Azure OpenAI is a Microsoft Azure service that provides powerful language models from OpenAI. This will help you getting started with AzureChatOpenAI chat models. For detailed documentation of all AzureChatOpenAI features and configurations head to the API reference.

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Overview

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Integration details

ClassPackageLocalSerializablePY supportDownloadsVersion
AzureChatOpenAI@langchain/openai❌✅✅NPM - DownloadsNPM - Version

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Model features

See the links in the table headers below for guides on how to use specific features.
Tool callingStructured outputJSON modeImage inputAudio inputVideo inputToken-level streamingToken usageLogprobs
✅✅✅✅❌❌✅✅✅

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Setup

Azure OpenAI is a cloud service to help you quickly develop generative AI experiences with a diverse set of prebuilt and curated models from OpenAI, Meta and beyond. LangChain.js supports integration with Azure OpenAI using the new Azure integration in the OpenAI SDK. You can learn more about Azure OpenAI and its difference with the OpenAI API on this page.

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Credentials

If you don’t have an Azure account, you can create a free account to get started. You’ll also need to have an Azure OpenAI instance deployed. You can deploy a version on Azure Portal following this guide. Once you have your instance running, make sure you have the name of your instance and key. You can find the key in the Azure Portal, under the “Keys and Endpoint” section of your instance. Then, if using Node.js, you can set your credentials as environment variables: 
AZURE_OPENAI_API_INSTANCE_NAME=<YOUR_INSTANCE_NAME>
AZURE_OPENAI_API_DEPLOYMENT_NAME=<YOUR_DEPLOYMENT_NAME>
AZURE_OPENAI_API_KEY=<YOUR_KEY>
AZURE_OPENAI_API_VERSION="2024-02-01"
If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below: 
# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"

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Installation

The LangChain AzureChatOpenAI integration lives in the @langchain/openai package: 

import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>

<Npm2Yarn>
  @langchain/openai @langchain/core
</Npm2Yarn>

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Instantiation

Now we can instantiate our model object and generate chat completions: 
import { AzureChatOpenAI } from "@langchain/openai"

const llm = new AzureChatOpenAI({
    model: "gpt-4o",
    temperature: 0,
    maxTokens: undefined,
    maxRetries: 2,
    azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY, // In Node.js defaults to process.env.AZURE_OPENAI_API_KEY
    azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_API_INSTANCE_NAME, // In Node.js defaults to process.env.AZURE_OPENAI_API_INSTANCE_NAME
    azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME, // In Node.js defaults to process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME
    azureOpenAIApiVersion: process.env.AZURE_OPENAI_API_VERSION, // In Node.js defaults to process.env.AZURE_OPENAI_API_VERSION
})

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Invocation

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

AIMessage {
  "id": "chatcmpl-9qrWKByvVrzWMxSn8joRZAklHoB32",
  "content": "J'adore la programmation.",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "completionTokens": 8,
      "promptTokens": 31,
      "totalTokens": 39
    },
    "finish_reason": "stop"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 31,
    "output_tokens": 8,
    "total_tokens": 39
  }
}

console.log(aiMsg.content)

J'adore la programmation.

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Chaining

We can chain our model with a prompt template like so: 
import { ChatPromptTemplate } from "@langchain/core/prompts"

const prompt = ChatPromptTemplate.fromMessages(
    [
        [
            "system",
            "You are a helpful assistant that translates {input_language} to {output_language}.",
        ],
        ["human", "{input}"],
    ]
)

const chain = prompt.pipe(llm);
await chain.invoke(
    {
        input_language: "English",
        output_language: "German",
        input: "I love programming.",
    }
)

AIMessage {
  "id": "chatcmpl-9qrWR7WiNjZ3leSG4Wd77cnKEVivv",
  "content": "Ich liebe das Programmieren.",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "completionTokens": 6,
      "promptTokens": 26,
      "totalTokens": 32
    },
    "finish_reason": "stop"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 26,
    "output_tokens": 6,
    "total_tokens": 32
  }
}

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Using Azure Managed Identity

If you’re using Azure Managed Identity, you can configure the credentials like this: 
import {
  DefaultAzureCredential,
  getBearerTokenProvider,
} from "@azure/identity";
import { AzureChatOpenAI } from "@langchain/openai";

const credentials = new DefaultAzureCredential();
const azureADTokenProvider = getBearerTokenProvider(
  credentials,
  "https://cognitiveservices.azure.com/.default"
);

const llmWithManagedIdentity = new AzureChatOpenAI({
  azureADTokenProvider,
  azureOpenAIApiInstanceName: "<your_instance_name>",
  azureOpenAIApiDeploymentName: "<your_deployment_name>",
  azureOpenAIApiVersion: "<api_version>",
});

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Using a different domain

If your instance is hosted under a domain other than the default openai.azure.com, you’ll need to use the alternate AZURE_OPENAI_BASE_PATH environment variable. For example, here’s how you would connect to the domain https://westeurope.api.microsoft.com/openai/deployments/{DEPLOYMENT_NAME}: 
import { AzureChatOpenAI } from "@langchain/openai";

const llmWithDifferentDomain = new AzureChatOpenAI({
  temperature: 0.9,
  azureOpenAIApiKey: "<your_key>", // In Node.js defaults to process.env.AZURE_OPENAI_API_KEY
  azureOpenAIApiDeploymentName: "<your_deployment_name>", // In Node.js defaults to process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME
  azureOpenAIApiVersion: "<api_version>", // In Node.js defaults to process.env.AZURE_OPENAI_API_VERSION
  azureOpenAIBasePath:
    "https://westeurope.api.microsoft.com/openai/deployments", // In Node.js defaults to process.env.AZURE_OPENAI_BASE_PATH
});

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Custom headers

You can specify custom headers by passing in a configuration field: 
import { AzureChatOpenAI } from "@langchain/openai";

const llmWithCustomHeaders = new AzureChatOpenAI({
  azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY, // In Node.js defaults to process.env.AZURE_OPENAI_API_KEY
  azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_API_INSTANCE_NAME, // In Node.js defaults to process.env.AZURE_OPENAI_API_INSTANCE_NAME
  azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME, // In Node.js defaults to process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME
  azureOpenAIApiVersion: process.env.AZURE_OPENAI_API_VERSION, // In Node.js defaults to process.env.AZURE_OPENAI_API_VERSION
  configuration: {
    defaultHeaders: {
      "x-custom-header": `SOME_VALUE`,
    },
  },
});

await llmWithCustomHeaders.invoke("Hi there!");
The configuration field also accepts other ClientOptions parameters accepted by the official SDK. Note: The specific header api-key currently cannot be overridden in this manner and will pass through the value from azureOpenAIApiKey.

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Migration from Azure OpenAI SDK

If you are using the deprecated Azure OpenAI SDK with the @langchain/azure-openai package, you can update your code to use the new Azure integration following these steps:
  1. Install the new @langchain/openai package and remove the previous @langchain/azure-openai package:

<Npm2Yarn>
  @langchain/openai
</Npm2Yarn>


npm uninstall @langchain/azure-openai
  1. Update your imports to use the new AzureChatOpenAI class from the @langchain/openai package: 
    import { AzureChatOpenAI } from "@langchain/openai";
  2. Update your code to use the new AzureChatOpenAI class and pass the required parameters: 
    const model = new AzureChatOpenAI({
  azureOpenAIApiKey: "<your_key>",
  azureOpenAIApiInstanceName: "<your_instance_name>",
  azureOpenAIApiDeploymentName: "<your_deployment_name>",
  azureOpenAIApiVersion: "<api_version>",
});
    Notice that the constructor now requires the azureOpenAIApiInstanceName parameter instead of the azureOpenAIEndpoint parameter, and adds the azureOpenAIApiVersion parameter to specify the API version.
    • If you were using Azure Managed Identity, you now need to use the azureADTokenProvider parameter to the constructor instead of credentials, see the Azure Managed Identity section for more details.
    • If you were using environment variables, you now have to set the AZURE_OPENAI_API_INSTANCE_NAME environment variable instead of AZURE_OPENAI_API_ENDPOINT, and add the AZURE_OPENAI_API_VERSION environment variable to specify the API version.

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API reference

For detailed documentation of all AzureChatOpenAI features and configurations head to the API reference.