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Google Vertex AI

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You are currently on a page documenting the use of Google Vertex models as text completion models. Many popular models available on Google Vertex are chat completion models.

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LangChain.js supports two different authentication methods based on whether you're running in a Node.js environment or a web environment.

Setup​

Node.js​

To call Vertex AI models in Node, you'll need to install the @langchain/google-vertexai package:

tip

See this section for general instructions on installing integration packages.

npm install @langchain/google-vertexai

You should make sure the Vertex AI API is enabled for the relevant project and that you've authenticated to Google Cloud using one of these methods:

  • You are logged into an account (using gcloud auth application-default login) permitted to that project.
  • You are running on a machine using a service account that is permitted to the project.
  • You have downloaded the credentials for a service account that is permitted to the project and set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path of this file. or
  • You set the GOOGLE_API_KEY environment variable to the API key for the project.

Web​

To call Vertex AI models in web environments (like Edge functions), you'll need to install the @langchain/google-vertexai-web package:

npm install @langchain/google-vertexai-web

Then, you'll need to add your service account credentials directly as a GOOGLE_VERTEX_AI_WEB_CREDENTIALS environment variable:

GOOGLE_VERTEX_AI_WEB_CREDENTIALS={"type":"service_account","project_id":"YOUR_PROJECT-12345",...}

You can also pass your credentials directly in code like this:

import { VertexAI } from "@langchain/google-vertexai";
// Or uncomment this line if you're using the web version:
// import { VertexAI } from "@langchain/google-vertexai-web";

const model = new VertexAI({
  authOptions: {
    credentials: {"type":"service_account","project_id":"YOUR_PROJECT-12345",...},
  },
});

Usage​

The entire family of gemini models are available by specifying the modelName parameter.

import { VertexAI } from "@langchain/google-vertexai";
// Or, if using the web entrypoint:
// import { VertexAI } from "@langchain/google-vertexai-web";

const model = new VertexAI({
  temperature: 0.7,
});
const res = await model.invoke(
  "What would be a good company name for a company that makes colorful socks?"
);
console.log({ res });
/*
{
  res: '* Hue Hues\n' +
    '* Sock Spectrum\n' +
    '* Kaleidosocks\n' +
    '* Threads of Joy\n' +
    '* Vibrant Threads\n' +
    '* Rainbow Soles\n' +
    '* Colorful Canvases\n' +
    '* Prismatic Pedals\n' +
    '* Sock Canvas\n' +
    '* Color Collective'
}
 */

API Reference:

  • VertexAI from @langchain/google-vertexai

Streaming​

Streaming in multiple chunks is supported for faster responses:

import { VertexAI } from "@langchain/google-vertexai";
// Or, if using the web entrypoint:
// import { VertexAI } from "@langchain/google-vertexai-web";

const model = new VertexAI({
  temperature: 0.7,
});
const stream = await model.stream(
  "What would be a good company name for a company that makes colorful socks?"
);

for await (const chunk of stream) {
  console.log("\n---------\nChunk:\n---------\n", chunk);
}

/*
---------
Chunk:
---------
 * Kaleidoscope Toes
* Huephoria
* Soleful Spectrum
*

---------
Chunk:
---------
  Colorwave Hosiery
* Chromatic Threads
* Rainbow Rhapsody
* Vibrant Soles
* Toe-tally Colorful
* Socktacular Hues
*

---------
Chunk:
---------
  Threads of Joy

---------
Chunk:
---------
*/

API Reference:

  • VertexAI from @langchain/google-vertexai

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