Step by Step Guide to Building an Azure OpenAI Service API Integration in Ruby

Introduction

Hey there, fellow Ruby enthusiast! Ready to dive into the world of AI with Azure OpenAI Service? You're in for a treat. We'll be using the nifty azure_openai_client package to make our lives easier. Buckle up!

Prerequisites

Before we jump in, make sure you've got:

• A Ruby environment that's up and running
• An Azure OpenAI Service account with an API key (if you don't have one, go grab it!)

Installation

Let's start by adding the azure_openai_client gem to your project. It's as simple as:

gem install azure_openai_client

Or add it to your Gemfile:

gem 'azure_openai_client'

Configuration

Now, let's set things up:

require 'azure_openai_client'

client = AzureOpenAIClient.new(
  api_key: ENV['AZURE_OPENAI_API_KEY'],
  endpoint: ENV['AZURE_OPENAI_ENDPOINT'],
  api_version: '2023-05-15'
)

Pro tip: Use environment variables for your API key and endpoint. Keep it secure!

Basic Usage

Time to make your first API call:

response = client.completions.create(
  model: 'text-davinci-003',
  prompt: 'Hello, Azure OpenAI!',
  max_tokens: 50
)

puts response.choices.first.text

Easy peasy, right?

Advanced Features

Streaming Responses

For those long responses, streaming is your friend:

client.completions.create(
  model: 'text-davinci-003',
  prompt: 'Write a short story',
  max_tokens: 200,
  stream: true
) do |chunk|
  print chunk.choices.first.text
end

Managing Different Models

Switching models is a breeze:

response = client.completions.create(
  model: 'text-curie-001',
  prompt: 'Explain quantum computing',
  max_tokens: 100
)

Handling Rate Limits

Be nice to the API:

begin
  response = client.completions.create(...)
rescue AzureOpenAIClient::RateLimitError => e
  puts "Oops! Rate limit hit. Retrying in #{e.retry_after} seconds."
  sleep e.retry_after
  retry
end

Error Handling

Always be prepared:

begin
  response = client.completions.create(...)
rescue AzureOpenAIClient::APIError => e
  puts "Error: #{e.message}"
end

Best Practices

• Keep your API key secret. Seriously.
• Use environment variables or a secure key management system.
• Implement proper error handling and retries.
• Consider caching responses for frequently asked queries.

Conclusion

And there you have it! You're now ready to harness the power of Azure OpenAI in your Ruby projects. Remember, with great power comes great responsibility. Use it wisely, and happy coding!

Want to dive deeper? Check out the Azure OpenAI documentation and the azure_openai_client GitHub repo.

Now go forth and build something awesome!