Respostas estruturadas

É possível configurar os modelos do Gemini para gerar respostas que aderem a um esquema JSON fornecido. Isso garante resultados previsíveis e com segurança de tipos, além de simplificar a extração de dados estruturados de textos não estruturados.

O uso de respostas estruturadas é ideal para:

  • Extração de dados:extraia informações específicas, como nomes e datas, de um texto.
  • Classificação estruturada:classifique o texto em categorias predefinidas.
  • Agentic Workflows:gere entradas estruturadas para ferramentas ou APIs.

Além de oferecer suporte ao esquema JSON na API REST, os SDKs de IA generativa do Google permitem definir esquemas usando Pydantic (Python) e Zod (JavaScript).

Exemplos de saída estruturada

Extrator de receitas

Este exemplo demonstra como extrair dados estruturados de um texto usando tipos básicos de esquema JSON, como object, array, string e integer.

Python

from google import genai
from pydantic import BaseModel, Field
from typing import List, Optional

class Ingredient(BaseModel):
    name: str = Field(description="Name of the ingredient.")
    quantity: str = Field(description="Quantity of the ingredient, including units.")

class Recipe(BaseModel):
    recipe_name: str = Field(description="The name of the recipe.")
    prep_time_minutes: Optional[int] = Field(description="Optional time in minutes to prepare the recipe.")
    ingredients: List[Ingredient]
    instructions: List[str]

client = genai.Client()

prompt = """
Please extract the recipe from the following text.
The user wants to make delicious chocolate chip cookies.
They need 2 and 1/4 cups of all-purpose flour, 1 teaspoon of baking soda,
1 teaspoon of salt, 1 cup of unsalted butter (softened), 3/4 cup of granulated sugar,
3/4 cup of packed brown sugar, 1 teaspoon of vanilla extract, and 2 large eggs.
For the best part, they'll need 2 cups of semisweet chocolate chips.
First, preheat the oven to 375°F (190°C). Then, in a small bowl, whisk together the flour,
baking soda, and salt. In a large bowl, cream together the butter, granulated sugar, and brown sugar
until light and fluffy. Beat in the vanilla and eggs, one at a time. Gradually beat in the dry
ingredients until just combined. Finally, stir in the chocolate chips. Drop by rounded tablespoons
onto ungreased baking sheets and bake for 9 to 11 minutes.
"""

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=prompt,
    response_format={
        "type": "text",
        "mime_type": "application/json",
        "schema": Recipe.model_json_schema()
    },
)

recipe = Recipe.model_validate_json(interaction.output_text)
print(recipe)

JavaScript

// Note: Ensure zod is installed (npm install zod)
import { GoogleGenAI } from "@google/genai";
import * as z from "zod";

const recipeJsonSchema = {
  type: "object",
  properties: {
    recipe_name: {
      type: "string",
      description: "The name of the recipe."
    },
    prep_time_minutes: {
        type: "integer",
        description: "Optional time in minutes to prepare the recipe."
    },
    ingredients: {
      type: "array",
      items: {
        type: "object",
        properties: {
          name: { type: "string", description: "Name of the ingredient."},
          quantity: { type: "string", description: "Quantity of the ingredient, including units."}
        },
        required: ["name", "quantity"]
      }
    },
    instructions: {
      type: "array",
      items: { type: "string" }
    }
  },
  required: ["recipe_name", "ingredients", "instructions"]
};

const recipeSchema = z.fromJSONSchema(recipeJsonSchema);

const client = new GoogleGenAI({});

const prompt = `
Please extract the recipe from the following text.
The user wants to make delicious chocolate chip cookies.
They need 2 and 1/4 cups of all-purpose flour, 1 teaspoon of baking soda,
1 teaspoon of salt, 1 cup of unsalted butter (softened), 3/4 cup of granulated sugar,
3/4 cup of packed brown sugar, 1 teaspoon of vanilla extract, and 2 large eggs.
For the best part, they'll need 2 cups of semisweet chocolate chips.
First, preheat the oven to 375°F (190°C). Then, in a small bowl, whisk together the flour,
baking soda, and salt. In a large bowl, cream together the butter, granulated sugar, and brown sugar
until light and fluffy. Beat in the vanilla and eggs, one at a time. Gradually beat in the dry
ingredients until just combined. Finally, stir in the chocolate chips. Drop by rounded tablespoons
onto ungreased baking sheets and bake for 9 to 11 minutes.
`;

const interaction = await client.interactions.create({
  model: "gemini-3.8-flash",
  input: prompt,
  response_format: {
    type: 'text',
    mime_type: 'application/json',
    schema: recipeJsonSchema
  },
});

const recipe = recipeSchema.parse(JSON.parse(interaction.output_text));
console.log(recipe);

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.CreateModelInteractionResponseFormat;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.ResponseFormat;
import com.google.genai.gaos.models.interactions.TextResponseFormat;
import com.google.genai.gaos.models.interactions.TextResponseFormatMimeType;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;

Client client = new Client();

Map<String, Object> ingredientProps = new HashMap<>();
Map<String, Object> nameProp = new HashMap<>();
nameProp.put("type", "string");
nameProp.put("description", "Name of the ingredient.");
ingredientProps.put("name", nameProp);

Map<String, Object> quantityProp = new HashMap<>();
quantityProp.put("type", "string");
quantityProp.put("description", "Quantity of the ingredient, including units.");
ingredientProps.put("quantity", quantityProp);

Map<String, Object> ingredientItemSchema = new HashMap<>();
ingredientItemSchema.put("type", "object");
ingredientItemSchema.put("properties", ingredientProps);
ingredientItemSchema.put("required", Arrays.asList("name", "quantity"));

Map<String, Object> properties = new HashMap<>();

Map<String, Object> recipeNameProp = new HashMap<>();
recipeNameProp.put("type", "string");
recipeNameProp.put("description", "The name of the recipe.");
properties.put("recipe_name", recipeNameProp);

Map<String, Object> prepTimeProp = new HashMap<>();
prepTimeProp.put("type", "integer");
prepTimeProp.put("description", "Optional time in minutes to prepare the recipe.");
properties.put("prep_time_minutes", prepTimeProp);

Map<String, Object> ingredientsProp = new HashMap<>();
ingredientsProp.put("type", "array");
ingredientsProp.put("items", ingredientItemSchema);
properties.put("ingredients", ingredientsProp);

Map<String, Object> instructionsProp = new HashMap<>();
instructionsProp.put("type", "array");
Map<String, Object> stringItem = new HashMap<>();
stringItem.put("type", "string");
instructionsProp.put("items", stringItem);
properties.put("instructions", instructionsProp);

Map<String, Object> recipeJsonSchema = new HashMap<>();
recipeJsonSchema.put("type", "object");
recipeJsonSchema.put("properties", properties);
recipeJsonSchema.put("required", Arrays.asList("recipe_name", "ingredients", "instructions"));

String prompt =
    "Please extract the recipe from the following text.\n"
        + "The user wants to make delicious chocolate chip cookies.\n"
        + "They need 2 and 1/4 cups of all-purpose flour, 1 teaspoon of baking soda,\n"
        + "1 teaspoon of salt, 1 cup of unsalted butter (softened), 3/4 cup of granulated sugar,\n"
        + "3/4 cup of packed brown sugar, 1 teaspoon of vanilla extract, and 2 large eggs.\n"
        + "For the best part, they'll need 2 cups of semisweet chocolate chips.\n"
        + "First, preheat the oven to 375°F (190°C). Then, in a small bowl, whisk together the flour,\n"
        + "baking soda, and salt. In a large bowl, cream together the butter, granulated sugar, and brown sugar\n"
        + "until light and fluffy. Beat in the vanilla and eggs, one at a time. Gradually beat in the dry\n"
        + "ingredients until just combined. Finally, stir in the chocolate chips. Drop by rounded tablespoons\n"
        + "onto ungreased baking sheets and bake for 9 to 11 minutes.";

CreateModelInteractionResponseFormat format =
    CreateModelInteractionResponseFormat.of(
        ResponseFormat.of(
            TextResponseFormat.builder()
                .mimeType(TextResponseFormatMimeType.APPLICATION_JSON)
                .schema(recipeJsonSchema)
                .build()));

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.of(prompt))
        .responseFormat(format)
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

System.out.println(interaction.outputText().orElse(""));

Go

package main

import (
    "context"
    "fmt"
    "log"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    recipeJsonSchema := map[string]any{
        "type": "object",
        "properties": map[string]any{
            "recipe_name": map[string]any{
                "type":        "string",
                "description": "The name of the recipe.",
            },
            "prep_time_minutes": map[string]any{
                "type":        "integer",
                "description": "Optional time in minutes to prepare the recipe.",
            },
            "ingredients": map[string]any{
                "type": "array",
                "items": map[string]any{
                    "type": "object",
                    "properties": map[string]any{
                        "name": map[string]any{
                            "type":        "string",
                            "description": "Name of the ingredient.",
                        },
                        "quantity": map[string]any{
                            "type":        "string",
                            "description": "Quantity of the ingredient, including units.",
                        },
                    },
                    "required": []string{"name", "quantity"},
                },
            },
            "instructions": map[string]any{
                "type": "array",
                "items": map[string]any{
                    "type": "string",
                },
            },
        },
        "required": []string{"recipe_name", "ingredients", "instructions"},
    }

    prompt := `Please extract the recipe from the following text.
The user wants to make delicious chocolate chip cookies.
They need 2 and 1/4 cups of all-purpose flour, 1 teaspoon of baking soda,
1 teaspoon of salt, 1 cup of unsalted butter (softened), 3/4 cup of granulated sugar,
3/4 cup of packed brown sugar, 1 teaspoon of vanilla extract, and 2 large eggs.
For the best part, they'll need 2 cups of semisweet chocolate chips.
First, preheat the oven to 375°F (190°C). Then, in a small bowl, whisk together the flour,
baking soda, and salt. In a large bowl, cream together the butter, granulated sugar, and brown sugar
until light and fluffy. Beat in the vanilla and eggs, one at a time. Gradually beat in the dry
ingredients until just combined. Finally, stir in the chocolate chips. Drop by rounded tablespoons
onto ungreased baking sheets and bake for 9 to 11 minutes.`

    format := interactions.NewCreateModelInteractionResponseFormat(
        interactions.NewResponseFormat(interactions.TextResponseFormat{
            MimeType: interactions.TextResponseFormatMimeTypeApplicationJSON.ToPointer(),
            Schema:   recipeJsonSchema,
        }),
    )

    resp, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(
            interactions.CreateModelInteraction{
                Model:          interactions.Model("gemini-3.8-flash"),
                Input:          interactions.NewInteractionsInput(prompt),
                ResponseFormat: &format,
            },
        ),
    })
    if err != nil {
        log.Fatal(err)
    }

    fmt.Println(resp.Interaction.GetOutputText())
}

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "gemini-3.8-flash",
      "input": "Please extract the recipe from the following text.\nThe user wants to make delicious chocolate chip cookies.\nThey need 2 and 1/4 cups of all-purpose flour, 1 teaspoon of baking soda,\n1 teaspoon of salt, 1 cup of unsalted butter (softened), 3/4 cup of granulated sugar,\n3/4 cup of packed brown sugar, 1 teaspoon of vanilla extract, and 2 large eggs.\nFor the best part, they will need 2 cups of semisweet chocolate chips.\nFirst, preheat the oven to 375°F (190°C). Then, in a small bowl, whisk together the flour,\nbaking soda, and salt. In a large bowl, cream together the butter, granulated sugar, and brown sugar\nuntil light and fluffy. Beat in the vanilla and eggs, one at a time. Gradually beat in the dry\ningredients until just combined. Finally, stir in the chocolate chips. Drop by rounded tablespoons\nonto ungreased baking sheets and bake for 9 to 11 minutes.",
      "response_format": {
        "type": "text",
        "mime_type": "application/json",
        "schema": {
          "type": "object",
          "properties": {
            "recipe_name": {
              "type": "string",
              "description": "The name of the recipe."
            },
            "prep_time_minutes": {
                "type": "integer",
                "description": "Optional time in minutes to prepare the recipe."
            },
            "ingredients": {
              "type": "array",
              "items": {
                "type": "object",
                "properties": {
                  "name": { "type": "string", "description": "Name of the ingredient."},
                  "quantity": { "type": "string", "description": "Quantity of the ingredient, including units."}
                },
                "required": ["name", "quantity"]
              }
            },
            "instructions": {
              "type": "array",
              "items": { "type": "string" }
            }
          },
          "required": ["recipe_name", "ingredients", "instructions"]
        }
      }
      }
    }'

Exemplo de resposta:

{
  "recipe_name": "Delicious Chocolate Chip Cookies",
  "ingredients": [
    { "name": "all-purpose flour", "quantity": "2 and 1/4 cups" },
    { "name": "baking soda", "quantity": "1 teaspoon" },
    { "name": "salt", "quantity": "1 teaspoon" },
    { "name": "unsalted butter (softened)", "quantity": "1 cup" },
    { "name": "granulated sugar", "quantity": "3/4 cup" },
    { "name": "packed brown sugar", "quantity": "3/4 cup" },
    { "name": "vanilla extract", "quantity": "1 teaspoon" },
    { "name": "large eggs", "quantity": "2" },
    { "name": "semisweet chocolate chips", "quantity": "2 cups" }
  ],
  "instructions": [
    "Preheat the oven to 375°F (190°C).",
    "In a small bowl, whisk together the flour, baking soda, and salt.",
    "In a large bowl, cream together the butter, granulated sugar, and brown sugar until light and fluffy.",
    "Beat in the vanilla and eggs, one at a time.",
    "Gradually beat in the dry ingredients until just combined.",
    "Stir in the chocolate chips.",
    "Drop by rounded tablespoons onto ungreased baking sheets and bake for 9 to 11 minutes."
  ]
}

Moderação de conteúdo

Este exemplo mostra anyOf para esquemas condicionais e enum para classificação, permitindo que a estrutura de saída varie com base no conteúdo.

Python

from google import genai
from pydantic import BaseModel, Field
from typing import Union, Literal

class SpamDetails(BaseModel):
    reason: str = Field(description="The reason why the content is considered spam.")
    spam_type: Literal["phishing", "scam", "unsolicited promotion", "other"] = Field(description="The type of spam.")

class NotSpamDetails(BaseModel):
    summary: str = Field(description="A brief summary of the content.")
    is_safe: bool = Field(description="Whether the content is safe for all audiences.")

class ModerationResult(BaseModel):
    decision: Union[SpamDetails, NotSpamDetails]

client = genai.Client()

prompt = """
Please moderate the following content and provide a decision.
Content: 'Congratulations! You''ve won a free cruise to the Bahamas. Click here to claim your prize: www.definitely-not-a-scam.com'
"""

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=prompt,
    response_format={
        "type": "text",
        "mime_type": "application/json",
        "schema": ModerationResult.model_json_schema()
    },
)

result = ModerationResult.model_validate_json(interaction.output_text)
print(result)

JavaScript

// Note: Ensure zod is installed (npm install zod)
import { GoogleGenAI } from "@google/genai";
import * as z from "zod";

const moderationResultJsonSchema = {
  type: "object",
  properties: {
    decision: {
      anyOf: [
        {
          type: "object",
          title: "SpamDetails",
          description: "Details for content classified as spam.",
          properties: {
            reason: { type: "string", description: "The reason why the content is considered spam." },
            spam_type: { type: "string", enum: ["phishing", "scam", "unsolicited promotion", "other"], description: "The type of spam." }
          },
          required: ["reason", "spam_type"]
        },
        {
          type: "object",
          title: "NotSpamDetails",
          description: "Details for content classified as not spam.",
          properties: {
            summary: { type: "string", description: "A brief summary of the content." },
            is_safe: { type: "boolean", description: "Whether the content is safe for all audiences." }
          },
          required: ["summary", "is_safe"]
        }
      ]
    }
  },
  required: ["decision"]
};

const moderationResultSchema = z.fromJSONSchema(moderationResultJsonSchema);

const client = new GoogleGenAI({});

const prompt = `
Please moderate the following content and provide a decision.
Content: 'Congratulations! You''ve won a free cruise to the Bahamas. Click here to claim your prize: www.definitely-not-a-scam.com'
`;

const interaction = await client.interactions.create({
  model: "gemini-3.8-flash",
  input: prompt,
  response_format: {
    type: 'text',
    mime_type: 'application/json',
    schema: moderationResultJsonSchema
  },
});

const result = moderationResultSchema.parse(JSON.parse(interaction.output_text));
console.log(result);

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.CreateModelInteractionResponseFormat;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.ResponseFormat;
import com.google.genai.gaos.models.interactions.TextResponseFormat;
import com.google.genai.gaos.models.interactions.TextResponseFormatMimeType;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;

Client client = new Client();

Map<String, Object> spamProps = new HashMap<>();
Map<String, Object> reasonProp = new HashMap<>();
reasonProp.put("type", "string");
reasonProp.put("description", "The reason why the content is considered spam.");
spamProps.put("reason", reasonProp);

Map<String, Object> spamTypeProp = new HashMap<>();
spamTypeProp.put("type", "string");
spamTypeProp.put("enum", Arrays.asList("phishing", "scam", "unsolicited promotion", "other"));
spamTypeProp.put("description", "The type of spam.");
spamProps.put("spam_type", spamTypeProp);

Map<String, Object> spamDetailsSchema = new HashMap<>();
spamDetailsSchema.put("type", "object");
spamDetailsSchema.put("title", "SpamDetails");
spamDetailsSchema.put("properties", spamProps);
spamDetailsSchema.put("required", Arrays.asList("reason", "spam_type"));

Map<String, Object> notSpamProps = new HashMap<>();
Map<String, Object> summaryProp = new HashMap<>();
summaryProp.put("type", "string");
summaryProp.put("description", "A brief summary of the content.");
notSpamProps.put("summary", summaryProp);

Map<String, Object> isSafeProp = new HashMap<>();
isSafeProp.put("type", "boolean");
isSafeProp.put("description", "Whether the content is safe for all audiences.");
notSpamProps.put("is_safe", isSafeProp);

Map<String, Object> notSpamDetailsSchema = new HashMap<>();
notSpamDetailsSchema.put("type", "object");
notSpamDetailsSchema.put("title", "NotSpamDetails");
notSpamDetailsSchema.put("properties", notSpamProps);
notSpamDetailsSchema.put("required", Arrays.asList("summary", "is_safe"));

Map<String, Object> decisionProp = new HashMap<>();
decisionProp.put("anyOf", Arrays.asList(spamDetailsSchema, notSpamDetailsSchema));

Map<String, Object> properties = new HashMap<>();
properties.put("decision", decisionProp);

Map<String, Object> moderationResultJsonSchema = new HashMap<>();
moderationResultJsonSchema.put("type", "object");
moderationResultJsonSchema.put("properties", properties);
moderationResultJsonSchema.put("required", Arrays.asList("decision"));

String prompt =
    "Please moderate the following content and provide a decision.\n"
        + "Content: 'Congratulations! You''ve won a free cruise to the Bahamas. Click here to claim your prize: www.definitely-not-a-scam.com'";

CreateModelInteractionResponseFormat format =
    CreateModelInteractionResponseFormat.of(
        ResponseFormat.of(
            TextResponseFormat.builder()
                .mimeType(TextResponseFormatMimeType.APPLICATION_JSON)
                .schema(moderationResultJsonSchema)
                .build()));

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.of(prompt))
        .responseFormat(format)
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

System.out.println(interaction.outputText().orElse(""));

Go

package main

import (
    "context"
    "fmt"
    "log"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    spamDetailsSchema := map[string]any{
        "type":  "object",
        "title": "SpamDetails",
        "properties": map[string]any{
            "reason": map[string]any{
                "type":        "string",
                "description": "The reason why the content is considered spam.",
            },
            "spam_type": map[string]any{
                "type":        "string",
                "enum":        []string{"phishing", "scam", "unsolicited promotion", "other"},
                "description": "The type of spam.",
            },
        },
        "required": []string{"reason", "spam_type"},
    }

    notSpamDetailsSchema := map[string]any{
        "type":  "object",
        "title": "NotSpamDetails",
        "properties": map[string]any{
            "summary": map[string]any{
                "type":        "string",
                "description": "A brief summary of the content.",
            },
            "is_safe": map[string]any{
                "type":        "boolean",
                "description": "Whether the content is safe for all audiences.",
            },
        },
        "required": []string{"summary", "is_safe"},
    }

    moderationResultJsonSchema := map[string]any{
        "type": "object",
        "properties": map[string]any{
            "decision": map[string]any{
                "anyOf": []any{spamDetailsSchema, notSpamDetailsSchema},
            },
        },
        "required": []string{"decision"},
    }

    prompt := "Please moderate the following content and provide a decision.\n" +
        "Content: 'Congratulations! You've won a free cruise to the Bahamas. Click here to claim your prize: www.definitely-not-a-scam.com'"

    format := interactions.NewCreateModelInteractionResponseFormat(
        interactions.NewResponseFormat(interactions.TextResponseFormat{
            MimeType: interactions.TextResponseFormatMimeTypeApplicationJSON.ToPointer(),
            Schema:   moderationResultJsonSchema,
        }),
    )

    resp, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(
            interactions.CreateModelInteraction{
                Model:          interactions.Model("gemini-3.8-flash"),
                Input:          interactions.NewInteractionsInput(prompt),
                ResponseFormat: &format,
            },
        ),
    })
    if err != nil {
        log.Fatal(err)
    }

    fmt.Println(resp.Interaction.GetOutputText())
}

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "gemini-3.8-flash",
      "input": "Please moderate the following content and provide a decision.\nContent: '\''Congratulations! You have won a free cruise to the Bahamas. Click here to claim your prize: www.definitely-not-a-scam.com'\''",
      "response_format": {
        "type": "text",
        "mime_type": "application/json",
        "schema": {
          "type": "object",
          "properties": {
            "decision": {
              "anyOf": [
                {
                  "type": "object",
                  "title": "SpamDetails",
                  "description": "Details for content classified as spam.",
                  "properties": {
                    "reason": { "type": "string", "description": "The reason why the content is considered spam." },
                    "spam_type": { "type": "string", "enum": ["phishing", "scam", "unsolicited promotion", "other"], "description": "The type of spam." }
                  },
                  "required": ["reason", "spam_type"]
                },
                {
                  "type": "object",
                  "title": "NotSpamDetails",
                  "description": "Details for content classified as not spam.",
                  "properties": {
                    "summary": { "type": "string", "description": "A brief summary of the content." },
                    "is_safe": { "type": "boolean", "description": "Whether the content is safe for all audiences." }
                  },
                  "required": ["summary", "is_safe"]
                }
              ]
            }
          },
          "required": ["decision"]
        }
      }
      }
    }'

Exemplo de resposta:

{
  "decision": {
    "reason": "The content is an unsolicited prize notification attempting to trick the user into clicking a suspicious link.",
    "spam_type": "scam"
  }
}

Estruturas recursivas

Este exemplo ilustra como definir um esquema recursivo, como um organograma.

Python

from google import genai
from pydantic import BaseModel, Field
from typing import List

class Employee(BaseModel):
    """Represents an employee in an organization."""
    name: str
    employee_id: int
    reports: List["Employee"] = Field(
        default_factory=list,
        description="A list of employees reporting to this employee."
    )

client = genai.Client()

prompt = """
Generate an organization chart for a small team.
The manager is Alice, who manages Bob and Charlie. Bob manages David.
"""

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=prompt,
    response_format={
        "type": "text",
        "mime_type": "application/json",
        "schema": Employee.model_json_schema()
    },
)

employee = Employee.model_validate_json(interaction.output_text)
print(employee)

JavaScript

// Note: Ensure zod is installed (npm install zod)
import { GoogleGenAI } from "@google/genai";
import * as z from "zod";

const employeeJsonSchema = {
  type: "object",
  properties: {
    name: { type: "string" },
    employee_id: { type: "integer" },
    reports: {
      type: "array",
      description: "A list of employees reporting to this employee.",
      items: {
        "$ref": "#"
      }
    }
  },
  required: ["name", "employee_id", "reports"]
};

const employeeSchema = z.fromJSONSchema(employeeJsonSchema);

const client = new GoogleGenAI({});

const prompt = `
Generate an organization chart for a small team.
The manager is Alice, who manages Bob and Charlie. Bob manages David.
`;

const interaction = await client.interactions.create({
  model: "gemini-3.8-flash",
  input: prompt,
  response_format: {
    type: 'text',
    mime_type: 'application/json',
    schema: employeeJsonSchema
  },
});

const employee = employeeSchema.parse(JSON.parse(interaction.output_text));
console.log(employee);

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.CreateModelInteractionResponseFormat;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.ResponseFormat;
import com.google.genai.gaos.models.interactions.TextResponseFormat;
import com.google.genai.gaos.models.interactions.TextResponseFormatMimeType;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.Map;

Client client = new Client();

Map<String, Object> properties = new HashMap<>();

Map<String, Object> nameProp = new HashMap<>();
nameProp.put("type", "string");
properties.put("name", nameProp);

Map<String, Object> idProp = new HashMap<>();
idProp.put("type", "integer");
properties.put("employee_id", idProp);

Map<String, Object> reportsProp = new HashMap<>();
reportsProp.put("type", "array");
reportsProp.put("description", "A list of employees reporting to this employee.");
reportsProp.put("items", Collections.singletonMap("$ref", "#"));
properties.put("reports", reportsProp);

Map<String, Object> employeeJsonSchema = new HashMap<>();
employeeJsonSchema.put("type", "object");
employeeJsonSchema.put("properties", properties);
employeeJsonSchema.put("required", Arrays.asList("name", "employee_id", "reports"));

String prompt =
    "Generate an organization chart for a small team.\n"
        + "The manager is Alice, who manages Bob and Charlie. Bob manages David.";

CreateModelInteractionResponseFormat format =
    CreateModelInteractionResponseFormat.of(
        ResponseFormat.of(
            TextResponseFormat.builder()
                .mimeType(TextResponseFormatMimeType.APPLICATION_JSON)
                .schema(employeeJsonSchema)
                .build()));

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.of(prompt))
        .responseFormat(format)
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

System.out.println(interaction.outputText().orElse(""));

Go

package main

import (
    "context"
    "fmt"
    "log"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    employeeJsonSchema := map[string]any{
        "type": "object",
        "properties": map[string]any{
            "name": map[string]any{
                "type": "string",
            },
            "employee_id": map[string]any{
                "type": "integer",
            },
            "reports": map[string]any{
                "type":        "array",
                "description": "A list of employees reporting to this employee.",
                "items": map[string]any{
                    "$ref": "#",
                },
            },
        },
        "required": []string{"name", "employee_id", "reports"},
    }

    prompt := "Generate an organization chart for a small team.\n" +
        "The manager is Alice, who manages Bob and Charlie. Bob manages David."

    format := interactions.NewCreateModelInteractionResponseFormat(
        interactions.NewResponseFormat(interactions.TextResponseFormat{
            MimeType: interactions.TextResponseFormatMimeTypeApplicationJSON.ToPointer(),
            Schema:   employeeJsonSchema,
        }),
    )

    resp, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(
            interactions.CreateModelInteraction{
                Model:          interactions.Model("gemini-3.8-flash"),
                Input:          interactions.NewInteractionsInput(prompt),
                ResponseFormat: &format,
            },
        ),
    })
    if err != nil {
        log.Fatal(err)
    }

    fmt.Println(resp.Interaction.GetOutputText())
}

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "gemini-3.8-flash",
      "input": "Generate an organization chart for a small team.\nThe manager is Alice, who manages Bob and Charlie. Bob manages David.",
      "response_format": {
        "type": "text",
        "mime_type": "application/json",
        "schema": {
          "type": "object",
          "properties": {
            "name": { "type": "string" },
            "employee_id": { "type": "integer" },
            "reports": {
              "type": "array",
              "description": "A list of employees reporting to this employee.",
              "items": {
                "$ref": "#"
              }
            }
          },
          "required": ["name", "employee_id", "reports"]
        }
      }
      }
    }'

Exemplo de resposta:

{
  "name": "Alice",
  "employee_id": 101,
  "reports": [
    {
      "name": "Bob",
      "employee_id": 102,
      "reports": [
        {
          "name": "David",
          "employee_id": 104,
          "reports": []
        }
      ]
    },
    {
      "name": "Charlie",
      "employee_id": 103,
      "reports": []
    }
  ]
}

Resultados de streaming

É possível transmitir saídas estruturadas, o que permite começar a processar a resposta enquanto ela é gerada. Os blocos transmitidos são strings JSON parciais válidas que podem ser concatenadas para formar o objeto JSON final.

Python

from google import genai
from pydantic import BaseModel
from typing import Literal

class Feedback(BaseModel):
    sentiment: Literal["positive", "neutral", "negative"]
    summary: str

client = genai.Client()
prompt = "The new UI is incredibly intuitive. Add a very long summary to test streaming!"

stream = client.interactions.create(
    model="gemini-3.8-flash",
    input=prompt,
    response_format={
        "type": "text",
        "mime_type": "application/json",
        "schema": Feedback.model_json_schema()
    },
    stream=True
)
for event in stream:
    if event.event_type == "step.delta":
        if event.delta.type == "text" and getattr(event.delta, "text", None):
            print(event.delta.text, end="", flush=True)

JavaScript

// Note: Ensure zod is installed (npm install zod)
import { GoogleGenAI } from "@google/genai";
import * as z from "zod";

const feedbackJsonSchema = {
  type: "object",
  properties: {
    sentiment: { type: "string", enum: ["positive", "neutral", "negative"] },
    summary: { type: "string" }
  },
  required: ["sentiment", "summary"]
};

const feedbackSchema = z.fromJSONSchema(feedbackJsonSchema);

const client = new GoogleGenAI({});

const stream = await client.interactions.create({
  model: "gemini-3.8-flash",
  input: "The new UI is incredibly intuitive. Add a very long summary!",
  response_format: {
    type: 'text',
    mime_type: 'application/json',
    schema: feedbackJsonSchema
  },
  stream: true,
});

for await (const event of stream) {
  if (event.event_type === "step.delta") {
    if (event.delta.type === "text") {
      process.stdout.write(event.delta.text);
    }
  }
}

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.CreateModelInteractionResponseFormat;
import com.google.genai.gaos.models.interactions.InteractionSSEEvent;
import com.google.genai.gaos.models.interactions.InteractionSSEStreamEvent;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.ResponseFormat;
import com.google.genai.gaos.models.interactions.StepDelta;
import com.google.genai.gaos.models.interactions.StepDeltaData;
import com.google.genai.gaos.models.interactions.TextDelta;
import com.google.genai.gaos.models.interactions.TextResponseFormat;
import com.google.genai.gaos.models.interactions.TextResponseFormatMimeType;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.gaos.models.operations.CreateInteractionResponse;
import com.google.genai.gaos.utils.EventStream;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;

Client client = new Client();

Map<String, Object> properties = new HashMap<>();

Map<String, Object> sentimentProp = new HashMap<>();
sentimentProp.put("type", "string");
sentimentProp.put("enum", Arrays.asList("positive", "neutral", "negative"));
properties.put("sentiment", sentimentProp);

Map<String, Object> summaryProp = new HashMap<>();
summaryProp.put("type", "string");
properties.put("summary", summaryProp);

Map<String, Object> feedbackJsonSchema = new HashMap<>();
feedbackJsonSchema.put("type", "object");
feedbackJsonSchema.put("properties", properties);
feedbackJsonSchema.put("required", Arrays.asList("sentiment", "summary"));

String prompt = "The new UI is incredibly intuitive. Add a very long summary to test streaming!";

CreateModelInteractionResponseFormat format =
    CreateModelInteractionResponseFormat.of(
        ResponseFormat.of(
            TextResponseFormat.builder()
                .mimeType(TextResponseFormatMimeType.APPLICATION_JSON)
                .schema(feedbackJsonSchema)
                .build()));

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.of(prompt))
        .responseFormat(format)
        .stream(true)
        .build();

CreateInteractionResponse response =
    client.interactions.create(CreateInteractionRequestBody.of(params));

try (EventStream<InteractionSSEStreamEvent> events = response.events()) {
  for (InteractionSSEStreamEvent streamEvent : events) {
    InteractionSSEEvent event = streamEvent.data().orElse(null);
    if (event instanceof StepDelta) {
      StepDeltaData data = ((StepDelta) event).delta().orElse(null);
      if (data instanceof TextDelta) {
        ((TextDelta) data).text().ifPresent(System.out::print);
      }
    }
  }
}

Go

package main

import (
    "context"
    "fmt"
    "log"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    feedbackJsonSchema := map[string]any{
        "type": "object",
        "properties": map[string]any{
            "sentiment": map[string]any{
                "type": "string",
                "enum": []string{"positive", "neutral", "negative"},
            },
            "summary": map[string]any{
                "type": "string",
            },
        },
        "required": []string{"sentiment", "summary"},
    }

    prompt := "The new UI is incredibly intuitive. Add a very long summary to test streaming!"

    format := interactions.NewCreateModelInteractionResponseFormat(
        interactions.NewResponseFormat(interactions.TextResponseFormat{
            MimeType: interactions.TextResponseFormatMimeTypeApplicationJSON.ToPointer(),
            Schema:   feedbackJsonSchema,
        }),
    )

    resp, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(
            interactions.CreateModelInteraction{
                Model:          interactions.Model("gemini-3.8-flash"),
                Input:          interactions.NewInteractionsInput(prompt),
                ResponseFormat: &format,
                Stream:         genai.Ptr(true),
            },
        ),
    })
    if err != nil {
        log.Fatal(err)
    }
    defer resp.InteractionSSEStreamEvent.Close()

    for resp.InteractionSSEStreamEvent.Next() {
        event := resp.InteractionSSEStreamEvent.Value()
        if stepDelta := event.GetDataStepDelta(); stepDelta != nil {
            if textDelta := stepDelta.GetDeltaText(); textDelta != nil {
                fmt.Print(textDelta.GetText())
            }
        }
    }
}

REST

curl -N -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "gemini-3.8-flash",
      "input": "The new UI is incredibly intuitive. Add a very long summary!",
      "response_format": {
        "type": "text",
        "mime_type": "application/json",
        "schema": {
          "type": "object",
          "properties": {
            "sentiment": { "type": "string", "enum": ["positive", "neutral", "negative"] },
            "summary": { "type": "string" }
          },
          "required": ["sentiment", "summary"]
        }
      },
      "stream": true
    }'

Respostas estruturadas com ferramentas

O Gemini 3 permite combinar saídas estruturadas com ferramentas integradas, incluindo embasamento com a Pesquisa Google, contexto de URL, execução de código, pesquisa de arquivos e chamada de função.

Python

from google import genai
from pydantic import BaseModel, Field
from typing import List

class MatchResult(BaseModel):
    winner: str = Field(description="The name of the winner.")
    final_match_score: str = Field(description="The final match score.")
    scorers: List[str] = Field(description="The name of the scorer.")

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-4-argon",
    input="Search for all details for the latest Euro.",
    tools=[{"type": "google_search"}, {"type": "url_context"}],
    response_format={
        "type": "text",
        "mime_type": "application/json",
        "schema": MatchResult.model_json_schema()
    },
)

result = MatchResult.model_validate_json(interaction.output_text)
print(result)

JavaScript

// Note: Ensure zod is installed (npm install zod)
import { GoogleGenAI } from "@google/genai";
import * as z from "zod";

const matchJsonSchema = {
  type: "object",
  properties: {
    winner: { type: "string" },
    final_match_score: { type: "string" },
    scorers: { type: "array", items: { type: "string" } }
  },
  required: ["winner", "final_match_score", "scorers"]
};

const matchSchema = z.fromJSONSchema(matchJsonSchema);

const client = new GoogleGenAI({});

const interaction = await client.interactions.create({
  model: "gemini-4-argon",
  input: "Search for all details for the latest Euro.",
  tools: [{type: "google_search"}, {type: "url_context"}],
  response_format: {
    type: 'text',
    mime_type: 'application/json',
    schema: matchJsonSchema
  },
});

const match = matchSchema.parse(JSON.parse(interaction.output_text));
console.log(match);

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.CreateModelInteractionResponseFormat;
import com.google.genai.gaos.models.interactions.GoogleSearch;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.ResponseFormat;
import com.google.genai.gaos.models.interactions.TextResponseFormat;
import com.google.genai.gaos.models.interactions.TextResponseFormatMimeType;
import com.google.genai.gaos.models.interactions.URLContext;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.Map;

Client client = new Client();

Map<String, Object> properties = new HashMap<>();

Map<String, Object> winnerProp = new HashMap<>();
winnerProp.put("type", "string");
winnerProp.put("description", "The name of the winner.");
properties.put("winner", winnerProp);

Map<String, Object> scoreProp = new HashMap<>();
scoreProp.put("type", "string");
scoreProp.put("description", "The final match score.");
properties.put("final_match_score", scoreProp);

Map<String, Object> scorersProp = new HashMap<>();
scorersProp.put("type", "array");
scorersProp.put("description", "The name of the scorer.");
scorersProp.put("items", Collections.singletonMap("type", "string"));
properties.put("scorers", scorersProp);

Map<String, Object> matchJsonSchema = new HashMap<>();
matchJsonSchema.put("type", "object");
matchJsonSchema.put("properties", properties);
matchJsonSchema.put("required", Arrays.asList("winner", "final_match_score", "scorers"));

CreateModelInteractionResponseFormat format =
    CreateModelInteractionResponseFormat.of(
        ResponseFormat.of(
            TextResponseFormat.builder()
                .mimeType(TextResponseFormatMimeType.APPLICATION_JSON)
                .schema(matchJsonSchema)
                .build()));

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-4-argon"))
        .input(InteractionsInput.of("Search for all details for the latest Euro."))
        .tools(Arrays.asList(GoogleSearch.builder().build(), URLContext.builder().build()))
        .responseFormat(format)
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

System.out.println(interaction.outputText().orElse(""));

Go

package main

import (
    "context"
    "fmt"
    "log"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    matchJsonSchema := map[string]any{
        "type": "object",
        "properties": map[string]any{
            "winner": map[string]any{
                "type":        "string",
                "description": "The name of the winner.",
            },
            "final_match_score": map[string]any{
                "type":        "string",
                "description": "The final match score.",
            },
            "scorers": map[string]any{
                "type":        "array",
                "description": "The name of the scorer.",
                "items": map[string]any{
                    "type": "string",
                },
            },
        },
        "required": []string{"winner", "final_match_score", "scorers"},
    }

    format := interactions.NewCreateModelInteractionResponseFormat(
        interactions.NewResponseFormat(interactions.TextResponseFormat{
            MimeType: interactions.TextResponseFormatMimeTypeApplicationJSON.ToPointer(),
            Schema:   matchJsonSchema,
        }),
    )

    resp, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(
            interactions.CreateModelInteraction{
                Model: interactions.Model("gemini-4-argon"),
                Input: interactions.NewInteractionsInput("Search for all details for the latest Euro."),
                Tools: []interactions.Tool{
                    interactions.NewTool(interactions.GoogleSearch{}),
                    interactions.NewTool(interactions.URLContext{}),
                },
                ResponseFormat: &format,
            },
        ),
    })
    if err != nil {
        log.Fatal(err)
    }

    fmt.Println(resp.Interaction.GetOutputText())
}

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-4-argon",
    "input": "Search for all details for the latest Euro.",
    "tools": [{"type": "google_search"}, {"type": "url_context"}],
    "response_format": {
      "type": "text",
      "mime_type": "application/json",
      "schema": {
        "type": "object",
        "properties": {
            "winner": {"type": "string"},
            "final_match_score": {"type": "string"},
            "scorers": {"type": "array", "items": {"type": "string"}}
        },
        "required": ["winner", "final_match_score", "scorers"]
      }
    }
  }'

Suporte a esquemas JSON

Para gerar um objeto JSON, configure response_format com um objeto (ou uma matriz que contenha um objeto) do tipo text e defina mime_type como application/json. O esquema precisa ser fornecido no campo schema.

O modo de saída estruturada do Gemini é compatível com um subconjunto da especificação do esquema JSON.

Os seguintes valores de type são aceitos:

  • string: para texto.
  • number: para usar pontos flutuantes.
  • integer: para números inteiros.
  • boolean: para valores verdadeiros ou falsos.
  • object: para dados estruturados com pares de chave-valor.
  • array: para listas de itens.
  • null: para permitir que uma propriedade seja nula, inclua "null" na matriz de tipo (por exemplo, {"type": ["string", "null"]}).

Essas propriedades descritivas ajudam a orientar o modelo:

  • title: uma breve descrição de uma propriedade.
  • description: uma descrição mais longa e detalhada de uma propriedade.

Propriedades específicas do tipo

Para valores de object:

  • properties: um objeto em que cada chave é um nome de propriedade e cada valor é um esquema para essa propriedade.
  • required: uma matriz de strings que lista quais propriedades são obrigatórias.
  • additionalProperties: controla se as propriedades não listadas em properties são permitidas. Pode ser um booleano ou um esquema.
  • Restrições de contagem não aceitas: minProperties e maxProperties não são aplicadas durante a decodificação restrita e são ignoradas pelo servidor. Valide as contagens de propriedades no código do aplicativo, se necessário.

Para valores de string:

  • enum: lista um conjunto específico de strings possíveis para tarefas de classificação.
  • format: especifica uma sintaxe para a string, como date-time, date, time.
  • Restrições de comprimento sem suporte: minLength e maxLength não são aplicadas durante a decodificação restrita e são ignoradas pelo servidor. Valide os tamanhos de string no código do aplicativo, se necessário.

Para os valores number e integer:

  • enum: lista um conjunto específico de valores numéricos possíveis.
  • minimum: o valor mínimo inclusivo.
  • maximum: o valor máximo inclusivo.

Para valores de array:

  • items: define o esquema para todos os itens na matriz.
  • prefixItems: define uma lista de esquemas para os primeiros N itens, permitindo estruturas semelhantes a tuplas.
  • minItems: o número mínimo de itens na matriz.
  • maxItems: o número máximo de itens na matriz.
  • Restrições de matriz não compatíveis: uniqueItems, contains, minContains e maxContains não são aplicadas durante a decodificação restrita e são ignoradas pelo servidor. Elimine a duplicação de itens ou verifique o conteúdo da matriz no código do aplicativo, se necessário.

Saídas estruturadas x chamada de função

Recurso Caso de uso principal
Respostas estruturadas Formatação da resposta final. Use quando quiser que a resposta do modelo esteja em um formato específico.
Chamadas de função Entre em ação durante a conversa. Use quando o modelo precisar pedir que você realize uma tarefa antes de fornecer uma resposta final.

Práticas recomendadas

  • Descrições claras:use o campo description para orientar o modelo.
  • Tipagem forte:use tipos específicos (integer, string, enum).
  • Engenharia de comandos:indique claramente o que você quer que o modelo faça.
  • Validação:embora a saída seja um JSON sintaticamente correto, sempre valide os valores no seu aplicativo.
  • Tratamento de erros:implemente um tratamento de erros robusto para saídas que estejam em conformidade com o esquema, mas que sejam semanticamente incorretas.

Limitações

  • Subconjunto de esquemas:nem todos os recursos do esquema JSON são compatíveis.
  • Complexidade do esquema:esquemas muito grandes ou profundamente aninhados podem ser rejeitados com um erro 400 INVALID_ARGUMENT que diz que o esquema produz uma restrição com muitos estados ou ramificações para veiculação. As causas comuns são limites grandes de maxItems (especialmente em matrizes aninhadas), nomes de propriedades ou tipos enumerados muito longos, tipos enumerados com muitos valores, sequências longas de propriedades opcionais, anyOf com muitas alternativas e limites numéricos de minimum/maximum. Para corrigir isso, reduza ou remova os limites de matrizes aninhadas, encurte os nomes ou intercale as propriedades obrigatórias entre as opcionais.
  • Restrições não aplicadas:minLength, maxLength, minProperties, maxProperties, uniqueItems, contains, minContains e maxContains (e as variantes snake_case) não são aplicadas pelo mecanismo de gramática de decodificação restrita. Se essas palavras-chave estiverem presentes no seu esquema (por exemplo, emitidas automaticamente pelo Field(min_length=...) do Pydantic ou pelo .min() do Zod), a API as remove e ignora automaticamente durante a normalização da solicitação, em vez de retornar um erro. minItems e maxItems são aplicados. Para aplicar as outras restrições, valide a saída analisada no lado do cliente (por exemplo, com Pydantic model_validate_json() ou Zod .parse()).