<Warning>
**You are currently on a page documenting the use of Google Vertex models as [text completion models](/oss/javascript/concepts/text_llms). Many popular models available on Google Vertex are [chat completion models](/oss/javascript/concepts/chat_models).**


You may be looking for [this page instead](/oss/javascript/integrations/chat/google_vertex_ai/).
</Warning>
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 supportPackage downloadsPackage latest
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: 
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>

<Npm2Yarn>
  @langchain/google-vertexai @langchain/core
</Npm2Yarn>

or for web environments:

<Npm2Yarn>
  @langchain/google-vertexai-web @langchain/core
</Npm2Yarn>

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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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Chaining

We can chain our completion model with a prompt template like so: 
import { PromptTemplate } from "@langchain/core/prompts"

const prompt = PromptTemplate.fromTemplate("How to say {input} in {output_language}:\n")

const chain = prompt.pipe(llm);
await chain.invoke(
  {
    output_language: "German",
    input: "I love programming.",
  }
)

"Ich liebe Programmieren."
Pronunciation guide:

Ich: [ɪç] (similar to "ikh" with a soft "ch" sound)
liebe: [ˈliːbə] (LEE-buh)
Programmieren: [pʁoɡʁaˈmiːʁən] (pro-gra-MEE-ren)

You could also say:
"Ich liebe es zu programmieren."
Which translates more literally to "I love to program." This version is a bit more formal or precise.
Pronunciation:

es: [ɛs] (like the letter "S")
zu: [tsuː] (tsoo)

Both versions are correct and commonly used.

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API reference

For detailed documentation of all VertexAI features and configurations head to the API reference: https://api.js.langchain.com/classes/langchain_google_vertexai.VertexAI.html