Azure OpenAI Access Request: A Step-by-Step Guide

To request access to Azure OpenAI, you'll need to submit a request through the Azure portal. This involves selecting the "Request access" button on the Azure OpenAI page.

First, ensure you have an Azure subscription and are logged in to the Azure portal. You can create a free account if you don't already have one.

Next, navigate to the Azure OpenAI page and click on the "Request access" button. You'll be prompted to provide some basic information about your organization and the purpose of your request.

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To request access to Azure OpenAI Service, you should first fill out a registration form as part of Microsoft's commitment to responsible AI. This is a necessary step before proceeding.

You'll need to request access for the specific models you want to use, such as GPT-3.5, GPT-3.5 Turbo, GPT-4, GPT-4 Turbo, and/or Embeddings Models.

By requesting access, you'll be able to use these models to follow along with the guide.

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To request access to Azure OpenAI Service, you should first fill out a registration form as part of Microsoft's commitment to responsible AI.

The registration form is necessary to access GPT-3.5, GPT-3.5 Turbo, GPT-4, GPT-4 Turbo, and/or Embeddings Models.

You can request access to one or more of these models to follow along with the tutorials and examples.

Filling out the registration form is a straightforward process that requires some basic information about yourself and your organization.

You'll need to provide details such as your name, email address, and job title, as well as information about your organization.

The registration form is designed to be easy to use and should only take a few minutes to complete.

Once you've submitted the form, you'll receive a confirmation email with instructions on how to access the Azure OpenAI Service.

To authenticate using Microsoft Entra ID, you'll need to create a TokenCredential bean in your configuration.

This bean is what allows an OpenAIClient instance to be created using the token credentials, making it a crucial step in the process.

The TokenCredential bean is available if you're using Microsoft Entra ID, formerly known as Azure Active Directory.

Having this bean available will automatically create an OpenAIClient instance, streamlining the authentication process.

If you're using Microsoft Entra ID, make sure to create this bean in your configuration to take advantage of its benefits.

By following these simple steps, you can ensure a smooth authentication process using Microsoft Entra ID.

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To request access to OpenAI, you'll need to get an OpenAI API key. This key will automatically set the endpoint to api.openai.com/v1.

You can obtain an OpenAI API key by following the instructions in the documentation. The key will be used to authenticate with the OpenAI service.

To configure the client to use OpenAI, you'll need to set the spring.ai.azure.openai.openai-api-key property with your OpenAI Key. This will replace the need to set the spring.ai.azure.openai.api-key property with your Azure OpenAI credentials.

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The spring.ai.azure.openai.chat.options.deployment-name property should be set to the name of the OpenAI model you wish to use. This is because OpenAI uses a Model Name, not a Deployment Name.

Here is a summary of the key settings you'll need to make:

By setting these properties, you'll be able to access OpenAI and use the OpenAI model you've chosen.

To get started with Azure OpenAI, you can start by exploring its advanced features, such as Azure Cognitive Search and vector databases with embedding models. You can also create an Azure OpenAI model using your own data stored in Azure Data Lake Gen2.

To do this, you'll need to create an Azure AI Search and an Azure Blob Storage with a container. Then, you can deploy your trained model to the Azure OpenAI instance you created. The model deployment process will provide you with an endpoint and a key, which you can use to call the Azure OpenAI service effectively.

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Here's a step-by-step guide to get you started:

With these steps, you'll be well on your way to accessing Azure OpenAI and exploring its capabilities.

You can easily set up and utilize the Azure OpenAI service by following a few simple steps.

First, create an Azure OpenAI instance as the first step. This will allow you to start using the service right away.

To test the service, use the search box to input a simple math query like ‘2 + 2 =?’. You should see the result displayed as ‘4’.

You can also test the service with a location query, such as ‘Where can I pick a train in Northampton?’. The system should provide the address of the train location in Northampton.

If you have the necessary permissions, you can deploy the Azure OpenAI instance with a single click, generating a Web App for you.

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The deployment process will also provide you with an endpoint and a key, enabling you to call the Azure OpenAI service effectively.

Here are the steps to create an Azure OpenAI instance:

To get started with Azure OpenAI, you'll need an Azure account with AzureML enabled. This will give you access to the advanced AI models and tools.

You'll also need to select the right region for both Azure OpenAI and Azure AI Search. Not all regions have Azure OpenAI available, so make sure to check the options.

Choose the Basic SKU for Azure AI Search to ensure you have the necessary capabilities for semantic search. If you're using Bring Your Own Data, pay attention to the search plan you select.

Here are some key features to explore as you become more familiar with Azure OpenAI:

Azure OpenAI is a powerful tool that allows developers to tap into the capabilities of OpenAI's models. It provides a scalable and secure way to integrate AI into applications.

To access Azure OpenAI, you'll need to submit a request, which will be reviewed and approved by Microsoft. This process typically takes a few days to a week.

Once approved, you can start using Azure OpenAI to build and deploy AI-powered applications.

Azure OpenAI is a cloud-based platform that allows developers to integrate OpenAI's AI models into their applications.

It's powered by Microsoft Azure, which provides a scalable and secure infrastructure for deploying and managing AI workloads.

Developers can use Azure OpenAI to build a wide range of applications, from chatbots and virtual assistants to content generation and decision-making tools.

The platform offers a variety of AI models, including text, image, and audio models, which can be used for tasks such as language translation, image recognition, and speech synthesis.

These models are highly customizable and can be fine-tuned for specific use cases, allowing developers to tailor the AI's performance to their application's needs.

Azure OpenAI also provides a range of tools and services for building, deploying, and managing AI models, including model training, deployment, and monitoring.

The Azure OpenAI model is quite impressive, especially when it comes to multimodal capabilities. It can understand and process information from various sources, including text, images, audio, and other data formats.

The Azure OpenAI gpt-4o model offers multimodal support, allowing it to incorporate a list of base64-encoded images or image URLs with the message. This is facilitated by the Spring AI's Message interface, which introduces the Media type.

The Media type encompasses data and details regarding media attachments in messages, utilizing Spring's org.springframework.util.MimeType and a java.lang.Object for the raw media data. This makes it easy to pass multiple images as well.

You can pass a single image, like the multimodal.test.png image, along with a text message, and the model will generate a response. For example, if you pass the multimodal.test.png image with the text message "Explain what do you see on this picture?", the model will generate a response.

The Azure OpenAI model can fuse user text with an image using the GPT_4_O model, as seen in the code example from OpenAiChatModelIT.java. This shows how the model can understand and respond to multimodal inputs.

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To access Azure OpenAI, you'll need to configure your project's dependencies and settings. Add the spring-ai-azure-openai dependency to your project's Maven pom.xml file or Gradle build.gradle file.

You'll also need to obtain your Azure OpenAI endpoint and API key from the Azure Portal. This involves setting two configuration properties: spring.ai.azure.openai.api-key and spring.ai.azure.openai.endpoint. You can set these properties by exporting environment variables or directly in your application.

Here's a breakdown of the required configuration properties:

Auto-configuration is a breeze with Spring AI. You can enable it by adding a dependency to your project's Maven pom.xml or Gradle build.gradle build files.

To auto-configure the Azure OpenAI Chat Client, you'll need to add the following dependency: you can reference the Spring Boot Azure AI Starter and its Autoconfiguration feature for the default configurations.

The Azure configuration aligns with the default configurations of the Spring Boot Azure AI Starter and its Autoconfiguration feature. This means you can rely on these configurations for your project.

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To get started, you'll need to obtain your Azure OpenAI endpoint and api-key from the Azure OpenAI Service section on the Azure Portal. Once you have these, you can set them as configuration properties.

Here are the configuration properties you'll need to set:

Remember to update the configuration property if you use a different Deployment Name.

To enable the AzureOpenAiChatModel, you'll need to add the spring-ai-azure-openai dependency to your project's Maven pom.xml file or Gradle build.gradle file.

The gpt-4o is actually the Deployment Name as presented in the Azure AI Portal. This is an important detail to keep in mind when configuring your model.

To set up the Azure API Key and Endpoint, you'll need to obtain your Azure OpenAI endpoint and api-key from the Azure OpenAI Service section on the Azure Portal. This involves setting two configuration properties: spring.ai.azure.openai.api-key and spring.ai.azure.openai.endpoint.

You can set these configuration properties by exporting environment variables, which is a convenient way to manage your settings. Alternatively, you can update the configuration property accordingly if you use a different Deployment Name.

Here are the configuration properties you'll need to set:

You can configure the default options for Azure OpenAI chat on start-up using the AzureOpenAiChatOptions.java file or the spring.ai.azure.openai.chat.options.* properties.

The AzureOpenAiChatOptions.java file provides model configurations, such as the model to use, the temperature, and the frequency penalty.

You can override the default options at runtime by adding new options to the Prompt call. This allows you to tailor the chat experience for specific requests.

For example, you can override the default model and temperature by adding new options to the Prompt call.

You can also use a portable ChatOptions instance, created with the ChatOptionsBuilder#builder(). This provides a flexible way to manage chat options.

Here's a brief overview of the runtime options: