Updates based on alpha run

docs/01_deploy_app_resources/0101.md

@@ -47,6 +47,10 @@ The key tasks are as follows:
     > **Important**: Abstractive summarization in the Azure AI Services Language service is only supported in a subset of regions. You must create your Azure AI Language service in one of the [supported regions](https://learn.microsoft.com/azure/ai-services/language-service/summarization/region-support) to allow abstractive summarization to be performed in a later exercise.
 
 7. Open the `DeployAzureResources.bicep` script in the `src/InfrastructureAsCode/` folder and change the value of **sqlAdminPassword** if you desire to have a password other than the default in the deployment script.
+
+    {: .note }
+    > In this lab, you will use a variety of passwords and API keys for the sake of simplicity. In a production environment, you should instead make use of managed identities over passwords or keys whenever possible.
+
 8. Use the `DeployAzureResources.bicep` script in the `src/InfrastructureAsCode/` folder to deploy resources out.
 
     {: .note }

docs/02_implement_function_calls/0202.md

@@ -22,7 +22,7 @@ The key tasks are as follows:
 3. Add the Semantic Kernel NuGet package to the Web API project.
 
     {: .important }
-    > You will need a version of Semantic Kernel that supports Azure.AI.OpenAI version 2. Semantic Kernel 1.17.2 only supports version 1 of the library, so you will need to use 1.18 or later. As of the time of writing, [the latest version of Semantic Kernel](https://www.nuget.org/packages/Microsoft.SemanticKernel/) is 1.18.1-rc so be sure to install that version or later.
+    > You will need a version of Semantic Kernel that supports Azure.AI.OpenAI version 2. Semantic Kernel 1.17.2 only supports version 1 of the library, so you will need to use 1.18 or later. As of the time of writing, [the latest version of Semantic Kernel](https://www.nuget.org/packages/Microsoft.SemanticKernel/) will be sufficient.
 
 4. Tag each of the methods in the `DatabaseService` class as kernel functions and provide a description for each method and input parameter.
 5. The `AppConfig.cs` file contains a simple configuration class. Update the `Program.cs` file to use this configuration class to build out a configuration. Add in any user secrets associated with the (implicit) `Program` class as well as environment variables, and then validate the configuration.
@@ -181,11 +181,8 @@ The key tasks are as follows:
 - To add the Semantic Kernel NuGet package, you can run the following command from the command line, in the Web API directory containing your `ContosoSuitesWebAPI.csproj` file.
 
   ```powershell
-  dotnet add package Microsoft.SemanticKernel --version 1.18.1-rc
+  dotnet add package Microsoft.SemanticKernel
   ```
-
-  {: .important }
-  > This example specifies a pre-release version of [the Semantic Kernel package](https://www.nuget.org/packages/Microsoft.SemanticKernel/). Once version 1.18 or later has reached a stable   state, you will not need to specify the version.
 
 - In order to tag a method in the `DatabaseService.cs` file, you will need to include the `Microsoft.SemanticKernel` and `System.ComponentModel` namespaces at the top of the file, along with the other **using** statements.
 

docs/03_implement_vector_search_in_cosmos_db_nosql/0301.md

@@ -82,7 +82,7 @@ The key tasks are as follows:
       - Data type: Select **float32**.
       - Distance function: Select **cosine**.
       - Dimensions: Enter **1536**. This is based on the number of dimensions generated by the `ada-text-embedding-002` model in Azure OpenAI.
-      - Index type: Select **quantizedFlat**. Given the number of dimensions being specified, 1536, the `flat` index type will not work, as it only supports a maximum of 505 dimensions for vectors. The `diskANN` index could also be used here.
+      - Index type: Select **diskANN**. Given the number of dimensions being specified, 1536, the `flat` index type will not work, as it only supports a maximum of 505 dimensions for vectors. The `quantizedFlat` index could also be used here. `diskANN` is a more efficient index type, but given the amount of data we are working with in this lab, you likely will not notice any difference in performance.
     - Select **OK** to create the container.
 
 </details>

docs/04_implement_audio_transcription/0403.md

@@ -56,7 +56,7 @@ The key tasks are as follows:
       - Data type: Select **float32**.
       - Distance function: Select **cosine**.
       - Dimensions: Enter **1536**. This is based on the number of dimensions generated by the `ada-text-embedding-002` model in Azure OpenAI.
-      - Index type: Select **quantizedFlat**. Given the number of dimensions being specified, 1536, the `flat` index type will not work, as it only supports a maximum of 505 dimensions for vectors. The `diskANN` index could also be used here.
+      - Index type: Select **diskANN**. Given the number of dimensions being specified, 1536, the `flat` index type will not work, as it only supports a maximum of 505 dimensions for vectors. The `quantizedFlat` index could also be used here. `diskANN` is a more efficient index type, but given the amount of data we are working with in this lab, you likely will not notice any difference in performance.
     - Select **OK** to create the container.
 - The completed version of the `make_azure_openai_embedding_request()` function is as follows: