Azure OpenAI Streaming Python Development and Deployment Guide

To get started with Azure OpenAI Streaming Python development, you'll need to install the Azure OpenAI SDK. This can be done using pip with the command `pip install azure-ai-openai`.

The Azure OpenAI SDK provides a Python client library for interacting with the Azure OpenAI service. This allows you to use the service's features, such as text generation and image processing, in your Python applications.

One of the key benefits of using the Azure OpenAI SDK is that it provides a simple and intuitive API for accessing the service's features. This makes it easy to get started with development and deployment.

The Azure OpenAI Streaming Python guide provides a step-by-step walkthrough of how to use the Azure OpenAI SDK to develop and deploy a Python application that uses the service's streaming capabilities.

To get started with Azure OpenAI streaming in Python, you'll need to set up your Azure OpenAI service. First, create an instance of Azure OpenAI services, which may require approval from Microsoft, taking around 24 hours to process.

You'll need to submit a special form to get approved, which can be found here. Access to OpenAI Services is granted on a per-subscription basis, so ensure you're using the same subscription for your new service.

Once approved, go to Azure OpenAI services and click Create, answering a few basic questions like pricing tier and location. If you're implementing a proof of concept, the location might not be crucial, but for business cases, it could be vital.

During the creation process, Azure will ask for the name of the instance, which will become part of the API endpoint URL. Note down the keys and endpoint URL found in the Keys and Endpoint tab on the left, in Resource Management section.

To deploy models, navigate to the Model Deployments tab on the left, under Resource Management. Select a model (like GPT-3.5 or GPT-4) and give it a name, which will be used in API calls.

Here's a quick rundown of the necessary tools and versions:

Before running the deployment, ensure you've cloned the repo to your local drive, opened a PowerShell terminal or command line, and logged in to your Azure subscription using azd auth login.

To get started with Azure OpenAI streaming in Python, you'll need to install the Azure Developer CLI (AZD) if you haven't already. This can be done by following the documentation home for AZD.

You'll also need to be approved for Azure OpenAI services on your Azure subscription, which can take around 24 hours. This involves applying through the provided link.

First, make sure you have one of the required versions of Python installed: 3.9, 3.10, or 3.11. I personally use 3.11.

Next, use NVM to install the latest version of Node, which is currently 20.11.0.

Now, let's get to the steps:

During the deployment process, you'll need to choose your subscription and location, with eastus being a recommended option.

The deployment process typically takes around 10-20 minutes, although document loading can take longer.

You can stream responses from the Azure OpenAI API using the openai package, making it easy to process large amounts of data.

The openai package allows you to stream responses from the API by passing a stream argument, which is particularly useful when dealing with large responses.

To enable streaming, simply set the stream argument to True, and the response type will change to an asynchronous generator.

This means you can use async for to process each chunk of the response, making it easier to handle large amounts of data.

Streaming responses can be especially helpful when working with the ChatCompletion API, where responses are returned in chunks.

By streaming responses, you can avoid loading the entire response into memory at once, which can be a significant performance improvement.

The openai package makes it easy to handle streaming responses, so you can focus on processing the data rather than worrying about memory constraints.

The demo details are as follows: 64 contributors have worked on this project as of 2/23/2024. The project was created on 2/5/2023 and has been updated within the last month. You can find the link to the documentation in the Project’s Readme and the Github project link is https://github.com/Azure-Samples/azure-search-openai-demo.

To get started with the demo, you'll need an Azure account, access to Azure OpenAI, and specific prerequisites including Git, Azure Developer CLI, Python 3.9, 3.10, or 3.11, and Node 14+.

The following technologies are used in this demo: Python, Node, React, Azure Web Apps, Azure OpenAI, Azure Storage, Azure AI Search (also known as Azure Search and Azure Cognitive Search), and Azure AI Document Intelligence (also known as Form Recognizer).

Here are the prerequisites to get the demo working: