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列挙型を使用した制御生成 JSON 出力

オブジェクトの説明と選択可能な値のリストを指定して、列挙型の値を含む JSON 形式のオブジェクトを出力します。

このコードサンプルを含む詳細なドキュメントについては、以下をご覧ください。

生成制御機能

コードサンプル

C#

このサンプルを試す前に、Vertex AI クイックスタート: クライアントライブラリの使用にある C# の設定手順を完了してください。詳細については、Vertex AI C# API のリファレンスドキュメントをご覧ください。

Vertex AI に対する認証を行うには、アプリケーションのデフォルト認証情報を設定します。詳細については、ローカル開発環境の認証を設定するをご覧ください。

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

このサンプルを試す前に、Vertex AI クイックスタート: クライアントライブラリの使用にある Go の設定手順を完了してください。詳細については、Vertex AI Go API のリファレンスドキュメントをご覧ください。

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

このサンプルを試す前に、Vertex AI クイックスタート: クライアントライブラリの使用にある Java の設定手順を完了してください。詳細については、Vertex AI Java API のリファレンスドキュメントをご覧ください。

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

このサンプルを試す前に、Vertex AI クイックスタート: クライアントライブラリの使用にある Python の設定手順を完了してください。詳細については、Vertex AI Python API のリファレンスドキュメントをご覧ください。

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,
#     }
# ]

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列挙型を使用した制御生成 JSON 出力

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コードサンプル

C#

Go

Java

Python

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