Azure AI Model Inference API | Azure AI Studio

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The Azure AI Model Inference is an API that exposes a common set of capabilities for foundational models and that can be used by developers to consume predictions from a diverse set of models in a uniform and consistent way. Developers can talk with different models deployed in Azure AI Studio without changing the underlying code they are using.

Benefits

Foundational models, such as language models, have indeed made remarkable strides in recent years. These advancements have revolutionized various fields, including natural language processing and computer vision, and they have enabled applications like chatbots, virtual assistants, and language translation services.

While foundational models excel in specific domains, they lack a uniform set of capabilities. Some models are better at specific task and even across the same task, some models may approach the problem in one way while others in another. Developers can benefit from this diversity by using the right model for the right job allowing them to:

  • Improve the performance in a specific downstream task.
  • Use more efficient models for simpler tasks.
  • Use smaller models that can run faster on specific tasks.
  • Compose multiple models to develop intelligent experiences.

Having a uniform way to consume foundational models allow developers to realize all those benefits without sacrificing portability or changing the underlying code.

Availability

The Azure AI Model Inference API is available in the following models:

Models deployed to serverless API endpoints:

Models deployed to managed inference:

The API is compatible with Azure OpenAI model deployments.

Capabilities

The following section describes some of the capabilities the API exposes. For a full specification of the API, view the reference section.

Modalities

The API indicates how developers can consume predictions for the following modalities:

  • Get info: Returns the information about the model deployed under the endpoint.
  • Text embeddings: Creates an embedding vector representing the input text.
  • Text completions: Creates a completion for the provided prompt and parameters.
  • Chat completions: Creates a model response for the given chat conversation.
  • Image embeddings: Creates an embedding vector representing the input text and image.

Inference SDK support

You can use streamlined inference clients in the language of your choice to consume predictions from models running the Azure AI model inference API.

Install the package azure-ai-inference using your package manager, like pip:

pip install azure-ai-inference

Then, you can use the package to consume the model. The following example shows how to create a client to consume chat completions:

import os
from azure.ai.inference import ChatCompletionsClient
from azure.core.credentials import AzureKeyCredential

model = ChatCompletionsClient(
    endpoint=os.environ["AZUREAI_ENDPOINT_URL"],
    credential=AzureKeyCredential(os.environ["AZUREAI_ENDPOINT_KEY"]),
)

Extensibility

The Azure AI Model Inference API specifies a set of modalities and parameters that models can subscribe to. However, some models may have further capabilities that the ones the API indicates. On those cases, the API allows the developer to pass them as extra parameters in the payload.

By setting a header extra-parameters: pass-through, the API will attempt to pass any unknown parameter directly to the underlying model. If the model can handle that parameter, the request completes.

The following example shows a request passing the parameter safe_prompt supported by Mistral-Large, which isn't specified in the Azure AI Model Inference API:

response = model.complete(
    messages=[
        SystemMessage(content="You are a helpful assistant."),
        UserMessage(content="How many languages are in the world?"),
    ],
    model_extras={
        "safe_mode": True
    }
)

Tip

The default value for extra-parameters is error which returns an error if an extra parameter is indicated in the payload. Alternatively, you can set extra-parameters: ignore to drop any unknown parameter in the request. Use this capability in case you happen to be sending requests with extra parameters that you know the model won't support but you want the request to completes anyway. A typical example of this is indicating seed parameter.

Models with disparate set of capabilities

The Azure AI Model Inference API indicates a general set of capabilities but each of the models can decide to implement them or not. A specific error is returned on those cases where the model can't support a specific parameter.

The following example shows the response for a chat completion request indicating the parameter reponse_format and asking for a reply in JSON format. In the example, since the model doesn't support such capability an error 422 is returned to the user.

from azure.ai.inference.models import ChatCompletionsResponseFormat
from azure.core.exceptions import HttpResponseError
import json

try:
    response = model.complete(
        messages=[
            SystemMessage(content="You are a helpful assistant."),
            UserMessage(content="How many languages are in the world?"),
        ],
        response_format={ "type": ChatCompletionsResponseFormat.JSON_OBJECT }
    )
except HttpResponseError as ex:
    if ex.status_code == 422:
        response = json.loads(ex.response._content.decode('utf-8'))
        if isinstance(response, dict) and "detail" in response:
            for offending in response["detail"]:
                param = ".".join(offending["loc"])
                value = offending["input"]
                print(
                    f"Looks like the model doesn't support the parameter '{param}' with value '{value}'"
                )
    else:
        raise ex

Tip

You can inspect the property details.loc to understand the location of the offending parameter and details.input to see the value that was passed in the request.

Content safety

The Azure AI model inference API supports Azure AI Content Safety. When using deployments with Azure AI Content Safety on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions.

The following example shows the response for a chat completion request that has triggered content safety.

from azure.ai.inference.models import AssistantMessage, UserMessage, SystemMessage

try:
    response = model.complete(
        messages=[
            SystemMessage(content="You are an AI assistant that helps people find information."),
            UserMessage(content="Chopping tomatoes and cutting them into cubes or wedges are great ways to practice your knife skills."),
        ]
    )

    print(response.choices[0].message.content)

except HttpResponseError as ex:
    if ex.status_code == 400:
        response = json.loads(ex.response._content.decode('utf-8'))
        if isinstance(response, dict) and "error" in response:
            print(f"Your request triggered an {response['error']['code']} error:\n\t {response['error']['message']}")
        else:
            raise ex
    else:
        raise ex

Getting started

The Azure AI Model Inference API is currently supported in certain models deployed as Serverless API endpoints and Managed Online Endpoints. Deploy any of the supported models and use the exact same code to consume their predictions.

The client library azure-ai-inference does inference, including chat completions, for AI models deployed by Azure AI Studio and Azure Machine Learning Studio. It supports Serverless API endpoints and Managed Compute endpoints (formerly known as Managed Online Endpoints).

Explore our samples and read the API reference documentation to get yourself started.