Vertex AI Node.js SDK

The Vertex AI Node.js SDK enables developers to use Google's state-of-the-art generative AI models (like Gemini) to build AI-powered features and applications.

See here for detailed samples using the Vertex AI Node.js SDK.

Before you begin

  1. Select or create a Cloud Platform project.
  2. Enable billing for your project.
  3. Enable the Vertex AI API.
  4. Set up authentication with a service account so you can access theAPI from your local workstation.

Installation

Install this SDK via NPM.

npm install @google-cloud/vertexai

Setup

To use the SDK, create an instance ofVertexAI by passing it your Google Cloud project ID and location. Then create a reference to a generative model.

const {VertexAI, HarmCategory, HarmBlockThreshold} = require('@google-cloud/vertexai');const project = 'your-cloud-project';const location = 'us-central1';const vertex_ai = newVertexAI({project: project, location: location});// Instantiate modelsconst generativeModel = vertex_ai.preview.getGenerativeModel({    model: 'gemini-pro',    // The following parameters are optional    // They can also be passed to individual content generation requests    safety_settings: [{category:HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, threshold:HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE}],    generation_config: {max_output_tokens: 256},  });const generativeVisionModel = vertex_ai.preview.getGenerativeModel({    model: 'gemini-pro-vision',});

Streaming content generation

async function streamGenerateContent() {  const request = {    contents: [{role: 'user', parts: [{text: 'How are you doing today?'}]}],  };  const streamingResp = await generativeModel.generateContentStream(request);  for await (const item of streamingResp.stream) {    console.log('stream chunk: ', JSON.stringify(item));  }  console.log('aggregated response: ', JSON.stringify(await streamingResp.response));};streamGenerateContent();

Streaming chat

async function streamChat() {  const chat = generativeModel.startChat();  const chatInput1 = "How can I learn more about Node.js?";  const result1 = await chat.sendMessageStream(chatInput1);  for await (const item of result1.stream) {      console.log(item.candidates[0].content.parts[0].text);  }  console.log('aggregated response: ', JSON.stringify(await result1.response));}streamChat();

Multi-part content generation

Providing a Google Cloud Storage image URI

async function multiPartContent() {    const filePart = {file_data: {file_uri: "gs://generativeai-downloads/images/scones.jpg", mime_type: "image/jpeg"}};    const textPart = {text: 'What is this a picture of?'};    const request = {        contents: [{role: 'user', parts: [textPart, filePart]}],      };    const streamingResp = await generativeVisionModel.generateContentStream(request);    for await (const item of streamingResp.stream) {      console.log('stream chunk: ', JSON.stringify(item));    }    const aggregatedResponse = await streamingResp.response;    console.log(aggregatedResponse.candidates[0].content);}multiPartContent();

Providing a base64 image string

async function multiPartContentImageString() {    // Replace this with your own base64 image string    const base64Image = 'iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8z8BQDwAEhQGAhKmMIQAAAABJRU5ErkJggg==';    const filePart = {inline_data: {data: base64Image, mime_type: 'image/jpeg'}};    const textPart = {text: 'What is this a picture of?'};    const request = {        contents: [{role: 'user', parts: [textPart, filePart]}],      };    const resp = await generativeVisionModel.generateContentStream(request);    const contentResponse = await resp.response;    console.log(contentResponse.candidates[0].content.parts[0].text);}multiPartContentImageString();

Multi-part content with text and video

async function multiPartContentVideo() {    const filePart = {file_data: {file_uri: 'gs://cloud-samples-data/video/animals.mp4', mime_type: 'video/mp4'}};    const textPart = {text: 'What is in the video?'};    const request = {        contents: [{role: 'user', parts: [textPart, filePart]}],      };    const streamingResp = await generativeVisionModel.generateContentStream(request);    for await (const item of streamingResp.stream) {      console.log('stream chunk: ', JSON.stringify(item));    }    const aggregatedResponse = await streamingResp.response;    console.log(aggregatedResponse.candidates[0].content);}multiPartContentVideo();

Content generation: non-streaming

async function generateContent() {  const request = {    contents: [{role: 'user', parts: [{text: 'How are you doing today?'}]}],  };  const resp = await generativeModel.generateContent(request);  console.log('aggregated response: ', JSON.stringify(await resp.response));};generateContent();

Counting tokens

async function countTokens() {    const request = {        contents: [{role: 'user', parts: [{text: 'How are you doing today?'}]}],      };    const resp = await generativeModel.countTokens(request);    console.log('count tokens response: ', resp);}countTokens();

License

The contents of this repository are licensed under theApache License, version 2.0.

Except as otherwise noted, the content of this page is licensed under theCreative Commons Attribution 4.0 License, and code samples are licensed under theApache 2.0 License. For details, see theGoogle Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-10-30 UTC.