Skip to main content

AzureChatOpenAI

This will help you getting started with AzureChatOpenAI chat models. For detailed documentation of all AzureChatOpenAI features and configurations head to the API reference.

Overview​

Integration details​

ClassPackageLocalSerializablePY supportPackage downloadsPackage latest
AzureChatOpenAI@langchain/openai❌✅✅NPM - DownloadsNPM - Version

Model features​

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

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.

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](https://docs.smith.langchain.com/) API key by uncommenting below:

```{=mdx}

```bash
# export LANGCHAIN_TRACING_V2="true"
# export LANGCHAIN_API_KEY="your-api-key"

### Installation

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

```{=mdx}

```bash npm2yarn
npm i @langchain/openai

## Instantiation

Now we can instantiate our model object and generate chat completions:

- TODO: Update model instantiation with relevant params.

::: {.cell execution_count=3}
``` {.typescript .cell-code}
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
})

:::

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.

Chaining​

We can chain our model with a prompt template like so:

  • TODO: Run cells so output can be seen.
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
  }
}

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

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 model2 = 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
});

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:
npm install @langchain/openai
npm uninstall @langchain/azure-openai


2. Update your imports to use the new `AzureChatOpenAI` class from the `@langchain/openai` package:
   ```typescript
   import { AzureChatOpenAI } from "@langchain/openai";

  1. 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.

API reference​

For detailed documentation of all AzureChatOpenAI features and configurations head to the API reference: https://api.js.langchain.com/classes/langchain_openai.AzureChatOpenAI.html

Was this page helpful?


You can also leave detailed feedback on GitHub.