结构化输出

您可以将 Gemini 模型配置为生成符合所提供的 JSON 架构的回答。这可确保获得可预测的类型安全结果，并简化从非结构化文本中提取结构化数据的过程。

使用结构化输出非常适合以下情况：

  • 数据提取：从文本中提取特定信息，例如姓名和日期。
  • 结构化分类：将文本分类为预定义的类别。
  • Agentic 工作流：为工具或 API 生成结构化输入。

除了在 REST API 中支持 JSON 架构之外，Google GenAI SDK 还允许使用 Pydantic (Python) 和 Zod (JavaScript) 定义架构。

结构化输出示例

食谱提取器

此示例演示了如何使用 object、array、string 和 integer 等基本 JSON 架构类型从文本中提取结构化数据。

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-3.5-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

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-3.5-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);

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-3.5-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"]
        }
      }
      }
    }'

示例回答：

{
  "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."
  ]
}

内容审核

此示例展示了 anyOf（用于条件架构）和 enum（用于分类），从而使输出结构能够根据内容而变化。

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-3.5-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

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-3.5-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);

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-3.5-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"]
        }
      }
      }
    }'

示例回答：

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

递归结构

此示例展示了如何定义递归架构，例如组织结构图。

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-3.5-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

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-3.5-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);

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "gemini-3.5-flash",
      "input": "Generate an organization chart for a small team.\nThe manager is Alice, who manages Bob and Charlie. Bob manages David.",
      "response_format": {
        "type": "text",
        "mime_type": "application/json",
        "schema": {
          "type": "object",
          "properties": {
            "name": { "type": "string" },
            "employee_id": { "type": "integer" },
            "reports": {
              "type": "array",
              "description": "A list of employees reporting to this employee.",
              "items": {
                "$ref": "#"
              }
            }
          },
          "required": ["name", "employee_id", "reports"]
        }
      }
      }
    }'

示例回答：

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

流式传输结果

您可以流式传输结构化输出，从而在生成响应时开始处理响应。流式传输的块是有效的 JSON 部分字符串，可以串联起来形成最终的 JSON 对象。

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-3.5-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" and event.delta.text:
        print(event.delta.text, end="")

JavaScript

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-3.5-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.type === "step.delta" && event.delta?.text) {
    process.stdout.write(event.delta.text);
  }
}

使用工具生成结构化输出

Gemini 3 可让您将结构化输出与内置工具相结合，包括依托 Google 搜索进行接地、网址上下文、代码执行、文件搜索和函数调用。

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

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);

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"]
      }
    }
  }'

JSON 架构支持

如需生成 JSON 对象，请使用 text 类型的对象（或包含对象的数组）配置 response_format，并将其 mime_type 设置为 application/json。应在 schema 字段中提供架构。

Gemini 的结构化输出模式支持部分 JSON 架构规范。

支持以下 type 值：

  • string：对于文本。
  • number：适用于浮点数。
  • integer：适用于整数。
  • boolean：对于 true 或 false 值。
  • object：适用于包含键值对的结构化数据。
  • array：适用于商品列表。
  • null：如需允许属性为 null，请在类型数组中添加 "null"（例如 {"type": ["string", "null"]}）。

这些描述性属性有助于引导模型：

  • title：属性的简短说明。
  • description：属性的更长、更详细的说明。

特定于类型的属性

对于 object 值：

  • properties：一个对象，其中每个键都是属性名称，每个值都是相应属性的架构。
  • required：一个字符串数组，列出了哪些属性是必需的。
  • additionalProperties：控制是否允许使用未在 properties 中列出的属性。可以是布尔值或架构。

对于 string 值：

  • enum：列出分类任务的一组特定可能字符串。
  • format：指定字符串的语法，例如 date-time、date、time。

对于 number 和 integer 值：

  • enum：列出了一组特定的可能数值。
  • minimum：包含在内的最小值。
  • maximum：最大值（含）。

对于 array 值：

  • items：定义数组中所有元素的架构。
  • prefixItems：为前 N 个项定义架构列表，从而实现类似元组的结构。
  • minItems：数组中的最小项数。
  • maxItems：数组中的项数上限。

结构化输出与函数调用

功能 主要使用场景
结构化输出 正在格式化最终回答。当您希望模型以特定格式回答时使用。
函数调用 在对话期间采取行动。当模型需要询问您是否执行某项任务，然后才能提供最终答案时使用。

最佳做法

  • 清晰的说明：使用 description 字段来引导模型。
  • 强类型：使用特定类型（integer、string、enum）。
  • 提示工程：明确说明您希望模型执行的操作。
  • 验证：虽然输出是语法正确的 JSON，但请务必在应用中验证值。
  • 错误处理：针对符合架构但语义不正确的输出实现强大的错误处理机制。

限制

  • 架构子集：并非所有 JSON 架构功能都受支持。
  • 架构复杂性：过大或嵌套过深的架构可能会被拒绝。