Getting Started with Azure OpenAI

Welcome to the exciting world of Azure OpenAI! This guide will walk you through the essential steps to get started with leveraging powerful large language models (LLMs) like GPT-3.5 and GPT-4 within the secure and scalable Azure cloud environment.

What is Azure OpenAI?

Azure OpenAI Service provides REST API access to OpenAI's powerful language models, including the GPT-3.5, GPT-4, and embedding models. These models can be used to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. Azure OpenAI offers enterprise-grade security, reliability, and compliance, making it ideal for building production-ready AI solutions.

Prerequisites

Before you begin, ensure you have the following:

An Azure Subscription. If you don't have one, you can sign up for a free trial.
Access to Azure OpenAI Service. Due to high demand, access is currently limited. You can apply for access.
A browser with internet access.

Step 1: Provision an Azure OpenAI Resource

Once you have access, the first step is to create an Azure OpenAI resource in your Azure subscription.

Navigate to the Azure portal.
Click on "Create a resource".
Search for "Azure OpenAI".
Click "Create".
Fill in the required details:

Subscription: Select your Azure subscription.
Resource group: Create a new one or select an existing one.
Region: Choose a region that supports Azure OpenAI.
Name: A unique name for your resource.
Pricing tier: Select "Standard S0".

Click "Review + create", and then "Create".

Step 2: Deploy a Model

After your Azure OpenAI resource is provisioned, you need to deploy a specific model to start using it. The Azure OpenAI Studio provides a user-friendly interface for this.

Go to your Azure OpenAI resource in the Azure portal.
Click on "Go to Azure OpenAI Studio".
In the Studio, navigate to the "Deployments" section.
Click "Create new deployment".
Select a model (e.g., gpt-35-turbo, text-davinci-003, or gpt-4 if available).
Give your deployment a name.
Click "Deploy".

Step 3: Get Your API Key and Endpoint

To interact with your deployed model programmatically, you'll need your API key and the endpoint URL.

In your Azure OpenAI resource in the Azure portal, go to "Keys and Endpoint".
Copy one of the keys (Key 1 or Key 2).
Copy the "Endpoint" URL.

Security Note: Treat your API keys like passwords. Do not expose them in client-side code or public repositories. Use environment variables or secure secret management solutions.

Step 4: Making Your First API Call

Now you can make calls to your deployed model. Here's a simple example using Python and the OpenAI Python client library:

                
import os
from openai import AzureOpenAI

# Load environment variables or hardcode securely
AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT")
AZURE_OPENAI_KEY = os.getenv("AZURE_OPENAI_KEY")
AZURE_OPENAI_DEPLOYMENT_NAME = os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME") # Your deployment name

client = AzureOpenAI(
    api_key=AZURE_OPENAI_KEY,
    api_version="2023-05-15", # Or the latest stable API version
    azure_endpoint=AZURE_OPENAI_ENDPOINT
)

try:
    response = client.chat.completions.create(
        model=AZURE_OPENAI_DEPLOYMENT_NAME,
        messages=[
            {
                "role": "system",
                "content": "You are a helpful assistant."
            },
            {
                "role": "user",
                "content": "What is Azure OpenAI?"
            }
        ]
    )
    print(response.choices[0].message.content)
except Exception as e:
    print(f"An error occurred: {e}")

                
            

To run this code:

Install the library: pip install openai
Set your environment variables for AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_KEY, and AZURE_OPENAI_DEPLOYMENT_NAME.
Execute the Python script.

Further Exploration

Azure OpenAI offers a wide range of capabilities beyond basic text generation. You can explore:

Fine-tuning: Adapt models to specific tasks and domains.
Embeddings: Generate vector representations of text for semantic search and comparison.
Image generation: Utilize models like DALL-E for creating unique images from text prompts.
Responsible AI: Implement content filtering and safety features.

The official Azure OpenAI documentation is an invaluable resource for in-depth information and advanced topics.

Congratulations! You've taken the first step towards harnessing the power of advanced AI with Azure OpenAI. Happy coding!