Strukturierte Ausgaben

Sie können Gemini-Modelle so konfigurieren, dass sie Antworten generieren, die einem bereitgestellten JSON-Schema entsprechen. So werden vorhersagbare, typsichere Ergebnisse erzielt und das Extrahieren strukturierter Daten aus unstrukturiertem Text wird vereinfacht.

Strukturierte Ausgaben eignen sich ideal für:

  • Datenextraktion:Bestimmte Informationen wie Namen und Datumsangaben aus Text extrahieren.
  • Strukturierte Klassifizierung:Text in vordefinierte Kategorien einordnen.
  • Agentenbasierte Workflows:Strukturierte Eingaben für Tools oder APIs generieren.

Die Google GenAI SDKs unterstützen nicht nur JSON-Schema in der REST API, sondern ermöglichen auch die Definition von Schemas mit Pydantic (Python) und Zod (JavaScript).

Beispiele für strukturierte Ausgaben

Rezept-Extraktor

In diesem Beispiel wird gezeigt, wie Sie strukturierte Daten aus Text extrahieren. Dazu werden grundlegende JSON-Schematypen wie object, array, string und integer verwendet.

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"]
        }
      }
      }
    }'

Beispielantwort:

{
  "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."
  ]
}

Inhalte moderieren

In diesem Beispiel werden anyOf für bedingte Schemas und enum für die Klassifizierung verwendet, sodass die Ausgabestruktur je nach Inhalt variieren kann.

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"]
        }
      }
      }
    }'

Beispielantwort:

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

Rekursive Strukturen

In diesem Beispiel wird veranschaulicht, wie ein rekursives Schema wie ein Organigramm definiert wird.

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"]
        }
      }
      }
    }'

Beispielantwort:

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

Ergebnisse streamen

Sie können strukturierte Ausgaben streamen und so mit der Verarbeitung der Antwort beginnen, während sie generiert wird. Die gestreamten Chunks sind gültige partielle JSON-Strings, die verkettet werden können, um das endgültige JSON-Objekt zu bilden.

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
    }'

Strukturierte Ausgaben mit Tools

Mit Gemini 3 können Sie strukturierte Ausgaben mit integrierten Tools kombinieren, darunter Fundierung mit der Google Suche, URL-Kontext, Codeausführung, Dateisuche und Funktionsaufruf.

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-3.1-pro-preview",
    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-3.1-pro-preview",
  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-3.1-pro-preview"))
        .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-3.1-pro-preview"),
                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-3.1-pro-preview",
    "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"]
      }
    }
  }'

Unterstützung von JSON-Schemas

Wenn Sie ein JSON-Objekt generieren möchten, konfigurieren Sie response_format mit einem Objekt (oder einem Array, das ein Objekt enthält) vom Typ text und legen Sie mime_type auf application/json fest. Das Schema sollte im Feld schema angegeben werden.

Der Modus für strukturierte Ausgabe von Gemini unterstützt eine Teilmenge der JSON-Schema-Spezifikation.

Folgende Werte für type werden unterstützt:

  • string: Für Text.
  • number: Für Gleitkommazahlen.
  • integer: Für Ganzzahlen.
  • boolean: Für „Wahr“- oder „Falsch“-Werte.
  • object: Für strukturierte Daten mit Schlüssel/Wert-Paaren.
  • array: Für Listen von Elementen.
  • null: Wenn ein Attribut null sein darf, fügen Sie "null" in das Typ-Array ein (z.B. {"type": ["string", "null"]}).

Diese beschreibenden Attribute helfen dem Modell:

  • title: Eine kurze Beschreibung einer Property.
  • description: Eine längere und detailliertere Beschreibung einer Unterkunft.

Typspezifische Eigenschaften

Für object-Werte:

  • properties: Ein Objekt, in dem jeder Schlüssel ein Attributname und jeder Wert ein Schema für dieses Attribut ist.
  • required: Ein Array von Strings, in dem die Pflichtattribute aufgeführt sind.
  • additionalProperties: Steuert, ob Attribute, die nicht in properties aufgeführt sind, zulässig sind. Kann ein boolescher Wert oder ein Schema sein.

Für string-Werte:

  • enum: Listet eine bestimmte Gruppe möglicher Strings für Klassifizierungsaufgaben auf.
  • format: Gibt eine Syntax für den String an, z. B. date-time, date, time.

Für number- und integer-Werte:

  • enum: Listet eine bestimmte Gruppe möglicher numerischer Werte auf.
  • minimum: Der kleinste inklusive Wert.
  • maximum: Der maximale inklusive Wert.

Für array-Werte:

  • items: Definiert das Schema für alle Elemente im Array.
  • prefixItems: Definiert eine Liste von Schemas für die ersten N Elemente, die Tupel-ähnliche Strukturen ermöglicht.
  • minItems: Die Mindestanzahl von Elementen im Array.
  • maxItems: Die maximale Anzahl von Elementen im Array.

Strukturierte Ausgaben im Vergleich zu Funktionsaufrufen

Funktion Primärer Anwendungsfall
Strukturierte Ausgaben Endgültige Antwort wird formatiert. Wird verwendet, wenn die Antwort des Modells in einem bestimmten Format erfolgen soll.
Funktionsaufrufe Aktionen während der Unterhaltung ausführen: Wird verwendet, wenn das Modell Sie bitten muss, eine Aufgabe auszuführen, bevor es eine endgültige Antwort geben kann.

Best Practices

  • Klare Beschreibungen:Verwenden Sie das Feld description, um das Modell anzuleiten.
  • Starke Typisierung:Verwenden Sie spezifische Typen (integer, string, enum).
  • Prompt Engineering:Machen Sie dem Modell deutlich, was es tun soll.
  • Validierung:Die Ausgabe ist zwar syntaktisch korrektes JSON, aber Sie sollten die Werte immer in Ihrer Anwendung validieren.
  • Fehlerbehandlung:Implementieren Sie eine robuste Fehlerbehandlung für schemakonforme, aber semantisch falsche Ausgaben.

Beschränkungen

  • Schema-Teilmenge:Nicht alle JSON-Schema-Funktionen werden unterstützt.
  • Schema-Komplexität:Sehr große oder tief verschachtelte Schemas werden möglicherweise abgelehnt.