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Use Azure OpenAI SDK

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In addition to REST APIs covered in the previous unit, users can also access Azure OpenAI models through C# and Python SDKs. The same functionality is available through both REST and these SDKs.

Note

Before interacting with the API using either SDK, you must create an Azure OpenAI resource in the Azure portal, deploy a model in that resource, and retrieve your endpoint and keys. Check out the Getting started with Azure OpenAI Service to learn how to do that.

For both SDKs covered in this unit, you need the endpoint and a key from your Azure OpenAI resource, and the name you gave for your deployed model. In the following code snippets, the following placeholders are used:

Placeholder name	Value
YOUR_ENDPOINT_NAME	This base endpoint is found in the Keys & Endpoint section in the Azure portal. It's the base endpoint of your resource, such as https://sample.openai.azure.com/.
YOUR_API_KEY	Keys are found in the Keys & Endpoint section in the Azure portal. You can use either key for your resource.
YOUR_DEPLOYMENT_NAME	This deployment name is the name provided when you deployed your model in the Azure OpenAI Studio.

Install libraries

First, install the client library for your preferred language. The C# SDK is a .NET adaptation of the REST APIs and built specifically for Azure OpenAI, however it can be used to connect to Azure OpenAI resources or non-Azure OpenAI endpoints. The Python SDK is built and maintained by OpenAI.

dotnet add package Azure.AI.OpenAI --prerelease

pip install openai

Configure app to access Azure OpenAI resource

Configuration for each language varies slightly, but both require the same parameters to be set. The necessary parameters are endpoint, key, and the name of your deployment, which is called the engine when sending your prompt to the model.

Add the library to your app, and set the required parameters for your client.

// Add OpenAI library
using Azure.AI.OpenAI;

// Define parameters and initialize the client
string endpoint = "<YOUR_ENDPOINT_NAME>";
string key = "<YOUR_API_KEY>";
string deploymentName = "<YOUR_DEPLOYMENT_NAME>"; 

OpenAIClient client = new OpenAIClient(new Uri(endpoint), new AzureKeyCredential(key));

# Add OpenAI library
from openai import AzureOpenAI

deployment_name = '<YOUR_DEPLOYMENT_NAME>' 

# Initialize the Azure OpenAI client
client = AzureOpenAI(
        azure_endpoint = '<YOUR_ENDPOINT_NAME>', 
        api_key='<YOUR_API_KEY>',  
        api_version="20xx-xx-xx" #  Target version of the API, such as 2024-02-15-preview
        )

Call Azure OpenAI resource

Once you've configured your connection to Azure OpenAI, send your prompt to the model.

// Build completion options object
ChatCompletionsOptions chatCompletionsOptions = new ChatCompletionsOptions()
{
    Messages =
    {
        new ChatRequestSystemMessage("You are a helpful AI bot."),
        new ChatRequestUserMessage("What is Azure OpenAI?"),
    },
    DeploymentName = deploymentName
};

// Send request to Azure OpenAI model
ChatCompletions response = client.GetChatCompletions(chatCompletionsOptions);

// Print the response
string completion = response.Choices[0].Message.Content;
Console.WriteLine("Response: " + completion + "\n");

response = client.chat.completions.create(
    model=deployment_name,
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is Azure OpenAI?"}
    ]
)
generated_text = response.choices[0].message.content

# Print the response
print("Response: " + generated_text + "\n")

The response object contains several values, such as total_tokens and finish_reason. The completion from the response object will be similar to the following completion:

"Azure OpenAI is a cloud-based artificial intelligence (AI) service that offers a range of tools and services for developing and deploying AI applications. Azure OpenAI provides a variety of services for training and deploying machine learning models, including a managed service for training and deploying deep learning models, a managed service for deploying machine learning models, and a managed service for managing and deploying machine learning models."

In both C# and Python, your call can include optional parameters including temperature and max_tokens. Examples of using those parameters are included in this module's lab.

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