Discover the Power of Azure OpenAI Model with Python

Table of Contents

1. Introduction
2. Deploying a Model in Azure
3. Consuming a Deployed Model in Azure
4. Setting up the Development Environment
5. Initializing the Variables
6. Making a Call to the Completion Endpoint
7. Using the OpenAI SDK
8. Printing the Result
9. Conclusion
10. Frequently Asked Questions

Article

Introduction

In this article, we will explore how to Consume a model that has already been deployed in Azure. We will learn the step-by-step process of setting up the development environment, initializing the variables, making a call to the completion endpoint, and printing the result. By following these instructions, You will be able to effectively consume the models deployed in Azure and utilize them in your Python applications.

Deploying a Model in Azure

Before we can consume a deployed model in Azure, it is important to first understand how to deploy a model. If you are not familiar with the deployment process, I recommend referring to my previous video tutorial where I explain the process in Detail.

Consuming a Deployed Model in Azure

Once you have successfully deployed a model in Azure, you can proceed with consuming it. Let's start by setting up the development environment.

Setting up the Development Environment

To consume a deployed model, we need to initialize a few important variables. First, we need to provide the deployment name. This can be obtained from the Azure portal.

Next, we need the API base, which is the endpoint we will be hitting. This endpoint is also available in the Azure portal. Additionally, we will need the API key, which can be obtained from the "Keys and Endpoints" section of the Azure portal.

Once we have these variables initialized, we can proceed with making a call to the completion endpoint.

Initializing the Variables

To initialize the variables, we import the necessary libraries, such as OpenAI and OS. We then assign values to the variables: deployment name, API base, API key, and API version. The API version should be the latest one available, which can be obtained from the documentation.

Making a Call to the Completion Endpoint

To make a call to the completion endpoint, we can use either the web API or the OpenAI SDK. In this article, we will use the OpenAI SDK as it is a popular choice.

To make the call, we define the prompt, which is the input for the completion. We can use the same prompt that we used earlier. We then Create a variable to store the result of the API call using the OpenAI SDK. We can also define additional parameters such as temperature and max tokens.

Finally, we print the result of the API call using the OpenAI SDK.

Using the OpenAI SDK

The OpenAI SDK provides a convenient way to Interact with the deployed models in Azure. By using the SDK, we can easily make API calls and retrieve the results.

Printing the Result

After making the API call using the OpenAI SDK, we can print the result. The result will be displayed in the format specified in the API response.

Conclusion

In this article, we have learned how to consume a model that has been deployed in Azure. We covered the steps for setting up the development environment, initializing the variables, making a call to the completion endpoint, and printing the result. By following these steps, you will be able to effectively utilize the deployed models in your Python applications.

Frequently Asked Questions

Q: Can I deploy multiple models in Azure? A: Yes, you can deploy multiple models in Azure. Each model will have its own deployment name and API endpoint.

Q: How can I obtain the API key for my deployed model? A: The API key for your deployed model can be obtained from the "Keys and Endpoints" section of the Azure portal.

Q: Are there any limitations on the size of the models that can be deployed in Azure? A: Yes, there are limitations on the size of the models that can be deployed in Azure. It is important to consider the size and complexity of the model when deploying it.

Q: Can I use languages other than Python to consume a deployed model in Azure? A: Yes, you can use other programming languages to consume a deployed model in Azure. However, in this article, we focused on using Python.

Q: What are some advanced scenarios that can be explored when consuming deployed models in Azure? A: Some advanced scenarios include fine-tuning the deployed models, integrating them with other Azure services, and scaling the deployment to handle higher loads.