@@ -38,7 +38,7 @@ Key considerations at this stage might include:
 - **Bias and ethical considerations**: Does the model produce any outputs that might perpetuate or promote harmful stereotypes?
 - **Risk and safety**: Are there any risks of the model generating unsafe or malicious content?
 
-You can explore [Azure AI Foundry benchmarks](./model-benchmarks.md)to evaluate and compare models on publicly available datasets, while also regenerating benchmark results on your own data. Alternatively, you can evaluate one of many base generative AI models via Azure AI Evaluation SDK as demonstrated, see [Evaluate model endpoints sample](https://github.com/Azure-Samples/azureai-samples/blob/main/scenarios/evaluate/evaluate_endpoints/evaluate_endpoints.ipynb).
+You can explore [Azure AI Foundry benchmarks](./model-benchmarks.md)to evaluate and compare models on publicly available datasets, while also regenerating benchmark results on your own data. Alternatively, you can evaluate one of many base generative AI models via Azure AI Evaluation SDK as demonstrated, see [Evaluate model endpoints sample](https://github.com/Azure-Samples/azureai-samples/blob/main/scenarios/evaluate/Supported_Evaluation_Targets/Evaluate_Base_Model_Endpoint/Evaluate_Base_Model_Endpoint.ipynb).
 
 ## Pre-production evaluation
 

@@ -59,7 +59,7 @@ Rerank multilingual performs well on multilingual benchmarks such as Miracl.
 
 Certain models in the model catalog can be deployed as a serverless API with pay-as-you-go billing. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. This deployment option doesn't require quota from your subscription.
 
-You can deploy the previously mentioned Cohere models as a service with pay-as-you-go billing. Cohere offers these models through the Microsoft Azure Marketplace and can change or update the terms of use and pricing of these models.
+You can deploy the previously mentioned Cohere models as a service with pay-as-you-go billing. Cohere offers these models through Microsoft Azure Marketplace and can change or update the terms of use and pricing of these models.
 
 ### Prerequisites
 
@@ -95,7 +95,7 @@ To create a deployment:
 1. Select the **Subscribe and Deploy** button. If this is your first time deploying the model in the project, you have to subscribe your project for the particular offering.
 
     > [!NOTE]
-    > This step requires that your account has the **Azure AI Developer role** permissions on the resource group, as listed in the prerequisites. Models that are offered by non-Microsoft providers (for example, Cohere models) are billed through the Azure Marketplace. For such models, you're required to subscribe your project to the particular model offering. Each project has its own subscription to the particular Azure Marketplace offering of the model, which allows you to control and monitor spending. Currently, you can have only one deployment for each model within a project.
+    > This step requires that your account has the **Azure AI Developer role** permissions on the resource group, as listed in the prerequisites. Models that are offered by non-Microsoft providers (for example, Cohere models) are billed through Azure Marketplace. For such models, you're required to subscribe your project to the particular model offering. Each project has its own subscription to the particular Azure Marketplace offering of the model, which allows you to control and monitor spending. Currently, you can have only one deployment for each model within a project.
 
 1. Once you subscribe the project for the particular Azure Marketplace offering, subsequent deployments of the _same_ offering in the _same_ project don't require subscribing again. If this scenario applies to you, there's a **Continue to deploy** option to select.
 
@@ -237,11 +237,11 @@ The `results` object is a dictionary with the following fields:
 
 Quota is managed per deployment. Each deployment has a rate limit of 200,000 tokens per minute and 1,000 API requests per minute. However, we currently limit one deployment per model per project. Contact Microsoft Azure Support if the current rate limits aren't sufficient for your scenarios. 
 
-Cohere models deployed as serverless APIs with pay-as-you-go billing are offered by Cohere through the Azure Marketplace and integrated with Azure AI Foundry for use. You can find the Azure Marketplace pricing when deploying the model.
+Cohere models deployed as serverless APIs with pay-as-you-go billing are offered by Cohere through Azure Marketplace and integrated with Azure AI Foundry for use. You can find Azure Marketplace pricing when deploying the model.
 
-Each time a project subscribes to a given offer from the Azure Marketplace, a new resource is created to track the costs associated with its consumption. The same resource is used to track costs associated with inference; however, multiple meters are available to track each scenario independently.
+Each time a project subscribes to a given offer from Azure Marketplace, a new resource is created to track the costs associated with its consumption. The same resource is used to track costs associated with inference; however, multiple meters are available to track each scenario independently.
 
-For more information on how to track costs, see [monitor costs for models offered throughout the Azure Marketplace](./costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace).
+For more information on how to track costs, see [monitor costs for models offered throughout Azure Marketplace](./costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace).
 
 
 

@@ -5,7 +5,7 @@ description: Learn how to deploy TimeGEN-1 with Azure AI Foundry.
 manager: scottpolly
 ms.service: azure-ai-studio
 ms.topic: how-to
-ms.date: 5/21/2024
+ms.date: 12/16/2024
 ms.reviewer: kritifaujdar
 reviewer: fkriti
 ms.author: mopeakande
@@ -28,23 +28,15 @@ The Nixtla TimeGEN-1 is a generative, pretrained forecasting and anomaly detecti
 
 Certain models in the model catalog can be deployed as a serverless API with pay-as-you-go billing. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. This deployment option doesn't require quota from your subscription.
 
-You can deploy TimeGEN-1 as a serverless API with pay-as-you-go billing. Nixtla offers TimeGEN-1 through the Microsoft Azure Marketplace. Nixtla can change or update the terms of use and pricing of this model.
+You can deploy TimeGEN-1 as a serverless API with pay-as-you-go billing. Nixtla offers TimeGEN-1 through Microsoft Azure Marketplace. Nixtla can change or update the terms of use and pricing of this model.
 
 ### Prerequisites
 
 - An Azure subscription with a valid payment method. Free or trial Azure subscriptions don't work. If you don't have an Azure subscription, create a [paid Azure account](https://azure.microsoft.com/pricing/purchase-options/pay-as-you-go) to begin.
-- An [Azure AI Foundry project](../how-to/create-projects.md). The serverless API model deployment offering for TimeGEN-1 is only available with projects created in these regions:
 
-    > [!div class="checklist"]
-    > * East US
-    > * East US 2
-    > * North Central US
-    > * South Central US
-    > * West US
-    > * West US 3
-    > * Sweden Central
+- An [Azure AI Foundry hub](../how-to/create-azure-ai-resource.md). The serverless API model deployment offering for Nixtla's TimeGEN-1 model is only available with hubs created in specific regions. For a list of these regions, see [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md#nixtla-models).
 
-    For a list of  regions that are available for each of the models supporting serverless API endpoint deployments, see [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md).
+- An [Azure AI Foundry project](../how-to/create-projects.md).
 
 - Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure AI Foundry portal. To perform the steps in this article, your user account must be assigned the __Azure AI Developer role__ on the resource group. For more information on permissions, visit [Role-based access control in Azure AI Foundry portal](../concepts/rbac-ai-studio.md).
 
@@ -89,20 +81,26 @@ These steps demonstrate the deployment of TimeGEN-1. To create a deployment:
 
 [!INCLUDE [open-catalog](../includes/open-catalog.md)]
 
-4. Search for and select **TimeGEN-1** to open its Details page.
+4. Select the model card of the model you want to deploy. In this article, you select **TimeGEN-1** to open the Model Details page.
+
 1. Select **Deploy** to open a serverless API deployment window for the model.
-1. Alternatively, you can initiate a deployment by starting from the **Models + endpoints** page in AI Foundry portal.
-    1. From the left navigation pane of your project, select **My assets** > **Models + endpoints**.
+1. Alternatively, you can initiate a deployment from your project in the AI Foundry portal as follows: 
+
+    1. From the left sidebar of your project, select **Models + Endpoints**.
     1. Select **+ Deploy model** > **Deploy base model**.
-    1. Search for and select **TimeGEN-1**. to open the Model's Details page.
+    1. Search for and select **TimeGEN-1** to open the Model Details page.
     1. Select **Confirm** to open a serverless API deployment window for the model.
-1. Your current project is specified for the deployment. To successfully deploy the TimeGEN-1 model, your project must be in one of the regions listed in the [Prerequisites](#prerequisites) section.
-1. In the deployment wizard, select the link to **Azure Marketplace Terms**, to learn more about the terms of use.
+
+1. In the deployment wizard, select the link to **Azure Marketplace Terms** to learn more about the terms of use.
 1. Select the **Pricing and terms** tab to learn about pricing for the selected model.
-1. Select the **Subscribe and Deploy** button. If this is your first time deploying the model in the project, you have to subscribe your project for the particular offering. This step requires that your account has the **Azure AI Developer role** permissions on the resource group, as listed in the prerequisites. Each project has its own subscription to the particular Azure Marketplace offering of the model, which allows you to control and monitor spending. Currently, you can have only one deployment for each model within a project.
+1. Select the **Subscribe and Deploy** button. If this is your first time deploying the model in the project, you have to subscribe your project for the particular offering.
+
+    > [!NOTE]
+    > This step requires that your account has the **Azure AI Developer role** permissions on the resource group, as listed in the prerequisites. Models that are offered by non-Microsoft providers (for example, Nixtla TimeGEN-1) are billed through Azure Marketplace. For such models, you're required to subscribe your project to the particular model offering. Each project has its own subscription to the particular Azure Marketplace offering of the model, which allows you to control and monitor spending. Currently, you can have only one deployment for each model within a project.
+
 1. Once you subscribe the project for the particular Azure Marketplace offering, subsequent deployments of the _same_ offering in the _same_ project don't require subscribing again. If this scenario applies to you,  there's a **Continue to deploy** option to select.
 1. Give the deployment a name. This name becomes part of the deployment API URL. This URL must be unique in each Azure region.
-1. Select **Deploy**. Wait until the deployment is ready and you're redirected to the Deployments page.
+1. Select **Deploy**. Wait until the deployment is ready and you're redirected to the **Model deployments** page.
 1. Return to the Deployments page, select the deployment, and note the endpoint's **Target** URI and the Secret **Key**. For more information on using the APIs, see the [reference](#reference-for-timegen-1-deployed-as-a-serverless-api) section.
 1. [!INCLUDE [Find your deployment details](../includes/find-deployments.md)]
 
@@ -273,11 +271,11 @@ This JSON sample is an example response:
 
 ### Cost and quota considerations for TimeGEN-1 deployed as a serverless API
 
-Nixtla offers TimeGEN-1 deployed as a serverless API through the Azure Marketplace. TimeGEN-1 is integrated with Azure AI Foundry for use. You can find more information about Azure Marketplace pricing when you deploy the model.
+Nixtla offers TimeGEN-1 deployed as a serverless API through Azure Marketplace. TimeGEN-1 is integrated with Azure AI Foundry for use. You can find more information about Azure Marketplace pricing when you deploy the model.
 
-Each time a project subscribes to a given offer from the Azure Marketplace, a new resource is created to track the costs associated with its consumption. The same resource is used to track costs associated with inference; however, multiple meters are available to track each scenario independently.
+Each time a project subscribes to a given offer from Azure Marketplace, a new resource is created to track the costs associated with its consumption. The same resource is used to track costs associated with inference; however, multiple meters are available to track each scenario independently.
 
-For more information about how to track costs, visit [monitor costs for models offered throughout the Azure Marketplace](./costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace).
+For more information about how to track costs, visit [monitor costs for models offered throughout Azure Marketplace](./costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace).
 
 Quota is managed per deployment. Each deployment has a rate limit of 200,000 tokens per minute and 1,000 API requests per minute. However, we currently limit one deployment per model per project. Contact Microsoft Azure Support if the current rate limits are insufficient for your scenarios.
 

@@ -415,6 +415,8 @@ pip install azure-monitor-opentelemetry
 Use the following code to enable instrumentation of the Azure AI Inference SDK and logging to your AI project:
 
 ```Python
+from azure.monitor.opentelemetry import configure_azure_monitor
+
 # Enable instrumentation of AI packages (inference, agents, openai, langchain)
 project.telemetry.enable()
 

@@ -0,0 +1,226 @@
+---
+title: Develop applications with Semantic Kernel and Azure AI Foundry
+titleSuffix: Azure AI Foundry
+description: Develop applications with Semantic Kernel and Azure AI Foundry.
+author: lgayhardt
+ms.author: lagayhar
+ms.reviewer: taochen
+ms.date: 12/04/2024
+ms.topic: how-to
+ms.service: azure-ai-studio
+manager: scottpolly
+---
+
+# Develop applications with Semantic Kernel and Azure AI Foundry
+
+In this article, you learn how to use [Semantic Kernel](/semantic-kernel/overview/) with models deployed from the Azure AI model catalog in Azure AI Foundry portal.
+
+## Prerequisites
+
+- An [Azure subscription](https://azure.microsoft.com).
+- An Azure AI project as explained at [Create a project in Azure AI Foundry portal](../create-projects.md).
+- A model supporting the [Azure AI model inference API](../../reference/reference-model-inference-api.md?tabs=python) deployed. In this example, we use a `Mistral-Large` deployment, but use any model of your preference. For using embeddings capabilities in LlamaIndex, you need an embedding model like `cohere-embed-v3-multilingual`.
+
+  - You can follow the instructions at [Deploy models as serverless APIs](../deploy-models-serverless.md).
+
+- Python **3.10** or later installed, including pip.
+- Semantic Kernel installed. You can do it with:
+
+    ```bash
+    pip install semantic-kernel
+    ```
+- In this example, we are working with the Azure AI model inference API, hence we install the relevant Azure dependencies. You can do it with:
+    ```bash
+    pip install semantic-kernel[azure]
+    ```
+
+## Configure the environment
+
+To use LLMs deployed in Azure AI Foundry portal, you need the endpoint and credentials to connect to it. Follow these steps to get the information you need from the model you want to use:
+
+1. Go to the [Azure AI Foundry portal](https://ai.azure.com/).
+1. Open the project where the model is deployed, if it isn't already open.
+1. Go to **Models + endpoints** and select the model you deployed as indicated in the prerequisites.
+1. Copy the endpoint URL and the key.
+
+    :::image type="content" source="../../media/how-to/inference/serverless-endpoint-url-keys.png" alt-text="Screenshot of the option to copy endpoint URI and keys from an endpoint." lightbox="../../media/how-to/inference/serverless-endpoint-url-keys.png":::
+
+    > [!TIP]
+    > If your model was deployed with Microsoft Entra ID support, you don't need a key.
+
+In this scenario, we placed both the endpoint URL and key in the following environment variables:
+
+```bash
+export AZURE_AI_INFERENCE_ENDPOINT="<your-model-endpoint-goes-here>"
+export AZURE_AI_INFERENCE_API_KEY="<your-key-goes-here>"
+```
+
+Once configured, create a client to connect to the endpoint:
+
+```python
+from semantic_kernel.connectors.ai.azure_ai_inference import AzureAIInferenceChatCompletion
+
+chat_completion_service = AzureAIInferenceChatCompletion(ai_model_id="<deployment-name>")
+```
+
+> [!TIP]
+> The client automatically reads the environment variables `AZURE_AI_INFERENCE_ENDPOINT` and `AZURE_AI_INFERENCE_API_KEY` to connect to the model. However, you can also pass the endpoint and key directly to the client via the `endpoint` and `api_key` parameters on the constructor.
+
+Alternatively, if your endpoint support Microsoft Entra ID, you can use the following code to create the client:
+
+```bash
+export AZURE_AI_INFERENCE_ENDPOINT="<your-model-endpoint-goes-here>"
+```
+
+```python
+from semantic_kernel.connectors.ai.azure_ai_inference import AzureAIInferenceChatCompletion
+
+chat_completion_service = AzureAIInferenceChatCompletion(ai_model_id="<deployment-name>")
+```
+
+> [!NOTE]
+> When using Microsoft Entra ID, make sure that the endpoint was deployed with that authentication method and that you have the required permissions to invoke it.
+
+### Azure OpenAI models
+
+If you're using an Azure OpenAI model, you can use the following code to create the client:
+
+```python
+from azure.ai.inference.aio import ChatCompletionsClient
+from azure.identity.aio import DefaultAzureCredential
+
+from semantic_kernel.connectors.ai.azure_ai_inference import AzureAIInferenceChatCompletion
+
+chat_completion_service = AzureAIInferenceChatCompletion(
+    ai_model_id="<deployment-name>",
+    client=ChatCompletionsClient(
+        endpoint=f"{str(<your-azure-open-ai-endpoint>).strip('/')}/openai/deployments/{<deployment_name>}",
+        credential=DefaultAzureCredential(),
+        credential_scopes=["https://cognitiveservices.azure.com/.default"],
+    ),
+)
+```
+
+## Inference parameters
+
+You can configure how inference is performed by using the `AzureAIInferenceChatPromptExecutionSettings` class:
+
+```python
+from semantic_kernel.connectors.ai.azure_ai_inference import AzureAIInferenceChatPromptExecutionSettings
+
+execution_settings = AzureAIInferenceChatPromptExecutionSettings(
+    max_tokens=100,
+    temperature=0.5,
+    top_p=0.9,
+    # extra_parameters={...},    # model-specific parameters
+)
+```
+
+## Calling the service
+
+Let's first call the chat completion service with a simple chat history:
+
+> [!TIP]
+> Semantic Kernel is an asynchronous library, so you need to use the asyncio library to run the code.
+>
+> ```python
+> import asyncio
+> 
+> async def main():
+>     ...
+>
+> if __name__ == "__main__":
+>     asyncio.run(main())
+> ```
+
+```python
+from semantic_kernel.contents.chat_history import ChatHistory
+
+chat_history = ChatHistory()
+chat_history.add_user_message("Hello, how are you?")
+
+response = await chat_completion_service.get_chat_message_content(
+    chat_history=chat_history,
+    settings=execution_settings,
+)
+print(response)
+```
+
+Alternatively, you can stream the response from the service:
+
+```python
+chat_history = ChatHistory()
+chat_history.add_user_message("Hello, how are you?")
+
+response = chat_completion_service.get_streaming_chat_message_content(
+    chat_history=chat_history,
+    settings=execution_settings,
+)
+
+chunks = []
+async for chunk in response:
+    chunks.append(chunk)
+    print(chunk, end="")
+
+full_response = sum(chunks[1:], chunks[0])
+```
+
+### Create a long-running conversation
+
+You can create a long-running conversation by using a loop:
+
+```python
+while True:
+    response = await chat_completion_service.get_chat_message_content(
+        chat_history=chat_history,
+        settings=execution_settings,
+    )
+    print(response)
+    chat_history.add_message(response)
+    chat_history.add_user_message(user_input = input("User:> "))
+```
+
+If you're streaming the response, you can use the following code:
+
+```python
+while True:
+    response = chat_completion_service.get_streaming_chat_message_content(
+        chat_history=chat_history,
+        settings=execution_settings,
+    )
+
+    chunks = []
+    async for chunk in response:
+        chunks.append(chunk)
+        print(chunk, end="")
+
+    full_response = sum(chunks[1:], chunks[0])
+    chat_history.add_message(full_response)
+    chat_history.add_user_message(user_input = input("User:> "))
+```
+
+## Use embeddings models
+
+Configure your environment similarly to the previous steps, but use the `AzureAIInferenceEmbeddings` class:
+
+```python
+from semantic_kernel.connectors.ai.azure_ai_inference import AzureAIInferenceTextEmbedding
+
+embedding_generation_service = AzureAIInferenceTextEmbedding(ai_model_id="<deployment-name>")
+```
+
+The following code shows how to get embeddings from the service:
+
+```python
+embeddings = await embedding_generation_service.generate_embeddings(
+    texts=["My favorite color is blue.", "I love to eat pizza."],
+)
+
+for embedding in embeddings:
+    print(embedding)
+```
+
+## Related content
+
+- [How to get started with Azure AI SDKs](sdk-overview.md)
+- [Reference for Semantic Kernel model integration](/semantic-kernel/concepts/ai-services/)
\ No newline at end of file

@@ -83,7 +83,7 @@ Cohere family models | Not available | Cohere-command-r-plus-08-2024 <br> Cohere
 JAIS | Not available | jais-30b-chat
 AI21 family models | Not available | Jamba-1.5-Mini <br> Jamba-1.5-Large
 Healthcare AI family Models | MedImageParse<BR>  MedImageInsight<BR>  CxrReportGen<BR>  Virchow<BR>  Virchow2<BR>  Prism<BR>  BiomedCLIP-PubMedBERT<BR>  microsoft-llava-med-v1.5<BR>  m42-health-llama3-med4<BR>  biomistral-biomistral-7b<BR>  microsoft-biogpt-large-pub<BR>  microsoft-biomednlp-pub<BR>  stanford-crfm-biomedlm<BR>  medicalai-clinicalbert<BR>  microsoft-biogpt<BR>  microsoft-biogpt-large<BR>  microsoft-biomednlp-pub<BR> | Not Available
-Phi-3 family models | Phi-3-mini-4k-Instruct <br> Phi-3-mini-128k-Instruct <br> Phi-3-small-8k-Instruct <br> Phi-3-small-128k-Instruct <br> Phi-3-medium-4k-instruct <br> Phi-3-medium-128k-instruct <br> Phi-3-vision-128k-Instruct <br> Phi-3.5-mini-Instruct <br> Phi-3.5-vision-Instruct <br> Phi-3.5-MoE-Instruct | Phi-3-mini-4k-Instruct <br> Phi-3-mini-128k-Instruct <br> Phi-3-small-8k-Instruct <br> Phi-3-small-128k-Instruct <br> Phi-3-medium-4k-instruct <br> Phi-3-medium-128k-instruct <br> <br> Phi-3.5-mini-Instruct <br> Phi-3.5-vision-Instruct <br> Phi-3.5-MoE-Instruct
+Microsoft Phi family models | Phi-3-mini-4k-Instruct <br> Phi-3-mini-128k-Instruct <br> Phi-3-small-8k-Instruct <br> Phi-3-small-128k-Instruct <br> Phi-3-medium-4k-instruct <br> Phi-3-medium-128k-instruct <br> Phi-3-vision-128k-Instruct <br> Phi-3.5-mini-Instruct <br> Phi-3.5-vision-Instruct <br> Phi-3.5-MoE-Instruct <br> Phi-4| Phi-3-mini-4k-Instruct <br> Phi-3-mini-128k-Instruct <br> Phi-3-small-8k-Instruct <br> Phi-3-small-128k-Instruct <br> Phi-3-medium-4k-instruct <br> Phi-3-medium-128k-instruct <br> <br> Phi-3.5-mini-Instruct <br> Phi-3.5-vision-Instruct <br> Phi-3.5-MoE-Instruct
 Nixtla | Not available | TimeGEN-1
 
 <!-- docutune:enable -->

@@ -23,15 +23,13 @@ CHAT_MODEL="gpt-4o-mini"
 EVALUATION_MODEL="gpt-4o-mini"
 ```
 
-If you changed the name of the models you deployed, or you want to try different models, update those names in this `.env` file.
+* Find your connection string in the Azure AI Foundry project you created in the [AI Foundry playground quickstart](../quickstarts/get-started-playground.md).  Open the project, then find the connection string on the **Overview** page.  Copy the connection string and paste it into the `.env` file.
 
-Find your connection string in the Azure AI Foundry project you created in the [AI Foundry playground quickstart](../quickstarts/get-started-playground.md).  Open the project, then find the connection string on the **Overview** page.  Copy the connection string and paste it into the `.env` file.
+    :::image type="content" source="../media/quickstarts/azure-ai-sdk/connection-string.png" alt-text="Screenshot shows the overview page of a project and the location of the connection string.":::
 
-:::image type="content" source="../media/quickstarts/azure-ai-sdk/connection-string.png" alt-text="Screenshot shows the overview page of a project and the location of the connection string.":::
+* If you don't yet have a search index, keep the value "example-index" for `AISEARCH_INDEX_NAME`. In Part 2 of this tutorial you'll create the index using this name. If you have previously created a search index that you want to use instead, update the value to match the name of that search index.  
 
-If you have a search index that you want to use, update the `AISEARCH_INDEX_NAME` value to match the name of your search index.  If you don't have one, you'll create one in Part 2 of this tutorial.
-
-If you changed the names of the models when you deployed them, update the values in the `.env` file to match the names you used.
+* If you changed the names of the models when you deployed them, update the values in the `.env` file to match the names you used.
 
 > [!TIP]
 > If you're working in VS Code, close and reopen the terminal window after you've saved changes in the `.env` file.

@@ -42,7 +42,7 @@ pip install azure-ai-projects azure-ai-inference azure-identity
 
 Create a file named **chat.py**.  Copy and paste the following code into it.
 
-:::code language="python" source="~/azureai-samples-nov2024/scenarios/inference/chat-app/chat-simple.py":::
+:::code language="python" source="~/azureai-samples-temp/scenarios/inference/chat-app/chat-simple.py":::
 
 ## Insert your connection string
 
@@ -70,9 +70,9 @@ Let's change the script to take input from a client application and generate a s
 
 1. Remove the last line of the script that prints a response.
 
-1. Now define a `get_chat_response` function that takes messages and context, generates a system message using a prompt template, and calls a model.  Add this code to your **chat.py** file:
+1. Now define a `get_chat_response` function that takes messages and context, generates a system message using a prompt template, and calls a model.  Add this code to your  existing **chat.py** file:
 
-    :::code language="python" source="~/azureai-samples-nov2024/scenarios/inference/chat-app/chat-template.py" id="chat_function":::
+    :::code language="python" source="~/azureai-samples-temp/scenarios/inference/chat-app/chat-template.py" id="chat_function":::
 
     > [!NOTE]
     > The prompt template uses mustache format.
@@ -81,9 +81,9 @@ Let's change the script to take input from a client application and generate a s
 
 1. Now simulate passing information from a frontend application to this function.  Add the following code to the end of your **chat.py** file.  Feel free to play with the message and add your own name.
 
-    :::code language="python" source="~/azureai-samples-nov2024/scenarios/inference/chat-app/chat-template.py" id="create_response":::
+    :::code language="python" source="~/azureai-samples-temp/scenarios/inference/chat-app/chat-template.py" id="create_response":::
 
-Run the script to see the response from the model with this new input.
+Run the revised script to see the response from the model with this new input.
 
 ```bash
 python chat.py

@@ -30,8 +30,6 @@ items:
     displayName: code
   - name: Get started using Azure OpenAI Assistants
     href: ../ai-services/openai/assistants-quickstart.md?context=/azure/ai-studio/context/context
-  - name: Use Azure AI Foundry with a screen reader
-    href: tutorials/screen-reader.md
 - name: Tutorials
   items:
   - name: Deploy an enterprise chat web app
@@ -303,6 +301,8 @@ items:
       - name: Develop with LlamaIndex
         href: how-to/develop/llama-index.md
         displayName: code,sdk
+      - name: Develop with Semantic Kernel 
+        href: how-to/develop/semantic-kernel.md
     - name: Trace generative AI apps
       items:
        - name: Tracing overview
@@ -462,6 +462,8 @@ items:
   items:
   - name: Support & help options
     href: ../ai-services/cognitive-services-support-options.md?context=/azure/ai-studio/context/context
+  - name: Use Azure AI Foundry with a screen reader
+    href: tutorials/screen-reader.md
   - name: Region support
     href: https://azure.microsoft.com/global-infrastructure/services/?products=cognitive-services
   - name: Azure updates