Azure OpenAI

Azure OpenAI is a great alternative to accessing the best models including GPT-4 and more in your private environments. Portkey provides complete support for Azure OpenAI.

With Portkey, you can take advantage of features like fast AI gateway access, observability, prompt management, and more, all while ensuring the secure management of your LLM API keys through a virtual key system.

Provider Slug**: azure-openai**

Portkey SDK Integration with Azure OpenAI

Portkey provides a consistent API to interact with models from various providers. To integrate Azure OpenAI with Portkey:

First, add your Azure details to Portkey's Virtual Keys

Here's a step-by-step guide:

1. Request access to Azure OpenAI here.

2. Create a resource in the Azure portal here. (This will be your Resource Name)

3. Deploy a model in Azure OpenAI Studio here. (This will be your Deployment Name)

4. Select your Foundation Model from the dropdowon on the modal.

5. Now, on Azure OpenAI studio, go to any playground (chat or completions), click on a UI element called "View code". Note down the API version & API key from here. (This will be your Azure API Version & Azure API Key)

When you input these details, the foundation model will be auto populated. More details in this guide.

If you do not want to add your Azure details to Portkey vault, you can also directly pass them while instantiating the Portkey client. More on that here.

Now, let's make a request using this virtual key!

1. Install the Portkey SDK

Add the Portkey SDK to your application to interact with Azure OpenAI's API through Portkey's gateway.

NodeJS

npm install --save portkey-ai

Python

pip install portkey-ai

2. Initialize Portkey with the Virtual Key

Set up Portkey with your virtual key as part of the initialization configuration. You can create a virtual key for Azure in the Portkey UI.

NodeJS SDK

import Portkey from 'portkey-ai'
 
const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
    virtualKey: "AZURE_VIRTUAL_KEY" // Your Azure Virtual Key
})

Python SDK

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
    virtual_key="AZURE_VIRTUAL_KEY"   # Replace with your virtual key for Azure
)

3. Invoke Chat Completions with Azure OpenAI

Use the Portkey instance to send requests to your Azure deployments. You can also override the virtual key directly in the API call if needed.

NodeJS SDK

const chatCompletion = await portkey.chat.completions.create({
    messages: [{ role: 'user', content: 'Say this is a test' }],
    model: 'gpt4', // This would be your deployment or model name
});

console.log(chatCompletion.choices);

Python SDK

completion = portkey.chat.completions.create(
    messages= [{ "role": 'user', "content": 'Say this is a test' }],
    model= 'custom_model_name'
)

print(completion.choices)

Managing Azure OpenAI Prompts

You can manage all prompts to Azure OpenAI in the Prompt Library. All the current models of OpenAI are supported and you can easily start testing different prompts.

Once you're ready with your prompt, you can use the portkey.prompts.completions.create interface to use the prompt in your application.

Image Generation

Portkey supports multiple modalities for Azure OpenAI and you can make image generation requests through Portkey's AI Gateway the same way as making completion calls.

Portkey NodeJS

import Portkey from 'portkey-ai'
 
const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY",
    virtualKey: "DALL-E_VIRTUAL_KEY" // Referencing a Dall-E Azure deployment with Virtual Key
})

const image = await portkey.images.generate({
  prompt:"Lucy in the sky with diamonds",
  size:"1024x1024"
})

Portkey Python

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",  
    virtual_key="DALL-E_VIRTUAL_KEY"   # Referencing a Dall-E Azure deployment with Virtual Key
)

image = portkey.images.generate(
  prompt="Lucy in the sky with diamonds",
  size="1024x1024"
)

Portkey's fast AI gateway captures the information about the request on your Portkey Dashboard. On your logs screen, you'd be able to see this request with the request and response.

Log view for an image generation request on Azure OpenAI

More information on image generation is available in the API Reference.

---

Making Requests Without Virtual Keys

Here's how you can pass your Azure OpenAI details & secrets directly without using the Virutal Keys feature.

Key Mapping

In a typical Azure OpenAI request,

curl https://.openai.azure.com/openai/deployments//chat/completions?api-version= \
  -H "Content-Type: application/json" \
  -H "api-key: " \
  -d '{"messages": [{"role": "system", "content": "Hello"}] }'

Parameter	Node SDK	Python SDK	REST Headers
AZURE RESOURCE NAME	azureResourceName	azure_resource_name	x-portkey-azure-resource-name
AZURE DEPLOYMENT NAME	azureDeploymentId	azure_deployment_id	x-portkey-azure-deployment-id
API VERSION	azureApiVersion	azure_api_version	x-portkey-azure-api-version
AZURE API KEY	Authorization: "Bearer + {API_KEY}"	Authorization = "Bearer + {API_KEY}"	Authorization
AZURE MODEL NAME	azureModelName	azure_model_name	x-portkey-azure-model-name

Example

Node

import Portkey from 'portkey-ai'
 
const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY",
    provider: "azure-openai",
    azureResourceName: "AZURE_RESOURCE_NAME",
    azureDeploymentId: "AZURE_DEPLOYMENT_NAME",
    azureApiVersion: "AZURE_API_VERSION",
    azureModelName: "AZURE_MODEL_NAME"
    Authorization: "Bearer API_KEY"
})

Python

from portkey_ai import Portkey

portkey = Portkey(
    api_key = "PORTKEY_API_KEY",  
    provider = "azure-openai",
    azure_resource_name = "AZURE_RESOURCE_NAME",
    azure_deployment_id = "AZURE_DEPLOYMENT_NAME",
    azure_api_version = "AZURE_API_VERSION",
    azure_model_name = "AZURE_MODEL_NAME",
    Authorization = "Bearer API_KEY"
)

REST

curl https://api.portkey.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AZURE_OPENAI_API_KEY" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-provider: azure-openai" \
  -H "x-portkey-azure-resource-name: $AZURE_RESOURCE_NAME" \
  -H "x-portkey-azure-deployment-id: $AZURE_DEPLOYMENY_ID" \
  -H "x-portkey-azure-model-name: $AZURE_MODEL_NAME" \
  -H "x-portkey-azure-api-version: $AZURE_API_VERSION" \
  -d '{
    "model": "gpt-4o",
    "messages": [{"role": "user","content": "Hello!"}]
  }'

How to Pass JWT (JSON Web Tokens)

If you have configured fine-grained access for Azure OpenAI and need to use JSON web token (JWT) in the Authorization header instead of the regular API Key, you can use the forwardHeaders parameter to do this.

Node

import Portkey from 'portkey-ai'
 
const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY",
    provider: "azure-openai",
    azureResourceName: "AZURE_RESOURCE_NAME",
    azureDeploymentId: "AZURE_DEPLOYMENT_NAME",
    azureApiVersion: "AZURE_API_VERSION",
    azureModelName: "AZURE_MODEL_NAME",
    authorization: "Bearer JWT_KEY", // Pass your JWT here
    forwardHeaders: [ "authorization" ]
})

Python

import Portkey from 'portkey-ai'
 
const portkey = new Portkey({
    api_key = "PORTKEY_API_KEY",
    provider = "azure-openai",
    azure_resource_name = "AZURE_RESOURCE_NAME",
    azure_deployment_id = "AZURE_DEPLOYMENT_NAME",
    azure_api_version = "AZURE_API_VERSION",
    azure_model_name = "AZURE_MODEL_NAME",
    authorization = "Bearer API_KEY", # Pass your JWT here
    forward_headers= [ "authorization" ]
)

For further questions on custom Azure deployments or fine-grained access tokens, reach out to us on [email protected]

Next Steps

The complete list of features supported in the SDK are available on the link below.

SDK

You'll find more information in the relevant sections:

1. Add metadata to your requests

2. Add gateway configs to your Azure OpenAI requests

3. Tracing Azure OpenAI requests

4. Setup a fallback from OpenAI to Azure OpenAI APIs