization aspect must be toggled on for this to appear.|
+
+:::image type="content" source="../../media/quickstarts/azure-ai-foundry/call-center-summarization.png" alt-text="A screenshot of an example of summarize for call center in azure AI studio." lightbox="../../media/quickstarts/azure-ai-foundry/call-center-summarization.png":::
+
+### Use Summarize text
+
+**Summarize text** is designed to summarize and extract key information at scale from text.
+
+In **Configuration** there are the following options:
+
+|Option              |Description                              |
+|--------------------|-----------------------------------------|
+|Extractive summarization  |  The service will produce a summary by extracting salient sentences.  |
+|Number of sentences| The number of sentences that Extractive summarization will extract.|
+|Abstractive summarization| the service will generate a summary with novel sentences.|
+|Summary length| The length of the summary generated by Abstractive summarization.|
+|Define keywords for summary focus (preview)| Helps focus summarization on a particular set of keywords.|
+
+After your operation is completed, the **Details** section contains the following fields for the selected methods of summarization:
+
+|Field | Description                |
+|------|----------------------------|
+|Extractive summary| Extracted sentences from the input text, ranked by detected relevance and prioritized for words in the **Defined keywords for summary focus** field, if any. Sentences are sorted by rank score of detected relevance (default) or order of appearance in the input text.|
+|Abstractive summary| A summary of the input text of the length chosen in the **Summary length** field and prioritized for words in the **Defined keywords for summary focus** field, if any.|
+
+:::image type="content" source="../../media/quickstarts/azure-ai-foundry/text-summarization.png" alt-text="A screenshot of an example of summarize text in azure AI studio." lightbox="../../media/quickstarts/azure-ai-foundry/text-summarization.png":::
\ No newline at end of file

@@ -7,7 +7,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 11/21/2024
+ms.date: 02/17/2025
 ms.author: jboback
 ms.custom: language-service-summarization, build-2024, ignite-2024
 ---

@@ -7,7 +7,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: quickstart
-ms.date: 11/21/2024
+ms.date: 02/17/2025
 ms.author: jboback
 ms.devlang: csharp
 # ms.devlang: csharp, java, javascript, python
@@ -49,6 +49,12 @@ zone_pivot_groups: programming-languages-text-analytics
 
 ::: zone-end
 
+::: zone pivot="azure-ai-foundry"
+
+[!INCLUDE [Azure AI Foundry quickstart](includes/quickstarts/azure-ai-foundry.md)]
+
+::: zone-end
+
 ## Clean up resources
 
 If you want to clean up and remove an Azure AI services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

@@ -13,6 +13,6 @@ To use Text Analytics for health, you submit raw unstructured text for analysis
 
 |Development option  |Description  |
 |---------|---------|
-|Language studio     | Language Studio is a web-based platform that lets you try entity linking with text examples without an Azure account, and your own data when you sign up. For more information, see the [Language Studio website](https://language.cognitive.azure.com/tryout/healthAnalysis) or [language studio quickstart](../../language-studio.md).         |
+|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use entity linking with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-studio/what-is-ai-studio.md).         |
 |REST API or Client library (Azure SDK)      | Integrate Text Analytics for health into your applications using the REST API, or the client library available in a variety of languages. For more information, see the [Text Analytics for health quickstart](../quickstart.md).        |
 | Docker container | Use the available Docker container to [deploy this feature on-premises](../how-to/use-containers.md). These docker containers enable you to bring the service closer to your data for compliance, security, or other operational reasons. |

@@ -0,0 +1,52 @@
+---
+author: jboback
+manager: nitinme
+ms.service: azure-ai-language
+ms.topic: include
+ms.date: 02/16/2025
+ms.author: jboback
+---
+
+## Prerequisites
+
+* [Create a Project in Foundry in the Azure AI Foundry Portal](../../../../../ai-studio/how-to/create-projects.md)
+
+## Navigate to the Azure AI Foundry Playground
+
+Using the left side pane, select **Playgrounds**. Then select the **Try the Language Playground** button.
+
+:::image type="content" source="../../media/quickstarts/azure-ai-foundry/foundry-playground-navigation.png" alt-text="The development lifecycle" lightbox="../../media/quickstarts/azure-ai-foundry/foundry-playground-navigation.png":::
+
+## Use Text Analytics for Health in the Azure AI Foundry Playground
+
+The **Language Playground** consists of four sections:
+
+* Top banner: You can select any of the currently available Language services here.
+* Right pane: This pane is where you can find the **Configuration** options for the service, such as the API and model version, along with features specific to the service.
+* Center pane: This pane is where you enter your text for processing. After the operation is run, some results are shown here.
+* Right pane: This pane is where **Details** of the run operation are shown.
+
+Here you can select the Text Analytics for Health capability by choosing the top banner tile, **Extract health information**.
+
+## Use Extract health information
+
+**Extract health information** is designed to identify and extract health information in text.
+
+In **Configuration** there are the following options:
+
+|Option              |Description                              |
+|--------------------|-----------------------------------------|
+|Select API version  | Select which version of the API to use.    |
+|Select model version| Select which version of the model to use.|
+|Select text language| Select which language the language is input in.|
+|Return output in FHIR structure| Returns the output in the Fast Healthcare Interoperability Resources (FHIR) structure.|
+
+After your operation is completed, the type of entity is displayed beneath each entity in the center pane and the **Details** section contains the following fields for each entity:
+
+|Field | Description                |
+|------|----------------------------|
+|Entity|The detected entity.|
+|Category| The type of entity that was detected.|
+|Confidence| How confident the model is in the correctness of identification of entity's type.|
+
+:::image type="content" source="../../media/quickstarts/azure-ai-foundry/text-analytics-for-health.png" alt-text="A screenshot of an example of extract health information in azure AI studio." lightbox="../../media/quickstarts/azure-ai-foundry/text-analytics-for-health.png":::

@@ -7,7 +7,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 10/21/2024
+ms.date: 02/17/2025
 ms.author: jboback
 ms.custom: language-service-health, ignite-2024
 ---

@@ -7,7 +7,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: quickstart
-ms.date: 11/21/2024
+ms.date: 02/16/2025
 ms.author: jboback
 ms.devlang: csharp
 # ms.devlang: csharp, java, javascript, python
@@ -51,6 +51,12 @@ This article contains Text Analytics for health quickstarts that help with using
 
 ::: zone-end
 
+::: zone pivot="azure-ai-foundry"
+
+[!INCLUDE [Azure AI Foundry quickstart](includes/quickstarts/azure-ai-foundry.md)]
+
+::: zone-end
+
 ## Clean up resources
 
 If you want to clean up and remove an Azure AI services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

@@ -10,6 +10,11 @@
             "redirect_url": "/azure/ai-studio/how-to/develop/vscode",
             "redirect_document_id": false
         },
+        {
+            "source_path_from_root": "/articles/ai-studio/how-to/develop/index-build-consume-sdk.md",
+            "redirect_url": "/azure/ai-studio/tutorials/copilot-sdk-create-resources",
+            "redirect_document_id": false
+        },
         {
             "source_path_from_root": "/articles/ai-studio/how-to/deploy-models.md",
             "redirect_url": "/azure/ai-studio/concepts/deployments-overview",

@@ -7,16 +7,16 @@ ms.service: azure-ai-foundry
 ms.custom:
   - ignite-2024
 ms.topic: overview
-ms.date: 11/09/2024
+ms.date: 02/20/2025
 ms.author: pafarley
 author: PatrickFarley
 ---
 
-# Content safety in the Azure AI Foundry portal
+# Content Safety in the Azure AI Foundry portal
 
 Azure AI Content Safety is an AI service that detects harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety includes various APIs that allow you to detect and prevent the output of harmful content. The interactive Content Safety **try out** page in [Azure AI Foundry portal](https://ai.azure.com) allows you to view, explore, and try out sample code for detecting harmful content across different modalities. 
 
-## Features 
+## Features
 
 You can use Azure AI Content Safety for many scenarios: 
 
@@ -44,6 +44,6 @@ You can use Azure AI Content Safety for many scenarios:
 Refer to the [Content Safety overview](/azure/ai-services/content-safety/overview) for supported regions, rate limits, and input requirements for all features. Refer to the [Language support](/azure/ai-services/content-safety/language-support) page for supported languages. 
 
 
-## Next step 
+## Next step
 
 Get started using Azure AI Content Safety in [Azure AI Foundry portal](https://ai.azure.com) by following the [How-to guide](./how-to/content-safety.md).
\ No newline at end of file

@@ -17,7 +17,7 @@ author: Blackmist
 
 # Role-based access control in Azure AI Foundry portal
 
-In this article, you learn how to manage access (authorization) to an Azure AI Foundry hub. Azure role-based access control (Azure RBAC) is used to manage access to Azure resources, such as the ability to create new resources or use existing ones. Users in your Microsoft Entra ID are assigned specific roles, which grant access to resources. Azure provides both built-in roles and the ability to create custom roles. 
+In this article, you learn how to manage access (authorization) to an [Azure AI Foundry](https://ai.azure.com) hub. Azure role-based access control (Azure RBAC) is used to manage access to Azure resources, such as the ability to create new resources or use existing ones. Users in your Microsoft Entra ID are assigned specific roles, which grant access to resources. Azure provides both built-in roles and the ability to create custom roles. 
 
 > [!WARNING]
 > Applying some roles might limit UI functionality in Azure AI Foundry portal for other users. For example, if a user's role does not have the ability to create a compute instance, the option to create a compute instance will not be available in studio. This behavior is expected, and prevents the user from attempting operations that would return an access denied error. 

@@ -17,7 +17,7 @@ author: Blackmist
 
 Vulnerability management involves detecting, assessing, mitigating, and reporting on any security vulnerabilities that exist in an organization's systems and software. Vulnerability management is a shared responsibility between you and Microsoft.
 
-This article discusses these responsibilities and outlines the vulnerability management controls that Azure AI Foundry provides. You learn how to keep your service instance and applications up to date with the latest security updates, and how to minimize the window of opportunity for attackers.
+This article discusses these responsibilities and outlines the vulnerability management controls that [Azure AI Foundry](https://ai.azure.com) provides. You learn how to keep your service instance and applications up to date with the latest security updates, and how to minimize the window of opportunity for attackers.
 
 ## Microsoft-managed VM images
 

@@ -14,7 +14,7 @@ author: Blackmist
 
 # Access on-premises resources from your Azure AI Foundry's managed network (preview)
 
-To access your non-Azure resources located in a different virtual network or located entirely on-premises from your Azure AI Foundry's managed virtual network, an Application Gateway must be configured. Through this Application Gateway, full end to end access can be configured to your resources.
+To access your non-Azure resources located in a different virtual network or located entirely on-premises from your [Azure AI Foundry](https://ai.azure.com)'s managed virtual network, an Application Gateway must be configured. Through this Application Gateway, full end to end access can be configured to your resources.
 
 Azure Application Gateway is a load balancer that makes routing decisions based on the URL of an HTTPS request. Azure Machine Learning supports using an application gateway to securely communicate with non-Azure resources. For more on Application Gateway, see [What is Azure Application Gateway](/azure/application-gateway/overview).
 

@@ -16,7 +16,7 @@ author: Blackmist
 
 # Autoscale Azure AI limits
 
-This article provides guidance for how you can manage and increase quotas for Azure AI services resources with Azure AI Foundry.
+This article provides guidance for how you can manage and increase quotas for Azure AI services resources with [Azure AI Foundry](https://ai.azure.com).
 
 ## Overview
 

@@ -12,7 +12,7 @@ ms.topic: how-to
 
 # Audit and manage Azure AI Foundry hubs and projects
 
-As a platform administrator, you can use policies to lay out guardrails for teams to manage their own resources. [Azure Policy](/azure/governance/policy/) helps audit and govern resource state. This article explains how you can use audit controls and governance practices for Azure AI Foundry.
+As a platform administrator, you can use policies to lay out guardrails for teams to manage their own resources. [Azure Policy](/azure/governance/policy/) helps audit and govern resource state. This article explains how you can use audit controls and governance practices for [Azure AI Foundry](https://ai.azure.com).
 
 ## Policies for Azure AI Foundry hubs and projects
 

@@ -15,7 +15,7 @@ zone_pivot_groups: azure-ai-studio-sdk-cli
 
 # How to configure a managed network for Azure AI Foundry hubs
 
-We have two network isolation aspects. One is the network isolation to access an Azure AI Foundry hub. Another is the network isolation of computing resources for both your hub and project (such as compute instance, serverless and managed online endpoint.) This document explains the latter highlighted in the diagram. You can use hub built-in network isolation to protect your computing resources.
+We have two network isolation aspects. One is the network isolation to access an [Azure AI Foundry](https://ai.azure.com) hub. Another is the network isolation of computing resources for both your hub and project (such as compute instance, serverless and managed online endpoint.) This document explains the latter highlighted in the diagram. You can use hub built-in network isolation to protect your computing resources.
 
 :::image type="content" source="../media/how-to/network/azure-ai-network-outbound.svg" alt-text="Diagram of hub network isolation." lightbox="../media/how-to/network/azure-ai-network-outbound.png":::
 

@@ -15,7 +15,7 @@ author: Blackmist
 
 # How to configure a private link for Azure AI Foundry hubs
 
-We have two network isolation aspects. One is the network isolation to access an Azure AI Foundry hub. Another is the network isolation of computing resources in your hub and projects such as compute instances, serverless, and managed online endpoints. This article explains the former highlighted in the diagram. You can use private link to establish the private connection to your hub and its default resources. This article is for Azure AI Foundry (hub and projects). For information on Azure AI services, see the [Azure AI services documentation](/azure/ai-services/cognitive-services-virtual-networks).
+We have two network isolation aspects. One is the network isolation to access an [Azure AI Foundry](https://ai.azure.com) hub. Another is the network isolation of computing resources in your hub and projects such as compute instances, serverless, and managed online endpoints. This article explains the former highlighted in the diagram. You can use private link to establish the private connection to your hub and its default resources. This article is for Azure AI Foundry (hub and projects). For information on Azure AI services, see the [Azure AI services documentation](/azure/ai-services/cognitive-services-virtual-networks).
 
 :::image type="content" source="../media/how-to/network/azure-ai-network-inbound.svg" alt-text="Diagram of Azure AI Foundry hub network isolation." lightbox="../media/how-to/network/azure-ai-network-inbound.png":::
 

@@ -20,7 +20,7 @@ author: Blackmist
 
 [!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
-In this article, you learn how to add a new connection in Azure AI Foundry portal.
+In this article, you learn how to add a new connection in [Azure AI Foundry portal](https://ai.azure.com).
 
 Connections are a way to authenticate and consume both Microsoft and other resources within your Azure AI Foundry projects. For example, connections can be used for prompt flow, training data, and deployments. [Connections can be created](../how-to/connections-add.md) exclusively for one project or shared with all projects in the same Azure AI Foundry hub. 
 
@@ -44,7 +44,7 @@ Here's a table of some of the available connection types in Azure AI Foundry por
 
 Follow these steps to create a new connection that's only available for the current project.
 
-1. Go to your project in Azure AI Foundry portal. If you don't have a project, [create a new project](./create-projects.md).
+1. Go to your project in [Azure AI Foundry portal](https://ai.azure.com). If you don't have a project, [create a new project](./create-projects.md).
 1. Select __Management center__ from the bottom left navigation.
 1. Select __Connected resources__ from the __Project__ section.
 1. Select __+ New connection__ from the __Connected resources__ section.

@@ -19,7 +19,7 @@ author: Blackmist
 
 [!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
-This article describes how you plan for and manage costs for Azure AI Foundry. First, you use the Azure pricing calculator to help plan for Azure AI Foundry costs before you add any resources for the service to estimate costs. Next, as you add Azure resources, review the estimated costs.
+This article describes how you plan for and manage costs for [Azure AI Foundry](https://ai.azure.com). First, you use the Azure pricing calculator to help plan for Azure AI Foundry costs before you add any resources for the service to estimate costs. Next, as you add Azure resources, review the estimated costs.
 
 > [!TIP]
 > Azure AI Foundry does not have a specific page in the Azure pricing calculator. Azure AI Foundry is composed of several other Azure services, some of which are optional. This article provides information on using the pricing calculator to estimate costs for these services.
@@ -60,7 +60,7 @@ When you create resources for a hub, resources for other Azure services are also
 | [Azure Machine Learning](https://azure.microsoft.com/pricing/details/machine-learning/) | Compute instances are needed to run [Visual Studio Code (Web or Desktop)](./develop/vscode.md) and [prompt flow](./prompt-flow.md) via Azure AI Foundry.<br/><br/>When you create a compute instance, the virtual machine (VM) stays on so it's available for your work.<br/><br/>Enable idle shutdown to save on cost when the VM is idle for a specified time period.<br/><br/>Or set up a schedule to automatically start and stop the compute instance to save cost when you aren't planning to use it. | 
 | [Azure Virtual Machine](https://azure.microsoft.com/pricing/details/virtual-machines/) | Azure Virtual Machines gives you the flexibility of virtualization for a wide range of computing solutions with support for Linux, Windows Server, SQL Server, Oracle, IBM, SAP, and more. |
 | [Azure Container Registry Basic account](https://azure.microsoft.com/pricing/details/container-registry) | Provides storage of private Docker container images, enabling fast, scalable retrieval, and network-close deployment of container workloads on Azure. |
-| [Azure Blob Storage](https://azure.microsoft.com/pricing/details/storage/blobs/) | Can be used to store [Azure AI Foundry project](./create-projects.md) files. |
+| [Azure Blob Storage](https://azure.microsoft.com/pricing/details/storage/blobs/) | Can be used to store Azure AI Foundry project files. |
 | [Key Vault](https://azure.microsoft.com/pricing/details/key-vault/) | A key vault for storing secrets. |
 | [Azure Private Link](https://azure.microsoft.com/pricing/details/private-link/) | Azure Private Link enables you to access Azure PaaS Services (for example, Azure Storage and SQL Database) over a private endpoint in your virtual network. |
 
@@ -97,10 +97,10 @@ When you use cost analysis, you view hub costs in graphs and tables for differen
 
 ### Monitor Azure AI Foundry project costs
 
-You can get to cost analysis from the [Azure portal](https://portal.azure.com). You can also get to cost analysis from the [Azure AI Foundry](https://ai.azure.com).
+You can get to cost analysis from the [Azure portal](https://portal.azure.com). You can also get to cost analysis from the [Azure AI Foundry portal](https://ai.azure.com).
 
 > [!IMPORTANT]
-> Your Azure AI Foundry project costs are only a subset of your overall application or solution costs. You need to monitor costs for all Azure resources used in your application or solution. For more information, see [Azure AI Foundry hubs](../concepts/ai-resources.md).
+> Your Azure AI Foundry project costs are only a subset of your overall application or solution costs. You need to monitor costs for all Azure resources used in your application or solution. For more information, see Azure AI Foundry hubs.
 
 For the examples in this section, assume that all Azure AI Foundry resources are in the same resource group. But you can have resources in different resource groups. For example, your Azure AI Search resource might be in a different resource group than your project.
 

@@ -17,7 +17,7 @@ author: Blackmist
 
 [!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
-Use a [Microsoft Bicep](/azure/azure-resource-manager/bicep/overview) template to create a hub for Azure AI Foundry. A template makes it easy to create resources as a single, coordinated operation. A Bicep template is a text document that defines the resources that are needed for a deployment. It might also specify deployment parameters. Parameters are used to provide input values when using the template.
+Use a [Microsoft Bicep](/azure/azure-resource-manager/bicep/overview) template to create a hub for [Azure AI Foundry](https://ai.azure.com). A template makes it easy to create resources as a single, coordinated operation. A Bicep template is a text document that defines the resources that are needed for a deployment. It might also specify deployment parameters. Parameters are used to provide input values when using the template.
 
 The template used in this article can be found at [https://github.com/Azure/azure-quickstart-templates/tree/master/quickstarts/microsoft.machinelearningservices/aistudio-basics](https://github.com/Azure/azure-quickstart-templates/tree/master/quickstarts/microsoft.machinelearningservices/aistudio-basics). Both the source `main.bicep` file and the compiled Azure Resource Manager template (`main.json`) file are available. This template creates the following resources:
 

@@ -18,7 +18,7 @@ author: Blackmist
 
 # How to create and manage an Azure AI Foundry hub
 
-In Azure AI Foundry portal, hubs provide the environment for a team to collaborate and organize work, and help you as a team lead or IT admin centrally set up security settings and govern usage and spend. You can create and manage a hub from the Azure portal or from the Azure AI Foundry portal, and then your developers can create projects from the hub.
+In [Azure AI Foundry portal](https://ai.azure.com), hubs provide the environment for a team to collaborate and organize work, and help you as a team lead or IT admin centrally set up security settings and govern usage and spend. You can create and manage a hub from the Azure portal or from the Azure AI Foundry portal, and then your developers can create projects from the hub.
 
 In this article, you learn how to create and manage a hub in Azure AI Foundry portal with the default settings so you can get started quickly. Do you need to customize security or the dependent resources of your hub? Then use [Azure portal](create-secure-ai-hub.md) or [template options](create-azure-ai-hub-template.md). 
 

@@ -18,7 +18,7 @@ ai-usage: ai-assisted
 
 # Use Terraform to create an Azure AI Foundry hub
 
-In this article, you use Terraform to create an Azure AI Foundry hub, a project, and AI services connection. A hub is a central place for data scientists and developers to collaborate on machine learning projects. It provides a shared, collaborative space to build, train, and deploy machine learning models. The hub is integrated with Azure Machine Learning and other Azure services, making it a comprehensive solution for machine learning tasks. The hub also allows you to manage and monitor your AI deployments, ensuring they're performing as expected.
+In this article, you use Terraform to create an [Azure AI Foundry](https://ai.azure.com) hub, a project, and AI services connection. A hub is a central place for data scientists and developers to collaborate on machine learning projects. It provides a shared, collaborative space to build, train, and deploy machine learning models. The hub is integrated with Azure Machine Learning and other Azure services, making it a comprehensive solution for machine learning tasks. The hub also allows you to manage and monitor your AI deployments, ensuring they're performing as expected.
 
 [!INCLUDE [About Terraform](~/azure-dev-docs-pr/articles/terraform/includes/abstract.md)]
 

@@ -6,7 +6,7 @@ ms.service: azure-ai-foundry
 ms.custom:
   - build-2024
 ms.topic: how-to
-ms.date: 11/21/2024
+ms.date: 02/21/2025
 ms.reviewer: meerakurup 
 ms.author: larryfr
 author: Blackmist
@@ -15,9 +15,7 @@ author: Blackmist
 
 # How to create a secure Azure AI Foundry hub and project with a managed virtual network
 
-[!INCLUDE [feature-preview](../includes/feature-preview.md)]
-
-You can secure your Azure AI Foundry hub, projects, and managed resources in a managed virtual network. With a managed virtual network, inbound access is only allowed through a private endpoint for your hub. Outbound access can be configured to allow either all outbound access, or only allowed outbound that you specify. For more information, see [Managed virtual network](configure-managed-network.md).
+You can secure your [Azure AI Foundry](https://ai.azure.com) hub, projects, and managed resources in a managed virtual network. With a managed virtual network, inbound access is only allowed through a private endpoint for your hub. Outbound access can be configured to allow either all outbound access, or only allowed outbound that you specify. For more information, see [Managed virtual network](configure-managed-network.md).
 
 > [!IMPORTANT]
 > The managed virtual network doesn't provide inbound connectivity for your clients. For more information, see the [Connect to the hub](#connect-to-the-hub) section. 
@@ -56,7 +54,7 @@ You can secure your Azure AI Foundry hub, projects, and managed resources in a m
 
         1. Select **Ok** to save the endpoint configuration.
 
-1. Select **Review + create**, then **Create** to create the hub. Once the hub has been created, any projects or compute instances created from the hub inherit the network configuration.
+1. Select **Review + create**, then **Create** to create the hub. Once the hub is created, any projects or compute instances created from the hub inherit the network configuration.
 
 ## Connect to the hub
 

@@ -18,7 +18,7 @@ author: Blackmist
 
 [!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
-In this article, you learn how to add a new connection using the Azure Machine Learning SDK.
+In this article, you learn how to add a new connection to [Azure AI Foundry](https://ai.azure.com) using the Azure Machine Learning SDK.
 
 Connections are a way to authenticate and consume both Microsoft and other resources within your Azure AI Foundry projects. For example, connections can be used for prompt flow, training data, and deployments. [Connections can be created](../../how-to/connections-add.md) exclusively for one project or shared with all projects in the same Azure AI Foundry hub. For more information, see [Connections in Azure AI Foundry portal](../../concepts/connections.md).
 

@@ -1,397 +0,0 @@
----
-title: How to build and consume an index using code
-titleSuffix: Azure AI Foundry
-description: This article provides instructions on how to build and consume an index using code.
-manager: scottpolly
-ms.service: azure-ai-foundry
-ms.custom:
-  - build-2024
-ms.topic: how-to
-ms.date: 11/21/2024
-ms.reviewer: dantaylo
-ms.author: sgilley
-author: sdgilley
----
-
-# How to build and consume an index using code
-
-[!INCLUDE [feature-preview](../../includes/feature-preview.md)]
-
-In this article, you learn how to create an index and consume it from code. To create an index locally, we use the `promptflow-rag` package. To create a remote index on the cloud, we use the `azure-ai-ml` package. We consume the indexes using `langchain`.
-
-## Prerequisites
-
-You must have:
-
-- An [Azure AI Foundry hub](../../how-to/create-azure-ai-resource.md) and [project](../../how-to/create-projects.md).
-
-- An [Azure AI Search service connection](../../how-to/connections-add.md#create-a-new-connection) to index the sample product and customer data. If you don't have an Azure AI Search service, you can create one from the [Azure portal](https://portal.azure.com/) or see the instructions [here](/azure/search/search-create-service-portal).
-- Models for embedding:
-    - You can use an ada-002 embedding model from Azure OpenAI. The instructions to deploy can be found [here](../deploy-models-openai.md).
-    - OR you can use any another embedding model deployed in your Azure AI Foundry project. In this example we use Cohere multi-lingual embedding. The instructions to deploy this model can be found [here](../deploy-models-cohere-embed.md).
-
-## Build and consume an index locally
-
-We can build and consume an index locally. 
-
-### Required packages for local index operations
-
-Install the following packages required for local index creation.
-
-```bash
-pip install promptflow-rag langchain langchain-openai
-```
-### Configure AI Search for local use
-
-We use Azure AI Search as the index store. To get started, we can set up the Azure AI Search service using the following code:
-
-```python
-import os
-# set credentials to your Azure AI Search instance
-os.environ["AZURE_AI_SEARCH_KEY"] = "<your-ai-search-key>"
-os.environ["AZURE_AI_SEARCH_ENDPOINT"] = "https://<your-ai-search-service>.search.windows.net"
-```
-
-### Build an index locally using Azure OpenAI embeddings
-
-To create an index that uses Azure OpenAI embeddings, we configure environment variables to connect to the model.
-
-```python
-import os
-# set credentials to your Azure OpenAI instance
-os.environ["OPENAI_API_VERSION"] = "2023-07-01-preview"
-os.environ["AZURE_OPENAI_API_KEY"] = "<your-azure-openai-api-key>"
-os.environ["AZURE_OPENAI_ENDPOINT"] = "https://<your-azure-openai-service>.openai.azure.com/"
-```
-
-Now let us build the index using the `build_index` function.
-
-```python
-from promptflow.rag.config import LocalSource, AzureAISearchConfig, EmbeddingsModelConfig
-from promptflow.rag import build_index
-
-local_index_aoai=build_index(
-    name="<your-index-name>" + "aoai",  # name of your index
-    vector_store="azure_ai_search",  # the type of vector store
-    embeddings_model_config=EmbeddingsModelConfig(
-        model_name="text-embedding-ada-002",
-        deployment_name="text-embedding-ada-002", # verify if your deployment name is same as model name
-    ),
-    input_source=LocalSource(input_data="<path-to-your-local-files>"),  # the location of your file/folders
-    index_config=AzureAISearchConfig(
-        ai_search_index_name="<your-index-name>" + "-aoai-store", # the name of the index store inside the azure ai search service
-    ),
-    tokens_per_chunk = 800, # Optional field - Maximum number of tokens per chunk
-    token_overlap_across_chunks = 0, # Optional field - Number of tokens to overlap between chunks
-)
-```
-
-The above code builds an index locally. It uses environment variables to get the AI Search service and also to connect to the Azure OpenAI embedding model. 
-
-### Build an index locally using other embedding models deployed in your Azure AI Foundry project
-
-To create an index that uses an embedding model deployed in your Azure AI Foundry project, we configure the connection to the model using a `ConnectionConfig` as shown below. The `subscription`, `resource_group` and `workspace` refers to the project where the embedding model is installed. The `connection_name` refers to the connection name for the model, which can be found in the Azure AI Foundry project settings page.
-
-```python
-from promptflow.rag.config import ConnectionConfig
-
-my_connection_config=ConnectionConfig(
-    subscription_id="<subscription_id>",
-    resource_group_name="<resource_group_name>",
-    workspace_name="<ai_foundry_project_name>",
-    connection_name="<serverless_connection_name>"
-    )
-```
-
-Now let us build the index using the `build_index` function.
-
-```python
-from promptflow.rag.config import LocalSource, AzureAISearchConfig, EmbeddingsModelConfig
-from promptflow.rag import build_index
-
-local_index_cohere=build_index(
-    name="<your-index-name>" + "cohere",  # name of your index
-    vector_store="azure_ai_search",  # the type of vector store
-    embeddings_model_config=EmbeddingsModelConfig(
-        model_name="cohere-embed-v3-multilingual", # in this example we use cohere multi lingual embedding
-        connection_config=my_connection_config # created in previous step
-    ),
-    input_source=LocalSource(input_data="<path-to-your-local-files>"),  # the location of your file/folders
-    index_config=AzureAISearchConfig(
-        ai_search_index_name="<your-index-name>" + "cohere-store", # the name of the index store inside the azure ai search service
-    ),
-    tokens_per_chunk = 800, # Optional field - Maximum number of tokens per chunk
-    token_overlap_across_chunks = 0, # Optional field - Number of tokens to overlap between chunks
-)
-```
-
-The above code builds an index locally. It uses environment variables to get the AI Search service and the connection config to connect to the embedding model. 
-
-### Consuming a local index
-
-The local index created can be used as a langchain retriever to consume it for search queries.
-
-```python
-from promptflow.rag import get_langchain_retriever_from_index
-
-# Get the OpenAI embedded Index
-retriever=get_langchain_retriever_from_index(local_index_aoai)
-retriever.get_relevant_documents("<your search query>")
-
-# Get the Cohere embedded Index
-retriever=get_langchain_retriever_from_index(local_index_cohere)
-retriever.get_relevant_documents("<your search query>")
-```
-### Registering the index in your Azure AI Foundry project (Optional)
-
-Optionally, you can register the index in your Azure AI Foundry project so that you or others who have access to your project can use it from the cloud. Before proceeding [install the required packages](#required-packages-for-remote-index-operations) for remote operations.
-
-#### Connect to the project
-
-```python
-# connect to the Azure AI Foundry project
-from azure.identity import DefaultAzureCredential
-from azure.ai.ml import MLClient
-
-client=MLClient(
-    DefaultAzureCredential(), 
-    subscription_id="<subscription_id>",
-    resource_group_name="<resource_group_name>",
-    workspace_name="<ai_foundry_project_name>"
-    )
-```
-The `subscription`, `resource_group` and `workspace` in the above code refers to the project you want to connect to.
-
-#### Register the index
-
-```python
-from azure.ai.ml.entities import Index
-
-# register the index with Azure OpenAI embeddings
-client.indexes.create_or_update(
-    Index(name="<your-index-name>" + "aoai", 
-          path=local_index_aoai, 
-          version="1")
-          )
-
-# register the index with cohere embeddings
-client.indexes.create_or_update(
-    Index(name="<your-index-name>" + "cohere", 
-          path=local_index_cohere, 
-          version="1")
-          )
-```
-
-> [!NOTE]
-> Environment variables are intended for convenience in a local environment. However, if you register a local index created using environment variables, the index may not function as expected because secrets from environment variables won't be transferred to the cloud index. To address this issue, you can use a `ConnectionConfig` or `connection_id` to create a local index before registering.
-
-## Build an index (remotely) in your Azure AI Foundry project
-
-We build an index in the cloud in your Azure AI Foundry project. 
-
-### Required packages for remote index operations
-
-Install the following packages required for remote index creation.
-
-```bash
-pip install azure-ai-ml promptflow-rag langchain langchain-openai
-```
-
-### Connect to the Azure AI Foundry project
-
-To get started, we connect to the project. The `subscription`, `resource_group` and `workspace` in the code below refers to the project you want to connect to.
-
-```python
-# connect to the Azure AI Foundry project
-from azure.identity import DefaultAzureCredential
-from azure.ai.ml import MLClient
-
-client=MLClient(
-    DefaultAzureCredential(), 
-    subscription_id="<subscription_id>",
-    resource_group_name="<resource_group_name>",
-    workspace_name="<ai_foundry_project_name>"
-    )
-```
-
-### Get the AI Search service connection
-
-This project should have a connection to the AI Search service. We retrieve the details from the project.
-
-```python
-ai_search_connection = client.connections.get("<ai_search_connection>")
-``` 
-
-### Connect to the embedding models
-
-You can connect to Azure OpenAI using Microsoft Entra ID connections or API key based connections.
-
-```python
-from azure.ai.ml.entities import IndexModelConfiguration
-## aoai connections - entra id
-aoai_connection = client.connections.get("<your_aoai_entra_id_connection>")
-embeddings_model_config = IndexModelConfiguration.from_connection(
-    aoai_connection, 
-    model_name="text-embedding-ada-002",
-    deployment_name="text-embedding-ada-002") # verify if your deployment name is same as model name
-
-## OR you can connect using API Key based connections 
-from azure.ai.ml.entities import IndexModelConfiguration
-## aoai connections - API Key
-aoai_connection = client.connections.get("<your_aoai_connection>", populate_secrets=True)
-embeddings_model_config = IndexModelConfiguration.from_connection(
-    aoai_connection, 
-    model_name="text-embedding-ada-002",
-    deployment_name="text-embedding-ada-002")
-```
-
-You can connect to embedding model deployed in your Azure AI Foundry project (non Azure OpenAI models) using the serverless connection. 
-
-```python
-from azure.ai.ml.entities import IndexModelConfiguration
-serverless_connection = client.connections.get("<my_embedding_model_severless_connection_name>")
-embeddings_model_config = IndexModelConfiguration.from_connection(cohere_serverless_connection)
-```
-
-### Select input data to build the index
-
-You can build the index from the following types of inputs:
-- Local files and folders
-- GitHub repositories
-- Azure Storage
-
-We can use the following code sample to use any of these sources and configure our `input_source`:
-
-```python
-# Local source
-from azure.ai.ml.entities import LocalSource
-
-input_source=LocalSource(input_data="<path-to-your-local-files>")
-
-# GitHub repository
-from azure.ai.ml.entities import GitSource
-
-input_source=GitSource(
-    git_url="https://github.com/rust-lang/book.git", # connecting to the RUST repo as an example
-    git_branch_name="main", 
-    git_connection_id="")
-
-# Azure Storage
-input_source_subscription = "<subscription>"
-input_source_resource_group = "<resource_group>"
-input_source_workspace = "<workspace>"
-input_source_datastore = "<datastore_name>"
-input_source_path = "path"
-
-input_source = f"azureml://subscriptions/{input_source_subscription}/resourcegroups/{input_source_resource_group}/workspaces/{input_source_workspace}/datastores/{input_source_datastore}/paths/{input_source_path}"
-```
-### Build the index on cloud
-
-Now we can build the index using the `ai_search_connection`, `embeddings_model_config` and `input_source`. We use the `build_index` function. If you're using an Azure Storage URL as your input source, you also need to provide a `UserIdentityConfiguration`.
-
-```python
-# from azure.ai.ml.entities.credentials import UserIdentityConfiguration # user specified identity used to access the data. Required when using an azure storage URL
-from azure.ai.ml.entities import AzureAISearchConfig
-
-client.indexes.build_index(
-    name="<index_name>", # name of your index
-    embeddings_model_config=embeddings_model_config, 
-    input_source=input_source, 
-    # input_source_credential=UserIdentityConfiguration(), # user specified identity used to access the data. Required when using an azure storage URL
-    index_config=AzureAISearchConfig(
-        ai_search_index_name="<index_name>",  # the name of the index store in AI search service
-        ai_search_connection_id=ai_search_connection.id, 
-    ),
-    tokens_per_chunk = 800, # Optional field - Maximum number of tokens per chunk
-    token_overlap_across_chunks = 0, # Optional field - Number of tokens to overlap between chunks
-)
-```
-
-Depending on the size of your input source data, the above steps might take some time to complete. Once the job completes, you can retrieve the index object.
-
-```python
-my_index=client.indexes.get(name="<index_name>", label="latest")
-```
-
-### Consuming a registered index from your project
-
-To consume a registered index from your project, you need to connect to the project and retrieve the index. The retrieved index can be used as a langhcain retriever to consume it. You can connect to the project with a `client` as shown here.
-
-```python
-from promptflow.rag import get_langchain_retriever_from_index
-
-my_index=client.indexes.get(
-    name="<registered_index_name>", 
-    label="latest")
-
-index_langchain_retriever=get_langchain_retriever_from_index(my_index.path)
-index_langchain_retriever.get_relevant_documents("<your search query>")
-```
-
-## A question and answer function to use the index
-
-We have seen how to build an index locally or in the cloud. Using this index, we build a QnA function that accepts a user question and provides an answer from the index data. First let us get the index as a langchain_retriever as shown [here](#consuming-a-registered-index-from-your-project). We now use this `retriever` in our function. This function uses the LLM as defined in the `AzureChatOpenAI` constructor. It uses the index as a langchain_retriever to query the data. We build a prompt template that accepts a context and a question. We use langchain's `RetrievalQA.from_chain_type` to put all these together and get us the answers.
-
-```python
-def qna(question: str, temperature: float = 0.0, prompt_template: object = None) -> str:
-    from langchain import PromptTemplate
-    from langchain.chains import RetrievalQA
-    from langchain_openai import AzureChatOpenAI
-
-    llm = AzureChatOpenAI(
-        openai_api_version="2023-06-01-preview",
-        api_key="<your-azure-openai-api-key>",
-        azure_endpoint="https://<your-azure-openai-service>.openai.azure.com/",
-        azure_deployment="<your-chat-model-deployment>", # verify the model name and deployment name
-        temperature=temperature,
-    )
-
-    template = """
-    System:
-    You are an AI assistant helping users answer questions given a specific context.
-    Use the following pieces of context to answer the questions as completely, 
-    correctly, and concisely as possible.
-    Your answer should only come from the context. Don't try to make up an answer.
-    Do not add documentation reference in the response.
-
-    {context}
-
-    ---
-
-    Question: {question}
-
-    Answer:"
-    """
-    prompt_template = PromptTemplate(template=template, input_variables=["context", "question"])
-
-    qa = RetrievalQA.from_chain_type(
-        llm=llm,
-        chain_type="stuff",
-        retriever=index_langchain_retriever,
-        return_source_documents=True,
-        chain_type_kwargs={
-            "prompt": prompt_template,
-        },
-    )
-
-    response = qa(question)
-
-    return {
-        "question": response["query"],
-        "answer": response["result"],
-        "context": "\n\n".join([doc.page_content for doc in response["source_documents"]]),
-    }
-```
-
-Let us ask a question to make sure we get an answer.
-
-```python
-result = qna("<your question>")
-print(result["answer"])
-```
-
-## Related content
-
-- [Create and consume an index from the Azure AI Foundry portal UI](../index-add.md)
-- [Get started building a chat app using the prompt flow SDK](../../quickstarts/get-started-code.md)
-- [Work with projects in VS Code](vscode.md)
\ No newline at end of file

@@ -21,7 +21,7 @@ Models deployed to Azure AI Foundry can be used with LangChain in two ways:
 
 - **Using the Azure AI model inference API:** All models deployed to Azure AI Foundry support the [Azure AI model inference API](../../../ai-foundry/model-inference/reference/reference-model-inference-api.md), which offers a common set of functionalities that can be used for most of the models in the catalog. The benefit of this API is that, since it's the same for all the models, changing from one to another is as simple as changing the model deployment being use. No further changes are required in the code. When working with LangChain, install the extensions `langchain-azure-ai`.
 
-- **Using the model's provider specific API:** Some models, like OpenAI, Cohere, or Mistral, offer their own set of APIs and extensions for LlamaIndex. Those extensions may include specific functionalities that the model support and hence are suitable if you want to exploit them. When working with LangChain, install the extension specific for the model you want to use, like `langchain-openai` or `langchain-cohere`.
+- **Using the model's provider specific API:** Some models, like OpenAI, Cohere, or Mistral, offer their own set of APIs and extensions for LangChain. Those extensions may include specific functionalities that the model support and hence are suitable if you want to exploit them. When working with LangChain, install the extension specific for the model you want to use, like `langchain-openai` or `langchain-cohere`.
 
 In this tutorial, you learn how to use the packages `langchain-azure-ai` to build applications with LangChain.
 

@@ -15,7 +15,7 @@ ms.reviewer: ambadal
 
 # Disable shared key access for your hub's storage account (preview)
 
-An Azure AI Foundry hub defaults to use of a shared key to access its default Azure Storage account. With key-based authorization, anyone who has the key and access to the storage account can access data.
+An [Azure AI Foundry](https://ai.azure.com) hub defaults to use of a shared key to access its default Azure Storage account. With key-based authorization, anyone who has the key and access to the storage account can access data.
 
 To reduce the risk of unauthorized access, you can disable key-based authorization, and instead use Microsoft Entra ID for authorization. This configuration uses a Microsoft Entra ID value to authorize access to the storage account. The identity used to access storage is either the user's identity or a managed identity. The user's identity is used to view data in the Azure Machine Learning studio, or to run a notebook while authenticated with the user's identity. The Azure Machine Learning service uses a managed identity to access the storage account - for example, when running a training job as the managed identity.
 

@@ -17,7 +17,7 @@ ms.date: 12/05/2024
 
 [!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
-To maximize your uptime, plan ahead to maintain business continuity and prepare for disaster recovery with Azure AI Foundry. Since Azure AI Foundry builds on [Azure Machine Learning architecture](/azure/machine-learning/concept-workspace), it's beneficial to reference the foundational architecture.
+To maximize your uptime, plan ahead to maintain business continuity and prepare for disaster recovery with [Azure AI Foundry](https://ai.azure.com). Since Azure AI Foundry builds on [Azure Machine Learning architecture](/azure/machine-learning/concept-workspace), it's beneficial to reference the foundational architecture.
 
 Microsoft strives to ensure that Azure services are always available. However, unplanned service outages might occur. We recommend having a disaster recovery plan in place for handling regional service outages. In this article, you learn how to:
 

@@ -44,7 +44,7 @@ Default limits vary by offer category type, such as free trial, pay-as-you-go, a
 
 ## Azure AI Foundry quota 
 
-The following actions in Azure AI Foundry portal consume quota: 
+The following actions in [Azure AI Foundry portal](https://ai.azure.com) consume quota: 
 
 - Creating a compute instance.
 - Building a vector index.

@@ -15,7 +15,7 @@ zone_pivot_groups: azure-ai-studio-sdk-cli
 
 # Use your data securely with the Azure AI Foundry portal playground
 
-Use this article to learn how to securely use Azure AI Foundry's playground chat on your data. The following sections provide our recommended configuration to protect your data and resources by using Microsoft Entra ID role-based access control, a managed network, and private endpoints. We recommend disabling public network access for Azure OpenAI resources, Azure AI Search resources, and storage accounts. Using selected networks with IP rules isn't supported because the services' IP addresses are dynamic.
+Use this article to learn how to securely use [Azure AI Foundry](https://ai.azure.com)'s playground chat on your data. The following sections provide our recommended configuration to protect your data and resources by using Microsoft Entra ID role-based access control, a managed network, and private endpoints. We recommend disabling public network access for Azure OpenAI resources, Azure AI Search resources, and storage accounts. Using selected networks with IP rules isn't supported because the services' IP addresses are dynamic.
 
 > [!NOTE]
 > Azure AI Foundry's managed virtual network settings apply only to Azure AI Foundry's managed compute resources, not platform as a service (PaaS) services like Azure OpenAI or Azure AI Search. When using PaaS services, there's no data exfiltration risk because the services are managed by Microsoft.

@@ -7,29 +7,27 @@ ms.service: azure-ai-foundry
 ms.custom:
   - build-2024
 ms.topic: how-to
-ms.date: 11/21/2024
+ms.date: 02/21/2025
 ms.reviewer: meerakurup
 ms.author: larryfr
 author: Blackmist
 ---
 
 # Troubleshoot connection to a project with a private endpoint
 
-[!INCLUDE [feature-preview](../includes/feature-preview.md)]
-
-When connecting to a project that has been configured with a private endpoint, you may encounter a 403 or a messaging saying that access is forbidden. Use the information in this article to check for common configuration problems that can cause this error.
+When connecting to an [Azure AI Foundry](https://ai.azure.com) project configured with a private endpoint, you might encounter a 403 or a messaging saying that access is forbidden. Use the information in this article to check for common configuration problems that can cause this error.
 
 ## Securely connect to your project
 
-To connect to a project that's secured behind a VNet, use one of the following methods:
+To connect to a project secured behind a virtual network, use one of the following methods:
 
-* [Azure VPN gateway](/azure/vpn-gateway/vpn-gateway-about-vpngateways) - Connects on-premises networks to the VNet over a private connection. Connection is made over the public internet. There are two types of VPN gateways that you might use:
+* [Azure VPN gateway](/azure/vpn-gateway/vpn-gateway-about-vpngateways) - Connects on-premises networks to the virtual network over a private connection. Connection is made over the public internet. There are two types of VPN gateways that you might use:
 
-    * [Point-to-site](/azure/vpn-gateway/vpn-gateway-howto-point-to-site-resource-manager-portal): Each client computer uses a VPN client to connect to the VNet.
-    * [Site-to-site](/azure/vpn-gateway/tutorial-site-to-site-portal): A VPN device connects the VNet to your on-premises network.
+    * [Point-to-site](/azure/vpn-gateway/vpn-gateway-howto-point-to-site-resource-manager-portal): Each client computer uses a VPN client to connect to the virtual network.
+    * [Site-to-site](/azure/vpn-gateway/tutorial-site-to-site-portal): A VPN device connects the virtual network to your on-premises network.
 
 * [ExpressRoute](https://azure.microsoft.com/services/expressroute/) - Connects on-premises networks into the cloud over a private connection. Connection is made using a connectivity provider.
-* [Azure Bastion](/azure/bastion/bastion-overview) - In this scenario, you create an Azure Virtual Machine (sometimes called a jump box) inside the VNet. You then connect to the VM using Azure Bastion. Bastion allows you to connect to the VM using either an RDP or SSH session from your local web browser. You then use the jump box as your development environment. Since it is inside the VNet, it can directly access the workspace.
+* [Azure Bastion](/azure/bastion/bastion-overview) - In this scenario, you create an Azure Virtual Machine (sometimes called a jump box) inside the virtual network. You then connect to the VM using Azure Bastion. Bastion allows you to connect to the VM using either an RDP or SSH session from your local web browser. You then use the jump box as your development environment. Since it is inside the virtual network, it can directly access the workspace.
 
 ## DNS configuration
 
@@ -51,8 +49,8 @@ The troubleshooting steps for DNS configuration differ based on whether you're u
 
     :::image type="content" source="../media/how-to/troubleshoot-secure-connection-project/dns-servers.png" alt-text="Screenshot of the DNS servers configuration." lightbox="../media/how-to/troubleshoot-secure-connection-project/dns-servers.png":::
 
-    * If this value is __Default (Azure-provided)__, then the VNet is using Azure DNS. Skip to the [Azure DNS troubleshooting](#azure-dns-troubleshooting) section.
-    * If there's a different IP address listed, then the VNet is using a custom DNS solution. Skip to the [Custom DNS troubleshooting](#custom-dns-troubleshooting) section.
+    * If this value is __Default (Azure-provided)__, then the virtual network is using Azure DNS. Skip to the [Azure DNS troubleshooting](#azure-dns-troubleshooting) section.
+    * If there's a different IP address listed, then the virtual network is using a custom DNS solution. Skip to the [Custom DNS troubleshooting](#custom-dns-troubleshooting) section.
 
 ### Custom DNS troubleshooting
 
@@ -97,7 +95,7 @@ When using Azure DNS for name resolution, use the following steps to verify that
     * If there's a Private DNS zone entry, but __no DNS zone group entry__, delete and recreate the Private Endpoint. When recreating the private endpoint, __enable Private DNS zone integration__.
     * If __DNS zone group__ isn't empty, select the link for the __Private DNS zone__ entry.
     
-        From the Private DNS zone, select __Virtual network links__. There should be a link to the VNet. If there isn't one, then delete and recreate the private endpoint. When recreating it, select a Private DNS Zone linked to the VNet or create a new one that is linked to it.
+        From the Private DNS zone, select __Virtual network links__. There should be a link to the virtual network. If there isn't one, then delete and recreate the private endpoint. When recreating it, select a Private DNS Zone linked to the virtual network or create a new one that is linked to it.
 
         :::image type="content" source="../media/how-to/troubleshoot-secure-connection-project/virtual-network-links.png" alt-text="Screenshot of the virtual network links for the Private DNS zone." lightbox="../media/how-to/troubleshoot-secure-connection-project/virtual-network-links.png":::
 
@@ -109,14 +107,14 @@ Check if DNS over HTTP is enabled in your web browser. DNS over HTTP can prevent
 
 * Mozilla Firefox: For more information, see [Disable DNS over HTTPS in Firefox](https://support.mozilla.org/en-US/kb/firefox-dns-over-https).
 * Microsoft Edge:
-    1. In Edge, select __...__ and then select __Settings__.
+    1. In Microsoft Edge, select __...__ and then select __Settings__.
     1. From settings, search for `DNS` and then disable __Use secure DNS to specify how to look up the network address for websites__.
     
         :::image type="content" source="../media/how-to/troubleshoot-secure-connection-project/disable-dns-over-http.png" alt-text="Screenshot of the use secure DNS setting in Microsoft Edge." lightbox="../media/how-to/troubleshoot-secure-connection-project/disable-dns-over-http.png":::
 
 ## Proxy configuration
 
-If you use a proxy, it may prevent communication with a secured project. To test, use one of the following options:
+If you use a proxy, it might prevent communication with a secured project. To test, use one of the following options:
 
 * Temporarily disable the proxy setting and see if you can connect.
 * Create a [Proxy auto-config (PAC)](https://wikipedia.org/wiki/Proxy_auto-config) file that allows direct access to the FQDNs listed on the private endpoint. It should also allow direct access to the FQDN for any compute instances.
@@ -125,12 +123,12 @@ If you use a proxy, it may prevent communication with a secured project. To test
 
 ## Troubleshoot configurations on connecting to storage
 
-When you create a project, a number of connections to Azure storage are auto-created for data upload scenarios and artifact storage including prompt flow. When your hub's associated Azure Storage account is having public network access set to 'Disabled', there may be a delay in these storage connections to be created. 
+When you create a project, several connections to Azure storage are automatically created for data upload and artifact storage, including prompt flow. When your hub's associated Azure Storage account is having public network access set to 'Disabled', there might be a delay in these storage connections to be created. 
 
 Try the following steps to troubleshoot:
-1. In Azure Portal, check the network settings of the storage account that is associated to your hub.
+1. In Azure portal, check the network settings of the storage account that is associated to your hub.
   * If public network access is set to __Enabled from selected virtual networks and IP addresses__, ensure the correct IP address ranges are added to access your storage account.
   * If public network access is set to __Disabled__, ensure you have a private endpoint configured from your Azure virtual network to your storage account with Target sub-resource as blob. In addition, you must grant the [Reader](/azure/role-based-access-control/built-in-roles#reader) role for the storage account private endpoint to the managed identity.
-2. In Azure Portal, navigate to your Azure AI Foundry hub. Ensure the managed virtual network is provisioned and the outbound private endpoint to blob storage is Active. For more on provisioning the managed virtual network, see [How to configure a managed network for Azure AI Foundry hubs](configure-managed-network.md).
+2. In Azure portal, navigate to your Azure AI Foundry hub. Ensure the managed virtual network is provisioned and the outbound private endpoint to blob storage is Active. For more on provisioning the managed virtual network, see [How to configure a managed network for Azure AI Foundry hubs](configure-managed-network.md).
 3. Navigate to Azure AI Foundry > your project > project settings. 
-4. Refresh the page. A number of connections should be created including 'workspaceblobstore'.
+4. Refresh the page. Several connections should be created including 'workspaceblobstore'.