Azure embeddings example¶

In this example we'll try to go over all operations for embeddings that can be done using the Azure endpoints.
This example focuses on embeddings but also touches some other operations that are also available using the API. This example is meant to be a quick way of showing simple operations and is not meant as a tutorial.

In [ ]:

import openai
from openai import cli

import openai from openai import cli

Setup¶

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.

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openai.api_version = '2022-12-01'
openai.api_base = '' # Please add your endpoint here

openai.api_version = '2022-12-01' openai.api_base = '' # Please add your endpoint here

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.

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openai.api_type = 'azure'
openai.api_key = ''  # Please add your api key here

openai.api_type = 'azure' openai.api_key = '' # Please add your api key here

(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.

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# from azure.identity import DefaultAzureCredential

# default_credential = DefaultAzureCredential()
# token = default_credential.get_token("https://cognitiveservices.azure.com/.default")

# openai.api_type = 'azure_ad'
# openai.api_key = token.token

# from azure.identity import DefaultAzureCredential # default_credential = DefaultAzureCredential() # token = default_credential.get_token("https://cognitiveservices.azure.com/.default") # openai.api_type = 'azure_ad' # openai.api_key = token.token

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".

(Optional) Deployments: Create programatically¶

We can also create a deployment using code:

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model = "text-similarity-curie-001"

# Now let's create the deployment
print(f'Creating a new deployment with model: {model}')
result = openai.Deployment.create(model=model, scale_settings={"scale_type":"standard"})
deployment_id = result["id"]

model = "text-similarity-curie-001" # Now let's create the deployment print(f'Creating a new deployment with model: {model}') result = openai.Deployment.create(model=model, scale_settings={"scale_type":"standard"}) deployment_id = result["id"]

(Optional) Deployments: Retrieving¶

Now let's check the status of the newly created deployment

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print(f'Checking for deployment status.')
resp = openai.Deployment.retrieve(id=deployment_id)
status = resp["status"]
print(f'Deployment {deployment_id} is with status: {status}')

print(f'Checking for deployment status.') resp = openai.Deployment.retrieve(id=deployment_id) status = resp["status"] print(f'Deployment {deployment_id} is with status: {status}')

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.

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print('While deployment running, selecting a completed one that supports embeddings.')
deployment_id = None
result = openai.Deployment.list()
for deployment in result.data:
    if deployment["status"] != "succeeded":
        continue
    
    model = openai.Model.retrieve(deployment["model"])
    if model["capabilities"]["embeddings"] != True:
        continue
    
    deployment_id = deployment["id"]
    break

if not deployment_id:
    print('No deployment with status: succeeded found.')
else:
    print(f'Found a succeeded deployment that supports embeddings with id: {deployment_id}.')

print('While deployment running, selecting a completed one that supports embeddings.') deployment_id = None result = openai.Deployment.list() for deployment in result.data: if deployment["status"] != "succeeded": continue model = openai.Model.retrieve(deployment["model"]) if model["capabilities"]["embeddings"] != True: continue deployment_id = deployment["id"] break if not deployment_id: print('No deployment with status: succeeded found.') else: print(f'Found a succeeded deployment that supports embeddings with id: {deployment_id}.')

Embeddings¶

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

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embeddings = openai.Embedding.create(deployment_id=deployment_id,
                                     input="The food was delicious and the waiter...")
                                
print(embeddings)

embeddings = openai.Embedding.create(deployment_id=deployment_id, input="The food was delicious and the waiter...") print(embeddings)

(Optional) Deployments: Delete¶

Finally let's delete the deployment

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print(f'Deleting deployment: {deployment_id}')
openai.Deployment.delete(sid=deployment_id)

print(f'Deleting deployment: {deployment_id}') openai.Deployment.delete(sid=deployment_id)