Vertex AI GoogleAuthError in nodeJS

I have a web server with one endpoint deployed on Heroku. Server purpose is to interact with Vertex AI gemini-pro LLM. Localy everything wors perfect because I am using a gCloud CLI auth but I want to implement a service account auth.

Here is the code sample of model init:

import { VertexAI } from "@google-cloud/vertexai";
import { GoogleAuth } from "google-auth-library";
import dotenv from "dotenv";

dotenv.config();

const gAuth = new GoogleAuth({
  credentials: {
    client_email: process.env.CLIENT_EMAIL,
    private_key: process.env.PRIVATE_KEY,
  },
});

const authClient = await gAuth.getClient();

const vertex_ai = new VertexAI({
  project: process.env.PROJECT_ID,
  location: process.env.LOCATION,
  googleAuth: authClient,
});

const model = "gemini-pro";
const generativeModel = vertex_ai.preview.getGenerativeModel({
  model: model,
  generation_config: {
    max_output_tokens: 8192,
    temperature: 0.8,
    top_p: 0.8,
    top_k: 5,
  },
});

export default generativeModel;

I checked the service acc role - it's "Editor" so the problem is in model init. Every time when trying to reach and endpoint I get the error:

2024-02-05T21:13:23.977555+00:00 app[web.1]: GoogleAuthError: 
2024-02-05T21:13:23.977557+00:00 app[web.1]: Unable to authenticate your request        
2024-02-05T21:13:23.977576+00:00 app[web.1]: Depending on your run time environment, you can get authentication by        
2024-02-05T21:13:23.977576+00:00 app[web.1]: - if in local instance or cloud shell: `!gcloud auth login`        
2024-02-05T21:13:23.977576+00:00 app[web.1]: - if in Colab:        
2024-02-05T21:13:23.977577+00:00 app[web.1]:     -`from google.colab import auth`        
2024-02-05T21:13:23.977577+00:00 app[web.1]:     -`auth.authenticate_user()`        
2024-02-05T21:13:23.977578+00:00 app[web.1]: - if in service account or other: please follow guidance in https://cloud.google.com/docs/authentication
2024-02-05T21:13:23.977579+00:00 app[web.1]: Error: Could not load the default credentials. Browse to https://cloud.google.com/docs/authentication/getting-started for more information.

UPD: I found the solution in migrating to using API key:

import { GoogleGenerativeAI } from "@google/generative-ai";
import dotenv from "dotenv";

dotenv.config();

const genAI = new GoogleGenerativeAI(process.env.API_KEY);

const model = "gemini-pro";
const generativeModel = genAI.getGenerativeModel({
  model: model,
  generation_config: {
    max_output_tokens: 8192,
    temperature: 0.8,
    top_p: 0.8,
    top_k: 5,
  },
});

export default generativeModel;

This is an example of how you can utilize Gemini AI through Vertex AI. Note that the credentials can be obtained from your Google Cloud account via a .json file. After obtaining the credentials, you can implement them in your app by placing them in the .env file to ensure security. This is a quick example of how you can use it.

https://www.npmjs.com/package/@google-cloud/vertexai

Gemini Pro(Streaming content generation,Streaming chat,Content generation: non-streaming)

Gemini Pro Vision(Providing a Google Cloud Storage image URI, Providing a base64 image string ,Multi-part content with text and video)

Example:

    const {
      VertexAI,
      HarmCategory,
      HarmBlockThreshold,
    } = require('@google-cloud/vertexai');
    
    
    
    const PROJECT_ID = '....';
    const LOCATION = 'us-central1';
    const GEMINI_PRO_MODEL_NAME = 'gemini-pro';
    const GEMINI_PRO_VISION_MODEL_NAME = 'gemini-pro-vision';
    
    const authOptions = {
      credentials: {
        client_email: '....',
        private_key: '.....',
      },
    };
    
    const vertexAiOptions = {
      project: PROJECT_ID,
      location: LOCATION,
      googleAuthOptions: authOptions,
    };
    const vertex_ai = new VertexAI(vertexAiOptions);
    
    const safetySettings = [
      {
        category: HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
        threshold: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
      },
    ];
    
    
    
    
    //             generativeModel  gemini pro
    
    
    
    
    const generativeModel = vertex_ai.preview.getGenerativeModel({
      model: GEMINI_PRO_MODEL_NAME,
      safety_settings: safetySettings,
      generation_config: {max_output_tokens: 256},
    });
    
    
    
    
     //          generativeVisionModel  gemini pro vision
    
    
    
    
    const generativeVisionModel = vertex_ai.preview.getGenerativeModel({
      model: GEMINI_PRO_VISION_MODEL_NAME,
    });
    
    
    
    // Content generation: non-streaming      : Gemini pro :
    
    
    
    async function nonStreaming() {
      const request = {
        contents: [{role: 'user', parts: [{text: 'who is james bond?'}]}],
      };
    
      try {
        const resp = await generativeModel.generateContent(request);
        const data = await resp.response;
        const textValue = data.candidates[0].content.parts[0].text;
        console.log('nonStreaming', textValue);
      } catch (error) {
        console.error('An error occurred during content generation:', error);
      }
    }

     nonStreaming();