Azure embeddings example

Note: There is a newer version of the openai library available. See https://github.com/openai/openai-python/discussions/742

This example will cover embeddings using the Azure OpenAI service.

Setup

First, we install the necessary dependencies.

! pip install "openai>=0.28.1,<1.0.0"

For the following sections to work properly we first have to setup some things. Let's start with the api_base and api_version. To find your api_base go to https://portal.azure.com, find your resource and then under "Resource Management" -> "Keys and Endpoints" look for the "Endpoint" value.

We next have to setup the api_type and api_key. We can either get the key from the portal or we can get it through Microsoft Active Directory Authentication. Depending on this the api_type is either azure or azure_ad.

Setup: Portal

Let's first look at getting the key from the portal. Go to https://portal.azure.com, find your resource and then under "Resource Management" -> "Keys and Endpoints" look for one of the "Keys" values.

Note: In this example, we configured the library to use the Azure API by setting the variables in code. For development, consider setting the environment variables instead:

OPENAI_API_BASE
OPENAI_API_KEY
OPENAI_API_TYPE
OPENAI_API_VERSION

(Optional) Setup: Microsoft Active Directory Authentication

Let's now see how we can get a key via Microsoft Active Directory Authentication. Uncomment the following code if you want to use Active Directory Authentication instead of keys from the portal.

A token is valid for a period of time, after which it will expire. To ensure a valid token is sent with every request, you can refresh an expiring token by hooking into requests.auth:

Deployments

In this section we are going to create a deployment that we can use to create embeddings.

Deployments: Create manually

Let's create a deployment using the text-similarity-curie-001 model. Create a new deployment by going to your Resource in your portal under "Resource Management" -> "Model deployments".

Deployments: Listing

Now because creating a new deployment takes a long time, let's look in the subscription for an already finished deployment that succeeded.

Embeddings

Now let's send a sample embedding to the deployment.