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 object、array、string 和 integer)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.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 值:
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 值:
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-time、date、time。
对于 number 和 integer 值:
enum:列出了一组特定的可能数值。minimum:包含的最小值。maximum:包含的最大值。
对于 array 值:
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(
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。