Generate Text With a Generative Model

import com.google.cloud.vertexai.VertexAI;
import com.google.cloud.vertexai.api.GenerateContentResponse;
import com.google.cloud.vertexai.generativeai.GenerativeModel;
import com.google.cloud.vertexai.generativeai.ResponseHandler;

public class QuestionAnswer {

  public static void main(String[] args) throws Exception {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "your-google-cloud-project-id";
    String location = "us-central1";
    String modelName = "gemini-1.5-flash-001";

    String output = simpleQuestion(projectId, location, modelName);
    System.out.println(output);
  }

  // Asks a question to the specified Vertex AI Gemini model and returns the generated answer.
  public static String simpleQuestion(String projectId, String location, String modelName)
      throws Exception {
    // Initialize client that will be used to send requests.
    // This client only needs to be created once, and can be reused for multiple requests.
    try (VertexAI vertexAI = new VertexAI(projectId, location)) {
      String output;
      GenerativeModel model = new GenerativeModel(modelName, vertexAI);
      // Send the question to the model for processing.
      GenerateContentResponse response = model.generateContent("Why is the sky blue?");
      // Extract the generated text from the model's response.
      output = ResponseHandler.getText(response);
      return output;
    }
  }
}

const {VertexAI} = require('@google-cloud/vertexai');

/**
 * TODO(developer): Update these variables before running the sample.
 */
const PROJECT_ID = process.env.CAIP_PROJECT_ID;
const LOCATION = process.env.LOCATION;
const MODEL = 'gemini-1.5-flash-001';

async function generateContent() {
  // Initialize Vertex with your Cloud project and location
  const vertexAI = new VertexAI({project: PROJECT_ID, location: LOCATION});

  // Instantiate the model
  const generativeModel = vertexAI.getGenerativeModel({
    model: MODEL,
  });

  const request = {
    contents: [
      {
        role: 'user',
        parts: [
          {
            text: 'Write a story about a magic backpack.',
          },
        ],
      },
    ],
  };

  console.log(JSON.stringify(request));

  const result = await generativeModel.generateContent(request);

  console.log(result.response.candidates[0].content.parts[0].text);
}

import vertexai

from vertexai.generative_models import GenerativeModel

vertexai.init(project=PROJECT_ID, location="us-central1")

model = GenerativeModel("gemini-1.5-flash-002")
response = model.generate_content("Write a story about a magic backpack.")

print(response.text)