Interactive text generation with a chatbot

This sample demonstrates how to use the Gemini model to generate text interactively.

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For detailed documentation that includes this code sample, see the following:

Code sample

C#

Before trying this sample, follow the C# setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI C# API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.


using Google.Cloud.AIPlatform.V1;
using System;
using System.Collections.Generic;
using System.Threading.Tasks;

public class MultiTurnChatSample
{
    public async Task<string> GenerateContent(
        string projectId = "your-project-id",
        string location = "us-central1",
        string publisher = "google",
        string model = "gemini-1.5-flash-001"
    )
    {
        // Create a chat session to keep track of the context
        ChatSession chatSession = new ChatSession($"projects/{projectId}/locations/{location}/publishers/{publisher}/models/{model}", location);

        string prompt = "Hello.";
        Console.WriteLine($"\nUser: {prompt}");

        string response = await chatSession.SendMessageAsync(prompt);
        Console.WriteLine($"Response: {response}");

        prompt = "What are all the colors in a rainbow?";
        Console.WriteLine($"\nUser: {prompt}");

        response = await chatSession.SendMessageAsync(prompt);
        Console.WriteLine($"Response: {response}");

        prompt = "Why does it appear when it rains?";
        Console.WriteLine($"\nUser: {prompt}");

        response = await chatSession.SendMessageAsync(prompt);
        Console.WriteLine($"Response: {response}");

        return response;
    }

    private class ChatSession
    {
        private readonly string _modelPath;
        private readonly PredictionServiceClient _predictionServiceClient;

        private readonly List<Content> _contents;

        public ChatSession(string modelPath, string location)
        {
            _modelPath = modelPath;

            _predictionServiceClient = new PredictionServiceClientBuilder
            {
                Endpoint = $"{location}-aiplatform.googleapis.com"
            }.Build();

            // Initialize contents to send over in every request.
            _contents = new List<Content>();
        }

        public async Task<string> SendMessageAsync(string prompt)
        {
            var content = new Content
            {
                Role = "USER",
                Parts =
                {
                    new Part { Text = prompt }
                }
            };
            _contents.Add(content);

            var generateContentRequest = new GenerateContentRequest
            {
                Model = _modelPath,
                GenerationConfig = new GenerationConfig
                {
                    Temperature = 0.9f,
                    TopP = 1,
                    TopK = 32,
                    CandidateCount = 1,
                    MaxOutputTokens = 2048
                }
            };
            generateContentRequest.Contents.AddRange(_contents);

            GenerateContentResponse response = await _predictionServiceClient.GenerateContentAsync(generateContentRequest);

            _contents.Add(response.Candidates[0].Content);

            return response.Candidates[0].Content.Parts[0].Text;
        }
    }
}

Go

Before trying this sample, follow the Go setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Go API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import (
	"context"
	"encoding/json"
	"fmt"
	"io"

	"cloud.google.com/go/vertexai/genai"
)

func makeChatRequests(ctx context.Context, w io.Writer, projectID, region, modelName string) error {
	client, err := genai.NewClient(ctx, projectID, region)

	if err != nil {
		return fmt.Errorf("error creating client: %w", err)
	}
	defer client.Close()

	gemini := client.GenerativeModel(modelName)
	chat := gemini.StartChat()

	send := func(message string) error {
		r, err := chat.SendMessage(ctx, genai.Text(message))
		if err != nil {
			return err
		}
		rb, err := json.MarshalIndent(r, "", "  ")
		if err != nil {
			return err
		}
		fmt.Fprintln(w, string(rb))
		return nil
	}

	if err := send("Hello"); err != nil {
		return err
	}
	if err := send("What are all the colors in a rainbow?"); err != nil {
		return err
	}
	return send("Why does it appear when it rains?")
}

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Java API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import com.google.cloud.vertexai.VertexAI;
import com.google.cloud.vertexai.api.GenerateContentResponse;
import com.google.cloud.vertexai.generativeai.ChatSession;
import com.google.cloud.vertexai.generativeai.GenerativeModel;
import com.google.cloud.vertexai.generativeai.ResponseHandler;
import java.io.IOException;

public class ChatDiscussion {

  public static void main(String[] args) throws IOException {
    // 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";

    chatDiscussion(projectId, location, modelName);
  }

  // Ask interrelated questions in a row using a ChatSession object.
  public static void chatDiscussion(String projectId, String location, String modelName)
      throws IOException {
    // 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)) {
      GenerateContentResponse response;

      GenerativeModel model = new GenerativeModel(modelName, vertexAI);
      // Create a chat session to be used for interactive conversation.
      ChatSession chatSession = new ChatSession(model);

      response = chatSession.sendMessage("Hello.");
      System.out.println(ResponseHandler.getText(response));

      response = chatSession.sendMessage("What are all the colors in a rainbow?");
      System.out.println(ResponseHandler.getText(response));

      response = chatSession.sendMessage("Why does it appear when it rains?");
      System.out.println(ResponseHandler.getText(response));
      System.out.println("Chat Ended.");
    }
  }
}

Python

Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Python API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import vertexai

from vertexai.generative_models import GenerativeModel, ChatSession

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"

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

model = GenerativeModel(model_name="gemini-1.5-flash-001")

chat = model.start_chat()

def get_chat_response(chat: ChatSession, prompt: str) -> str:
    response = chat.send_message(prompt)
    return response.text

prompt = "Hello."
print(get_chat_response(chat, prompt))

prompt = "What are all the colors in a rainbow?"
print(get_chat_response(chat, prompt))

prompt = "Why does it appear when it rains?"
print(get_chat_response(chat, prompt))

What's next

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