如要存取 Gemini Pro 和 Flash 模型，我們建議 Android 開發人員使用 Firebase AI Logic Gemini Developer API。您不需要信用卡即可開始使用，而且免費方案配額十分充足。向少數使用者驗證整合功能後，即可切換至付費層級，擴大規模。
開始使用
直接從應用程式與 Gemini API 互動前，您需要先完成幾項工作，包括熟悉提示，以及設定 Firebase 和應用程式以使用 SDK。
測試提示
您可以透過實驗找出最適合 Android 應用程式的措辭、內容和格式。Google AI Studio 是一種 IDE，可用於設計應用程式用例的提示原型。
為您的用途建立合適的提示，與其說是科學，不如說是一門藝術，因此實驗至關重要。如要進一步瞭解提示，請參閱 Firebase 說明文件。
確認提示沒問題後，按一下「<>」按鈕，即可取得可加入程式碼的程式碼片段。
設定 Firebase 專案，並將應用程式連結至 Firebase
準備好從應用程式呼叫 API 後，請按照 Firebase AI Logic 入門指南「步驟 1」中的操作說明，在應用程式中設定 Firebase 和 SDK。
新增 Gradle 依附元件
將下列 Gradle 依附元件新增至應用程式模組：
Kotlin
dependencies {
// ... other androidx dependencies
// Import the BoM for the Firebase platform
implementation(platform("com.google.firebase:firebase-bom:34.2.0"))
// Add the dependency for the Firebase AI Logic library When using the BoM,
// you don't specify versions in Firebase library dependencies
implementation("com.google.firebase:firebase-ai")
}
Java
dependencies {
// Import the BoM for the Firebase platform
implementation(platform("com.google.firebase:34.2.0"))
// Add the dependency for the Firebase AI Logic library When using the BoM,
// you don't specify versions in Firebase library dependencies
implementation("com.google.firebase:firebase-ai")
// Required for one-shot operations (to use `ListenableFuture` from Guava
// Android)
implementation("com.google.guava:guava:31.0.1-android")
// Required for streaming operations (to use `Publisher` from Reactive
// Streams)
implementation("org.reactivestreams:reactive-streams:1.0.4")
}
初始化生成式模型
首先，請例項化 GenerativeModel 並指定模型名稱：
Kotlin
val model = Firebase.ai(backend = GenerativeBackend.googleAI())
.generativeModel("gemini-2.5-flash")
Java
GenerativeModel firebaseAI = FirebaseAI.getInstance(GenerativeBackend.googleAI())
.generativeModel("gemini-2.5-flash");
GenerativeModelFutures model = GenerativeModelFutures.from(firebaseAI);
進一步瞭解可搭配 Gemini Developer API 使用的模型。您也可以進一步瞭解如何設定模型參數。
從應用程式與 Gemini Developer API 互動
您已設定 Firebase 和應用程式來使用 SDK，現在可以從應用程式與 Gemini Developer API 互動。
生成文字
如要生成文字回覆，請使用提示呼叫 generateContent()。
Kotlin
scope.launch {
val response = model.generateContent("Write a story about a magic backpack.")
}
Java
Content prompt = new Content.Builder()
.addText("Write a story about a magic backpack.")
.build();
ListenableFuture<GenerateContentResponse> response = model.generateContent(prompt);
Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {
@Override
public void onSuccess(GenerateContentResponse result) {
String resultText = result.getText();
[...]
}
@Override
public void onFailure(Throwable t) {
t.printStackTrace();
}
}, executor);
從圖片和其他媒體生成文字
您也可以根據包含文字、圖片或其他媒體的提示生成文字。呼叫 generateContent() 時，您可以將媒體做為內嵌資料傳遞。
舉例來說，如要使用點陣圖，請使用 image 內容類型：
Kotlin
scope.launch {
val response = model.generateContent(
content {
image(bitmap)
text("what is the object in the picture?")
}
)
}
Java
Content content = new Content.Builder()
.addImage(bitmap)
.addText("what is the object in the picture?")
.build();
ListenableFuture<GenerateContentResponse> response = model.generateContent(content);
Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {
@Override
public void onSuccess(GenerateContentResponse result) {
String resultText = result.getText();
[...]
}
@Override
public void onFailure(Throwable t) {
t.printStackTrace();
}
}, executor);
如要傳遞音訊檔案，請使用 inlineData 內容類型：
Kotlin
val contentResolver = applicationContext.contentResolver
val inputStream = contentResolver.openInputStream(audioUri).use { stream ->
stream?.let {
val bytes = stream.readBytes()
val prompt = content {
inlineData(bytes, "audio/mpeg") // Specify the appropriate audio MIME type
text("Transcribe this audio recording.")
}
val response = model.generateContent(prompt)
}
}
Java
ContentResolver resolver = getApplicationContext().getContentResolver();
try (InputStream stream = resolver.openInputStream(audioUri)) {
File audioFile = new File(new URI(audioUri.toString()));
int audioSize = (int) audioFile.length();
byte audioBytes = new byte[audioSize];
if (stream != null) {
stream.read(audioBytes, 0, audioBytes.length);
stream.close();
// Provide a prompt that includes audio specified earlier and text
Content prompt = new Content.Builder()
.addInlineData(audioBytes, "audio/mpeg") // Specify the appropriate audio MIME type
.addText("Transcribe what's said in this audio recording.")
.build();
// To generate text output, call `generateContent` with the prompt
ListenableFuture<GenerateContentResponse> response = model.generateContent(prompt);
Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {
@Override
public void onSuccess(GenerateContentResponse result) {
String text = result.getText();
Log.d(TAG, (text == null) ? "" : text);
}
@Override
public void onFailure(Throwable t) {
Log.e(TAG, "Failed to generate a response", t);
}
}, executor);
} else {
Log.e(TAG, "Error getting input stream for file.");
// Handle the error appropriately
}
} catch (IOException e) {
Log.e(TAG, "Failed to read the audio file", e);
} catch (URISyntaxException e) {
Log.e(TAG, "Invalid audio file", e);
}
如要提供影片檔案，請繼續使用 inlineData 內容類型：
Kotlin
val contentResolver = applicationContext.contentResolver
contentResolver.openInputStream(videoUri).use { stream ->
stream?.let {
val bytes = stream.readBytes()
val prompt = content {
inlineData(bytes, "video/mp4") // Specify the appropriate video MIME type
text("Describe the content of this video")
}
val response = model.generateContent(prompt)
}
}
Java
ContentResolver resolver = getApplicationContext().getContentResolver();
try (InputStream stream = resolver.openInputStream(videoUri)) {
File videoFile = new File(new URI(videoUri.toString()));
int videoSize = (int) videoFile.length();
byte[] videoBytes = new byte[videoSize];
if (stream != null) {
stream.read(videoBytes, 0, videoBytes.length);
stream.close();
// Provide a prompt that includes video specified earlier and text
Content prompt = new Content.Builder()
.addInlineData(videoBytes, "video/mp4")
.addText("Describe the content of this video")
.build();
// To generate text output, call generateContent with the prompt
ListenableFuture<GenerateContentResponse> response = model.generateContent(prompt);
Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {
@Override
public void onSuccess(GenerateContentResponse result) {
String resultText = result.getText();
System.out.println(resultText);
}
@Override
public void onFailure(Throwable t) {
t.printStackTrace();
}
}, executor);
}
} catch (IOException e) {
e.printStackTrace();
} catch (URISyntaxException e) {
e.printStackTrace();
}
同樣地，您也可以傳遞 PDF (application/pdf) 和純文字 (text/plain) 文件，並將各自的 MIME 類型做為參數傳遞。
多輪對話
您也可以支援多輪對話。使用 startChat() 函式初始化即時通訊。您可以選擇提供訊息記錄給模型。然後呼叫 sendMessage() 函式傳送即時通訊訊息。
Kotlin
val chat = model.startChat(
history = listOf(
content(role = "user") { text("Hello, I have 2 dogs in my house.") },
content(role = "model") { text("Great to meet you. What would you like to know?") }
)
)
scope.launch {
val response = chat.sendMessage("How many paws are in my house?")
}
Java
Content.Builder userContentBuilder = new Content.Builder();
userContentBuilder.setRole("user");
userContentBuilder.addText("Hello, I have 2 dogs in my house.");
Content userContent = userContentBuilder.build();
Content.Builder modelContentBuilder = new Content.Builder();
modelContentBuilder.setRole("model");
modelContentBuilder.addText("Great to meet you. What would you like to know?");
Content modelContent = userContentBuilder.build();
List<Content> history = Arrays.asList(userContent, modelContent);
// Initialize the chat
ChatFutures chat = model.startChat(history);
// Create a new user message
Content.Builder messageBuilder = new Content.Builder();
messageBuilder.setRole("user");
messageBuilder.addText("How many paws are in my house?");
Content message = messageBuilder.build();
// Send the message
ListenableFuture<GenerateContentResponse> response = chat.sendMessage(message);
Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {
@Override
public void onSuccess(GenerateContentResponse result) {
String resultText = result.getText();
System.out.println(resultText);
}
@Override
public void onFailure(Throwable t) {
t.printStackTrace();
}
}, executor);
生成圖像
Gemini 2.5 Flash 圖像模型 (又稱 Nano Banana) 可運用世界知識和推理能力生成及編輯圖片。這項功能會生成與內容相關的圖片，並順暢地混合或交錯顯示文字和圖片。此外，Gemini 也能根據長篇文字序列生成準確的圖像，並支援對話式圖像編輯，同時保留脈絡。
除了 Gemini，您也可以使用 Imagen 模型，特別是需要生成高品質圖像，且要求照片寫實、藝術細節或特定風格時。不過，對於 Android 應用程式的大多數用戶端用途而言，Gemini 已經綽綽有餘。
本指南說明如何使用 Android 適用的 Firebase AI Logic SDK，透過 Gemini 2.5 Flash Image 模型生成圖片。如要進一步瞭解如何使用 Gemini 生成圖片，請參閱「使用 Gemini 版 Firebase 生成圖片」說明文件。如要使用 Imagen 模型，請參閱相關文件。
初始化生成式模型
例項化 GenerativeModel 並指定模型名稱
gemini-2.5-flash-image-preview。確認您設定 responseModalities 時，同時納入 TEXT 和 IMAGE。
Kotlin
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
modelName = "gemini-2.5-flash-image-preview",
// Configure the model to respond with text and images (required)
generationConfig = generationConfig {
responseModalities = listOf(ResponseModality.TEXT,
ResponseModality.IMAGE)
}
)
Java
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI()).generativeModel(
"gemini-2.5-flash-image-preview",
// Configure the model to respond with text and images (required)
new GenerationConfig.Builder()
.setResponseModalities(Arrays.asList(ResponseModality.TEXT, ResponseModality.IMAGE))
.build()
);
GenerativeModelFutures model = GenerativeModelFutures.from(ai);
生成圖像 (僅輸入文字)
你可以提供純文字提示，指示 Gemini 模型生成圖片：
Kotlin
// Provide a text prompt instructing the model to generate an image
val prompt = "A hyper realistic picture of a t-rex with a blue bag pack roaming a pre-historic forest."
// To generate image output, call `generateContent` with the text input
val generatedImageAsBitmap = model.generateContent(prompt)
.candidates.first().content.parts.filterIsInstance<ImagePart>()
.firstOrNull()?.image
Java
// Provide a text prompt instructing the model to generate an image
Content prompt = new Content.Builder()
.addText("Generate an image of the Eiffel Tower with fireworks in the background.")
.build();
// To generate an image, call `generateContent` with the text input
ListenableFuture<GenerateContentResponse> response = model.generateContent(prompt);
Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {
@Override
public void onSuccess(GenerateContentResponse result) {
// iterate over all the parts in the first candidate in the result object
for (Part part : result.getCandidates().get(0).getContent().getParts()) {
if (part instanceof ImagePart) {
ImagePart imagePart = (ImagePart) part;
// The returned image as a bitmap
Bitmap generatedImageAsBitmap = imagePart.getImage();
break;
}
}
}
@Override
public void onFailure(Throwable t) {
t.printStackTrace();
}
}, executor);
編輯圖片 (輸入文字和圖片)
你可以在提示中提供文字和一或多張圖片，要求 Gemini 模型編輯現有圖片：
Kotlin
// Provide an image for the model to edit
val bitmap = BitmapFactory.decodeResource(context.resources, R.drawable.scones)
// Provide a text prompt instructing the model to edit the image
val prompt = content {
image(bitmap)
text("Edit this image to make it look like a cartoon")
}
// To edit the image, call `generateContent` with the prompt (image and text input)
val generatedImageAsBitmap = model.generateContent(prompt)
.candidates.first().content.parts.filterIsInstance<ImagePart>().firstOrNull()?.image
// Handle the generated text and image
Java
// Provide an image for the model to edit
Bitmap bitmap = BitmapFactory.decodeResource(resources, R.drawable.scones);
// Provide a text prompt instructing the model to edit the image
Content promptcontent = new Content.Builder()
.addImage(bitmap)
.addText("Edit this image to make it look like a cartoon")
.build();
// To edit the image, call `generateContent` with the prompt (image and text input)
ListenableFuture<GenerateContentResponse> response = model.generateContent(promptcontent);
Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {
@Override
public void onSuccess(GenerateContentResponse result) {
// iterate over all the parts in the first candidate in the result object
for (Part part : result.getCandidates().get(0).getContent().getParts()) {
if (part instanceof ImagePart) {
ImagePart imagePart = (ImagePart) part;
Bitmap generatedImageAsBitmap = imagePart.getImage();
break;
}
}
}
@Override
public void onFailure(Throwable t) {
t.printStackTrace();
}
}, executor);
透過多輪對話反覆編輯圖片
如要以對話方式編輯圖片，可以使用多輪對話。 這樣一來，後續要求就能微調編輯內容，而不必重新傳送原始圖片。
首先，使用 startChat() 初始化對話，並視需要提供訊息記錄。然後，後續訊息請使用 sendMessage()：
Kotlin
// Provide an image for the model to edit
val bitmap = BitmapFactory.decodeResource(context.resources, R.drawable.scones)
// Create the initial prompt instructing the model to edit the image
val prompt = content {
image(bitmap)
text("Edit this image to make it look like a cartoon")
}
// Initialize the chat
val chat = model.startChat()
// To generate an initial response, send a user message with the image and text prompt
var response = chat.sendMessage(prompt)
// Inspect the returned image
var generatedImageAsBitmap = response
.candidates.first().content.parts.filterIsInstance<ImagePart>().firstOrNull()?.image
// Follow up requests do not need to specify the image again
response = chat.sendMessage("But make it old-school line drawing style")
generatedImageAsBitmap = response
.candidates.first().content.parts.filterIsInstance<ImagePart>().firstOrNull()?.image
Java
// Provide an image for the model to edit
Bitmap bitmap = BitmapFactory.decodeResource(resources, R.drawable.scones);
// Initialize the chat
ChatFutures chat = model.startChat();
// Create the initial prompt instructing the model to edit the image
Content prompt = new Content.Builder()
.setRole("user")
.addImage(bitmap)
.addText("Edit this image to make it look like a cartoon")
.build();
// To generate an initial response, send a user message with the image and text prompt
ListenableFuture<GenerateContentResponse> response = chat.sendMessage(prompt);
// Extract the image from the initial response
ListenableFuture<@Nullable Bitmap> initialRequest = Futures.transform(response,
result -> {
for (Part part : result.getCandidates().get(0).getContent().getParts()) {
if (part instanceof ImagePart) {
ImagePart imagePart = (ImagePart) part;
return imagePart.getImage();
}
}
return null;
}, executor);
// Follow up requests do not need to specify the image again
ListenableFuture<GenerateContentResponse> modelResponseFuture = Futures.transformAsync(
initialRequest,
generatedImage -> {
Content followUpPrompt = new Content.Builder()
.addText("But make it old-school line drawing style")
.build();
return chat.sendMessage(followUpPrompt);
}, executor);
// Add a final callback to check the reworked image
Futures.addCallback(modelResponseFuture, new FutureCallback<GenerateContentResponse>() {
@Override
public void onSuccess(GenerateContentResponse result) {
for (Part part : result.getCandidates().get(0).getContent().getParts()) {
if (part instanceof ImagePart) {
ImagePart imagePart = (ImagePart) part;
Bitmap generatedImageAsBitmap = imagePart.getImage();
break;
}
}
}
@Override
public void onFailure(Throwable t) {
t.printStackTrace();
}
}, executor);
最佳做法和限制
- 輸出格式：系統會以 PNG 格式生成圖片，最大尺寸為 1024 像素。
- 輸入類型：模型不支援音訊或影片輸入，無法生成圖片。
- 支援的語言：為獲得最佳成效，請使用下列語言：
英文 (
en)、墨西哥西班牙文 (es-mx)、日文 (ja-jp)、簡體中文 (zh-cn) 和北印度文 (hi-in)。 - 生成問題：
- 系統不一定會生成圖片，有時只會輸出文字。明確要求輸出圖片 (例如：「生成圖片」、「在過程中提供圖片」、「更新圖片」)。
- 模型可能會中途停止生成內容。請再試一次或改用其他提示。
- 模型可能會以圖片形式生成文字。嘗試明確要求文字輸出內容 (例如：「生成敘事文字和插圖」)。
詳情請參閱 Firebase 說明文件。
後續步驟
- 請參閱 GitHub 上的 Android Quickstart Firebase 範例應用程式和 Android AI 範例目錄。
- 準備將應用程式發布到正式環境，包括設定 Firebase App Check，防止未經授權的用戶端濫用 Gemini API。
- 如要進一步瞭解 Firebase AI Logic，請參閱 Firebase 說明文件。