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This will help you get started with AzureAIChatCompletionsModel chat models. For detailed documentation of all AzureAIChatCompletionsModel features and configurations, head to the API reference The AzureAIChatCompletionsModel class uses the Azure AI Foundry SDK. AI Foundry has several chat models, including AzureOpenAI, Cohere, Llama, Phi-3/4, and DeepSeek-R1, among others. You can find information about their latest models and their costs, context windows, and supported input types in the Azure docs.

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Overview

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Integration details

ClassPackageLocalSerializableJS supportDownloadsVersion
AzureAIChatCompletionsModellangchain-azure-ai❌✅✅PyPI - DownloadsPyPI - Version

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Model features

Tool callingStructured outputJSON modeImage inputAudio inputVideo inputToken-level streamingNative asyncToken usageLogprobs
✅✅✅✅❌❌✅✅✅✅

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Setup

To access AzureAIChatCompletionsModel models, you’ll need to create an Azure account, get an API key, and install the langchain-azure-ai integration package.

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Credentials

Head to the Azure docs to see how to create your deployment and generate an API key. Once your model is deployed, you click the ‘get endpoint’ button in AI Foundry. This will show you your endpoint and api key. Once you’ve done this, set the AZURE_AI_CREDENTIAL and AZURE_AI_ENDPOINT environment variables: 
import getpass
import os

if not os.getenv("AZURE_AI_CREDENTIAL"):
    os.environ["AZURE_AI_CREDENTIAL"] = getpass.getpass(
        "Enter your AzureAIChatCompletionsModel API key: "
    )

if not os.getenv("AZURE_AI_ENDPOINT"):
    os.environ["AZURE_AI_ENDPOINT"] = getpass.getpass(
        "Enter your model endpoint: "
    )
If you want to get automated tracing of your model calls, you can also set your LangSmith API key by uncommenting below: 
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

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Installation

The LangChain AzureAIChatCompletionsModel integration lives in the langchain-azure-ai package: 
pip install -qU langchain-azure-ai

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Instantiation

Now we can instantiate our model object and generate chat completions: 
from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel

llm = AzureAIChatCompletionsModel(
    model_name="gpt-4",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2,
)

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Invocation

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg

AIMessage(content="J'adore programmer.", additional_kwargs={}, response_metadata={'model': 'gpt-4o-2024-05-13', 'token_usage': {'input_tokens': 31, 'output_tokens': 4, 'total_tokens': 35}, 'finish_reason': 'stop'}, id='run-c082dffd-b1de-4b3f-943f-863836663ddb-0', usage_metadata={'input_tokens': 31, 'output_tokens': 4, 'total_tokens': 35})

print(ai_msg.content)

J'adore programmer.

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API reference

For detailed documentation of all AzureAIChatCompletionsModel features and configurations, head to the API reference: python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html
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