AI SDK ProvidersGoogle Vertex AI

Google Vertex Provider

The Google Vertex provider for the Vercel AI SDK contains language model support for the Google Vertex AI APIs.

The Google Vertex provider is not compatible with edge environments.


The Google provider is available in the @ai-sdk/google-vertex module. You can install it with

pnpm install @ai-sdk/google-vertex

Provider Instance

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

import { vertex } from '@ai-sdk/google-vertex';

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

import { createVertex } from '@ai-sdk/google-vertex';
const vertex = createVertex({
project: 'my-project', // optional
location: 'us-central1', // optional

You can use the following optional settings to customize the Google Generative AI provider instance:

  • project string

    The Google Cloud project ID that you want to use for the API calls. It uses the GOOGLE_VERTEX_PROJECT environment variable by default.

  • location string

    The Google Cloud location that you want to use for the API calls, e.g. us-central1. It uses the GOOGLE_VERTEX_LOCATION environment variable by default.

  • googleAuthOptions object

    Optional. The Authentication options used by the Google Auth Library:

    • authClient object An AuthClient to use.

    • keyFilename string Path to a .json, .pem, or .p12 key file.

    • keyFile string Path to a .json, .pem, or .p12 key file.

    • credentials object Object containing client_email and private_key properties, or the external account client options.

    • clientOptions object Options object passed to the constructor of the client.

    • scopes string | string[] Required scopes for the desired API request.

    • projectId string Your project ID.

    • universeDomain string The default service domain for a given Cloud universe.

Language Models

You can create models that call the Vertex API using the provider instance. The first argument is the model id, e.g. gemini-1.5-pro.

const model = vertex('gemini-1.5-pro');

Google Vertex 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 = vertex('gemini-1.5-pro', {
topK: 0.2,

The following optional settings are available for Google Vertex models:

  • topK number

    Optional. The maximum number of tokens to consider when sampling.

    Models use nucleus sampling or combined Top-k and nucleus sampling. Top-k sampling considers the set of topK most probable tokens. Models running with nucleus sampling don't allow topK setting.

  • safetySettings Array<{ category: string; threshold: string }>

    Optional. Safety settings for the model.

    • category string

      The category of the safety setting. Can be one of the following:

    • threshold string

      The threshold of the safety setting. Can be one of the following:

      • BLOCK_NONE


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

import { vertex } from '@ai-sdk/google-vertex'
import { generateText } from 'ai'
const { text } = await generateText({
model: vertex('gemini-1.5-pro')
prompt: 'Write a vegetarian lasagna recipe for 4 people.'

Google Vertex language models can also be used in the streamText and streamUI functions (see AI SDK Core and AI SDK RSC).

Model Capabilities

ModelImage InputObject GenerationTool UsageTool Streaming