Liste objetos numa imagem no formato JSON

Gere texto formatado em JSON que liste os objetos que o modelo consegue identificar a partir de uma determinada imagem.

Exemplo de código

C#

Antes de experimentar este exemplo, siga as C#instruções de configuração no início rápido do Vertex AI com bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API C# Vertex AI.

Para se autenticar no Vertex AI, configure as Credenciais padrão da aplicação. Para mais informações, consulte o artigo Configure a autenticação para um ambiente de desenvolvimento local.

public async Task<string> GenerateContentWithResponseSchema6(
    string projectId = "your-project-id",
    string location = "us-central1",
    string publisher = "google",
    string model = "gemini-2.0-flash-001")
{

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

    var responseSchema = new OpenApiSchema
    {
        Type = Type.Object,
        Properties =
        {
            ["playlist"] = new()
            {
                Type = Type.Array,
                Items = new()
                {
                    Type = Type.Object,
                    Properties =
                    {
                        ["artist"] = new() { Type = Type.String },
                        ["song"] = new() { Type = Type.String },
                        ["era"] = new() { Type = Type.String },
                        ["released"] = new() { Type = Type.Integer }
                    }
                }
            },
            ["time_start"] = new() { Type = Type.String }
        }
    };

    string prompt = @"
    We have two friends of the host who have requested a few songs for us to play. We're going to start this playlist at 8:15.
    They'll want to hear Black Hole Sun by Soundgarden because their son was born in 1994. They will also want Loser by Beck
    coming right after which is a funny choice considering it's also the same year as their son was born, but that's probably
    just a coincidence. Add Take On Me from A-ha to the list since they were married when the song released in 1985. Their final
    request is Sweet Child O' Mine by Guns N Roses, which I think came out in 1987 when they both finished university.
    Thank you, this party should be great!";

    var generateContentRequest = new GenerateContentRequest
    {
        Model = $"projects/{projectId}/locations/{location}/publishers/{publisher}/models/{model}",
        Contents =
        {
            new Content
            {
                Role = "USER",
                Parts =
                {
                    new Part { Text = prompt }
                }
            }
        },
        GenerationConfig = new GenerationConfig
        {
            ResponseMimeType = "application/json",
            ResponseSchema = responseSchema
        },
    };

    GenerateContentResponse response = await predictionServiceClient.GenerateContentAsync(generateContentRequest);

    string responseText = response.Candidates[0].Content.Parts[0].Text;
    Console.WriteLine(responseText);

    return responseText;
}

Go

Antes de experimentar este exemplo, siga as Goinstruções de configuração no início rápido do Vertex AI com bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Go Vertex AI.

Para se autenticar no Vertex AI, configure as Credenciais padrão da aplicação. Para mais informações, consulte o artigo Configure a autenticação para um ambiente de desenvolvimento local.

import (
	"context"
	"errors"
	"fmt"
	"io"

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

// controlledGenerationResponseSchema6 shows how to make sure the generated output
// will always be valid JSON and adhere to a specific schema.
func controlledGenerationResponseSchema6(w io.Writer, projectID, location, modelName string) error {
	// location := "us-central1"
	// modelName := "gemini-2.0-flash-001"
	ctx := context.Background()
	client, err := genai.NewClient(ctx, projectID, location)
	if err != nil {
		return fmt.Errorf("unable to create client: %w", err)
	}
	defer client.Close()

	model := client.GenerativeModel(modelName)

	model.GenerationConfig.ResponseMIMEType = "application/json"

	// Build an OpenAPI schema, in memory
	model.GenerationConfig.ResponseSchema = &genai.Schema{
		Type: genai.TypeArray,
		Items: &genai.Schema{
			Type: genai.TypeArray,
			Items: &genai.Schema{
				Type: genai.TypeObject,
				Properties: map[string]*genai.Schema{
					"object": {
						Type: genai.TypeString,
					},
				},
			},
		},
	}

	// These images in Cloud Storage are viewable at
	// https://storage.googleapis.com/cloud-samples-data/generative-ai/image/office-desk.jpeg
	// https://storage.googleapis.com/cloud-samples-data/generative-ai/image/gardening-tools.jpeg

	img1 := genai.FileData{
		MIMEType: "image/jpeg",
		FileURI:  "gs://cloud-samples-data/generative-ai/image/office-desk.jpeg",
	}

	img2 := genai.FileData{
		MIMEType: "image/jpeg",
		FileURI:  "gs://cloud-samples-data/generative-ai/image/gardening-tools.jpeg",
	}

	prompt := "Generate a list of objects in the images."

	res, err := model.GenerateContent(ctx, img1, img2, genai.Text(prompt))
	if err != nil {
		return fmt.Errorf("unable to generate contents: %v", err)
	}

	if len(res.Candidates) == 0 ||
		len(res.Candidates[0].Content.Parts) == 0 {
		return errors.New("empty response from model")
	}

	fmt.Fprint(w, res.Candidates[0].Content.Parts[0])
	return nil
}

Java

Antes de experimentar este exemplo, siga as Javainstruções de configuração no início rápido do Vertex AI com bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Java Vertex AI.

Para se autenticar no Vertex AI, configure as Credenciais padrão da aplicação. Para mais informações, consulte o artigo Configure a autenticação para um ambiente de desenvolvimento local.

import com.google.cloud.vertexai.VertexAI;
import com.google.cloud.vertexai.api.GenerateContentResponse;
import com.google.cloud.vertexai.api.GenerationConfig;
import com.google.cloud.vertexai.api.Schema;
import com.google.cloud.vertexai.api.Type;
import com.google.cloud.vertexai.generativeai.ContentMaker;
import com.google.cloud.vertexai.generativeai.GenerativeModel;
import com.google.cloud.vertexai.generativeai.PartMaker;
import com.google.cloud.vertexai.generativeai.ResponseHandler;
import java.io.IOException;

public class ControlledGenerationSchema6 {
  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "genai-java-demos";
    String location = "us-central1";
    String modelName = "gemini-2.0-flash-001";

    controlGenerationWithJsonSchema6(projectId, location, modelName);
  }

  // Generate responses that are always valid JSON and comply with a JSON schema
  public static String controlGenerationWithJsonSchema6(
      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)) {
      GenerationConfig generationConfig = GenerationConfig.newBuilder()
          .setResponseMimeType("application/json")
          .setResponseSchema(Schema.newBuilder()
              .setType(Type.ARRAY)
              .setItems(Schema.newBuilder()
                  .setType(Type.OBJECT)
                  .putProperties("object", Schema.newBuilder().setType(Type.STRING).build())
                  .build())
              .build())
          .build();

      GenerativeModel model = new GenerativeModel(modelName, vertexAI)
          .withGenerationConfig(generationConfig);

      // These images in Cloud Storage are viewable at
      // https://storage.googleapis.com/cloud-samples-data/generative-ai/image/office-desk.jpeg
      // https://storage.googleapis.com/cloud-samples-data/generative-ai/image/gardening-tools.jpeg

      GenerateContentResponse response = model.generateContent(
          ContentMaker.fromMultiModalData(
              PartMaker.fromMimeTypeAndData("image/jpeg",
                  "gs://cloud-samples-data/generative-ai/image/office-desk.jpeg"),
              PartMaker.fromMimeTypeAndData("image/jpeg",
                  "gs://cloud-samples-data/generative-ai/image/gardening-tools.jpeg"),
              "Generate a list of objects in the images."
          )
      );

      String output = ResponseHandler.getText(response);
      System.out.println(output);
      return output;
    }
  }
}

O que se segue?

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