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Vertex AI Gemini API
Stay organized with collections
Save and categorize content based on your preferences.
If you're new to the Gemini API, the Gemini Developer API is the
recommended API provider for Android Developers. But if you have specific
data location requirements or you are already embedded in the
Vertex AI or Google Cloud environment, you can use the Vertex AI Gemini API.
Getting started
Before you interact with the Vertex AI Gemini API directly from your app, you
can experiment with prompts in Vertex AI Studio.
Set up a Firebase project and connect your app to Firebase
Once you're ready to call the API from your app, follow the instructions in
"Step 1" of the Firebase AI Logic getting started guide to
set up Firebase and enable required APIs and services.
Add the Gradle dependencies
Add the following Gradle dependencies to your app module:
Kotlin
dependencies {
// ... other androidx dependencies
// Import the BoM for the Firebase platform
implementation(platform("com.google.firebase:firebase-bom:34.16.0"))
// Add the dependencies for the Firebase AI Logic and App Check libraries
// When using the BoM, you don't specify versions in Firebase library dependencies
implementation("com.google.firebase:firebase-ai")
implementation("com.google.firebase:firebase-appcheck-debug")
}
Java
dependencies {
// Import the BoM for the Firebase platform
implementation(platform("com.google.firebase:34.16.0"))
// Add the dependencies for the Firebase AI Logic and App Check libraries
// When using the BoM, you don't specify versions in Firebase library dependencies
implementation("com.google.firebase:firebase-ai")
implementation("com.google.firebase:firebase-appcheck-debug")
// 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")
}
Configure the App Check debug provider for local development
Starting early July 2026, as part of the guided setup workflow for AI Logic
in the Firebase console, Firebase App Check is automatically enforced to protect
the Gemini API. For local development, you need to configure the
App Check debug provider to bypass attestation while still maintaining the
enforcement of App Check.
In your debug build, configure App Check to use the debug provider
factory:
Kotlin
Firebase.initialize(context = this)
Firebase.appCheck.installAppCheckProviderFactory(
DebugAppCheckProviderFactory.getInstance(),
)
Java
FirebaseApp.initializeApp(/*context=*/ this);
FirebaseAppCheck firebaseAppCheck = FirebaseAppCheck.getInstance();
firebaseAppCheck.installAppCheckProviderFactory(
DebugAppCheckProviderFactory.getInstance());
Obtain your debug token:
Run your app in the emulator or on your test device.
Look for the App Check debug token in your logs. For example:
D DebugAppCheckProvider: Enter this debug secret into the allow list
in the Firebase Console for your project: 123a4567-b89c-12d3-e456-789012345678
Copy the token (for example, 123a4567-b89c-12d3-e456-789012345678).
Register your debug token with App Check:
In the Firebase console, go to the
Security > App Check > Apps tab.
Find your app, click the overflow menu
(more_vert), and then select
Manage debug tokens.
Follow the on-screen instructions to register your debug token.
For details about the debug provider (including how to get a new debug token),
check out the official App Check docs.
Here are some critical points about the
App Check debug provider:
Keep your debug token and debug build private. Don't
commit your debug token to a public repository, and don't ship your debug
token or debug build in production builds of your app.
Register your app with a production attestation provider before
releasing to end users.
You'll need to
register your app with a production App Check attestation provider
(for example, Play Integrity)
so that your end-users can use your feature with App Check enforced.
Initialize the generative model
Start by instantiating a GenerativeModel and specifying the model name:
Kotlin
val model = Firebase.ai(backend = GenerativeBackend.vertexAI())
.generativeModel("gemini-2.5-flash")
VertexAiGeminiApi.kt
Java
GenerativeModel firebaseAI = FirebaseAI.getInstance(GenerativeBackend.vertexAI())
.generativeModel("gemini-2.5-flash");
GenerativeModelFutures model = GenerativeModelFutures.from(firebaseAI);
VertexAiGeminiApiJava.java
In the Firebase documentation, you can learn more about the
available Gemini models. You can also learn about
configuring model parameters.
Generate text
To generate a text response, call generateContent() with your prompt.
Kotlin
suspend fun generateText(model: GenerativeModel) {
// Note: generateContent() is a suspend function, which integrates well
// with existing Kotlin code.
val response = model.generateContent("Write a story about a magic backpack.")
// ...
}
VertexAiGeminiApi.kt
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);
VertexAiGeminiApiJava.java
Similar to the Gemini Developer API, you can also pass images, audio, video, and
files with your text prompt. For details, see
Interact with the Gemini Developer API from your app.
To learn more about Firebase AI Logic SDK, read the
Firebase documentation.
Send feedback
Content and code samples on this page are subject to the licenses described in the Content License. Java and OpenJDK are trademarks or registered trademarks of Oracle and/or its affiliates.
Last updated 2026-07-15 UTC.
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