本快速入门将介绍如何安装我们的库、发出第一个请求、流式传输响应、构建多轮对话以及使用工具。
您可以通过以下两种方式向 Gemini API 发送请求：
- （推荐） Interactions API 是一种新的原语，内置支持多步工具使用、编排和复杂的推理流程（通过类型化执行步骤）。未来，除了核心 Mainline 系列之外的新模型，以及新的智能体功能和工具，都将仅在 Interactions API 上推出。
generateContent提供了一种从模型生成无状态响应的方法。虽然我们建议使用 Interactions API，但generateContent也完全受支持。
本版本的快速入门使用 Interactions API 向 Gemini API 发送请求。
准备工作
如需使用 Gemini API，您需要拥有一个 API 密钥，以便对请求进行身份验证、强制执行安全限制，以及跟踪您账号的使用情况。
在 AI Studio 中免费创建一个项目，即可开始使用：
安装 Google GenAI SDK
Python
使用 Python 3.9 及更高版本，通过以下 pip 命令安装 google-genai 软件包：
pip install -q -U google-genai
JavaScript
使用 Node.js v18 及更高版本，通过以下 npm 命令安装 Google Gen AI SDK（适用于 TypeScript 和 JavaScript）：
npm install @google/genai
生成文本
使用 interactions.create 方法生成文本回答。
Python
from google import genai
client = genai.Client()
interaction = client.interactions.create(
model="gemini-3.5-flash",
input="Explain how AI works in a few words"
)
print(interaction.output_text)
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({});
async function main() {
const interaction = await ai.interactions.create({
model: "gemini-3.5-flash",
input: "Explain how AI works in a few words",
});
console.log(interaction.output_text);
}
main();
REST
curl -X POST \
"https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-H "Api-Revision: 2026-05-20" \
-d '{
"model": "gemini-3.5-flash",
"input": "Explain how AI works in a few words"
}'
逐字逐句给出回答
默认情况下，模型会在完成整个生成过程后返回回答。为了获得更快、更具互动性的体验，您可以以流式传输方式获取生成的响应块。
Python
stream = client.interactions.create(
model="gemini-3.5-flash",
input="Explain how AI works in detail",
stream=True
)
for event in stream:
if event.event_type == "step.delta":
if event.delta.type == "text":
print(event.delta.text, end="", flush=True)
JavaScript
async function main() {
const stream = await ai.interactions.create({
model: "gemini-3.5-flash",
input: "Explain how AI works in detail",
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);
}
}
}
}
main();
REST
# Use alt=sse for streaming
curl -X POST \
"https://generativelanguage.googleapis.com/v1beta/interactions?alt=sse" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-H "Api-Revision: 2026-05-20" \
--no-buffer \
-d '{
"model": "gemini-3.5-flash",
"input": "Explain how AI works in detail",
"stream": true
}'
多轮对话
Gemini API 内置了对构建多轮对话的支持。
只需将上一次互动返回的 id 作为 previous_interaction_id 参数传递，服务器就会自动管理对话记录。
Python
interaction1 = client.interactions.create(
model="gemini-3.5-flash",
input="I have 2 dogs in my house."
)
print("Response 1:", interaction1.output_text)
interaction2 = client.interactions.create(
model="gemini-3.5-flash",
input="How many paws are in my house?",
previous_interaction_id=interaction1.id
)
print("Response 2:", interaction2.output_text)
JavaScript
async function main() {
const interaction1 = await ai.interactions.create({
model: "gemini-3-flash-preview",
input: "I have 2 dogs in my house.",
});
console.log("Response 1:", interaction1.output_text);
const interaction2 = await ai.interactions.create({
model: "gemini-3-flash-preview",
input: "How many paws are in my house?",
previous_interaction_id: interaction1.id,
});
console.log("Response 2:", interaction2.output_text);
}
main();
REST
# Turn 1: Start the conversation
RESPONSE1=$(curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Api-Revision: 2026-05-20" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "gemini-3-flash-preview",
"input": "I have 2 dogs in my house."
}')
# Extract the interaction ID
INTERACTION_ID=$(echo "$RESPONSE1" | jq -r '.id')
# Turn 2: Continue the conversation
curl -X POST \
"https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Api-Revision: 2026-05-20" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d "{
\"model\": \"gemini-3-flash-preview\",
\"input\": \"How many paws are in my house?\",
\"previous_interaction_id\": \"$INTERACTION_ID\"
}"
使用工具
通过依托 Google 搜索对回答进行接地来扩展模型的功能，以便访问实时网络内容。模型会自动决定何时进行搜索、执行查询，并合成包含引用的回答。
以下示例演示了如何启用 Google 搜索：
Python
interaction = client.interactions.create(
model="gemini-3-flash-preview",
input="Who won the euro 2024?",
tools=[{"type": "google_search"}]
)
print(interaction.output_text)
for step in interaction.steps:
if step.type == "model_output":
for content_block in step.content:
if content_block.type == "text" and content_block.annotations:
print("\nCitations:")
for annotation in content_block.annotations:
if annotation.type == "url_citation":
print(f" - [{annotation.title}]({annotation.url})")
JavaScript
async function main() {
const interaction = await ai.interactions.create({
model: "gemini-3-flash-preview",
input: "Who won the euro 2024?",
tools: [{ type: "google_search" }]
});
console.log(interaction.output_text);
for (const step of interaction.steps) {
if (step.type === 'model_output') {
for (const contentBlock of step.content) {
if (contentBlock.type === 'text' && contentBlock.annotations) {
console.log("\nCitations:");
for (const annotation of contentBlock.annotations) {
if (annotation.type === 'url_citation') {
console.log(` - [${annotation.title}](${annotation.url})`);
}
}
}
}
}
}
}
main();
REST
curl -X POST \
"https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Api-Revision: 2026-05-20" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gemini-3-flash-preview",
"input": "Who won the euro 2024?",
"tools": [{"type": "google_search"}]
}'
Gemini API 还支持其他内置工具：
- 代码执行：让模型能够编写和运行 Python 代码来解决复杂的数学问题。
- 网址上下文：可让模型根据您提供的特定网页网址生成回答。
- 文件搜索：可让您上传文件，并使用语义搜索根据文件内容生成回答。
- Google 地图：可根据位置数据生成回答，并搜索地点、路线和地图。
- 计算机使用：让模型与虚拟计算机屏幕、键盘和鼠标互动，以执行任务。
调用自定义函数
使用函数调用将模型连接到您的自定义工具和 API。模型会确定何时调用您的函数，并返回一个 function_call 步骤，其中包含供您的应用执行的实参。
此示例声明了一个模拟温度函数，并检查模型是否想要调用该函数。
Python
import json
weather_function = {
"type": "function",
"name": "get_current_temperature",
"description": "Gets the current temperature for a given location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name, e.g. San Francisco",
},
},
"required": ["location"],
},
}
interaction = client.interactions.create(
model="gemini-3-flash-preview",
input="What's the temperature in London?",
tools=[weather_function],
)
fc_step = None
for step in interaction.steps:
if step.type == "function_call":
fc_step = step
break
if fc_step:
print(f"Model requested function: {fc_step.name} with args {fc_step.arguments}")
mock_result = {"temperature": "15C", "condition": "Cloudy"}
final_interaction = client.interactions.create(
model="gemini-3-flash-preview",
input=[
{
"type": "function_result",
"name": fc_step.name,
"call_id": fc_step.id,
"result": [{"type": "text", "text": json.dumps(mock_result)}],
}
],
tools=[weather_function],
previous_interaction_id=interaction.id,
)
print("Final Response:", final_interaction.output_text)
JavaScript
async function main() {
const weatherFunction = {
type: 'function',
name: 'get_current_temperature',
description: 'Gets the current temperature for a given location.',
parameters: {
type: 'object',
properties: {
location: {
type: 'string',
description: 'The city name, e.g. San Francisco',
},
},
required: ['location'],
},
};
const interaction = await ai.interactions.create({
model: 'gemini-3-flash-preview',
input: "What's the temperature in London?",
tools: [weatherFunction],
});
const fcStep = interaction.steps.find(s => s.type === 'function_call');
if (fcStep) {
console.log(`Model requested function: ${fcStep.name}`);
const mockResult = { temperature: "15C", condition: "Cloudy" };
const finalInteraction = await ai.interactions.create({
model: 'gemini-3-flash-preview',
input: [{
type: 'function_result',
name: fcStep.name,
call_id: fcStep.id,
result: [{ type: 'text', text: JSON.stringify(mockResult) }]
}],
tools: [weatherFunction],
previous_interaction_id: interaction.id,
});
console.log("Final Response:", finalInteraction.output_text);
}
}
main();
REST
curl -X POST \
"https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Api-Revision: 2026-05-20" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gemini-3-flash-preview",
"input": "What'\''s the temperature in London?",
"tools": [{
"type": "function",
"name": "get_current_temperature",
"description": "Gets the current temperature for a given location.",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string", "description": "The city name"}
},
"required": ["location"]
}
}]
}'
后续步骤
现在，您已开始使用 Gemini API，接下来可以探索以下指南来构建更高级的应用：