Merge branch 'main' of github.com:MicrosoftDocs/azure-ai-docs-pr into 245675-data-aoai-sec

Author: Blackmist

articles/ai-services/openai/dall-e-quickstart.md

@@ -5,11 +5,11 @@ description: Learn how to get started generating images with Azure OpenAI Servic
 #services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-openai
-ms.custom: devx-track-python, devx-track-dotnet, devx-track-extended-java, devx-track-go, devx-track-js
+ms.custom: devx-track-python, devx-track-dotnet, devx-track-extended-java, devx-track-go, devx-track-js, devx-track-ts
 ms.topic: quickstart
 author: PatrickFarley
 ms.author: pafarley
-ms.date: 08/21/2024
+ms.date: 09/06/2024
 zone_pivot_groups: openai-quickstart-dall-e
 ---
 

articles/ai-services/openai/gpt-v-quickstart.md

@@ -5,7 +5,7 @@ description: Use this article to get started using Azure OpenAI to deploy and us
 services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-openai
-ms.custom: devx-track-python
+ms.custom: devx-track-python, devx-track-js, devx-track-ts
 ms.topic: quickstart
 author: PatrickFarley
 ms.author: pafarley
@@ -39,6 +39,12 @@ Get started using GPT-4 Turbo with images with the Azure OpenAI Service.
 
 ::: zone-end
 
+::: zone pivot="programming-language-javascript"
+
+[!INCLUDE [JavaScript quickstart](includes/gpt-v-javascript.md)]
+
+::: zone-end
+
 ## Next steps
 
 * Learn more about these APIs in the [GPT-4 Turbo with Vision how-to guide](./gpt-v-quickstart.md)

articles/ai-services/openai/how-to/json-mode.md

@@ -20,6 +20,9 @@ JSON mode allows you to set the models response format to return a valid JSON ob
 > [!NOTE]
 > While JSON mode is still supported, when possible we recommend using [structured outputs](./structured-outputs.md). Like JSON mode structured outputs generates valid JSON, but with the added benefit that you can constrain the model to use a specific JSON schema.
 
+>[!NOTE]
+> Currently Structured outputs is not supported on [bring your own data](../concepts/use-your-data.md) scenario.
+
 ## JSON mode support
 
 JSON mode is only currently supported with the following models:

articles/ai-services/openai/how-to/structured-outputs.md

@@ -16,6 +16,9 @@ recommendations: false
 
 Structured outputs make a model follow a [JSON Schema](https://json-schema.org/overview/what-is-jsonschema) definition that you provide as part of your inference API call. This is in contrast to the older [JSON mode](./json-mode.md) feature, which guaranteed valid JSON would be generated, but was unable to ensure strict adherence to the supplied schema. Structured outputs is recommended for function calling, extracting structured data, and building complex multi-step workflows.
 
+>[!NOTE]
+> Currently Structured outputs is not supported on [bring your own data](../concepts/use-your-data.md) scenario.
+
 ## Supported models
 
 Currently only `gpt-4o` version: `2024-08-06` supports structured outputs.

articles/ai-services/openai/includes/dall-e-javascript.md

@@ -8,19 +8,30 @@ ms.service: azure-ai-openai
 ms.topic: include
 author: PatrickFarley
 ms.author: pafarley
-ms.date: 08/24/2023
+ms.date: 09/06/2024
 ---
 
 Use this guide to get started generating images with the Azure OpenAI SDK for JavaScript.
 
-[Library source code](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai) | [Package (npm)](https://www.npmjs.com/package/@azure/openai) | [Samples](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/openai/Azure.AI.OpenAI/tests/Samples)
+[Reference documentation](https://platform.openai.com/docs/api-reference/images/create) | [Source code](https://github.com/openai/openai-node) | [Package (npm)](https://www.npmjs.com/package/openai) | [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples)
 
 ## Prerequisites
 
+#### [TypeScript](#tab/typescript)
+
+- An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true)
+- [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule)
+- [TypeScript](https://www.typescriptlang.org/download/)
+- An Azure OpenAI resource created in a supported region (see [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)). For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
+
+
+#### [JavaScript](#tab/javascript)
+
 - An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true)
 - [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule)
 - An Azure OpenAI resource created in a supported region (see [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)). For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
 
+---
 
 ## Setup
 
@@ -39,10 +50,10 @@ npm init
 
 ## Install the client library
 
-Install the Azure OpenAI client library for JavaScript with npm:
+Install the client libraries with:
 
 ```console
-npm install @azure/openai
+npm install openai @azure/identity
 ```
 
 Your app's _package.json_ file will be updated with the dependencies.
@@ -51,43 +62,123 @@ Your app's _package.json_ file will be updated with the dependencies.
 
 Create a new file named _ImageGeneration.js_ and open it in your preferred code editor. Copy the following code into the _ImageGeneration.js_ file:
 
-```javascript
-const { OpenAIClient, AzureKeyCredential } = require("@azure/openai");
+#### [TypeScript](#tab/typescript)
+
+```typescript
+import "dotenv/config";
+import { AzureOpenAI } from "openai";
 
 // You will need to set these environment variables or edit the following values
-const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] ;
-const azureApiKey = process.env["AZURE_OPENAI_API_KEY"] ;
+const endpoint = process.env["AZURE_OPENAI_ENDPOINT"];
+const apiKey = process.env["AZURE_OPENAI_API_KEY"];
+
+// Required Azure OpenAI deployment name and API version
+const apiVersion = "2024-07-01";
+const deploymentName = "dall-e-3";
 
 // The prompt to generate images from
 const prompt = "a monkey eating a banana";
-const size = "256x256";
+const numberOfImagesToGenerate = 1;
+
+function getClient(): AzureOpenAI {
+  return new AzureOpenAI({
+    endpoint,
+    apiKey,
+    apiVersion,
+    deployment: deploymentName,
+  });
+}
+async function main() {
+  console.log("== Image Generation ==");
+
+  const client = getClient();
+
+  const results = await client.images.generate({
+    prompt,
+    size: "1024x1024",
+    n: numberOfImagesToGenerate,
+    model: "",
+    style: "vivid", // or "natural"
+  });
+
+  for (const image of results.data) {
+    console.log(`Image generation result URL: ${image.url}`);
+  }
+}
+
+main().catch((err) => {
+  console.error("The sample encountered an error:", err);
+});
+```
+
+1. Build the application with the following command:
+
+    ```console
+    tsc
+    ```
+
+1. Run the application with the following command:
 
-// The number of images to generate
-const n = 2;
+    ```console
+    node ImageGeneration.js
+    ```
 
+
+#### [JavaScript](#tab/javascript)
+
+```javascript
+require("dotenv/config");
+const { AzureOpenAI } = require("openai");
+
+// You will need to set these environment variables or edit the following values
+const endpoint = process.env["AZURE_OPENAI_ENDPOINT"];
+const apiKey = process.env["AZURE_OPENAI_API_KEY"];
+
+// Required Azure OpenAI deployment name and API version
+const apiVersion = "2024-07-01";
+const deploymentName = "dall-e-3";
+
+// The prompt to generate images from
+const prompt = "a monkey eating a banana";
+const numberOfImagesToGenerate = 1;
+
+function getClient() {
+  return new AzureOpenAI({
+    endpoint,
+    apiKey,
+    apiVersion,
+    deployment: deploymentName,
+  });
+}
 async function main() {
-    console.log("== Batch Image Generation ==");
-  
-    const client = new OpenAIClient(endpoint, new AzureKeyCredential(azureApiKey));
-    const deploymentName = "dall-e";
-    const results = await client.getImages(deploymentName, prompt, { n, size });
-  
-    for (const image of results.data) {
-      console.log(`Image generation result URL: ${image.url}`);
-    }
-    //console.log(`Image generation result URL: ${results.result.status}`);
+  console.log("== Image Generation ==");
+
+  const client = getClient();
+
+  const results = await client.images.generate({
+    prompt,
+    size: "1024x1024",
+    n: numberOfImagesToGenerate,
+    model: "",
+    style: "vivid", // or "natural"
+  });
+
+  for (const image of results.data) {
+    console.log(`Image generation result URL: ${image.url}`);
+  }
 }
 
 main().catch((err) => {
-console.error("The sample encountered an error:", err);
+  console.error("The sample encountered an error:", err);
 });
 ```
 
 Run the script with the following command:
 
 ```console
-node _ImageGeneration.js
+node ImageGeneration.js
 ```
+---
 
 ## Output