Output JSON pembuatan terkontrol dengan enum

Menampilkan objek berformat JSON dengan nilai enum, dengan deskripsi objek dan daftar nilai yang dapat dipilih.

Mempelajari lebih lanjut

Untuk dokumentasi mendetail yang menyertakan contoh kode ini, lihat artikel berikut:

• Pembuatan terkontrol

Contoh kode

C#

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan C# di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API C# Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

public async Task<string> GenerateContentWithResponseSchema4(
    string projectId = "your-project-id",
    string location = "us-central1",
    string publisher = "google",
    string model = "gemini-1.5-pro-001")
{

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

    var responseSchema = new OpenApiSchema
    {
        Type = Type.Array,
        Items = new()
        {
            Type = Type.Object,
            Properties =
            {
                ["to_discard"] = new() { Type = Type.Integer },
                ["subcategory"] = new() { Type = Type.String },
                ["safe_handling"] = new() { Type = Type.Integer },
                ["item_category"] = new()
                {
                    Type = Type.String,
                    Enum =
                    {
                        "clothing",
                        "winter apparel",
                        "specialized apparel",
                        "furniture",
                        "decor",
                        "tableware",
                        "cookware",
                        "toys"
                    }
                },
                ["for_resale"] = new() { Type = Type.Integer },
                ["condition"] = new()
                {
                    Type = Type.String,
                    Enum =
                    {
                        "new in package",
                        "like new",
                        "gently used",
                        "used",
                        "damaged",
                        "soiled"
                    }
                }
            }
        }
    };

    string prompt = @"
        Item description:
        The item is a long winter coat that has many tears all around the seams and is falling apart.
        It has large questionable stains on it.";

    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

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Go di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Go Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

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

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

// controlledGenerationResponseSchema4 shows how to make sure the generated output
// will always be valid JSON and adhere to a specific schema.
func controlledGenerationResponseSchema4(w io.Writer, projectID, location, modelName string) error {
	// location := "us-central1"
	// modelName := "gemini-1.5-pro-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.TypeObject,
			Properties: map[string]*genai.Schema{
				"to_discard":    {Type: genai.TypeInteger},
				"subcategory":   {Type: genai.TypeString},
				"safe_handling": {Type: genai.TypeString},
				"item_category": {
					Type: genai.TypeString,
					Enum: []string{
						"clothing",
						"winter apparel",
						"specialized apparel",
						"furniture",
						"decor",
						"tableware",
						"cookware",
						"toys",
					},
				},
				"for_resale": {Type: genai.TypeInteger},
				"condition": {
					Type: genai.TypeString,
					Enum: []string{
						"new in package",
						"like new",
						"gently used",
						"used",
						"damaged",
						"soiled",
					},
				},
			},
		},
	}

	prompt := `
		Item description:
		The item is a long winter coat that has many tears all around the seams and is falling apart.
		It has large questionable stains on it.
	`

	res, err := model.GenerateContent(ctx, 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

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Java di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Java Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

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.GenerativeModel;
import com.google.cloud.vertexai.generativeai.ResponseHandler;
import java.io.IOException;
import java.util.Arrays;

public class ControlledGenerationSchema4 {
  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-1.5-pro-001";

    controlGenerationWithJsonSchema4(projectId, location, modelName);
  }

  // Generate responses that are always valid JSON and comply with a JSON schema
  public static String controlGenerationWithJsonSchema4(
      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)) {
      Schema itemSchema = Schema.newBuilder()
          .setType(Type.OBJECT)
          .putProperties("to_discard", Schema.newBuilder().setType(Type.INTEGER).build())
          .putProperties("subcategory", Schema.newBuilder().setType(Type.STRING).build())
          .putProperties("safe_handling", Schema.newBuilder().setType(Type.INTEGER).build())
          .putProperties("item_category", Schema.newBuilder()
              .setType(Type.STRING)
              .addAllEnum(Arrays.asList(
                  "clothing", "winter apparel", "specialized apparel", "furniture",
                  "decor", "tableware", "cookware", "toys"))
              .build())
          .putProperties("for_resale", Schema.newBuilder().setType(Type.INTEGER).build())
          .putProperties("condition", Schema.newBuilder()
              .setType(Type.STRING)
              .addAllEnum(Arrays.asList(
                  "new in package", "like new", "gently used", "used", "damaged", "soiled"))
              .build())
          .build();

      GenerationConfig generationConfig = GenerationConfig.newBuilder()
          .setResponseMimeType("application/json")
          .setResponseSchema(Schema.newBuilder()
              .setType(Type.ARRAY)
              .setItems(itemSchema)
              .build())
          .build();

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

      GenerateContentResponse response = model.generateContent(
          "Item description:\n"
              + "The item is a long winter coat that has many tears all around the seams "
              + "and is falling apart.\n"
              + "It has large questionable stains on it."
      );

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

Python

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Python di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Python Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

import vertexai

from vertexai.generative_models import GenerationConfig, GenerativeModel

# TODO(developer): Update and un-comment below line
# PROJECT_ID = "your-project-id"
vertexai.init(project=PROJECT_ID, location="us-central1")

response_schema = {
    "type": "ARRAY",
    "items": {
        "type": "OBJECT",
        "properties": {
            "to_discard": {"type": "INTEGER"},
            "subcategory": {"type": "STRING"},
            "safe_handling": {"type": "INTEGER"},
            "item_category": {
                "type": "STRING",
                "enum": [
                    "clothing",
                    "winter apparel",
                    "specialized apparel",
                    "furniture",
                    "decor",
                    "tableware",
                    "cookware",
                    "toys",
                ],
            },
            "for_resale": {"type": "INTEGER"},
            "condition": {
                "type": "STRING",
                "enum": [
                    "new in package",
                    "like new",
                    "gently used",
                    "used",
                    "damaged",
                    "soiled",
                ],
            },
        },
    },
}

prompt = """
    Item description:
    The item is a long winter coat that has many tears all around the seams and is falling apart.
    It has large questionable stains on it.
"""

model = GenerativeModel("gemini-1.5-pro-002")

response = model.generate_content(
    prompt,
    generation_config=GenerationConfig(
        response_mime_type="application/json", response_schema=response_schema
    ),
)

print(response.text)
# Example response:
# [
#     {
#         "condition": "damaged",
#         "item_category": "clothing",
#         "subcategory": "winter apparel",
#         "to_discard": 123,
#     }
# ]

Langkah selanjutnya

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