Gemini API 可運用 Gemini 模型，根據文字、圖片、影片和音訊等各種輸入內容生成文字。
以下是採用單一文字輸入的基本範例：
Python
from google import genai client = genai.Client() response = client.models.generate_content( model="gemini-2.5-flash", contents="How does AI work?" ) print(response.text) JavaScript
import { GoogleGenAI } from "@google/genai"; const ai = new GoogleGenAI({}); async function main() { const response = await ai.models.generateContent({ model: "gemini-2.5-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, err := genai.NewClient(ctx, nil) if err != nil { log.Fatal(err) } result, _ := client.Models.GenerateContent( ctx, "gemini-2.5-flash", genai.Text("Explain how AI works in a few words"), nil, ) fmt.Println(result.Text()) } Java
import com.google.genai.Client; import com.google.genai.types.GenerateContentResponse; public class GenerateContentWithTextInput { public static void main(String[] args) { Client client = new Client(); GenerateContentResponse response = client.models.generateContent("gemini-2.5-flash", "How does AI work?", null); System.out.println(response.text()); } } REST
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent" \ -H "x-goog-api-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-2.5-flash:generateContent'; const options = { method: 'POST', contentType: 'application/json', headers: { 'x-goog-api-key': apiKey, }, 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); } 使用 Gemini 2.5 思考
2.5 Flash 和 Pro 模型預設會啟用「思考」功能，以提升品質，但執行時間可能會較長，且權杖用量會增加。
使用 2.5 Flash 時，您可以將思考預算設為零，停用思考功能。
詳情請參閱思考指南。
Python
from google import genai from google.genai import types client = genai.Client() response = client.models.generate_content( model="gemini-2.5-flash", contents="How does AI work?", config=types.GenerateContentConfig( thinking_config=types.ThinkingConfig(thinking_budget=0) # Disables thinking ), ) print(response.text) JavaScript
import { GoogleGenAI } from "@google/genai"; const ai = new GoogleGenAI({}); async function main() { const response = await ai.models.generateContent({ model: "gemini-2.5-flash", contents: "How does AI work?", config: { thinkingConfig: { thinkingBudget: 0, // Disables thinking }, } }); console.log(response.text); } await main(); Go
package main import ( "context" "fmt" "os" "google.golang.org/genai" ) func main() { ctx := context.Background() client, err := genai.NewClient(ctx, nil) if err != nil { log.Fatal(err) } result, _ := client.Models.GenerateContent( ctx, "gemini-2.5-flash", genai.Text("How does AI work?"), &genai.GenerateContentConfig{ ThinkingConfig: &genai.ThinkingConfig{ ThinkingBudget: int32(0), // Disables thinking }, } ) fmt.Println(result.Text()) } Java
import com.google.genai.Client; import com.google.genai.types.GenerateContentConfig; import com.google.genai.types.GenerateContentResponse; import com.google.genai.types.ThinkingConfig; public class GenerateContentWithThinkingConfig { public static void main(String[] args) { Client client = new Client(); GenerateContentConfig config = GenerateContentConfig.builder() // Disables thinking .thinkingConfig(ThinkingConfig.builder().thinkingBudget(0)) .build(); GenerateContentResponse response = client.models.generateContent("gemini-2.5-flash", "How does AI work?", config); System.out.println(response.text()); } } REST
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent" \ -H "x-goog-api-key: $GEMINI_API_KEY" \ -H 'Content-Type: application/json' \ -X POST \ -d '{ "contents": [ { "parts": [ { "text": "How does AI work?" } ] } ], "generationConfig": { "thinkingConfig": { "thinkingBudget": 0 } } }' 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-2.5-flash:generateContent'; const options = { method: 'POST', contentType: 'application/json', headers: { 'x-goog-api-key': apiKey, }, 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); } 系統指令和其他設定
你可以使用系統指令引導 Gemini 模型行為。如要這麼做，請傳遞 GenerateContentConfig 物件。
Python
from google import genai from google.genai import types client = genai.Client() response = client.models.generate_content( model="gemini-2.5-flash", config=types.GenerateContentConfig( system_instruction="You are a cat. Your name is Neko."), contents="Hello there" ) print(response.text) JavaScript
import { GoogleGenAI } from "@google/genai"; const ai = new GoogleGenAI({}); async function main() { const response = await ai.models.generateContent({ model: "gemini-2.5-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, err := genai.NewClient(ctx, nil) if err != nil { log.Fatal(err) } config := &genai.GenerateContentConfig{ SystemInstruction: genai.NewContentFromText("You are a cat. Your name is Neko.", genai.RoleUser), } result, _ := client.Models.GenerateContent( ctx, "gemini-2.5-flash", genai.Text("Hello there"), config, ) fmt.Println(result.Text()) } Java
import com.google.genai.Client; import com.google.genai.types.Content; import com.google.genai.types.GenerateContentConfig; import com.google.genai.types.GenerateContentResponse; import com.google.genai.types.Part; public class GenerateContentWithSystemInstruction { public static void main(String[] args) { Client client = new Client(); GenerateContentConfig config = GenerateContentConfig.builder() .systemInstruction( Content.fromParts(Part.fromText("You are a cat. Your name is Neko."))) .build(); GenerateContentResponse response = client.models.generateContent("gemini-2.5-flash", "Hello there", config); System.out.println(response.text()); } } REST
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent" \ -H "x-goog-api-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-2.5-flash:generateContent'; const options = { method: 'POST', contentType: 'application/json', headers: { 'x-goog-api-key': apiKey, }, 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 物件也可用來覆寫預設生成參數，例如溫度。
Python
from google import genai from google.genai import types client = genai.Client() response = client.models.generate_content( model="gemini-2.5-flash", contents=["Explain how AI works"], config=types.GenerateContentConfig( temperature=0.1 ) ) print(response.text) JavaScript
import { GoogleGenAI } from "@google/genai"; const ai = new GoogleGenAI({}); async function main() { const response = await ai.models.generateContent({ model: "gemini-2.5-flash", contents: "Explain how AI works", config: { 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, err := genai.NewClient(ctx, nil) if err != nil { log.Fatal(err) } temp := float32(0.9) topP := float32(0.5) topK := float32(20.0) config := &genai.GenerateContentConfig{ Temperature: &temp, TopP: &topP, TopK: &topK, ResponseMIMEType: "application/json", } result, _ := client.Models.GenerateContent( ctx, "gemini-2.5-flash", genai.Text("What is the average size of a swallow?"), config, ) fmt.Println(result.Text()) } Java
import com.google.genai.Client; import com.google.genai.types.GenerateContentConfig; import com.google.genai.types.GenerateContentResponse; public class GenerateContentWithConfig { public static void main(String[] args) { Client client = new Client(); GenerateContentConfig config = GenerateContentConfig.builder().temperature(0.1f).build(); GenerateContentResponse response = client.models.generateContent("gemini-2.5-flash", "Explain how AI works", config); System.out.println(response.text()); } } REST
curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-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" } ] } ], "generationConfig": { "stopSequences": [ "Title" ], "temperature": 1.0, "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, 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-2.5-flash:generateContent'; const options = { method: 'POST', contentType: 'application/json', headers: { 'x-goog-api-key': apiKey, }, 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); } 如需可設定參數的完整清單及其說明，請參閱 API 參考資料中的 GenerateContentConfig。
多模態輸入內容
Gemini API 支援多模態輸入內容，可讓您結合文字和媒體檔案。以下範例說明如何提供圖片：
Python
from PIL import Image from google import genai client = genai.Client() image = Image.open("/path/to/organ.png") response = client.models.generate_content( model="gemini-2.5-flash", contents=[image, "Tell me about this instrument"] ) print(response.text) JavaScript
import { GoogleGenAI, createUserContent, createPartFromUri, } from "@google/genai"; const ai = new GoogleGenAI({}); async function main() { const image = await ai.files.upload({ file: "/path/to/organ.png", }); const response = await ai.models.generateContent({ model: "gemini-2.5-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, err := genai.NewClient(ctx, nil) if err != nil { log.Fatal(err) } 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-2.5-flash", contents, nil, ) fmt.Println(result.Text()) } Java
import com.google.genai.Client; import com.google.genai.Content; import com.google.genai.types.GenerateContentResponse; import com.google.genai.types.Part; public class GenerateContentWithMultiModalInputs { public static void main(String[] args) { Client client = new Client(); Content content = Content.fromParts( Part.fromText("Tell me about this instrument"), Part.fromUri("/path/to/organ.jpg", "image/jpeg")); GenerateContentResponse response = client.models.generateContent("gemini-2.5-flash", content, null); System.out.println(response.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-2.5-flash:generateContent" \ -H "x-goog-api-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-2.5-flash:generateContent'; const options = { method: 'POST', contentType: 'application/json', headers: { 'x-goog-api-key': apiKey, }, 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()) }; } 如需提供圖片的替代方法和更進階的圖片處理方式，請參閱圖片理解指南。API 也支援文件、影片和音訊輸入內容和理解。
逐句顯示回覆
根據預設，整個生成程序完成後，模型才會傳回回覆。
如要進行更流暢的互動，請使用串流功能，逐步接收生成的 GenerateContentResponse 執行個體。
Python
from google import genai client = genai.Client() response = client.models.generate_content_stream( model="gemini-2.5-flash", contents=["Explain how AI works"] ) for chunk in response: print(chunk.text, end="") JavaScript
import { GoogleGenAI } from "@google/genai"; const ai = new GoogleGenAI({}); async function main() { const response = await ai.models.generateContentStream({ model: "gemini-2.5-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, err := genai.NewClient(ctx, nil) if err != nil { log.Fatal(err) } stream := client.Models.GenerateContentStream( ctx, "gemini-2.5-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) } } Java
import com.google.genai.Client; import com.google.genai.ResponseStream; import com.google.genai.types.GenerateContentResponse; public class GenerateContentStream { public static void main(String[] args) { Client client = new Client(); ResponseStream<GenerateContentResponse> responseStream = client.models.generateContentStream( "gemini-2.5-flash", "Write a story about a magic backpack.", null); for (GenerateContentResponse res : responseStream) { System.out.print(res.text()); } // To save resources and avoid connection leaks, it is recommended to close the response // stream after consumption (or using try block to get the response stream). responseStream.close(); } } REST
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:streamGenerateContent?alt=sse" \ -H "x-goog-api-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-2.5-flash:streamGenerateContent'; const options = { method: 'POST', contentType: 'application/json', headers: { 'x-goog-api-key': apiKey, }, 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); } 多輪對話 (即時通訊)
我們的 SDK 提供功能，可將多輪提示和回覆收集到對話中，方便您追蹤對話記錄。
Python
from google import genai client = genai.Client() chat = client.chats.create(model="gemini-2.5-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) JavaScript
import { GoogleGenAI } from "@google/genai"; const ai = new GoogleGenAI({}); async function main() { const chat = ai.chats.create({ model: "gemini-2.5-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, err := genai.NewClient(ctx, nil) if err != nil { log.Fatal(err) } 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-2.5-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) } } Java
import com.google.genai.Chat; import com.google.genai.Client; import com.google.genai.types.Content; import com.google.genai.types.GenerateContentResponse; public class MultiTurnConversation { public static void main(String[] args) { Client client = new Client(); Chat chatSession = client.chats.create("gemini-2.5-flash"); GenerateContentResponse response = chatSession.sendMessage("I have 2 dogs in my house."); System.out.println("First response: " + response.text()); response = chatSession.sendMessage("How many paws are in my house?"); System.out.println("Second response: " + response.text()); // Get the history of the chat session. // Passing 'true' to getHistory() returns the curated history, which excludes // empty or invalid parts. // Passing 'false' here would return the comprehensive history, including // empty or invalid parts. ImmutableList<Content> history = chatSession.getHistory(true); System.out.println("History: " + history); } } REST
curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent \ -H "x-goog-api-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-2.5-flash:generateContent'; const options = { method: 'POST', contentType: 'application/json', headers: { 'x-goog-api-key': apiKey, }, 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); } 串流功能也可用於多輪對話。
Python
from google import genai client = genai.Client() chat = client.chats.create(model="gemini-2.5-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) JavaScript
import { GoogleGenAI } from "@google/genai"; const ai = new GoogleGenAI({}); async function main() { const chat = ai.chats.create({ model: "gemini-2.5-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, err := genai.NewClient(ctx, nil) if err != nil { log.Fatal(err) } 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-2.5-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) } } Java
import com.google.genai.Chat; import com.google.genai.Client; import com.google.genai.ResponseStream; import com.google.genai.types.GenerateContentResponse; public class MultiTurnConversationWithStreaming { public static void main(String[] args) { Client client = new Client(); Chat chatSession = client.chats.create("gemini-2.5-flash"); ResponseStream<GenerateContentResponse> responseStream = chatSession.sendMessageStream("I have 2 dogs in my house.", null); for (GenerateContentResponse response : responseStream) { System.out.print(response.text()); } responseStream = chatSession.sendMessageStream("How many paws are in my house?", null); for (GenerateContentResponse response : responseStream) { System.out.print(response.text()); } // Get the history of the chat session. History is added after the stream // is consumed and includes the aggregated response from the stream. System.out.println("History: " + chatSession.getHistory(false)); } } REST
curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:streamGenerateContent?alt=sse \ -H "x-goog-api-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-2.5-flash:streamGenerateContent'; const options = { method: 'POST', contentType: 'application/json', headers: { 'x-goog-api-key': apiKey, }, 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); } 支援的模型
Gemini 系列的所有模型都支援生成文字。如要進一步瞭解模型及其功能，請前往「模型」頁面。
最佳做法
提示撰寫訣竅
如要生成基本文字，通常只要使用零樣本提示詞即可，不需要範例、系統指令或特定格式。
如要取得更符合需求的輸出內容：
- 使用系統指令引導模型。
- 提供幾個輸入和輸出範例，引導模型。這通常稱為「少量樣本」提示。
如需更多提示，請參閱提示工程指南。
結構化輸出內容
在某些情況下，您可能需要 JSON 等結構化輸出內容。請參閱結構化輸出指南，瞭解如何操作。