Skip to main content

Authorization

Follow this guide to create a new Azure AI Studio credential that will allow Nexla to authenticate to and exchange data with your Azure AI Studio deployment.
azure_llm_api_auth.png

Azure AI Studio

Prerequisites​

Before creating an Azure AI Studio credential in Nexla, you need to create an Azure AI Studio deployment and obtain an API key. Azure AI Studio uses API Key authentication with Bearer token authentication for secure access to language models.

Azure AI Studio Deployment Setup​

Azure AI Studio provides access to language models through Azure deployments. You need to create a deployment in Azure AI Studio and obtain the necessary credentials to access the models.

  1. Access Azure AI Studio: Sign in to your Azure AI Studio portal or access it through the Azure Portal.

  2. Create or Select a Deployment: Create a new deployment or select an existing deployment of a language model (e.g., GPT-4, GPT-3.5, or other available models). The deployment provides access to the model through a dedicated endpoint.

  3. Obtain API Key: Navigate to your deployment settings or the Azure AI Studio resource settings to obtain your API key. The API key is used to authenticate requests to your Azure AI Studio deployment.

  4. Identify Deployment Details: Note the following information about your deployment:

    • Model Name: The name of the model deployment (e.g., gpt-4o, gpt-35-turbo)
    • Region: The Azure region where your deployment is located (e.g., eastus2, westeurope, asia)
    • Model Version: The API version for the model (e.g., 2023-03-15-preview, 2024-02-15-preview)
    • Base URL: The full endpoint URL for your deployment, typically in the format https://{model_name}.{region}.models.ai.azure.com

  5. Review Model Access: Ensure your Azure subscription and deployment have the necessary permissions and quotas to access the language models you want to use.

For detailed information about creating deployments and managing Azure AI Studio resources, see the Azure AI Studio documentation.

Create an Azure AI Studio Credential​

  • To create a new Azure AI Studio credential, after selecting the data source/destination type, click the Add Credential tile to open the Add New Credential overlay.

New Credential Overlay – Azure AI Studio

AzureAIStudioCred.png

Credential Name & Description​

  1. Enter a name for the credential in the Credential Name field and a short, meaningful description in the Credential Description field.

    Resource descriptions are recommended but are not required. They should be used provide information about the resource purpose, data freshness, etc. that can help the owner and other users efficiently understand and utilize the resource.

Azure AI Studio Configuration​

  1. Enter the name of the model you want to use in the Model Name field. This should be the name of your Azure AI Studio model deployment (e.g., gpt-4o, gpt-35-turbo, gpt-4). The model name is used to construct the API endpoint URL for your deployment.

  2. Enter your Azure AI Studio API key in the API Key Value field. This should be the API key obtained from your Azure AI Studio deployment or resource settings. The API key is used for Bearer token authentication in the Authorization header for all API requests.

    The API key provides secure access to your Azure AI Studio deployment. Keep your API key secure and do not share it publicly or commit it to version control systems. The API key is sent in the Authorization: Bearer {api_key} header for all API requests. If you suspect your API key has been compromised, regenerate it immediately in your Azure AI Studio resource settings. For detailed information about Azure AI Studio authentication, see the Azure AI Studio documentation.

  3. Enter the API version for the model in the Model Version field. This should be the API version associated with your model deployment (e.g., 2023-03-15-preview, 2024-02-15-preview, 2024-06-01). The API version determines which features and response formats are available.

  4. Enter the Azure region where your deployment is located in the Region field. This should be the Azure region identifier (e.g., eastus2, westeurope, asia, southeastasia). The region is used to construct the API endpoint URL. The default value is eastus2 if not specified.

  5. Enter the complete base URL for your Azure AI Studio deployment in the Base Url for the LLM Call field. This should be the full endpoint URL in the format https://{model_name}.{region}.models.ai.azure.com (e.g., https://gpt-4o.eastus2.models.ai.azure.com). The base URL is used as the endpoint for API calls to your deployment.

    The base URL must match your Azure AI Studio deployment endpoint. Ensure the URL format is correct and includes the model name and region. The base URL is combined with the API path (e.g., /chat/completions) and API version query parameter to form the complete endpoint URL for API requests.

Save the Credential​

  1. Once all of the relevant steps in the above sections have been completed, click the Save button at the bottom of the overlay to save the configured credential.

  2. The newly added credential will now appear in a tile on the Authenticate screen during data source/destination creation and can be selected for use with a new data source or destination.