Azure

The azure provider is an interface to Azure. It shares configuration settings with the OpenAI provider.

Setup

There are two ways to authenticate with Azure:

Option 1: API Key Authentication

Set the AZURE_API_KEY environment variable and configure your deployment:

providers:
  - id: azure:chat:deploymentNameHere
    config:
      apiHost: 'xxxxxxxx.openai.azure.com'

Option 2: Client Credentials Authentication

Set the following environment variables or config properties:

• AZURE_CLIENT_ID / azureClientId
• AZURE_CLIENT_SECRET / azureClientSecret
• AZURE_TENANT_ID / azureTenantId

Optionally, you can also set:

• AZURE_AUTHORITY_HOST / azureAuthorityHost (defaults to 'https://login.microsoftonline.com')
• AZURE_TOKEN_SCOPE / azureTokenScope (defaults to 'https://cognitiveservices.azure.com/.default')

Then configure your deployment:

providers:
  - id: azure:chat:deploymentNameHere
    config:
      apiHost: 'xxxxxxxx.openai.azure.com'

Option 3: Azure CLI Authentication

Authenticate with Azure CLI using az login before running promptfoo. This is the fallback option if the parameters for the previous options are not provided.

Optionally, you can also set:

• AZURE_TOKEN_SCOPE / azureTokenScope (defaults to 'https://cognitiveservices.azure.com/.default')

Then configure your deployment:

providers:
  - id: azure:chat:deploymentNameHere
    config:
      apiHost: 'xxxxxxxx.openai.azure.com'

Provider Types

• azure:chat:<deployment name> - uses the given deployment (for chat endpoints such as gpt-35-turbo, gpt-4)
• azure:completion:<deployment name> - uses the given deployment (for completion endpoints such as gpt-35-instruct)

Environment Variables

The Azure OpenAI provider supports the following environment variables:

Environment Variable	Config Key	Description	Required
AZURE_API_KEY	apiKey	Your Azure OpenAI API key	No*
AZURE_API_HOST	apiHost	API host	No
AZURE_API_BASE_URL	apiBaseUrl	API base URL	No
AZURE_BASE_URL	apiBaseUrl	Alternative API base URL	No
AZURE_DEPLOYMENT_NAME	-	Default deployment name	Yes
AZURE_CLIENT_ID	azureClientId	Azure AD application client ID	No*
AZURE_CLIENT_SECRET	azureClientSecret	Azure AD application client secret	No*
AZURE_TENANT_ID	azureTenantId	Azure AD tenant ID	No*
AZURE_AUTHORITY_HOST	azureAuthorityHost	Azure AD authority host	No
AZURE_TOKEN_SCOPE	azureTokenScope	Azure AD token scope	No

* Either AZURE_API_KEY OR the combination of AZURE_CLIENT_ID, AZURE_CLIENT_SECRET, and AZURE_TENANT_ID must be provided.

Note: For API URLs, you only need to set one of AZURE_API_HOST, AZURE_API_BASE_URL, or AZURE_BASE_URL. If multiple are set, the provider will use them in that order of preference.

Default Deployment

If AZURE_DEPLOYMENT_NAME is set, it will be automatically used as the default deployment when no other provider is configured. This makes Azure OpenAI the default provider when:

1. No OpenAI API key is present (OPENAI_API_KEY is not set)
2. Azure authentication is configured (either via API key or client credentials)
3. AZURE_DEPLOYMENT_NAME is set

For example, if you have these environment variables set:

AZURE_DEPLOYMENT_NAME=gpt-4
AZURE_API_KEY=your-api-key
AZURE_API_HOST=your-host.openai.azure.com

Or these client credential environment variables:

AZURE_DEPLOYMENT_NAME=gpt-4
AZURE_CLIENT_ID=your-client-id
AZURE_CLIENT_SECRET=your-client-secret
AZURE_TENANT_ID=your-tenant-id
AZURE_API_HOST=your-host.openai.azure.com

Then Azure OpenAI will be used as the default provider for all operations including:

• Dataset generation
• Grading
• Suggestions
• Synthesis

Because embedding models are distinct from text generation, to set an embedding provider you must specify AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME.

Note that any moderation tasks will still use the OpenAI API.

Configuration

The YAML configuration can override environment variables and set additional parameters:

providers:
  - id: azure:chat:deploymentNameHere
    config:
      apiHost: 'xxxxxxxx.openai.azure.com'
      # Authentication (Option 1: API Key)
      apiKey: 'your-api-key'

      # Authentication (Option 2: Client Credentials)
      azureClientId: 'your-azure-client-id'
      azureClientSecret: 'your-azure-client-secret'
      azureTenantId: 'your-azure-tenant-id'
      azureAuthorityHost: 'https://login.microsoftonline.com' # Optional
      azureTokenScope: 'https://cognitiveservices.azure.com/.default' # Optional

      # OpenAI parameters
      temperature: 0.5
      max_tokens: 1024

tip

All other OpenAI provider environment variables and configuration properties are supported.

Using client credentials

To use client credentials for authentication with Azure, first install the peer dependency:

npm i @azure/identity

Then set the following configuration variables:

providers:
  - id: azure:chat:deploymentNameHere
    config:
      apiHost: 'xxxxxxxx.openai.azure.com'
      azureClientId: 'your-azure-client-id'
      azureClientSecret: 'your-azure-client-secret'
      azureTenantId: 'your-azure-tenant-id'
      azureAuthorityHost: 'https://login.microsoftonline.com' # Optional
      azureTokenScope: 'https://cognitiveservices.azure.com/.default' # Optional

These credentials will be used to obtain an access token for the Azure OpenAI API.

The azureAuthorityHost defaults to 'https://login.microsoftonline.com' if not specified. The azureTokenScope defaults to 'https://cognitiveservices.azure.com/.default', the scope required to authenticate with Azure Cognitive Services.

You must also install a peer dependency from Azure:

npm i @azure/identity

Model-graded tests

Model-graded assertions such as factuality or llm-rubric use gpt-4.1-2025-04-14 by default. If you are using Azure, you must override the grader to point to your Azure deployment.

The easiest way to do this for all your test cases is to add the defaultTest property to your config:

defaultTest:
  options:
    provider:
      id: azure:chat:gpt-4-deployment-name
      config:
        apiHost: 'xxxxxxx.openai.azure.com'

However, you can also do this for individual assertions:

# ...
assert:
  - type: llm-rubric
    value: Do not mention that you are an AI or chat assistant
    provider:
      id: azure:chat:xxxx
      config:
        apiHost: 'xxxxxxx.openai.azure.com'

Or individual tests:

# ...
tests:
  - vars:
      # ...
    options:
      provider:
        id: azure:chat:xxxx
        config:
          apiHost: 'xxxxxxx.openai.azure.com'
    assert:
      - type: llm-rubric
        value: Do not mention that you are an AI or chat assistant

Similarity

The similar assertion type requires an embedding model such as text-embedding-ada-002. Be sure to specify a deployment with an embedding model, not a chat model, when overriding the grader.

AI Services

You may also specify deployment_id and dataSources, used to integrate with the Azure AI Search API.

providers:
  - id: azure:chat:deploymentNameHere
    config:
      apiHost: 'xxxxxxxx.openai.azure.com'
      deployment_id: 'abc123'
      dataSources:
        - type: AzureCognitiveSearch
          parameters:
            endpoint: '...'
            key: '...'
            indexName: '...'

(The inconsistency in naming convention between deployment_id and dataSources reflects the actual naming in the Azure API.)

Configuration

These properties can be set under the provider config key`:

General config

Name	Description
apiHost	API host.
apiBaseUrl	Base URL of the API (used instead of host).
apiKey	API key.
apiVersion	API version.

Azure-specific config

Name	Description
azureClientId	Azure identity client ID.
azureClientSecret	Azure identity client secret.
azureTenantId	Azure identity tenant ID.
azureAuthorityHost	Azure identity authority host.
azureTokenScope	Azure identity token scope.
deployment_id	Azure cognitive services deployment ID.
dataSources	Azure cognitive services parameter for specifying data sources.

OpenAI config:

Name	Description
o1	Set to true if your Azure deployment uses an o1 model. Since Azure allows custom model naming, this flag is required to properly handle o1 models which do not support certain parameters. (Deprecated, use isReasoningModel instead)
isReasoningModel	Set to true if your Azure deployment uses a reasoning model (o1, o3-mini, etc.). This is the preferred flag over the deprecated o1 flag.
max_completion_tokens	Maximum number of tokens to generate for reasoning models. Only used when isReasoningModel or o1 is set to true.
reasoning_effort	Allows you to control how long the reasoning model thinks before answering, 'low', 'medium' or 'high'. Only used when isReasoningModel or o1 is set to true.
temperature	Controls randomness of the output. Not supported for reasoning models and will be automatically excluded when isReasoningModel or o1 is true.
max_tokens	Maximum number of tokens to generate. Not supported for reasoning models and will be automatically excluded when isReasoningModel or o1 is true.
top_p	Controls nucleus sampling.
frequency_penalty	Penalizes new tokens based on their frequency.
presence_penalty	Penalizes new tokens based on their presence.
best_of	Generates multiple outputs and chooses the best.
functions	Specifies functions available for use.
function_call	Controls automatic function calling.
response_format	Specifies the format of the response.
stop	Specifies stop sequences for the generation.
passthrough	Anything under passthrough will be sent as a top-level request param

Using Reasoning Models (o1, o3-mini)

Azure OpenAI now supports reasoning models like o1 and o3-mini. These models operate differently from standard models with specific requirements:

1. They use max_completion_tokens instead of max_tokens
2. They don't support temperature (it's ignored)
3. They accept a reasoning_effort parameter ('low', 'medium', 'high')

Since Azure allows custom deployment names that don't necessarily reflect the underlying model type, you must explicitly set the isReasoningModel flag to true in your configuration when using reasoning models. This works with both chat and completion endpoints:

# For chat endpoints
providers:
  - id: azure:chat:my-o3-mini-deployment
    config:
      apiHost: 'xxxxxxxx.openai.azure.com'
      # Set this flag to true for reasoning models (o1, o3-mini)
      isReasoningModel: true
      # Use max_completion_tokens instead of max_tokens
      max_completion_tokens: 25000
      # Optional: Set reasoning effort (default is 'medium')
      reasoning_effort: 'medium'

# For completion endpoints
providers:
  - id: azure:completion:my-o1-deployment
    config:
      apiHost: 'xxxxxxxx.openai.azure.com'
      isReasoningModel: true
      max_completion_tokens: 25000
      reasoning_effort: 'high'

Note: The o1 flag is still supported for backward compatibility, but isReasoningModel is preferred as it more clearly indicates its purpose.

Using Variables with Reasoning Effort

You can use variables in your configuration to dynamically adjust the reasoning effort based on your test cases:

# Configure different reasoning efforts based on test variables
prompts:
  - 'Solve this complex math problem: {{problem}}'

providers:
  - id: azure:chat:my-o3-mini-deployment
    config:
      apiHost: 'xxxxxxxx.openai.azure.com'
      isReasoningModel: true
      max_completion_tokens: 25000
      # This will be populated from the test case variables
      reasoning_effort: '{{effort_level}}'

tests:
  - vars:
      problem: 'What is the integral of x²?'
      effort_level: 'low'
  - vars:
      problem: 'Prove the Riemann hypothesis'
      effort_level: 'high'

Environment Variables

These parameters can be configured directly in your configuration file as shown above.

Troubleshooting

If you encounter this error when using reasoning models:

API response error: unsupported_parameter Unsupported parameter: 'max_tokens' is not supported with this model. Use 'max_completion_tokens' instead.

This means you're using a reasoning model without setting the isReasoningModel flag. Update your config as shown above.

Using DeepSeek Models

Azure AI supports DeepSeek models such as DeepSeek-R1. Like other reasoning models, these require specific configuration:

1. Set isReasoningModel: true
2. Use max_completion_tokens instead of max_tokens
3. Set API version to '2024-05-01-preview' (or latest available)

promptfooconfig.yaml

providers:
  - id: azure:chat:DeepSeek-R1
    config:
      apiHost: 'your-deployment-name.services.ai.azure.com'
      apiVersion: '2024-05-01-preview'
      isReasoningModel: true
      max_completion_tokens: 2048
      reasoning_effort: 'medium' # Options: low, medium, high

For model-graded assertions, you can configure your defaultTest to use the same provider:

defaultTest:
  options:
    provider:
      id: azure:chat:DeepSeek-R1
      config:
        apiHost: 'your-deployment-name.services.ai.azure.com'
        apiVersion: '2024-05-01-preview'
        isReasoningModel: true
        max_completion_tokens: 2048

Adjust reasoning_effort to control response quality vs. speed: low for faster responses, medium for balanced performance (default), or high for more thorough reasoning on complex tasks.

Assistants

To eval an OpenAI assistant on Azure, first create a deployment for the assistant and create an assistant in the Azure web UI.

Then install the peer dependency locally:

npm i @azure/openai-assistants

Next, record the assistant ID and set up your provider like so:

providers:
  - id: azure:assistant:asst_E4GyOBYKlnAzMi19SZF2Sn8I
    config:
      apiHost: yourdeploymentname.openai.azure.com

Be sure to replace the assistant ID and the name of your deployment.

Function Tools with Assistants

Azure OpenAI Assistants support custom function tools. You can define functions in your configuration and provide callback implementations to handle them:

providers:
  - id: azure:assistant:your_assistant_id
    config:
      apiHost: your-resource-name.openai.azure.com
      # Load function tool definition
      tools: file://tools/weather-function.json
      # Define function callback inline
      functionToolCallbacks:
        # Use an external file
        get_weather: file://callbacks/weather.js:getWeather
        # Or use an inline function
        get_weather: |
          async function(args) {
            try {
              const parsedArgs = JSON.parse(args);
              const location = parsedArgs.location;
              const unit = parsedArgs.unit || 'celsius';
              // Function implementation...
              return JSON.stringify({
                location,
                temperature: 22,
                unit,
                condition: 'sunny'
              });
            } catch (error) {
              return JSON.stringify({ error: String(error) });
            }
          }

Using Vector Stores with Assistants

Azure OpenAI Assistants support vector stores for enhanced file search capabilities. To use a vector store with your assistant, first create a vector store in the Azure Portal or via the API, then configure your assistant to use it:

providers:
  - id: azure:assistant:your_assistant_id
    config:
      apiHost: your-resource-name.openai.azure.com
      # Add tools for file search
      tools:
        - type: file_search
      # Configure vector store IDs
      tool_resources:
        file_search:
          vector_store_ids:
            - 'your_vector_store_id'
      # Optional parameters
      temperature: 1
      top_p: 1
      apiVersion: '2024-05-01-preview'

Make sure to:

1. Set up a tool with type: file_search
2. Configure the tool_resources.file_search.vector_store_ids array with your vector store IDs
3. Set the appropriate apiVersion (recommended: 2024-05-01-preview or later)

Simple Example

Here's an example of a simple full assistant eval:

prompts:
  - 'Write a tweet about {{topic}}'

providers:
  - id: azure:assistant:your_assistant_id
    config:
      apiHost: your-resource-name.openai.azure.com

tests:
  - vars:
      topic: bananas

For complete working examples of Azure OpenAI Assistants with various tool configurations, check out the azure-openai-assistant example directory.

See the guide on How to eval OpenAI assistants for more information on how to compare different models, instructions, and more.

See Also

• OpenAI Provider - The base provider that Azure shares configuration with
• Evaluating Assistants - Learn how to compare different models and instructions
• Azure OpenAI Assistant Examples - Complete working examples with various tool configurations