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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.You may be looking for this page instead.
Google Vertex is a service that exposes all foundation models available in Google Cloud, like gemini-1.5-pro, gemini-1.5-flash, etc. This will help you get started with VertexAI completion models (LLMs) using LangChain. For detailed documentation on VertexAI features and configuration options, please refer to the API reference.

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Overview

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Integration details

ClassPackageLocalSerializablePY supportDownloadsVersion
VertexAI@langchain/google-vertexai❌✅✅NPM - DownloadsNPM - Version

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Setup

LangChain.js supports two different authentication methods based on whether you’re running in a Node.js environment or a web environment. To access VertexAI models you’ll need to create a Google Cloud Platform (GCP) account, get an API key, and install the @langchain/google-vertexai integration package.

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Credentials

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Node.js

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.

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Web

To call Vertex AI models in web environments (like Edge functions), you’ll need to install the @langchain/google-vertexai-web package. 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",...},
  },
});
If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below: 
# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"

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Installation

The LangChain VertexAI integration lives in the @langchain/google-vertexai package: 
npm install @langchain/google-vertexai @langchain/core
or for web environments: 
npm install @langchain/google-vertexai-web @langchain/core

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Instantiation

Now we can instantiate our model object and generate chat completions: 
import { VertexAI } from "@langchain/google-vertexai-web"

const llm = new VertexAI({
  model: "gemini-pro",
  temperature: 0,
  maxRetries: 2,
  // other params...
})

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Invocation

const inputText = "VertexAI is an AI company that "

const completion = await llm.invoke(inputText)
completion

offers a wide range of cloud computing services and artificial intelligence solutions to businesses and developers worldwide.

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API reference

For detailed documentation of all VertexAI features and configurations head to the API reference.
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