如需访问 Gemini Pro 和 Flash 模型，我们建议 Android 开发者使用 Gemini Developer API（通过 Firebase AI Logic）。它让您无需信用卡即可开始使用，并提供宽裕的免费层级。在小部分用户群中验证集成后，您可以通过切换到付费层级来扩大规模。
使用入门
在直接从应用中与 Gemini API 互动之前，您需要先完成一些操作，包括熟悉提示以及设置 Firebase 和应用以使用 SDK。
使用提示进行实验
通过实验提示，您可以为 Android 应用找到最佳措辞、内容和格式。Google AI Studio 是一款 IDE，您可以使用它为应用的使用情形设计提示并制作原型。
为您的用例创建合适的提示与其说是科学，不如说是艺术，因此实验至关重要。如需详细了解提示，请参阅 Firebase 文档。
对提示感到满意后，点击“<>”按钮即可获取可添加到代码中的代码段。
设置 Firebase 项目并将应用连接到 Firebase
准备好从应用中调用 API 后，请按照 Firebase AI Logic 入门指南的“第 1 步”中的说明在应用中设置 Firebase 和 SDK。
添加 Gradle 依赖项
将以下 Gradle 依赖项添加到应用模块中：
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")
}
初始化生成模型
首先，实例化 GenerativeModel 并指定模型名称：
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);
详细了解可与 Gemini Developer API 搭配使用的可用模型。您还可以详细了解如何配置模型参数。
从应用中与 Gemini Developer API 互动
现在，您已设置 Firebase 和应用以使用该 SDK，接下来就可以从应用中与 Gemini Developer API 进行交互了。
生成文本
如需生成文本回答，请使用提示调用 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);
根据图片和其他媒体内容生成文本
您还可以根据包含文字、图片或其他媒体内容的提示生成文本。调用 generateContent() 时，您可以将媒体作为内嵌数据传递。
例如，如需使用位图，请使用 image 内容类型：
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);
如需传递音频文件，请使用 inlineData 内容类型：
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);
}
如需提供视频文件，请继续使用 inlineData 内容类型：
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();
}
同样，您也可以传递 PDF (application/pdf) 和纯文本 (text/plain) 文档，只需将各自的 MIME 类型作为参数传递即可。
多轮聊天
您还可以支持多轮对话。使用 startChat() 函数初始化聊天。您可以选择性地向模型提供消息历史记录。然后，调用 sendMessage() 函数来发送聊天消息。
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);
生成图片
Gemini 2.5 Flash Image 模型（又称 Nano Banana）可利用世界知识和推理能力生成和修改图片。它会生成与上下文相关的图片，并无缝融合或交织文本和图片输出。它还可以生成包含长文本序列的准确视觉效果，并支持在保持上下文的同时进行对话式图片编辑。
除了 Gemini 之外，您还可以使用 Imagen 模型，尤其是在需要生成逼真、具有艺术细节或特定风格的高质量图片时。不过，对于大多数 Android 应用的客户端用例，Gemini 已经绰绰有余。
本指南介绍了如何使用适用于 Android 的 Firebase AI Logic SDK 来使用 Gemini 2.5 Flash Image 模型。如需详细了解如何使用 Gemini 生成图片，请参阅在 Firebase 上使用 Gemini 生成图片文档。如果您有兴趣使用 Imagen 模型，请查看相关文档。
初始化生成模型
实例化 GenerativeModel 并指定模型名称 gemini-2.5-flash-image-preview。验证您是否已将 responseModalities 配置为同时包含 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);
生成图片（仅限文本输入）
您可以通过提供纯文本提示来指示 Gemini 模型生成图片：
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);
修改图片（文本和图片输入）
您可以在提示中同时提供文本和一张或多张图片，让 Gemini 模型修改现有图片：
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);
通过多轮对话迭代和修改图片
如需采用对话式图片修改方法，您可以使用多轮对话。 这样一来，您无需重新发送原始图片，即可通过后续请求来优化修改。
首先，使用 startChat() 初始化聊天，可以选择性地提供消息历史记录。然后，对后续消息使用 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
- 输出格式：生成的图片为 PNG 格式，最大尺寸为 1024 像素。
- 输入类型：模型不支持音频或视频输入来生成图片。
- 语言支持：为获得最佳性能，请使用以下语言：英语 (
en)、墨西哥西班牙语 (es-mx)、日语 (ja-jp)、简体中文 (zh-cn) 和印地语 (hi-in)。 - 生成问题：
- 图片生成可能不会始终触发，有时只会生成文字输出。尝试明确要求生成图片输出（例如，“生成图片”“在您操作过程中提供图片”“更新图片”）。
- 模型可能会中途停止生成。请重试或尝试使用其他提示。
- 模型可能会以图片形式生成文本。尝试明确要求生成文本输出（例如，“生成叙事文本及插图”）。
如需了解详情，请参阅 Firebase 文档。
后续步骤
- 查看 GitHub 上的 Android 快速入门 Firebase 示例应用和 Android AI 示例目录。
- 准备将应用用于生产环境，包括设置 Firebase App Check 以保护 Gemini API 免遭未经授权的客户端滥用。
- 如需详细了解 Firebase AI Logic，请参阅 Firebase 文档。