本快速入门介绍了如何安装我们的库，以及如何使用标准 generateContent 方法发出第一个请求、流式传输响应、构建多轮对话和使用工具。
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 models.generate_content 方法生成文本回答。
Python
from google import genai
client = genai.Client()
response = client.models.generate_content(
model="gemini-3.5-flash",
contents="Explain how AI works in a few words"
)
print(response.text)
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({});
async function main() {
const response = await ai.models.generateContent({
model: "gemini-3.5-flash",
contents: "Explain how AI works in a few words",
});
console.log(response.text);
}
main();
REST
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.5-flash:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
"contents": [
{
"parts": [
{
"text": "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
response = client.models.generate_content_stream(
model="gemini-3.5-flash",
contents="Explain how AI works in detail"
)
for chunk in response:
print(chunk.text, end="", flush=True)
JavaScript
async function main() {
const responseStream = await ai.models.generateContentStream({
model: "gemini-3.5-flash",
contents: "Explain how AI works in detail",
});
for await (const chunk of responseStream) {
process.stdout.write(chunk.text);
}
}
main();
REST
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.5-flash:streamGenerateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
--no-buffer \
-X POST \
-d '{
"contents": [
{
"parts": [
{
"text": "Explain how AI works in detail"
}
]
}
]
}'
多轮对话
对于多轮对话，SDK 提供有状态的 chats 辅助程序，以构建可自动管理对话历史记录的多轮对话体验。
Python
chat = client.chats.create(model="gemini-3.5-flash")
response1 = chat.send_message("I have 2 dogs in my house.")
print("Response 1:", response1.text)
response2 = chat.send_message("How many paws are in my house?")
print("Response 2:", response2.text)
JavaScript
async function main() {
const chat = ai.chats.create({ model: "gemini-3.5-flash" });
let response = await chat.sendMessage({ message: "I have 2 dogs in my house." });
console.log("Response 1:", response.text);
response = await chat.sendMessage({ message: "How many paws are in my house?" });
console.log("Response 2:", response.text);
}
main();
REST
# REST is stateless. You must pass the full conversation history in the request.
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.5-flash:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
"contents": [
{
"role": "user",
"parts": [{"text": "I have 2 dogs in my house."}]
},
{
"role": "model",
"parts": [{"text": "That is nice! Two dogs mean you have plenty of company."}]
},
{
"role": "user",
"parts": [{"text": "How many paws are in my house?"}]
}
]
}'
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、执行查询并合成回答。
Python
from google import genai
from google.genai import types
config = types.GenerateContentConfig(
tools=[types.Tool(google_search=types.GoogleSearch())]
)
response = client.models.generate_content(
model="gemini-3.5-flash",
contents="Who won the euro 2024?",
config=config
)
print(response.text)
metadata = response.candidates[0].grounding_metadata
if metadata.web_search_queries:
print("\nSearch queries executed:")
for query in metadata.web_search_queries:
print(f" - {query}")
if metadata.grounding_chunks:
print("\nSources:")
for chunk in metadata.grounding_chunks:
print(f" - [{chunk.web.title}]({chunk.web.uri})")
JavaScript
async function main() {
const response = await ai.models.generateContent({
model: "gemini-3.5-flash",
contents: "Who won the euro 2024?",
config: {
tools: [{ googleSearch: {} }]
}
});
console.log(response.text);
const metadata = response.candidates[0]?.groundingMetadata;
if (metadata?.webSearchQueries) {
console.log("\nSearch queries executed:");
for (const query of metadata.webSearchQueries) {
console.log(` - ${query}`);
}
}
if (metadata?.groundingChunks) {
console.log("\nSources:");
for (const chunk of metadata.groundingChunks) {
console.log(` - [${chunk.web.title}](${chunk.web.uri})`);
}
}
}
main();
REST
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.5-flash:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-X POST \
-d '{
"contents": [
{
"parts": [
{"text": "Who won the euro 2024?"}
]
}
],
"tools": [
{
"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，并在回答中返回 functionCall 以供您的应用执行。
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
from google import genai
from google.genai import types
weather_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"],
},
}
tools = types.Tool(function_declarations=[weather_function])
config = types.GenerateContentConfig(tools=[tools])
contents = ["What's the temperature in London?"]
response = client.models.generate_content(
model="gemini-3.5-flash",
contents=contents,
config=config,
)
part = response.candidates[0].content.parts[0]
if part.function_call:
fc = part.function_call
print(f"Model requested function: {fc.name} with args {fc.args}")
mock_result = {"temperature": "15C", "condition": "Cloudy"}
contents.append(response.candidates[0].content)
fn_response_part = types.Part.from_function_response(
name=fc.name,
response=mock_result,
id=fc.id
)
contents.append(types.Content(role="user", parts=[fn_response_part]))
final_response = client.models.generate_content(
model="gemini-3.5-flash",
contents=contents,
config=config,
)
print("Final Response:", final_response.text)
JavaScript
import { GoogleGenAI, Type } from '@google/genai';
async function main() {
const weatherFunction = {
name: 'get_current_temperature',
description: 'Gets the current temperature for a given location.',
parameters: {
type: Type.OBJECT,
properties: {
location: {
type: Type.STRING,
description: 'The city name, e.g. San Francisco',
},
},
required: ['location'],
},
};
const contents = [{
role: 'user',
parts: [{ text: "What's the temperature in London?" }]
}];
const response = await ai.models.generateContent({
model: 'gemini-3.5-flash',
contents: contents,
config: {
tools: [{ functionDeclarations: [weatherFunction] }],
},
});
if (response.functionCalls && response.functionCalls.length > 0) {
const fc = response.functionCalls[0];
console.log(`Model requested function: ${fc.name}`);
const mockResult = { temperature: "15C", condition: "Cloudy" };
contents.push(response.candidates[0].content);
contents.push({
role: 'user',
parts: [{
functionResponse: {
name: fc.name,
response: mockResult,
id: fc.id
}
}]
});
const finalResponse = await ai.models.generateContent({
model: 'gemini-3.5-flash',
contents: contents,
config: {
tools: [{ functionDeclarations: [weatherFunction] }],
},
});
console.log("Final Response:", finalResponse.text);
}
}
main();
REST
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.5-flash:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
"contents": [
{
"role": "user",
"parts": [{"text": "What'\''s the temperature in London?"}]
}
],
"tools": [
{
"functionDeclarations": [
{
"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"]
}
}
]
}
]
}'
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：