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。这可确保结果具有可预测性且类型安全,并简化了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场景:

  • 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: 将文本分类到预定义的类别中。
  • 代理工作流: 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。

此示例演示了如何使用基本 JSON 架构类型(例如 objectarraystringinteger)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-flash-preview",
    input=prompt,
    response_format={
        "type": "text",
        "mime_type": "application/json",
        "schema": Recipe.model_json_schema()
    },
)

recipe = Recipe.model_validate_json(interaction.steps[-1].content[0].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-flash-preview",
  input: prompt,
  response_format: {
    type: 'text',
    mime_type: 'application/json',
    schema: recipeJsonSchema
  },
});

const recipe = recipeSchema.parse(JSON.parse(interaction.steps.at(-1).content[0].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-flash-preview",
      "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."
  ]
}

流式传输结果

您可以流式传输结构化输出,以便在生成回答时开始处理回答。流式传输的块是有效的 JSON 部分字符串,可以连接起来形成最终的 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-flash-preview",
    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-flash-preview",
  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);
  }
}

使用工具的结构化输出

借助 Gemini 3,您可以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.steps[-1].content[0].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.steps.at(-1).content[0].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,请使用类型为 text 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。架构应在 schema 字段中提供。

Gemini 的结构化输出模式支持 JSON 架构规范的子集。

支持以下 type 值:

  • string:适用于文本。
  • number:适用于浮点数。
  • integer:适用于整数。
  • boolean:适用于 true 或 false 值。
  • object:适用于包含键值对的结构化数据。
  • array:适用于项列表。
  • 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

  • properties:一个对象,其中每个键都是属性名称,每个值都是该属性的架构。
  • 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 properties 中未列出的属性。可以是布尔值或架构。

对于 string

  • enum:列出了一组特定的可能字符串,用于分类任务。
  • format:指定字符串的语法,例如 date-timedatetime

对于 numberinteger

  • enum:列出了一组特定的可能数值。
  • minimum:包含的最小值。
  • maximum:包含的最大值。

对于 array

  • items:定义数组中所有项的架构。
  • prefixItems:为前 N 个项定义架构列表,允许使用类似元组的结构。
  • minItems:数组中的最小项数。
  • maxItems:数组中的最大项数。

模型支持

模型 结构化输出
Gemini 3.1 Pro 预览版 ✔️
Gemini 3 Flash 预览版 ✔️
Gemini 2.5 Pro ✔️
Gemini 2.5 Flash ✔️
Gemini 2.5 Flash-Lite ✔️
Gemini 2.0 Flash ✔️*
Gemini 2.0 Flash-Lite ✔️*

* Gemini 2.0 需要显式 propertyOrdering 列表。

结构化输出与函数调用

功能 主要用途
结构化输出 设置最终回答的格式。当您希望模型的 回答 采用特定格式时使用。
函数调用 在对话期间采取行动。当模型需要 要求您 执行任务,然后才能提供最终回答时使用。

最佳实践

  • 清晰的说明: 使用 description 字段来引导模型。
  • 强类型: 使用特定类型(integerstringenum)。
  • 提示工程: 明确说明您希望模型执行的操作。
  • 验证: 虽然输出在语法上是正确的 JSON,但请务必在应用中验证值。
  • 错误处理: 为符合架构但在语义上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: 非常大或深度嵌套的架构可能会被拒绝。