Update DocumentDB vector quickstarts for new OpenAI infra

azure/documentdb/quickstart-agent-go.md

@@ -38,6 +38,9 @@ You can use the Azure Developer CLI to create the required Azure resources by ru
     - Add your client IP address to the cluster's firewall rules. For more information, see [Grant access from your IP address](/azure/documentdb/how-to-configure-firewall#grant-access-from-your-ip-address).
   - For passwordless authentication, Role Based Access Control (RBAC) enabled
 
+> [!NOTE]
+> The infrastructure deploys Azure OpenAI with the **Standard** SKU (not GlobalStandard). You can customize the SKU and model parameters using `azd env set` before deployment. See the sample's README for available parameters.
+
 ### Development tools
 
 - [Go](https://golang.org/dl/) 1.22 or later
@@ -120,6 +123,9 @@ AZURE_DOCUMENTDB_COLLECTION=hotel_data
 AZURE_DOCUMENTDB_INDEX_NAME=vectorIndex
 ```
 
+> [!TIP]
+> Unlike some databases, DocumentDB allows you to create and drop vector indexes at any time after container creation. You don't need to define the vector indexing policy at container creation time.
+
 ## Project structure
 
 The project follows the standard Go project layout. Your directory structure should look like the following structure:

azure/documentdb/quickstart-agent-nodejs.md

@@ -87,6 +87,15 @@ Use the Azure Developer CLI (`azd`) to provision the required Azure OpenAI and D
 
 > [!TIP]
 > Run `azd env get-values` at any time to view the current environment values.
+>
+> To export these values to a `.env` file, run:
+>
+> ```bash
+> azd env get-values > .env
+> ```
+
+> [!NOTE]
+> The infrastructure deploys Azure OpenAI with the **Standard** SKU (not GlobalStandard). You can customize the SKU and model parameters using `azd env set` before deployment. See the sample's README for available parameters.
 
 ## Configure environment variables
 
@@ -186,6 +195,9 @@ VECTOR_INDEX_ALGORITHM=vector-ivf
 EMBEDDING_DIMENSIONS=1536
 ```
 
+> [!TIP]
+> Unlike some databases, DocumentDB allows you to create and drop vector indexes at any time after container creation. You don't need to define the vector indexing policy at container creation time.
+
 ## Project structure
 
 The project follows a standard Node.js/TypeScript project layout. Your directory structure should look like the following structure:

azure/documentdb/quickstart-dotnet-vector-search.md

@@ -28,6 +28,9 @@ The app uses a sample hotel dataset in a JSON file with pre-calculated vectors f
 
 [!INCLUDE[Prerequisites - Vector Search Quickstart](includes/prerequisite-quickstart-vector-search-model.md)]
 
+> [!NOTE]
+> The infrastructure deploys Azure OpenAI with the **Standard** SKU (not GlobalStandard). You can customize the SKU and model parameters using `azd env set` before deployment. See the sample's README for available parameters.
+
 - [.NET 8.0 SDK](https://dotnet.microsoft.com/download/dotnet/8.0) or later
 
     - [C# extension for Visual Studio Code](https://marketplace.visualstudio.com/items?itemName=ms-dotnettools.csharp)

azure/documentdb/quickstart-go-vector-search.md

@@ -23,6 +23,9 @@ Find the [sample code](https://github.com/Azure-Samples/documentdb-samples/tree/
 
 [!INCLUDE[Prerequisites - Vector Search Quickstart](includes/prerequisite-quickstart-vector-search-model.md)]
 
+> [!NOTE]
+> The infrastructure deploys Azure OpenAI with the **Standard** SKU (not GlobalStandard). You can customize the SKU and model parameters using `azd env set` before deployment. See the sample's README for available parameters.
+
 - [Go](https://golang.org/dl/) version 1.24 or later
 
 ## Create data file with vectors
@@ -171,6 +174,9 @@ vector-search-quickstart
 
 ----
 
+> [!TIP]
+> Unlike some databases, DocumentDB allows you to create and drop vector indexes at any time after container creation. You don't need to define the vector indexing policy at container creation time.
+
 ## Create code for vector search
 
 ### [DiskANN](#tab/tab-diskann)

azure/documentdb/quickstart-java-vector-search.md

@@ -27,6 +27,9 @@ The app uses a sample hotel dataset in a JSON file with pre-calculated vectors f
 
 [!INCLUDE[Prerequisites - Vector Search Quickstart](includes/prerequisite-quickstart-vector-search-model.md)]
 
+> [!NOTE]
+> The infrastructure deploys Azure OpenAI with the **Standard** SKU (not GlobalStandard). You can customize the SKU and model parameters using `azd env set` before deployment. See the sample's README for available parameters.
+
 - [Java 21](/java/openjdk/download) or later
 
 - [Maven 3.6](https://maven.apache.org/download.cgi) or later

azure/documentdb/quickstart-nodejs-vector-search.md

@@ -28,6 +28,9 @@ Find the [sample code](https://github.com/Azure-Samples/documentdb-samples/tree/
 
 [!INCLUDE[Prerequisites - Vector Search Quickstart](includes/prerequisite-quickstart-vector-search-model.md)]
 
+> [!NOTE]
+> The infrastructure deploys Azure OpenAI with the **Standard** SKU (not GlobalStandard). You can customize the SKU and model parameters using `azd env set` before deployment. See the sample's README for available parameters.
+
 - [Node.js LTS](https://nodejs.org/download/)
 
 - [TypeScript](https://www.typescriptlang.org/download): Install TypeScript globally:
@@ -180,6 +183,9 @@ touch src/utils.ts
 
 ----
 
+> [!TIP]
+> Unlike some databases, DocumentDB allows you to create and drop vector indexes at any time after container creation. You don't need to define the vector indexing policy at container creation time.
+
 ## Create code for vector search
 
 ### [DiskANN](#tab/tab-diskann)

azure/documentdb/quickstart-python-vector-search.md

@@ -29,6 +29,9 @@ Find the [sample code](https://github.com/Azure-Samples/documentdb-samples/tree/
 
 [!INCLUDE[Prerequisites - Vector Search Quickstart](includes/prerequisite-quickstart-vector-search-model.md)]
 
+> [!NOTE]
+> The infrastructure deploys Azure OpenAI with the **Standard** SKU (not GlobalStandard). You can customize the SKU and model parameters using `azd env set` before deployment. See the sample's README for available parameters.
+
 - [Python](https://www.python.org/downloads/) 3.9 or greater
 
 ## Create data file with vectors
@@ -156,6 +159,9 @@ touch src/utils.py
 
 ----
 
+> [!TIP]
+> Unlike some databases, DocumentDB allows you to create and drop vector indexes at any time after container creation. You don't need to define the vector indexing policy at container creation time.
+
 ## Create code for vector search