Azure OpenAI Provider

The Azure OpenAI provider contains language model support for the Azure OpenAI chat API.

Setup

The Azure OpenAI provider is available in the @ai-sdk/azure module. You can install it with

pnpm add @ai-sdk/azure

Provider Instance

You can import the default provider instance azure from @ai-sdk/azure:

import { azure } from '@ai-sdk/azure';

If you need a customized setup, you can import createAzure from @ai-sdk/azure and create a provider instance with your settings:

import { createAzure } from '@ai-sdk/azure';

const azure = createAzure({
  resourceName: 'your-resource-name', // Azure resource name
  apiKey: 'your-api-key',
});

You can use the following optional settings to customize the OpenAI provider instance:

• resourceName string

  Azure resource name. It defaults to the AZURE_RESOURCE_NAME environment variable.

  The resource name is used in the assembled URL: https://{resourceName}.openai.azure.com/openai/deployments/{modelId}{path}. You can use baseURL instead to specify the URL prefix.

• apiKey string

  API key that is being sent using the api-key header. It defaults to the AZURE_API_KEY environment variable.

• apiVersion string

  Sets a custom api version. Defaults to 2024-10-01-preview.

• baseURL string

  Use a different URL prefix for API calls, e.g. to use proxy servers.

  Either this or resourceName can be used. When a baseURL is provided, the resourceName is ignored.

  With a baseURL, the resolved URL is {baseURL}/{modelId}{path}.

• headers Record<string,string>

  Custom headers to include in the requests.

• fetch (input: RequestInfo, init?: RequestInit) => Promise<Response>

  Custom fetch implementation. Defaults to the global fetch function. You can use it as a middleware to intercept requests, or to provide a custom fetch implementation for e.g. testing.

Language Models

The Azure OpenAI provider instance is a function that you can invoke to create a language model:

const model = azure('your-deployment-name');

You need to pass your deployment name as the first argument.

Reasoning Models

Azure exposes the thinking of DeepSeek-R1 in the generated text using the <think> tag. You can use the extractReasoningMiddleware to extract this reasoning and expose it as a reasoning property on the result:

import { azure } from '@ai-sdk/azure';
import { wrapLanguageModel, extractReasoningMiddleware } from 'ai';

const enhancedModel = wrapLanguageModel({
  model: azure('your-deepseek-r1-deployment-name'),
  middleware: extractReasoningMiddleware({ tagName: 'think' }),
});

You can then use that enhanced model in functions like generateText and streamText.

Example

You can use OpenAI language models to generate text with the generateText function:

import { azure } from '@ai-sdk/azure';
import { generateText } from 'ai';

const { text } = await generateText({
  model: azure('your-deployment-name'),
  prompt: 'Write a vegetarian lasagna recipe for 4 people.',
});

OpenAI language models can also be used in the streamText, generateObject, and streamObject functions (see AI SDK Core).

Provider Options

When using OpenAI language models on Azure, you can configure provider-specific options using providerOptions.openai. More information on available configuration options are on the OpenAI provider page.

const messages = [
  {
    role: 'user',
    content: [
      {
        type: 'text',
        text: 'What is the capital of the moon?',
      },
      {
        type: 'image',
        image: 'https://example.com/image.png',
        providerOptions: {
          openai: { imageDetail: 'low' },
        },
      },
    ],
  },
];

const { text } = await generateText({
  model: azure('your-deployment-name'),
  providerOptions: {
    openai: {
      reasoningEffort: 'low',
    },
  },
});

Chat Models

Azure OpenAI chat models support also some model specific settings that are not part of the standard call settings. You can pass them as an options argument:

const model = azure('your-deployment-name', {
  logitBias: {
    // optional likelihood for specific tokens
    '50256': -100,
  },
  user: 'test-user', // optional unique user identifier
});

The following optional settings are available for OpenAI chat models:

• logitBias Record<number, number>

  Modifies the likelihood of specified tokens appearing in the completion.

  Accepts a JSON object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100. You can use this tokenizer tool to convert text to token IDs. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.

  As an example, you can pass {"50256": -100} to prevent the token from being generated.

• logprobs boolean | number

  Return the log probabilities of the tokens. Including logprobs will increase the response size and can slow down response times. However, it can be useful to better understand how the model is behaving.

  Setting to true will return the log probabilities of the tokens that were generated.

  Setting to a number will return the log probabilities of the top n tokens that were generated.

• parallelToolCalls boolean

  Whether to enable parallel function calling during tool use. Default to true.

• user string

  A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.

Responses Models

You can use the Azure OpenAI responses API with the azure.responses(deploymentName) factory method.

const model = azure.responses('your-deployment-name');

Further configuration can be done using OpenAI provider options. You can validate the provider options using the OpenAIResponsesProviderOptions type.

import { azure, OpenAIResponsesProviderOptions } from '@ai-sdk/azure';
import { generateText } from 'ai';

const result = await generateText({
  model: azure.responses('your-deployment-name'),
  providerOptions: {
    openai: {
      parallelToolCalls: false,
      store: false,
      user: 'user_123',
      // ...
    } satisfies OpenAIResponsesProviderOptions,
  },
  // ...
});

The following provider options are available:

• parallelToolCalls boolean Whether to use parallel tool calls. Defaults to true.

• store boolean Whether to store the generation. Defaults to true.

• metadata Record<string, string> Additional metadata to store with the generation.

• previousResponseId string The ID of the previous response. You can use it to continue a conversation. Defaults to undefined.

• instructions string Instructions for the model. They can be used to change the system or developer message when continuing a conversation using the previousResponseId option. Defaults to undefined.

• user string A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Defaults to undefined.

• reasoningEffort 'low' | 'medium' | 'high' Reasoning effort for reasoning models. Defaults to medium. If you use providerOptions to set the reasoningEffort option, this model setting will be ignored.

• strictSchemas boolean Whether to use strict JSON schemas in tools and when generating JSON outputs. Defaults to true.

The Azure OpenAI responses provider also returns provider-specific metadata:

const { providerMetadata } = await generateText({
  model: azure.responses('your-deployment-name'),
});

const openaiMetadata = providerMetadata?.openai;

The following OpenAI-specific metadata is returned:

• responseId string The ID of the response. Can be used to continue a conversation.

• cachedPromptTokens number The number of prompt tokens that were a cache hit.

• reasoningTokens number The number of reasoning tokens that the model generated.

Web Search

The Azure OpenAI responses provider supports web search through the azure.tools.webSearchPreview tool.

You can force the use of the web search tool by setting the toolChoice parameter to { type: 'tool', toolName: 'web_search_preview' }.

const result = await generateText({
  model: azure.responses('your-deployment-name'),
  prompt: 'What happened in San Francisco last week?',
  tools: {
    web_search_preview: azure.tools.webSearchPreview({
      // optional configuration:
      searchContextSize: 'high',
      userLocation: {
        type: 'approximate',
        city: 'San Francisco',
        region: 'California',
      },
    }),
  },
  // Force web search tool:
  toolChoice: { type: 'tool', toolName: 'web_search_preview' },
});

// URL sources
const sources = result.sources;

PDF support

The Azure OpenAI Responses API supports reading PDF files. You can pass PDF files as part of the message content using the file type:

const result = await generateText({
  model: azure.responses('your-deployment-name'),
  messages: [
    {
      role: 'user',
      content: [
        {
          type: 'text',
          text: 'What is an embedding model?',
        },
        {
          type: 'file',
          data: fs.readFileSync('./data/ai.pdf'),
          mimeType: 'application/pdf',
          filename: 'ai.pdf', // optional
        },
      ],
    },
  ],
});

The model will have access to the contents of the PDF file and respond to questions about it. The PDF file should be passed using the data field, and the mimeType should be set to 'application/pdf'.

Completion Models

You can create models that call the completions API using the .completion() factory method. The first argument is the model id. Currently only gpt-35-turbo-instruct is supported.

const model = azure.completion('your-gpt-35-turbo-instruct-deployment');

OpenAI completion models support also some model specific settings that are not part of the standard call settings. You can pass them as an options argument:

const model = azure.completion('your-gpt-35-turbo-instruct-deployment', {
  echo: true, // optional, echo the prompt in addition to the completion
  logitBias: {
    // optional likelihood for specific tokens
    '50256': -100,
  },
  suffix: 'some text', // optional suffix that comes after a completion of inserted text
  user: 'test-user', // optional unique user identifier
});

The following optional settings are available for Azure OpenAI completion models:

• echo: boolean

  Echo back the prompt in addition to the completion.

• logitBias Record<number, number>

  Modifies the likelihood of specified tokens appearing in the completion.

  Accepts a JSON object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100. You can use this tokenizer tool to convert text to token IDs. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.

  As an example, you can pass {"50256": -100} to prevent the <|endoftext|> token from being generated.

• logprobs boolean | number

  Return the log probabilities of the tokens. Including logprobs will increase the response size and can slow down response times. However, it can be useful to better understand how the model is behaving.

  Setting to true will return the log probabilities of the tokens that were generated.

  Setting to a number will return the log probabilities of the top n tokens that were generated.

• suffix string

  The suffix that comes after a completion of inserted text.

• user string

  A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.

Embedding Models

You can create models that call the Azure OpenAI embeddings API using the .embedding() factory method.

const model = azure.embedding('your-embedding-deployment');

Azure OpenAI embedding models support several aditional settings. You can pass them as an options argument:

const model = azure.embedding('your-embedding-deployment', {
  dimensions: 512 // optional, number of dimensions for the embedding
  user: 'test-user' // optional unique user identifier
})

The following optional settings are available for Azure OpenAI embedding models:

• dimensions: number

  The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.

• user string

  A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.

Image Models

You can create models that call the Azure OpenAI image generation API (DALL-E) using the .imageModel() factory method. The first argument is your deployment name for the DALL-E model.

const model = azure.imageModel('your-dalle-deployment-name');

Azure OpenAI image models support several additional settings. You can pass them as an options argument:

const model = azure.imageModel('your-dalle-deployment-name', {
  user: 'test-user', // optional unique user identifier
  responseFormat: 'url', // 'url' or 'b64_json', defaults to 'url'
});

Example

You can use Azure OpenAI image models to generate images with the generateImage function:

import { azure } from '@ai-sdk/azure';
import { experimental_generateImage as generateImage } from 'ai';

const { image } = await generateImage({
  model: azure.imageModel('your-dalle-deployment-name'),
  prompt: 'A photorealistic image of a cat astronaut floating in space',
  size: '1024x1024', // '1024x1024', '1792x1024', or '1024x1792' for DALL-E 3
});

// image contains the URL or base64 data of the generated image
console.log(image);

Model Capabilities

Azure OpenAI supports DALL-E 2 and DALL-E 3 models through deployments. The capabilities depend on which model version your deployment is using:

Transcription Models

You can create models that call the Azure OpenAI transcription API using the .transcription() factory method.

The first argument is the model id e.g. whisper-1.

const model = azure.transcription('whisper-1');

You can also pass additional provider-specific options using the providerOptions argument. For example, supplying the input language in ISO-639-1 (e.g. en) format will improve accuracy and latency.

import { experimental_transcribe as transcribe } from 'ai';
import { azure } from '@ai-sdk/azure';
import { readFile } from 'fs/promises';

const result = await transcribe({
  model: azure.transcription('whisper-1'),
  audio: await readFile('audio.mp3'),
  providerOptions: { azure: { language: 'en' } },
});

The following provider options are available:

• timestampGranularities string[] The granularity of the timestamps in the transcription. Defaults to ['segment']. Possible values are ['word'], ['segment'], and ['word', 'segment']. Note: There is no additional latency for segment timestamps, but generating word timestamps incurs additional latency.

• language string The language of the input audio. Supplying the input language in ISO-639-1 format (e.g. 'en') will improve accuracy and latency. Optional.

• prompt string An optional text to guide the model's style or continue a previous audio segment. The prompt should match the audio language. Optional.

• temperature number The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If set to 0, the model will use log probability to automatically increase the temperature until certain thresholds are hit. Defaults to 0. Optional.

• include string[] Additional information to include in the transcription response.

Model Capabilities