本快速入門導覽課程說明如何安裝程式庫、提出第一個要求、串流回應、建立多輪對話，以及使用工具。
您可以透過下列 2 種方式傳送 Gemini API 要求：
- (建議) Interactions API 是新的基本功能，內建支援多步驟工具使用、自動化調度管理，以及透過型別執行步驟進行複雜的推論流程。日後，除了核心主線系列之外的新模型，以及新的代理能力和工具，都只會在 Interactions API 上推出。
generateContent可讓您從模型生成無狀態的回覆。雖然我們建議使用 Interactions API，但generateContent也完全支援。
本快速入門導覽課程使用 Interactions API 將要求傳送至 Gemini API。
事前準備
如要使用 Gemini API，您必須有 API 金鑰，才能驗證要求、強制執行安全限制，以及追蹤帳戶的使用情形。
在 AI Studio 上免費建立一個，即可開始使用：
安裝 Google GenAI SDK
Python
使用 Python 3.9 以上版本，透過下列 pip 指令安裝 google-genai 套件：
pip install -q -U google-genai
JavaScript
使用 Node.js v18 以上版本，透過下列 npm 指令安裝 Google Gen AI SDK for TypeScript and JavaScript：
npm install @google/genai
生成文字
使用 interactions.create 方法生成文字回覆。
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"
}'
逐句回覆
根據預設，整個生成程序完成後，模型才會傳回回覆。如要獲得更快速的互動體驗，可以串流回應區塊。
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: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-H "Api-Revision: 2026-05-20" \
--no-buffer \
-d '{
"model": "gemini-3.5-flash",
"input": "Explain how AI works in detail",
"stream": true
}'
多轉折對話
Gemini API 內建支援功能，可建構多輪對話。只要將先前互動傳回的 id 做為 previous_interaction_id 參數傳遞，伺服器就會自動管理對話記錄。
Python
interaction1 = client.interactions.create(
model="gemini-3.5-flash",
input="I have 2 dogs in my house."
)
print("Response 1:", interaction1.output_text)
interaction2 = client.interactions.create(
model="gemini-3.5-flash",
input="How many paws are in my house?",
previous_interaction_id=interaction1.id
)
print("Response 2:", interaction2.output_text)
JavaScript
async function main() {
const interaction1 = await ai.interactions.create({
model: "gemini-3-flash-preview",
input: "I have 2 dogs in my house.",
});
console.log("Response 1:", interaction1.output_text);
const interaction2 = await ai.interactions.create({
model: "gemini-3-flash-preview",
input: "How many paws are in my house?",
previous_interaction_id: interaction1.id,
});
console.log("Response 2:", interaction2.output_text);
}
main();
REST
# Turn 1: Start the conversation
RESPONSE1=$(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
以 Google 搜尋建立回覆基準，存取即時網路內容，擴展模型功能。模型會自動決定搜尋時機、執行查詢，並綜合整理附有引文的回覆。
以下範例說明如何啟用 Google 搜尋：
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 也支援其他內建工具：
- 執行程式碼： 讓模型編寫及執行 Python 程式碼，解決複雜的數學問題。
- 網址內容：根據您提供的特定網頁網址，生成回覆內容。
- 檔案搜尋：可上傳檔案，並使用語意搜尋功能，根據檔案內容提供回覆。
- Google 地圖：可根據位置資料提供回覆，並搜尋地點、路線和地圖。
- 電腦使用：讓模型與虛擬電腦螢幕、鍵盤和滑鼠互動，以執行工作。
呼叫自訂函式
使用函式呼叫，將模型連結至自訂工具和 API。模型會判斷何時呼叫函式，並傳回 function_call 步驟和引數，供應用程式執行。
這個範例會宣告模擬溫度函式，並檢查模型是否要呼叫該函式。
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"]
}
}]
}'
後續步驟
現在您已開始使用 Gemini API，請參閱下列指南，建構更進階的應用程式：