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Google Vertex AI


This API is new and may change in future LangChain.js versions.

The GoogleVertexAIMultimodalEmbeddings class provides additional methods that are parallels to the embedDocuments() and embedQuery() methods:

  • embedImage() and embedImageQuery() take node Buffer objects that are expected to contain an image.
  • embedMedia() and embedMediaQuery() take an object that contain a text string field, an image Buffer field, or both and returns a similarly constructed object containing the respective vectors.

Note: The Google Vertex AI embeddings models have different vector sizes than OpenAI's standard model, so some vector stores may not handle them correctly.

  • The textembedding-gecko model in GoogleVertexAIEmbeddings provides 768 dimensions.
  • The multimodalembedding@001 model in GoogleVertexAIMultimodalEmbeddings provides 1408 dimensions.


The Vertex AI implementation is meant to be used in Node.js and not directly in a browser, since it requires a service account to use.

Before running this code, you should make sure the Vertex AI API is enabled for the relevant project in your Google Cloud dashboard 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.
npm install google-auth-library @langchain/community


Here's a basic example that shows how to embed image queries:

import fs from "fs";
import { GoogleVertexAIMultimodalEmbeddings } from "langchain/experimental/multimodal_embeddings/googlevertexai";

const model = new GoogleVertexAIMultimodalEmbeddings();

// Load the image into a buffer to get the embedding of it
const img = fs.readFileSync("/path/to/file.jpg");
const imgEmbedding = await model.embedImageQuery(img);
console.log({ imgEmbedding });

// You can also get text embeddings
const textEmbedding = await model.embedQuery(
"What would be a good company name for a company that makes colorful socks?"
console.log({ textEmbedding });

API Reference:

Advanced usage

Here's a more advanced example that shows how to integrate these new embeddings with a LangChain vector store.

import fs from "fs";
import { GoogleVertexAIMultimodalEmbeddings } from "langchain/experimental/multimodal_embeddings/googlevertexai";
import { FaissStore } from "@langchain/community/vectorstores/faiss";
import { Document } from "@langchain/core/documents";

const embeddings = new GoogleVertexAIMultimodalEmbeddings();

const vectorStore = await FaissStore.fromTexts(
["dog", "cat", "horse", "seagull"],
[{ id: 2 }, { id: 1 }, { id: 3 }, { id: 4 }],

const img = fs.readFileSync("parrot.jpeg");
const vectors: number[] = await embeddings.embedImageQuery(img);
const document = new Document({
pageContent: img.toString("base64"),
// Metadata is optional but helps track what kind of document is being retrieved
metadata: {
id: 5,
mediaType: "image",

// Add the image embedding vectors to the vector store directly
await vectorStore.addVectors([vectors], [document]);

// Use a similar image to the one just added
const img2 = fs.readFileSync("parrot-icon.png");
const vectors2: number[] = await embeddings.embedImageQuery(img2);

// Use the lower level, direct API
const resultTwo = await vectorStore.similaritySearchVectorWithScore(
console.log(JSON.stringify(resultTwo, null, 2));

Document {
pageContent: '<BASE64 ENCODED IMAGE DATA>'
metadata: {
id: 5,
mediaType: "image"
Document {
pageContent: 'seagull',
metadata: {
id: 4

API Reference:

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