Gemini API는 Gemini 모델을 활용하여 텍스트, 이미지, 동영상, 오디오를 비롯한 다양한 입력에서 텍스트 출력을 생성할 수 있습니다.
다음은 단일 텍스트 입력을 사용하는 기본 예입니다.
Python
from google import genai client = genai.Client(api_key="GEMINI_API_KEY") response = client.models.generate_content( model="gemini-2.0-flash", contents=["How does AI work?"] ) print(response.text) 자바스크립트
import { GoogleGenAI } from "@google/genai"; const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" }); async function main() { const response = await ai.models.generateContent({ model: "gemini-2.0-flash", contents: "How does AI work?", }); console.log(response.text); } await main(); Go
package main import ( "context" "fmt" "os" "google.golang.org/genai" ) func main() { ctx := context.Background() client, _ := genai.NewClient(ctx, &genai.ClientConfig{ APIKey: os.Getenv("GEMINI_API_KEY"), Backend: genai.BackendGeminiAPI, }) result, _ := client.Models.GenerateContent( ctx, "gemini-2.0-flash", genai.Text("Explain how AI works in a few words"), nil, ) fmt.Println(result.Text()) } REST
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY" \ -H 'Content-Type: application/json' \ -X POST \ -d '{ "contents": [ { "parts": [ { "text": "How does AI work?" } ] } ] }' Apps Script
// See https://developers.google.com/apps-script/guides/properties // for instructions on how to set the API key. const apiKey = PropertiesService.getScriptProperties().getProperty('GEMINI_API_KEY'); function main() { const payload = { contents: [ { parts: [ { text: 'How AI does work?' }, ], }, ], }; const url = `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=${apiKey}`; const options = { method: 'POST', contentType: 'application/json', payload: JSON.stringify(payload) }; const response = UrlFetchApp.fetch(url, options); const data = JSON.parse(response); const content = data['candidates'][0]['content']['parts'][0]['text']; console.log(content); } 시스템 안내 및 구성
시스템 안내를 사용하여 Gemini 모델의 동작을 안내할 수 있습니다. 이렇게 하려면 GenerateContentConfig 객체를 전달합니다.
Python
from google import genai from google.genai import types client = genai.Client(api_key="GEMINI_API_KEY") response = client.models.generate_content( model="gemini-2.0-flash", config=types.GenerateContentConfig( system_instruction="You are a cat. Your name is Neko."), contents="Hello there" ) print(response.text) 자바스크립트
import { GoogleGenAI } from "@google/genai"; const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" }); async function main() { const response = await ai.models.generateContent({ model: "gemini-2.0-flash", contents: "Hello there", config: { systemInstruction: "You are a cat. Your name is Neko.", }, }); console.log(response.text); } await main(); Go
package main import ( "context" "fmt" "os" "google.golang.org/genai" ) func main() { ctx := context.Background() client, _ := genai.NewClient(ctx, &genai.ClientConfig{ APIKey: os.Getenv("GEMINI_API_KEY"), Backend: genai.BackendGeminiAPI, }) config := &genai.GenerateContentConfig{ SystemInstruction: genai.NewContentFromText("You are a cat. Your name is Neko.", genai.RoleUser), } result, _ := client.Models.GenerateContent( ctx, "gemini-2.0-flash", genai.Text("Hello there"), config, ) fmt.Println(result.Text()) } REST
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY" \ -H 'Content-Type: application/json' \ -d '{ "system_instruction": { "parts": [ { "text": "You are a cat. Your name is Neko." } ] }, "contents": [ { "parts": [ { "text": "Hello there" } ] } ] }' Apps Script
// See https://developers.google.com/apps-script/guides/properties // for instructions on how to set the API key. const apiKey = PropertiesService.getScriptProperties().getProperty('GEMINI_API_KEY'); function main() { const systemInstruction = { parts: [{ text: 'You are a cat. Your name is Neko.' }] }; const payload = { systemInstruction, contents: [ { parts: [ { text: 'Hello there' }, ], }, ], }; const url = `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=${apiKey}`; const options = { method: 'POST', contentType: 'application/json', payload: JSON.stringify(payload) }; const response = UrlFetchApp.fetch(url, options); const data = JSON.parse(response); const content = data['candidates'][0]['content']['parts'][0]['text']; console.log(content); } GenerateContentConfig 객체를 사용하면 온도와 같은 기본 생성 매개변수를 재정의할 수도 있습니다.
Python
from google import genai from google.genai import types client = genai.Client(api_key="GEMINI_API_KEY") response = client.models.generate_content( model="gemini-2.0-flash", contents=["Explain how AI works"], config=types.GenerateContentConfig( max_output_tokens=500, temperature=0.1 ) ) print(response.text) 자바스크립트
import { GoogleGenAI } from "@google/genai"; const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" }); async function main() { const response = await ai.models.generateContent({ model: "gemini-2.0-flash", contents: "Explain how AI works", config: { maxOutputTokens: 500, temperature: 0.1, }, }); console.log(response.text); } await main(); Go
package main import ( "context" "fmt" "os" "google.golang.org/genai" ) func main() { ctx := context.Background() client, _ := genai.NewClient(ctx, &genai.ClientConfig{ APIKey: os.Getenv("GEMINI_API_KEY"), Backend: genai.BackendGeminiAPI, }) temp := float32(0.9) topP := float32(0.5) topK := float32(20.0) maxOutputTokens := int32(100) config := &genai.GenerateContentConfig{ Temperature: &temp, TopP: &topP, TopK: &topK, MaxOutputTokens: maxOutputTokens, ResponseMIMEType: "application/json", } result, _ := client.Models.GenerateContent( ctx, "gemini-2.0-flash", genai.Text("What is the average size of a swallow?"), config, ) fmt.Println(result.Text()) } REST
curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY \ -H 'Content-Type: application/json' \ -X POST \ -d '{ "contents": [ { "parts": [ { "text": "Explain how AI works" } ] } ], "generationConfig": { "stopSequences": [ "Title" ], "temperature": 1.0, "maxOutputTokens": 800, "topP": 0.8, "topK": 10 } }' Apps Script
// See https://developers.google.com/apps-script/guides/properties // for instructions on how to set the API key. const apiKey = PropertiesService.getScriptProperties().getProperty('GEMINI_API_KEY'); function main() { const generationConfig = { temperature: 1, topP: 0.95, topK: 40, maxOutputTokens: 8192, responseMimeType: 'text/plain', }; const payload = { generationConfig, contents: [ { parts: [ { text: 'Explain how AI works in a few words' }, ], }, ], }; const url = `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=${apiKey}`; const options = { method: 'POST', contentType: 'application/json', payload: JSON.stringify(payload) }; const response = UrlFetchApp.fetch(url, options); const data = JSON.parse(response); const content = data['candidates'][0]['content']['parts'][0]['text']; console.log(content); } 구성 가능한 매개변수와 설명의 전체 목록은 API 참조의 GenerateContentConfig를 참고하세요.
멀티모달 입력
Gemini API는 멀티모달 입력을 지원하므로 텍스트와 미디어 파일을 결합할 수 있습니다. 다음 예는 이미지를 제공하는 방법을 보여줍니다.
Python
from PIL import Image from google import genai client = genai.Client(api_key="GEMINI_API_KEY") image = Image.open("/path/to/organ.png") response = client.models.generate_content( model="gemini-2.0-flash", contents=[image, "Tell me about this instrument"] ) print(response.text) 자바스크립트
import { GoogleGenAI, createUserContent, createPartFromUri, } from "@google/genai"; const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" }); async function main() { const image = await ai.files.upload({ file: "/path/to/organ.png", }); const response = await ai.models.generateContent({ model: "gemini-2.0-flash", contents: [ createUserContent([ "Tell me about this instrument", createPartFromUri(image.uri, image.mimeType), ]), ], }); console.log(response.text); } await main(); Go
package main import ( "context" "fmt" "os" "google.golang.org/genai" ) func main() { ctx := context.Background() client, _ := genai.NewClient(ctx, &genai.ClientConfig{ APIKey: os.Getenv("GEMINI_API_KEY"), Backend: genai.BackendGeminiAPI, }) imagePath := "/path/to/organ.jpg" imgData, _ := os.ReadFile(imagePath) parts := []*genai.Part{ genai.NewPartFromText("Tell me about this instrument"), &genai.Part{ InlineData: &genai.Blob{ MIMEType: "image/jpeg", Data: imgData, }, }, } contents := []*genai.Content{ genai.NewContentFromParts(parts, genai.RoleUser), } result, _ := client.Models.GenerateContent( ctx, "gemini-2.0-flash", contents, nil, ) fmt.Println(result.Text()) } REST
# Use a temporary file to hold the base64 encoded image data TEMP_B64=$(mktemp) trap 'rm -f "$TEMP_B64"' EXIT base64 $B64FLAGS $IMG_PATH > "$TEMP_B64" # Use a temporary file to hold the JSON payload TEMP_JSON=$(mktemp) trap 'rm -f "$TEMP_JSON"' EXIT cat > "$TEMP_JSON" << EOF { "contents": [ { "parts": [ { "text": "Tell me about this instrument" }, { "inline_data": { "mime_type": "image/jpeg", "data": "$(cat "$TEMP_B64")" } } ] } ] } EOF curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY" \ -H 'Content-Type: application/json' \ -X POST \ -d "@$TEMP_JSON" Apps Script
// See https://developers.google.com/apps-script/guides/properties // for instructions on how to set the API key. const apiKey = PropertiesService.getScriptProperties().getProperty('GEMINI_API_KEY'); function main() { const imageUrl = 'http://image/url'; const image = getImageData(imageUrl); const payload = { contents: [ { parts: [ { image }, { text: 'Tell me about this instrument' }, ], }, ], }; const url = `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=${apiKey}`; const options = { method: 'POST', contentType: 'application/json', payload: JSON.stringify(payload) }; const response = UrlFetchApp.fetch(url, options); const data = JSON.parse(response); const content = data['candidates'][0]['content']['parts'][0]['text']; console.log(content); } function getImageData(url) { const blob = UrlFetchApp.fetch(url).getBlob(); return { mimeType: blob.getContentType(), data: Utilities.base64Encode(blob.getBytes()) }; } 이미지를 제공하는 다른 방법과 고급 이미지 처리에 관한 자세한 내용은 이미지 이해 가이드를 참고하세요. 이 API는 문서, 동영상, 오디오 입력 및 이해도 지원합니다.
스트리밍 응답
기본적으로 모델은 전체 생성 프로세스가 완료된 후에만 대답을 반환합니다.
더 원활한 상호작용을 위해 스트리밍을 사용하여 GenerateContentResponse 인스턴스가 생성될 때마다 점진적으로 수신합니다.
Python
from google import genai client = genai.Client(api_key="GEMINI_API_KEY") response = client.models.generate_content_stream( model="gemini-2.0-flash", contents=["Explain how AI works"] ) for chunk in response: print(chunk.text, end="") 자바스크립트
import { GoogleGenAI } from "@google/genai"; const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" }); async function main() { const response = await ai.models.generateContentStream({ model: "gemini-2.0-flash", contents: "Explain how AI works", }); for await (const chunk of response) { console.log(chunk.text); } } await main(); Go
package main import ( "context" "fmt" "os" "google.golang.org/genai" ) func main() { ctx := context.Background() client, _ := genai.NewClient(ctx, &genai.ClientConfig{ APIKey: os.Getenv("GEMINI_API_KEY"), Backend: genai.BackendGeminiAPI, }) stream := client.Models.GenerateContentStream( ctx, "gemini-2.0-flash", genai.Text("Write a story about a magic backpack."), nil, ) for chunk, _ := range stream { part := chunk.Candidates[0].Content.Parts[0] fmt.Print(part.Text) } } REST
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:streamGenerateContent?alt=sse&key=${GEMINI_API_KEY}" \ -H 'Content-Type: application/json' \ --no-buffer \ -d '{ "contents": [ { "parts": [ { "text": "Explain how AI works" } ] } ] }' Apps Script
// See https://developers.google.com/apps-script/guides/properties // for instructions on how to set the API key. const apiKey = PropertiesService.getScriptProperties().getProperty('GEMINI_API_KEY'); function main() { const payload = { contents: [ { parts: [ { text: 'Explain how AI works' }, ], }, ], }; const url = `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:streamGenerateContent?key=${apiKey}`; const options = { method: 'POST', contentType: 'application/json', payload: JSON.stringify(payload) }; const response = UrlFetchApp.fetch(url, options); const data = JSON.parse(response); const content = data['candidates'][0]['content']['parts'][0]['text']; console.log(content); } 멀티턴 대화 (채팅)
Google의 SDK는 여러 번의 프롬프트와 응답을 채팅으로 수집하는 기능을 제공하므로 대화 기록을 쉽게 추적할 수 있습니다.
Python
from google import genai client = genai.Client(api_key="GEMINI_API_KEY") chat = client.chats.create(model="gemini-2.0-flash") response = chat.send_message("I have 2 dogs in my house.") print(response.text) response = chat.send_message("How many paws are in my house?") print(response.text) for message in chat.get_history(): print(f'role - {message.role}',end=": ") print(message.parts[0].text) 자바스크립트
import { GoogleGenAI } from "@google/genai"; const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" }); async function main() { const chat = ai.chats.create({ model: "gemini-2.0-flash", history: [ { role: "user", parts: [{ text: "Hello" }], }, { role: "model", parts: [{ text: "Great to meet you. What would you like to know?" }], }, ], }); const response1 = await chat.sendMessage({ message: "I have 2 dogs in my house.", }); console.log("Chat response 1:", response1.text); const response2 = await chat.sendMessage({ message: "How many paws are in my house?", }); console.log("Chat response 2:", response2.text); } await main(); Go
package main import ( "context" "fmt" "os" "google.golang.org/genai" ) func main() { ctx := context.Background() client, _ := genai.NewClient(ctx, &genai.ClientConfig{ APIKey: os.Getenv("GEMINI_API_KEY"), Backend: genai.BackendGeminiAPI, }) history := []*genai.Content{ genai.NewContentFromText("Hi nice to meet you! I have 2 dogs in my house.", genai.RoleUser), genai.NewContentFromText("Great to meet you. What would you like to know?", genai.RoleModel), } chat, _ := client.Chats.Create(ctx, "gemini-2.0-flash", nil, history) res, _ := chat.SendMessage(ctx, genai.Part{Text: "How many paws are in my house?"}) if len(res.Candidates) > 0 { fmt.Println(res.Candidates[0].Content.Parts[0].Text) } } REST
curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY \ -H 'Content-Type: application/json' \ -X POST \ -d '{ "contents": [ { "role": "user", "parts": [ { "text": "Hello" } ] }, { "role": "model", "parts": [ { "text": "Great to meet you. What would you like to know?" } ] }, { "role": "user", "parts": [ { "text": "I have two dogs in my house. How many paws are in my house?" } ] } ] }' Apps Script
// See https://developers.google.com/apps-script/guides/properties // for instructions on how to set the API key. const apiKey = PropertiesService.getScriptProperties().getProperty('GEMINI_API_KEY'); function main() { const payload = { contents: [ { role: 'user', parts: [ { text: 'Hello' }, ], }, { role: 'model', parts: [ { text: 'Great to meet you. What would you like to know?' }, ], }, { role: 'user', parts: [ { text: 'I have two dogs in my house. How many paws are in my house?' }, ], }, ], }; const url = `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=${apiKey}`; const options = { method: 'POST', contentType: 'application/json', payload: JSON.stringify(payload) }; const response = UrlFetchApp.fetch(url, options); const data = JSON.parse(response); const content = data['candidates'][0]['content']['parts'][0]['text']; console.log(content); } 스트리밍은 멀티턴 대화에도 사용할 수 있습니다.
Python
from google import genai client = genai.Client(api_key="GEMINI_API_KEY") chat = client.chats.create(model="gemini-2.0-flash") response = chat.send_message_stream("I have 2 dogs in my house.") for chunk in response: print(chunk.text, end="") response = chat.send_message_stream("How many paws are in my house?") for chunk in response: print(chunk.text, end="") for message in chat.get_history(): print(f'role - {message.role}', end=": ") print(message.parts[0].text) 자바스크립트
import { GoogleGenAI } from "@google/genai"; const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" }); async function main() { const chat = ai.chats.create({ model: "gemini-2.0-flash", history: [ { role: "user", parts: [{ text: "Hello" }], }, { role: "model", parts: [{ text: "Great to meet you. What would you like to know?" }], }, ], }); const stream1 = await chat.sendMessageStream({ message: "I have 2 dogs in my house.", }); for await (const chunk of stream1) { console.log(chunk.text); console.log("_".repeat(80)); } const stream2 = await chat.sendMessageStream({ message: "How many paws are in my house?", }); for await (const chunk of stream2) { console.log(chunk.text); console.log("_".repeat(80)); } } await main(); Go
package main import ( "context" "fmt" "os" "google.golang.org/genai" ) func main() { ctx := context.Background() client, _ := genai.NewClient(ctx, &genai.ClientConfig{ APIKey: os.Getenv("GEMINI_API_KEY"), Backend: genai.BackendGeminiAPI, }) history := []*genai.Content{ genai.NewContentFromText("Hi nice to meet you! I have 2 dogs in my house.", genai.RoleUser), genai.NewContentFromText("Great to meet you. What would you like to know?", genai.RoleModel), } chat, _ := client.Chats.Create(ctx, "gemini-2.0-flash", nil, history) stream := chat.SendMessageStream(ctx, genai.Part{Text: "How many paws are in my house?"}) for chunk, _ := range stream { part := chunk.Candidates[0].Content.Parts[0] fmt.Print(part.Text) } } REST
curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:streamGenerateContent?alt=sse&key=$GEMINI_API_KEY \ -H 'Content-Type: application/json' \ -X POST \ -d '{ "contents": [ { "role": "user", "parts": [ { "text": "Hello" } ] }, { "role": "model", "parts": [ { "text": "Great to meet you. What would you like to know?" } ] }, { "role": "user", "parts": [ { "text": "I have two dogs in my house. How many paws are in my house?" } ] } ] }' Apps Script
// See https://developers.google.com/apps-script/guides/properties // for instructions on how to set the API key. const apiKey = PropertiesService.getScriptProperties().getProperty('GEMINI_API_KEY'); function main() { const payload = { contents: [ { role: 'user', parts: [ { text: 'Hello' }, ], }, { role: 'model', parts: [ { text: 'Great to meet you. What would you like to know?' }, ], }, { role: 'user', parts: [ { text: 'I have two dogs in my house. How many paws are in my house?' }, ], }, ], }; const url = `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:streamGenerateContent?key=${apiKey}`; const options = { method: 'POST', contentType: 'application/json', payload: JSON.stringify(payload) }; const response = UrlFetchApp.fetch(url, options); const data = JSON.parse(response); const content = data['candidates'][0]['content']['parts'][0]['text']; console.log(content); } 지원되는 모델
Gemini 계열의 모든 모델은 텍스트 생성을 지원합니다. 모델 및 기능에 대해 자세히 알아보려면 모델 페이지를 참고하세요.
권장사항
프롬프트 팁
기본 텍스트 생성의 경우 예시, 시스템 안내 또는 특정 형식이 필요하지 않은 제로샷 프롬프트로 충분합니다.
맞춤형 출력을 위해 다음을 실행합니다.
자세한 내용은 프롬프트 엔지니어링 가이드를 참고하세요.
구조화된 출력
경우에 따라 JSON과 같은 구조화된 출력이 필요할 수 있습니다. 방법을 알아보려면 구조화된 출력 가이드를 참고하세요.
다음 단계
- Gemini API 시작 Colab을 사용해 보세요.
- Gemini의 이미지, 동영상, 오디오, 문서 이해 기능을 살펴보세요.
- 멀티모달 파일 프롬프트 전략에 대해 알아보세요.