Google Vertex Provider

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

Setup

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:

    • HARM_CATEGORY_UNSPECIFIED
    • HARM_CATEGORY_HATE_SPEECH
    • HARM_CATEGORY_DANGEROUS_CONTENT
    • HARM_CATEGORY_HARASSMENT
    • HARM_CATEGORY_SEXUALLY_EXPLICIT

  • threshold string

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

    • HARM_BLOCK_THRESHOLD_UNSPECIFIED
    • BLOCK_LOW_AND_ABOVE
    • BLOCK_MEDIUM_AND_ABOVE
    • BLOCK_ONLY_HIGH
    • BLOCK_NONE

Example

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