@google-cloud/vertexai

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 the API from your local workstation.

Installation

Install this SDK via NPM.

npm install @google-cloud/vertexai

Available Gemini models in Vertex

For the latest list of available Gemini models in Vertex, please refer to Google Cloud Generative AI page

Setup

To use the SDK, create an instance of VertexAI 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';
// For the latest list of available Gemini models in Vertex, please refer to https://cloud.google.com/vertex-ai/docs/generative-ai/learn/models#gemini-models
const textModel =  'gemini-1.0-pro';
const visionModel = 'gemini-1.0-pro-vision';

const vertex_ai = new VertexAI({project: project, location: location});

// Instantiate models
const generativeModel = vertex_ai.getGenerativeModel({
    model: textModel,
    // 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.getGenerativeModel({
    model: visionModel,
});

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 picture about?'};
    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 picture about?'};
    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();

Function calling

The Node SDK supports function calling via sendMessage, sendMessageStream, generateContent, and generateContentStream. We recommend using it through chat methods (sendMessage or sendMessageStream) but have included examples of both approaches below.

Function declarations and response

This is an example of a function declaration and function response, which are passed to the model in the snippets that follow.

const functionDeclarations = [
  {
    function_declarations: [
      {
        name: "get_current_weather",
        description: 'get weather in a given location',
        parameters: {
          type: FunctionDeclarationSchemaType.OBJECT,
          properties: {
            location: {type: FunctionDeclarationSchemaType.STRING},
            unit: {
              type: FunctionDeclarationSchemaType.STRING,
              enum: ['celsius', 'fahrenheit'],
            },
          },
          required: ['location'],
        },
      },
    ],
  },
];

const functionResponseParts = [
  {
    functionResponse: {
      name: "get_current_weather",
      response:
          {name: "get_current_weather", content: {weather: "super nice"}},
    },
  },
];

Function calling with chat

async function functionCallingChat() {
  // Create a chat session and pass your function declarations
  const chat = generativeModel.startChat({
    tools: functionDeclarations,
  });

  const chatInput1 = 'What is the weather in Boston?';

  // This should include a functionCall response from the model
  const result1 = await chat.sendMessageStream(chatInput1);
  for await (const item of result1.stream) {
    console.log(item.candidates[0]);
  }
  const response1 = await result1.response;

  // Send a follow up message with a FunctionResponse
  const result2 = await chat.sendMessageStream(functionResponseParts);
  for await (const item of result2.stream) {
    console.log(item.candidates[0]);
  }

  // This should include a text response from the model using the response content
  // provided above
  const response2 = await result2.response;
}

functionCallingChat();

Function calling with generateContentStream

async function functionCallingGenerateContentStream() {
  const request = {
    contents: [
      {role: 'user', parts: [{text: 'What is the weather in Boston?'}]},
      {role: 'model', parts: [{functionCall: {name: 'get_current_weather', args: {'location': 'Boston'}}}]},
      {role: 'function', parts: functionResponseParts}
    ],
    tools: functionDeclarations,
  };
  const streamingResp =
      await generativeModel.generateContentStream(request);
  for await (const item of streamingResp.stream) {
    console.log(item.candidates[0]);
  }
}

functionCallingGenerateContentStream();

License

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

Changelog

0.5.0 (2024-02-29)

Features

• Introduce Request Timeout Configuration (1b37f40)

Bug Fixes

• correct UsageMetadata schema (10bc676)
• include usageMetadata in stream aggregated response (a1154c9)